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Updated: Jun 17, 2025

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Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD

Seongyeon Na1, Heewon Jeong2, Ilgook Kim3

  • 1Department of Civil, Urban, Earth and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.

Journal of Hazardous Materials
|August 9, 2024
PubMed
Summary

Predicting radionuclide migration in soil is vital. A new multimodal model integrates soil properties and adsorption factors, achieving high accuracy in predicting the distribution coefficient (Kd) for safer nuclear facility management.

Keywords:
AdsorptionDistribution coefficientMultimodal modelRadionuclide

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Area of Science:

  • Environmental Science
  • Geochemistry
  • Nuclear Engineering

Background:

  • The distribution coefficient (Kd) is critical for predicting radionuclide migration in soils.
  • Existing models often fail to capture the complex interplay of geological and environmental factors influencing Kd, especially unique soil properties.
  • Accurate Kd prediction is essential for environmental safety assessments around nuclear facilities.

Purpose of the Study:

  • To develop a novel multimodal technique for predicting radionuclide distribution coefficients (Kd) in soils.
  • To integrate diverse data sources, including physicochemical conditions, X-ray fluorescence (XRF), and X-ray diffraction (XRD) data.
  • To provide a cost-effective and accurate method for assessing radionuclide adsorption mechanisms in soil environments.

Main Methods:

  • Development and training of a multimodal model comprising three sub-networks.
  • Sub-networks were designed to process distinct data domains: soil adsorption factors (physicochemical), XRF spectra, and XRD spectra (inherent soil properties).
  • Model performance was evaluated using coefficient of determination (R2) and root mean squared error (RMSE) on natural log-transformed Kd values.

Main Results:

  • The multimodal model demonstrated high predictive performance with R2 = 0.84 and RMSE = 0.89 for log-transformed Kd.
  • Identified influential peaks in XRD spectra related to inherent soil properties.
  • Determined that soil pH and calcium oxide (CaO) content were significant variables from soil adsorption factors and XRF data, respectively.

Conclusions:

  • This study presents the first multimodal model for simultaneously incorporating inherent soil properties and adsorption factors to predict Kd.
  • The proposed technique offers a robust, cost-effective, and novel approach to understanding radionuclide adsorption in soils.
  • Findings support the application of multimodal modeling for enhanced environmental safety and risk assessment at nuclear facilities.