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Integrating Compositional Data Analysis (CoDA) and Random Forest for lithology-specific geochemical baseline

Federica Meloni1, Caterina Gozzi2, Jacopo Cabassi3

  • 1Department of Earth Sciences, Via G. La Pira, 4, 50121, Firenze, Italy; CNR-IGG, Via G. La Pira, 4, 50121,, Firenze, Italy.

The Science of the Total Environment
|December 15, 2025
PubMed
Summary

This study introduces a new method combining data analysis and machine learning to determine accurate geochemical baselines in complex mining areas. This helps differentiate natural soil conditions from pollution, crucial for environmental management.

Keywords:
Compositional Data AnalysisGeochemical baselinePTERandom ForestSoils

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Area of Science:

  • Environmental Geochemistry
  • Applied Geology
  • Data Science in Earth Sciences

Background:

  • Accurate geochemical baselines are vital for distinguishing natural soil composition from anthropogenic contamination in mining-affected regions.
  • Geologically complex areas with historical mining pose challenges for establishing reliable background values.
  • Existing methods may be insufficient in areas with long-term industrial impact and diverse geological formations.

Purpose of the Study:

  • To develop and validate an improved methodology for defining lithology-specific geochemical baselines.
  • To accurately distinguish natural geochemical variations from legacy contamination in soils.
  • To support regulatory decisions and remediation strategies in complex geological settings.

Main Methods:

  • Integration of Compositional Data Analysis (CoDa) with the Random Forest algorithm for robust lithological classification.
  • Analysis of over 300 topsoil and subsoil samples for mineralogy and trace elements (Hg, As, Sb, Cr, Cu, Co, V, Ni).
  • Application of robust statistical methods (median ± 2MAD) for baseline value computation.

Main Results:

  • Successfully estimated geochemical baselines for Hg, As, and Sb, demonstrating strong lithological control across volcanic and sedimentary domains.
  • Identified significant variability in baseline values linked to geological settings, aiding in distinguishing natural anomalies from contamination.
  • Observed baseline values exceeding Italian legal thresholds for Hg and As, highlighting the need for site-specific regulations.

Conclusions:

  • The proposed workflow effectively establishes lithology-specific geochemical baselines in complex, historically mined areas.
  • The methodology provides crucial insights into the natural variability and resilience of different geological domains.
  • Findings underscore the necessity of tailored regulatory limits and the potential for broader application in similar contaminated regions.