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This study introduces a new method using Principal Component Analysis (PCA) to map rare-earth element (REE) distributions in soils. This approach simplifies data interpretation and reveals soil properties without normalization challenges.

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Area of Science:

  • Environmental Science
  • Geochemistry
  • Chemometrics

Background:

  • Rare-earth elements (REE) are crucial in technology and environmental studies.
  • Current REE analysis relies on normalization, which can lead to inconsistent results.
  • Interpreting REE distribution in natural environments requires robust analytical methods.

Purpose of the Study:

  • To develop an alternative, more interpretable method for analyzing REE concentration profiles.
  • To create REE chemometric maps using Principal Component Analysis (PCA).
  • To correlate REE patterns with soil properties for enhanced environmental monitoring.

Main Methods:

  • Application of Principal Component Analysis (PCA) to REE concentration data.
  • Creation of REE chemometric maps using an RGB color scale based on principal components.
  • Analysis of non-normalized REE data to assess methodological consistency.

Main Results:

  • PCA effectively visualizes REE distribution on geographical maps, highlighting compositional similarities and differences.
  • The method provides unsupervised insights into soil properties by correlating them with REE patterns.
  • Results obtained from non-normalized data are consistent with those from normalized data.

Conclusions:

  • The proposed PCA-based chemometric mapping offers a powerful, interpretable alternative to traditional REE analysis.
  • This approach overcomes normalization challenges and enhances understanding of soil characteristics and evolution.
  • The methodology facilitates environmental monitoring and reveals new connections between seemingly unrelated soil samples.