Unsupervised pattern-recognition and radiological risk assessment applied to the evaluation of behavior of rare earth elements, Th, and U in monazite sand

Environ Sci Pollut Res Int. 2022 Nov;29(55):83417-83425. doi: 10.1007/s11356-022-21632-w. Epub 2022 Jun 28.

Abstract

The Brazilian coast is rich in monazite which is found in beach sand deposits. In this study, the composition of the monazite sands from beaches of State of Espírito Santo, Brazil, was investigated. The concentrations of rare earth elements (REEs), Th, and U were determined by inductively coupled plasma mass spectrometry (ICP-MS). In the studied region, the mean concentration of investigated elements increased in the following order: Tm < Yb < Ho < Lu < Eu < Er < Tb < Dy < U < Y < Th < Gd < Sm < Pr < Nd < La < Ce. The sampling sites were classified into three clusters and discriminated by the concentrations of REEs, Th, and U found. In general, the radiological risk indices were higher than the established limits, and the risk of developing cancer was estimated to be higher than the world average.

Keywords: Artificial intelligence; KSOM; Monazite; Radiation; Rare earth elements.

MeSH terms

  • Brazil
  • Metals, Rare Earth* / analysis
  • Risk Assessment
  • Sand*

Substances

  • monazite
  • Sand
  • Metals, Rare Earth