4.7 Article

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

Journal

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
Volume 29, Issue 55, Pages 83417-83425

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s11356-022-21632-w

Keywords

Artificial intelligence; KSOM; Monazite; Radiation; Rare earth elements

Funding

  1. Foundation of Research Support of Espirito Santo (FAPES)
  2. Coordination for the Improvement of Higher Education Personnel (CAPES) [001]
  3. Brazilian National Council for Scientific and Technological Development (CNPq)
  4. Laboratory for Research and Development of Methodologies for Oil Analysis (LabPetro/UFES)
  5. Sao Paulo Research Foundation (FAPESP) [2021/01187-3]

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The Brazilian coast is rich in monazite, a mineral found in beach sand deposits. This study focused on the composition of monazite sands from beaches in the State of Espirito Santo, Brazil. The concentrations of rare earth elements, thorium, and uranium were determined and found to exceed established limits, indicating an elevated radiological risk and higher risk of developing cancer compared to the world average.
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 Espirito 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.

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