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Rare Earth Elements in Acidic Systems – Biotic and Abiotic Impacts

  • Anja Grawunder
  • Dirk Merten
Chapter
Part of the Soil Biology book series (SOILBIOL, volume 31)

Abstract

Rare earth elements (REE) are heavy metals with increasing technical application and importance in science. They are found in minerals like monazite and bastnaesite, which contain especially La and Ce. In waters, REE abundance strongly depends on pH, with acid mine drainage influence resulting in higher REE concentrations. The range of REE in solids as well as in precipitation, river water, seawater, and groundwater without AMD influence, and in acidic waters is reviewed. When plotting REE abundances against the atomic numbers, the natural REE abundance shows a characteristic zig-zag pattern due to the higher stability of even masses. To recognize slight variations in the behavior of REE in different samples, concentrations are normalized to a reference standard resulting in a characteristic graph called REE pattern. These patterns can be used to identify processes leading to different distributions, e.g., related to the influence of microorganisms. In AMD-influenced areas, consortia of microorganisms adapted to these conditions colonize the soils. These organisms can tolerate high metal concentrations, low pH conditions, and low nutrient supply. In bioremediation, such microorganisms are applied to improve the metal uptake from soil into plants since they support the element solubilization and transfer. However, bacteria and fungi are also able to stabilize metals in the soil by intracellular or extracellular complexation, thus minimizing the translocation to plants, depending on species and environmental conditions. To differentiate processes, REE may be applied which is discussed in this chapter.

Keywords

Acid Mine Drainage Laser Ablation Inductively Couple Plasma Mass Spectrometry Rare Earth Element Pattern Rare Earth Element Rare Earth Element Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of GeosciencesFriedrich Schiller University JenaJenaGermany

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