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Low Level Determination of Trace Metals in Arctic Sea Water and Snow by Differential Pulse Anodic Stripping Voltammetry

  • Léon Mart
  • Hans-Wolfgang Nürnberg
  • David Dyrssen
Part of the NATO Conference Series book series (NATOCS, volume 9)

Abstract

The work described took place on board the Swedish icebreaker “Ymer” during the second leg of the arctic expedition “Ymer 80”, from August 9th to September 25th, 1980. Sea water from different depths at various locations between Greenland and Franz Josef Land was sampled. Surface water and samples from 10–60 m depth were taken from ice floes. Deep-sea samples were collected by an automatic device working after the principle of the CIT sampler. Snow samples, mainly fresh fallen snow, were collected into widemouth bottles of 0.5 1 volume.

Analysis was performed directly on board in a clean room container. An advanced electrochemical determination procedure, dif?ferential pulse anodic stripping voltammetry at a rotating glassy carbon mercury film electrode was used for Cd, Pb and Cu analysis. Surface samples of the Arctic Sea with a mean of 0.078 nmol kg−1 Cd, 0.072 nmol kg−1 Pb, 1.49 nmol kg−1 Cu and 1.70 nmol kg−1 Ni fit into the usual pattern of unpolluted oceanographic areas. Cadmium correlates well with nutrients and shows a 2–3 fold increase with depth, thus behaving in general analogously as in the Atlantic.

Fresh fallen snow yielded trace metal results by far lower then data reported from comparable areas like Greenland. Only data of condensed and agglomerated snow from the last winter season 79/80 agreed well with Pb results from other authors.

All the reported nickel and cobalt results have been analyzed at home, using a new, very sensitive voltammetric method based on interfacial preconcentration as adsorbed dimethylglyoxime chelates.

Keywords

Trace Metal Arctic Ocean Level Determination Arctic Water Trace Metal 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 Science+Business Media New York 1983

Authors and Affiliations

  • Léon Mart
    • 1
  • Hans-Wolfgang Nürnberg
    • 1
  • David Dyrssen
    • 2
  1. 1.Institute of Applied Physical Chemistry Chemistry DepartmentNuclear Research CenterJuelichFederal Republic of Germany
  2. 2.Department of Analytical and Marine ChemistryChalmers University of Technology and University of GothenburgGöteborgSweden

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