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Homologue distribution patterns of 2,3,7,8-chloro-substituted PCDD/F in Bavarian soils

Overview of attention for article published in Environmental Sciences Europe, November 2017
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Title
Homologue distribution patterns of 2,3,7,8-chloro-substituted PCDD/F in Bavarian soils
Published in
Environmental Sciences Europe, November 2017
DOI 10.1186/s12302-017-0126-9
Pubmed ID
Authors

Raimund Prinz

Abstract

Soil-background values of PCDD/F concentrations are usually conveniently displayed as toxic equivalent (TEq), being a bulk parameter of all relevant 7 PCDD- and 10 PCDF-congeneres, chloro-subsidized at the 2nd, 3rd, 7th and 8th carbon atom. Data here are ample, not so survey on congenere/homologue patterns occurring in soils. The sufficient number of samples taken within this analysis allowed first a well-grounded evaluation. OCDD proved to be the dominant congenere in all samples (forest and agriculture), however, in considerably different concentrations. As expected, highest level was detected in forest organic layers, followed by forest top soils, cropland- and grassland top soils. Although highest in concentration, OCDD only contributes to 0.17% (both forest organic- and top soil layer), respectively, 0.4% (cropland) and 0.3% (grassland) to TEq. The influence of lower chlorinated homologues on TEq is strongest in forest top soils (72%) followed by 67% (forest organic layers), cropland top soil (63%) and grassland (61%). Although all homologue patterns (forest and agriculture) give a fairly similar picture, a test of significance (χ(2)) proved a different population of forest samples. The resemblance of the homologue patterns throughout all utilization suggests that agent levels are explained mainly by diffuse atmospheric depositions rather than by specific land use input.

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The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 25%
Unknown 3 75%
Readers by discipline Count As %
Engineering 1 25%
Unknown 3 75%