4.7 Article

Per- and polyfluoroalkyl substances in the German environment - Levels and patterns in different matrices

Journal

SCIENCE OF THE TOTAL ENVIRONMENT
Volume 740, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.scitotenv.2020.140116

Keywords

PFAS; PFAA-precursors; Emerging substances; PFAS-incident; POPs; Soil

Funding

  1. German Envirnment Agency [25062]

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Per- and polyfluoroalkyl substances (PFAS) in the environment mostly originate from emissions of previously unregulated PEAS. However, there are also many documented incidents of accidental releases. To track such releases, it is essential to distinguish between typical background contamination and legally relevant incidents. This requires a comprehensive overview of all PFAS present in the environment, which is currently only possible to a limited extent due to the large variety of individual compounds. In the present study, a multimethod for capturing 41 PEAS including perfluoroalkyl acid (PFAA) precursors is introduced. The applicability of the method was tested on terrestrial, freshwater and marine samples from the German Environmental Specimen Bank (ESB), thereby providing a rough overview of PFAS contamination in German environment. Special focus was put on soil samples from ESB sites across Germany in comparison to soil samples from a polluted site in south-west Germany. The method was successfully applied to environmental samples. In total, 31 PEAS were detected, among them PEAR precursors and fluorinated ethers. Substance patterns differed between sites and matrices. In ESB soil samples from 2014 (n = 11), the sum of all captured PEAS ranged between 0.75 and 19.5 mu g kg(-1) dry weight (dw), while concentrations between 416 mu g kg(-1) and 3530 mu g kg(-1) were detected in samples from the incident site (n = 10). In other matrices, total PEAS concentrations were magnitudes lower. Highest concentrations were observed for PFOS in bream livers from the Saale (226 mu g kg(-1)). Given the heterogeneous patterns, it will require further broadly-based monitoring data to allow for a solid estimation of relevant background levels. The data provided here may support the differentiation between background levels and hotspot contaminations. (C) 2020 The Authors. Published by Elsevier B.V.

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