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Titel |
Spatial variability of POPs in European background air |
VerfasserIn |
A. K. Halse, M. Schlabach, S. Eckhardt, A. Sweetman, K. C. Jones, K. Breivik |
Medientyp |
Artikel
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Sprache |
Englisch
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ISSN |
1680-7316
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Digitales Dokument |
URL |
Erschienen |
In: Atmospheric Chemistry and Physics ; 11, no. 4 ; Nr. 11, no. 4 (2011-02-17), S.1549-1564 |
Datensatznummer |
250009363
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Publikation (Nr.) |
copernicus.org/acp-11-1549-2011.pdf |
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Zusammenfassung |
Passive air samplers (PAS) were deployed at 86 European background sites
during summer 2006 in order (i) to gain further insight into spatial patterns
of persistent organic pollutants (POPs) in European background air and,
(ii) to evaluate PAS as an alternative sampling technique under EMEP
(Co-operative programme for monitoring and evaluation of the long-range
transmissions of air pollutants in Europe). The samples were analyzed for
selected PCBs, HCHs, DDTs, HCB, PAHs and chlordanes, and air concentrations
were calculated on the basis of losses of performance reference compounds.
Air concentrations of PCBs were generally lowest in more remote areas of
northern Europe with elevated levels in more densely populated areas.
γ-HCH was found at elevated levels in more central parts of Europe,
whereas α-HCH, β-HCH and DDTs showed higher concentrations in
the south-eastern part. There was no clear spatial pattern in the
concentrations for PAHs, indicative of influence by local sources, rather
than long range atmospheric transport (LRAT). HCB was evenly distributed
across Europe, while the concentrations of chlordanes were typically low or
non-detectable. A comparison of results obtained on the basis of PAS and
active air sampling (AAS) illustrated that coordinated PAS campaigns have
the potential serve as useful inter-comparison exercises within and across
existing monitoring networks. The results also highlighted limitations of
the current EMEP measurement network with respect to spatial coverage. We
finally adopted an existing Lagrangian transport model (FLEXPART) as
recently modified to incorporate key processes relevant for POPs to evaluate
potential source regions affecting observed concentrations at selected
sites. Using PCB-28 as an example, the model predicted concentrations which
agreed within a factor of 3 with PAS measurements for all except 1 out of
the 17 sites selected for this analysis. |
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