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| Titel |
A novel model evaluation approach focusing on local and advected contributions to urban PM2.5 levels – application to Paris, France |
| VerfasserIn |
H. Petetin, M. Beekmann, J. Sciare, M. Bressi, A. Rosso, O. Sanchez, V. Ghersi |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 4 ; Nr. 7, no. 4 (2014-07-18), S.1483-1505 |
| Datensatznummer |
250115665
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| Publikation (Nr.) |
 copernicus.org/gmd-7-1483-2014.pdf |
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| Zusammenfassung |
| Aerosol simulations in chemistry transport models (CTMs) still suffer from
numerous uncertainties, and diagnostic evaluations are required to point out
major error sources. This paper presents an original approach to evaluate
CTMs based on local and imported contributions in a large megacity rather
than urban background concentrations. The study is applied to the CHIMERE
model in the Paris region (France) and considers the fine particulate matter
(PM2.5) and its main chemical constituents (elemental and organic
carbon, nitrate, sulfate and ammonium), for which daily measurements are
available during a whole year at various stations (PARTICULES project).
Back-trajectory data are used to locate the upwind station, from which the
concentration is identified as the import, the local production being
deduced from the urban concentration by subtraction. Uncertainties on these
contributions are quantified. Small biases in urban background PM2.5
simulations (bias of +16%) hide significant error compensations between
local and advected contributions, as well as in PM2.5 chemical
compounds. In particular, winter time organic matter (OM) imports appear strongly
underestimated while local OM and elemental carbon (EC) production is overestimated all along
the year. Erroneous continental wood burning emissions and missing secondary organic aerosol (SOA)
pathways may explain errors on advected OM, while the carbonaceous compounds is likely to be related to errors in emissions and dynamics.
A statistically significant local formation of nitrate is also highlighted
from observations, but missed by the model. Together with the overestimation
of nitrate imports, it leads to a bias of +51% on the local PM2.5
contribution. Such an evaluation finally gives more detailed insights on
major gaps in current CTMs on which future efforts are needed. |
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