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| Titel |
Particulate matter mapping in the European Alps from MODIS, SEVIRI, and in-situ measurements |
| VerfasserIn |
E. Emili, C. Popp, M. Zebisch, S. Wunderle, M. Petitta |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250065646
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| Zusammenfassung |
| In this study, we investigate the spatially homogenous mapping of particulate matter over the
complex topography of the European Alpine region by means of remote sensing and
ground-based measurements. Knowledge about the spatio-temporal distribution and
atmospheric evolution of particulate matter is of great interest because higher levels of PM
can affect human health and therefore, such information can be used by authorities to take
counteractions like e.g. traffic restrictions. The study area is frequently influenced by high
PM concentrations, especially when atmospheric inversions occur during winter. Major
anthropogenic aerosol sources in the European Alps include traffic, wood burning for heating
and cooking, and industrial activities. Wefirst apply a linear model to relate aerosol optical
depth (AOD) from the geostationary Spinning Enhanced Visible and InfraRed Imager
(SEVIRI) and polar orbiting Moderate Resolution Imaging Spectroradiometer (MODIS)
together with boundary layer height (BLH) to surface PM10 concentrations in order to derive
spatially homogenous maps of PM10 over the study region for 2008-2009. In parallel, maps
of PM10 are computed by inverse distance interpolation of in-situ measurements. Both
(SEVIRI and MODIS) satellite based PM10 estimates reveal a moderate performance
with a correlation coefficient (R) of ~0.6 and a root mean square error (RMSE) of
around 10 μg m-1. In contrary, the sole inverse distance interpolation of in-situ
measurements produces more accurate PM10 maps (R~0.8, RMSE < 6 μg m-1).
Subsequently, the two separate maps are combined through an assimilation scheme where
the interpolated maps serve as background field which is up-dated by the satellite
product. However, this step only leads to a small improvement in accuracy when
most of the in-situ sites are excluded from the interpolation simulating a much
sparser network. We conclude that satellite based PM10 maps in the European
Alpine region are of limited additional value due to the relatively good coverage
of the existing in-situ network, difficult terrain for remote sensing applications
(topography, snow and cloud coverage), and inaccuracies with regard to spaceborne AOD
retrievals. However, PM remote sensing is of great interest in regions with a sparser
in-situ network (> 100km) and the presented approach can be generally applied to
test the additional information provided by PM estimates based on remote sensing
data. |
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