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Titel |
Fast emission estimates in China and South Africa constrained by satellite observations |
VerfasserIn |
Bas Mijling, Ronald van der A |
Konferenz |
EGU General Assembly 2013
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Medientyp |
Artikel
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Sprache |
Englisch
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Digitales Dokument |
PDF |
Erschienen |
In: GRA - Volume 15 (2013) |
Datensatznummer |
250080094
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Zusammenfassung |
Emission inventories of air pollutants are crucial information for policy makers and form
important input data for air quality models. Unfortunately, bottom-up emission inventories,
compiled from large quantities of statistical data, are easily outdated for emerging economies
such as China and South Africa, where rapid economic growth change emissions accordingly.
Alternatively, top-down emission estimates from satellite observations of air constituents
have important advantages of being spatial consistent, having high temporal resolution, and
enabling emission updates shortly after the satellite data become available. However,
constraining emissions from observations of concentrations is computationally
challenging.
Within the GlobEmission project (part of the Data User Element programme of ESA) a
new algorithm has been developed, specifically designed for fast daily emission estimates of
short-lived atmospheric species on a mesoscopic scale (0.25 Ã 0.25 degree) from satellite
observations of column concentrations. The algorithm needs only one forward model run
from a chemical transport model to calculate the sensitivity of concentration to emission,
using trajectory analysis to account for transport away from the source. By using a Kalman
filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is
made.
We apply the algorithm for NOx emission estimates in East China and South Africa,
using the CHIMERE chemical transport model together with tropospheric NO2 column
retrievals of the OMI and GOME-2 satellite instruments. The observations are used to
construct a monthly emission time series, which reveal important emission trends
such as the emission reduction measures during the Beijing Olympic Games, and
the impact and recovery from the global economic crisis. The algorithm is also
able to detect emerging sources (e.g. new power plants) and improve emission
information for areas where proxy data are not or badly known (e.g. shipping emissions).
The new emission inventories result in a better agreement between observations
and simulations of air pollutant concentrations, facilitating improved air quality
forecasts. |
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