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| Titel |
Recovery rates from line-integrated NH3 and CH4 measurements using
backward Lagrangian stochastic dispersion modelling |
| VerfasserIn |
Christoph Häni, Karl Voglmeier, Markus Jocher, Christof Ammann |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250154458
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| Publikation (Nr.) |
 EGU/EGU2017-19557.pdf |
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| Zusammenfassung |
| Inverse dispersion modelling, i.a. backward Lagrangian stochastic (bLS) dispersion
modelling, has become a popular way to estimate trace gas losses from field measurements
(Harper et al., 2011).
Numerous investigations using bLS modelling include methane (CH4) and ammonia
(NH3) emission estimations based on experimental plots with dimensions between
approximately 102 to 104 m2.
Whereas for CH4 deposition processes can be neglected, NH3 has a strong affinity to any
surface and is therefore efficiently deposited. In general, bLS models treat the modelled gases
as inert gases. Such a standard bLS approach will underestimate NH3 emissions due to the
neglecting of the dry deposition process.
We conducted a release experiment with an artificial source that consisted of 36
individual orifices mimicking a circular area source with a radius of 10 m. We
released a gas mixture consisting of 5% NH3 and 95% CH4. We simultaneously
measured line integrated NH3 and CH4 concentrations upwind and downwind of the
source using open-path measuring systems (miniDOAS, Sintermann et al., 2016;
GasFinder, Boreal Laser, Inc., Edmonton, Alberta, Canada) and calculated corresponding
recovery rates using a bLS model (Flesch et al., 2004). With the direct comparison
of calculated NH3 and CH4 recovery rates we can quantify the amount of NH3
deposited. An attempt was made to include a simple dry deposition scheme in the bLS
model.
References
Flesch, T.K., Wilson, J.D., Harper, L.A., Crenna, B.P., Sharpe, R.R., 2004. Deducing
ground-to-air emissions from observed trace gas concentrations: A field trial. J. Appl.
Meteorol. 43 (3), 487–502.
Harper, L.A., Denmead, O.T., Flesch, T.K., 2011. Micrometeorological techniques for
measurement of enteric greenhouse gas emissions. Anim. Feed Sci. Technol. 166-167,
227–239.
Sintermann, J., Dietrich, K., Häni, C., Bell, M., Jocher, M., Neftel, A., 2016. A
miniDOAS instrument optimised for ammonia field measurements. Atmos. Meas. Tech. 9 (6),
2721–2734. 10.5194/amt-9-2721-2016. |
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