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| Titel |
Quantifying methane and nitrous oxide emissions from the UK and Ireland using a national-scale monitoring network |
| VerfasserIn |
A. L. Ganesan, A. J. Manning, A. Grant, D. Young, D. E. Oram, W. T. Sturges, J. B. Moncrieff, S. O'Doherty |
| 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 ; 15, no. 11 ; Nr. 15, no. 11 (2015-06-11), S.6393-6406 |
| Datensatznummer |
250119805
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| Publikation (Nr.) |
 copernicus.org/acp-15-6393-2015.pdf |
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| Zusammenfassung |
| The UK is one of several countries around the world that has enacted
legislation to reduce its greenhouse gas emissions. In this study, we present
top-down emissions of methane (CH4) and nitrous oxide (N2O) for the
UK and Ireland over the period August~2012 to August~2014. These emissions
were inferred using measurements from a network of four sites around the two
countries. We used a hierarchical Bayesian inverse framework to infer fluxes
as well as a set of covariance parameters that describe uncertainties in the
system. We inferred average UK total emissions of 2.09
(1.65–2.67) Tg yr−1 CH4 and 0.101 (0.068–0.150) Tg yr−1
N2O and found our derived UK estimates to be generally lower than the a
priori emissions, which consisted primarily of anthropogenic sources and with
a smaller contribution from natural sources. We used sectoral distributions
from the UK National Atmospheric Emissions Inventory (NAEI) to determine
whether these discrepancies can be attributed to specific source sectors.
Because of the distinct distributions of the two dominant CH4 emissions
sectors in the UK, agriculture and waste, we found that the inventory may be
overestimated in agricultural CH4 emissions. We found that annual mean
N2O emissions were consistent with both the prior and the anthropogenic
inventory but we derived a significant seasonal cycle in emissions. This
seasonality is likely due to seasonality in fertilizer application and in
environmental drivers such as temperature and rainfall, which are not
reflected in the annual resolution inventory. Through the hierarchical
Bayesian inverse framework, we quantified uncertainty covariance parameters
and emphasized their importance for high-resolution emissions estimation. We
inferred average model errors of approximately 20 and 0.4 ppb and
correlation timescales of 1.0 (0.72–1.43) and 2.6 (1.9–3.9) days for CH4
and N2O, respectively. These errors are a combination of transport model
errors as well as errors due to unresolved emissions processes in the
inventory. We found the largest CH4 errors at the Tacolneston station in
eastern England, which may be due to sporadic emissions from landfills and
offshore gas in the North Sea. |
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