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Quantifying Methane and Nitrous Oxide Emissions from the Uk Using a Dense Monitoring Network : Volume 15, Issue 1 (09/01/2015)

By Ganesan, A. L.

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Book Id: WPLBN0003997231
Format Type: PDF Article :
File Size: Pages 30
Reproduction Date: 2015

Title: Quantifying Methane and Nitrous Oxide Emissions from the Uk Using a Dense Monitoring Network : Volume 15, Issue 1 (09/01/2015)  
Author: Ganesan, A. L.
Volume: Vol. 15, Issue 1
Language: English
Subject: Science, Atmospheric, Chemistry
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Grant, A., Manning, A. J., Young, D., Sturges, W. T., Moncrieff, J. B., Oram, D. E.,...O'doherty, S. (2015). Quantifying Methane and Nitrous Oxide Emissions from the Uk Using a Dense Monitoring Network : Volume 15, Issue 1 (09/01/2015). Retrieved from http://cloud-library.org/


Description
Description: School of Chemistry, University of Bristol, Bristol, UK. The UK is one of several countries around the world that has enacted legislation to reduce its greenhouse gas emissions. Monitoring of emissions has been done through a detailed sectoral level bottom-up inventory (UK National Atmospheric Emissions Inventory, NAEI) from which national totals are submitted yearly to the United Framework Convention on Climate Change. In parallel, the UK government has funded four atmospheric monitoring stations to infer emissions through top-down methods that assimilate atmospheric observations. 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. 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 emissions of 2.08 (1.72–2.47) Tg yr−1 CH4 and 0.105 (0.087–0.127) Tg yr−1 N2O and found our derived estimates to be generally lower than the inventory. We used sectoral distributions from the 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 also found that N2O fertilizer emissions from the NAEI may be overestimated and 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 is possibly to do with sporadic emissions from landfills and offshore gas in the North Sea.

Summary
Quantifying methane and nitrous oxide emissions from the UK using a dense monitoring network

Excerpt
Bergamaschi, P., Corazza, M., Karstens, U., Athanassiadou, M., Thompson, R. L., Pison, I., Manning, A. J., Bousquet, P., Segers, A., Vermeulen, A. T., Janssens-Maenhout, G., Schmidt, M., Ramonet, M., Meinhardt, F., Aalto, T., Haszpra, L., Moncrieff, J., Popa, M. E., Lowry, D., Steinbacher, M., Jordan, A., O'Doherty, S., Piacentino, S., and Dlugokencky, E.: Top-down estimates of European CH4 and N2O emissions based on four different inverse models, Atmos. Chem. Phys. Discuss., 14, 15683–15734, doi:10.5194/acpd-14-15683-2014, 2014.; Bloom, A. A., Palmer, P. I., Fraser, A., and Reay, D. S.: Seasonal variability of tropical wetland CH4 emissions: the role of the methanogen-available carbon pool, Biogeosciences, 9, 2821–2830, doi:10.5194/bg-9-2821-2012, 2012.; Bousquet, P., Ciais, P., Miller, J., Dlugokencky, E., Hauglustaine, D., Prigent, C., Van der Werf, G., Peylin, P., Brunke, E., Carouge, C., Langenfelds, R. L., Lathière, J., Papa, F., Ramonet, M., Schmidt, M., Steele, L. P., Tyler, S., and White, J.: Contribution of anthropogenic and natural sources to atmospheric methane variability, Nature, 443, 439–443, 2006.; Corazza, M., Bergamaschi, P., Vermeulen, A. T., Aalto, T., Haszpra, L., Meinhardt, F., O'Doherty, S., Thompson, R., Moncrieff, J., Popa, E., Steinbacher, M., Jordan, A., Dlugokencky, E., Brühl, C., Krol, M., and Dentener, F.: Inverse modelling of European N2O emissions: assimilating observations from different networks, Atmos. Chem. Phys., 11, 2381–2398, doi:10.5194/acp-11-2381-2011, 2011.; Fung, I., John, J., Lerner, J., Matthews, E., Prather, M., Steele, L. P., and Fraser, P. J.: Three-dimensional model synthesis of the global methane cycle, J. Geophys. Res., 96, 13033–13065, doi:10.1029/91JD01247, 1991.; Ganesan, A. L., Chatterjee, A., Prinn, R. G., Harth, C. M., Salameh, P. K., Manning, A. J., Hall, B. D., Mühle, J., Meredith, L. K., Weiss, R. F., O'Doherty, S., and Young, D.: The variability of methane, nitrous oxide and sulfur hexafluoride in Northeast India, Atmos. Chem. Phys., 13, 10633–10644, doi:10.5194/acp-13-10633-2013, 2013.; Ganesan, A. L., Rigby, M., Zammit-Mangion, A., Manning, A. J., Prinn, R. G., Fraser, P. J., Harth, C. M., Kim, K.-R., Krummel, P. B., Li, S., Mühle, J., O'Doherty, S. J., Park, S., Salameh, P. K., Steele, L. P., and Weiss, R. F.: Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods, Atmos. Chem. Phys., 14, 3855–3864, doi:10.5194/acp-14-3855-2014, 2014.; Hall, B. D., Dutton, G. S., Mondeel, D. J., Nance, J. D., Rigby, M., Butler, J. H., Moore, F. L., Hurst, D. F., and Elkins, J. W.: Improving measurements of SF6 for the study of atmospheric transport and emissions, Atmos. Meas. Tech., 4, 2441–2451, doi:10.5194/amt-4-2441-2011, 2011.; Jones, A., Thomson, D. J., Hort, M. C., and Devenish, B.: The UK Met Office's next-generation atmospheric dispersion model, NAME III, in: Air Pollution Modeling and Its Application XVII, edited by: Borrego, C. and Norman, A.-L., Springer, New York, USA., 580–589, 2007.; JRC/PBL: Joint Research Centre of the European Commission (JRC) /Netherlands Environmental Assessment Agency (PBL), Emission Database for Global Atmospheric Research (EDGAR), release version 4.2, available at: http://edgar.jrc.ec.europa.eu (12 May 2014) 2011.; Manizza, M., Keeling, R. F., and Nevison, C. D.: On the p

 

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