Articles | Volume 15, issue 11
https://doi.org/10.5194/acp-15-6393-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-15-6393-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
11 Jun 2015
Research article |
| 11 Jun 2015
Quantifying methane and nitrous oxide emissions from the UK and Ireland using a national-scale monitoring network
A. L. Ganesan
CORRESPONDING AUTHOR
School of Chemistry, University of Bristol, Bristol, UK
A. J. Manning
Hadley Centre, Met Office, Exeter, UK
A. Grant
School of Chemistry, University of Bristol, Bristol, UK
School of Chemistry, University of Bristol, Bristol, UK
D .E. Oram
School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, UK
W. T. Sturges
School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, UK
J. B. Moncrieff
School of Geosciences, University of Edinburgh, Edinburgh, UK
S. O'Doherty
School of Chemistry, University of Bristol, Bristol, UK
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- Inference of spatial heterogeneity in surface fluxes from eddy covariance data: A case study from a subarctic mire ecosystem P. Levy et al. 10.1016/j.agrformet.2019.107783
46 citations as recorded by crossref.
- Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. 10.1002/env.2754
- The impact of temporal variability in prior emissions on the optimization of urban anthropogenic emissions of CO2, CH4 and CO using in-situ observations I. Super et al. 10.1016/j.aeaoa.2021.100119
- A measurement-based verification framework for UK greenhouse gas emissions: an overview of the Greenhouse gAs Uk and Global Emissions (GAUGE) project P. Palmer et al. 10.5194/acp-18-11753-2018
- A Bayesian framework for deriving sector-based methane emissions from top-down fluxes D. Cusworth et al. 10.1038/s43247-021-00312-6
- Determining methane mole fraction at a landfill site using the Figaro Taguchi gas sensor 2611-C00 and wind direction measurements A. Shah et al. 10.1039/D3EA00138E
- Estimating the effect of nitrogen fertilizer on the greenhouse gas balance of soils in Wales under current and future climate M. Abdalla et al. 10.1007/s10113-016-0958-7
- Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network E. White et al. 10.5194/acp-19-4345-2019
- Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion A. Zammit-Mangion et al. 10.1016/j.spasta.2016.06.005
- A complete rethink is needed on how greenhouse gas emissions are quantified for national reporting A. Leip et al. 10.1016/j.atmosenv.2017.12.006
- Elevated temperature shifts soil N cycling from microbial immobilization to enhanced mineralization, nitrification and denitrification across global terrestrial ecosystems Z. Dai et al. 10.1111/gcb.15211
- Greenhouse gas observation network design for Africa A. Nickless et al. 10.1080/16000889.2020.1824486
- The increasing atmospheric burden of the greenhouse gas sulfur hexafluoride (SF6) P. Simmonds et al. 10.5194/acp-20-7271-2020
- UK greenhouse gas measurements at two new tall towers for aiding emissions verification A. Stavert et al. 10.5194/amt-12-4495-2019
- Unmanned aerial vehicle observations of cold venting from exploratory hydraulic fracturing in the United Kingdom A. Shah et al. 10.1088/2515-7620/ab716d
- Regional CO2 inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0 G. Monteil & M. Scholze 10.5194/gmd-14-3383-2021
- Quantifying fossil fuel methane emissions using observations of atmospheric ethane and an uncertain emission ratio A. Ramsden et al. 10.5194/acp-22-3911-2022
- Suitability of a Non-Dispersive Infrared Methane Sensor Package for Flux Quantification Using an Unmanned Aerial Vehicle A. Shah et al. 10.3390/s19214705
- EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012 G. Janssens-Maenhout et al. 10.5194/essd-11-959-2019
- Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO2 Emissions S. Jeong et al. 10.1021/acs.est.4c09395
- Inverse modelling of European CH4 emissions during 2006–2012 using different inverse models and reassessed atmospheric observations P. Bergamaschi et al. 10.5194/acp-18-901-2018
- Measurements of methane and nitrous oxide in human breath and the development of UK scale emissions B. Dawson et al. 10.1371/journal.pone.0295157
- Assessing London CO2, CH4 and CO emissions using aircraft measurements and dispersion modelling J. Pitt et al. 10.5194/acp-19-8931-2019
- Atmospheric observations show accurate reporting and little growth in India’s methane emissions A. Ganesan et al. 10.1038/s41467-017-00994-7
- Simultaneous detection of ambient methane, nitrous oxide, and water vapor using an external-cavity quantum cascade laser X. Cui et al. 10.1016/j.atmosenv.2018.07.001
- Direct high-precision radon quantification for interpreting high-frequency greenhouse gas measurements D. Kikaj et al. 10.5194/amt-18-151-2025
- Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. 10.5194/acp-16-14371-2016
- Magnitude and seasonal variation of N2O and CH4 emissions over a mixed agriculture-urban region X. Tong et al. 10.1016/j.agrformet.2023.109433
- Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo M. Lunt et al. 10.5194/gmd-9-3213-2016
- Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling S. Henne et al. 10.5194/acp-16-3683-2016
- Greenhouse gas measurements from a UK network of tall towers: technical description and first results K. Stanley et al. 10.5194/amt-11-1437-2018
- Country-scale greenhouse gas budgets using shipborne measurements: a case study for the UK and Ireland C. Helfter et al. 10.5194/acp-19-3043-2019
- Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment L. Cartwright et al. 10.5194/amt-12-4659-2019
- Nitrous Oxide Emissions Estimated With the CarbonTracker‐Lagrange North American Regional Inversion Framework C. Nevison et al. 10.1002/2017GB005759
- A decade of CH4, CO and N2O in situ measurements at Lauder, New Zealand: assessing the long-term performance of a Fourier transform infrared trace gas and isotope analyser D. Smale et al. 10.5194/amt-12-637-2019
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- Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion A. Zammit-Mangion et al. 10.1016/j.chemolab.2015.09.006
- High-resolution inverse modelling of European CH4 emissions using the novel FLEXPART-COSMO TM5 4DVAR inverse modelling system P. Bergamaschi et al. 10.5194/acp-22-13243-2022
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- Step Change in Improving the Accuracy of Nitrous Oxide Reference Materials for Underpinning Atmospheric Composition Measurements K. Charoenpornpukdee et al. 10.1021/acs.analchem.3c02160
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Short summary
The UK is one of several countries to enact legislation to reduce its greenhouse gas emissions. We present top-down emissions of methane and nitrous oxide for the UK and Ireland over 2012-2014. We inferred average UK emissions of 2.09Tg/yr CH4 and 0.101Tg/yr N2O and used sectoral distributions to determine whether these discrepancies can be attributed to specific source sectors. We found the agricultural sector likely to be overestimated in the bottom-up emissions inventories of both gases.
The UK is one of several countries to enact legislation to reduce its greenhouse gas emissions....
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