Continuous CH4 and  d13CH4 measurements in London demonstrate under-reported natural gas leakage

2021 ◽  
Author(s):  
Eric Saboya ◽  
Giulia Zazzeri ◽  
Heather Graven ◽  
Alistair J. Manning ◽  
Sylvia Englund Michel
Keyword(s):  
Natural Gas ◽  
Atmospheric Chemistry ◽  
Dispersion Model ◽  
Measurement Data ◽  
Atmospheric Methane ◽  
Bottom Up ◽  
Keeling Plot ◽  
Regional Emissions ◽  
The Uk ◽  
Gas Leaks

<p>Assessment of bottom-up greenhouse gas emissions estimates through independent methods is needed to demonstrate whether reported values are accurate or if bottom-up methodologies need to be refined. Previous studies of measurements of atmospheric methane (CH<sub>4</sub>) in London revealed that inventories substantially underestimated the amount of natural gas CH<sub>4</sub><sup> 1,2</sup>. We report atmospheric CH<sub>4</sub> concentrations and δ<sup>13</sup>CH<sub>4</sub> measurements from Imperial College London since early 2018 using a Picarro G2201-i analyser. Measurements from Sept. 2019-Oct. 2020 were compared to the values simulated using the dispersion model NAME coupled with the UK national atmospheric emissions inventory, NAEI, and the global inventory, EDGAR, for emissions outside the UK. Simulations of CH<sub>4</sub> concentration and δ<sup>13</sup>CH<sub>4</sub> values were generated using nested NAME back-trajectories with horizontal spatial resolutions of 2 km, 10 km and 30 km. Observed concentrations were underestimated in the simulations by 22 % for all data, and by 16 % when using only 13:00-17:00 data. There was no correlation between the measured and simulated δ<sup>13</sup>CH<sub>4</sub> values. On average, simulated natural gas mole fractions accounted for 28 % of the CH<sub>4 </sub>added by regional emissions, and simulated water sector mole fractions accounted for 32 % of the CH<sub>4</sub>added by regional emissions. To estimate the isotopic source signatures for individual pollution events, an algorithm was created for automatically analysing measurement data by using the Keeling plot approach. Nearly 70 % of isotopic source values were higher than -50 ‰, suggesting the primary CH<sub>4 </sub>sources in London are natural gas leaks. The model-data comparison of δ<sup>13</sup>CH<sub>4 </sub>and Keeling plot results both indicate that emissions due to natural gas leaks in London are being underestimated in the UK NAEI and EDGAR.</p><p> </p><p><sup>1 </sup>Helfter, C. et al. (2016), Atmospheric Chemistry and Physics, 16(16), pp. 10543-10557</p><p><sup>2</sup> Zazzeri, G. et al. (2017), Scientific Reports, 7(1), pp. 1-13</p>

scholarly journals Continuous CH<sub>4</sub> and δ<sup>13</sup>CH<sub>4</sub> measurements in London demonstrate under-reported natural gas leakage

2021 ◽  
Author(s):  
Eric Saboya ◽  
Giulia Zazzeri ◽  
Heather Graven ◽  
Alistair J. Manning ◽  
Sylvia Englund Michel
Keyword(s):  
Natural Gas ◽  
Dispersion Model ◽  
Measurement Data ◽  
Atmospheric Methane ◽  
Good Correspondence ◽  
Bottom Up ◽  
Gas Leakage ◽  
Keeling Plot ◽  
The Uk ◽  
Gas Leaks

Abstract. Assessment of bottom-up greenhouse gas emissions estimates through independent methods is needed to demonstrate whether reported values are accurate or if bottom-up methodologies need to be refined. We report atmospheric methane (CH4) mole fractions and δ13CH4 measurements from Imperial College London since early 2018 using a Picarro G2201-i analyser. Measurements from March 2018 to October 2020 were compared to simulations of CH4 mole fractions and δ13CH4 produced using the NAME dispersion model coupled with the UK National Atmospheric Emissions Inventory, UK NAEI, and the global inventory, EDGAR, with model spatial resolutions of ~2 km, ~10 km, and ~25 km. Observed mole fractions were underestimated by 30–35 % in the NAEI simulations. In contrast, a good correspondence between observations and EDGAR simulations was seen. There was no correlation between the measured and simulated δ13CH4 values for either NAEI or EDGAR, however, suggesting the inventories’ sectoral attributions are incorrect. On average, natural gas sources accounted for 20–28 % of the above background CH4 in the NAEI simulations, and only 6–9 % in the EDGAR simulations. In contrast, nearly 84 % of isotopic source values calculated by Keeling plot analysis (using measurement data from the afternoon) of individual pollution events were higher than −45 ‰, suggesting the primary CH4 sources in London are actually natural gas leaks. The simulation-observation comparison of CH4 mole fractions suggests that total emissions in London are much higher than the NAEI estimate (0.04 Tg CH4 yr−1) but close to, or slightly lower than the EDGAR estimate (0.10 Tg CH4 yr−1). However, the simulation-observation comparison of δ13CH4 and the Keeling plot results indicate that emissions due to natural gas leaks in London are being underestimated in both the UK NAEI and EDGAR.

Atmospheric Methane Concentration Allows Estimating Natural Gas Leaks in Heating Systems in Tandil, Argentina

2019 ◽  
Vol 48 (3) ◽  
pp. 762-769
Author(s):  
Victoria S. Fusé ◽  
José I. Gere ◽  
Daiana Urteaga ◽  
M. Paula Juliarena ◽  
Sergio A. Guzmán ◽  
...  
Keyword(s):  
Natural Gas ◽  
Methane Concentration ◽  
Atmospheric Methane ◽  
Heating Systems ◽  
Gas Leaks

scholarly journals Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set

2015 ◽  
Vol 15 (16) ◽  
pp. 9413-9433 ◽  
Author(s):  
S. Eckhardt ◽  
B. Quennehen ◽  
D. J. L. Olivié ◽  
T. K. Berntsen ◽  
R. Cherian ◽  
...  
Keyword(s):  
Black Carbon ◽  
Atmospheric Chemistry ◽  
Emission Inventory ◽  
Dispersion Model ◽  
Measurement Data ◽  
Particle Dispersion ◽  
The Arctic ◽  
Late Winter ◽  
Data Set ◽  
Arctic Haze

Abstract. The concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of 2 years (2008–2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC or rBC and sulfate concentrations quite well, compared to previous comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January–March underestimated by 59 and 37 % for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44 % for July–September), but with overestimates as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is 3 times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modeling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution.

scholarly journals Exploiting satellite measurements to reduce uncertainties in UK bottom-up NO<sub>x</sub> emission estimates

2021 ◽  
Author(s):  
Richard J. Pope ◽  
Rebecca Kelly ◽  
Eloise A. Marais ◽  
Ailish M. Graham ◽  
Chris Wilson ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Spatiotemporal Variability ◽  
Nox Emissions ◽  
Ozone Monitoring Instrument ◽  
Top Down ◽  
Bottom Up ◽  
Emissions Inventory ◽  
Monitoring Instrument ◽  
Hazardous Pollutants ◽  
The Uk

Abstract. Nitrogen oxides (NOx, NO+NO2) are potent air pollutants which directly impact on human health and which aid the formation of other hazardous pollutants such as ozone (O3) and particulate matter. In this study, we use satellite tropospheric column nitrogen dioxide (TCNO2) data to evaluate the spatiotemporal variability and magnitude of the United Kingdom (UK) bottom-up National Atmospheric Emissions Inventory (NAEI) NOx emissions. Although emissions and TCNO2 represent different quantities, for UK city sources we find a spatial correlation of ~0.5 between the NAEI NOx emissions and TCNO2 from the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI), suggesting a good spatial distribution of emission sources in the inventory. Between 2005 and 2015, the NAEI total UK NOx emissions and long-term TCNO2 record from the Ozone Monitoring Instrument (OMI), averaged over England, show decreasing trends of 4.4 % and 2.2 %, respectively. Top-down NOx emissions were derived in this study by applying a simple mass balance approach to TROPOMI observed downwind NO2 plumes from city sources. Overall, these top-down estimates were consistent with the NAEI, but for larger cities such as London and Manchester the inventory is significantly (> 25 %) less than the top-down emissions. This NAEI NOx emission underestimate is supported by comparing simulations from the GEOS-Chem atmospheric chemistry model, driven by the NAEI emissions, with satellite and surface NO2 observations over the UK. This yields substantial model negative biases, providing further evidence to demonstrate that the NAEI may be underestimating NOx emissions in London and Manchester.

scholarly journals Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set

2015 ◽  
Vol 15 (7) ◽  
pp. 10425-10477 ◽  
Author(s):  
S. Eckhardt ◽  
B. Quennehen ◽  
D. J. L. Olivié ◽  
T. K. Berntsen ◽  
R. Cherian ◽  
...  
Keyword(s):  
Black Carbon ◽  
Atmospheric Chemistry ◽  
Emission Inventory ◽  
Dispersion Model ◽  
Measurement Data ◽  
Particle Dispersion ◽  
The Arctic ◽  
Late Winter ◽  
Data Set ◽  
Arctic Haze

Abstract. The concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of two years (2008–2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry-transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry-climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC/rBC and sulfate concentrations quite well, compared to past comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January-March underestimated by 59 and 37% for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44% for July–September), but with over- as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is three times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modelling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution.

scholarly journals HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 2: The bottom–up module

2020 ◽  
Vol 13 (3) ◽  
pp. 873-903
Author(s):  
Marc Guevara ◽  
Carles Tena ◽  
Manuel Porquet ◽  
Oriol Jorba ◽  
Carlos Pérez García-Pando
Keyword(s):  
Atmospheric Chemistry ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Companion Paper ◽  
Point Sources ◽  
Air Pollutant ◽  
Pollutant Emission ◽  
Emission Model ◽  
Bottom Up ◽  
Multi Scale

Abstract. We describe the bottom–up module of the High-Elective Resolution Modelling Emission System version 3 (HERMESv3), a Python-based and multi-scale modelling tool intended for the processing and computation of atmospheric emissions for air quality modelling. HERMESv3 is composed of two separate modules: the global_regional module and the bottom_up module. In a companion paper (Part 1, Guevara et al., 2019a) we presented the global_regional module. The bottom_up module described in this contribution is an emission model that estimates anthropogenic emissions at high spatial- (e.g. road link level,) and temporal- (hourly) resolution using state-of-the-art calculation methods that combine local activity and emission factors along with meteorological data. The model computes bottom–up emissions from point sources, road transport, residential and commercial combustion, other mobile sources, and agricultural activities. The computed pollutants include the main criteria pollutants (i.e. NOx, CO, NMVOCs (non-methane volatile organic compounds), SOx, NH3, PM10 and PM2.5) and greenhouse gases (i.e. CO2 and CH4, only related to combustion processes). Specific emission estimation methodologies are provided for each source and are mostly based on (but not limited to) the calculation methodologies reported by the European EMEP/EEA air pollutant emission inventory guidebook. Meteorologically dependent functions are also included to take into account the dynamical component of the emission processes. The model also provides several functionalities for automatically manipulating and performing spatial operations on georeferenced objects (shapefiles and raster files). The model is designed so that it can be applicable to any European country or region where the required input data are available. As in the case of the global_regional module, emissions can be estimated on several user-defined grids, mapped to multiple chemical mechanisms and adapted to the input requirements of different atmospheric chemistry models (CMAQ, WRF-Chem and MONARCH) as well as a street-level dispersion model (R-LINE). Specific emission outputs generated by the model are presented and discussed to illustrate its capabilities.

scholarly journals Comparison of the HadGEM2 climate-chemistry model against in-situ and SCIAMACHY atmospheric methane data

2014 ◽  
Vol 14 (9) ◽  
pp. 12967-13020 ◽  
Author(s):  
G. D. Hayman ◽  
F. M. O'Connor ◽  
M. Dalvi ◽  
D. B. Clark ◽  
N. Gedney ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Land Surface ◽  
Earth System Model ◽  
System Model ◽  
High Latitudes ◽  
Earth System ◽  
Atmospheric Methane ◽  
Surface Sites ◽  
Atmospheric Observations ◽  
The Uk

Abstract. Wetlands are a major emission source of methane (CH4) globally. In this study, we have evaluated wetland emission estimates derived using the UK community land surface model (JULES, the Joint UK Land Earth Simulator) against atmospheric observations of methane, including, for the first time, total methane columns derived from the SCIAMACHY instrument on board the ENVISAT satellite. Two JULES wetland emission estimates were investigated: (a) from an offline run driven with CRU-NCEP meteorological data and (b) from the same offline run in which the modelled wetland fractions were replaced with those derived from the Global Inundation Extent from Multi-Satellites (GIEMS) remote sensing product. The mean annual emission assumed for each inventory (181 Tg CH4 per annum over the period 1999–2007) is in line with other recently-published estimates. There are regional differences as the unconstrained JULES inventory gave significantly higher emissions in the Amazon and lower emissions in other regions compared to the JULES estimates constrained with the GIEMS product. Using the UK Hadley Centre's Earth System model with atmospheric chemistry (HadGEM2), we have evaluated these JULES wetland emissions against atmospheric observations of methane. We obtained improved agreement with the surface concentration measurements, especially at northern high latitudes, compared to previous HadGEM2 runs using the wetland emission dataset of Fung (1991). Although the modelled monthly atmospheric methane columns reproduced the large–scale patterns in the SCIAMACHY observations, they were biased low by 50 part per billion by volume (ppb). Replacing the HadGEM2 modelled concentrations above 300 hPa with HALOE–ACE assimilated TOMCAT output resulted in a significantly better agreement with the SCIAMACHY observations. The use of the GIEMS product to constrain JULES-derived wetland fraction improved the description of the wetland emissions in JULES and gave a good description of the seasonality observed at surface sites influenced by wetlands, especially at high latitudes. We found that the annual cycles observed in the SCIAMACHY measurements and at many of the surface sites influenced by non-wetland sources could not be reproduced in these HadGEM2 runs. This suggests that the emissions over certain regions (e.g., India and China) are possibly too high and/or the monthly emission patterns for specific sectors are incorrect. The comparisons presented in this paper have shown that the performance of the JULES wetland scheme is comparable to that of other process-based land surface models. We have identified areas for improvement in this and the atmospheric chemistry components of the HadGEM Earth System model. The Earth Observation datasets used here will be of continued value in future evaluations of JULES and the HadGEM family of models.

scholarly journals HERMESv3, a stand-alone multiscale atmospheric emission modelling framework – Part 2: bottom-up module

2019 ◽  
Author(s):  
Marc Guevara ◽  
Carles Tena ◽  
Manuel Porquet ◽  
Oriol Jorba ◽  
Carlos Pérez García-Pando
Keyword(s):  
Atmospheric Chemistry ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Road Transport ◽  
Companion Paper ◽  
Point Sources ◽  
Air Pollutant ◽  
Pollutant Emission ◽  
Emission Model ◽  
Bottom Up

Abstract. We describe the bottom-up module of the High-Elective Resolution Modelling Emission System version 3 (HERMESv3), a python-based and multiscale modelling tool intended for the processing and computation of atmospheric emissions for air quality modelling. HERMESv3 is composed of two separate modules: the global_regional module and the bottom_up module. In a companion paper (Part 1, Guevara et al., 2019) we presented the global_regional module. The bottom_up module described in this contribution is an emission model that estimates anthropogenic emissions at high spatial (e.g. road link level) and temporal (hourly) resolution using state-of-the-art calculation methods that combine local activity and emission factors along with meteorological data. The model computes bottom-up emissions from point sources, road transport, residential and commercial combustion, other mobile sources and agricultural activities. The computed pollutants include main criteria pollutants (i.e. NOx, CO, NMVOC, SOx, NH3, PM10 and PM2.5) and greenhouse gases (i.e. CO2 and CH4, only related to combustion processes). Specific emission estimation methodologies are provided for each source, and are mostly based on (but not limited to) the calculation methodologies reported by the European EMEP/EEA air pollutant emission inventory guidebook. Meteorological-dependent functions are also included to take into account the dynamical component of the emission processes. The model also provides several functionalities for automatically manipulating and performing spatial operations on georeferenced objects (shapefiles and raster files). The model is designed so that it can be applicable to any European country/region where the required input data is available. As in the case of the global_regional module, emissions can be estimated on several user-defined grids, mapped to multiple chemical mechanisms and adapted to the input requirements of different atmospheric chemistry models (CMAQ, WRF-Chem and MONARCH) as well as a street-level dispersion model (R-LINE). Specific emission outputs generated by the model are presented and discussed to illustrate its capabilities.

scholarly journals Comparison of the HadGEM2 climate-chemistry model against in situ and SCIAMACHY atmospheric methane data

2014 ◽  
Vol 14 (23) ◽  
pp. 13257-13280 ◽  
Author(s):  
G. D. Hayman ◽  
F. M. O'Connor ◽  
M. Dalvi ◽  
D. B. Clark ◽  
N. Gedney ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Land Surface ◽  
Earth System Model ◽  
System Model ◽  
Climatic Research Unit ◽  
Earth System ◽  
Atmospheric Methane ◽  
Surface Sites ◽  
Atmospheric Observations ◽  
The Uk

Abstract. Wetlands are a major emission source of methane (CH4) globally. In this study, we evaluate wetland emission estimates derived using the UK community land surface model (JULES, the Joint UK Land Earth Simulator) against atmospheric observations of methane, including, for the first time, total methane columns derived from the SCIAMACHY instrument on board the ENVISAT satellite. Two JULES wetland emission estimates are investigated: (a) from an offline run driven with Climatic Research Unit–National Centers for Environmental Prediction (CRU-NCEP) meteorological data and (b) from the same offline run in which the modelled wetland fractions are replaced with those derived from the Global Inundation Extent from Multi-Satellites (GIEMS) remote sensing product. The mean annual emission assumed for each inventory (181 Tg CH4 per annum over the period 1999–2007) is in line with other recently published estimates. There are regional differences as the unconstrained JULES inventory gives significantly higher emissions in the Amazon (by ~36 Tg CH4 yr−1) and lower emissions in other regions (by up to 10 Tg CH4 yr−1) compared to the JULES estimates constrained with the GIEMS product. Using the UK Hadley Centre's Earth System model with atmospheric chemistry (HadGEM2), we evaluate these JULES wetland emissions against atmospheric observations of methane. We obtain improved agreement with the surface concentration measurements, especially at high northern latitudes, compared to previous HadGEM2 runs using the wetland emission data set of Fung et al. (1991). Although the modelled monthly atmospheric methane columns reproduce the large-scale patterns in the SCIAMACHY observations, they are biased low by 50 part per billion by volume (ppb). Replacing the HadGEM2 modelled concentrations above 300 hPa with HALOE–ACE assimilated TOMCAT output results in a significantly better agreement with the SCIAMACHY observations. The use of the GIEMS product to constrain the JULES-derived wetland fraction improves the representation of the wetland emissions in JULES and gives a good description of the seasonality observed at surface sites influenced by wetlands, especially at high latitudes. We find that the annual cycles observed in the SCIAMACHY measurements and at many of the surface sites influenced by non-wetland sources cannot be reproduced in these HadGEM2 runs. This suggests that the emissions over certain regions (e.g. India and China) are possibly too high and/or the monthly emission patterns for specific sectors are incorrect. The comparisons presented in this paper show that the performance of the JULES wetland scheme is comparable to that of other process-based land surface models. We identify areas for improvement in this and the atmospheric chemistry components of the HadGEM Earth System model. The Earth Observation data sets used here will be of continued value in future evaluations of JULES and the HadGEM family of models.

scholarly journals Estimates of sub-national methane emissions from inversion modelling

2018 ◽  
Author(s):  
Sarah Connors ◽  
Alistair J. Manning ◽  
Andrew D. Robinson ◽  
Stuart N. Riddick ◽  
Grant L. Forster ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Dispersion Model ◽  
Methane Emissions ◽  
Particle Dispersion ◽  
Eastern Region ◽  
Atmospheric Methane ◽  
Atmospheric Measurements ◽  
East Anglia ◽  
The Uk

Abstract. Methane is a strong contributor to global climate change, yet our current understanding and quantification of its sources and their variability is incomplete. There is a growing need for comparisons between emission estimates produced using bottom-up inventory approaches and top-down inversion techniques based on atmospheric measurements, especially at higher spatial resolutions. To meet this need, this study presents using an inversion approach based on the Inversion Technique for Emissions Modelling (InTEM) framework and measurements from four sites in East Anglia, United Kingdom. Atmospheric methane concentrations were recorded at 1–2 minute time-steps at each location within the region of interest. These observations, coupled with the UK Met Office's Lagrangian particle dispersion model, NAME (Numerical Atmospheric dispersion Modelling Environment), were used within InTEM2014 to produce methane emission estimates for a 1-year period (June 2013–May 2014) in this eastern region of the UK (~ 100 × 150 km) at high spatial resolution (up to 4 × 4 km). InTEM2014 was able to produce realistic emissions estimates for East Anglia, and highlighted potential areas of difference from the UK National Atmospheric Emissions Inventory (NAEI). As this study was part of the UK Greenhouse gAs Uk and Global Emissions (GAUGE) project, observations were included within a national inversion using all eleven measurement sites across the UK to directly compare emission estimates for the East Anglia Region. Results show similar methane estimates for the East Anglia region. Methane emissions from Norfolk and Suffolk show good agreement with the estimates in NAEI, with differences of ~ 5 %. Larger differences are found for Cambridgeshire where our estimate is 22.5 % lower than that of NAEI. The addition of the EA sites within the national inversion system enabled finer spatial resolution and a decrease in the associated uncertainty for that area. Further development of our approach to include a more robust analysis of the methane concentration in the air entering this region and the uncertainty associated with the resulting emissions would strengthen this inverse method. Nonetheless, our results show there is value in high spatial resolution measurement networks and the resulting inversion emission estimates.

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