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Atmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modeling. Atmospheric nitrous oxide (N2O) measurements made in the United Kingdom (UK) and Republic of Ireland are used to derive monthly N2O emissions for 2013–2022 using two different inverse methods. We find mean UK emissions of 90.5 ± 23.0 (1σ) and 111.7 ± 32.1 (1σ) Gg N2O yr−1 for 2013–2022, and corresponding trends of −0.68 ± 0.48 (1σ) Gg N2O yr−2 and −2.10 ± 0.72 (1σ) Gg N2O yr−2, respectively, for the two inverse methods. The UK National Atmospheric Emissions Inventory (NAEI) reported mean N2O emissions of 73.9 ± 1.7 (1σ) Gg N2O yr−1 across this period, which is 22%–51% smaller than the emissions derived from atmospheric data. We infer a pronounced seasonal cycle in N2O emissions, with a peak occurring in the spring and a second smaller peak in the late summer for certain years. The springtime peak has a long seasonal decline that contrasts with the sharp rise and fall of N2O emissions estimated from the bottom‐up UK Emissions Model (UKEM). Bayesian inference is used to minimize the seasonal cycle mismatch between the average top‐down (atmospheric data‐based) and bottom‐up (process model and inventory‐based) seasonal emissions at a sub‐sector level. Increasing agricultural manure management and decreasing synthetic fertilizer N2O emissions reduces some of the discrepancy between the average top‐down and bottom‐up seasonal cycles. Other possibilities could also explain these discrepancies, such as missing emissions from NH3 deposition, but these require further investigation. Plain Language Summary: Atmospheric nitrous oxide (N2O) is an important greenhouse gas and ozone depleting substance. Atmospheric N2O measurements made in the United Kingdom (UK) and Republic of Ireland were used to derive UK N2O emissions for 2013–2022 using two inverse methods. UK emissions derived using atmospheric N2O measurements were on average 22%–51% higher than emissions reported in the UK National Atmospheric Emissions Inventory. A pronounced seasonal cycle in N2O emissions is inferred from the atmospheric N2O observations which contrasts the seasonal N2O emissions estimated in the bottom‐up (process model and inventory‐based) UK Emissions Model (UKEM). We find increasing agricultural manure management N2O emissions and decreasing synthetic fertilizer N2O emissions reduces some of the discrepancy between the seasonal cycles. Key Points: Atmospheric N2O measurements from 2013 to 2022 are used to evaluate the UK's reported emissions using two inverse methodsEmissions derived from atmospheric data are on average 22%–51% higher than the UK's national emissions inventory values across 2013–2022Agreement between the average top‐down and bottom‐up seasonal emissions was improved by decreasing bottom‐up synthetic fertilizer emissions [ABSTRACT FROM AUTHOR]

Hydrofluorocarbons (HFCs) are powerful anthropogenic greenhouse gases (GHGs) with high global-warming potentials (GWPs). They have been widely used as refrigerants, insulation foam-blowing agents, aerosol propellants, and fire suppression agents. Since the mid-1990s, emissions of HFCs have been increasing rapidly as they are used in many applications to replace ozone-depleting chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) whose consumption and production have been phased out under the Montreal Protocol (MP). Due to the high GWP of HFCs, the Kigali Amendment to the MP requires the phasedown of production and consumption of HFCs to gradually achieve an 80 %–85 % reduction by 2047, starting in 2019 for non-Article 5 (developed) countries with a 10 % reduction against each defined baseline and later schedules for Article 5 (developing) countries. In this study, we have examined long-term high-precision measurements of atmospheric abundances of five major HFCs (HFC-134a, HFC-143a, HFC-32, HFC-125, and HFC-152a) at Gosan station, Jeju Island, South Korea, from 2008 to 2020. Background abundances of HFCs gradually increased, and the inflow of polluted air masses with elevated abundances from surrounding source regions were detected over the entire period. From these pollution events, we inferred regional and country-specific HFC emission estimates using two independent Lagrangian particle dispersion models and Bayesian inversion frameworks (FLEXPART-FLEXINVERT + and NAME-InTEM). The spatial distribution of the derived "top-down" (measurement based) emissions for all HFCs shows large fluxes from megacities and industrial areas in the region. Our most important finding is that HFC emissions in eastern China and Japan have sharply increased from 2016 to 2018. The contribution of East Asian HFC emissions to the global total increased from 9 % (2008–2014) to 13 % (2016–2020). In particular, HFC emissions in Japan (Annex I country) rose rapidly from 2016 onward, with accumulated total inferred HFC emissions being ∼ 114 Gg yr -1 , which is ∼ 76 Gg yr -1 higher for 2016–2020 than the "bottom-up" (i.e., based on activity data and emission factors) emissions of ∼ 38 Gg yr -1 reported to the United Nations Framework Convention on Climate Change (UNFCCC). This is likely related to the increase in domestic demand in Japan for refrigerants and air-conditioning-system-related products and incomplete accounting. A downward trend of HFC emissions that started in 2019 reflects the effectiveness of the F-gas policy in Japan. Eastern China and South Korea, though not obligated to report to the UNFCCC, voluntarily reported emissions, which also show differences between top-down and bottom-up emission estimates, demonstrating the need for atmospheric measurements, comprehensive data analysis, and accurate reporting for precise emission management. Further, the proportional contribution of each country's CO 2 -equivalent HFC emissions has changed over time, with HFC-134a decreasing and HFC-125 increasing. This demonstrates the transition in the predominant HFC substances contributing to global warming in each country. [ABSTRACT FROM AUTHOR]

We investigate the use of atmospheric oxygen (O 2) and carbon dioxide (CO 2) measurements for the estimation of the fossil fuel component of atmospheric CO 2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O 2 and CO 2 , minimizing the influence of terrestrial biosphere fluxes – is simulated at three sites in the UK, two of which make APO measurements. We present a set of model experiments that estimate the sensitivity of APO simulations to key inputs: fluxes from the ocean, fossil fuel flux magnitude and distribution, the APO baseline, and the exchange ratio of O 2 to CO 2 fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compare three ocean O 2 flux estimates from the NEMO–ERSEM, the ECCO–Darwin ocean model, and the Jena CarboScope (JC) APO inversion. The sensitivity of APO to fossil fuel emission magnitudes and to terrestrial biosphere and fossil fuel exchange ratios is investigated through Monte Carlo sampling within literature uncertainty ranges and by comparing different inventory estimates. We focus our model–data analysis on the year 2015 as ocean fluxes are not available for later years. As APO measurements are only available for one UK site at this time, our analysis focuses on the Weybourne station. Model–data comparisons for two additional UK sites (Heathfield and Ridge Hill) in 2021, using ocean flux climatologies, are presented in the Supplement. Of the factors that could potentially compromise simulated APO-derived fossil fuel CO 2 (ffCO 2) estimates, we find that the ocean O 2 flux estimate has the largest overall influence at the three sites in the UK. At times, this influence is comparable in magnitude to the contribution of simulated fossil fuel CO 2 to simulated APO. We find that simulations using different ocean fluxes differ from each other substantially. No single model estimate, or a model estimate that assumed zero ocean flux, provided a significantly closer fit than any other. Furthermore, the uncertainty in the ocean contribution to APO could lead to uncertainty in defining an appropriate regional background from the data. Our findings suggest that the contribution of non-terrestrial sources needs to be better accounted for in model simulations of APO in the UK to reduce the potential influence on inferred fossil fuel CO 2 using APO. [ABSTRACT FROM AUTHOR]

Atmospheric methane (CH 4) is the second-most-important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO 2). Anthropogenic sources account for ∼ 60 % of global CH 4 emissions, of which 20 % come from oil and gas exploration, production and distribution. High-resolution satellite-based imaging spectrometers are becoming important tools for detecting and monitoring CH 4 point source emissions, aiding mitigation. However, validation of these satellite measurements, such as those from the commercial GHGSat satellite constellation, has so far not been documented for active leaks. Here we present the monitoring and quantification, by GHGSat's satellites, of the CH 4 emissions from an active gas leak from a downstream natural gas distribution pipeline near Cheltenham, UK, in the spring and summer of 2023 and provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys. We also use a Lagrangian transport model, the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME), to estimate the flux from both satellite- and ground-based observation methods and assess the leak's contribution to observed concentrations at a local tall tower site (30 km away). We find GHGSat's emission estimates to be in broad agreement with those made from the in situ measurements. During the study period (March–June 2023) GHGSat's emission estimates are 236–1357 kg CH 4 h -1 , whereas the mobile surface measurements are 634–846 kg CH 4 h -1. The large variability is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181–1243 kg CH 4 h -1 , which are lower than the satellite- and mobile-survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat's observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including unplanned fugitive releases that are inherently challenging to identify, track, and estimate their impact and duration. Rapid, widespread access to such data to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing anthropogenic contributions to climate change. [ABSTRACT FROM AUTHOR]

Hydrofluorocarbons (HFCs) are powerful anthropogenic greenhouse gases (GHGs) with high global warming potentials (GWPs). They have been widely used as refrigerants, insulation foam blowing agents, aerosol propellants, and fire suppression agents. Since the mid-1990s, emissions of HFCs have been increasing rapidly as they are used in many applications to replace ozone depleting chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) whose consumption and production have been phased out under the Montreal Protocol (MP). Due to the high GWP of HFCs, the Kigali amendment to the MP requires the phase-down of production and consumption of HFCs to gradually achieve an 80-85% reduction by 2047 starting in 2019 for non-Article 5 (developed) countries with a 10% reduction against each defined baseline and later schedules for Article 5 (developing) countries. In this study, we have examined long-term high precision measurements of atmospheric abundances of 5 major HFCs (HFC-134a, HFC-143a, HFC-32, HFC-125, and HFC-152a) at Gosan station, Jeju Island, South Korea from 2008 to 2020. Background abundances of HFCs gradually increased, and the inflow of polluted air masses with elevated abundances from surrounding source regions were detected over the entire period. From these pollution events, we inferred regional and country-specific HFC emission estimates using two independent Lagrangian particle dispersion models and Bayesian inversion frameworks (FLEXPART-FLEXINVERT+ and NAME-InTEM). The spatial distribution of the derived "top-down" (measurement based) emissions for all HFCs shows large fluxes from megacities and industrial areas in the region. Our most important finding is that HFC emissions in eastern China and Japan have sharply increased since 2016. The contribution of East Asian HFC emissions to the global total increased from 9% (2008-2015) to 15% (2016-2020). In particular, HFCs emissions in Japan (Annex 1 country) rose rapidly from 2016 onward, with accumulated total inferred HFCs emissions being ~76 Gg/yr higher for 2016-2020 than the "bottom-up" (i.e., based on activity data and emission factors) emissions reported to the United Nations Framework Convention on Climate Change (UNFCCC). This is likely related to the increase in domestic demand in Japan for refrigerants and air-conditioning system-related products and incomplete accounting. A downward trend of HFCs emissions that started in 2019 reflects the effectiveness of the F-gas policy in Japan. Eastern China and South Korea, though not obligated to report to UNFCCC, voluntarily reported emissions, which also show differences between top-down and bottom-up emission estimates, demonstrating the need for atmospheric measurements, comprehensive data analysis and accurate reporting for precise emissions management. [ABSTRACT FROM AUTHOR]

Isotopic measurements of atmospheric methane are valuable for the verification of bottom‐up atmospheric emissions inventories. The balance of sources in emissions inventories must be consistent with the δ13C‐CH4 isotopic record in the air. Long‐term records of both methane mole fraction and δ13C from five sites across the UK are presented, showing post‐2007 growth in CH4 and negative trend in δ13C, consistent with global background sites. Miller‐Tans analyses of atmospheric measurements identified that the δ13C signature of the methane source mix varied between −50.1 and −56.1‰, with less depleted δ13C signatures at sites receiving air from urban areas, consistent with an increased proportion of thermogenic sources. Isotopic signatures calculated for all sites are more enriched than those expected from the bottom‐up emissions inventory, suggesting that inventories for the UK either underestimate contributions of thermogenic/pyrogenic emissions or overestimate biogenic sources. Plain Language Summary: Methane is a powerful greenhouse gas. The UK has committed to net zero carbon emissions by 2050 and has signed the Global Methane Pledge, which aims to reduce methane emissions by 30% by 2030, compared to 2020 levels. This study analyses long‐term measurements of methane and its carbon isotope ratio (δ13C) collected by Royal Holloway, University of London, across five UK sites. The δ13C source signature value acts as a fingerprint, distinguishing between different sources of methane. Bulk regional isotopic source signatures for each site were calculated, showing that urban sites are dominated by thermogenic emissions (e.g., gas leaks) and rural sites by agricultural emissions (e.g., cattle). The data presented in this study are compared with data from the UK National Atmospheric Emissions Inventory (NAEI). According to the data that were collected by Royal Holloway, University of London, the NAEI underestimates the relative contribution of fossil fuel sources in the UK, such as gas from pipelines in domestic and industrial settings, or overestimates biogenic sources. This has important implications for the design of UK greenhouse gas reduction strategies. Key Points: Long‐term records of CH4 mole fraction and δ13C‐CH4 from five UK sites show an increase in CH4 and a decrease in δ13CThe Miller‐Tans method can be used to calculate bulk regional δ13C‐CH4 source signaturesThe relative contribution of thermogenic/pyrogenic emissions across the UK may be underestimated in inventories [ABSTRACT FROM AUTHOR]

Atmospheric methane (CH4) is the second most important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO2). Anthropogenic sources account for ~60% of global CH4 emissions, of which 20% come from oil & gas exploration, production and distribution. High-resolution satellite-based imaging spectrometers are becoming important tools for detecting and monitoring CH4 point source emissions, aiding mitigation. However, validation of these satellite measurements, such as those from the commercial GHGSat satellite constellation, has so far not been documented for active leaks. Here we present the monitoring and quantification, by GHGSat’s satellites, of the CH4 emissions from an active gas leak from a downstream natural gas distribution pipeline near Cheltenham, UK in Spring/Summer 2023, and provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys. We also use a Lagrangian transport model, NAME, to estimate the flux from both satellite and groundbased observation methods and assess the leak’s contribution to observed concentrations at a local tall tower site (30 km away). We find GHGSat’s emission estimates to be in broad agreement with those made from the in-situ measurements. During the study period (March-June 2023) GHGSat’s emission estimates are 236 - 1357 kg CH4 hr-1 whereas the mobile surface measurements are 886 -998 kg CH4 hr-1 The large variation is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181-1243 kg CH4 hr-1, which are lower than the satellite- and mobile survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat’s observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including for unplanned fugitive releases that are inherently challenging to identify, track and estimate their impact and duration. Rapid access to such evidence to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing the anthropogenic contribution to climate change. [ABSTRACT FROM AUTHOR]

Radiocarbon (14C) is a powerful tracer of fossil emissions because fossil fuels are entirely depleted in 14C, but observations of 14CO2 and especially 14CH4 in urban regions are sparse. We present the first observations of 14C in both methane (CH4) and carbon dioxide (CO2) in an urban area (London) using a recently developed sampling system. We find that the fossil fraction of CH4 and the atmospheric concentration of fossil CO2 are consistently higher than simulated values using the atmospheric dispersion model NAME coupled with emission inventories. Observed net biospheric uptake in June–July is not well correlated with simulations using the SMURF model with NAME. The results show the partitioning of fossil and biospheric CO2 and CH4 in cities can be evaluated and improved with 14C observations when the nuclear power plants influence is negligible. Plain Language Summary: Radiocarbon (14C) is an ideal tracer of fossil emissions, as fossil fuels have lost all 14C during millions of years of burial underground. When fossil carbon is re‐introduced into the atmosphere, it exerts a strong dilution of the radiocarbon to total carbon ratio. By measuring this ratio in the atmosphere, we can quantify fossil methane and carbon dioxide emissions. This is the first combined study of 14C in both atmospheric methane and carbon dioxide at regional scale. Key Points: Atmospheric radiocarbon measurements in central London reveal higher fossil CH4 and CO2 present, compared to simulationsRadiocarbon measurements show biospheric uptake of CO2 in July that is stronger than simulationsNuclear power plants interfere with radiocarbon measurements in London when air is coming from Europe [ABSTRACT FROM AUTHOR]

Production and consumption of CFC-11 (trichlorofluoromethane, CCl 3 F), CFC-12 (dichlorodifluoromethane, CCl 2 F 2) and CCl 4 (carbon tetrachloride) are controlled under the regulations of the Montreal Protocol and have been phased out globally since 2010. Only CCl 4 is still widely produced as a chemical feedstock. After 2010, emissions of CFC-11 and CFC-12 should therefore mostly originate from existing banks (e.g. from foams, mobile air conditioning units and refrigerators); however evidence has emerged of an increase in global emissions of CFC-11 in the last decade, some of which has not been fully accounted for. The motivation for this work was to assess the emissions of CFC-11, CFC-12 and CCl 4 from western Europe. All countries in this region have been subject to the controls of the Montreal Protocol since the late 1980s and, as non-Article 5 Parties, have been prohibited from producing CFCs and CCl 4 for dispersive use since 1996. Four different inverse modelling systems are used to estimate emissions of these gases from 2008 to 2021 using data from four atmospheric measurement stations: Mace Head (Ireland), Jungfraujoch (Switzerland), Monte Cimone (Italy) and Tacolneston (UK). The average of the four model studies found that western European emissions of CFC-11, CFC-12 and CCl 4 between 2008 and 2021 were declining at 3.5 % yr -1 (2.7 % yr -1 –4.8 % yr -1), 7.7 % yr -1 (6.3 % yr -1 –8.0 % yr -1) and 4.4 % yr -1 (2.6 % yr -1 –6.4 % yr -1), respectively. Even though the emissions were declining throughout the period, the area including northern France, Belgium, the Netherlands and Luxembourg showed consistently elevated emissions of CFC-11 compared with the surrounding regions. Emissions of CFC-12 were slightly elevated in the same region. CCl 4 emissions were the highest in the south of France. France had the highest emissions of all three gases over the period 2008–2021. Emissions from western Europe (2008–2021) were on average 2.4 ± 0.4 Gg (CFC-11), 1.3 ± 0.3 Gg (CFC-12) and 0.9 ± 0.2 Gg (CCl 4). Our estimated decline in emissions of CFC-11 is consistent with a western European bank release rate of 3.4 % (2.6 %–4.5 %). This study concludes that emissions of CFC-11, CFC-12 and CCl 4 have all declined from 2008 to 2021 in western Europe. Therefore, no evidence is found that western European emissions contributed to the unexplained part of the global increase in atmospheric concentrations of CFC-11 observed in the last decade. [ABSTRACT FROM AUTHOR]

Atmospheric methane (CH 4) concentrations are rising, which are expected to lead to a corresponding increase in the global seasonal cycle amplitude (SCA) – the difference between its seasonal maximum and minimum values. The reaction between CH 4 and its main sink, OH, is dependent on the amount of CH 4 and OH in the atmosphere. The concentration of OH varies seasonally, and due to the increasing burden of CH 4 in the atmosphere, it is expected that the SCA of CH 4 will increase due to the increased removal of CH 4 through a reaction with OH in the atmosphere. Spatially varying changes in the SCA could indicate long-term persistent variations in the seasonal sources and sinks, but such SCA changes have not been investigated. Here we use surface flask measurements and a 3D chemical transport model (TOMCAT) to diagnose changes in the SCA of atmospheric CH 4 between 1995–2020 and attribute the changes regionally to contributions from different sectors. We find that the observed SCA decreased by 4 ppb (7.6 %) in the northern high latitudes (NHLs; 60–90 ∘ N), while the SCA increased globally by 2.5 ppb (6.5 %) during this time period. TOMCAT reproduces the change in the SCA at observation sites across the globe. Therefore, we use it to attribute regions which are contributing to the changes in the NHL SCA, which shows an unexpected change in the SCA that differs from the rest of the world. We find that well-mixed background CH 4 , likely from emissions originating in, and transported from, more southerly latitudes has the largest impact on the decreasing SCA in the NHLs (56.5 % of total contribution to NHLs). In addition to the background CH 4 , recent emissions from Canada, the Middle East, and Europe contribute 16.9 %, 12.1 %, and 8.4 %, respectively, to the total change in the SCA in the NHLs. The remaining contributions are due to changes in emissions and transport from other regions. The three largest regional contributions are driven by increases in summer emissions from the Boreal Plains in Canada, decreases in winter emissions across Europe, and a combination of increases in summer emissions and decreases in winter emissions over the Arabian Peninsula and Caspian Sea in the Middle East. These results highlight that changes in the observed seasonal cycle can be an indicator of changing emission regimes in local and non-local regions, particularly in the NHL, where the change is counterintuitive. [ABSTRACT FROM AUTHOR]

We investigate the use of oxygen (O2) and carbon dioxide (CO2) measurements for the estimation of the fossil fuel component of atmospheric CO2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O2 and CO2, minimising the influence of terrestrial biosphere fluxes – is simulated at three sites in the UK, two of which make atmospheric APO measurements. We present a set of model experiments that estimate the sensitivity of APO simulations to key inputs: fluxes from the ocean, fossil fuel flux magnitude and distribution, the APO baseline, and the ratio of O2 to CO2 fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compared three ocean O2 flux estimates, from the NEMO – ERSEM and ECCO-Darwin ocean models, and the Jena CarboScope APO inversion. The sensitivity of APO to fossil fuel emission magnitudes and to terrestrial biosphere and fossil fuel exchange ratios was investigated through Monte Carlo sampling within literature uncertainty ranges, and by comparing different inventory estimates. Of the factors that could potentially compromise APO-derived fossil fuel CO2 estimates, we find that the ocean O2 flux estimate has the largest overall influence at the three sites in the UK. At times, this influence is comparable to the contribution to APO of simulated fossil fuel CO2. We find that simulations using different ocean fluxes differ from each other substantially, with no single model estimate, or a simulation with zero ocean flux, providing a significantly closer fit to the observations. Furthermore, the uncertainty in the ocean contribution to APO could lead to uncertainty in defining an appropriate regional background from the data. Our findings suggest that the contribution of non-terrestrial sources need to be well accounted for, in order to reduce their potential influence on inferred fossil fuel CO2. [ABSTRACT FROM AUTHOR]

Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships ("footprints") for use in applications such as greenhouse gas (GHG) flux inversions. Because a single model simulation is required for each data point, LPDMs do not scale well to applications with large data sets such as flux inversions using satellite observations. Here, we develop a proof-of-concept machine learning emulator for LPDM footprints over a ∼ 350 km × 230 km region around an observation point, and test it for a range of in situ measurement sites from around the world. As opposed to previous approaches to footprint approximation, it does not require the interpolation or smoothing of footprints produced by the LPDM. Instead, the footprint is emulated entirely from meteorological inputs. This is achieved by independently emulating the footprint magnitude at each grid cell in the domain using gradient-boosted regression trees with a selection of meteorological variables as inputs. The emulator is trained based on footprints from the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) for 2014 and 2015, and the emulated footprints are evaluated against hourly NAME output from 2016 and 2020. When compared to CH 4 concentration time series generated by NAME, we show that our emulator achieves a mean R -squared score of 0.69 across all sites investigated between 2016 and 2020. The emulator can predict a footprint in around 10 ms, compared to around 10 min for the 3D simulator. This simple and interpretable proof-of-concept emulator demonstrates the potential of machine learning for LPDM emulation. [ABSTRACT FROM AUTHOR]

Production and consumption of CFC-11 (trichlorofluoromethane, CCl3F), CFC-12 dichlorodifluoromethane, CCl2F2) and CCl4 (carbon tetrachloride) are controlled under the regulations of the Montreal Protocol and have been phased out globally for dispersive use since 2010. Only CCl4 is still widely produced under exemption as a chemical feedstock (non-dispersive use). After 2010, emissions of CFC-11 and CFC-12 should therefore mostly originate from existing banks (e.g. foams and refrigerators), however evidence emerged of an increase in global emissions of CFC-11, which was in part attributed to eastern China. Emissions of CFC-11, CFC-12 and CCl4 have subsequently declined in this region, however the total global increase in CFC-11 was not fully accounted for. The motivation for this work was to assess the emissions of CFC-11 and the associated gases, CFC-12 and CCl4, from Western Europe. All countries in this region have been subject to the controls of the Montreal Protocol since the late 1980s, and, as non-Article-5 Parties, have been prohibited from producing CFCs and CCl4 for dispersive use since 1995. Four different inverse modelling systems are used to estimate emissions of these gases from 2008–2021 using data from four atmospheric measurement stations: Mace Head (Ireland), Jungfraujoch (Switzerland), Monte Cimone (Italy) and Tacolneston (UK). The average of the four model studies found that Western European emissions of CFC-11, CFC-12 and CCl4 between 2008 and 2021 were declining at 3.5 (2.7–4.8) %, 7.7 (6.3–8.0) % and 4.4 (2.6–6.4) % yr−1 respectively. Throughout this period, the highest CFC-11 emissions were in Northern France and Benelux (Belgium, the Netherlands and Luxembourg). Emissions of CFC-12 co-located in this region were slightly higher than elsewhere in Western Europe, and also showed some enhancement of CCl4 emissions. However for CCl4, emissions were highest in the south of France. France had the highest emissions of CFC-11, CFC-12 and CCl4 over the period 2008–2021. Emissions from Western Europe (2008–2021) were on average 2.4 ± 0.4 Gg (CFC-11), 1.3 ± 0.3 Gg (CFC-12), 0.9 ± 0.2 Gg (CCl4). This study concludes that the emissions of CFC-11 from Northern France and Benelux are unlikely to be the result of new production. Our estimated decline in emissions of CFC-11 is consistent with a Western European bank release rate of 3.4 (2.6–4.5) %, which is in the upper half of the published range. [ABSTRACT FROM AUTHOR]

Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships ("footprints") for use in applications such as greenhouse gas (GHG) flux inversions. Because a single model simulation is required for each data point, LPDMs do not scale well to applications with large data sets such as flux inversions using satellite observations. Here, we develop a proof-of-concept machine learning emulator for LPDM footprints over a ~350 km by 230 km region around an observation point, and test it for a range of in situ measurement sites from around the world. As opposed to previous approaches to footprint approximation, it does not require the interpolation or smoothing of footprints produced by the LPDM. Instead, the footprint is emulated entirely from meteorological inputs. This is achieved by independently emulating the footprint magnitude at each grid cell in the domain using gradient-boosted regression trees (GBRTs) with a selection of meteorological variables as inputs. The emulator is trained based on footprints from the UK Met Office Numerical Atmospheric dispersion Modelling Environment (NAME) for 2014 and 2015, and the emulated footprints are evaluated against hourly NAME output from 2016 and 2020. When compared to CH4 concentration time series generated by NAME, we show that our emulator achieves a mean R-squared score of 0.69 across all sites investigated between 2016 and 2020. The emulator can predict a footprint in around 10 ms, compared to around 10 minutes for the 3D simulator. This simple and interpretable proof-of-concept emulator demonstrates the potential of machine learning for LPDM emulation. [ABSTRACT FROM AUTHOR]

Global emissions of the ozone-depleting gas HCFC-141b (1,1-dichloro-1-fluoroethane, CH 3 CCl 2 F) derived from measurements of atmospheric mole fractions increased between 2017 and 2021 despite a fall in reported production and consumption of HCFC-141b for dispersive uses. HCFC-141b is a controlled substance under the Montreal Protocol, and its phase-out is currently underway, after a peak in reported consumption and production in developing (Article 5) countries in 2013. If reported production and consumption are correct, our study suggests that the 2017–2021 rise is due to an increase in emissions from the bank when appliances containing HCFC-141b reach the end of their life, or from production of HCFC-141b not reported for dispersive uses. Regional emissions have been estimated between 2017–2020 for all regions where measurements have sufficient sensitivity to emissions. This includes the regions of northwestern Europe, east Asia, the United States and Australia, where emissions decreased by a total of 2.3 ± 4.6 Gg yr -1 , compared to a mean global increase of 3.0 ± 1.2 Gg yr -1 over the same period. Collectively these regions only account for around 30 % of global emissions in 2020. We are not able to pinpoint the source regions or specific activities responsible for the recent global emission rise. [ABSTRACT FROM AUTHOR]

We present a method for estimating fossil fuel methane emissions using observations of methane and ethane, accounting for uncertainty in their emission ratio. The ethane:methane emission ratio is incorporated as a spatially and temporally variable parameter in a Bayesian model, with its own prior distribution and uncertainty. We find that using an emission ratio distribution mitigates bias from using a fixed, potentially incorrect emission ratio and that uncertainty in this ratio is propagated into posterior estimates of emissions. A synthetic data test is used to show the impact of assuming an incorrect ethane:methane emission ratio and demonstrate how our variable parameter model can better quantify overall uncertainty. We also use this method to estimate UK methane emissions from high-frequency observations of methane and ethane from the UK Deriving Emissions linked to Climate Change (DECC) network. Using the joint methane–ethane inverse model, we estimate annual mean UK methane emissions of approximately 0.27 (95 % uncertainty interval 0.26–0.29) Tgyr-1 from fossil fuel sources and 2.06 (1.99–2.15) Tgyr-1 from non-fossil fuel sources, during the period 2015–2019. Uncertainties in UK fossil fuel emissions estimates are reduced on average by 15 % and up to 35 % when incorporating ethane into the inverse model, in comparison to results from the methane-only inversion. [ABSTRACT FROM AUTHOR]

Atmospheric measurements can be used as a tool to evaluate national greenhouse gas inventories through inverse modelling. Using 8 years of continuous methane (CH 4) concentration data, this work assesses the United Kingdom's (UK) CH 4 emissions over the period 2013–2020. Using two different inversion methods, we find mean emissions of 2.10 ± 0.09 and 2.12 ± 0.26 Tg yr -1 between 2013 and 2020, an overall trend of - 0.05 ± 0.01 and - 0.06 ± 0.04 Tg yr -2 and a 2 %–3 % decrease each year. This compares with the mean emissions of 2.23 Tg yr -1 and the trend of - 0.03 Tg yr -2 (1 % annual decrease) reported in the UK's 2021 inventory between 2013 and 2019. We examine how sensitive these estimates are to various components of the inversion set-up, such as the measurement network configuration, the prior emissions estimate, the inversion method and the atmospheric transport model used. We find the decreasing trend to be due, primarily, to a reduction in emissions from England, which accounts for 70 % of the UK CH 4 emissions. Comparisons during 2015 demonstrate consistency when different atmospheric transport models are used to map the relationship between sources and atmospheric observations at the aggregation level of the UK. The posterior annual national means and negative trend are found to be consistent across changes in network configuration. We show, using only two monitoring sites, that the same conclusions on mean UK emissions and negative trend would be reached as using the full six-site network, albeit with larger posterior uncertainties. However, emissions estimates from Scotland fail to converge on the same posterior under different inversion set-ups, highlighting a shortcoming of the current observation network in monitoring all of the UK. Although CH 4 emissions in 2020 are estimated to have declined relative to previous years, this decrease is in line with the longer-term emissions trend and is not necessarily a response to national lockdowns. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We present a method for estimating fossil fuel methane emissions using observations of methane and ethane, accounting for uncertainty in their emission ratio. The ethane:methane emission ratio is incorporated as a variable parameter in a Bayesian model, with its own prior distribution and uncertainty. We find that using an emission ratio distribution mitigates bias from using a fixed, potentially incorrect emission ratio and that uncertainty in this ratio is propagated into posterior estimates of 5 emissions. A synthetic data test is used to show the impact of assuming an incorrect ethane:methane emission ratio and demonstrate how our variable parameter model can better quantify overall uncertainty. We also use this method to estimate UK methane emissions from high-frequency observations of methane and ethane from the UK Deriving Emissions linked to Climate Change (DECC) network. Using the joint methane-ethane inverse model, we estimate annual mean UK methane emissions of approximately 0.27 (95% uncertainty interval 0.26-0.29) Tg y-1 from fossil fuel sources and 2.06 (1.99-2.15) 10 Tg y-1 from non-fossil fuel sources, during the period 2015-2019. Uncertainties in UK fossil fuel emissions estimates are reduced on average by 15%, and up to 35%, when incorporating ethane into the inverse model, in comparison to results from the methane-only inversion. [ABSTRACT FROM AUTHOR]

National greenhouse gas inventories (GHGIs) are submitted annually to the United Nations Framework Convention on Climate Change (UNFCCC). They are estimated in compliance with Intergovernmental Panel on Climate Change (IPCC) methodological guidance using activity data, emission factors and facility-level measurements. For some sources, the outputs from these calculations are very uncertain. Inverse modelling techniques that use high-quality, long-term measurements of atmospheric gases have been developed to provide independent verification of national GHGIs. This is considered good practice by the IPCC as it helps national inventory compilers to verify reported emissions and to reduce emission uncertainty. Emission estimates from the InTEM (Inversion Technique for Emission Modelling) model are presented for the UK for the hydrofluorocarbons (HFCs) reported to the UNFCCC (HFC-125, HFC-134a, HFC-143a, HFC-152a, HFC-23, HFC-32, HFC-227ea, HFC-245fa, HFC-43-10mee and HFC-365mfc). These HFCs have high global warming potentials (GWPs), and the global background mole fractions of all but two are increasing, thus highlighting their relevance to the climate and a need for increasing the accuracy of emission estimation for regulatory purposes. This study presents evidence that the long-term annual increase in growth of HFC-134a has stopped and is now decreasing. For HFC-32 there is an early indication, its rapid global growth period has ended, and there is evidence that the annual increase in global growth for HFC-125 has slowed from 2018. The inverse modelling results indicate that the UK implementation of European Union regulation of HFC emissions has been successful in initiating a decline in UK emissions from 2018. Comparison of the total InTEM UK HFC emissions in 2020 with the average from 2009–2012 shows a drop of 35 % , indicating progress toward the target of a 79 % decrease in sales by 2030. The total InTEM HFC emission estimates (2008–2018) are on average 73 (62–83) % of, or 4.3 (2.7–5.9) Tg CO2 -eq yr-1 lower than, the total HFC emission estimates from the UK GHGI. There are also significant discrepancies between the two estimates for the individual HFCs. [ABSTRACT FROM AUTHOR]

Perfluorocarbons (PFCs) are amongst the most potent greenhouse gases listed under the United Nations Framework Convention on Climate Change (UNFCCC). With atmospheric lifetimes on the order of thousands to tens of thousands of years, PFC emissions represent a permanent alteration to the global atmosphere on human timescales. While the industries responsible for the vast majority of these emissions – aluminium smelting and semi-conductor manufacturing – have made efficiency improvements and introduced abatement measures, the global mean mole fractions of three PFCs, namely tetrafluoromethane (CF 4 , PFC-14), hexafluoroethane (C 2 F 6 , PFC-116) and octafluoropropane (C 3 F 8 , PFC-218), continue to grow. In this study, we update baseline growth rates using in situ high-frequency measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) and, using data from four European stations, estimate PFC emissions for northwest Europe. The global growth rate of CF 4 decreased from 1.3 ppt yr -1 in 1979 to 0.6 ppt yr -1 around 2010 followed by a renewed steady increase to 0.9 ppt yr -1 in 2019. For C 2 F 6 , the growth rate grew to a maximum of 0.125 ppt yr -1 around 1999, followed by a decline to a minimum of 0.075 ppt yr -1 in 2009, followed by weak growth thereafter. The C 3 F 8 growth rate was around 0.007 ppt yr -1 until the early 1990s and then quickly grew to a maximum of 0.03 ppt yr -1 in 2003–2004. Following a period of decline until 2012 to 0.015 ppt yr -1 , the growth rate slowly increased again to ∼ 0.017 ppt yr -1 in 2019. We used an inverse modelling framework to infer PFC emissions for northwest Europe. No statistically significant trend in regional emissions was observed for any of the PFCs assessed. For CF 4 , European emissions in early years were linked predominantly to the aluminium industry. However, we link large emissions in recent years to a chemical manufacturer in northwest Italy. Emissions of C 2 F 6 are linked to a range of sources, including a semi-conductor manufacturer in Ireland and a cluster of smelters in Germany's Ruhr valley. In contrast, northwest European emissions of C 3 F 8 are dominated by a single source in northwest England, raising the possibility of using emissions from this site for a tracer release experiment. [ABSTRACT FROM AUTHOR]

Environmental context: Chlorofluorocarbons (CFCs) are potent greenhouse and stratospheric ozone depleting trace gases. Their atmospheric concentrations are in decline, thanks to global production and consumption controls imposed by the Montreal Protocol. In recent years, the rates of decline of CFC atmospheric concentrations, especially for CFC-11 (CCl3F), are not as large as anticipated under the Protocol, resulting in renewed efforts to estimate CFC consumption and/or emissions to possibly identify new or poorly quantified sources. Australian emissions of chlorofluorocarbons (CFCs) have been estimated from atmospheric CFC observations by both inverse modelling and interspecies correlation techniques, and from CFC production, import and consumption data compiled by industry and government. Australian and global CFC emissions show similar temporal behaviour, with emissions peaking in the late-1980s and then declining by ~10 % per year through to the present. Australian CFC emissions since 1978 account for less than 1 % of global emissions and therefore make a correspondingly small contribution to stratospheric ozone depletion. The current CFC emissions in Australia are likely from 'banks' of closed-cell foams, and refrigeration–air conditioning equipment now more than 20 years old. There is no evidence of renewed consumption or emissions of CFCs in Australia. The reduction in CFC emissions has made a significant contribution to reducing Australian greenhouse gas emissions. Environmental context. Chlorofluorocarbons (CFCs) are potent greenhouse and stratospheric ozone depleting trace gases. Their atmospheric concentrations are in decline, thanks to global production and consumption controls imposed by the Montreal Protocol. In recent years, the rates of decline of CFC atmospheric concentrations, especially for CFC-11 (CCl3F), are not as large as anticipated under the Protocol, resulting in renewed efforts to estimate CFC consumption and/or emissions to possibly identify new or poorly quantified sources. [ABSTRACT FROM AUTHOR]

We report a 40-year history of SF6 atmospheric mole fractions measured at the Advanced Global Atmospheric Gases Experiment (AGAGE) monitoring sites, combined with archived air samples, to determine emission estimates from 1978 to 2018. Previously we reported a global emission rate of 7.3±0.6 Gg yr-1 in 2008 and over the past decade emissions have continued to increase by about 24% to 9.04±0.35 Gg yr-1 in 2018. We show that changing patterns in SF6 consumption from developed (Kyoto Protocol Annex-1) to developing countries (non-Annex-1) and the rapid global expansion of the electric power industry, mainly in Asia, have increased the demand for SF6-insulated switchgear, circuit breakers, and transformers. The large bank of SF6 sequestered in this electrical equipment provides a substantial source of emissions from maintenance, replacement, and continuous leakage. Other emissive sources of SF6 occur from the magnesium, aluminium, and electronics industries as well as more minor industrial applications. More recently, reported emissions, including those from electrical equipment and metal industries, primarily in the Annex-1 countries, have declined steadily through substitution of alternative blanketing gases and technological improvements in less emissive equipment and more efficient industrial practices. Nevertheless, there are still demands for SF6 in Annex-1 countries due to economic growth, as well as continuing emissions from older equipment and additional emissions from newly installed SF6-insulated electrical equipment, although at low emission rates. In addition, in the non-Annex-1 countries, SF6 emissions have increased due to an expansion in the growth of the electrical power, metal, and electronics industries to support their continuing development. There is an annual difference of 2.5-5 Gg yr-1 (1990-2018) between our modelled top-down emissions and the UNFCCC-reported bottom-up emissions (United Nations Framework Convention on Climate Change), which we attempt to reconcile through analysis of the potential contribution of emissions from the various industrial applications which use SF6. We also investigate regional emissions in East Asia (China, S. Korea) and western Europe and their respective contributions to the global atmospheric SF6 inventory. On an average annual basis, our estimated emissions from the whole of China are approximately 10 times greater than emissions from western Europe. In 2018, our modelled Chinese and western European emissions accounted for ~ 36% and 3.1 %, respectively, of our global SF6 emissions estimate. [ABSTRACT FROM AUTHOR]

We report a 40-year history of SF 6 atmospheric mole fractions measured at the Advanced Global Atmospheric Gases Experiment (AGAGE) monitoring sites, combined with archived air samples, to determine emission estimates from 1978 to 2018. Previously we reported a global emission rate of 7.3±0.6 Gg yr -1 in 2008 and over the past decade emissions have continued to increase by about 24 % to 9.04±0.35 Gg yr -1 in 2018. We show that changing patterns in SF 6 consumption from developed (Kyoto Protocol Annex-1) to developing countries (non-Annex-1) and the rapid global expansion of the electric power industry, mainly in Asia, have increased the demand for SF 6 -insulated switchgear, circuit breakers, and transformers. The large bank of SF 6 sequestered in this electrical equipment provides a substantial source of emissions from maintenance, replacement, and continuous leakage. Other emissive sources of SF 6 occur from the magnesium, aluminium, and electronics industries as well as more minor industrial applications. More recently, reported emissions, including those from electrical equipment and metal industries, primarily in the Annex-1 countries, have declined steadily through substitution of alternative blanketing gases and technological improvements in less emissive equipment and more efficient industrial practices. Nevertheless, there are still demands for SF 6 in Annex-1 countries due to economic growth, as well as continuing emissions from older equipment and additional emissions from newly installed SF 6 -insulated electrical equipment, although at low emission rates. In addition, in the non-Annex-1 countries, SF 6 emissions have increased due to an expansion in the growth of the electrical power, metal, and electronics industries to support their continuing development. There is an annual difference of 2.5–5 Gg yr -1 (1990–2018) between our modelled top-down emissions and the UNFCCC-reported bottom-up emissions (United Nations Framework Convention on Climate Change), which we attempt to reconcile through analysis of the potential contribution of emissions from the various industrial applications which use SF 6. We also investigate regional emissions in East Asia (China, S. Korea) and western Europe and their respective contributions to the global atmospheric SF 6 inventory. On an average annual basis, our estimated emissions from the whole of China are approximately 10 times greater than emissions from western Europe. In 2018, our modelled Chinese and western European emissions accounted for ∼36 % and 3.1 %, respectively, of our global SF 6 emissions estimate. [ABSTRACT FROM AUTHOR]

We present a method to infer spatially and spatio-temporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model. The method allows fast computation of spatial emissions using a hierarchical Bayesian framework as an alternative to Markov chain Monte Carlo algorithms. The spatial emissions follow a Gaussian process with a Matérn correlation structure which can be represented by a Gaussian Markov random field through a stochastic partial differential equation approach. The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields. Combining an autoregressive temporal correlation and the Matérn field provides a full spatio-temporal correlation structure. We first demonstrate the method on a synthetic data example and follow this using a well-studied test case of inferring UK methane emissions from tall tower measurements of atmospheric mole fraction. Results from these two test cases show that this method can accurately estimate regional greenhouse gas emissions, accounting for spatio-temporal uncertainties that have traditionally been neglected in atmospheric inverse modelling. [ABSTRACT FROM AUTHOR]