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.

Top-down greenhouse gas measurements can be used to independently assess the accuracy of bottom-up emission estimates. We report atmospheric methane (CH₄) mole fractions and δ¹³CH₄ measurements from Imperial College London from early 2018 onwards 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 (Numerical Atmospheric-dispersion Modelling Environment) dispersion model coupled with the UK National Atmospheric Emissions Inventory, UK NAEI, and a global inventory, the Emissions Database for Global Atmospheric Research (EDGAR), with model spatial resolutions of ∼ 2, ∼ 10, and ∼ 25 km. Simulation–measurement comparisons are used to evaluate London emissions and the source apportionment in the global (EDGAR) and UK national (NAEI) emission inventories. 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 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.

The unprecedented reductions in anthropogenic emissions over the COVID-19 lockdowns were utilised to investigate the response of ozone (O₃) concentrations to changes in its precursors across various UK sites. Ozone, volatile organic compounds (VOCs) and NO_x (NO+NO₂) data were obtained for a 3-year period encompassing the pandemic period (January 2019–December 2021), as well as a pre-pandemic year (2017), to better understand the contribution of precursor emissions to O₃ fluctuations. Compared with pre-lockdown levels, NO and NO₂ declined by up to 63% and 42%, respectively, over the lockdown periods, with the most significant changes in pollutant concentrations recorded across the urban traffic sites. O₃ levels correspondingly increased by up to 30%, consistent with decreases in the [NO]/[NO₂] ratio for O₃ concentration response. Analysis of the response of O₃ concentrations to the NO_x reductions suggested that urban traffic, suburban background and suburban industrial sites operate under VOC-limited regimes, while urban background, urban industrial and rural background sites are NO_x-limited. This was in agreement with the [VOC]/[NO_x] ratios determined for the London Marylebone Road (LMR; urban traffic) site and the Chilbolton Observatory (CO; rural background) site, which produced values below and above 8, respectively. Conversely, [VOC]/[NO_x] ratios for the London Eltham (LE; suburban background) site indicated NO_x-sensitivity, which may suggest the [VOC]/[NO_x] ratio for O₃ concentration response may have had a slight NO_x-sensitive bias. Furthermore, O₃ concentration response with [NO]/[NO₂] and [VOC]/[NO_x] were also investigated to determine their relevance and accuracy in identifying O₃-NO_x-VOC relationships across UK sites. While the results obtained via utilisation of these metrics would suggest a shift in photochemical regime, it is likely that variation in O₃ during this period was primarily driven by shifts in oxidant (OX; NO₂ + O₃) equilibrium as a result of decreasing NO₂, with increased O₃ transported from Europe likely having some influence.

We investigate the use of atmospheric 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, 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 O₂ to CO₂ fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compare three ocean O₂ 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 CO₂ (ffCO₂) 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 in magnitude to the contribution of simulated fossil fuel CO₂ 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 CO₂ using APO.

Atmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modelling. Here, atmospheric nitrous oxide (N ₂ O) measurements made in the United Kingdom (U.K.) and Republic of Ireland are used to derive monthly N ₂ O emissions for 2013-2022 using two different inverse methods. We find mean U.K. emissions of 90.5±23.0 (1\(\sigma\)) and 111.7±32.1 (1\(\sigma\)) Gg N ₂ O yr ^-1 for 2013-2022, and corresponding trends of -0.68±0.48 (1\(\sigma\)) Gg N ₂ O yr ^-2 and -2.10±0.72 (1\(\sigma\)) Gg N ₂ O yr ^-2 , respectively for the two inverse methods. The U.K. National Atmospheric Emissions Inventory (NAEI) reported mean N ₂ O emissions of 73.9 Gg N ₂ O yr ^-1 across this period, which is 14-33% smaller than the emissions derived from atmospheric data. We infer a pronounced seasonal cycle in N ₂ O 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 N ₂ O emissions estimated from the bottom-up U.K. 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 N ₂ O 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 NH ₃ deposition, but these require further investigation.