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Global emissions of the ozone depleting gas 1,1-dichloro-1-fluoroethane (HCFC-141b, CH3CCl2F), derived from measurements of atmospheric mole fractions, have been rising between 2017–2021 despite a fall in reported production and consumption for dispersive uses. This study evaluates the possible drivers behind this renewed rise in emissions. 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 countries (Article 5) in 2013. If reported production and consumption are correct, it suggests that the 2017–2021 rise is due to an increase in emissions from the bank when HCFC-141b containing appliances 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 USA and Australia, where emissions decreased by a total of 1.6 ± 3.9 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 a third of global emissions in 2020. Therefore we are not able to pinpoint the source regions or specific activities responsible for the recent global emission rise.

Emissions of the potent greenhouse gas perfluorocyclobutane (c-C4F8, PFC-318, octafluorocyclobutane) into the global atmosphere inferred from atmospheric measurements have been increasing sharply since the early 2000s. We find that these inferred emissions are highly correlated with the production of hydrochlorofluorocarbon-22 (HCFC-22, CHClF2) for feedstock (FS) uses, because almost all HCFC-22 FS is pyrolyzed to produce (poly)tetrafluoroethylene ((P)TFE) and hexafluoropropylene (HFP), a process in which c-C4F8 is a known by-product, causing a significant fraction of global c-C4F8 emissions. We find a global emission factor of ∼0.003 kg c-C4F8 per kilogram of HCFC-22 FS pyrolyzed. Mitigation of these c-C4F8 emissions, e.g., through process optimization, abatement, or different manufacturing processes, such as refined methods of electrochemical fluorination and waste recycling, could reduce the climate impact of this industry. While it has been shown that c-C4F8 emissions from developing countries dominate global emissions, more atmospheric measurements and/or detailed process statistics are needed to quantify c-C4F8 emissions at country to facility levels.

Inverse modeling is a widely used top-down method to infer greenhouse gas (GHG) emissions and their spatial distribution based on atmospheric observations. The errors associated with inverse modeling have multiple sources, such as observations and a-priori emission estimates, but they are often dominated by the transport model error. Here, we utilize the Lagrangian Particle Dispersion Model (LPDM) FLEXPART, driven by the meteorological fields of the regional numerical weather prediction model COSMO. The main source of errors in LPDMs is the turbulence diffusion parameterization and the meteorological fields. The latter are outputs of an Eulerian model. Recently, we introduced an improved parameterization scheme of the turbulence diffusion in FLEXPART, which significantly improves FLEXPART-COSMO simulations at 1 km resolution. We exploit F-gases measurements from two extended field campaigns on the Swiss Plateau (in Beromünster and Sottens) and we conduct both high- (1 km) and low-resolution (7 km) FLEXPART transport simulations that are then used in a Bayesian analytical inversion to estimate spatial emission distributions. Our results for four F-gases (HFC-134a, HFC-125, HFC-32, SF6) indicate that both high-resolution inversions and a dense measurement network significantly improve the ability to estimate the spatial distribution of emissions. Furthermore, the total emission estimates from the high-resolution inversions (351±44 Mg yr−1 for HFC-134a, 101±21 Mg yr−1 for HFC-125, 50±8 Mg yr−1 for HFC-32, 9.0±1.1 Mg yr−1 for SF6) are significantly higher compared to the low-resolution inversions (20–40 % increase) and result in total a-posteriori emission estimates that are closer to national inventory values as reported to the UNFCCC (10–20 % difference between high-resolution inversion estimates and inventory values compared to 30–40 % difference between the low-resolution inversion estimates and inventory values). Specifically, we attribute these improvements to a better representation of the atmospheric flow in complex terrain in the high-resolution model, partly induced by the more realistic topography. We further conduct numerous sensitivity inversions, varying different parameters and variables of our Bayesian inversion framework to explore the whole range of uncertainty in the inversion errors (e.g., inversion grid, spatial distribution of a-priori emissions, covariance parameters like baseline uncertainty and spatial correlation length, temporal resolution of the assimilated observations, observation network, seasonality of emissions). From the above-mentioned parameters, we find that the uncertainty of the mole fraction baseline and the spatial distribution of the a-priori emissions have the largest impact on the a-posteriori total emission estimates and their spatial distribution. This study is a step towards mitigating the errors associated with the transport models and better characterizing the uncertainty inherent in the inversion error. Improvements in the latter will facilitate the validation and standardization of the national GHG emission inventories and support policymakers.

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 1978–2018. Previously we reported a global emission rate of 7.3 ± 0.6 Gigagrams (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, electronics industries and more minor industrial applications. More recently, reported emissions, including those from electrical equipment and metal industries, primarily in the Annex-1 countries, have steadily declined through substitution of alternative blanketing gases and technological improvements in less emissive equipment and more efficient industrial practices. Conversely, 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 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, 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.

We present a method to infer spatially and spatiotemporally 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 spatiotemporal 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 spatiotemporal uncertainties that have traditionally been neglected in atmospheric inverse modelling.

We reconstruct atmospheric abundances of the potent greenhouse gas c-C4F8 (perfluorocyclobutane, perfluorocarbon PFC-318) from measurements of in situ, archived, firn, and aircraft air samples with precisions of ~ 1–2 % reported on the SIO-14 gravimetric calibration scale. Combined with inverse methods, we found near zero atmospheric abundances from the early 1900s to the early 1960s, after which they rose sharply, reaching 1.66 ppt (parts per trillion dry-air mole fraction) in 2017. Global c-C4F8 emissions rose from near zero in the 1960s to ~ 1.2 Gg yr−1 in the late 1970s to late 1980s, then declined to ~ 0.8 Gg yr−1 in the mid-1990s to early 2000s, followed by a rise since the early 2000s to ~ 2.2 Gg yr−1 in 2017. These emissions are significantly larger than inventory based emission estimates. Estimated emissions from eastern Asia rose from 0.36 Gg yr−1 in 2010 to 0.73 Gg yr−1 in 2016 and 2017, 31 % of global emissions, mostly from eastern China. We estimate emissions of 0.14 Gg yr−1 from Northern and Central India in 2016 and find evidence for significant emissions from Russia. In contrast, recent emissions from North Western Europe and Australia are estimated to be small (≤ 1 % each). We conclude that emissions from China, India and Russia are likely related to production of polytetrafluoroethylene (PTFE, “Teflon”) and other fluoropolymers that are based on the pyrolysis of hydrochlorofluorocarbon HCFC-22 (CHClF2) in which c-C4F8 is a known by-product. The semiconductor sector, where c-C4F8 is used, is estimated to be a small source. Without an obvious correlation with population density, incineration of waste containing fluoropolymers is probably a minor source, and we find no evidence of emissions from electrolytic production of aluminum in Australia. While many possible emissive uses of c-C4F8 are known, the start of significant emissions may well be related to the advent of commercial PTFE production in 1947. Process controls or abatement to reduce c-C4F8 by-product were probably not in place in the early decades, explaining the increase in emissions. With the advent of by-product reporting requirements to the United Nations Framework Convention on Climate Change (UNFCCC) in the 1990s, concern about climate change and product stewardship, abatement, and perhaps the collection of c-C4F8 by-product for use in the semiconductor industry where it can be easily abated, it is conceivable that emissions in developed countries were stabilized and then reduced, explaining the observed emission reduction in the 1980s and 1990s. Concurrently, production of PTFE in China began to increase rapidly. Without emission reduction requirements, it is plausible that global emissions today are dominated by China and other developing countries, in agreement with our analysis. We predict that c-C4F8 emissions will continue to rise and that c-C4F8 will become the second most important emitted PFC in terms of CO2-equivalent emissions within a year or two. The 2017 radiative forcing of c-C4F8 (0.52 mW m−2) is small but emissions of c-C4F8 and other PFCs, due to their very long atmospheric lifetimes, essentially permanently alter Earth's radiative budget and should be reduced. Significant emissions outside of the investigated regions clearly show that observational capabilities and reporting requirements need to be improved to understand global and country scale emissions of PFCs and other synthetic greenhouse gases and ozone depleting substances.

We present a method to derive atmospheric-observation-based estimates of carbon dioxide (CO2) fluxes at the national scale, demonstrated using data from a network of surface tall tower sites across the UK and Ireland over the period 2013–2014. The inversion is carried out using simulations from a Lagrangian chemical transport model and an innovative hierarchical Bayesian Markov chain Monte Carlo (MCMC) framework, which addresses some of the traditional problems faced by inverse modelling studies, such as subjectivity in the specification of model and prior uncertainties. Biospheric fluxes related to gross primary productivity and terrestrial ecosystem respiration are solved separately in the inversion and then combined a posteriori to determine net primary productivity. Two different models, CARDAMOM and JULES, provide prior estimates for these fluxes. We carry out separate inversions to assess the impact of these different priors on the posterior flux estimates and evaluate the differences between the prior and posterior estimates in terms of missing model components. The Numerical Atmospheric dispersion Modelling Environment (NAME) is used to relate fluxes to the measurements taken across the regional network. Posterior CO2 estimates from the two inversions agree within estimated uncertainties, despite large differences in the prior fluxes from the different models. With our method, averaging results from 2013 and 2014, we find a total annual net biospheric flux for the UK of −8 ± 79 Tg CO2 yr−1 (CARDAMOM prior) and −64 ± 85 Tg CO2 yr−1 (JULES prior), where -ve values represent an uptake of CO2. These biospheric CO2 estimates show that annual UK biospheric sources and sinks are roughly in balance. These annual mean estimates are consistently higher than the prior estimates, which show much more pronounced uptake in the summer months.