Explore the vast range of titles available.

Sources and budgets of the persistent, anthropogenic compound trifluoroacetate (TFA) are poorly quantified across different environmental media. Recently, the introduction of hydrofluoroolefins and the continued use of other fluorinated compounds has increased environmental levels of TFA. Here, we present concentrations of TFA observed in precipitation and surface waters in Switzerland during three years of continuous monitoring and in archived water samples, collected since 1984. Mean observed TFA concentrations ranged from 0.30 to 0.96 µg L−1 across 14 precipitation sites and from 0.33 to 0.88 µg L−1 across 9 river sites in 2021–2023 – a four-to-six-fold increase since 1996/1997. Simulated atmospheric degradation of known TFA precursors accounted for 63 % (58 %–70 %) of the observed deposition (48 % (41 %–54 %) hydrofluoroolefins and 15 % (12 %–18 %) long-lived fluorinated gases; mean and range across sites) for sites on the Swiss Plateau. In Switzerland, atmospheric (wet+dry) deposition of TFA amounted to 24.5 ± 9.6 Mg yr−1, whereas TFA terrestrial inputs from the degradation of plant protection products (PPP) in soils, estimated from the literature, ranged from 2.9 to 11.8 Mg yr−1, depending on the assumption on degradation efficiency. TFA inputs from the degradation of PPP dominated 2–3 times over atmospheric deposition in Swiss croplands. These inputs were balanced by exports through the major rivers, 31 ± 4 Mg yr−1. Archived precipitation samples from the period 1986 to 2020 revealed that TFA was formed in the atmosphere before the introduction of known atmospheric precursors, whereas in the 1990s TFA deposition increased along with their concentrations. However, simulated atmospheric degradation underestimates summertime TFA deposition five-fold. Continued use of fluorinated compounds is likely to enhance TFA deposition in the future. Additional environmental monitoring and source attribution studies are paramount for refining the assessment of TFA sources and levels for potential health and environmental risks.

Atmospheric non-methane hydrocarbon concentrations began declining in the 1970s. Surface and column measurements show that Northern Hemisphere ethane concentrations are now rising, probably due to North American oil and natural gas emissions. Non-methane hydrocarbons such as ethane are important precursors to tropospheric ozone and aerosols. Using data from a global surface network and atmospheric column observations we show that the steady decline in the ethane mole fraction that began in the 1970s 1 , 2 , 3 halted between 2005 and 2010 in most of the Northern Hemisphere and has since reversed. We calculate a yearly increase in ethane emissions in the Northern Hemisphere of 0.42 (±0.19) Tg yr −1 between mid-2009 and mid-2014. The largest increases in ethane and the shorter-lived propane are seen over the central and eastern USA, with a spatial distribution that suggests North American oil and natural gas development as the primary source of increasing emissions. By including other co-emitted oil and natural gas non-methane hydrocarbons, we estimate a Northern Hemisphere total non-methane hydrocarbon yearly emission increase of 1.2 (±0.8) Tg yr −1 . Atmospheric chemical transport modelling suggests that these emissions could augment summertime mean surface ozone by several nanomoles per mole near oil and natural gas production regions. Methane/ethane oil and natural gas emission ratios could suggest a significant increase in associated methane emissions; however, this increase is inconsistent with observed leak rates in production regions and changes in methane’s global isotopic ratio.

HFC-134a is the most prevalent hydrofluorocarbon used as a replacement for ozone-depleting CFCs and HCFCs. Due to its high global warming potential, it is regulated under various European and global frameworks, underscoring the importance of tracking its emissions. Emissions derived by the commonly used, bottom-up, methodology are affected by a certain degree of uncertainty. The bottom-up estimates can be aided with an independent top-down estimate based on atmospheric observations combined with an atmospheric transport model. This study presents HFC-134a emissions for Europe, with a specific focus on Italy, from 2008 to 2023. The emissions were estimated using a Bayesian inversion methodology, based on atmospheric observations collected at four European stations. Our analysis reveals a slightly increasing trend in HFC-134a emissions for Italy from 2008 to 2015 of 0.17 Gg yr - 1 , followed by a steady decrease thereafter, highlighting the effect of European regulation on fluorinated gases that came into force in 2014. We observed a reduction in HFC-134a emissions in the Po Basin inferred from the inversion method for 2020, likely due to mobility restrictions imposed during the COVID-19 pandemic. The observed mild seasonality in emissions may be partly attributed to higher air-conditioning activity during summer. Comparison with the Italian National Emission Inventory indicates an improvement in iterative bottom-up estimates, with the 2024 inventory emission trend post-2015 aligning closely with our inversion results. This study emphasises the need for collaboration between the two independent approaches to enhance the accuracy of emission estimates. Such cooperation is crucial to narrowing the gap in quantifying emissions of potent greenhouse gases and effectively assessing the progress of international policies and regulations.

Background Non-target screening consists in searching a sample for all present substances, suspected or unknown, with very little prior knowledge about the sample. This approach has been introduced more than a decade ago in the field of water analysis, together with dedicated compound identification tools, but is still very scarce for indoor and atmospheric trace gas measurements, despite the clear need for a better understanding of the atmospheric trace gas composition. For a systematic detection of emerging trace gases in the atmosphere, a new and powerful analytical method is gas chromatography (GC) of preconcentrated samples, followed by electron ionisation, high resolution mass spectrometry (EI-HRMS). In this work, we present data analysis tools to enable automated fragment formula annotation for unknown compounds measured by GC-EI-HRMS. Results Based on co-eluting mass/charge fragments, we developed an innovative data analysis method to reliably reconstruct the chemical formulae of the fragments, using efficient combinatorics and graph theory. The method does not require the presence of the molecular ion, which is absent in ∼ 40% of EI spectra. Our method has been trained and validated on >50 halocarbons and hydrocarbons, with 3–20 atoms and molar masses of 30–330 g mol - 1 , measured with a mass resolution of approx. 3500. For >90% of the compounds, more than 90% of the annotated fragment formulae are correct. Cases of wrong identification can be attributed to the scarcity of detected fragments per compound or the lack of isotopic constraint (no minor isotopocule detected). Conclusions Our method enables to reconstruct most probable chemical formulae independently from spectral databases. Therefore, it demonstrates the suitability of EI-HRMS data for non-target analysis and paves the way for the identification of substances for which no EI mass spectrum is registered in databases. We illustrate the performances of our method for atmospheric trace gases and suggest that it may be well suited for many other types of samples. The L-GPL licenced Python code is released under the name ALPINAC for ALgorithmic Process for Identification of Non-targeted Atmospheric Compounds.

Measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) combined with a global 12-box model of the atmosphere have long been used to estimate global emissions and surface mean mole fraction trends of atmospheric trace gases. Here, we present annually updated estimates of these global emissions and mole fraction trends for 42 compounds through 2023 measured by the AGAGE network, including chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride, nitrogen trifluoride, methane, nitrous oxide, and selected other compounds. The data sets are available at https://doi.org/10.5281/zenodo.15372480 (Western et al., 2025). We describe the methodology to derive global mole fraction and emissions trends, which includes the calculation of semihemispheric monthly mean mole fractions, the mechanics of the 12-box model and the inverse method that is used to estimate emissions from the observations and model. Finally, we present examples of the emissions and mole fraction data sets for the 42 compounds.

Anthropogenic increases in atmospheric greenhouse gas concentrations are the main driver of current and future climate change. The integrated assessment community has quantified anthropogenic emissions for the shared socio-economic pathway (SSP) scenarios, each of which represents a different future socio-economic projection and political environment. Here, we provide the greenhouse gas concentrations for these SSP scenarios – using the reduced-complexity climate–carbon-cycle model MAGICC7.0. We extend historical, observationally based concentration data with SSP concentration projections from 2015 to 2500 for 43 greenhouse gases with monthly and latitudinal resolution. CO2 concentrations by 2100 range from 393 to 1135 ppm for the lowest (SSP1-1.9) and highest (SSP5-8.5) emission scenarios, respectively. We also provide the concentration extensions beyond 2100 based on assumptions regarding the trajectories of fossil fuels and land use change emissions, net negative emissions, and the fraction of non-CO2 emissions. By 2150, CO2 concentrations in the lowest emission scenario are approximately 350 ppm and approximately plateau at that level until 2500, whereas the highest fossil-fuel-driven scenario projects CO2 concentrations of 1737 ppm and reaches concentrations beyond 2000 ppm by 2250. We estimate that the share of CO2 in the total radiative forcing contribution of all considered 43 long-lived greenhouse gases increases from 66 % for the present day to roughly 68 % to 85 % by the time of maximum forcing in the 21st century. For this estimation, we updated simple radiative forcing parameterizations that reflect the Oslo Line-By-Line model results. In comparison to the representative concentration pathways (RCPs), the five main SSPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are more evenly spaced and extend to lower 2100 radiative forcing and temperatures. Performing two pairs of six-member historical ensembles with CESM1.2.2, we estimate the effect on surface air temperatures of applying latitudinally and seasonally resolved GHG concentrations. We find that the ensemble differences in the March–April–May (MAM) season provide a regional warming in higher northern latitudes of up to 0.4 K over the historical period, latitudinally averaged of about 0.1 K, which we estimate to be comparable to the upper bound (∼5 % level) of natural variability. In comparison to the comparatively straight line of the last 2000 years, the greenhouse gas concentrations since the onset of the industrial period and this studies' projections over the next 100 to 500 years unequivocally depict a “hockey-stick” upwards shape. The SSP concentration time series derived in this study provide a harmonized set of input assumptions for long-term climate science analysis; they also provide an indication of the wide set of futures that societal developments and policy implementations can lead to – ranging from multiple degrees of future warming on the one side to approximately 1.5 ∘C warming on the other.

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.

Atmospheric greenhouse gas (GHG) concentrations are at unprecedented, record-high levels compared to the last 800 000 years. Those elevated GHG concentrations warm the planet and – partially offset by net cooling effects by aerosols – are largely responsible for the observed warming over the past 150 years. An accurate representation of GHG concentrations is hence important to understand and model recent climate change. So far, community efforts to create composite datasets of GHG concentrations with seasonal and latitudinal information have focused on marine boundary layer conditions and recent trends since the 1980s. Here, we provide consolidated datasets of historical atmospheric concentrations (mole fractions) of 43 GHGs to be used in the Climate Model Intercomparison Project – Phase 6 (CMIP6) experiments. The presented datasets are based on AGAGE and NOAA networks, firn and ice core data, and archived air data, and a large set of published studies. In contrast to previous intercomparisons, the new datasets are latitudinally resolved and include seasonality. We focus on the period 1850–2014 for historical CMIP6 runs, but data are also provided for the last 2000 years. We provide consolidated datasets in various spatiotemporal resolutions for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as 40 other GHGs, namely 17 ozone-depleting substances, 11 hydrofluorocarbons (HFCs), 9 perfluorocarbons (PFCs), sulfur hexafluoride (SF6), nitrogen trifluoride (NF3) and sulfuryl fluoride (SO2F2). In addition, we provide three equivalence species that aggregate concentrations of GHGs other than CO2, CH4 and N2O, weighted by their radiative forcing efficiencies. For the year 1850, which is used for pre-industrial control runs, we estimate annual global-mean surface concentrations of CO2 at 284.3 ppm, CH4 at 808.2 ppb and N2O at 273.0 ppb. The data are available at https://esgf-node.llnl.gov/search/input4mips/ and http://www.climatecollege.unimelb.edu.au/cmip6 . While the minimum CMIP6 recommendation is to use the global- and annual-mean time series, modelling groups can also choose our monthly and latitudinally resolved concentrations, which imply a stronger radiative forcing in the Northern Hemisphere winter (due to the latitudinal gradient and seasonality).

Halocarbons contribute to global warming and stratospheric ozone depletion. They are emitted to the atmosphere by various anthropogenic activities. To determine Swiss national halocarbon emissions, we applied top-down methods, which rely on atmospheric concentration observations sensitive to the targeted emissions. We present 12 months (September 2019 to August 2020) of continuous atmospheric observations of 28 halocarbons from a measurement campaign at the Beromünster tall tower in Switzerland. The site is sensitive to the Swiss Plateau, which is the most densely populated area of Switzerland. Therefore, the measurements are well suited to derive Swiss halocarbon emissions. Emissions were calculated by two different top-down methods, i.e. a tracer ratio method (TRM), with carbon monoxide (CO) as the independent tracer, and a Bayesian inversion (BI), based on atmospheric transport simulations using FLEXPART–COSMO. The results were compared to previously reported top-down emission estimates, based on measurements at the high-Alpine site of Jungfraujoch, and to the bottom-up Swiss national greenhouse gas (GHG) inventory, as annually reported to the United Nations Framework Convention on Climate Change (UNFCCC). We observed moderately elevated concentrations of chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs), both banned from production and consumption in Europe. The corresponding emissions are likely related to the ongoing outgassing from older foams and refrigerators and confirm the widespread historical use of these substances. For the major hydrofluorocarbons (HFCs), HFC-125 (CHF2CF3) and HFC-32 (CH2F2), our calculated emissions of 100 ± 34 and 45 ± 14 Mg yr−1 are in good agreement with the numbers reported in the Swiss inventory, whereas, for HFC-134a (CH2FCF3), our result of 280 ± 89 Mg yr−1 is more than 30 % lower than the Swiss inventory. For HFC-152a (CH3CHF2), our top-down result of 21 ± 5 Mg yr−1 is significantly higher than the number reported in the Swiss inventory. For the other investigated HFCs, perfluorocarbons (PFCs), SF6 and NF3, Swiss emissions were small and in agreement with the inventory. Finally, we present the first country-based emission estimates for three recently phased-in, unregulated hydrofluoroolefins (HFOs), HFO-1234yf (CF3CF=CH2), HFO-1234ze(E) ((E)-CF3CH=CHF), and HCFO-1233zd(E) ((E)-CF3CH=CHCl). For these three HFOs, we calculated Swiss emissions of 15 ± 4, 34 ± 14, and 7 ± 1 Mg yr−1, respectively.

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 (FLEXible PARTicle Dispersion Model), driven by the meteorological fields of the regional numerical weather prediction model COSMO. The main sources of errors in LPDMs are 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-gas measurements from two extended field campaigns on the Swiss Plateau (in Beromünster and Sottens), and we conduct both high-resolution (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 spatial distribution of the 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 abovementioned 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 national GHG emission inventories and support policymakers.