Abstract. 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.

Many organic contaminants entering the aquatic environment feature stereogenic structural elements that give rise to enantiomerism. While abiotic processes usually act identical on enantiomers, biotic processes, such as biodegradation often result in enantiomeric fractionation (EFr), i.e. the change of the relative abundance of enantiomers. Therefore, EFr offers the opportunity to differentiate biodegradation in complex environmental systems from abiotic processes. In this study, an achiral-chiral two-dimensional liquid chromatographic method for the enantioseparation of selected pharmaceuticals was developed. This method was then applied to determine the enantiomeric compositions of 8 chiral pharmaceuticals in 20 water-sediment test flumes and test EFr as an indicator of biodegradation. While all 8 substances were attenuated by at least 60%, 5 (atenolol, metoprolol, celiprolol, propranolol, flecainide) displayed EFr. No EFr was observed for citalopram, fluoxetine and venlafaxine despite almost complete attenuation (80 to 100%). Celiprolol, a barely studied beta-blocker, revealed the most distinct EFr among all investigated substances, however, EFr varied considerably with biodiversity. Celiprolol-H2 was identified as a biological transformation product, possibly formed by reduction of the celiprolol keto group through a highly regio-and enantioselective carbonyl reductase. While celiprolol-H2 was observed across all flumes, as expected, its formation was faster in flumes with high bacterial diversity where also EFr was highest. Overall, EFr and transformation product formation together served as good indicators of biological processes; however, the strong dependence of EFr on biodiversity limits its usefulness in complex environmental systems.

Abstract. 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.