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Ammonia (NH3), a significant precursor of particulate matter, is the most important alkaline gas in the atmosphere and directly affects biodiversity, ecosystems, soil acidification. It also indirectly affects climate and human health. In addition, its concentrations are constantly rising because of the increasing feeding needs of the global population accompanied by a larger use of fertilizers and animal farming. The combination of its increasing atmospheric levels with its environmental and human impact has led many countries to adopt abatement strategies in order to conform with respective regulations. While the significance of ammonia is pronounced, its emissions are often associated with large uncertainties, while its atmospheric abundance is difficult to measure. However, during the last decade, several satellite products have been developed that measure ammonia very effectively, with low uncertainty, and most importantly, with a global coverage. Here, we use satellite observations of column ammonia in combination with an inversion algorithm to derive ammonia emissions with a high resolution over Europe for the period 2013–2020. Ammonia emissions peak in Northern Europe due to agricultural application and livestock management and the local maxima are found over Western Europe (industrial activity) and over Spain (pig farming). Our calculations show that these emissions have decreased by −26 % since 2013 (from 5431 Gg in 2013 to 3994 Gg in 2020) showing that the abatement strategies adopted by the European Union have been very efficient. The slight increase (+4.4 %) reported in 2015 is also reproduced here and is attributed to some European countries exceeding annual emission targets. Ammonia emissions are low in winter (286 Gg) and peak in summer (563 Gg) and are dominated by the temperature dependent volatilization of ammonia from the soil. The largest emission decreases were observed in Central and Eastern Europe (−38 %) and in Western Europe (−37 %), while smaller decreases were recorded in Northern (−17 %) and Southern Europe (−7.6 %). Our results are associated with relatively low uncertainties reaching a maximum of 42 %; when complemented against independent ground-based observations, modelled concentrations using the posterior emissions showed improved statistics, also following the observed seasonal trends. The posterior emissions presented here also agree well with respective estimates reported in the literature and inferred from different methodologies. These results indicate that the posterior emissions of ammonia calculated with satellite measurements and combined with our adapted inverse modelling framework constitute a robust basis for European NH3 estimates and show the de facto evolution of ammonia emissions since 2013. [ABSTRACT FROM AUTHOR]

The paper introduces the upcoming outputs of the Lexico-Semantic Database Project (LSD-Czech) supported by The Technology Agency of the Czech Republic (TA CR) [TL02000041]: a Czech online conceptual dictionary and a lexico-semantic database. It reviews the phases of the implementation of the project and its results to be made available by the end of 2022. It describes both the dictionary and the database (from which the dictionary is generated). While the dictionary is intended for the general user, the database will serve linguists and experts in natural language processing and language data application. The paper briefly discusses the field of onomasiological lexicography, focusing on what is an onomasiological dictionary, what dictionaries of this type exist for Czech, and introduces the two dictionaries on which the database is founded, Haller's CČesky slovník věcný a synonymický (1969--86) and Klégr's Tezaurus jazyka českého (2007). The focus of the paper is on the description of the functions and features of the dictionary, the steps whereby to search in it and the envisaged future improvements. Finally, it gives the specifications of the database and the next steps planned in its development. [ABSTRACT FROM AUTHOR]

Low concentrations of 106Ru were detected across Europe at the turn of September and October 2017. The origin of 106Ru has still not been confirmed; however, current studies agree that the release occurred probably near Mayak in the southern Urals. The source reconstructions are mostly based on an analysis of concentration measurements coupled with an atmospheric transport model. Since reasonable temporal resolution of concentration measurements is crucial for proper source term reconstruction, the standard 1-week sampling interval could be limiting. In this paper, we present an investigation of the usability of the newly developed AMARA (Autonomous Monitor of Atmospheric Radioactive Aerosol) and CEGAM (carousel gamma spectrometry) real-time monitoring systems, which are based on the gamma-ray counting of aerosol filters and allow for determining the moment when 106Ru arrived at the monitoring site within approx. 1 h and detecting activity concentrations as low as several mBq m -3 in 4 h intervals. These high-resolution data were used for inverse modeling of the 106Ru release. We perform backward runs of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric transport model driven with meteorological data from the Global Forecast System (GFS), and we construct a source–receptor sensitivity (SRS) matrix for each grid cell of our domain. Then, we use our least squares with adaptive prior covariance (LS-APC) method to estimate possible locations of the release and the source term of the release. With Czech monitoring data, the use of concentration measurements from the standard regime and from the real-time regime is compared, and a better source reconstruction for the real-time data is demonstrated in the sense of the location of the source and also the temporal resolution of the source. The estimated release location, Mayak, and the total estimated source term, 237±107 TBq, are in agreement with previous studies. Finally, the results based on the Czech monitoring data are validated with the IAEA-reported (International Atomic Energy Agency) dataset with a much better spatial resolution, and the agreement between the IAEA dataset and our reconstruction is demonstrated. In addition, we validated our findings also using the FLEXPART (FLEXible PARTicle dispersion) model coupled with meteorological analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). [ABSTRACT FROM AUTHOR]

Abstract Low concentrations of 106Ru were detected across Europe at the turn of September and October 2017. The origin of 106Ru has still not been confirmed; however, current studies agree that the release occurred probably near Mayak in the southern Urals. The source reconstructions are mostly based on an analysis of concentration measurements coupled with an atmospheric transport model. Since reasonable temporal resolution of concentration measurements is crucial for proper source term reconstruction, the standard one week sampling interval could be limiting. In this paper, we present an investigation of the usability of the newly developed AMARA and CEGAM real-time monitoring systems, which are based on the gamma-ray counting of aerosol filters. These high resolution data were used for inverse modeling of the 106Ru release. We perform backward runs of the Hysplit atmospheric transport model driven with meteorological data from the global forecast system (GFS) and we construct a source-receptor sensitivity (SRS) matrix for each grid cell of our domain. Then, we use our least-squares with adaptive prior covariance (LS-APC) method to estimate possible locations of the release and the source term of the release. On Czech monitoring data, the use of concentration measurements from the standard regime and from the real-time regime is compared and better source reconstruction for the real-time data is demonstrated in the sense of the location of the source and also the temporal resolution of the source. The estimated release location, Mayak, and the total estimated source term, 237 ± 107 TBq, are in agreement with previous studies. Finally, the results based on the Czech monitoring data are validated with the IAEA reported dataset with a much better spatial resolution, and the agreement between the IAEA dataset and our reconstruction is demonstrated. [ABSTRACT FROM AUTHOR]

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