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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]

Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem wherein the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties, e.g., in measurements or atmospheric transport modeling; hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information: the prior source term, also known as the first guess, and regularization parameters for the shape of the source term. While the first guess is based on information independent of the measurements, such as the physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term and regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3. [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]

Determination of a source term of an accidental release of radioactive material into the atmosphere is very important for evaluating emergency situations and their consequences. However, knowledge of the source term and its composition is typically vague and uncertain. One possible way to obtain the source term is inverse modeling in which an atmospheric transport model is combined with field measurements. The most accessible measurements are those from gamma dose rate (GDR) detectors. However, GDR measurements represent a sum of contribution from all nuclides from both plume and deposition which makes the problem particularly difficult. The same difficulty arises when the measurements can not distinguish contribution from another species in the release, such as nuclides attached to different particle sizes. We propose a Bayesian method for recovery of the source term from GDR measurements where a priori knowledge on ratios of different species is given in the form of bounds. This knowledge is incorporated into the model of covariance matrix of the source term. The Bayesian methodology allows to handle uncertain knowledge on the nuclide ratios as well as unknown temporal correlations of the source term. We evaluate and compare the proposed method with other state‐of‐the‐art methods on a twin experiment of a non‐stationary release of 16 nuclides from the Czech nuclear power plant Temelin being registered by the Austrian GDR monitoring network. Real‐world validation of the approach is performed on the latest measurements of concentration and deposition of caesium‐137 from the Chernobyl accident, where we estimate composition of the source term from different particle sizes (species). The estimated source term is in very good agreement with previously reported results and the calculated species ratios are supported by the available observations. We study the problem of multi‐specie source term estimation from gamma dose rate (GDR) measurements which is crucial in emergency situations after atmospheric releases of radioactive material. However, GDR measurements sum contributions of activities from all nuclides, making the source term estimation particularly difficult. We propose a novel hierarchical probabilistic model for multi‐specie source term estimation. Specifically, the model includes prior knowledge on specie ratios in the form of intervals. Validation is given on Chernobyl dataset. [ABSTRACT FROM AUTHOR]

Estimation of pollutant releases into the atmosphere is an important problem in the environmental sciences. It is typically formalized as an inverse problem using a linear model that can explain observable quantities (e.g. concentrations or deposition values) as a product of the source-receptor sensitivity (SRS) matrix obtained from an atmospheric transport model multiplied by the unknown source term vector. Since this problem is typically ill-posed, current state-of-the-art methods are based on regularization of the problem and solution of a formulated optimization problem. This procedure depends on manual settings of uncertainties that are often very poorly quantified, effectively making them tuning parameters. We formulate a probabilistic model, that has the same maximum likelihood solution as the conventional method using pre-specified uncertainties. Replacement of the maximum likelihood solution by full Bayesian estimation allows to estimate also all tuning parameters from the measurements. The estimation procedure is based on the Variational Bayes approximation which is evaluated by an iterative algorithm. The resulting method is thus very similar to the conventional approach, but with the possibility to estimate also all tuning parameters from the observations. The proposed algorithm is tested and compared with the state-of-the-art method on data from the European Tracer Experiment (ETEX) where advantages of the new method are demonstrated. A MATLAB implementation of the proposed algorithm is available for download. [ABSTRACT FROM AUTHOR]

We combine observations from Western USA and inverse modelling to constrain global atmospheric emissions of microplastics (MPs) and microfibers (MFs). The latter are used further to model their global atmospheric dynamics. Global annual MP emissions were calculated as 9.6 ± 3.6 Tg and MF emissions as 6.5 ± 2.9 Tg. Global average monthly MP concentrations were 47 ng m-3 and 33 ng m-3 for MFs, at maximum. The largest deposition of agricultural MPs occurred close to the world's largest agricultural regions. Road MPs mostly deposited in the East Coast of USA, Central Europe, and Southeastern Asia; MPs resuspended with mineral dust near Sahara and Middle East. Only 1.8% of the emitted mass of oceanic MPs was transferred to land, and 1.4% of land MPs to ocean; the rest were deposited in the same environment. Previous studies reported that 0.74–1.9 Tg y-1 of land-based atmospheric MPs/MFs (< 5 mm) are transported to the ocean, while riverine transport is between 3.3 and 14 Tg y-1. We calculate that 0.418 ± 0.201 Tg y-1 MPs/MFs (size up to 250 and 2500 µm) were transported from the land to ocean (large particles were ignored). Model validation against observations showed that particle removal must be urgently updated in global models. [Display omitted] • About 9.6 and 6.5 Tg y-1 of microplastics and microfibers 1 are globally released. • Global average monthly surface MPs (MFs) were 47 ng m-3 (33 ng m-3) at maximum. • 1.8% of the emitted microplastics from ocean to land, 1.4% from land to ocean. • Validation suggests that removal of microplastics in global models needs update. • Results can be used as a proxy of the expected global levels for experimentalists. [ABSTRACT FROM AUTHOR]

Estimation of a source term, i.e. release rate, of atmospheric radionuclide emissions is of key interest for nuclear emergency response and further accident analysis. The source term estimate is, however, often very inaccurate due to biases in atmospheric transport and used meteorological analysis. We propose a method for atmospheric plume bias correction which uses not only concentrations modeled at a measuring site but also the information on concentration gradient from the neighborhood of each measuring site, i.e. information already available from the atmospheric transport model. To properly regularize the model, we propose an elastic model of the plume bias correction based on regularization with the use of known topology of the measurement network. The proposed plume bias correction method can be coupled with an arbitrary source term estimation algorithm and can be instantly applied to any other atmospheric release of hazardous material. We demonstrate the method in two real cases. First, we use data from the European Tracer Experiment to validate the methodology. Second, we use data from the 106Ru occurrence over Europe in 2017 to demonstrate the methodology in a more demanding case where agreement with state-of-the-art estimates is shown with much better reconstruction of measurements. [Display omitted] • Fully interpretable bias correction method based on measuring network topology. • Usage of gradient of the modelled concentration in the neighborhood of each sensor. • Significant improvement of measurement-reconstruction mismatch. • Applicable as plug-in to any selected inverse method, demonstration on two of them. • Validation using controlled ETEX experiment and ruthenium-106 release in 2017. [ABSTRACT FROM AUTHOR]

• The excited states of pyrimidine bases are some of their derivatives addressed. • Absorption electronic spectral lines were determined using several DFT functional and post-HF techniques. • Lifetimes of all the molecules were evaluated using OM2/MNDO methods within hopping dynamics for a swarm of semiclassical MD trajectories. • Lifetime of the first excited state is estimated as:τ 2 = 0.45, 0.27, and 0.13 ps for Cyt, Thy, and Ura, respectively. • Mssssuch longer time is necessary for the remaining derivatives. Vertical absorption spectra of selected molecules (pyrimidine nucleobases and 5-azacytosin, 2-amino-1,3,5-triazine, 2,4-diamino-1,3,5-triazine, 2,4,6-triamino-1,3,5-triazine, and s-triazine) were performed using TD-DFT, post-HF, and semiempirical OM2/MNDO methods. From comparing a relatively large set of functionals from the different rungs of Jacob's DFT ladder, the important role of the exact exchange interaction is demonstrated. TD-DFT results with the ωB2GP-PLYP functional (and a few others) are comparable with the post-HF methods. An important finding is also a good accuracy of the semiempirical OM2 approximation, which is used for estimations of lifetimes of the excited states for the same set of molecules. The lifetimes' determination is based on a stochastical treatment of a swarm of MD trajectories. The time-dependent Schrödinger equation is solved for the electronic degrees of freedom while the dynamics of nuclei is treated classically in each trajectory step applying Tully's fewest switch algorithm. The probability of individual states in time is determined and compared with both experimental and computational studies. Our results are in fair accord with available experiments. The nucleobases relatively quickly deactivate – all the relaxation times are below 0.5 ps (in very good accord with measured values). Much longer lifetimes (a few hundred ps) were obtained for other molecules: 5-azacytosin, 2,4-diamino-1,3,5-triazine, and 2,4,6-triamino-1,3,5-triazine. Also, in agreement with experimental data, 2-amino-1,3,5-triazine returns to the ground state on a nanosecond scale. [Display omitted] [ABSTRACT FROM AUTHOR]

The primary research, which is brought by this paper, focuses on medium and large-sized enterprises in sectors of information and communication technologies (ICT). It investigates their attitude to the ICT that they use. Based on these ICT, the article is looking for trends set by the companies. Then, these trends could be considered as an inspiration for companies from other sectors of business. The research results have shown that the specially developed software belongs between the most often implemented ICT tools in the last seven years by the questionnaire companies. The research also showed that this ICT tool of all, which they have implemented, is the most beneficial at a tactical level. Another part of analysis, where the companies should chose benefits that arose from the implementation, pointed out that they are focused on two basic clusters of benefits. The first contains: improved teamwork and increased efficiency and effectiveness. The other cluster contains: reduced costs, improved service quality and improved integration of business functions. The research also showed that the surveyed companies consider the ICT only as a tool, which simplifies work. [ABSTRACT FROM AUTHOR]

This article focuses on the formation of complex trajectories of radiological background fields for atypical accidental discharges of radioactivity into the atmosphere during very low wind speed episodes (calms). Within several hours of a calm meteorological situation, a relatively significant level of radioactivity can be accumulated around the source. In the next stage, the calm situation is assumed to terminate and convective movement of the air immediately starts. A packet of accumulated radioactivity, which has the form of multiple Gaussian puffs, is drifted by wind. Consequently, the pollution is disseminated over the terrain. Significant transport of radioactivity even behind the protective zone of a nuclear facility (up to between 15 and 20 km) has been observed. Original optional statistical pre-processing of calm's results is inserted between the calm and convective stages of the trajectory generation which may improve performance of following computationally expensive methods of Bayesian filtering. Determination of complex trajectory passing through both calm and convective stages of the release scenario represents inevitable prior information for prospective assimilation techniques. Draft twin experiment is outlined for a simple assimilation scenario for re-estimation of the main model parameters based on a notional monitoring network in the outer convective region. [ABSTRACT FROM AUTHOR]

The linear inverse problem is often formulated as y = Mx + e (Seibert, 2001), where y is thethe vector of observations, M is the source-receptor-sensitivity (SRS) matrix, x is theunknown source term, and e is the model residue. The key task is to estimate thesource term x. The measurements y are typically in the form of concentrations orgamma dose rates. The SRS matrix M is computed using an atmospheric transportmodel coupled with meteorological data. Since the inverse problem is ill-conditionedand subject to many errors, some form of regularization is required. A commonlyused regularization is the knowledge of the prior source term, also known as a firstguess. The result is thus influenced by this choice, which may be a drawback incases when very uncertain or limited information is available to construct suchprior. In this contribution, we are going to investigate the influence of the prior source term onthe posterior estimate of the source term. We considered the conventional optimizationapproach with loss function (Eckhardt et al., 2008) as well as probabilistic methodLS-APC with automatic selection of the tuning parameters (Tichý et al., 2016).To study the influence, it is necessary to have a dataset with ground truth sourceterm, therefore, we show the results on a twin simulation study as well as on realdata from the controlled tracer experiment ETEX. Specifically, we use the originalrelease as a prior source term, and compare results with those using inaccurate priorobtained by shifting, scaling, and blurring of the ground truth. Using this approach,stability and reliability of the estimated source term based on corrupted prior sourceterms and selected regularization parameters can be quantified and compared fordifferent methods. We show that the LS-APC method is more robust when littleinformation is available, but the conventional method can give good results when it is welltuned. Citations: Eckhardt, S., Prata, A. J., Seibert, P., Stebel, K., and Stohl, A.: Esti- mation of the verticalprofile of sulfur dioxide injection into the atmosphere by a volcanic eruption using satellitecolumn mea- surements and inverse transport modeling, Atmos. Chem. Phys., 8, 3881–3897,https://doi.org/10.5194/acp-8-3881-2008, 2008. Seibert, P.: Iverse modelling with a lagrangian particle disperion model: application topoint releases over limited time intervals, in: Air Pollution Modeling and its Application XIV,Springer, 381–389, 2001. Tichý, O., Šmídl, V., Hofman, R., and Stohl, A.: LS-APC v1.0: a tuning-free methodfor the linear inverse problem and its ap- plication to source-term determination,Geosci. Model Dev., 9, 4297–4311, https://doi.org/10.5194/gmd-9-4297-2016, 2016. [ABSTRACT FROM AUTHOR]