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Real-time measurement of radionuclide concentrations and its impact on inverse modeling of 106 Ru release in the fall of 2017

机译:2017年下降106 ru释放对放射性核素浓度的实时测量及其对逆建模的影响

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Low concentrations of 106 Ru were detected across Europe at the turn of September and October 2017. The origin of 106 Ru 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 106 Ru 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 106 Ru 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).
机译:欧洲于9月和2017年10月在欧洲检测到低浓度106 ru。106 ru的起源仍未得到确认;然而,目前的研究一致认为,南部乌拉尔斯的Mayak可能会发生释放。源重建主要基于与大气传输模型耦合的浓度测量的分析。由于浓度测量的合理时间分辨率对于适当的源期重建至关重要,因此标准的1周采样间隔可能是限制性的。在本文中,我们展示了对新开发的Amara(大气放射性气溶胶)和CeGAM(Carousel Gamma光谱)实时监测系统的可用性的调查,该系统基于气溶胶过滤器的伽马射线计数允许确定106 ru在约会内到达监测站点时的时刻。 1?H和检测相对于几MBQΔ3的活性浓度低4Ω3间隔。这些高分辨率数据用于106 ru释放的反向建模。我们对来自全球预测系统(GFS)的气象数据驱动的混合单粒子拉格朗日集成轨迹(HYSPLIT)大气传输模型的倒退运行,我们为我们的每个网格单元构成源 - 受体敏感性(SRS)矩阵领域。然后,我们使用我们的最小二乘法与Adaptive的先前协方差(LS-APC)方法来估计释放的可能位置和释放的源期限。利用捷克监测数据,比较了从标准制度和实时制度的浓度测量,并且在源的位置和时间的位置上对实时数据进行了更好的源重建解决来源的解决。估计的释放位置,Mayak和总估计源期限237±107?TBQ,与之前的研究一致。最后,基于捷克监测数据的结果与具有更好的空间分辨率的IAEA报告(国际原子能机构)数据集,并证明了IAEA数据集之间的协议和我们的重建。此外,我们还使用与欧洲中距离(ECMWF)的气象分析相结合的Flexpart(柔性粒子分散)模型进行了验证了我们的研究结果。

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