Abstract. 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.

中文翻译：

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放射性核素浓度的实时测量及其对2017年秋季¹⁰⁶ Ru释放逆模型的影响

摘要。摘要低浓度¹⁰⁶在九月和十月2017年的起源之交在欧洲进行检测茹¹⁰⁶茹尚未得到证实；但是，目前的研究认为释放可能发生在乌拉尔山脉南部的Mayak附近。源的重建主要基于对浓度测量值的分析以及大气传输模型。由于浓度测量值的合理时间分辨率对于正确调整源术语至关重要，因此标准的一周采样间隔可能会受到限制。在本文中，我们对基于气溶胶过滤器的伽马射线计数的最新开发的AMARA和CEGAM实时监控系统的可用性进行了调查。这些高分辨率数据用于^106的逆建模汝释放。我们使用来自全球预报系统（GFS）的气象数据执行Hysplit大气传输模型的后向运行，并为我们域中的每个网格单元构造一个源-受体敏感性（SRS）矩阵。然后，我们使用带有自适应先验协方差的最小二乘法（LS-APC）方法来估计发布的可能位置和发布的源项。在捷克监测数据上，比较了标准方案和实时方案中浓度测量的使用，并从源的位置以及时间分辨率的角度证明了实时数据更好的源重构。来源。估计的释放位置Mayak和总的估计源期限237±107 TBq与先前的研究一致。最后，