Sun Dajie, Wainwright Haruko, Suresh Ishita, Seki Akiyuki, Takemiya Hiroshi, Saito Kimiaki
University of California, Berkeley, USA.
Lawrence Berkeley National Laboratory, USA; Massachusetts Institute of Technology, USA.
J Environ Radioact. 2022 Oct;251-252:106946. doi: 10.1016/j.jenvrad.2022.106946. Epub 2022 Jun 22.
In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.
在本文中，我们开发了一种方法来估算福岛第一核电站（FDNPP）周边辐射空气剂量率的时空分布。在我们的探索性数据分析中，我们发现：（1）剂量率的时间演变由对数线性衰减趋势和空气剂量率波动组成，这些波动在相邻监测站之间存在空间相关性；（2）对数线性环境衰减趋势的斜率可以表示为表观初始剂量率、坐标位置、土地利用类型和土壤类型的函数。基于这些观察结果，我们首先使用随机森林方法根据这些预测变量估计每个位置的对数线性衰减趋势。然后，我们开发了一种结合高斯过程模型的改进卡尔曼滤波器，以估计给定位置和时间的剂量率时间序列。我们将此方法应用于福岛疏散区（截至2017年3月），该区域包括17个监测站位置（有2014年至2018年期间收集的监测数据集），并生成了剂量率地图的时间序列。我们的结果表明，这种方法使我们能够利用有限的时空测量数据准确地对辐射剂量率地图进行时空预测。
