Detailed deposition density maps constructed by large-scale soil sampling for gamma-ray emitting radioactive nuclides from the Fukushima Dai-ichi Nuclear Power Plant accident.
Soil deposition density maps of gamma-ray emitting radioactive nuclides from the Fukushima Dai-ichi Nuclear Power Plant (NPP) accident were constructed on the basis of results from large-scale soil sampling. In total 10,915 soil samples were collected at 2168 locations. Gamma rays emitted from the samples were measured by Ge detectors and analyzed using a reliable unified method. The determined radioactivity was corrected to that of June 14, 2011 by considering the intrinsic decay constant of each nuclide. Finally the deposition maps were created for (134)Cs, (137)Cs, (131)I, (129m)Te and (110m)Ag. The radioactivity ratio of (134)Cs-(137)Cs was almost constant at 0.91 regardless of the locations of soil sampling. The radioactivity ratios of (131)I and (129m)Te-(137)Cs were relatively high in the regions south of the Fukushima NPP site. Effective doses for 50 y after the accident were evaluated for external and inhalation exposures due to the observed radioactive nuclides. The radiation doses from radioactive cesium were found to be much higher than those from the other radioactive nuclides.
A multiscale Bayesian data integration approach for mapping air dose rates around the Daiichi Nuclear Power Plant.
This paper presents a multiscale data integration method to estimate the spatial distribution of air dose rates in the regional scale around the Fukushima Daiichi Nuclear Power Plant. We integrate various types of datasets, such as ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi- type datasets in a consistent manner. The Bayesian method allows us to quantify the uncertainty in the estimates, and to provide the con ﬁ dence intervals that are critical for robust decision-making. Although this approach is primarily data-driven, it has great ﬂ exibility to include mechanistic models for representing radiation transport or other complex correlations. We demonstrate our approach using three types of datasets collected at the same time over Fukushima City in Japan: (1) coarse-resolution airborne surveys covering the entire area, (2) car surveys along major roads, and (3) walk surveys in multiple neighborhoods. Results show that the method can successfully integrate three types of datasets and create an integrated map (including the con ﬁ dence intervals) of air dose rates over the domain in high resolution. Moreover, this study provides us with various insights into the characteristics of each dataset, as well as radiocaesium distribution. In particular, the urban areas show high heterogeneity in the contaminant distribution due to human activities as well as large discrepancy among different surveys due to such heterogeneity.
A model for predicting heat transfer through gypsum‐board/wood‐stud walls exposed to fire
To facilitate the development of cost-effective and flexible design options there is a need to develop models to predict the fire resistance of wood-frame building assemblies. Such assemblies often derive much of their fire resistance from a protective membrane composed of gypsum board. A simple two-dimensional computer model is presented to predict heat transfer through gypsum-board/wood-stud walls exposed to fire. Input data for the thermophysical properties of gypsum board were measured exploying standard bench-scale tests. Input data for wood were selected from the literature. Small-and full-scale fire resistance tests were conducted on gypsum-board/wood-stud wall assemblies to provide data for the validation of the model. The model is shown to predict heat transfer through these walls rather well.