A multiscale Bayesian data integration approach for mapping air dose rates around the Fukushima Daiichi Nuclear Power Plant. (February 2017)

• Record Type: Journal Article
• Authors: Wainwright, Haruko M.; Seki, Akiyuki; Chen, Jinsong; Saito, Kimiaki
• Is Part Of: Journal of environmental radioactivity. Volume 167(2017:Feb.)
• Page Start: 62
• Page End: 69
• Publication Date: 2017-02
• Subjects: Mapping of air dose rates -- Bayesian hierarchical models -- Fukushima Daiichi Nuclear Power Plant accident -- Geostatistics -- Multi-scale -- Data integration; Radioactivity -- Periodicals; Radiation, Background -- Periodicals; Radioecology -- Periodicals; Radioactive pollution -- Periodicals; Environmental Pollutants -- Periodicals; Radioactive Pollutants -- Periodicals; Radioactivity -- Periodicals; Radioécologie -- Périodiques; Pollution radioactive -- Périodiques; Fond de rayonnement -- Périodiques; 539.752
• DOI: 10.1016/j.jenvrad.2016.11.033
• Languages: English
• ISSNs: 0265-931X

Abstract

Abstract: 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 confidence intervals that are critical for robust decision-making. Although this approach is primarily data-driven, it has great flexibility 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 confidence 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. Highlights: A Bayesian data integration method is proposed to map air dose rates. The method integrated walk, car and air survey data with different accuracy/scales. The method provided a detailed dose-rate map with the uncertainty quantified.