Characterizing regional-scale temporal evolution of air dose rates after the Fukushima Daiichi Nuclear Power Plant accident

Highlights: • Created the integrated dose rate maps from 2014 to 2016 based on walk, car and airborne survey data around the Fukushima Dai-ichi NPP. • Visualized the spatial heterogeneity of dose rate reduction in the regional scale; including decontamination and anthropologic effects. • Used the integrated map as the initialization in the data-driven dose prediction model to predict the radiation dose rate map in 2026. - Abstract: In this study, we quantify the temporal changes of air dose rates in the regional scale around the Fukushima Dai-ichi Nuclear Power Plant in Japan, and predict the spatial distribution of air dose rates in the future. We first apply the Bayesian geostatistical method developed by Wainwright et al. (2017) to integrate multiscale datasets including 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. We apply this method to the datasets from three years: 2014 to 2016. The temporal changes among the three integrated maps enables us to characterize the spatiotemporal dynamics of radiation air dose rates. The data-driven ecological decay model is then coupled with the integrated map to predict future dose rates. Results show that the air dose rates are decreasing consistently across the region. While slower in the forested region, the decrease is particularly significant in the town area. The decontamination has contributed to significant reduction of air dose rates. By 2026, the air dose rates will continue to decrease, and the area above 3.8 μSv/h will be almost fully contained within the non-residential forested zone.