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

Abstract

In this paper, 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, andmore » the area above 3.8 μSv/h will be almost fully contained within the non-residential forested zone.« less

Authors:
 [1]; ORCiD logo [2];  [3];  [4]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Japan Atomic Energy Agency, Ibaraki (Japan)
  3. Japan Atomic Energy Agency, Fukushima (Japan)
  4. Japan Atomic Energy Agency, Tokyo (Japan)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1460346
Alternate Identifier(s):
OSTI ID: 1548446
Grant/Contract Number:  
AC02-05CH11231; AWD00000626
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Environmental Radioactivity
Additional Journal Information:
Journal Volume: 189; Journal Issue: C; Related Information: © 2018 Elsevier Ltd; Journal ID: ISSN 0265-931X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
61 RADIATION PROTECTION AND DOSIMETRY; 54 ENVIRONMENTAL SCIENCES

Citation Formats

Wainwright, Haruko M., Seki, Akiyuki, Mikami, Satoshi, and Saito, Kimiaki. Characterizing regional-scale temporal evolution of air dose rates after the Fukushima Daiichi Nuclear Power Plant accident. United States: N. p., 2018. Web. doi:10.1016/j.jenvrad.2018.04.006.
Wainwright, Haruko M., Seki, Akiyuki, Mikami, Satoshi, & Saito, Kimiaki. Characterizing regional-scale temporal evolution of air dose rates after the Fukushima Daiichi Nuclear Power Plant accident. United States. https://doi.org/10.1016/j.jenvrad.2018.04.006
Wainwright, Haruko M., Seki, Akiyuki, Mikami, Satoshi, and Saito, Kimiaki. Tue . "Characterizing regional-scale temporal evolution of air dose rates after the Fukushima Daiichi Nuclear Power Plant accident". United States. https://doi.org/10.1016/j.jenvrad.2018.04.006. https://www.osti.gov/servlets/purl/1460346.
@article{osti_1460346,
title = {Characterizing regional-scale temporal evolution of air dose rates after the Fukushima Daiichi Nuclear Power Plant accident},
author = {Wainwright, Haruko M. and Seki, Akiyuki and Mikami, Satoshi and Saito, Kimiaki},
abstractNote = {In this paper, 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.},
doi = {10.1016/j.jenvrad.2018.04.006},
journal = {Journal of Environmental Radioactivity},
number = C,
volume = 189,
place = {United States},
year = {Tue Apr 24 00:00:00 EDT 2018},
month = {Tue Apr 24 00:00:00 EDT 2018}
}

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Cited by: 3 works
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Figures / Tables:

Fig. 1 Fig. 1: (a) Evacuation designated area and (b) land cover types (blue= urban, green= cropland and yellow= forest). In (a), the red region is the evacuation designated area as of April 2017. The green region is where the restriction order was lifted in April 2017.

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