Title: Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi Nuclear Power Plant

Abstract

Radiation air dose rates near the Fukushima Daiichi Nuclear Power Plant (FDNPP) have been steadily decreasing over the past eight years since the release of radioactive elements in March 2011. Currently, the radiation monitoring program is expected to transition to long-term monitoring after most of the remediation activities are completed. The main long-term monitoring objectives are to (1) confirm the continuing reduction of contaminant and hazard levels, (2) provide assurance for the public, (3) accumulate the basic datasets for scientific knowledge and future preparation, and (4) detect changes or anomalies in contaminant mobility (if they occur), or any unexpected processes or events. In this work, we have developed a methodology for optimizing the monitoring locations of radiation air dose-rate monitoring. Our approach consists of three steps in order to determine monitoring locations in a systematic manner: (1) prioritizing the critical locations, such as schools or regulatory requirement locations, (2) diversifying locations that cover the key environmental controls that are known to influence contaminant mobility and distributions, and (3) capturing the heterogeneity of radiation air-dose rates across the domain. Therefore, for the second step, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such asmore » elevation and land-cover types. For the third step, we use a Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Employing an integrated dose-rate map derived from Bayesian geostatistical methods as a reference map, we distribute the monitoring locations in such a way as to capture the heterogeneity of the reference map. Our results have shown that this approach allows us to select monitoring locations in a systematic manner such that the heterogeneity of air dose rates is captured by the minimal number of monitoring locations.« less

Authors:

Sun, Dajie ^[1]; Wainwright, Haruko M. ^[2]; Oroza, Carlos A. ^[3]; Seki, Akiyuki ^[4]; Mikami, Satoshi ^[4]; Takemiya, Hiroshi ^[4]; Saito, Kimiaki ^[4]

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+ Show Author Affiliations

1. Univ. of California, Berkeley, CA (United States)
2. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
3. Univ. of Utah, Salt Lake City, UT (United States)
4. Japan Atomic Energy Agency (JAEA), Tokyo (Japan)

Publication Date:

Mon May 18 00:00:00 EDT 2020

Research Org.:

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC); Japan Atomic Energy Agency (JAEA)

OSTI Identifier:

1782171

Alternate Identifier(s):

OSTI ID: 1702194

Grant/Contract Number:  

AC02-05CH11231; AWD00000626; AC02-05CH1123

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Environmental Radioactivity

Additional Journal Information:

Journal Volume: 220-221; Journal ID: ISSN 0265-931X

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; Fukushima; dose rate; network optimization; machine learning; long-term monitoring