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Wainwright, Haruko*; Oroza, C.*; Sun, D.*; Seki, Akiyuki; Mikami, Satoshi; Saito, Kimiaki
45th Annual Waste Management Conference (WM 2019); Encouraging Young Men & Women to Achieve Their Goals in Radwaste Management, Vol.7, p.4346 - 4356, 2020/01
In this work, we have developed a methodology for optimizing the sampling locations of radiation air dose-rate monitoring. Three steps are taken in order to determine sampling locations in a systematic manner: (1) prioritizing the critical locations, such as schools or regulatory requirement locations, (2) diversifying locations across 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. Our results have shown that increasing the number of sampling locations can better capture the heterogeneity of dose rates, although the estimation error does not decrease further after a certain number of samples. We have also found that when there are restrictions such as pre-existing monitoring locations or the ones along roads, the spatial estimation becomes poor even with the same number of monitoring locations.
Wainwright, Haruko*; Seki, Akiyuki; Mikami, Satoshi; Saito, Kimiaki
44th Annual waste management conference (WM 2018); Nuclear and industrial robotics, remote systems and other emerging technology, Vol.8, p.5013 - 5017, 2018/08
A Bayesian hierarchical modeling approach was developed to integrate multiscale datasets, and also to estimate the spatial distribution of air dose rates in high resolution over space. In this study, we aim to extend this approach and predict the area of the evacuation zone in the future. We coupled the integrated map with the data-driven ecological decay model. Results show that the area of evacuation zone will shrink significantly in the next twenty years.
Wainwright, Haruko*; Seki, Akiyuki; Chen, J.*; Saito, Kimiaki
Proceedings of International Waste Management Symposia 2017 (WM2017) (Internet), 8 Pages, 2017/03
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, multitype 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. We showed the demonstration of Bayesian data integration approach for the Fukushima evacuation zones with high air dose rates.