1 Introduction
Radiation measurements and monitoring in the region around the Fukushima
Daiichi nuclear power plant (NPP) have been performed continuously since the
accident [1, 2]. Such mapping is essential for protecting the public, guiding
decontamination efforts, estimating the amount of decontamination waste, and also
in planning the return of evacuated residents. Radiation measurements have been
conducted using various techniques such as portable hand-held monitors, car-borne
surveys, and airborne surveys. Soil samples have been collected to assess the extent
of contamination in the terrestrial environment [3].
Despite such large-scale and continuous efforts, there are still significant challenges in mapping the radiation dose rates and radionuclide contamination. Detailed
ground-based measurements have revealed that the radiation dose rates and contamination are both quite heterogeneous, often with many hotspots [4]. Although
many datasets are becoming available, it has been difficult to integrate those
datasets, since each type of data has a different level of accuracy and represents a
different support scale (i.e., spatial coverage and resolution). For example, although
ground-based car-borne data provide high-resolution air dose rates, car-borne data
are limited to the locations along roads [5]. Airborne surveys have been extensively
used to map dose rates in the regional spatial coverage (e.g., 100 km radius) [6];
such data are, however, known to exhibit some discrepancies with co-located
ground-based measurements. These discrepancies result mainly from the differences in support volume, since airborne measurements represent the average dose
rate over a much larger area (typically a several-hundred-meter radius) than
ground-based measurements (*several tens of meters).
In environmental science, monitoring and spatial-temporal mapping of various
properties—such as CO 2 concentration, wind velocity or reactive transport properties in subsurface—have been the focus of extensive research. Although many
traditional datasets have been sparse in time and space, more recently available
datasets can cover large areas, such as remote sensing data in atmospheric/terrestrial
sciences and data from geophysical techniques in subsurface science. Such datasets,
however, are known to have some discrepancy with traditional point measurements,
because they tend to have a larger support volume (or lower resolution), such that
each pixel represents the average of heterogeneous properties in the vicinity.
Various approaches have been proposed to integrate remote-sensing or geophysical
datasets with traditional point measurements [7–9]. Many of them are based on
geostatistics, a powerful tool for characterizing spatial heterogeneity (or correlation)
structure based on available datasets [8–10]. In addition, a Bayesian framework is
often used to integrate different datasets consistently and also to quantify the
uncertainty associated with the estimated maps [7, 9].
In this study, we develop a Bayesian data-integration approach to estimate the
spatial distribution of air dose rates and radionuclide contamination in high resolution across the regional scale (several kilometers to several tens of kilometers).
We integrate various radiation measurements, with particular focus on airborne and
58
H.M. Wainwright et al.
Radiation measurements and monitoring in the region around the Fukushima
Daiichi nuclear power plant (NPP) have been performed continuously since the
accident [1, 2]. Such mapping is essential for protecting the public, guiding
decontamination efforts, estimating the amount of decontamination waste, and also
in planning the return of evacuated residents. Radiation measurements have been
conducted using various techniques such as portable hand-held monitors, car-borne
surveys, and airborne surveys. Soil samples have been collected to assess the extent
of contamination in the terrestrial environment [3].
Despite such large-scale and continuous efforts, there are still significant challenges in mapping the radiation dose rates and radionuclide contamination. Detailed
ground-based measurements have revealed that the radiation dose rates and contamination are both quite heterogeneous, often with many hotspots [4]. Although
many datasets are becoming available, it has been difficult to integrate those
datasets, since each type of data has a different level of accuracy and represents a
different support scale (i.e., spatial coverage and resolution). For example, although
ground-based car-borne data provide high-resolution air dose rates, car-borne data
are limited to the locations along roads [5]. Airborne surveys have been extensively
used to map dose rates in the regional spatial coverage (e.g., 100 km radius) [6];
such data are, however, known to exhibit some discrepancies with co-located
ground-based measurements. These discrepancies result mainly from the differences in support volume, since airborne measurements represent the average dose
rate over a much larger area (typically a several-hundred-meter radius) than
ground-based measurements (*several tens of meters).
In environmental science, monitoring and spatial-temporal mapping of various
properties—such as CO 2 concentration, wind velocity or reactive transport properties in subsurface—have been the focus of extensive research. Although many
traditional datasets have been sparse in time and space, more recently available
datasets can cover large areas, such as remote sensing data in atmospheric/terrestrial
sciences and data from geophysical techniques in subsurface science. Such datasets,
however, are known to have some discrepancy with traditional point measurements,
because they tend to have a larger support volume (or lower resolution), such that
each pixel represents the average of heterogeneous properties in the vicinity.
Various approaches have been proposed to integrate remote-sensing or geophysical
datasets with traditional point measurements [7–9]. Many of them are based on
geostatistics, a powerful tool for characterizing spatial heterogeneity (or correlation)
structure based on available datasets [8–10]. In addition, a Bayesian framework is
often used to integrate different datasets consistently and also to quantify the
uncertainty associated with the estimated maps [7, 9].
In this study, we develop a Bayesian data-integration approach to estimate the
spatial distribution of air dose rates and radionuclide contamination in high resolution across the regional scale (several kilometers to several tens of kilometers).
We integrate various radiation measurements, with particular focus on airborne and
58
H.M. Wainwright et al.