A multiscale Bayesian data integration approach for mapping air dose rates around the Fukushima Daiichi Nuclear Power Plant

Resilient design in nuclear energy: Critical lessons from a cross-disciplinary analysis of the Fukushima Dai-ichi nuclear accident

This paper presents a multidisciplinary analysis of the Fukushima Dai-ichi Nuclear Power Plant accident. Along with the latest observations and simulation studies, we synthesize the time-series and event progressions during the accident across multiple disciplines, including in-plant physics and engineering systems, operators' actions, emergency responses, meteorology, radionuclide release and transport, land contamination, and health impacts. We identify three key factors that exacerbated the consequences of the accident: (1) the failure of Unit 2 containment venting, (2) the insufficient integration of radiation measurements and meteorology data in the evacuation strategy, and (3) the limited risk assessment and emergency preparedness. We conclude with new research and development directions to improve the resilience of nuclear energy systems and communities, including (1) meteorology-informed proactive venting, (2) machine learning-enabled adaptive evacuation zones, and (3) comprehensive risk-informed emergency planning while leveraging the experience from responses to other disasters.

Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.

A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima

Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. There is a scientific need to understand the characteristics and geostatistical variability of big spatial data from these diverse sources if they are to be used for decision making. Crowdsourced radiation measurements can be visualized as raw, often overlapping, points or processed for an aggregated comparison with traditional sources to confirm patterns of elevated radiation levels. However, crowdsourced data from citizen-led projects do not typically use a spatial sampling method so classical geostatistical techniques may not seamlessly be applied. Standard aggregation and interpolation methods were adapted to represent variance, sampling patterns, and the reliability of modeled trends. Finally, a Bayesian approach was used to model the spatial distribution of crowdsourced radiation measurements around Fukushima and quantify uncertainty introduced by the spatial data characteristics. Bayesian kriging of the crowdsourced data captures hotspots and the probabilistic approach could provide timely contextualized information that can improve situational awareness during hazards. This paper calls for the development of methods and metrics to clearly communicate spatial uncertainty by evaluating data characteristics, representing observational gaps and model error, and providing probabilistic outputs for decision making.

High-Resolution Spatio-Temporal Estimation of Net Ecosystem Exchange in Ice-Wedge Polygon Tundra Using In Situ Sensors and Remote Sensing Data

Land-atmosphere carbon exchange is known to be extremely heterogeneous in arctic ice-wedge polygonal tundra regions. In this study, a Kalman filter-based method was developed to estimate the spatio-temporal dynamics of daytime average net ecosystem exchange (NEEday) at 0.5-m resolution over a 550 m by 700 m study site. We integrated multi-scale, multi-type datasets, including normalized difference vegetation indices (NDVIs) obtained from a novel automated mobile sensor system (or tram system) and a greenness index map obtained from airborne imagery. We took advantage of the significant correlations between NDVI and NEEday identified based on flux chamber measurements. The weighted average of the estimated NEEday within the flux-tower footprint agreed with the flux tower data in term of its seasonal dynamics. We then evaluated the spatial variability of the growing season average NEEday, as a function of polygon geomorphic classes; i.e., the combination of polygon types—which are known to present different degradation stages associated with permafrost thaw—and microtopographic features (i.e., troughs, centers and rims). Our study suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.

Estimation of fukushima radio-cesium deposits by airborne surveys: Sensitivity to the flight-line spacing

After Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, airborne gamma-ray detection was used for regional mapping of soil contamination. For such surveys, the flight-line spacing is an important factor controlling the quality of contamination maps. In this study, cesium-137 (¹³⁷Cs) ground activity is interpolated and mapped using ordinary kriging method; thereafter the error of interpolation is evaluated as a function of flight-line spacing. The analyses were conducted in six 20 km × 20 km test sites with distance of less than 80 km from the FDNPP. In each site, the ordinary kriging estimators were applied to different selections of flight-lines of decreasing density, then punctual and classification errors were calculated. It is demonstrated that these variables are highly correlated (r² > 0.78): increasing the flight-line spacing for 1 km increases the errors from 3% to 9%, depending on the site location. Therefore, flight-line spacing could be designed as a function of acceptable error, determined in the monitoring objectives.

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

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. For the second step, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such as 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.

Advances in detection algorithms for radiation monitoring

This paper presents a review of up-to-date advancements in detection algorithms employed in radiation monitoring for generating radiation maps of ground contamination and tracking radioactive release into the atmosphere. Detection algorithms for true count processing, spectroscopy processing, and plume tracking are discussed in chronological order of development. Process steps of detection include height correction, solid-angle correction, background radioactivity correction, Compton continuum elimination, de-noising of gamma-radiation spectra, and recording of plume passage events.

Summary of temporal changes in air dose rates and radionuclide deposition densities in the 80 km zone over five years after the Fukushima Nuclear Power Plant accident

We summarized temporal changes in air dose rates and radionuclide deposition densities over five years in the 80 km zone based on large-scale environmental monitoring data obtained continuously after the Fukushima Nuclear Power Plant (NPP) accident, including those already reported in the present and previous special issues. After the accident, multiple radionuclides deposited on the ground were detected over a wide area; radiocesium was found to be predominantly important from the viewpoint of long-term exposure. The relatively short physical half-life of ¹³⁴Cs (2.06 y) has led to considerable reductions in air dose rates. The reduction in air dose rates owing to the radioactive decay of radiocesium was more than 60% over five years. Furthermore, the air dose rates in environments associated with human lives decreased at a considerably faster rate than expected for radioactive decay. The average air dose rate originating from the radiocesium deposited in the 80 km zone was lower than that predicted from radioactive decay by a factor of 2-3 at five years after the accident. Vertical penetration of radiocesium into the ground contributed greatly to the reduction in air dose rate because of an increase in the shielding of gamma rays; the estimated average reduction in air dose rate was approximately 25% with penetration compared to that without penetration. The average air dose rate measured in undisturbed fields in the 80 km zone was estimated to be reduced owing to decontamination by approximately 20% compared to that without decontamination. The average deposition density of radiocesium in undisturbed fields has decreased owing to radioactive decay, indicating that the migration of radiocesium in the horizontal direction has generally been slow. Nevertheless, in human living environments, horizontal radiocesium movement is considered to contribute significantly to the reduction in air dose rate. The contribution of horizontal radiocesium movement to the decrease in air dose rate was estimated to vary by up to 30% on average. Massive amounts of environmental data were used in extended analyses, such as the development of a predictive model or integrated air dose rate maps according to different measurement results, which facilitated clearer characterization of the contamination conditions. Ecological half-lives were evaluated in several studies by using a bi-exponential model. Short-term ecological half-lives were shorter than one year in most cases, while long-term ecological half-lives were different across the studies. Even though the general tendency of decrease in air dose rates and deposition densities in the 80 km zone were elucidated as summarized above, their trend was found to vary significantly according to location. Therefore, site-specific analysis is an important task in the future.

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

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.

Spatial properties of soil analyses and airborne measurements for reconnaissance of soil contamination by 137Cs after Fukushima nuclear accident in 2011

Following Fukushima nuclear disaster, several data gathering campaigns surveyed the radionuclide propagation in the environment. However, the acquired datasets do not have the same sampling dimension. For example, the airborne measurements are some sort of averaging over a circular field of view, beneath the sensor; while the soil analyses are much more punctual. The objective of this work is to compare the soil samples and an airborne survey to investigate whether these two datasets reflect the same spatial patterns or not. This is prerequisite for combining the multiresolution data to create and update the contamination map in a post-accidental situation. The analyses were performed on square tiles of 20 km side to study large- and small-dimension variations in ¹³⁷Cs deposition. The former was modelled by fitting a plane (called trend) to the georeferenced data points; and the latter was modelled by computing the difference (called residual) between the trend and the initial data. Dip direction and dip angle of trends as well as minimum spatial correlation distance and anisotropy of residuals were computed for both the soil and airborne datasets and compared. Dip directions are compatible in 73% of the tiles and dip angles are generally close. Anisotropy directions are compatible in 49% of the tiles and minimum spatial correlation distances are significantly more marked for the airborne dataset. The soil samples and airborne measurements are therefore more in agreement in large-dimension (trend) rather than in small-dimension (residual) variations. More generally, both the datasets allow highlighting the main contamination plumes distinguishable because of high concentration values. The airborne dataset yet appears to be more powerful to quantify spatial correlations, which could be linked to the contamination mechanisms.

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

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.