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This paper describes the outline and development plan for “Advanced Reactor Knowledge- and Artificial Intelligence (AI)-aided design integration approach through the whole plant lifecycle (ARKADIA),” which the Japan Atomic Energy Agency has begun developing to transform advanced nuclear reactor design to meet expectations of a safe, economic, and sustainable carbon-free energy source. ARKADIA will realize AI-aided integrated numerical analysis to offer the best possible solutions for any challenge that could arise in the design and operation of a nuclear plant, including optimization of safety equipment as well as structures, systems, and components. State-of-the-art numerical simulation technologies and a knowledge base that stores data and insights from past nuclear reactor development projects and R&D are integrated with AI. In the first phase of development, ARKADIA-design and ARKADIA-safety will be constructed individually, with the first target of a sodium-cooled reactor. In a subsequent phase, everything will be integrated into a single entity that is technology inclusive and applicable not only to advanced rectors with a variety of concepts, coolants, configurations, and output levels but also to existing light-water reactors.

Massive environmental monitoring has been conducted continuously since the Fukushima Daiichi Nuclear Power accident in March of 2011 by different monitoring methods that have different features together with migration studies of radiocesium in diverse environments. These results have clarified the characteristics of radiological environments and their temporal change around the Fukushima site. At three months after the accident, multiple radionuclides including radiostrontium and plutonium were detected in many locations; and it was confirmed that radiocesium was most important from the viewpoint of long-term exposure. Radiation levels around the Fukushima site have decreased greatly over time. The decreasing trend was found to change variously according to local conditions. The air dose rates in environments related to human living have decreased faster than expected from radioactive decay by a factor of 2–3 on average; those in pure forest have decreased more closely to physical decay. The main causes of air dose rate reduction were judged to be radioactive decay, movement of radiocesium in vertical and horizontal directions, and decontamination. Land-use categories and human activities have significantly affected the reduction tendency. Difference in the air dose rate reduction trends can be explained qualitatively according to the knowledge obtained in radiocesium migration studies; whereas, the quantitative explanation for individual sites is an important future challenge. The ecological half-lives of air dose rates have been evaluated by several researchers, and a short-term half-life within 1 year was commonly observed in the studies. An empirical model for predicting air dose rate distribution was developed based on statistical analysis of an extensive car-borne survey dataset, which enabled the prediction with confidence intervals. Different types of contamination maps were integrated to better quantify the spatial data. The obtained data were used for extended studies such as for identifying the main reactor that caused the contamination of arbitrary regions and developing standard procedures for environmental measurement and sampling. Annual external exposure doses for residents who intended to return to their homes were estimated as within a few millisieverts. Different forms of environmental data and knowledge have been provided for wide spectrum of people. Diverse aspects of lessons learned from the Fukushima accident, including practical ones, must be passed on to future generations.

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.

An enormous amount of environmental monitoring data has been acquired by various organisations for the evaluation and implementation of countermeasures to mitigate the effects of the accident at the Fukushima Daiichi Nuclear Power Plant. However, it is difficult to collate, compare, and analyse this data because it was published in different formats at different sites according to the respective objectives of the publishing organisations. Moreover, these organisations have been accumulating data in large volumes for over nine years after the accident. We established procedures to collect this data, convert them into a unified format, classify them according to categories, and make the data accessible on a web-based database system. The database contains environmental monitoring data on air dose rates, ground deposition densities, and concentrations in various environmental samples such as soil, water, and food. This data is being provided not only in numerical format for quantitative analysis but also as distribution maps and time-series graphs for visual understanding. The database system enabled us to spatially and temporally compare large volumes of monitoring data. By using the database functions, characteristics of some representative data in the database was clarified.

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.

© 2018 Elsevier Ltd 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.

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.

© 2016 Elsevier Ltd This paper presents a multiscale data integration method to estimate the spatial distribution of air dose rates in the regional scale around the Fukushima Daiichi Nuclear Power Plant. 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, multi-type 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. Although this approach is primarily data-driven, it has great flexibility to include mechanistic models for representing radiation transport or other complex correlations. We demonstrate our approach using three types of datasets collected at the same time over Fukushima City in Japan: (1) coarse-resolution airborne surveys covering the entire area, (2) car surveys along major roads, and (3) walk surveys in multiple neighborhoods. Results show that the method can successfully integrate three types of datasets and create an integrated map (including the confidence intervals) of air dose rates over the domain in high resolution. Moreover, this study provides us with various insights into the characteristics of each dataset, as well as radiocaesium distribution. In particular, the urban areas show high heterogeneity in the contaminant distribution due to human activities as well as large discrepancy among different surveys due to such heterogeneity.

In nuclear emergencies, it is especially important to carry out a wide range of environmental monitoring and provide the data immediately so as to understand the current distribution of radionuclides and investigate countermeasures. Therefore, it is indispensable for a nuclear emergency response to establish a system that supports rapid provision of these data. The authors have been developing the software platform by integrating technologies of environmental monitoring, information processing and network communication, based on the experience of the Fukushima Daiichi Nuclear Accident. It was discovered that the platform is effective in reducing the time needed to publish the monitoring data. Reducing the cost and workload for publishing the monitoring data is also important because monitoring should be continued over a few decades in the case of the Fukushima accident. The authors' platform is expected to help to mitigate the problem, too.