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

Sun, Daniel; Wainwright, Haruko M.; Oroza, Carlos A.; Seki, Atsushi; Mikami, Shuichi; Takemiya, Hiroshi; Saitô, Kimiaki

doi:10.1016/j.jenvrad.2020.106281

Cited by 16 publications

(9 citation statements)

References 28 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The area of the maximum radioactivity with spiking in the measurement coincides with the location of the devastating plant of 37°25′23″N and 141°01′59 ″E as could be seen from the contour plot in Figure 2 [26,27]. On the other hand, major contributors of radionuclide based on the municipality are shown in Figure 3 through Pareto analysis.…”

Section: Geographical Distribution and Radiation Levelmentioning

confidence: 51%

STUDY OF TELLURIUM-129m (129mTe) GROUND DEPOSITION FOLLOWING FUKUSHIMA NUCLEAR DISASTER: DESCRIPTIVE ANALYSIS OF UNSCEAR DATABASE USING STATISTICAL PROCESS TECHNIQUES

Eissa¹

2021

Mugla Journal of Science and Technology

View full text Add to dashboard Cite

One of the most shocking accidents that have impacted the world recently was the devastation of the Fukushima-Daiichi nuclear power plant following a major earthquake followed by a strong 15-meter Tsunami wave affecting the east coast region. One of the major adverse consequences of this catastrophic event is the release and dissemination of radioactive materials in the surrounding environment. Tellurium-129m is one of the comprehensively traced fission products to assess the magnitude of spreading by measuring ground soil deposition of radioactivity. Radiation level/location relationship study was tested using a simple and inexpensive statistical software package using unique Statistical Process Control methodologies for the United Nations Scientific Committee on the Effects of Atomic Radiation database that demonstrated spiking in radioactivity. Sampling points were geographically associated with the radioactivity level with the spot of the maximum radionuclide concentration could be linked with the damaged plant location. More than three-fifths of the measured radioactivity are confined in an area of about 640 km 2 covering five municipalities namely Namie, and Futaba towns, Litate village and Iwaki city. The latitude-oriented chart showed a sudden rise in deposition followed by a gradual decline. Excessive Tellurium-129m activity could be observed in the area of the catastrophic event.

show abstract

Section: Geographical Distribution and Radiation Levelmentioning

confidence: 51%

STUDY OF TELLURIUM-129m (129mTe) GROUND DEPOSITION FOLLOWING FUKUSHIMA NUCLEAR DISASTER: DESCRIPTIVE ANALYSIS OF UNSCEAR DATABASE USING STATISTICAL PROCESS TECHNIQUES

Eissa¹

2021

Mugla Journal of Science and Technology

View full text Add to dashboard Cite

show abstract

“…Sasaki et al (Sasaki et al 2021) applied ANNs to develop a new method of visualizing the ambient dose-rate distribution around the Fukushima Daiichi NPPs. Sun et al (Sun et al 2020) developed a methodology for optimizing the monitoring locations of long-term radiation air dose-rate monitoring near the Fukushima Daiichi NPPs. Zhang and Hu (Zhang and Hu 2020) proposed a real-time method for radionuclide estimation in NPP wastewater using ANNs.…”

Section: Plant Safety Assessment -Accidental Radiological Release And...mentioning

confidence: 99%

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

Bao

Zhang

et al. 2022

View full text Add to dashboard Cite

This report presents the project Idaho National Laboratory conducted for the Nuclear Regulatory Commission (NRC) to explore the advanced computational tools and techniques, such as artificial intelligence (AI) and machine learning (ML), for operating nuclear plants. The report reviews the nuclear data sources, with the focus on operating experience data, that could be applied by advanced computational tools and techniques. Plant-specific and generic (national and international) data from different sources are described. The report describes the relationships between statistics and AI/ML and then introduces the most widely used AI/ML algorithms in both supervised and unsupervised learning. The report reviews the recent applications of advanced computational tools and techniques in various fields of nuclear industry, such as reactor system design and analysis, plant operation and maintenance, and nuclear safety and risk analysis. The report presents the insights from the project on the potential applicability of AI/ML techniques in improving advanced computational capabilities, how the advanced tools and techniques could contribute to the understanding of safety and risk, and what information would be needed to provide meaningful insights to decision makers.The report also documents an NRC survey on the current state of commercial nuclear power operations relative to the use of AI and ML tools as well as the role of AI/ML tools in nuclear power operations, which was published by the NRC as in the Federal Register Notice NRC-2021-0048 in April 2021. A summary of the survey, including the survey questions, survey participants, survey responses, and the conclusions and insights derived from the survey, is provided in the report.Finally, the report investigates potential applications of using AI/ML in operating nuclear power plants and advanced reactors (both advanced light-water reactors and advanced non-light-water reactors) to improve nuclear plant safety and efficiency. Three main application fields are considered: plant safety and security assessments; plant degradation modeling, fault and accident diagnosis and prognosis; and plant operation and maintenance efficiency improvement. v TABLE OF CONTENTS ABSTRACT .

show abstract

“…After the accident in Fukushima prefecture and neighboring areas the ambient air dose rates have been continuously measured by various methods through car and air-borne surveys, and insitu techniques [17]. Long term measurements can assure the public, monitor hazard levels reduction, and give insights about future preparations like decontamination [18]. Fortunately, the observations show that the amount of air doses has been reduced significantly in residential areas compared to the forests and remote areas, owing to the radioactive decay of radiocesium, decontamination processes, the activity of the inhabitants, and the penetration into the deeper depths of the soil [19,20].…”

Section: Jrncmentioning

confidence: 99%

A mobile simulation and ARIMA modeling for prediction of air radiation dose rates

Salh

Külahçı

Aközcan

2021

J Radioanal Nucl Chem

View full text Add to dashboard Cite

A spatial simulation method in .mp4 format was proposed to determine Fukushima radioactive fallout transport and the Absorbed Dose Rate, Annual Effective Dose Equivalent, and Excess Lifetime Cancer Risk were determined for 10 months after the accident (March 11 2011). The findings of this study demonstrate that an appropriate ARIMA model can be applied for radiation dose time-series in the case of nuclear reactor accidents like Chernobyl and Fukushima to predict the future air dose rates, which can provide valuable information in determining the evacuation zones, decontamination processes, and radiation protection progresses. The model forecasted results and the actual observation data in the same period shows a gradual decrease in the air dose rates during the prediction period. Moreover, there is a good agreement between them as the prediction and observation scatter plot follows each other with small variations. These results provide important insights into the predictability of ARIMA models; thus, the models were utilized to forecast the air dose rates for the period (January 2020 -October 2020).

show abstract

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

Cited by 16 publications

References 28 publications

STUDY OF TELLURIUM-129m (129mTe) GROUND DEPOSITION FOLLOWING FUKUSHIMA NUCLEAR DISASTER: DESCRIPTIVE ANALYSIS OF UNSCEAR DATABASE USING STATISTICAL PROCESS TECHNIQUES

STUDY OF TELLURIUM-129m (129mTe) GROUND DEPOSITION FOLLOWING FUKUSHIMA NUCLEAR DISASTER: DESCRIPTIVE ANALYSIS OF UNSCEAR DATABASE USING STATISTICAL PROCESS TECHNIQUES

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

A mobile simulation and ARIMA modeling for prediction of air radiation dose rates

Contact Info

Product

Resources

About