Abstract

Abstract. The Chernobyl nuclear accident, and more recently the Fukushima accident, highlighted that the largest source of error on consequences assessment is the source term, including the time evolution of the release rate and its distribution between radioisotopes. Inverse modeling methods, which combine environmental measurements and atmospheric dispersion models, have proven efficient in assessing source term due to an accidental situation (Gudiksen, 1989; Krysta and Bocquet, 2007; Stohl et al., 2012a; Winiarek et al., 2012). Most existing approaches are designed to use air sampling measurements (Winiarek et al., 2012) and some of them also use deposition measurements (Stohl et al., 2012a; Winiarek et al., 2014). Some studies have been performed to use dose rate measurements (Duranova et al., 1999; Astrup et al., 2004; Drews et al., 2004; Tsiouri et al., 2012) but none of the developed methods were carried out to assess the complex source term of a real accident situation like the Fukushima accident. However, dose rate measurements are generated by the most widespread measurement system, and in the event of a nuclear accident, these data constitute the main source of measurements of the plume and radioactive fallout during releases. This paper proposes a method to use dose rate measurements as part of an inverse modeling approach to assess source terms. The method is proven efficient and reliable when applied to the accident at the Fukushima Daiichi Nuclear Power Plant (FD-NPP). The emissions for the eight main isotopes 133Xe, 134Cs, 136Cs, 137Cs, 137mBa, 131I, 132I and 132Te have been assessed. Accordingly, 105.9 PBq of 131I, 35.8 PBq of 132I, 15.5 PBq of 137Cs and 12 134 PBq of noble gases were released. The events at FD-NPP (such as venting, explosions, etc.) known to have caused atmospheric releases are well identified in the retrieved source term. The estimated source term is validated by comparing simulations of atmospheric dispersion and deposition with environmental observations. In total, it was found that for 80% of the measurements, simulated and observed dose rates agreed within a factor of 2. Changes in dose rates over time have been overall properly reconstructed, especially in the most contaminated areas to the northwest and south of the FD-NPP. A comparison with observed atmospheric activity concentration and surface deposition shows that the emissions of caesiums and 131I are realistic but that 132I and 132Te are probably underestimated and noble gases are likely overestimated. Finally, an important outcome of this study is that the method proved to be perfectly suited to emergency management and could contribute to improve emergency response in the event of a nuclear accident.

Highlights

  • The Great East Japan Earthquake followed by a tsunami that occurred in Japan in March 2011 led to the most significant nuclear accident since the one that occurred at Chernobyl in 1986

  • This paper proposes a method based on inverse modeling techniques to estimate source term by using dose rate measurements

  • We have proposed an effective method of reconstructing the source term of a nuclear accident

Read more

Summary

Introduction

The Great East Japan Earthquake followed by a tsunami that occurred in Japan in March 2011 led to the most significant nuclear accident since the one that occurred at Chernobyl in 1986. The estimates of emissions obtained using simple methods were first proposed by Chino et al (2011) and later reassessed by Katata et al (2012) and Terada et al (2012) Their approach is based on the use of activity concentrations and surface activity measurements and part of the dose rate measurements. Stohl et al (2012a) and Winiarek et al (2012) propose estimates of source terms obtained by inverse modeling In both cases, measurements of activity concentration are used. This paper proposes a method based on inverse modeling techniques to estimate source term by using dose rate measurements.

Dose rate measurements
Inverse modeling of accidental release using dose rate measurements
Step 1: defining the a priori information
Step 2: identification of potential release periods
Step 3: estimating source term
Towards an implementation of the method
Model simulations
Source term
Comparison with observations
Comparison with the dose rate measurements
Comparison with the surface activity measurements
Conclusions and prospects
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call