Estimation of the caesium-137 source term from the Fukushima Daiichi nuclear power plant using a consistent joint assimilation of air concentration and deposition observations

TLDR: Inverse modeling techniques can be used to estimate the amount of radionuclides and the temporal profile of the source term released in the atmosphere during the accident of the Fukushima Daiichi nuclear power plant in March 2011.

About: This article is published in Atmospheric Environment.The article was published on 2014-01-01 and is currently open access. It has received 93 citations till now.

Figures

Table 2: Estimation of parameters and corresponding reconstructed released activity for caesium-137 source reconstruction using only activity concentration observations in the air.

Figure 3: Full line: Temporal profile of reconstructed caesium-137 source term using (a): activity concentration of137Cs measurements in the air only, (b): total cumulated deposited137Cs measurements only, (c): the activity concentration in the air, the daily fallout measurements and the total cumulated deposition raw measurements, (d): the activity concentration in the air, the daily fallout measurements andthe super-observations computed from the total cumulated deposited137Cs. Dashed lines: posterior uncertainty of the source term co puted using a Monte Carlo simulation.

Table 3: Estimation of parameters and corresponding reconstructed released activity for caesium-137 source using activity concentration, fallout daily measurements and total deposition data.r1, r2 andr3 represents respectively the errors variances of these thredata sets.

Figure 5: Map of observed and simulated deposited137Cs. Upper left (a): measurements from MEXT. Upper right (b): simulation with source reconstructed using only137Cs activity concentrations in the air. Lower left (c): simulation with source reconstructed using137Cs activity concentrations in the air, daily measurements of deposited137Cs and measurements of total cumulated deposited137Cs. Lower right (d): simulation with source reconstructed using137Cs activity concentrations in the air, daily measurements of deposited137Cs and super-observations computed from measurements of total cumulated deposited137Cs. The displayed values are in kBq m−2

Table 1: Configuration and physical parametrisations of the WRFmodel. A two-way nesting technique is used between domain 1 anddomain 2.

Figure 1: Map of the simulation domains used in WRF (dashed-linedomains) and in P3D (full-line domains). Two-way nesting is used in the WRF simulations and one-way nesting in the P3D simulation. A triangle marks the location of the Fukushima Daiichi nuclear power plant.

Frequently asked questions

Q1. Why is the observability of the plume reduced?

Due to the meteorological conditions which have often transported the radionuclides towards the Pacific Ocean, and to the low number of activity concentration data, the observability of the plume is reduced. 

Q2. What is the way to use the proposed methods?

The proposed methods can of course be applied to inverse modelling using only one data set, such as activity concentration in the air data (the first data set described in Section 3.1 and which contains 104 observations). 

Q3. What is the reason why the system gave too much weight to the third raw data set?

The assumption that the observation errors are uncorrelated led the system to give toomuch weight to the third raw data set in the inverse modelling algorithm. 

Q4. What is the posterior uncertainty of the reconstructed source term?

Then the standard deviation of the estimators ensemble is used to estimate the posterior uncertainty of the reconstructed source term. 

Q5. How many direct simulations are needed to fill the Jacobian matrix H column by column?

A total of 504 direct simulations are thus performed to fill the Jacobian matrix H column by column, and no adjoint model is needed in this process following Abida and Bocquet (2009); Winiarek et al. (2011). 

Q6. What is the route to deal with densely distributed data?

Another route that can be chosen to deal with densely distributed data is the thinning of observations which consists in the optimal selection of observations.