An inverse source estimation method is proposed to reconstruct emission rates of multi-radionuclides using local gamma dose rate measurements under the data assimilation framework. It involves the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational data assimilation (PODEn4DVar) algorithm and a transfer coefficient matrix (TCM) created using FLEXPART, a Lagrangian atmospheric dispersion model. PODEn4DVar is a hybrid data assimilation method that exploits the strengths of both the ensemble Kalman filter (EnKF) and the 4DVar assimilation method. With an explicit expression of control (state) variables in the cost functional, the data assimilation process is substantially simplified than traditional 4D variational method. By setting a unit emission rate and running the ATDM model (FLEXPART in this article) driven by meteorological fields forecasted with WRF, we get the transfer coefficient matrix with the progression of nuclear accident. TCM not only acts as observation operator in PODEn4DVar, but also eliminates the control run in traditional data assimilation framework. The method is tested by twin experiments with ratios of nuclides assumed to be known. With pseudo observations based on Fukushima Daiichi nuclear power plant (FDNPP) accident, most of the emission rates were estimated accurately, except under conditions when wind blew off land toward the sea and at extremely slow wind speeds near the FDNPP. Because of the long duration of accident and variability of meteorological fields, measurements from land only in local area is unable to offer enough information to support emergency response. With abundant measurements of gamma dose rate, emission rates can be reconstructed sequentially with the progression of nuclear accident. Therefore, the proposed method has the potential to be applied to nuclear emergency response after improvement.
Predicting terrestrial <sup>222</sup>Rn flux using gamma dose rate as a proxy
