Abstract

The emission inventories of cesium-137 resulting from the wildfires in the Chernobyl Exclusion Zone (3–24 April 2020) and from the dust storm (16–17 April 2020), which resuspended contaminated ash, were estimated using inverse modeling. The goal of this work was to take into account uncertainties of inexactly known source term and meteorological input data for evaluation of emission inventories and their confidence limits, by developing Ensemble Iterative Source Inversion Method (EISIM). A set of source receptor matrices (SRMs) was calculated using FLEXPART atmospheric transport code with varying source term parameters (size distribution of emitted particles, height distribution of emissions) and meteorological input data. The covariance matrix of model errors was estimated by ensemble averaging of model results obtained after multiplication of the pre-calculated SRMs by the estimated emission inventories at the current iteration step. The emission inventories at each iteration step were evaluated for each of the ensemble members by solving the conventional variational source inversion problem. With iterations, the variance of model error was reduced by an order of magnitude. The estimated total emission of 137Cs from wildfires was 448 GBq, close to the first guess estimation. By using emission inventories within the obtained confidence limits (from 39 to 1530 GBq), different combinations of source term parameters and input meteorological data, FLEXPART could fit observations with a correlation coefficient of more than 0.6 and a normalized mean squared error of less than 10. The obtained estimate of the total emission resulting from the wind resuspension during the dust storm was 27 GBq. The respective estimated confidence interval was from 3 to 93 GBq. By analyzing model error statistics, some of the source term parameters could be reliably evaluated. The fraction of the fine particles (0.25 μm) in total emissions Wsf(0.25)≈0.1 and the fraction of emission below a bottom height of convective plume Wh(1)≈0.5.

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