EGU22-670, updated on 28 Apr 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Emissions of radioactive aerosols during wildfires and dust storm in Chernobyl Exclusion Zone in April 2020 estimated by means of ensemble inverse modeling

Ivan Kovalets1, Mykola Talerko2, Roman Synkevych1, Serhii Koval1, and Oleg Udovenko3
Ivan Kovalets et al.
  • 1Institute of Mathematical Machines and Systems Problems NAS of Ukraine, Environmental Informatics, Kyiv, Ukraine (
  • 2Institute for Safety Problems of Nuclear Power Plants NASU, Kyiv, Ukraine
  • 3Ukrainian Center of Environmental and Water Projects, Kyiv, Ukraine

The dynamics of emissions of radioactive aerosols during powerful wildfires (3-23 April 2020) and dust storm (16-17 April 2020) in the Chernobyl Exclusion Zone (ChEZ) was estimated using an ensemble inverse method. The unique feature of this event is that the wildfires of unprecedented power in ChEZ were combined with the dust storm on 16-17 April 2020, which covered the Northern-West and Central Ukraine. Due to both events, the levels of Cs-137 concentrations in air were increased significantly above the background levels. In our study, the ensemble covariance matrices of model errors were calculated by a series of runs of the FLEXPART atmospheric transport model using different input meteorological data (22 meteorological datasets produced by Global Ensemble Forecasting System GEFS) and different sets of model parameters describing the size distribution of particles and height distribution of releases. Simulations covered the period from 3rd to 27th of April 2020. The prior estimates for the temporal dynamics of emissions were taken from [1]. Measurements of Cs-137 concentration in air collected by different countries and presented in [2] were used for source inversion. The vertical extensions of releases from different sources were estimated based on the data of the CAMS Global Fire Assimilation System. The fractions of emissions below plume bottom and between plume bottom and plume top heights were allowed to vary in different ensemble runs. It is shown that varying all the mentioned parameters (meteorological data, particle size distribution, and the parameters of emission distribution by height) significantly affected the results of the calculated temporal dynamics of emissions during the wildfires. However, the variability of meteorological data had the largest overall influence on the results. Confidence intervals for emissions from wildfires and dust storm (16-17 April) were obtained by processing the ensemble of estimates. The estimated total emissions of Cs-137 from the wildfires ranged from about 200 to about 1000 GBq. The total estimates of Cs-137 emissions due to the dust storm estimated by inverse modeling appeared to be considerably less than the emissions from the wildfires on the same days. At the same time, the levels of air pollution by common contaminants (PM2.5 and ash) observed in Kyiv were strongly dominated by the dust storm because the area covered by the dust storm was much greater than the area of ChEZ.


  • Talerko, M., Kovalets, I., Lev, T., Igarashi,  Y., Romanenko, O.  (2021) Simulation study of the radionuclide atmospheric transport after wildland fires in the Chernobyl Exclusion Zone in April 2020. Atmospheric Pollution Research, 12(3) 193-204. DOI:1016/j.apr.2021.01.010
  • Masson O., Romanenko O., Saunier O., Kirieiev S., Protsak V., Laptev G., Voitsekhovych O., Durand V., Coppin F. [et al.] (2021) Europe-Wide Atmospheric Radionuclide Dispersion by Unprecedented Wildfires in the Chernobyl Exclusion Zone, April 2020. Environmental Science & Technology, 55(20) 13834-13848. DOI: 10.1021/acs.est.1c03314

How to cite: Kovalets, I., Talerko, M., Synkevych, R., Koval, S., and Udovenko, O.: Emissions of radioactive aerosols during wildfires and dust storm in Chernobyl Exclusion Zone in April 2020 estimated by means of ensemble inverse modeling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-670,, 2022.