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The paper presents the results of the simulation of secondary air pollution scenario in Ukraine due to the wind lift of anthropogenic radionuclides during a dust storm in April 2020. A variant of the Bagnold formula was used to parameterize the intensity of radionuclide resuspension. To set the initial pollution of the territory of Ukraine, the reconstruction of meteorological conditions and fallout of Cs-137 after the disaster at the Chernobyl nuclear power plant was carried out through the use of the RODOS nuclear emergency response system and the WRF meteorological model. For the normalized root-mean-square error of the calculated total fallout in the 50-km zone around the Chernobyl NPP the value NMSE=4,5 was obtained. A decrease in the levels of pollution of the Earth's surface during the time after the accident due to radioactive decay and other environmental processes was estimated. The distribution of contamination of the Earth's surface obtained in this way was used to assess the intensity of wind rise and atmospheric transport of radionuclides during a dust storm on April 16–18, 2020. The calculations were carried out using the CALPUFF model. The input meteorological data were the results of the WRF-Ukraine weather forecasting system. In the calculation of secondary contamination, the effect of fires in the Chernobyl Exclusion Zone (ChEZ) was not taken into account. The calculated average daily concentrations of Cs-137 in the air were maximum for the first day of the dust storm (April 16), when the wind speed was maximum (13 m/s with gusts up to 19 m/s). Average daily concentrations on April 16 ranged from the background values (5,8·10-6 Bq/m3 in Kyiv) to 2,2·10-3 Bq/m3 (ChNPP). The obtained estimates are much less than the permissible concentrations (0.8 Bq/m3). At the same time consideravle exceedance of background values were predicted in a large part of Ukraine – from Rivne NPP (2,2·10-5 Bq/m3) to Kharkiv (1,3·10-5 Bq / m3). In the vicinity of the ChEZ in the cities of Chernihiv and Slavutich, the obtained estimates of daily average concentration were 1,6·10-4 Bq/m3 and 3,2·10-4 Bq/m3 respectively.

<p>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.</p><p><strong>References</strong></p><ul><li>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</li> <li>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</li> </ul>

The paper reviews the methods for identifying an unknown source of pollution by inverse mod-eling and information systems for air pollution forecasting and analysis. Several different for-eign and Ukrainian air pollution forecasting systems, such as the European Union's Nuclear Emergency Response System RODOS, have been developed on the basis of atmospheric transport models. However, the key data that determine the quality of forecasting in such sys-tems are the characteristics of the emission sources. In the case of detection of pollution from an unknown emission source, there should be performed inverse simulation. The use of the RODOS system, as well as other existing forecasting systems for such a task is possible but it requires multiple manual start of calculations of atmospheric transfer models in the reverse mode. Presented in the paper results of the application of inverse modeling methods during ra-diation incidents of the last decade demonstrate that modern methods of inverse modeling are sufficiently developed to set the task of automating inverse modeling in information systems for air pollution analysis and forecasting. Even though these methods not always can exactly identify the source of emissions due to the lack of measurements and poor conditioning of the inverse atmospheric transport problem, their application always leads to a significant reduction (by an order of magnitude or more) in the search for unknown sources compared to the detec-tion of pollutants. At present, in the existing forecasting systems the methods of inverse model-ing are only partially automated, namely for the case of known location and unknown emissions of the source of pollution. Therefore, this paper proposes the architecture of the future system for identifying unknown sources of emissions by inverse modeling.