The spatial and temporal distributions of atmospheric aerosols have been simulated using the GEOS-Chem model over the sparsely investigated Eastern European region. The spatial distribution of the particulate matter (PM_2.5) concentration, mineral dust, black carbon, organic aerosols, sea salt, as well as nitrate, sulfate, and ammonium aerosols during 2016–2017 were considered. The aerosols’ concentration, seasonality and spatial features were determined for the region. Particulate matter (PM_2.5) contamination prevails in Poland in late autumn and winter. The monthly mean PM_2.5 concentration reached 55 µg m⁻³ over the Moscow region in the early spring of both years. The mineral dust concentration varied significantly, reaching 40 µg m⁻³ over the southwestern part of Eastern Europe in March 2016. The areas most polluted by black carbon aerosols were the central and southern parts of Poland in the winter. The organic aerosols’ concentration was the largest in March and April, reaching 10 µg m⁻³ over East Belarus. The sea salt aerosol concentration increased in the coastal regions in winter due to the wind strength. Mineral dust aerosols in Eastern Europe are mainly composed of dust, partially transported from the Ukrainian steppe and partially from the Saharan Desert.

Extraordinarily high aerosol contamination was observed in the atmosphere over the city of Kyiv, Ukraine, during the March–April 2020 period. The source of contamination was the large grass and forest fires in the northern part of Ukraine and the Kyiv region. The level of PM2.5 load was investigated using newly established AirVisual sensor mini-networks in five areas of the city. The aerosol data from the Kyiv AERONET sun-photometer site were analyzed for that period. Aerosol optical depth, Ångström exponent, and the aerosol particles properties (particle size distribution, single-scattering albedo, and complex refractive index) were analyzed using AERONET sun-photometer observations. The smoke particles observed at Kyiv site during the fires in general correspond to aerosol with optical properties of biomass burning aerosol. The variability of the optical properties and chemical composition indicates that the aerosol particles in the smoke plumes over Kyiv city were produced by different burning materials and phases of vegetation fires at different times. The case of enormous PM2.5 aerosol contamination in the Kyiv city reveals the need to implement strong measures for forest fire control and prevention in the Kyiv region, especially in its northwest part, where radioactive contamination from the Chernobyl disaster is still significant.

Extraordinary high aerosol contamination observed in the atmosphere over Kyiv city, Ukraine, during the March – April 2020 period. The source of contamination was the large grass and forest fires in the northern part of Ukraine and the Kyiv region. The level of PM2.5 load investigated using newly established AirVisual sensors mini-network in five areas of the city. The aerosol data from the Kyiv AERONET sun-photometer site analyzed for that period. Aerosol optical depth, Angstrom exponent, and the aerosol particles properties (particles size distribution, single-scattering albedo, and complex refractive index) were analyzed using AERONET sun-photometer observations. The smoke particles observed at Kyiv site during the fires in general correspond to aerosol with optical properties of biomass burning particles. The variability of the optical properties and chemical composition indicates that the aerosol particles in the smoke plumes over Kyiv were produced by different burning material and phases of vegetation fires at different time. The case of enormous PM2.5 aerosol contamination in Kyiv city reveals the need to accept strong measures for forest fire control and prevention in Kyiv region, especially in the north-west region where radioactive contamination from Chornobyl disaster is still significant.

To estimate the spatial and temporal distribution of aerosol optical depth (AOD), we used the optimal interpolation (OI). In OI, observational data and a model forecast are linearly combined according to their relative accuracies. Weight coefficients are chosen to minimize the mean-square error in the estimate. To obtain weight coefficients, correlations between model errors in the different grid points are used. In classical OI, only spatial correlations are considered. We used spatial and temporal correlation functions. To obtain error statistics, we used observations from European stations of ground-based sun photometers, the Aerosol Robotic Network (AERONET), and simulations by a chemical transport model GEOS-Chem, assuming a negligible error of AERONET AOD observations. The estimates of the daily mean AOD distribution over Europe are obtained. The reduction of the root-mean-square error of the AOD estimate based on the OI method in comparison with the GEOS-Chem model results is discussed.

In April 2024, the transport of mineral dust from the Sahara Desert was observed over Ukraine. This phenomenon, unusual for the region, resulted in reduced visibility, "red rain," degraded air quality, and altered atmospheric aerosol properties over Kyiv. To better understand the impact of this event, sun photometers and modeling efforts were used to analyze the changes in aerosol characteristics and the atmospheric influence of mineral dust transport. Observations from the AERONET Kyiv station indicated significant changes in aerosol characteristics. Specifically, there was an increase in aerosol optical depth (AOD) and coarse-mode AOD, while the Angstrom exponent (AE) and fine-mode AOD showed a decline. Cluster analysis of these parameters revealed temporal patterns and correlations between the observed changes. The size distribution analysis highlighted the dominant influence of coarse particles. Additionally, the single scattering albedo (SSA) and refractive index values were affected, reflecting the presence of mineral dust compared to typical conditions. The GEOS-Chem chemical transport model further indicated changes in mineral dust concentrations, suggesting its notable impact on Ukraine's territory. Additionally, the HYSPLIT model was utilized in this study to analyze backward trajectories of air masses, providing crucial information about their movement before reaching the territory of Ukraine and identifying their origins.