of risk assessment WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide Global update 2005 Summary of risk assessment WHO/SDE/PHE/OEH/06.02

The TROPOspheric Monitoring Instrument (TROPOMI) significantly improved the quality of air pollution monitoring. The comparison against other data sources showed acceptable results and good correlation. However, highly polluted areas remain the reason for large uncertainties. In our study, we compared TROPOMI NO 2 , CO, HCHO, and SO 2 data against ground-level measurements from Ukrainian monitoring sites, which could be considered rather unusual due to their close location to large anthropogenic emission sources. These sites were established in the former USSR to reflect the maximal air pollution levels. TROPOMI detected all cities with huge anthropogenic emissions in 2019-2020 and qualitatively reflected air pollution in Ukraine. However, direct comparison against individual monitoring sites showed mainly insignificant correlation, which was not much improved with cloudiness filtering, spatial averaging, and considering boundary layer height. We show that on an hour-to-daily scale, differences appear when remote sensing at a particular time cannot catch the elevated pollution levels in the boundary layer after air mass transportation. On a seasonal scale, the correlation decreases because of the differences in seasonality between near-surface and column pollutants' content. We discuss that the relationship between TROPOMI and ground-level measurements depends on the season, the continuance of industrial emissions, and the features of individual emission sources.

The paper aims to define the main features and principles of seasonal and interannual NO2 variations in Ukrainian industrial cities. Using ground-based measurements for 15-year period, it shows weak NO2 seasonal variability that could intensify in case of three regularities. These regularities depend on impact of natural conditions during anthropogenic emissions growth and redistribution between emission sources. Most industrial cities are characterized by positive trends even if stationary industrial emissions fall. NO2 interannual changes forms under variety of fluctuations. However, 6.2- and 9.3-year periods have the biggest impact and might be explained by low-frequent lunar tidal forces through its influence on meteorological conditions.

Introduction. Air pollution heterogeneity and rapid urbanization impose numerous constraints on available near-surface air quality monitoring. The solution for effective warning comes with the integration of different data, including remote sensing. Satellite data cannot answer whether dangerous pollution levels are observed; however, it provides a complete picture and may detect air pollution transportation towards or away from cities. The possibilities for effective near-real time (NRTI) monitoring have significantly improved with the launch of the Sentinel-5P satellite. The study aimed to describe the developed system for NRTI air pollution monitoring over Kharkiv, Kryvyi Rig, Kyiv, and Odesa based on NO2 and CO data derived from the Sentinel-5P satellite. Data and methodology. The NRTI System was developed for tropospheric NO2 and total CO column number densities based on the Sentinel-5P NRTI products. After satellite scanning of Ukrainian territory, the NRTI System goes live in 2-3 hours. It is fully automatic, and modules were written using Python, VB.NET, and batch-scripting. Results. The NRTI System includes four main phases: preparatory, source data downloading, processing and post-processing with visualization, archiving, and result distribution among users. Source data filtering with a quality assurance index and downscaling with linear kriging interpolation were developed. The output of the NRTI System is data in regular grids with a spatial resolution of 0.02o×0.02o. Based on the NRTI System work during October – December 2021, we conducted preliminary analyses to understand the possibilities of data usage. Higher NO2 content was observed in Kyiv and Kharkiv, where traffic emissions play a crucial role in air quality worsening. The use of daily time series allowed the detection of an increase in NO2 variance during the heating season, as well as plume distribution from cities to rural areas due to the prevailing wind. CO content is more homogeneous; however, higher values were observed in industrial Kryvyi Rig and Odesa. It is emphasized the huge impact of shipping CO emissions on air quality in Odesa. The temporal averaging of the NRTI System output allowed us to define the most polluted districts within the cities of interest. We intend to continue developing the presented NRTI System and develop the same algorithms for all cities with populations greater than 500 000 people in order to provide operational air pollution monitoring based on satellite data.

The state of air pollution monitoring in Kyiv city was investigated. There were discussed the relevance of observation system optimization, advantages and disadvantages of the current monitoring network for air pollution in the urban atmosphere. The united complex approach was used for justification of mentioned optimization, which was combined with emission inventories databases, meteorological air pollution potential characteristics, ratified ground-based measurements data of main pollutants, and demographic urban features. The paper discusses main meteorological parameters which drive pollutants’ dispersion. Analysis provide evidence of its huge impact for the pollution regime formation and tendency to the decline of air quality, which must be taken in consideration during optimization for atmospheric air monitoring. The process of optimization for atmospheric air monitoring takes into account the orographic urban features used mainly for the purpose of statistically valid data provision. Therefore, in small microclimatic zones the monitoring sites are located within relief bodies, which are representative for the area. The research estimates results of boundary pollutants’ content caused by middle and high stationary emission sources, defined from the methodology connected with combined IEM diffusive model. Analysis of observations confirms the accuracy of defined structure for urban pollution fields. The combine usage of modeling results and observations allows increasing of atmospheric air quality estimations and helps to optimize the network with minimal amount of necessary representative sites.