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The measurements of submicron aerosol and black carbon (BC) surface concentrations, and carbon monoxide (CO) total column in 1992-2012 in Beijing and Moscow are illustrated. The specific features in the long-term variations in the studied impurities in these megacities are discussed. The level of pollution with all three impurities in Beijing is substantially higher than in Moscow. From 1992 to 1999, the monthly means of black carbon and aerosol increased in Beijing. These concentrations substantially decreased beginning from 2000. From 2007 to 2011, black carbon decreased and submicron aerosol increased. In 1996-2003 the urban part of CO total column (TC) in Beijing was on average higher than in 2006-2012 by a factor of 1.4. The anthropogenic part of CO in Moscow decreased in 2006-2012. High aerosol and CO concentrations, comparable with concentrations rather typical of Beijing, were observed in Moscow only during wildfires in 2010. Using the cluster analysis statistical methods, it has been indicated that the main sources of the air pollution in Beijing are located 100-500 km southward. [ABSTRACT FROM AUTHOR]

The new-generation sensor TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 precursor (S5P) satellite is promising for monitoring air pollutants with greater spatial resolution, especially for China, which suffers from severe pollution. As tropospheric NO2 vertical column densities (VCDs) from TROPOMI have become available since February 2018, this study presents the comparisons of NO2 data measured by TROPOMI and its predecessor Ozone Monitoring Instrument (OMI) over China, together with validation against ground Multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements. At the nationwide scale, we used two different filters performed for the TROPOMI data (named TROPOMI50 and TROPOMI75), and the TROPOMI50 yielded larger values than TROPOMI75. The TROPOMI NO2 datasets from different filters show consistent spatial patterns with OMI, and the correlation coefficient values were both above 0.93. However, linear regression indicates that NO2 loadings in TROPOMI is about 2/3 to 4/5 of those in OMI, which is presumably due to a different cloud mask and uncertainties of air mass factors. The absolute difference is prominent over the high pollution areas such as Jing-Jin-Ji region and during winter and autumn, exceeding 0.6 × 1016 molecules cm−2 (molec cm−2). However, the NO2 concentrations retrieved from TROPOMI50 in the southern China may be somewhat higher than OMI. When it comes to the local-scale Jing-Jin-Ji hotspot, the analysis focuses on a comparison to TROPOMI75. TROPOMI manifests high quality and exhibits a significantly better performance of representing spatial variability. In contrast, OMI shows fewer effective pixels and does a poor job of capturing local details due to its row anomaly and low resolution. The absolute difference between two datasets shows the same seasonal behavior with NO2 variation, which is most striking in the winter (0.31 × 1016 molec cm−2) and is lowest in the summer (0.05 × 1016 molec cm−2). Furthermore, the ground MAX-DOAS instrument in Xianghe station, the representative site in Jing-Jin-Ji, is used to assess the skill of satellite retrievals. It turns out that both OMI and TROPOMI underestimate the observations, ranging from 30% to 50%, with OMI being less biased. In spite of the negative drift, the temporal structures of changes derived from OMI and TROPOMI closely match the ground-based records, since the correlation coefficients are above 0.8 and 0.95 for daily and monthly scales, respectively. Overall, TROPOMI NO2 retrievals are better suited for applications in China as well as the Jing-Jin-Ji hotspot due to its higher spatial resolution, although some improvements are also needed in the near future. [ABSTRACT FROM AUTHOR]