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.

The spatial and temporal distributions of the carbon monoxide (CO) concentration were calculated with the Regional Atmospheric Modeling System and Hybrid Particle and Concentration Transport model (RAMS/HYPACT) in the provinces near Moscow during the abnormally hot summer of 2010. The forest, steppe and meadow hot spots were defined by the satellite data MCD14ML (MODIS Terra and Aqua satellite data). The calculations indicated that the surface CO concentrations from the model were two times less than the experimental data obtained from the Moscow State University (MSU) station and Zvenigorod Scientific Station (ZSS). Conversely, the total column CO concentrations obtained from the model were two to three times larger than the experimental values obtained from the Obukhov Institute of Atmospheric Physics (OIAP) and ZSS stations. The vertical transfer of pollutants was overestimated. Tentatively, it could be assumed that an aerosol influence in the model calculations is a reason for the overestimation. The comparisons between the wind speed, temperature and humidity profiles calculated in the model with the data from the standard balloon sounding exhibited good agreement. The CO total column data of the Measurements of Pollution in the Troposphere (MOPITTv5 NIR and TIR/NIR) obtained from the OIAP and ZSS stations appear more realistic than do the MOPITTv4 data. However, the surface MOPITT values of CO concentration for Moscow have the large distinction from the ground measurements. A careful proposal regarding satellite orbit optimization was made, which could improve future spectrometric measurements, such as the MOPITT, Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) measurements.

Biomass burning is an important and changing component of global and hemispheric carbon cycles. Boreal forest fires in Russia and Canada are significant sources of the greenhouse gases carbon dioxide (CO₂) and methane (CH₄). The influence of carbon monoxide (CO) on the greenhouse effect is practically absent; its main absorption bands of 4.6 and 2.3 μm are far away from the climatically important spectral regions. Meanwhile, CO concentrations in fire plumes are closely related to CO₂ and CH₄ emissions from fires. On the other hand, satellite measurements of CO are much simpler than those of the aforementioned gases. The Atmospheric Infrared Sounder (AIRS) operating in the Thermal IR spectral region has provided a satellite-based CO data set since October 2002. This satellite data allow to estimate CO emissions from biomass burning north of 30° N using a simple two-box mass-balance model. These results correlate closely with independently estimated CO emissions from the GFED4c bottom-up database. In 2021, both estimate record high emissions throughout the preceding two decades, double the annual emissions compared to previous periods. There have been two years with extremely high emissions (2003 and 2021) but for the rest of the data, an upward trend with a rate of 3.6 ± 2.2 Tg CO yr⁻² (4.8 ± 2.7% yr⁻¹) was found. A similar rate of CO emissions can be seen in the GFED4c data.

This paper presents the results of a comprehensive analysis of measurements of CO total content (CO TC) at stations of the Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences (IAP RAS), in Moscow and Moscow oblast. Also CO in-situ data obtained from automated stations of the Mosecomonitoring network, satellite monitoring results and information on the parameters of the atmospheric boundary layer in Moscow and the surrounding regions are analyzed. The long-term variability of CO TC and meteorological parameters is investigated, the carbon monoxide accumulation characteristics in calm days in the atmospheric boundary layer are obtained. A decrease in the average annual values of TC CO in 2000–2018 was found in Moscow (–2.56 ± 0.52%/year) and at the Zvenigorod Scientific Station (ZSS) (‒1.15 ± 0.37%/year). However, the rate of decrease in both sites is different in different seasons and periods. After about 2007–2008, the rate of CO TC reduction decreased at both sites. In 2008–2018, at the ZSS, an increase in CO TC was recorded in the summer and autumn months at a rate of about 0.7%/year. An increase in the wind speed in the atmospheric boundary layer of Moscow in different periods of 2000–2018 at a rate of 0.4–1.6%/year has been determined. At the same time, no statistically significant changes in wind speed were found in Kaluga oblast. The recurrence of calm days in Moscow in 2006–2017 decreased (–7.06 ± 3.96%/year) with a decrease in the anthropogenic part of the CO content in the same period (–6.72 ± 3.48%/year). The results indicate the influence of the climatic (meteorological) factor on air quality in Moscow.

Spatial and temporal distributions of isoprene and its oxidation products, methyl vinyl ketone and methacrolein in the Far East region of Russia were investigated. The measurement data were obtained from a mobile laboratory, which moved along the Trans-Siberian railway and from WRF-CHEM (Weather Research and Forecasting Chemical Model) and GEOS-CHEM (Goddard Earth Observing System Chemical Model) simulations. During the simulations, the RACM-MIM and MOZART mechanisms, included in the mesoscale WRF-CHEM model, as well as the Caltech Isoprene Scheme (CIS), built in the global GEOS-CHEM model, have been used. We found that the temporal distribution of the measured isoprene is in good agreement with the simulations. The measured isoprene, methyl vinyl ketone (MVK) and methacrolein (MACR) concentrations demonstrate pronounced diurnal variations. The correlation between the measured isoprene and MVK + MACR was good (R ~ 0.60–0.86). However, the simulated correlation between MVK + MACR and isoprene is very low, with the data for the night-time and daytime values varying. The simulated MVK + MACR to isoprene ratio, in comparison with the experimental result, has pronounced diurnal variations. During twilight and the night-time, the simulated MVK + MACR to isoprene ratio is more than 10. We propose that, due to the validity of the kinetic equations only in the homogeneous system, all chemical and transport (CTM) models, based on these kinetic equations, are not able to show an adequate simulation at night in the weak mixing atmosphere, when the atmospheric structure becomes heterogeneous. At moderate latitudes, we recommend the use of the turbulent Damköhler number and the Kolmogorov Damköhler numbers, which characterize the limits of CTM applicability, as the quality flags at the air quality forecast simulations.