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This review contains the most important results obtained by Russian scientists in studying atmospheric ozone in 2015–2018 and is a part of the Russian National Report on Meteorology and Atmospheric Sciences that was prepared for the International Association of Meteorology and Atmospheric Sciences (IAMAS). This report was considered and approved at the 27th General Assembly of the International Union of Geodesy and Geophysics (IUGG). A list of main Russian publications on atmospheric ozone and its precursors is appended to it.

Airborne particles of mineral dust play a key role in Earth’s climate system and affect human activities around the globe. The numerical weather modeling community has undertaken considerable efforts to accurately forecast these dust emissions. Here, for the first time in the literature, we thoroughly describe and document the Air Force Weather Agency (AFWA) dust emission scheme for the Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosol model within the Weather Research and Forecasting model with chemistry (WRF-Chem) and compare it to the other dust emission schemes available in WRF-Chem. The AFWA dust emission scheme addresses some shortcomings experienced by the earlier GOCART-WRF scheme. Improved model physics are designed to better handle emission of fine dust particles by representing saltation bombardment. WRF-Chem model performance with the AFWA scheme is evaluated against observations of dust emission in southwest Asia and compared to emissions predicted by the other schemes built into the WRF-Chem GOCART model. Results highlight the relative strengths of the available schemes, indicate the reasons for disagreement, and demonstrate the need for improved soil source data.

Airborne particles of mineral dust play a key role in Earth's climate system and affect human activities around the globe. The numerical weather modeling community has undertaken considerable efforts to accurately forecast these dust emissions. Here, for the first time in the literature, we thoroughly describe and document the Air Force Weather Agency (AFWA) dust emission scheme for the Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosol model within the Weather Research and Forecasting model with chemistry (WRF-Chem) and compare it to the other dust emission schemes available in WRF-Chem. The AFWA dust emission scheme addresses some shortcomings experienced by the earlier GOCART-WRF scheme. Improved model physics are designed to better handle emission of fine dust particles by representing saltation bombardment. WRF-Chem model performance with the AFWA scheme is evaluated against observations of dust emission in southwest Asia and compared to emissions predicted by the other schemes built into the WRF-Chem GOCART model. Results highlight the relative strengths of the available schemes, indicate the reasons for disagreement, and demonstrate the need for improved soil source data.

Total OH reactivity measurements were conducted on the Peking University campus (Beijing) in August 2013 and in Heshan (Guangdong province) from October to November 2014. The daily median OH reactivity was 20 ± 11 s−1 in Beijing and 31 ± 20 s−1 in Heshan, respectively. The data in Beijing showed a distinct diurnal pattern with the maxima over 27 s−1 in the early morning and minima below 16 s−1 in the afternoon. The diurnal pattern in Heshan was not as evident as in Beijing. Missing reactivity, defined as the difference between measured and calculated OH reactivity, was observed at both sites, with 21 % missing reactivity in Beijing and 32 % missing reactivity in Heshan. Unmeasured primary species, such as branched alkenes, could contribute to missing reactivity in Beijing, especially during morning rush hours. An observation-based model with the RACM2 (Regional Atmospheric Chemical Mechanism version 2) was used to understand the daytime missing reactivity in Beijing by adding unmeasured oxygenated volatile organic compounds and simulated intermediates of the degradation from primary volatile organic compounds (VOCs). However, the model could not find a convincing explanation for the missing reactivity in Heshan, where the ambient air was found to be more aged, and the missing reactivity was presumably attributed to oxidized species, such as unmeasured aldehydes, acids and dicarbonyls. The ozone production efficiency was 21 % higher in Beijing and 30 % higher in Heshan when the model was constrained by the measured reactivity, compared to the calculations with measured and modeled species included, indicating the importance of quantifying the OH reactivity for better understanding ozone chemistry.

The ROx (=OH + HO2 + RO2) budget and O3 production at an urban site in central China (Wuhan) during autumn were simulated and analyzed for the first time using a UW Chemical Model 0-D box model constrained by in situ observational data. The daytime average OH, HO2, and RO2 concentrations were 2.2 × 106, 1.0 × 108, and 5.2 × 107 molecules cm−3, respectively. The average daytime O3 production rate was 8.8 ppbv h−1, and alkenes were the most important VOC species for O3 formation (contributing 45%) at this site. Our sensitivity test indicated that the atmospheric environment in Wuhan during autumn belongs to the VOC-limited regime. The daily average HONO concentration at this site during the study period reached 1.1 ppbv and played an important role in the oxidative capacity of the atmosphere. Without the source of excess HONO, the average daytime OH, HO2, RO2, and O3 production rates decreased by 36%, 26%, 27%, and 31% respectively. A correlation between the HONO to NO2 heterogeneous conversion efficiency and PM2.5 × SWR was found at this site; based on this relationship, if the PM2.5 concentration met the World Health Organization air quality standard (25 µg m−3), the O3 production rate in this city would decrease by 19% during late autumn. The burning of agricultural biomass severely affected the air quality in Wuhan during summer and autumn. Agricultural burning was found to account for 18% of the O3 formation during the study period. Our results suggest that VOC control and a ban on agricultural biomass burning should be considered as high-priority measures for improving the air quality in this region.

The spatiotemporal variations of surface air pollutants (O3, NO2, SO2, CO, and PM10) with four land-use types, residence (R), commerce (C), industry (I) and greenbelt (G), have been investigated at 283 stations in South Korea during 2002–2013, using routinely observed data. The volatile organic compound (VOC) data at nine photochemical pollutant monitoring stations available since 2007 were utilized in order to examine their effect on the ozone chemistry. The land-use types, set by the Korean government, were generally consistent with the satellite-derived land covers and with the previous result showing anti-correlation between O3 and NO2 in diverse urban areas. The relationship between the two pollutants in the Seoul Metropolitan Area (SMA) residence land-use areas was substantially different from that outside of the SMA, probably due to the local differences in vehicle emissions. The highest concentrations of air pollutants in the diurnal, weekly, and annual cycles were found in industry for SO2 and PMPM10, in commerce for NO2 and CO, and in greenbelt for O3. The concentrations of air pollutants, except for O3, were generally higher in big cities during weekdays, while O3 showed its peak in suburban areas or small cities during weekends. The weekly cycle and trends of O3 were significantly out of phase with those of NO2, particularly in the residential and commercial areas, suggesting that vehicle emission was a major source in those areas. The ratios of VOCs to NO2 for each of the land-use types were in the order of I (10.2) > C (8.7) > G (3.9) > R (3.6), suggesting that most areas in South Korea were likely to be VOC-limited for ozone chemistry. The pollutants (NO2, SO2, CO, and PMPM10 except for O3 have decreased, most likely due to the effective government control. The total oxidant values (OX = O3 + NO2) with the land-use types were analyzed for the local and regional (or background) contributions of O3, respectively, and the order of OX (ppb) was C (57.4) > R (53.6) > I (50.7) > G (45.4), indicating the greenbelt observation was close to the background.

We present a comprehensive overview of particulate air quality across the five major metropolitan areas of South Africa (Cape Town, Bloemfontein, Johannesburg and Tshwane (Gauteng Province), the Industrial Highveld Air Quality Priority Area (HVAPA), and Durban), based on a decadal (1 January 2000 to 31 December 2009) aerosol climatology from multiple satellite platforms and a detailed analysis of ground-based data from 19 sites throughout Gauteng. Data include Aerosol Optical Depth (AOD550, 555) from Aqua (550 nm), Terra (550 nm), and MISR (555 nm) platforms, Ängström Exponent (α550/865, 470/660) from Aqua (550/865 nm) and Terra (470/660 nm), Ultraviolet Aerosol Index (UVAI) from TOMS, and model results from the Goddard Ozone Chemistry Aerosol Radiation and Transport (GOCART) model. Results in Cape Town are distinct, owing to a typically clean, marine airmass origin and infrequent continental influence. At continentally-influenced sites, AOD550, AOD555, α550/865, α470/660 and UVAI reach maxima (0.12–0.20, 1.0–1.8, and 1.0–1.2, respectively) during late winter and early spring (August–October), coinciding with a period of enhanced dust generation and the maximum frequency of close-proximity and subtropical fires identified by MODIS Fire Information for Resource Management System (FIRMS). The adjacent metropolitan and industrial Gauteng and HVAPA areas have been identified as a megacity based on NO2 concentrations, but AOD is a factor of 3–6 lower than other megacities worldwide. GOCART results suggest that the contributions of organics and black carbon to AOD are significantly enhanced during biomass burning season (ASO), but that sulfate is the most significant contributor to AOD (~70–80%) through the rest of the year. Dust appears to be underestimated by GOCART emissions inventories at continentally-influenced metropolitan areas of South Africa. Ground monitoring sites were classified according to site type: (1) township and informal settlement sites with domestic burning influence, (2) urban and suburban residential sites with no domestic burning in the immediate vicinity, (3) industrial sites, and (4) one traffic site situated at a major freeway interchange. PM10 concentrations in township areas are 56% higher than in developed residential areas and 78% higher than in industrial areas as an annual average, with PM10 in townships 63 and 136% higher than developed residential and industrial areas, respectively, in winter (June, July, August). Monthly PM10 and PM2.5 concentrations reach annual maxima during winter at all sites except in industrial areas. At industrial sites, maxima in PM10 and PM2.5 tend to occur during summer (December–February), when photochemical generation of secondary aerosol is expected and when deep and unstable boundary layers allow high stack emissions (emitted above the boundary layer during winter) to reach the ground in close proximity to point sources. Diurnal profiles of PM10 and PM2.5 display maxima during morning (06:00–09:00 LT) and evening (17:00–22:00 LT) at nearly every site – especially during winter – and underscore the importance of domestic burning as a major source of primary particles. Multi-year averages indicate that evening maxima at some township sites average in excess of 400 μg m−3. These results from the urban/industrial Gauteng area quantitatively confirm previous studies suggesting that the lowest-income populations of South Africa experience the poorest air quality, and demonstrate that domestic burning results in frequent exposure to high concentrations of particulate pollution in the region comprising the cities of Johannesburg and Tshwane. While remotely-sensed data are frequently used as a proxy for ground air quality, we report poor correlations between PM concentrations and satellite parameters and suggest that this practice is not appropriate in metropolitan South Africa. Disagreement between satellite and ground data may be attributed to a number of factors: (1) vertical inhomogeneity and stratified pollution layers aloft during much of the year, (2) extremely shallow winter boundary layers, (3) discrepancy between satellite passover times and elevated diurnal PM concentrations, and (4) poor spatial resolution of satellites compared with highly localized PM sources. While remotely-sensed data provide a good picture of regional, seasonal properties of column aerosol, a complete understanding of South Africa's air quality at the ground will necessitate more extensive monitoring at the ground and intensive, multi-platform campaigns to understand the relationship between ground and satellite data.

We describe the publicly available dataset from the Global OZone Chemistry And Related Datasets for the Stratosphere (GOZCARDS) project, and provide some results, with a~focus on hydrogen chloride (HCl), water vapor (H2O), and ozone (O3). This dataset is a global long-term stratospheric Earth System Data Record (ESDR), consisting of monthly zonal mean time series starting as early as 1979. The data records are based on high quality measurements from several NASA satellite instruments and ACE-FTS on SCISAT. We examine consistency aspects between the various datasets. To merge ozone records, the time series are debiased by calculating average offsets with respect to SAGE II during periods of measurement overlap, whereas for other species, the merging derives from an averaging procedure based on overlap periods. The GOZCARDS files contain mixing ratios on a common pressure/latitude grid, as well as standard errors and other diagnostics; we also present estimates of systematic uncertainties in the merged products. Monthly mean temperatures for GOZCARDS were also produced, based directly on data from the Modern-Era Retrospective analysis for Research and Applications (MERRA). The GOZCARDS HCl merged product comes from HALOE, ACE-FTS and (for the lower stratosphere) Aura MLS data. After a~rapid rise in upper stratospheric HCl in the early 1990s, the rate of decrease in this region for 1997–2010 was between 0.4 and 0.7% yr−1. On shorter timescales (6 to 8 years), the rate of decrease peaked in 2004–2005 at about 1% yr−1, and has since levelled off, at ~0.5 yr−1. With a delay of 6–7 years, these changes roughly follow total surface chlorine, whose behavior vs. time arises from inhomogeneous changes in the source gases. Since the late 1990s, HCl decreases in the lower stratosphere have occurred with pronounced latitudinal variability at rates sometimes exceeding 1–2 yr−1. There has been a significant reversal in the changes of lower stratospheric HCl abundances and columns for 2005–2010, in particular at northern midlatitudes and in the deep tropics, where short-term increases are observed. However, lower stratospheric HCl tendencies appear to be reversing after about 2011, with (short-term) decreases at northern midlatitudes and some increasing tendencies at southern midlatitudes. For GOZCARDS H2O, covering the stratosphere and mesosphere, the same instruments as for HCl are used, along with UARS MLS stratospheric H2O data (1991–1993). We display seasonal to decadal-type variability in H2O from 22 years of data. In the upper mesosphere, the anti-correlation between H2O and solar flux is now clearly visible over two full solar cycles. Lower stratospheric tropical H2O has exhibited two periods of increasing values, followed by fairly sharp drops, the well-documented 2000–2001 decrease, and another recent decrease in 2011–2013. Tropical decadal variability peaks just above the tropopause. Between 1991 and 2013, both in the tropics and on a near-global basis, H2O has decreased by ~ 5–10% in the lower stratosphere, but about a 10% increase is observed in the upper stratosphere and lower mesosphere. However, recent tendencies may not hold for the long-term, and the addition of a few years of data can significantly modify trend results. For ozone, we used SAGE I, SAGE II, HALOE, UARS and Aura MLS, and ACE-FTS data to produce a~merged record from late 1979 onward, using SAGE II as the primary reference for aligning (debiasing) the other datasets. Other adjustments were needed in the upper stratosphere to circumvent temporal drifts in SAGE II O3 after June 2000, as a result of the (temperature-dependent) data conversion from a density/altitude to a mixing ratio/pressure grid. Unlike the 2 to 3% increase in near-global column ozone after the late 1990s reported by some, GOZCARDS stratospheric column O3 values do not show a recent upturn of more than 0.5 to 1%; continuing studies of changes in global ozone profiles, as well as ozone columns, are warranted. A brief mention is also made of other currently available, commonly-formatted GOZCARDS satellite data records for stratospheric composition, namely those for N2O and HNO3.