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Climate change has the potential to increase surface ozone (O3) concentrations, known as the “ozone–climate penalty”, through changes to atmospheric chemistry, transport and dry deposition. In the tropics, the response of surface O3 to changing climate is relatively understudied but has important consequences for air pollution and human and ecosystem health. In this study, we evaluate the change in surface O3 due to climate change over South America and Africa using three state-of-the-art Earth system models that follow the Shared Socioeconomic Pathway 3-7.0 emission scenario from CMIP6. In order to quantify changes due to climate change alone, we evaluate the difference between simulations including climate change and simulations with a fixed present-day climate. We find that by 2100, models predict an ozone–climate penalty in areas where O3 is already predicted to be high due to the impacts of precursor emissions, namely urban and biomass burning areas, although on average, models predict a decrease in surface O3 due to climate change. We identify a small but robust positive trend in annual mean surface O3 over polluted areas. Additionally, during biomass burning seasons, seasonal mean O3 concentrations increase by 15 ppb (model range 12 to 18 ppb) in areas with substantial biomass burning such as the arc of deforestation in the Amazon. The ozone–climate penalty in polluted areas is shown to be driven by an increased rate of O3 chemical production, which is strongly influenced by NOx concentrations and is therefore specific to the emission pathway chosen. Multiple linear regression finds the change in NOx concentration to be a strong predictor of the change in O3 production, whereas increased isoprene emission rate is positively correlated with increased O3 destruction, suggesting NOx-limited conditions over the majority of tropical Africa and South America. However, models disagree on the role of climate change in remote, low-NOx regions, partly because of significant differences in NOx concentrations produced by each model. We also find that the magnitude and location of the ozone–climate penalty in the Congo Basin has greater inter-model variation than that in the Amazon, so further model development and validation are needed to constrain the response in central Africa. We conclude that if the climate were to change according to the emission scenario used here, models predict that forested areas in biomass burning locations and urban populations will be at increasing risk of high O3 exposure, irrespective of any direct impacts on O3 via the prescribed emission scenario.

Surface ozone is usually measured in national networks, including the monitoring of gaseous components important for determining air quality and the short-term forecast of surface ozone. Here we consider the option of forecasting surface ozone based on measurements of only surface ozone and several weather parameters. This low-cost configuration can increase the number of locations that provide short-term surface ozone forecast important to local communities. 24 h prediction of the 1-h averaged concentration of surface ozone were presented for rural (Belsk, 20.79° E, 51.84° N) and suburban site (Racibórz, 18.19° E, 50.08° N) in Poland for the period 2018–2021 via simple statistical models dealing with a limited number of predictors. Multiple linear regression (MLR) and artificial neural network (ANN) models were examined separately for each season of the year using temperature, relative humidity, an hour of the day, and 1-day lagged surface ozone values. The performance of ANN (with R2 = 0.81 in Racibórz versus R2 = 0.75 at Belsk) was slightly better than the MLR model (with R2 = 0.78 in Racibórz versus R2 = 0.71 at Belsk). These statistical models were compared with advanced chemical–transport models provided by the Copernicus Atmosphere Monitoring Service. Despite the simplicity of the statistical models, they showed better performance in all seasons, with the exception of winter.

High-levels of surface ozone (O3) pollution threaten human and environmental health. Chongqing, a mountainous municipality located in southwest China, is exposed to serious O3 pollution and requires more studies. Due to its complex terrain and always foggy weather, it is difficult to maintain many in-situ sites in Chongqing, and Chemical Transportation Model (CTM) simulations are also challenged. The recently launched (in 2017) Sentinel-5p satellite provides O3 columns with advanced spatiotemporal resolution. Without the dependence on CTMs, we linked O3 columns and surface monitoring data from 2019 to 2021 in virtue of a deep forest machine-learning model. Compared with another widely used machine-learning model and previous studies, our results showed great advantages in estimating surface O3 on a daily scale. Validated against in-situ sites in Chongqing, averaged R2 of cross-validations reached 0.9 while the root mean squared error (RMSE) and mean bias error (MBE) were 13.57 and 0.37 μg/m3. We found out that the model performance is associated with relative height difference between training sites and the test site. The model performed stably when the height difference was lower than 200 m, but obvious performance degradation was seen when the height difference exceeding 400 m.

Surface level ozone pollution imposes significant crop yield damages. However, the quantification has mainly involved chamber experiments, which may not be representative of results in farm fields. Additionally, the relative impacts of ozone under future climate change and their possible interactions remain poorly understood. Here we attempt to empirically fill this gap using historical county-level crop yield, ozone, and climate data in the United States. We explore ozone impacts on corn, soybeans, spring wheat, winter wheat, barley, cotton, peanuts, rice, sorghum, and sunflowers. We also incorporate a variety of climatic variables to investigate potential ozone-climate interactions. The results shed light on future yield consequences of ozone and climate change individually and jointly under a projected climate scenario. Our findings indicate significant negative impacts of ozone exposure for eight of the ten crops we examined, excepting barley and winter wheat. Meanwhile, corn exhibits to be more sensitive to ozone than soybeans. These results differ from those found under chamber experiments. We also find rising temperatures tend to worsen ozone damages while water supplies mitigate that. We find that the average annual historical damages from ozone reached $6.03 billion (in 2015 U.S. dollar) from 1980 to 2015. Finally, our results suggest that the damages caused by climate change-induced ozone elevation are much smaller than the damages caused by the direct effects of climate change itself.

Surface ozone concentrations (O3) during the period 2006–2021 are analysed at Córdoba city (southern Iberian Peninsula) in suburban and urban sampling sites. The aims are to present the levels and temporal variations, to explore trends and to quantity the variation in O3 concentrations in the context of the COVID-19 lockdown. The O3 means are higher in the suburban station (62 µg m−3 and 51.3 µg m−3), being the information level threshold only exceeded twice during this period. The daily evolution shows a maximum at about 17:00 UTC, whereas the minimum is reached at about 9:00 UTC, with higher levels in the suburban station. The seasonal evolution of this daily cycle also presents monthly differences in shape and intensity between stations. The trends are analysed by means of daily averages and daily 5th and 95th percentiles, and they show a similar increase in all of these parameters, with special emphasis on the daily P95 concentrations, with 0.27 µg m−3 year−1 and 0.24 µg m−3 year−1. Finally, the impact of the COVID-19 lockdown shows a decline in O3 concentrations over 10%.

Tropospheric ozone is an important atmospheric pollutant as well as an efficient greenhouse gas. Beijing is one of the cities with the most serious ozone pollution. However, long-term date of observed ozone in Beijing are limited. In this paper, we combine the measurements of the In-service Aircraft for a Global Observing System (IAGOS), ozonesonde observations as well as the recently available ozone monitoring network observations to produce a unique data record of surface ozone (at 14:00 Beijing time) in Beijing from 1995 to 2020. Using this merged dataset, we investigate the variability in surface ozone in Beijing on multiple timescales. The long-term change is primarily characterized by a sudden drop in 2011–2012 with an insignificant linear trend during the full period. Based on CAM-chem model simulations, meteorological factors played important roles in the 2011–2012 ozone drop. Before and after this sudden drop, ozone levels in Beijing increased significantly by 0.42 ± 0.27 ppbv year−1 before 2011 and 0.43 ± 0.41 ppbv year−1 after 2013. We also found a substantial increase in the amplitude of the ozone annual cycle in Beijing, which has not been documented in previous studies. This is consistent with ozone increases in summer and ozone decreases in winter. In addition, the results by the Ensemble Empirical Mode Decomposition (EEMD) analysis indicate significant interannual variations in ozone levels in Beijing with different time oscillation periods, which may be associated with natural variabilities and subsequent changes in meteorological conditions.

We present information on ozone concentration in the surface air layer in the second half of 2020. Data were obtained at 13 stations located in different regions of Russia. We estimated the excess over hygienic standards of the Russian Federation, both in the second half of the year and throughout 2020. It is shown that the daily average maximum permissible concentration of ozone is regularly exceeded at all stations. There are cases of exceeding the one-time maximum permissible concentration.

Fire is an important source of ozone (O3) precursors. The formation of surface O3 can cause damage to vegetation and reduce stomatal conductance. Such processes can feed back to inhibit dry deposition and indirectly enhance surface O3. Here, we apply a fully coupled chemistry–vegetation model to estimate the indirect contributions of global fires to surface O3 through O3–vegetation feedback during 2005–2012. Fire emissions directly increase the global annual mean O3 by 1.2 ppbv (5.0 %) with a maximum of 5.9 ppbv (24.4 %) averaged over central Africa by emitting a substantial number of precursors. Considering O3–vegetation feedback, fires additionally increase surface O3 by 0.5 ppbv averaged over the Amazon in October, 0.3 ppbv averaged over southern Asia in April, and 0.2 ppbv averaged over central Africa in April. During extreme O3–vegetation interactions, such a feedback can rise to >0.6 ppbv in these fire-prone areas. Moreover, large ratios of indirect-to-direct fire O3 are found in eastern China (3.7 %) and the eastern US (2.0 %), where the high ambient O3 causes strong O3–vegetation interactions. With the likelihood of increasing fire risks in a warming climate, fires may promote surface O3 through both direct emissions and indirect chemistry–vegetation feedbacks. Such indirect enhancement will cause additional threats to public health and ecosystem productivity.

We collected hourly ground ozone observations (more than 6 million records), meteorological data, remote sensing products, and social-economic information, and applied the Long Short-Term Memory (LSTM) recurrent neural networks to map surface ozone concentrations data (HrSOD) at an hourly time-step and a 0.1°× 0.1° resolution across China during 2005-2020.The gridded ozone concentration data are provided in NetCDF format in ppb. The daily data is a NetCDF file and the file is named "YYYYMMDD.nc", where "YYYY", "MM" and "DD'' refer to the year, month and day of the file.

We collected hourly ground ozone observations (more than 6 million records), meteorological data, remote sensing products, and social-economic information, and applied the Long Short-Term Memory (LSTM) recurrent neural networks to map surface ozone concentrations data (HrSOD) at an hourly time-step and a 0.1°× 0.1° resolution across China during 2005-2020.The gridded ozone concentration data are provided in NetCDF format in ppb. The daily data is a NetCDF file and the file is named "YYYYMMDD.nc", where "YYYY", "MM" and "DD'' refer to the year, month and day of the file.

International audience; Rising air pollution by surface ozone (O 3) in China has induced extensive efforts to control ozone generation in major urban and industrial areas, yet mechanisms ruling the ozone production and loss are not well understood. In particular, ozone levels are strongly influenced by meteorological factors such as relative humidity, but this has been explored only in local situations, and the effect of relative humidity on ozone levels in warm seasons on a large scale in China is still unknown. Here we studied surface ozone, relative humidity, temperature, and other meteorological variables in 74 major cities in China during 2017-2018, focusing on the warm seasons in seven regions. Results show that ozone levels decrease with increasing relative humidity in all cities, with an average correlation coefficient of − 0.58, ranging from − 0.17 in Zhangjiakou to − 0.84 in Hengshui. At high relative humidity levels, above 75%, average ozone levels ranged from 44.6 to 122.5 μg m −3 , which is lower than Chinese quality threshold of hourly average ozone level of 200 μg m −3. The decreases of ozone with relative humidity were more pronounced at high temperature, above 30 °C, than below 25 °C. The increases of ozone with temperature were more pronounced at low relative humidity, below 40%. Overall, our findings reveal that mechanisms ruling surface ozone levels are similar on a large scale. This is promising to design common methods of climate engineering to protect human health.

The rising concentration of ground-level ozone (O3) reduces crop yield via increased oxidative stress. Application of ethylenediurea (EDU) protects plants from O3 and could thereby serve as a means to estimate the crop yield losses due to ambient O3 (AO3). However, no study but a few exceptions has ever compared the yield loss estimates from EDU application with those from O3 elevation experiments. Here, we estimated yield loss to AO3 in rice cultivars across the 3 types, indica, japonica, and hybrid, by an EDU application in the field, and compared the yield losses with those estimated with dose-response relationships based on O3 elevation experiments. Relative yield loss (RYL) in the EDU application was estimated at 16% across the rice types on an assumption of a 100% efficiency for protection of crop yield by EDU. This estimate of RYL was close to the 15% RYL estimated from the O3 elevation experiments when a common sensitivity to O3 is assumed across the cultivars. The rice yield loss due to AO3 was thus consistent between the two approaches supporting the idea of EDU application for the yield loss estimation. When only hybrids are focused, however, the RYL from EDU application (16%) was much lower than the 34% RYL from the O3 elevation experiments, which indicates only a 37% yield protection by EDU in the hybrid rice. The incomplete protection by EDU and its genetic variability indicates the need to quantify the efficiency of protection from AO3-induced yield loss as estimated with O3 manipulating experiments.

In order to better understand the tempo-spatial characteristics of surface ozone (O3) on the regional scale of Yangtze River Delta (YRD), China, cluster analysis was applied to surface O3 monitored from 2016 to 2017 at 110 state-controlled stations. Eight spatial regions with distinct patterns of O3 variations were identified. The exceedances over the Grade I (100 µg/m3) and Grade II (160 µg/m3) National Ambient Air Quality Standard of maximum daily average 8 h O3 (MDA8 O3) varied within 99 ~ 192 and 1 ~ 41 days, respectively, for different subregions. The central and east YRD were the most polluted subregions with higher incidence of O3 episodes, while the southwest and southeast YRD were relatively clean. The spatial disparity of O3 over YRD was strongly affected by local emissions, with larger precursor emissions contributing to higher frequencies of O3 pollution and larger amplitudes of temporal variations. The regional O3 pollution mainly occurred in spring, with higher peaks of MDA8 O3 being observed during April ~ May for most subregions. While for east YRD, O3 pollution was most serious in summer, when its monthly MDA8 O3 reached the highest value (141.0 µg/m3). During the whole study period, the spatial differences of weekly/monthly amplitudes of MDA8 O3 were similar to those of NO2 among the eight subregions, indicating the significant influence of NO2 on O3 over YRD on the yearly timescale. This influence was especially evident in warm seasons for the coastal subregions, where O3 production was controlled by NOx. While for the west inland subregion, O3 was less affected by NO2 variation, indicating a NOx-saturated characteristic of O3 formation.

The review is based on the results of operation of the total ozone (TO) monitoring system in Russia and neighboring countries that functions in the operational mode at the Central Aerological Observatory. The monitoring system uses data from the national network equipped with M-124 filter ozonometers being under the methodological supervision of the Main Geophysical Observatory. The quality of the functioning of the entire system is operationally controlled using the OMI satellite equipment observations (NASA, USA). Basic TO observation data are generalized for each month of the fourth quarter of 2020, for the fourth quarter, and for the whole year. Data of routine observations of surface ozone in the Moscow region are also presented.

In the present study, various solar and atmospheric parameters, namely solar radiation, surface ozone, relative humidity, air temperature, and wind speed, have been studied first time for the annular solar eclipse of June 21, 2020, at seven Indian stations simultaneously. All the seven different stations are selected along the solar eclipse path having the maximum eclipse magnitude of 92% and above. The various atmospheric parameters have shown the change in behavior during the solar eclipse duration. The incoming solar radiation has a maximum 95.97% decrease at Ludhiana at the time of maximum obscuration compared to the previous day. The surface ozone concentration has shown a decrease for all the seven stations during the maximum obscuration period. A maximum of 9.25% decrease in surface temperature has been observed nearly after 30 min of the time of maximum obscuration of the eclipse. Relative humidity started increasing at all the stations and showed an inverse trend from temperature. The wind speed was found to suppress during the maximum solar eclipse which may be attributed to stabilization of atmospheric boundary layer due to cooling.

Anomalously low ozone concentrations in the surface atmosphere in central Russia were recorded in spring of 2020. The increase in the ozone content usual for spring due to a seasonal increase in the temperature and light was followed by a monotonic decrease. The maximum daily values of surface ozone concentrations averaged over the month in April 2020 were three times less than the values recorded at the same time in 2019. Both a decrease in regional background ozone concentrations in the near-surface atmosphere and a decrease in the intensity of its photochemical formation were observed. The most probable cause of the observed phenomenon is a decrease in combustion product emissions into the atmosphere in China, Europe, and Russia due to the introduction of special regimes in connection with the COVID-19 pandemic.

The present study aims to understand how increasing surface ozone and fine particulate matter concentrations affect wheat crop productivity under ambient conditions. A pot experiment was conducted spanning over a period of 117 days starting from December 2016 to April 2017 at one of the receptor locations in Delhi characterized with high levels of surface ozone and fine particulate matter. The study site recorded highest concentrations of PM1, PM2.5, PM10 and surface ozone of 159±77 μg m-3, 172±79 μg m-3, 280±108 μg m-3 and 335±18 μg m-3, respectively during the crop cycle indicating the high levels of air pollutants at the site. The crops were treated with ascorbic acid under different experimental setups. A large number of growth, biochemical and yield parameters were evaluated at the vegetative, reproductive and grain formation stage of the crop cycle. Results indicated that the chlorophyll content and harvest yield of crops grown under ambient conditions were ~23% and ~14% lower than those of crops grown under controlled environment. Furthermore, a ~13%, 5%, 15% and 10% decline in root length, plant height, number of tillers and number of leaves was observed in crops that were exposed to only surface ozone in comparison to crops exposed to only fine particulate matter under vegetative stage, respectively. Relative water content, chlorophyll content and air pollution tolerance index observed ~56%, 23% and 61% decline with fully exposed setup in comparison control setup in the vegetative stage, while ~57%, 23% and 44% decline was observed in the reproductive stage. Experiments also suggested that surface ozone had a more pronounced influence on overall productivity of wheat crops in comparison to fine particulate matter.

Accurate nationwide spatiotemporal distribution of ambient ozone product is critical for environment & health departments and for researches to investigate the influence of ozone for epidemiological studies. Our hybrid method, the novel CAMS (The Copernicus Atmosphere Monitoring Service) ozone improvement (CAO3_I) method, is the first attempt to predict ambient ozone by improving CAMS ozone (CAO3) products. For this novel framework, the SVM (Support Vector Machine) has been adopted for classification through the most significant regional ozone patterns which were extracted through the REOF (Rotated Empirical Orthogonal Function) technique. For each classified region, meteorological data, geographical data, CAMS ozone and ground-sites ozone are fed into random forest for regional regulation training and prediction. The CAO3_I method has shown its great feasibility in daily ozone surface distribution prediction. Based on daily averaged ozone concentrations for each station (STO3), the performance of CAO3 products (R2 = 0.35, RMSE = 25.77 μg/m3, MAPE = 42.06) have significantly improved to CAO3_I (R2 = 0.81, RMSE = 14.10 μg/m3, MAPE = 22.37), which shows above 97.4% R2 and RMSE have been improved. Our model is also capable to predict high level ozone concentrations in summer where the R2 has improved from 0.37 (for CAO3) to 0.81. In comparison with ground monitoring stations, the CAMS ozone improvement results can excellently reflect the distribution of daily ground-level ozone concentration and outperform previous statistical models in predicting ambient O3 concentrations. Therefore, the prediction results and our proposed model can be used for future epidemiological studies and air pollution controlling programs.

Information on the ozone concentration in the surface air and troposphere in the first half of 2020 is presented. The data were acquired at 13 stations in different Russian regions; vertical distributions were obtained with the use of an aircraft laboratory. The excess over the Russian Federation hygienic standards is assessed; the daily average ozone concentration is found to be regularly higher the maximum permissible concentrations at most stations. At some stations, there are features the seasonal variations in the ozone concentration in the period under study different from previous years.

Statistical relationships between surface ozone (O-3) concentration, precursor species and meteorological conditions in continental South Africa were examined from data obtained from measurement stations in north-eastern South Africa. Three multivariate statistical methods were applied in the investigation, i.e. multiple linear regression (MLR), principal component analysis (PCA) and -regression (PCR), and generalised additive model (GAM) analysis. The daily maximum 8-h moving average O-3 concentrations were considered in these statistical models (dependent variable). MLR models indicated that meteorology and precursor species concentrations are able to explain similar to 50% of the variability in daily maximum O-3 levels. MLR analysis revealed that atmospheric carbon monoxide (CO), temperature and relative humidity were the strongest factors affecting the daily O-3 variability. In summer, daily O-3 variances were mostly associated with relative humidity, while winter O-3 levels were mostly linked to temperature and CO. PCA indicated that CO, temperature and relative humidity were not strongly collinear. GAM also identified CO, temperature and relative humidity as the strongest factors affecting the daily variation of O-3. Partial residual plots found that temperature, radiation and nitrogen oxides most likely have a non-linear relationship with O-3,while the relationship with relative humidity and CO is probably linear. An inter-comparison between O-3 levels modelled with the three statistical models compared to measured O-3 concentrations showed that the GAM model offered a slight improvement over the MLR model. These findings emphasise the critical role of regional-scale O-3 precursors coupled with meteorological conditions in daily variances of O-3 levels in continental South Africa.

Tropospheric NO2 and ozone simulations have large uncertainties, but their biases, seasonality, and trends can be improved with NO2 assimilations. We perform global top-down estimates of monthly NOx emissions using two Ozone Monitoring Instrument (OMI) NO2 retrievals (NASAv3 and DOMINOv2) from 2005 to 2016 through a hybrid 4D-Var/mass balance inversion. Discrepancy in NO2 retrieval products is a major source of uncertainties in the top-down NOx emission estimates. The different vertical sensitivities in the two NO2 retrievals affect both magnitude and seasonal variations of top-down NOx emissions. The 12-year averages of regional NOx budgets from the NASA posterior emissions are 37 % to 53 % smaller than the DOMINO posterior emissions. Consequently, the DOMINO posterior surface NO2 simulations greatly reduced the negative biases in China (by 15 %) and the US (by 22 %) compared to surface NO2 measurements. Posterior NOx emissions show consistent trends over China, the US, India, and Mexico constrained by the two retrievals. Emission trends are less robust over South America, Australia, western Europe, and Africa, where the two retrievals show less consistency. NO2 trends have more consistent decreases (by 26 %) with the measurements (by 32 %) in the US from 2006 to 2016 when using the NASA posterior emissions. The performance of posterior ozone simulations has spatial heterogeneities from region to region. On a global scale, ozone simulations using NASA-based emissions alleviate the double peak in the prior simulation of global ozone seasonality. The higher abundances of NO2 from the DOMINO posterior simulations increase the global background ozone concentrations and therefore reduce the negative biases more than the NASA posterior simulations using GEOS-Chem v12 at remote sites. Compared to surface ozone measurements, posterior simulations have more consistent magnitude and interannual variations than the prior estimates, but the performance from the NASA-based and DOMINO-based emissions varies across ozone metrics. The limited availability of remote-sensing data and the use of prior NOx diurnal variations hinder improvement of ozone diurnal variations from the assimilation, and therefore have mixed performance on improving different ozone metrics. Additional improvements in posterior NO2 and ozone simulations require more precise and consistent NO2 retrieval products, more accurate diurnal variations of NOx and VOC emissions, and improved simulations of ozone chemistry and depositions.

The review is compiled on the basis of the results of operation of the total ozone (TO) monitoring in Russia and adjoining territories functioning in the operational mode at the Central Aerological Observatory (CAO). The monitoring system uses data from the national network equipped with M-124 filter ozonometers under the methodological supervision of the Main Geophysical Observatory. The quality of the functioning of the entire system is operationally controlled in CAO based on the OMI satellite equipment observations (NASA, USA). Basic TO observation data are generalized for each month of the third quarter of 2020 and for the third quarter. Data of routine observations of surface ozone values in the Moscow region are also considered.

Previous studies have emphasized that the decrease in photolysis rate at the surface induced by the light extinction of aerosols could weaken ozone photochemistry and then reduce surface ozone. However, quantitative studies have shown that weakened photochemistry leads to a much greater reduction in the net chemical production of ozone, which does not match the reduction in surface ozone. This suggests that in addition to photochemistry, some other physical processes related to the variation of ozone should also be considered. In this study, the Weather Research and Forecasting with Chemistry (WRF-Chem) model coupled with the ozone source apportionment method was applied to determine the mechanism of ozone reduction induced by aerosols over central East China (CEC). Our results showed that weakened ozone photochemistry led to a significant reduction in ozone net chemical production, which occurred not only at the surface but also within the lowest several hundred meters in the planetary boundary layer (PBL). Meanwhile, a larger ozone gradient was formed in the vertical direction, which led to the high concentrations of ozone aloft being entrained by turbulence from the top of the PBL to the surface and partly counteracting the reduction in surface ozone. In addition, contribution from dry deposition was weakened due to the decrease in surface ozone concentration. The reduction in the ozone's sink also slowed down the rate of the decrease in surface ozone. Ozone in the upper layer of the PBL was also reduced, which was induced by much ozone aloft being entrained downward. Therefore, by affecting the photolysis rate, the impact of aerosols was a reduction in ozone not only at the surface but also throughout the entire PBL during the daytime over CEC in this study. The ozone source apportionment results showed that 41.4 %–66.3 % of the reduction in surface ozone came from local and adjacent source regions, which suggested that the impact of aerosols on ozone from local and adjacent regions was more significant than that from long-distance regions. The results also suggested that while controlling the concentration of aerosols, simultaneously controlling ozone precursors from local and adjacent source regions is an effective way to suppress the increase in surface ozone over CEC at present.

The earth’s surface ozone levels are becoming very significant due to their negative impact on human health, vegetation and climate. In this study, the methodology based on ensemble approach embodied linear and nonlinear behaviors was developed. It was applied for prediction of ozone concentration using dataset (2013–2016) of gaseous pollutants (O3, CO, NOx, MHC, TNMHCs) and meteorological variables as input variables. The daily O3 max/O3 min ratio of 10.9 marks the peculiar ozone pollution in the area. The fourteen prediction algorithms and their possible combinations of ensemble models were employed in this paper. Compared with individual models, the ensemble model approach showed an index of agreement of 0.91, the accuracy of 95.5% and mean absolute error of − 0.001 ppb between the predicted and observed diurnal cycle and daily averaged data of the year 2016 for benchmark analysis.

Air pollution caused by particulate matter has significantly improved in China in recent years since the implementation of a series of stringent clean-air regulations. However, surface ozone concentrations have increased, especially in developed city clusters, such as the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Sichuan Basin regions. Due to the complexity and nonlinearity of the ozone formation, accurately locating major sources of ozone and its precursors is an important basis for the formulation of cost-effective pollution control strategies. In this paper, the authors systematically summarize the reported results and outcomes of the methods and main conclusions of ozone source apportionment (regions and categories) in China from the published literature, based on observation-based methods and emission-based methods, respectively. The authors aim to provide a comprehensive understanding of ozone pollution and reliable references for the formulation of air pollution prevention policies in China.

Urban ozone (O3) formation can be limited by NOx, VOCs, or both, complicating the design of effective O3 abatement plans. A satellite-retrieved ratio of formaldehyde to NO2 (HCHO/NO2), developed from theory and modeling, has previously been used to indicate O3 formation chemistry. Here, we connect this space-based indicator to spatiotemporal variations in O3 recorded by on-the-ground monitors over major U.S. cities. High-O3 events vary nonlinearly with OMI HCHO and NO2, and the transition from VOC-limited to NOx-limited O3 formation regimes occurs at higher HCHO/NO2 value (3 to 4) than previously determined from models, with slight intercity variations. To extend satellite records back to 1996, we develop an approach to harmonize observations from GOME and SCIAMACHY that accounts for differences in spatial resolution and overpass time. Two-decade (1996-2016) multisatellite HCHO/NO2 captures the timing and location of the transition from VOC-limited to NOx-limited O3 production regimes in major U.S. cities, which aligns with the observed long-term changes in urban-rural gradient of O3 and the reversal of O3 weekend effect. Our findings suggest promise for applying space-based HCHO/NO2 to interpret local O3 chemistry, particularly with the new-generation satellite instruments that offer finer spatial and temporal resolution.

China’s nationwide ozone monitoring network initiated in 2013 has observed severe surface ozone pollution. This network, combined with the recent Tropospheric Ozone Assessment Report (TOAR) data set, offers a more comprehensive view on global surface ozone distribution and trends. Here, we report quantitative estimates of the warm-season (April–September) surface ozone trends and resulting health impacts at Chinese cities in 2013–2019. Both the parametric and nonparametric linear trends for 12 ozone metrics relevant to human health and vegetation exposure are derived. We find that all ozone metrics averaged from Chinese urban sites have increased significantly since 2013. The warm-season daily maximum 8-h average (MDA8) ozone levels increased by 2.4 ppb (5.0%) year–¹, with over 90% of the sites showing positive trends and 30% with trends larger than 3.0 ppb year–¹. These rates are among the fastest trends, even faster in some Chinese cities, compared with the urban ozone trends in any other region worldwide reported in TOAR. Ozone metrics reflecting the cumulative exposure effect on human health and vegetation such as SOMO35 and AOT40 have increased at even faster rates (>10% year–¹). We estimate that the total premature respiratory mortalities attributable to ambient MDA8 ozone exposure in 69 Chinese cities are 64,370 in 2019, which has increased by 60% compared to 2013 levels and requires urgent attention.

The review is compiled on the basis of the results of operation of the total ozone (TO) monitoring system in Russia and neighboring countries functioning in operational mode at the Central Aerological Observatory. The monitoring system uses data from the national network equipped with M-124 filter ozonometers being under the methodological supervision of the Main Geophysical Observatory. The quality of the functioning of the entire system is operationally controlled using the OMI satellite equipment observations (NASA, USA). Basic TO observation data are generalized for each month of the fourth quarter of 2019, for the fourth quarter, and for the whole year. Data of routine observations of surface ozone values in the Moscow region, Crimea, and on the Kola Peninsula are also presented.

This study investigated the adverse effect of surface ozone on the maize yield using a unique panel from 880 counties in China. To identify the impact of elevated surface ozone concentrations, we constructed an econometric model by controlling the impact of climate variables and related economic variables. This study also considered the potential spatial correlation in the measurement of the impact of surface ozone on maize yield. Results confirmed that the increase of ozone concentration decreased the maize yield. Moreover, maize was found to be the most sensitive to ozone at the end of the second month of the growing season. The average annual loss of maize caused by ozone pollution is about 4.234 million tons in 2013–2015, accounting for 1.9% of the average output.

Although much attention has been paid to investigating and controlling air pollution in China, the trends of air-pollutant concentrations on a national scale have remained unclear. Here, we quantitatively investigated the variation of air pollutants in China using long-term comprehensive data sets from 2013 to 2017, during which Chinese government made major efforts to reduce anthropogenic emission in polluted regions. Our results show a significant decreasing trend in the PM2.5 concentration in heavily polluted regions of eastern China, with an annual decrease of ∼7% compared with measurements in 2013. The measured decreased concentrations of SO2, NO2 and CO (a proxy for anthropogenic volatile organic compounds) could explain a large fraction of the decreased PM2.5 concentrations in different regions. As a consequence, the heavily polluted days decreased significantly in corresponding regions. Concentrations of organic aerosol, nitrate, sulfate, ammonium and chloride measured in urban Beijing revealed a remarkable reduction from 2013 to 2017, connecting the decreases in aerosol precursors with corresponding chemical components closely. However, surface-ozone concentrations showed increasing trends in most urban stations from 2013 to 2017, which indicates stronger photochemical pollution. The boundary-layer height in capital cities of eastern China showed no significant trends over the Beijing–Tianjin–Hebei, Yangtze River Delta and Pearl River Delta regions from 2013 to 2017, which confirmed the reduction in anthropogenic emissions. Our results demonstrated that the Chinese government was successful in the reduction of particulate matter in urban areas from 2013 to 2017, although the ozone concentration has increased significantly, suggesting a more complex mechanism of improving Chinese air quality in the future.

In recent years, ozone pollution has been increasing in some parts of the world. In this study, we used the Beijing–Tianjin–Tangshan (BJ-TJ-TS) urban agglomeration region as a case study and used satellite remotely sensed inversion data and hourly ground monitoring observations of surface ozone concentrations, meteorological data, and other factors from 2016 to 2019 to explore the spatiotemporal dynamic characteristics of surface ozone concentration and its pollution levels. We also investigated their coupling relationships with meteorological factors, including temperature, pressure, relative humidity, wind velocity, and sunshine duration, in order to support the development of effective control measures for regional ozone pollution. The results revealed that the surface ozone concentration throughout the BJ-TJ-TS region from 2016 to 2019 exhibited an overall pattern of high values in the northwest and low values in the southeast, as well as an obvious difference between built-up and non-built-up areas (especially in Beijing). Meanwhile, a notable increasing trend of ozone levels was discovered in the BJ and TJ areas from 2016 to 2019, whereas this upward trend was not evident in the TS area. In all three areas, the highest monthly average ozone values occurred in the summer month of June, while the lowest monthly average levels occurred in the winter month of December. Their diurnal variation values reached a maximum value at approximately 3:00–4:00 p.m. and a minimum value at approximately 7:00 a.m. It is clear that high temperature, long sunshine duration, low atmospheric pressure, and weak wind velocity conditions, as well as certain relative humidity levels, readily led to high-concentration ozone pollution. Meanwhile, the daily average values of the five meteorological factors on days with Grade I and Grade II ozone pollution displayed different characteristics.

Efficient and accurate real-time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large-scale numerical simulations. In this work, we introduce a hybrid model (VAE-GAN) combining a generative adversarial network (GAN) with a variational autoencoder (VAE) to learn the dynamic ozone distributions in spatial and temporal spaces. The VAE-GAN model can not only decipher the complex nonlinear relationship between the inputs (the past states/ozone and meteorological factors) and outputs (ozone), but also provide ozone forecasts for a long lead-time beyond the training period. The performance of VAE-GAN is demonstrated in hourly and daily spatio-temporal ozone forecasts over China. The training datasets from 2013 to 2017 and validation datasets from 2018 to 2019 are the collection of data from the air quality reanalysis datasets. With the use of VAE, large dataset sizes are decreased by three orders of magnitude, enabling hourly and daily forecasts to be computed in seconds. Results show that the VAE-GAN achieves a reasonable accuracy in the prediction of both the spatial and temporal evolution patterns of hourly and daily ozone fields, as compared to the Nested Air Quality Prediction Modeling System (commonly used in China), the reanalysis data and observations during the validation period. Thus, the VAE-GAN is a cost-effective tool for large data-driven predictions, which can potentially reinforce air pollution prediction efforts in providing risk assessment and management in a timely manner.

The discovery of causal structures behind a phenomenon under investigation has been at the heart of scientific inquiry since the beginning. Randomized control trials, the gold standard for causal analysis, may not always be feasible, such as in the domain of climate sciences. In the absence of interventional data, we are forced to depend only on observational data. This study demonstrates the application of one such causal discovery algorithm using a neural network for identifying the drivers of surface ozone variability in Antarctica. The analyses reveal the overarching influence of the stratosphere on the surface ozone variability in Antarctica, buttressed by the southern annular mode and tropospheric wave forcing in mid-latitudes. We find no significant and robust evidence for the influence of tropical teleconnection on the ground-level ozone in Antarctica. As the field of atmospheric science is now replete with a massive stock of observational data, both satellite and ground-based, this tool for automated causal structure discovery might prove to be invaluable for scientific investigation and flawless decision making.

This work presents an analysis of the effect of climate change on surface ozone discussing the related penalties and benefits around the globe from the global modelling perspective based on simulations with five CMIP6 (Coupled Model Intercomparison Project Phase 6) Earth System Models. As part of AerChemMIP (Aerosol Chemistry Model Intercomparison Project) all models conducted simulation experiments considering future climate (ssp370SST) and present-day climate (ssp370pdSST) under the same future emissions trajectory (SSP3-7.0). A multi-model global average climate change benefit on surface ozone of −0.96 ± 0.07 ppbv °C−1 is calculated which is mainly linked to the dominating role of enhanced ozone destruction with higher water vapour abundances under a warmer climate. Over regions remote from pollution sources, there is a robust decline in mean surface ozone concentration on an annual basis as well as for boreal winter and summer varying spatially from −0.2 to −2 ppbv °C−1, with strongest decline over tropical oceanic regions. The implication is that over regions remote from pollution sources (except over the Arctic) there is a consistent climate change benefit for baseline ozone due to global warming. However, ozone increases over regions close to anthropogenic pollution sources or close to enhanced natural biogenic volatile organic compounds emission sources with a rate ranging regionally from 0.2 to 2 ppbv C−1, implying a regional surface ozone penalty due to global warming. Overall, the future climate change enhances the efficiency of precursor emissions to generate surface ozone in polluted regions and thus the magnitude of this effect depends on the regional emission changes considered in this study within the SSP3_7.0 scenario. The comparison of the climate change impact effect on surface ozone versus the combined effect of climate and emission changes indicates the dominant role of precursor emission changes in projecting surface ozone concentrations under future climate change scenarios.

Increased tropospheric ozone (O3) and high temperatures affect human health during heat waves. Here, we perform a source attribution that considers separately the formation of German surface ozone from emitted NOx and volatile organic compound (VOC) precursors during two peak ozone events that took place in 2015 and 2018 which were associated with elevated temperatures. Results showed that peak ozone concentrations can be primarily attributed to nearby emissions of anthropogenic NOx (from Germany and immediately neighboring countries) and biogenic VOC. Outside of these high ozone episodes, baseline ozone concentrations are attributed primarily to long-range transport, with ozone due to remote anthropogenic NOx emissions and methane oxidation adding to the tropospheric ozone background. We show that a significant contribution to modeled O3 coming from German NOx or VOC emissions occurs mostly in southern Germany, emphasizing that the production of ozone depends on the local interplay between NOx and VOC precursors. Shipping activities in the Baltic and North seas have a large impact on ozone predicted in coastal areas, yet a small amount of ozone from these sources can also be seen far inland, showing the importance of transported ozone on pollution levels. We have also shown that changes in circulation patterns during the peak O3 episodes observed in Germany during the 2015 and 2018 heat waves can affect the contribution of different NOx emission sources to total O3; thus, the possible influence of multiple upwind source regions should be accounted for when mitigation strategies are designed. Our study also highlights the good correlation between ozone coming from German biogenic VOC emissions and total ozone, although the diurnal variation in the ozone coming from biogenic sources is not dominated by the diurnal variation in biogenic emissions, and the peaks of ozone from biogenic sources are disconnected from local emission peaks. This suggests that the formation of O3 from local German biogenic VOC emissions is not the sole factor that influences the ozone formation, and other meteorological and chemical processes affect the diel variation of ozone with a biogenic origin. Overall, this study helps to demonstrate the importance of a source attribution method to understand the sources of O3 in Germany and can be a useful tool that will help to design effective mitigation strategies.

Surface ozone (O3) is a secondary air pollutant, harmful to human health and vegetation. To provide a long-term study of O3 concentrations in Portugal (study period: 2009–2019), a statistical analysis of ozone trends in rural stations (where the highest concentrations can be found) was first performed. Additionally, the effect of nitrogen oxides (NOx) and meteorological variables on O3 concentrations were evaluated in different environments in northern Portugal. A decreasing trend of O3 concentrations was observed in almost all monitoring stations. However, several exceedances to the standard values legislated for human health and vegetation protection were recorded. Daily and seasonal O3 profiles showed high concentrations in the afternoon and summer (for all inland rural stations) or spring (for Portuguese islands). The high number of groups obtained from the cluster analysis showed the difference of ozone behaviour amongst the existent rural stations, highlighting the effectiveness of the current geographical distribution of monitoring stations. Stronger correlations between O3, NO, and NO2 were detected at the urban site, indicating that the O3 concentration was more NOx-sensitive in urban environments. Solar radiation showed a higher correlation with O3 concentration regarding the meteorological influence. The wind and pollutants transport must also be considered in air quality studies. The presented results enable the definition of air quality policies to prevent and/or mitigate unfavourable outcomes from O3 pollution.

Air pollutants can be transported to the pristine regions such as the Tibetan Plateau, by monsoon and stratospheric intrusion. The Tibetan Plateau region has limited local anthropogenic emissions, while this region is influenced strongly by transport of heavy emissions mainly from South Asia. We conducted a comprehensive study on various air pollutants (PM2.5, total gaseous mercury, and surface ozone) at Nam Co Station in the inland Tibetan Plateau. Monthly mean PM2.5 concentration at Nam Co peaked in April before monsoon season, and decreased during the whole monsoon season (June–September). Monthly mean total gaseous mercury concentrations at Nam Co peaked in July and were in high levels during monsoon season. The Indian summer monsoon acted as a facilitator for transporting gaseous pollutants (total gaseous mercury) but a suppressor for particulate pollutants (PM2.5) during the monsoon season. Different from both PM2.5 and total gaseous mercury variabilities, surface ozone concentrations at Nam Co are primarily attributed to stratospheric intrusion of ozone and peaked in May. The effects of the Indian summer monsoon and stratospheric intrusion on air pollutants in the inland Tibetan Plateau are complex and require further studies.

China has seen dramatic emission changes from 2010, especially after the implementation of Clean Air Action in 2013, with significant air quality and human health benefits observed. Air pollutants, such as PM2.5 and surface ozone, as well as their precursors, have long enough lifetime in the troposphere which can be easily transported downwind. So emission changes in China will not only change the regional air quality domestically, but also affect the air quality in downwind regions. In this study, we use a global chemistry transport model to simulate the influence on both domestic and foreign air quality from the emission change from 2010 to 2017 in China. By applying the health impact functions derived from epidemiology studies, we then quantify the changes in air pollution-related (including both PM2.5 and O3) mortality burdens at regional and global scales. The majority of air pollutants in China reach their peak values around 2012 and 2013. Compared with the year 2010, the population-weighted annual PM2.5 in China increases till 2011 (94.1 μg m−3), and then begins to decrease. In 2017, the population-weighted annual PM2.5 decreases by 17.6 %, compared with the values in 2010 (84.7 μg m−3). The estimated national PM2.5 concentration changes in China are comparable with previous studies using fine-resolution regional models, though our model tends to overestimate PM2.5 from 2013 to 2017 when evaluated with surface observation in China during the same periods. The emission changes in China increased the global PM2.5-related mortality burdens from 2010 to 2013, by 27,700 (95 %CI: 23,900–31, 400) deaths yr−1 in 2011, and 13, 300 (11,400–15,100) deaths yr−1 in 2013, among which at least 93 % occurred in China. The sharp emission decreases after 2013 bring significant benefits for reduced avoided premature mortality in 2017, reaching 108, 800 (92,800–124,800) deaths yr−1 globally, among which 92 % happening in China. Different trend as PM2.5, the annual maximum daily 8-hr ozone in China increased, and also the ozone-related premature deaths, ranging from 3,600 (2,700–4,300) deaths yr−1 in 2011 (75 % of global total increased premature deaths), and 8,500 (6,500–9,900) deaths yr−1 in 2017 (143 % of the global total). Downwind regions, such as South Korea, Japan, and U.S. generally see a decreased O3-related mortality burden after 2013 as a combination of increased export of ozone and decreased export of ozone precursors. In general, we conclude that the sharp emission reductions in China after 2013 bring benefits of improved air quality and reduced premature deaths associated with air pollution at global scale. The benefits are dominated by the PM2.5 decreases since the ozone is shown to actually increase with the emission decrease.

After a continuous increase in surface ozone (O3) level from 2013 to 2019, the overall summertime O3 concentration across China showed a significant reduction in 2020. In contrast to this overall reduction in surface O3 across China, unexpected surface O3 enhancements of 10.2 ± 0.8 ppbv (23.4 %) were observed in May–June 2020 vs. 2019 over the Sichuan basin (SCB), China. In this study, we use high resolution nested-grid GEOS-Chem simulation, the eXtreme Gradient Boosting (XGBoost) machine learning method and the exposure−response relationship to determine the drivers and evaluate the health risks of the unexpected surface O3 enhancements. We first use the XGBoost machine learning method to correct the GEOS-Chem model-to-measurement O3 discrepancy over the SCB. The relative contributions of meteorology and anthropogenic emissions changes to the unexpected surface O3 enhancements are then quantified with the combination of GEOS-Chem and XGBoost models. In order to assess the health risks caused by the unexpected O3 enhancements over the SCB, total premature death mortalities are estimated. The results show that changes in anthropogenic emissions caused 0.9 ± 0.1 ppbv of O3 reduction and changes in meteorology caused 11.1 ± 0.7 ppbv of O3 increase in May–June 2020 vs. 2019. The meteorology-induced surface O3 increase is mainly attributed to significant increases in temperature and downward potential vorticity, and decreases in precipitation, specific humidity and cloud fractions over the SCB and surrounding regions in May–June 2020 vs. 2019. These changes in meteorology combined with the complex basin effect enhance downward transport of O3 from upper troposphere, enhance biogenic emissions of volatile organic compounds (VOCs) and nitrogen oxides (NOx), speed up O3 chemical production, and inhabit the ventilation of O3 and its precursors, and therefore account for the surface O3 enhancements over the SCB. The total premature mortality due to the unexpected surface O3 enhancements over the SCB has increased by 89.8 % in May–June 2020 vs. 2019.

The Indian megacity of Delhi suffers from some of the poorest air quality in the world. While ambient NO2 and particulate matter (PM) concentrations have received considerable attention in the city, high ground-level ozone (O3) concentrations are an often overlooked component of pollution. O3 can lead to significant ecosystem damage and agricultural crop losses, and adversely affect human health. During October 2018, concentrations of speciated non-methane hydrocarbon volatile organic compounds (C2–C13), oxygenated volatile organic compounds (o-VOCs), NO, NO2, HONO, CO, SO2, O3, and photolysis rates, were continuously measured at an urban site in Old Delhi. These observations were used to constrain a detailed chemical box model utilising the Master Chemical Mechanism v3.3.1. VOCs and NOx (NO + NO2) were varied in the model to test their impact on local O3 production rates, P(O3), which revealed a VOC-limited chemical regime. When only NOx concentrations were reduced, a significant increase in P(O3) was observed; thus, VOC co-reduction approaches must also be considered in pollution abatement strategies. Of the VOCs examined in this work, mean morning P(O3) rates were most sensitive to monoaromatic compounds, followed by monoterpenes and alkenes, where halving their concentrations in the model led to a 15.6 %, 13.1 %, and 12.9 % reduction in P(O3), respectively. P(O3) was not sensitive to direct changes in aerosol surface area but was very sensitive to changes in photolysis rates, which may be influenced by future changes in PM concentrations. VOC and NOx concentrations were divided into emission source sectors, as described by the Emissions Database for Global Atmospheric Research (EDGAR) v5.0 Global Air Pollutant Emissions and EDGAR v4.3.2_VOC_spec inventories, allowing for the impact of individual emission sources on P(O3) to be investigated. Reducing road transport emissions only, a common strategy in air pollution abatement strategies worldwide, was found to increase P(O3), even when the source was removed in its entirety. Effective reduction in P(O3) was achieved by reducing road transport along with emissions from combustion for manufacturing and process emissions. Modelled P(O3) reduced by ∼ 20 ppb h−1 when these combined sources were halved. This study highlights the importance of reducing VOCs in parallel with NOx and PM in future pollution abatement strategies in Delhi.

In the present study, continuous measurements of Surface Ozone (O3), Oxides of Nitrogen (NOx (NO + NO2)), and carbon monoxide (CO), monitored at five different locations in Delhi National Capital Region have been studied for the period 2013–2019. The five monitoring locations used are namely IMD Lodi Road, IGI Airport Palam, CV Raman Dheerpur, CRRI Mathura Road, and NCMRWF Noida. The average hourly concentration of O3, NO, NO2, CO, NOx (NO + NO2), and OX(NO2 + O3) are found in the range of 32.44 ppb to 36.57 ppb, 19.46 to 28.09 ppb, 20.83 to 26.89 ppb,1.67 to 1.89 ppm,43.04 to 54.99 ppb, and 54.06 to 60.99 ppb respectively during the study period. Diurnal variation of NOx and CO Concentrations show higher values during the morning (0600-0900h) and late evening (1900-2400h) hours while the highest concentrations of ozone have been observed during afternoon hours. The relationship between NO, NO2, and surface O3 as a function of NOx has also been examined during daylight hours (0500hrs IST to 1900 hrs IST) and chemical coupling of the three species i.e. NO, NO2 and O3 have been studied. The ground-level concentration of Ozone have been found to decrease with increasing NOx concentration during daytime. The variations in concentrations of oxidants (NO2 + O3) with the concentration of [NOx] have been studied to examine the contributing pollution sources of oxidants at all the study sites. The average rate of change of O3 concentrations (dO3/dt) has been examined at all five locations. The monthly and diurnal variation of oxidants [OX] at all the study locations has shown a strong positive correlation with temperature whereas a negative correlation with humidity.

Emissions of nitrogen oxides (NOx = NO + NO2) in the United States have declined significantly during the past three decades. However, satellite observations since 2009 indicate total column NO2 is...

The nationwide lockdown in India to curb the spread of Coronavirus disease 2019 (COVID-19) led to colossal reduction in anthropogenic emissions. Here, we investigated the impact of lockdown on surface ozone (O3) and nitrogen dioxide (NO2) over a tropical coastal station – Thumba, Thiruvananthapuram (8.5°N, 76.9°E). Daytime as well as night-time NO2 showed reduction by 0.8 (40%) and 2.3 (35%) ppbv, respectively during the lockdown period of 25–30 March 2020 as compared with the same period of previous 3 years. Unlike many urban locations, daytime surface O3 is found to be dramatically reduced by 15 ppbv (36%) with O3 production rate being lower by a factor of 3 during the lockdown. Interestingly, a feature of O3-hump during the onset of land breeze typically observed during 1997–1998 has reappeared with magnitude of 5–10 ppbv. A photochemical box model, capturing this feature, revealed that significant O3 sustained till onset of land breeze over the land due to weaker titration with NOx during lockdown. It is suggested that the transport of this O3 rich air with onset of land breeze led to the observed hump. Our measurements unravel a remarkable impact of the COVID-19 lockdown on the chemistry and dynamics of O3 over this tropical coastal environment. Supplementary Information The online version contains supplementary material available at 10.1007/s12040-021-01666-3.

Dry deposition is a major sink for tropospheric ozone; nevertheless, studies on its effects on ozone distribution are very limited over the rapidly developing South Asian region. We performed numer...

This study analyzed the increase in the surface ozone over Beijing-Tianjin-Hebei (BTH) and its surrounding areas during the period of 2005–2018 using satellite-retrieved data. First, a geographically weighted regression (GWR) model was developed to estimate the surface ozone concentration (SOC) between 2005 and 2018 based on Ozone Monitoring Instrument (OMI) ozone profiles. The resultant values and their growth rate were then analyzed. The SOC exhibited significant variation in the spatial distribution over the study area, with the maximum and the minimum values occurring in the southeast and the northeast, respectively. The latter region also displayed the highest SOC growth rate, however, whereas the southwest displayed the lowest one. Additionally, prominent seasonality was observed in the SOC: The concentration peaked during the warm season and troughed during the cold season, but the growth rate showed the opposite trend. The values during the cold season greatly affected the annual spatial distribution and the growth rate of the SOC, whereas those during the warm season significantly contributed to the annual concentration. From 2005 till 2018, the SOC showed an upward trend with an average growth rate of 3.4 µg m–3 y–1, with a greater increase in the second half (2012–2018) than the first half (2005–2011) of the study period because of the stronger photochemical reactions caused by the continual increase in HCHO during summer and the weaker NO titration effect caused by the rapid decrease in NO2 during winter. With constantly rising levels of HCHO and a VOCs-limited regime in the study area, we must formulate an effective reduction scheme for VOCs and NO2 co-emissions in order to mitigate the surface ozone pollution, despite the risk that decreasing the NO2 will lead to a certain increase in the SOC.

The review is based on the results of the operation of the total ozone (TO) monitoring system in Russia and adjoining territories that functions in the operational mode at the Central Aerological Observatory. The monitoring system uses data from the national network equipped with M-124 filter ozonometers being under the methodological supervision of the Main Geophysical Observatory. The quality of the system functioning is operationally controlled using OMI satellite equipment observations (NASA, the USA). Basic TO observation data are generalized for each month of the third quarter of 2019 and for the third quarter. Data of routine observations of surface ozone values in the Moscow region and Crimea are also considered.