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Seasonality and reduced nitric oxide titration dominated ozone increase during COVID-19 lockdown in eastern China


With improving PM2.5 air quality, the tropospheric ozone (O3) has become the top issue of China’s air pollution control. Here, we combine comprehensive observational data analysis with models to unveil the contributions of different processes and precursors to the change of O3 during COVID-19 lockdown in the Yangtze River Delta (YRD), one of the most urbanized megacity regions of eastern China. Despite a 44 to 47% reduction in volatile organic compounds (VOCs) and nitrogen oxides (NOx) emissions, maximum daily 8-h average (MDA8) ozone concentrations increase from 28 ppbv in pre-lockdown to 43 ppbv in lockdown period. We reproduce this transition with the WRF-Chem model, which shows that ~80% of the increase in MDA8 is due to meteorological factors (seasonal variation and radiation), and ~20% is due to emission reduction. We find that daytime photochemistry does not lead to an increase but rather a decrease of daytime O3 production during the lockdown. However, the reduced O3 production is overwhelmed by the weakened nitric oxide (NO) titration resulting in a net increase of O3 concentration. Although the emission reduction increases O3 concentration, it leads to a decrease in the Ox (O3 + NO2) concentration, suggesting reduced atmospheric oxidation capacity on a regional scale. The dominant effect of NO titration demonstrates the importance of prioritizing VOCs reduction, especially from solvent usage and the petrochemical industry with high emission ratios of VOCs/NOx.


Ozone (O3) pollution, a threat to both human health1 and the ecosystem2, would become the top issue of China’s future air pollution control as the gradual improvement of particulate pollution3,4. The Coronavirus Disease 2019 (COVID-19) lockdown led to nationwide reductions of air pollutants emissions in China, which largely reduced ambient concentrations of major air pollutants on a national scale indicated by both the satellite5 and ground-based measurements6,7. However, O3 concentrations showed nationwide enhancement in China during lockdown6,8,9 compared to that before lockdown, which was expected as in many polluted regions the reduced nitrogen oxides (NOx) would have served as a sink for OH radicals, suppressing O3 production and also sequestered O3 in temporary reservoirs10. The magnitude of O3 changes in a given region depends on a number of local factors, i.e., NOx level, the volatile organic compounds (VOCs) level or reactivity, oxidant level, as well as meteorology11. The mechanisms leading to the upsurge of O3 in China during lockdown was still under debate. Especially, some recent studies have even highlighted this increase of ozone as well as the enhanced oxidation capacity aggravated the haze pollution in north of China12,13,14, while other studies attributed the haze to the impact of an anomaly in meteorological conditions15,16,17. Considering the reduction during lockdown was an extreme scenario or a superior limit of reduction (with current technology), it caused great concern about the mitigation of ozone pollution in China in the future.

Here, we combined observations and model simulations to decouple these processes, resolve their individual contributions, and elucidate the true effect of emission reduction on O3 changes, aiming to reveal the underlying chemistry and mitigation of ozone pollution in China. The Yangtze River Delta (YRD) region, one of the most polluted regions located in eastern China was selected as the focusing area to utilize the readily accessible observation dataset (supersite + monitoring network) and emission inventories18.

Results and discussion

Meteorology dominating ozone increase during lockdown

Figure 1 shows the evolution of air pollution before and during the lockdown in the YRD region. Compared to the before COVID lockdown period (donated as Pre-C period from December 1st in 2019 to January 23rd in 2020, as we think January 1st to 23rd is not a good option, which were mostly rainy/heavy cloudy days as shown in Fig. 1), ozone precursors, e.g., NO2 and VOCs largely decreased during lockdown (denoted as Lockdown period from January 24th to February 29th in 2020) by 61 and 38%, respectively, while O3 concentrations almost doubled, i.e., 88% increase. These variations are consistent with results reported for other regions in early studies6,8,13. However, we noticed a significantly lower solar radiation before lockdown periods (Supplementary Fig. 1). The lower radiation is expected to reduce O3 concentration (Supplementary Fig. 2) and enlarges its difference from the O3 of the lockdown period. Moreover, the lockdown occurred during the seasonal alternation in the northern hemisphere, with gradually increasing radiation and temperature, which may lead to an additional increase of O3 in the lockdown period19. As shown in Supplementary Fig. 3, the seasonal variation of O3 in 2020 is comparable to those in other years (e.g., 2017 to 2019), which apparently dominates the O3 increase in this period.

Fig. 1: Evolution of air pollution before and during the COVID-19 lockdown in the YRD region.
figure 1

Panels a and b present the observations and comparison of model simulations and observations, respectively. Observed values (except VOCs) were daily averages of observations from 41 major cities (list in Supplementary Table 2) in the YRD region. The shade of lines represented the range of mean value ±50% standard deviation across the 41 cities (spatial variability). VOCs data were based on the measurements at SAES supersite in Shanghai. The brown dash line presented the linear fitting result of daily O3 concentrations from December to February in the past 3 years (2017–2019). The data of solar radiation were the average of the daily maximum at five sites in the YRD region from the national meteorological observing stations. The shade areas in panel b indicate rainy time. Here, we defined the period from 1st December to 23rd January as before lockdown (denoted as Pre-COVID or Pre-C, and the period from 24th January to 29th February as during lockdown (denoted as Lockdown or Lock).

To decouple the impact of meteorology (including seasonal trend and meteorological anomalies) from emission reduction during the lockdown, a state-of-art 3-D air quality model20 was employed (Methods for details and model validation). The Base case and the COVID case scenarios represent cases without and with COVID lockdown emission reduction, respectively. The difference between the two scenarios (Base - COVID) represents the effect of emission reductions while the difference between Lockdown and Pre-C periods in the Base case represents the effects of meteorology. To identify the distinct effects of NOx and VOCs reductions during the lockdown, we have also run a scenario of the same setting as the COVID case but without VOCs reductions (COVID-NOx case). As shown in Supplementary Figs. 1, 4, our model simulations (COVID case) can well capture the variations of O3 and NO2 as well as their differences between Lockdown and Pre-C periods (Supplementary Fig. 5).

Figure 2 (and Supplementary Fig. 6) shows that the meteorology is the main driver of the rise of O3 in the YRD region during the Lockdown period (compared to the Pre-C period), contributing about two-thirds (10.2 ppbv) of the O3 increase. Among the meteorological effects, the more frequent rainy/heavy cloudy days during the Pre-C period largely reduced the solar radiation (by around one-third), which may lead to a 24% reduction in the O3 according to long-term statistics (Methods and Supplementary Fig. 2), and the seasonal variation may explain 58% of the O3 increase (Methods and Supplementary Fig. 3).

Fig. 2: Impacts of COVID-19 lockdown and meteorology on ozone and Ox (Ox = O3 + NO2) in YRD region.
figure 2

Impact of lockdown emission reduction and meteorology on a The average O3. b Ozone diurnal pattern. c The average Ox. d Ox diurnal pattern. “Pre-C”, average concentrations before lockdown. “Lockdown”, average concentrations during lockdown. “Met”, meteorology impact was calculated as the difference between before and during the lockdown in the Base case and scaled by observations. “COVID-NOx”, the impact of NOx reductions during the lockdown, calculated by the modeled differences between the Base case and COVID-NOx case (the same as the COVID case but without VOCs reductions) during the lockdown period and scaled by observations. “COVID-VOCs”, the impact of VOCs reductions during lockdown period, calculated by the differences between the COVID case and COVID-NOx case (without NOx reductions) during lockdown period and scaled by observations.

Different response of O3 and Ox

As shown in Fig. 2, the emission reductions elevate the O3 concentration in YRD by 5.0 ppbv, around one-third of its apparent increase. Then comes the question of why a large emission reduction would worsen the O3 pollution. Earlier studies have attributed this O3 increase to fast photochemical production of ozone and nonlinear ozone chemistry during lockdown periods12,13. To better understand the mechanism, we analyzed the response and budget of O3 and Ox (Ox = NO2 + O3) separately. Compared to O3, Ox is more conserved a parameter upon titration by NO, representing atmospheric oxidation capacity on a regional scale. Besides average O3, we introduced additional parameters for reference, i.e., the daily maximum (MDA8) and minimum (MinDA8) of 8-h moving average O3 and their difference between MDA8 and MinDA8 O3 (ΔDA8 O3).

Figure 2 shows the distinct response of O3 and Ox to the emission reduction. In contrast to an elevated O3 concentration, the overall Ox concentrations are comparable between Pre-C and Lockdown periods according to both modeling and observations (Supplementary Fig. 5). The emission reduction itself leads to a decrease in Ox concentrations for both NOx and VOCs reductions (the red and yellow bars in Fig. 2). Specifically, VOCs reductions contributed to 81% (1.1 ppbv) of Ox decline, compared to 19% from NOx reductions, which, however, were largely offset by the adverse impact of meteorology (blue bar in Fig. 2). The reduced Ox implies a reduced oxidation capacity on a regional scale, which is also supported by reduced tropospheric O3 column concentration as in the work of Wang et al.21. Our results challenge the results of recent studies which suggest a regional increase of ozone production and increase of atmospheric oxidation capacity due to emission reductions during lockdown12,13.

To understand the distinct response of O3 and Ox and the underlying mechanism, we performed process analyses investigating the change of individual reactions upon emission reductions. Figure 3 shows the calculated budget amount for O3 and Ox in the lockdown period with and without emission reductions. For O3, its in situ chemical production results primarily from the reaction of O3p + O2, while its in situ chemical loss results mainly from O3 + NO. According to the budget analysis, the emission reduction during lockdown didn’t lead to a fast chemical production of O3, but rather a 42% reduced production. The reason for a higher O3 during lockdown is mainly driven by a reduced chemical loss via NO titration processes besides the meteorology effect. The decrease of NOx emission leads to a reduced titration effect which compensates the reduced O3 production and further increases the O3 concentration. This indicates a VOCs-limited regime and highlights the importance of reducing VOCs emission in the control of O3 pollution in the YRD region. For Ox, the in situ chemical production results primarily from reactions of HO2 + NO, RO2 + NO, and NO3 + NO, and the chemical loss results mainly from the reactions of OH + NO2 and O3 + NO2. According to the budget analysis in Fig. 3, the emission reduction during lockdown also reduces both chemical production and consumption of Ox, similar to that of O3. However, the reduction in Ox production is greater than the reduction in Ox consumption, resulting in a decrease in Ox concentration (Fig. 4).

Fig. 3: The budget of O3 and Ox in the lockdown period with and without emission reductions in the YRD region based on model simulations.
figure 3

a The daily average of the O3 budget. b The diurnal variation of O3 budget. c The daily average of Ox budget. d The diurnal variation of Ox budget. Here, Ox is defined as Ox = O3 + NO2. In panels b and d, the solid columns and lines indicate the results with lockdown emission reductions (COVID case), while the light color columns and dashed lines are those without emission reductions (Base case).

Fig. 4: Diagram of impacts of meteorology and emission reductions on the ozone concentration in the YRD region during COVID lockdown.
figure 4

The meteorology effects dominate the increase of both O3 and Ox concentrations. The emission reduction doesn’t lead to a fast chemical production of O3, but rather a reduced production. The increase of O3 is mainly driven by a greater reduction in the chemical loss of O3 via NO titration processes.

The increase of O3 in response to NOx emission reductions demonstrates the challenge of O3 control in YRD in the near future. So when are we expecting a positive effect of NOx control? To answer this question, we investigated the observed response of O3 relevant parameters to the COVID lockdown (emission reduction) at different locations (monitoring sites) of YRD, sorted by different parameters. As shown in Supplementary Fig. 7, the lockdown effect appears to correlate with NO2 concentrations (red squares), which is also well captured by our model simulations (green squares). To gain more insight beyond the correlation, we further explore the response of O3 changes to the ratios of modeled VOCs to NOx concentrations (which is considered a key determinant of the O3 formation regime) in all simulated grids in YRD.

As shown in Fig. 5 and Supplementary Fig. 8, the lockdown effect largely depends on the ratio of VOCs/NOx. In regions of low VOCs/NOx ratio, both O3 concentration and MDA8 O3 tend to increase while in regions of high VOCs/NOx ratio, the lockdown leads to reduce O3 and MDA8 O3. We find an empirical threshold VOCs/NOx ratio of ~8 ppbC/ppbv (~6.5 ppbC/ppbv for MDA8 O3) where the lockdown effect turns from negative (increasing O3) to positive (reducing O3). In YRD, ~10% of regions are in the positive effect regime, mostly located in Zhejiang province in the southwest of the YRD region while the rest areas are in a negative effect regime (Supplementary Fig. 9). We have also found that the VOCs/NOx ratios are well correlated with the NOx concentration, i.e., regions of higher NOx concentrations show lower VOCs/NOx ratios (Supplementary Fig. 10), and the threshold of VOCs/NOx ratio corresponds to NOx concentration ~10 ppbv (Supplementary Fig. 9). This explains the observed dependence of O3 changes on NO2 concentrations at different monitoring sites (Supplementary Fig. 7) aforementioned. As shown in Fig. 5, the individual response of MDA8 and MinDA8 to the lockdown is also in agreement with that of regional averages (Supplementary Fig. 11): under most VOCs/NOx ratios, the lockdown leads to a reduced Ox and daytime O3 formation (ΔDA8 O3), and an increase of MinDA8 O3 due to reduced NO titration. The dependence of ozone changes on the ratios of VOCs/NOx was similar to that on LN/Q which indicated the fraction of free radicals which are removed by reacting with NOx (Supplementary Fig. 12).

Fig. 5: Dependence of changes of ozone with and without lockdown reductions on VOCs/NOx ratios.
figure 5

The x-axis was the simulated VOCs/NOx ratios in each grid from the Base case grouped over 0.1 ppbC/ppbv VOCs/NOx bins, which indicated the present status in the YRD region. There were 862 grids in the YRD domain in the model. The horizontal bar presented the dependence of changes of ozone between the Base and COVID cases during lockdown period on VOCs/NOx ratios (those from Base case during lockdown), with the color bar axis as well as the x-axis, and the blank areas indicated no data samples under these ratios. The lines were the frequency of different VOCs/NOx ratios from the Base case indicated by the y and x arises.

Implications for ozone pollution mitigation

The strong titration effect and VOCs-limited regimes have important implications in the development of an effective O3 control strategy in YRD. With a combination of strong low-carbon policy and air pollution control policy, the VOCs and NOx emission are expected to be further reduced by 31 and 42% in 2030 compared to 202022, which is within the range of emission reduction during the COVID lockdown (VOCs by ~44%, NOx ~47%). Thus, the observed and modeled O3 formation regimes identified during the COVID period can also be used to predict the response of future O3 pollution by 2030. Given the widely-distributed NO titration effect in YRD, we would expect an increase of winter/spring O3 in most YRD regions by 2030 in spite of continuous NOx and VOCs reduction as shown in Supplementary Fig. 13 (black dashed lines). Such a negative effect of emission controls suggests us rethink of an alternative strategy. Under the same final emission control target (VOCs by ~44%, NOx ~47%) in 2030, the O3 concentration in the next decade can be changed by a different timetable of VOCs/NOx reduction. As shown in Supplementary Fig. 13, when we prioritize the VOCs control (red line), the 10-year average O3 would be largely reduced, become substantially lower than that from the simultaneous reduction of both NOx and VOCs. The other way round, if we postpone the VOCs reduction, the average O3 would be largely elevated. Overall, we may benefit more from an early implementation of VOCs reduction and a relatively late reduction of NOx in the control of winter O3. The major uncertainties of this study can be attributed to the lack of VOCs measurements on a regional scale. At the SAES site, modeled and measured VOCs show a generally good agreement, especially for HCHO and ethylene while the modeled toluene and xylenes are higher than the observations (Supplementary Fig. 14), suggesting a potential overestimation of these species in the MEIC emission inventory.

In practice, the changes in NOx emissions and VOCs emissions are often coupled because they share several common sources. More effective control relies on more precise information of emissions, and to which degree their reductions can be decoupled to form a different timetable. Supplementary Table 1 shows a characteristic emission inventory of YRD18, where different sources show distinct ratios of NOx to VOCs emissions from ~0.1 to 37.0 (mass ratio). Power sectors, heavy industry, and mobile sources show high ratios of NOx to VOCs emission, which take up 84 and 21% of NOx and VOCs emissions, respectively. While, solvent usage including both industrial and residential sources23 and the petrochemical industry contributes 9 and 49% to NOx and VOCs emissions, respectively, and show low ratios of NOx to VOCs emission. Therefore, under ideal conditions (without considering the different costs), the priority of emission controls should be given to solvent usage and the petrochemical industry which mainly emit VOCs but few NOx18.

Note that the present results are obtained based on the COVID lockdown in winter. We may expect a different response of O3 formation to NOx and VOCs regime in the summer season. For example, the emission ratio of VOCs/NOx is higher in summer than that in winter, due to more VOCs emissions24 (from plant and evaporation sources like solvent use and fuel leakage) and less NOx emissions (no heating in the north of China) in summer, which is also confirmed by the observations in Shanghai megacity located in eastern YRD (Supplementary Fig. 15). Solar radiation is generally stronger in summer and the benefit of VOCs reductions should be larger. Thus, we may expect weakened negative effects of NOx control in summer, and a transition from NO titration regime even toward a NOx-limited regime. In this case, we may not only benefit from advancing the control of VOCs in winter/spring but also benefit from the control of NOx in summer. Then we may further optimize the emission control strategies by precisely changing the ratio of emission reduction in different seasons.


Gases data

Hourly ground-based measurements data of ozone (O3) and nitrogen dioxides (NO2) concentrations from 2017 to 2020 were obtained from the public website of China National Environmental Monitoring Center. There were 190 sites located in the YRD region (41 cities). We firstly obtained the city level results by averaging the station level data and then averaged the city level results to output regional values. The maximum and minimum of 8-h moving average of O3 (MDA8 O3 and MinDA8 O3) were calculated, and their differences were defined as ΔDA8 O3 which suggested the daily production of O3 to some extent.

Volatile organic compounds (VOCs) data

VOCs data from January 2019 to February 2020 in Shanghai were measured by an online high-performance gas chromatograph with a flame ionization detector and mass spectrometer (GC-FID/MS) at the campus of the Shanghai Academy of Environmental Sciences (SAES) with a time resolution of 1 h. VOCs were identified and quantified by the 56 USA Photochemical Assessment Monitoring Stations (PAMS) standard gas mixture (Spectra Gases Inc., USA). A detailed description of the measurements could be found in our previous study24.

Meteorological data

Hourly surface solar radiation and precipitation measurements data were from national meteorological observing stations operated by the China Meteorological Administration. Overall, there are five monitoring stations in the YRD region, located in Shanghai, Nanjing, Huai’an, Hangzhou, and Hefei. The regional results were the average of the data at the above sites.

Formaldehyde data

Daily tropospheric formaldehyde (HCHO) vertical column densities (VCDs) in the YRD region from January 24 to February 29 of 2020 were retrieved from the Ozone Mapping and Profiler Suite (OMPS) onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite at 3.5 km resolution25, which were used to validate the simulated densities in the model.

Emissions inventory

We develop a dynamic emission estimation method to provide daily emission input to the model simulation. The emission dataset covers nine major categories18, including power plants, industrial process sources, industrial solvent-use sources, mobile sources, dust sources, oil storage and transportation sources, residential sources, waste treatment and disposal sources, and agricultural sources. Based on the baseline emission inventory, we further integrated real-time activity data related to the industry, transportation, and residential lives to dynamically characterize the daily emission changes during the COVID-19 outbreak26. Emission changes of industrial sources were characterized using continuous emission monitoring system (CEMS) and industrial electricity consumption (IEC) data. Emissions from gasoline and diesel vehicles were calculated based on real-time traffic flow data. Ship and aircraft emissions were estimated using an automatic identification system (AIS) and landing and take-off (LTO) data of flights. Dust monitoring system (DMS) and population migration index (PMI) developed by Baidu ( were used to estimate the emission changes of dust- and residential-related sources. As a result, NOx and VOCs emissions decreased by about 47 and 44% during COVID lockdown compared to that before, respectively.

Regional air quality modeling settings

The model used in this study was based on a specific version of the WRF-Chem model with modification by our previous study20. Briefly, the modeling framework is constructed on a single domain of 100 (west-east) × 70 (south-north) × 30 (vertical layers) grid cells with a horizontal resolution of 20 km. The initial and boundary conditions for meteorology and chemistry are derived from 1.0° × 1.0° NCEP FNL data and global-scale MOZART outputs, respectively. Observation nudging27 is used to nudge the modeled temperature, wind fields, and humidity towards the observations at the surface layer ( Due to the use of observational nudging (sites location shown in red dots), our base simulation could reasonably reproduce the observed 2-m temperature, 2-m relative humidity, 10-m wind speed, and wind direction (sites location shown in blue circles) in most cases (Supplementary Fig. 16). Muli-resolution Emission Inventory for China (MEIC) of the year 2019 was used for anthropogenic emissions28, while those in YRD region were replaced by the local emissions18. The Model of Emissions of Gases and Aerosols from Nature (MEGEN) scheme29 was used for biogenic VOCs emissions. The hourly biomass burning emissions data were provided by the Fire Inventory from NCAR30. The normalized mean bias (NMB), normalized mean error (NME), and the correlation (R) were used to evaluate the model performance in simulations of O3 relevant parameters and NO2 on a spatial scale (Supplementary Tables 24). The model could also well capture the temporal variations of O3 relevant parameters and NO2 as well as their changes before and during the lockdown, as shown in Supplementary Figs. 4, 5 and Supplementary Table 5. The model performance on VOCs simulations was validated to some extent by the satellite observed spatial distribution of HCHO VCDs (Supplementary Fig. 17) and the observations at the campus of the Shanghai Academy of Environmental Sciences in Shanghai, China (Supplementary Fig. 14).


In this study, we simulated three scenarios, including Base, COVID, and COVID-NOx cases, which were all constrained by meteorology from December 1st in 2019 to February 29th in 2020. The Base case was driven by the fixed emissions (emissions in 2019) without reductions over the total study period. The COVID case was driven by the real emissions through scaling the emissions by the daily-to-daily ratios calculated above. The COVID-NOx case driven by similar emissions to the COVID case but without VOCs reductions was employed to exactly apportion the impact of NOx and VOCs reductions on ozone changes during the lockdown.

Period definition

To better illustrate the changes of ozone before and during the COVID-19 lockdown, we grouped it into two periods. One was before the COVID-19 lockdown (from December 1st in 2019 to January 23rd in 2020), denoted as “Pre-C”. The other one was during the COVID-19 lockdown (from January 24th to February 29th in 2020), denoted as “Lockdown”. Different from the previous studies5,8, we extended the Pre-C period to December in 2019 in this study, mainly due to there were only two sunny days on a regional scale in the YRD region in January of 2020 (Fig. 1), considering the large influence of meteorology (in particular the solar radiation) to ozone concentrations (Supplementary Fig. 2).

Impact of solar radiation

Among the meteorological effects, the more frequent rainy/heavy cloudy days during the Pre-C period largely reduced the solar radiation (by around one-third). In order to estimate the impact of higher solar radiation during the lockdown, a linear fitting relationship between the solar radiation and ozone concentrations was obtained based on long-term statistics of observations (Supplementary Fig. 2). Accordingly, the increase of solar radiation during lockdown may lead to a 24% increase in the O3.

Impact of seasonal variations

The lockdown occurred during the seasonal alternation in the northern hemisphere, with gradually increasing SR and temperature, which may lead to an additional increase of O3 in the lockdown periods. The historical trend of ozone in the past 3 years (from 2017 to 2019) could also confirm the seasonal increase. Here, the linear fitting trend (brown dashed line in Fig. 1) of ozone concentrations was used to estimate the O3 changes before and during lockdown periods primarily, which may explain 58% of the O3 increase.

Data availability

All data included in this study could be downloaded at (The file name is “Increase of O3 during COVID-20220118.xlsx”).

Code availability

The codes used in this study are available upon request.


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This work was supported by the National Key R&D Program of China (Grants No. 2018YFC0213800) and the Science and Technology Commission of the Shanghai Municipality (Grants No. 20ZR1447800). The authors thank Prof. Liu Cheng from the University of Science and Technology of China for the sharing of tropospheric formaldehyde (HCHO) vertical column densities data.


Open Access funding enabled and organized by Projekt DEAL.

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H.S., C.H., and Y.C. designed the study. H.W., H.S., and C.H. analyzed the data and wrote the paper. Y.C., W.T., and S.W. performed the model simulations. Y.G., S.J., Q.W., S.L. made VOCs measurements. C.H., R.Y., J.A., Q.H. developed the dynamical emissions. All authors contributed to improve the paper. H.W., C.H., and W.T. contributed equally to this work.

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Correspondence to Cheng Huang, Yafang Cheng or Hang Su.

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Wang, H., Huang, C., Tao, W. et al. Seasonality and reduced nitric oxide titration dominated ozone increase during COVID-19 lockdown in eastern China. npj Clim Atmos Sci 5, 24 (2022).

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