The outbreak of coronavirus disease 2019 (COVID-19) has severely threatened public health worldwide, leading to millions of deaths 1 . China, where the first case of COVID-19 was reported in the city of Wuhan, imposed country-wide measures from January 23 to February 13, 2020 to prevent the spread of the disease, including social distancing, teleworking, and closure of non-essential businesses 2, 3 . These restrictions drastically reduced transportation and industrial activities, resulting in a sharp decrease in emissions of air pollutants 4, 5 .
The huge and large-scale emission reductions during the COVID-19 lockdown can be treated as a natural outdoor experiment to improve our understanding of the air pollutants' response to emission control. According to satellite and surface observations, NO2 concentrations decreased by over 50% in China during the lockdown period 6, 7 . The concentrations of other pollutants, including SO2, particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), particulate matter with an aerodynamic diameter less than 10 μm (PM10), and carbon monoxide (CO), also declined in a large area of China 8 . However, surface ozone (O3) concentrations in northern China increased by factors of 1.5-2.0 6 . Similar O3 increases have been reported in southern Europe, India, and Brazil despite the large decrease in other pollutants 9, 10, 11 . However, the underlying factors driving the O3 changes during the city lockdowns remain unclear.
This study analyzes surface O3 data across China before and during the country-wide lockdown. We find that O3 decreased in southern China while increasing in most other regions during the lockdown. Using a regional chemistry transport model, we isolate the impacts of meteorological changes and anthropogenic emission reductions on O3. Our results highlight the importance of meteorological influences on the short-term O3 changes and the diverse response of O3 to its precursors' emission reduction in different climatic and emission-mix regions.
generally made a greater contribution to the O3 variation than did the emission reductions. For the whole-day average, the meteorological changes made a significantly positive contribution to O3 in central (by 5-10 ppbv) and northern (by 2-10 ppbv) China, but a negative contribution (by 1-2 ppbv) in southern China, Tibet, and eastern Neimenggu (Fig. 2b) . The emission reduction contributed positively to O3 (by 2-5 ppbv) in the North China Plain, Yangtze River Delta, and some other urban areas, but negatively (by 0-4 ppbv) in other regions (Fig. 2c) . During daytime, the effect of meteorological changes on O3 was similar to those for the whole-day average, but the positive contribution to O3 was slightly lower (Fig. 2e) . The positive contribution of emission reduction to O3 was also lower in daytime than the all-day average, with positive contributions of 2-5 ppbv in the North China Plain and negative contributions in a broader rural area (Fig. 2f ). During nighttime, the meteorological contributions to the O3 mixing ratio were less strongly positive (Fig. 2h) , whereas those due to emission reductions were more strongly positive (Fig. 2i) . Figure 3 shows the observed and simulated O3 changes in the NC, CC, and SC regions and the simulated impacts of meteorological changes and emission reductions. The observed O3 changes in these regions were generally captured by the simulation. However, the model underestimated the increase in NC and CC and over-predicted the decrease in SC, which can be attributed to the uncertainties of the meteorological simulations and estimated emission reductions across China (see Supplementary Information for detailed discussions). Overall, the results showed that the meteorological changes and emission reductions both played an important role in the O3 changes in these regions. For the whole-day average, the O3 increase in NC was attributed to the comparably sized contributions from both meteorological changes (3.1 ppbv) and emission reductions (3.3 ppbv) (Fig. 3a) . In CC (Fig. 3b) , the meteorological change (6.7 ppbv) was the primary cause of the O3 increase, whereas the contribution of emission reduction was much lower (0.8 ppbv). In SC (Fig.  3c) , the meteorological changes (-1.7 ppbv) and emission reductions (-0.7 ppbv) both contributed to the O3 decrease. The trends in daytime were similar to the whole-day trends. During nighttime, the emission reduction contributed positively to O3 in all three regions (including SC), and its impact was stronger; the effect of meteorological changes weakened at night. Impacts of meteorological changes on O3. The impacts of meteorological changes on O3 for the NC, CC, and SC regions can be explained by the changes in the overall weather pattern and specific meteorological factors. In winter, China's land area is generally controlled by a cold high-pressure system (Fig. S4 ). During our study period, the center of this high-pressure system was located in northern China, moving southward from the pre-CLD to the CLD period, with weakening strength. During this time, the high-pressure system therefore became increasingly dominant in southern China, and the strengthened southward winds brought colder and drier air masses from the north (Fig. S5c) , which decreased the temperature and specific humidity in local areas ( Fig. S5a and b) . In contrast, in central and northern China, the winds shifted to a more northward direction, transporting warmer and wetter air masses from the south (Fig. S5c) , which increased the temperature and specific humidity ( Fig. S5a and b) . During daytime, the decrease in temperature in southern China, accompanied by increases in central and northern China, weakened and enhanced, respectively, the surface O3 chemical production 12, 13 , contributing to the respective decrease and increase in O3. Biogenic emission is an important source of VOCs and thereby contributes to O3 formation in China 14 . The temperature changes led to an increase and a decrease in biogenic emissions in central and southern China, respectively (Fig. S6) . Thus, the temperature changes increased and decreased O3 in central and southern China, respectively, by influencing chemical reaction rates directly and altering biogenic emissions indirectly.
The changes in the weather pattern also resulted in less cloud and precipitation in northern and central China, but more cloud and precipitation in southern China ( Fig. S5e and f) . Cloud can reduce the amount of solar radiation reaching the surface and thus the chemical production of O3 15 , while precipitation can also reduce O3 through the scavenging of its precursors 16, 17 . These opposite changes in cloud fraction and precipitation therefore helped to increase O3 in central and northern China and decrease O3 in southern China. Furthermore, in northern and central China, the significant increase in planetary boundary layer height during the lockdown promoted the transport of O3 from the upper air to the surface, contributing to the O3 increase in these regions 18, 19 . The increase in specific humidity in northern and central China and the decrease in southern China might also have contributed negatively and positively to the O3 mixing ratios in those regions, respectively 20, 21 (Fig.  S5b ). During nighttime, the changes in meteorological factors were similar to those in daytime (Fig.  S7) . However, the changes in temperature and cloud fraction exerted smaller impacts on O3 changes than those in daytime, considering the negligible biogenic emissions and solar radiation at nighttime. Thus, the effects of meteorological changes in the NC, CC, and SC regions during nighttime were generally smaller than during daytime (Fig. 3) , and they became negligible in SC (Fig. 3c) .
Response of O3 to emission reductions. We further investigated the impact of multi-pollutant reductions on the O3 changes. Because transportation and industrial activities were reduced significantly during the lockdown and they were the major sources of NOx (>80%) and VOCs (>60%) (Fig. S8) , the reductions of NOx and VOC emissions were more significant than those for CO, particulate matter (PM), and SO2 (Fig. 4a , c, e). The estimated NOx emission reductions were 42%, 48%, and 51% in the NC, CC, and SC regions, respectively, while the respective reductions for VOC emissions were 32%, 28%, and 45%. The relationship between O3 and its precursor emission is non-linear, especially for NOx. The reduction of NOx emission contributed positively to O3 in a large area of China due to the NO titration effect during nighttime (Fig. S9c) , whereas the impact of NOx reduction was more complicated during daytime ( The response of O3 to the emission reductions depended on the levels of NOx and VOC reductions in different regions. For the whole-day average, in NC, the positive contribution of the NOx reduction counteracted the negative contribution of the VOC reduction, leading to a substantial net O3 increase (Fig. 4b) . In CC, the contributions of the NOx and VOC reductions were comparable in magnitude, and their opposite impacts resulted in only a slight change in O3 (Fig. 4d ). In SC, the impact of the NOx reduction on O3 was smaller than that of the reduction of VOCs, leading to a net decrease in O3 (Fig. 4f) . The impacts of emission reductions on O3 during daytime were similar to those for the whole-day average. During nighttime, the effect of the VOC reduction was weakened due to the lower rate of degradation of VOCs by radicals compared with daytime, and the O3 level increased in all three regions due to the NOx reduction.
The above results show that the contribution of NOx reductions (with NOx falling by 42%-51%) to the rise in O3 decreased from NC to CC and to SC, indicating the decreasing level of NOx saturation from north to south. In contrast, the impact of the VOC reduction on O3 increased from north to south, which can in part be attributed to the regional variation of VOC reductions. In the SC region, transportation and industry are the predominant sources of VOCs (97%, compared with 85% and 60% in NC and CC, respectively) (Fig. S8 ). During the CLD period, the reduction of VOC emission in SC (45%) was significant and comparable with the NOx reduction (51%). In contrast, the VOC reductions in the NC (32%) and CC (28%) regions were much lower (Fig. 4a , c) and could not offset the positive impact of NOx reduction on O3. The residential sector (mainly household coal burning) is an important source of VOC emission in the NC and CC regions, whereas its contribution is smaller in SC. The residential emissions in some northern rural areas increased during the CLD period because many migrant workers came back for the Chinese New Year holiday and were stranded there due to the lockdown 4, 8 .
The drastic emission reductions during the COVID-19 outbreak offered an unprecedented realworld scenario to examine the human impacts on the air quality and obtain knowledge to help design more effective air pollution control policies. The responses of air quality to short-term emission controls have been studied during a number of public and political events in China, such as the Beijing Summer Olympic Games (August 2008) 24, 25 , the Nanjing Youth Olympic Games (August 2014) 26, 27 , the Asia-Pacific Economic Cooperation (APEC) meeting in Beijing (November 2014) 28, 29 , the Grand Military Parade in Beijing (September 2015) 28, 30 , and the G20 summit in Hangzhou (September 2016) 31, 32 . During these events, various emission-reducing measures were implemented in the cities concerned and their surrounding areas. Whereas atmospheric concentrations of primary air pollutants (NOx, CO, primary PM, and SO2) in the concerned cities generally decreased in response to the temporary control measures, the O3 concentrations showed mixed responses. O3 decreased during the Grand Military Parade 30 and the Nanjing Youth Olympic Games 33 but increased during the Beijing Olympic Games 25 , G20 summit 31 , and APEC meeting 29 . The different O3 responses can be qualitatively attributed to differences in the meteorological conditions (including regional transport of air masses) and different control measures implemented by the local governments.
Compared with the previously studied situations, the COVID-19 lockdown is unique in that emissions decreased across the whole country (and later the entire world) as opposed to a specific city or region, and the decreases were also much more drastic than those due to transportation restrictions alone. Moreover, the COVID-19 lockdown took place in winter, whereas the previous interventions occurred in summer and autumn, when meteorology and atmospheric chemistry are likely to be different from winter. The present study found differences in the responses of surface O3 to the emission decreases during the COVID-19 lockdown in northern (large increase) and southern (moderate decrease) China. Meteorology played a key role in the O3 changes during the lockdown, highlighting the necessity of taking meteorology into account when assessing the shortterm response of O3 (and other air pollutants) to emission reductions 34 .
The COVID-19 lockdown led to larger reductions in NOx and VOCs than in SO2 and PM (Fig.  4) . As NOx and VOCs are precursors of O3, the changes in O3 are more sensitive to their reduction than to the reduction in PM (and SO2), which could increase in the photolysis rates and reduce uptake of reactive gases. Previous studies showed that the increase in summer O3 concentration from 2013 to 2017 in China was due to a larger reduction of PM and SO2 than of NOx 35 . The findings of such studies on the response of O3 to different real-world emission reduction scenarios demonstrate the complex relationship of O3 with not only its precursors (NOx and VOC) but also particulates, highlighting the need for a holistic strategy to control both primary pollutants and secondary pollutants like O3.
In conclusion, the first country-wide lockdown during the COVID-19 outbreak in China drastically reduced transportation and industrial activities, leading to sharp declines in air pollutant emissions from these sectors. However, atmospheric O3 responded differently in the northern and southern regions, which can be explained by changes in meteorology and differences in the O3 chemistry regimes and the magnitudes of precursor reductions in these regions. Our study shows that the extent of VOC reduction during lockdown, which suppressed O3 formation, was insufficient to offset the NO titration effect in northern China, and that larger reductions of VOCs (e.g., from residential sectors) would have been needed to reduce the O3 concentration in the north. The rising O3 concentration in northern China during the COVID-19 lockdown and in recent winters should receive greater attention because O3 boosts the atmospheric oxidative capacity and therefore production of secondary aerosols 5 Two experiments were conducted to investigate the impacts of meteorological changes and emission reduction on O3 during the CLD period. The baseline experiment was simulated with the same anthropogenic emissions for the pre-CLD and CLD periods. Based on the baseline run, the other experiment (Reduction Case) was performed with 70% and 40% emission reductions of transportation and industry, respectively, during the CLD period. Because there is no official emission inventory for the CLD period, these emission reductions were estimated according to the literature 4, 5 . Comparing these two experiments, the O3 changes during the CLD period relative to the pre-CLD period for the Reduction Case were considered to be entirely due to the meteorological changes and emission reductions. The impacts of the meteorological changes were quantified by subtracting the O3 mixing ratios of the CLD period from those of the pre-CLD period for the baseline experiment, while the impacts of emission reduction were estimated by comparing the O3 mixing ratio during the CLD period between the Reduction Case and the baseline experiment. Furthermore, we individually reduced the emissions of NOx, VOCs, CO, PM, and SO2 during the CLD period to elucidate the response of O3 to each pollutant's reduction.
The performance of the CMAQ model in simulating the O3, NO2, PM2.5, SO2, and CO concentrations for the Reduction Case was evaluated ( Fig. S10 and Table S2) , showing reasonable agreements with the respective surface observations. Details of the emission estimation and the model evaluation are presented in Supplementary Information.
The codes or data used in this study are available upon request from Yiming Liu (yming.liu@polyu.edu.hk) and Tao Fig. 1a. (b, d, f) The impacts of different pollutant emission reductions due to the lockdown on O3 changes for the NC, CC, and SC regions. The O3 changes for the all-day average, daytime average, and nighttime average are presented. Note that the error bars mark the standard deviations within the region.
The most recent available data on China's anthropogenic emissions are from 2017 (MEIC, http://www.meicmodel.org). We estimated the emissions for 2020 based on the MEIC 2017 according to the trends in these emissions in recent years (Zheng et al., 2018) . Major emission reduction measures have implemented since 2013 in China. Table S1 shows the scaling factors from 2017 to 2020 for NO2, PM (particulate matter, including PM10, PM2.5, and its components), and SO2 in the power plant, industry, transportation, and residential sectors. The VOC emission was assumed to be unchanged from 2017 to 2020 because it increased by only 2% from 2013 to 2017 (Zheng et al., 2018) . The NO2, PM, and SO2 emissions from transportation were also assumed constant from 2017 to 2020, considering that they had changed little during 2015-2017. The NOx emission in the residential and industrial sectors was assumed to be the same in 2020 as in 2017 in view of its flat trend in recent years. Because the NOx emission from power plants decreased by ~47% from 2013 to 2017 (11.7% per year), we assumed it to further decrease by 35% from 2017 to 2020. The same approaches were applied to the reductions of the PM and SO2 emissions from 2017 to 2020. With these adjustments, we derived an estimated anthropogenic emission inventory for China in 2020. The model-simulated pollutant concentrations using this inventory showed a reasonable agreement with the surface measurement data (see the Model evaluation section below).
We estimated the emission reductions during the COVID-19 lockdown period according to the recent literature Huang et al., 2020) . Transportation and industry were the sectors most affected by the COVID-19 lockdown in China. For the transportation sector, the decrease in national traffic volume was estimated at 70% during the lockdown according to transportation index data (Huang et al., 2020) . The industry emissions were assumed to decrease by 40% across China in our study. We kept the emissions from power plants, residences, and agriculture unchanged because they were less affected by the city lockdowns Huang et al., 2020) . Fig.  4 presents the reductions of NOx, VOC, CO, PM, and SO2 emissions due to the 70% and 40% reductions of transportation and industry emissions during the lockdown, which generally match the estimation by Huang et al. (2020) .
Statistical parameters were calculated to validate the model performance in simulating the air pollutant concentrations from January 2 to February 12, 2020, including the mean observation (OBS), mean simulation (SIM), mean bias (MB), mean absolute gross error (MAGE), root mean square error (RMSE), index of agreement (IOA), and correlation coefficient (r). The equations of these statistical parameters can be found in Fan et al. (2013) . Table S2 shows the evaluation results for the simulated concentrations of SO2, NO2, CO, O3 and PM2.5 in China. Generally, the CMAQ model faithfully reproduced the observed pollutant concentrations with low biases. SO2 and PM2.5 were slightly overestimated with mean biases of 1.1 ppbv and 8.6 μg/m 3 , respectively, which suggested that the emissions of SO2 and aerosols might have been more heavily reduced in 2020 than we estimated. CO was underestimated by the model with a mean bias of 0.33 ppmv. These biases of the simulated SO2, PM2.5, and CO concentrations have little impact on our results because the reductions of those pollutants exerted much smaller effects on O3 changes than did the reductions in NOx and VOCs. The model slightly underestimated the NO2 mixing ratios with a mean bias of -4.1 ppbv. This underprediction was partly attributed to the catalytic conversion method used to measure NO2 in the national network, which can overestimate NO2 (Zhang et al., 2017; Fu et al., 2019) . The observed O3 mixing ratios were well captured by the model with a much smaller bias (2.6 ppbv). These evaluation results suggested reasonable estimations of the anthropogenic emissions for the year 2020 and during the lockdown. Fig. S10 shows the scatterplots of simulated and observed hourly O3, SO2, and PM2.5 concentrations. Most of the points fall within the range between 1:2 and 2:1 (the ratios between the simulated and observed concentrations). The general increase in O3 and decreases in SO2 and PM2.5 during the CLD period compared with the pre-CLD period were well captured by the model (see also Fig. S3 for the O3 changes across China), which indicated the faithful estimation of anthropogenic emission reductions during the lockdown. Fig. 3 shows that the model underestimated the O3 increase in the NC and CC regions and over-predicted the decrease in SC, which can be explained as follows. The simulated O3 mixing ratio and its increases were lower than the observed values in the NC region, possibly because of the overestimation of the NO titration effect due to the under-predicted planetary boundary layer height, especially during nighttime (Petersen et al., 2019) . Moreover, the degree of emission reductions from transportation and industries might vary between regions with either positive or negative biases compared with our unified estimations across China, which would have affected the accuracy of the O3 simulations in these three regions in various ways. Nevertheless, the model was able to faithfully capture the observed O3 increases in NC and CC and decrease in SC.
Overall, despite some uncertainties, the CMAQ model performance is acceptable and can support further analysis of O3 changes during the COVID-19 city lockdowns. Figure S5 : Changes in daytime temperature at 2 m height, specific humidity at 2 m height, wind field at 10 m height, planetary boundary layer (PBL) height, cloud fraction, and precipitation during CLD period relative to pre-CLD period. In panel (c), the shaded color and vector represent the wind speed and wind direction, respectively. Figure S6 : Biogenic isoprene emissions during the pre-CLD and CLD periods and their difference (CLD minus pre-CLD). Figure S7 : Changes in nighttime temperature at 2 m height, specific humidity at 2 m height, wind field at 10 m height, PBL height, cloud fraction, and precipitation during the CLD period relative to the pre-CLD period. In panel (c), the shaded color and vector represent the wind speed and wind direction, respectively. Industry  Residence  Transportation   NOx  -35%  ---SO2  -40%  -40%  -25%  -PM --20% -30% - 