The COVID-19 has become a severe threat to global public health since it was initially reported in January 2020 (Zhu et al. 2020) . The World Health Organization (WHO) declared COVID-19 as a global pandemic on Mar 11 2020, because of the rapid spread across the world: the reported confirmed cases are about 6400 thousand globally with 380 thousand deaths by June 1 2020 (http://www.chinacdc.cn). An important reason of the global outbreak of COVID-19 is lacking specific antiviral therapies and vaccines, and thus, the control strategy depends on isolation of cases and contact tracing to reduce the transmission rate (Chinazzi et al. 2020 , Li et al. 2020 , which has resulted in unprecedented lockdowns across the world.
As a precursor to ozone and secondary aerosols, NO2 is one of the most important pollutants and plays a key role in tropospheric chemistry. Tropospheric NO2 concentrations are strongly affected by fossil fuel combustions, such as power generation, industrial and transportation emissions (Jiang et al. 2018) . The short lifetime of tropospheric NO2 (few hours at the surface) makes it an ideal tracer for local anthropogenic emissions, as it exhibits marked responses to perturbations in economic activities (Mijling et al. 2009 , Wang et al. 2015 , Tong et al. 2016 . The economic activity changes, due to the intensive lockdowns to mitigate the COVID-19, are expected to affect tropospheric NO2 (Zhang et al. 2020) , however, their actual influences are still uncertain, e.g., the "flawed estimates of the effects of lockdown measures on air quality derived from satellite observations" as suggested by the European Centre for Medium-Range Weather Forecasts (ECMWF 2020).
An important task of the international community, in 2020, is to understand the impacts of anthropogenic activity changes due to COVID-19 controls on atmospheric environment. In this 3 work, we investigate the responses of tropospheric NO2 to COVID-19 control measures over China, South Korea, and Italy to analyze the influence of lockdown measures on tropospheric NO2, particularly, the responses of tropospheric NO2 to the pandemic developments (i.e., start, maximum, and mitigation of pandemic spreads).
Figure 1a shows tropospheric NO2 columns (OMI-QA4ECV, Boersma et al. 2018, See SI) over E. China, normalized in the 50-10 days before Jan 25 2020 (Spring Festival in 2020). The data over China are shifted for 2015-2019 to account for the economic cycles due to the Spring Festival.
The reference time (RT , Table 1 ) is set to Jan 25 for the following two reasons: 1) the Spring Festival is a good indication for Chinese economic cycles; 2) tropospheric NO2 in the 50-10 days before Jan 25 were not affected by (about 50 million) and Italy (about 60 million), the tropospheric NO2 over N. Italy is normalized in the 50-10 days before Feb 28 (RT, about 200 daily new confirmed cases) to ensure tropospheric NO2 in the 50-10 days before the RTs were not affected by 1f) .
As shown in Figure 1 , the normalized tropospheric NO2 changes (2020 vs. 2015-2019) exhibit the following relations with the COVID-19 pandemic developments: 1) Agreements in tropospheric NO2 before the pandemic outbreaks: 50-0 days before the RT for E. China; 50-10 days before the RTs for South Korea and N. Italy.
2) Agreements in tropospheric NO2 with pandemic mitigation: 60-80 days after the RT for E. China; 40-80 days after the RT for South Korea; 50-70 days after the RT for N. Italy.
3) Large differences in tropospheric NO2 by more than 40%, coincident with the pandemic outbreaks. Figure 2 shows the distributions of tropospheric OMI NO2 columns over these three nations. Figure 1 , we find marked reductions of tropospheric NO2 in the 10-30 days after the RTs over E. China, South Korea, and N. Italy in 2020. The reductions of tropospheric NO2 are widely observable over these three nations. Furthermore, the difference between tropospheric NO2 in 2020 and 2015-2019 increased on the RT for E. China, but in about 10 days before the RTs for South Korea and N. Italy, suggesting a 10-day delay in the response of tropospheric NO2 to the pandemic development in China compared to in South Korea and Italy. The delayed response in China could be due to the strong inhibition of the Spring Festival on Chinese economic activities, e.g., the E. China-averaged tropospheric NO2 dropped by about 50% within 10 days prior to the national holiday (Figure 1a ), which is even stronger than the perturbation due to COVID-19 controls. The perturbation in tropospheric NO2 in the initial pandemic stage in China may have been covered by the inhibition due to the Spring Festival.
We have demonstrated large perturbations in tropospheric NO2 by more than 40% accompanied with the outbreaks of COVID-19. However, it is still unclear whether the perturbations were caused by anthropogenic or non-anthropogenic processes (e.g., large-scale anomaly in meteorological conditions). 
The above analysis indicates the important influences of anthropogenic activities on the observed tropospheric NO2 changes. As shown in Table 1 2) South Korea: maximum quarantine in Gyeongsangbuk-Do (the province that COVID-19 was initially outbreak in South Korea) on Feb 25 2020 (#2, YNA 2020). As shown in Figure   1c , the quarantine (#2) matches well with the start of the 40% perturbation in tropospheric NO2.
3) Italy: lockdown in N. Italy on Mar 7 2020 (#3, BBC 2020); all unnecessary commercial activities stopped on Mar 11 (#4, Repubblica 2020). As shown in Figure 1e The similar responses of OMI NO2 and derived anthropogenic NOx emissions to the pandemic outbreaks provide support to our conclusion. The relative uncertainties in the derived NOx emissions are larger than those in OMI NO2. It could be partially associated with the regionspecific data filters ( Figure S1 , See SI), which were not considered in the global assimilation. In addition, the perturbations in OMI NO2 will become about 30% without the region-specific data filters ( Figure S2 
The OMI instrument on the Aura spacecraft has a spatial resolution of 13 km x 24 km (nadir view), which is in a sun-synchronous ascending polar orbit with a local equator crossing time . y-axis: regional daily average of tropospheric OMI NO2 columns; x-axis: regional average of tropospheric OMI NO2 columns in the period of ± 15 days by excluding the current day.
Besides the aforementioned filters, the regional averaged OMI NO2 data are affected by the different daily coverage of satellite data. Figure S1 shows the relations between daily average of tropospheric OMI NO2 and the average of its neighbouring days (± 15 days without the current day). Large deviation from the 1:1 relationship means the daily average of OMI NO2 is pronounced higher (or lower) than its neighbouring days. The following region-specific filters (green lines in Figure S1 ) are supplemented in our analysis: emission is optimized in data assimilation. This is to avoid the difficulty associated with optimizing the spatiotemporal structure in background errors for each category source separately. In our analysis, individual emission sources were estimated using the emission ratio between different categories in the a priori emission inventories. The forecast model is MIROC-Chem (Watanabe et al., 2011) .
The COVID-19 confirmed case data is downloaded at the Chinese Center for Disease Control and Prevention network (http://www.chinacdc.cn/), in which the data is provided by the National Health Commission (NHC) and the World Health Organization(WHO). As shown in Figure S3 , the data from the NHC/WHO is consistent 