Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is classified as a member of the order Nidovirales and part of the Coronaviridae family of viruses (Richman et al., 2016) . Stemming from this family of viruses is coronavirus disease 2019 , first reported to the World Health Organization (WHO) in December 2019 (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen; last access: 5 May 2020) with the first cases occurring in the city of Wuhan in Hubei Province, China. With the spread of the COVID-19, countries throughout the globe have issued lockdowns to combat the spread of the virus. Since the incipient state of the disease, the lockdowns have affected both people and industries during the epidemic and reduced concentrations of pollutants in the atmosphere by a significant amount . Regarding this, it is of great importance to investigate the impact of the COVID-19 outbreak and subsequent lockdown situations on atmospheric constituents to achieve a better understanding of the effects of the pandemic on the atmosphere.
The major criteria pollutants in the atmosphere are sulfur dioxide (SO 2 ), carbon monoxide (CO), nitrogen oxides (NO X ), particulate matter (PM), ozone (O 3 ), and volatile organic compounds (VOCs) (Zhang et al., percent of non-accidental deaths within Chinese cities, and PM 10 concentrations were significantly associated with these mortalities (Chen et al., 2011; Chen et al., 2012) . It is estimated that at least 50,000 people in China die from chronic obstructive pulmonary disease (COPD) resulting from ozone concentrations that exceed safe levels and exposure time . The major ozone precursors responsible for these deaths are VOCs, NO x , and CO (Placet et al., 2000) ; thus, reducing these precursors is critical to reducing tropospheric ozone levels. While PM concentrations originate from direct PM emissions, some PM concentrations originate from emission precursors (e.g., SO 2 , VOCs, NO x , and NH 3 ) through secondary formation within the atmosphere (Hodan et al., 2004) . Considering this, although COVID-19 is responsible for many deaths throughout the world, lockdown situations resulting from this pandemic can indirectly save lives through the decrease of pollutants from the atmosphere.
On January 23, 2020, Wuhan and several other cities within the Hubei Province were placed on lockdown, with Beijing and surrounding cities imposing travel restrictions the following day (WHO Timeline, 2020). On March 3, Japan closed primary and secondary schools, limited the hours of operation of many businesses, and supported stay-at-home work (Rush, 2020) . At the same time, although a comprehensive restriction order was not imposed within Seoul, the government announced a voluntary stay-at-home advisory for the area at the end of February (Kim and Denyer, 2020; Normile, 2020) . The effects of the mandatory shutdown of a large portion of the Chinese industry have provided a unique opportunity to analyze atmospheric changes that coincide with reductions in emissions and pollutants.
The last several decades have witnessed considerable advancements in the development of remote sensing applications in assessing, forecasting, and managing air quality (Mhawish et al., 2018) . More specifically, for diverse research applications such as the moderate resolution imaging spectroradiometer (MODIS) satellite program (Masuoka et al., 1998) . For instance, remote sensing data enable the identification of NO x -sensitive or NO x -saturated regimes (Choi, 2013) and constrain NO X emissions by NO 2 column remote sensing observations (Martin et al., 2003) . Remote sensing tools provide the capability to measure emissions and pollution where in situ measurement systems are not readily available (Benaissa et al., 2019) .
Geostationary remote sensing satellites such as the Advanced Himawari Imager (AHI) have dramatically improved the number of bands, spatial resolutions, and temporal frequencies and thus provide a diverse set of data products from the surface (e.g., sea surface temperature and vegetation) and atmospheric features (e.g., aerosols and stratospheric ozone) in a specific region (Bessho et al., 2016; Kramar et al., 2016; Zhang et al., 2019) . Moreover, TROPOspheric Monitoring Instrument (TROPOMI) has been developed and deployed on the Copernicus Sentinel-5 Precursor satellite in 2017. TROPOMI, with a sunsynchronous orbit, measures global key atmospheric pollutants (Veefkind et al., 2012) at significantly improved spatio-temporal resolutions (Köhler et al., 2018) . The combination of such diverse remote sensing satellites enables comprehensive remote sensing data validation, application, and analyses (de Laat et al., 2019; Lee et al., 2020) .
The findings of significant changes in emissions provide researchers with a unique opportunity to analyze emission and pollutant transport within East Asia. In this study, we have leveraged the capability of satellite remote sensing to investigate the impact of the COVID-19 outbreak and subsequent lockdowns on declining pollutant levels in East Asia. This is the first comprehensive study investigating the impact analyze the impact of drastic changes in anthropogenic emissions and meteorology to the significance of the change of atmospheric constituents due to the lockdown. In addition, we analyze the reasons for the significant increase in constituents despite the decrease in emissions during the lockdown period.
We analyzed pollutant concentrations over East Asia (21° to 46° N and 111° to 146° E), including specific regions and cities such as the Beijing-Tianjin-Hebei (BTH) region, the Wuhan area, the Seoul metropolitan area (SMA), and the Tokyo metropolitan area (TMA). The BTH (36.05° to 42.66° N and 113.45° to 119.83° E), including the municipalities of Beijing and Tianjin and another 11 cities in the Hebei Province is a heavily industrialized area and one of the most polluted regions in China Zhao et al., 2018) . To investigate the effects of the COVID-19 outbreak in metropolitan areas close to China, we selected a 3° × 3° area around the Wuhan city center and a 1° × 1° area around the SMA and the TMA. See the study area in Figure 1 . In this study, we used satellite remote sensing data to analyze four major pollutants and the AOD, measured at 500 nanometers (nm), to investigate the intensity of emission reductions resulting from the lockdowns enforced by officials during the COVID-19 outbreak. We analyzed concentrations of nitrogen dioxide (NO 2 ), formaldehyde (HCHO), sulfur dioxide (SO 2 ), and carbon monoxide (CO) and used NO 2 and SO 2 as proxies for PM 2.5 . We also investigated ozone production efficiency (OPE) and created a distribution map of the formaldehyde to nitrogen dioxide ratio (FNR) over the study area to investigate the chemical sensitivity of ozone production (OP) to its precursors over East Asia, and analyzed the impact of the lockdown on the chemical sensitivity of OP. We acquired the tropospheric column density of NO 2 and formaldehyde, along with total column densities of SO 2 and CO, from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (Sentinel-5P) satellite and gathered 106 swath images of TROPOMI for February 2019 and February 2020 (henceforth referred to as 2019 and 2020). Before August 6, 2019, the spatial resolution of the TROPOMI images was 7 × 3.5 km for NO 2 , formaldehyde and SO 2 , and 7 × 7 km for CO. After this time, the spatial resolution was updated to 5.5 × 3.5 km and 5.5 × 7 km, respectively. To address the difference, all of the images from 2019 and 2020 were resampled at a resolution of 7 × 3.5 km for NO 2 , formaldehyde, and SO 2 and 7 × 7 km for CO. We used all the swath images with a temporal resolution of 101.5 minutes over the study area to create daily images for the two months. In addition, we obtained daily AOD images for 2019 and 2020 from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite, a geostationary satellite over East Asia at longitude 140.7° East. In total, we analyzed 56 daily AOD images with the spatial We downloaded daily meteorological data from the Global Land Data Assimilation System (GLDAS) (https://ldas.gsfc.nasa.gov/gldas; last access: 5 May 2020) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2; last access: 5 May 2020) for 2019 and 2020. We obtained parameters, including air temperature (referred to in this study as "temperature"), specific humidity (referred to as "humidity"), and surface pressure (referred to as "pressure"), with the spatial resolution of 0.25 × 0.25 degrees, from GLDAS and eastward and northward components of wind speed, with the spatial resolution of 0.5 × 0.625 degrees, from MERRA-2. Then we performed comparative analyses of the four pollutants, the AOD, and meteorological parameters for 2019 and 2020 over the study area and matched pixels from satellite images to the GLDAS grid cells nearest to their centroids. Figure S1 shows the flowchart representing the research methodology. 
The AOD is a measure of the extinction of electromagnetic radiation at a specific wavelength owing to the presence of particles such as pollutants, dust, and smoke in the atmospheric column (Chudnovsky et al., 2014) . Since pollutants in the atmosphere absorb and scatter sunlight, reductions in the pollutant levels are expected to reduce AOD. It should be noted that pollutants are not the only factors altering AOD. Other elements (e.g., sea salt, dust) also influence the AOD value. Therefore, these factors can influence the direct relationship between air pollutant concentrations and AOD levels. We used the BSC-DREAM8b model, operated by the Barcelona Supercomputing Center (http://www.bsc.es/ess/bsc-dustdaily-forecast/; last access: 2 August 2020) to remove dusty days from February 2019 and February 2020.
This way, we eliminated the major effects of dust on AOD levels in the study area. Figure 2 shows the distribution of the AOD over the study area in 2019 and 2020. Major decreases in AOD values are observed in regions most impacted by the COVID-19 outbreak. The monthly AOD decreased by nearly 31% and 62% (see Table 2 ) in the BTH and Wuhan, respectively, from 2019 to 2020. Moreover, there were major reductions in the standard deviation of AOD levels within the BTH and especially Wuhan in 2020. The differences between maximum and minimum values of AOD levels within these two regions decreased, resulting in more homogeneous AOD levels within BTH and especially Wuhan. The AOD in the TMA, however, did not change dramatically since this metropolitan area was not under major lockdown conditions in February 2020.
Unlike BTH and Wuhan regions, the SMA experienced an increase in AOD levels. We attribute this increase to the significantly higher AOD levels in upwind regions of the SMA in 2020 than in 2019. High AOD levels in upwind regions of the SMA were likely due to the frequent dust storms in the eastern part of the Gobi Desert, located in regions northwest of the SMA. Although we removed the days with major transport of dust from desert regions, there is usually a consistent transport of dust to the upwind regions J o u r n a l P r e -p r o o f Journal Pre-proof of SMA, making AOD levels of these regions higher than other neighboring areas. Regarding this, the effects of wind transport played an important role in the higher AOD levels of SMA in 2020. Wind patterns in the study area ( Figure S3 ) show that north-westerly winds were the main wind patterns affecting the SMA in 2020. In addition, the area was not as significantly affected by the COVID-19 outbreak and thus had less stringent lockdown situations than BTH and Wuhan. Because of the relatively high frequency of missing values in the daily AOD images, this study did not analyze the relationships between the AOD and meteorological factors. 
NO 2 primarily enters the atmosphere from both the burning of fossil fuels and the photochemical oxidation of nitric oxide emitted from combustion processes, soils, plants, and so on (Choi et al., 2009; Jacobson, 2005) . Sunlight breaks the NO 2 down during the morning, which explains the lower concentrations of NO 2 during the midday or afternoon (Jacobson, 2005; Reed et al., 2016; Trebs et al., 2009 ). The lockdown provided conditions suitable for the atmosphere and sunlight to reduce NO 2 levels in East Asia. While the NO 2 column obtained from satellite images (see Figure 4 ) showed dramatic reductions in NO 2 concentrations over the study area, especially in Wuhan and BTH, where concentrations of NO 2 declined nearly 54% and 83%, respectively, it showed only a moderate reduction of about 33% and 19% in the SMA and the TMA, respectively (see Table 3 for details). The extent of the reductions in NO 2 levels in these four regions is consistent with the severity of the COVID-19 outbreak and lockdown situations within these areas. It is worth noting that South Korea and Japan, like many J o u r n a l P r e -p r o o f Journal Pre-proof other countries, enforced strict surveillance measures at airports, seaports, and border crossings to prevent the spread of COVID-19 into their countries. Management strategies that reduced travel (e.g., restricting air travel and enforcing airport lockdowns) contributed to the fewer infections and fewer lockdowns.
Highly stringent lockdowns represent a significant determinant of major reductions in NO 2 concentrations in BTH and Wuhan. Wuhan was the most impacted region in East Asia, which experienced the most stringent lockdown situations compared to other regions of the study area. In this regard, the most reductions of the NO 2 concentrations happened in this region. Moreover, according to This is consistent with the extent of the COVID-19 outbreak and its resulting stay-at-home strategies in the four regions of the study area. Table 4 shows relatively high correlations between daily NO 2 concentrations and daily temperature, pressure, and humidity in the four regions in 2019, but most of the correlations have decreased in 2020,
indicating that lockdowns were the main factor responsible for the decrease in NO 2 levels. For example, while the daily temperature and daily NO 2 concentrations in Wuhan were strongly correlated in 2019, their correlation was nearly zero in 2020. In 2020, the temperature in Wuhan was much higher than it was in 2019, but it showed no significant changes in other regions (see Table 4 and Figure S4 ). Normally, as temperature increases, concentrations of H 2 O increase, which results in increased OH concentrations. The increased OH partly expedites the formation of nitric acid from NO 2 and OH, which reduces the lifetime of NO 2 . Moreover, an increase in the temperature can promote the upward motion of the air , which leads to more efficient diffusion conditions and decreases NO 2 levels. The slight correlation between daily temperature and daily NO 2 levels in Wuhan indicates that meteorological factors had very little impact on reducing the NO 2 levels of Wuhan in 2020. 
Formaldehyde is released into the atmosphere by both anthropogenic and natural sources. Anthropogenic formaldehyde sources are mostly vehicle exhaust/emissions, different stationary combustion sources, and industrial emissions while natural sources include live and decaying plants, biomass burning, and other organic matters (Luecken et al., 2012; Pan et al., 2015; Shim et al., 2005) . Responses to the COVID-19 outbreak influenced tropospheric concentrations of formaldehyde. As shown in Table 5 , formaldehyde
Journal Pre-proof levels in East Asia decreased by 13% in BTH, 10% in Wuhan, 22% in the SMA, and 8% in the TMA. volcanoes (Hand et al., 2012; Mishra et al., 2016; Speight, 2017; Wei et al., 2014) . Table 6 shows that
Wuhan is the only region (of the four main study regions) that experienced significant reductions (almost 71%) in the column density of SO 2 from the previous year while both the SMA and the TMA showed an increase in SO 2 levels. Moreover, the column density of SO 2 over BTH remained unchanged in 2020. As mentioned before, BTH is one of the most industrialized regions in China. As Figure S5 shows, BTH has 27 coal-fired power plants while Wuhan has only three. Although the COVID-19 outbreak and the following lockdown significantly decreased SO 2 emissions from vehicle exhaust, the high number of coal-fired power plants and industrial sections in BTH could explain why SO 2 levels remained unchanged. It is also worth mentioning that because of the lockdown and stay-at-home advisory, residents remained in their homes, which increased electricity usage during cold days. The increased demand forced power plants to produce more electricity, resulting in higher emissions of SO 2 . Unlike in BTH, in Wuhan, several industrial sections either shut down or worked with limited capacity (Esfandiari and Morris, 2020; Kawakami and Tabeta, 2020) . Since Wuhan is home to only three power plants, industry rather than coal-fired power plants is the main source of SO 2 emissions in Wuhan . Thus, we attribute the significant reductions in SO 2 levels in Wuhan to the shutdown of the industrial sections and the stoppage of full-scale production of many industries (Esfandiari and Morris, 2020; Kawakami and Tabeta, 2020) , the main sources of SO 2 emissions in Wuhan. In addition, fewer vehicle emissions also partially decreased levels of SO 2 in Wuhan. Figure 6 shows the distribution of the total column density of As mentioned before, the total column density of SO 2 in the SMA and the TMA was 38 and 243 percent higher in 2020 than it was in 2019. Figures S6 and S7 show few coal-fired power plants in these areas, indicating other possible reasons for their dramatic increase in SO 2 levels. Figure S3 displays the wind patterns in the study area and upwind regions of the SMA and the TMA. The wind patterns, displayed in Figure 6 , reveal that upwind regions of these areas in 2020 were much more polluted than they were in 2019. Considering the velocity of wind emanating from upwind regions ( Figure S3 ) and the one to three days lifetime of SO 2 in the atmosphere (Lee et al., 2011; Rotstayn and Lohmann, 2002) , along with the close proximity of their upwind regions, we conclude that SO 2 was transported from the polluted upwind regions, resulting in a dramatic increase in SO 2 levels in these two areas. Furthermore, Figure S3 displays only surface wind speeds; wind speeds at higher altitudes, however, are expected to be much higher.
Thus, owing to the relatively high frequency of missing values in the daily measurements of the total column density of SO 2 within the four regions, we did not analyze the daily trends of SO 2 . Table S4 displays the correlation between each pixel showing the changes in SO 2 concentrations from 2019 to 2020 and its corresponding pixels showing changes in the meteorological patterns from 2019 to 2020 over the four regions. It also shows that meteorological factors are not the determining factors of changes in the SO 2 levels in each region. To reach a more valid conclusion, one should investigate the relationships between daily SO 2 levels and daily meteorological factors in a detailed analysis. Moreover, standard deviation of SO 2 concentrations in Wuhan decreased dramatically in 2020, compared to 2019, indicating a more homogeneous levels of SO 2 in all areas within the Wuhan region in 2020. 
As a major source of CO in the atmosphere is incomplete combustion by automobiles, trucks, and airplanes. Other important sources are wood and grass burning (Choi et al., 2010; Jacobson, 2005) .
Similar to the total column density of the three pollutants and the AOD, CO also decreased by about 8%
in BTH, 4% in Wuhan, 6% in the SMA, and 1% in the TMA (see Table 7 ). Figure 7 shows the J o u r n a l P r e -p r o o f distribution of the column density of CO over the study area in February 2019 and February 2020. One explanation for the smaller decrease in CO concentrations is that like HCHO, anthropogenic CO emissions resulting from less human activity declined; however, biogenic isoprene emission increased because of the increases in temperature, particularly in Wuhan. The rise in temperature resulted in the oxidation of more biogenic VOC emissions, which increased concentrations of HCHO and CO. Another is that CO has a moderate lifetime of almost two months in the atmosphere (Choi et al., 2010; Lelieveld et al., 2002; Pouyaei et al., 2020) . Although emissions of CO decreased significantly, it would take about two months for CO concentrations in the atmosphere to begin to decrease. Table S4 J o u r n a l P r e -p r o o f 3.6. Formaldehyde to Nitrogen Dioxide Ratio (FNR) and chemical regimes TROPOMI data showed that the decrease in nitrogen dioxide (NO 2 ) and formaldehyde (HCHO) concentrations during the lockdown period significantly influenced the sensitivity of ozone formation to J o u r n a l P r e -p r o o f ozone precursors, nitrogen oxides (NO x = NO + NO 2 ), and volatile organic compounds (VOCs). Several studies (Duncan et al., 2010; Haagen-Smit, 1952) have found that in the presence of sunlight, NO x, and VOCs are the pollutants most responsible for the formation of high ozone concentrations. The photochemical oxidation of VOCs leads to the production of hydroperoxyl (HO 2 ) and organic peroxy radicals (RO 2 ), which in turn increase the rate of the catalytic cycling of NO to NO 2 and the formation of high concentrations of ozone at ground level. The cycle is terminated by the oxidation of NO 2 to nitric acid (HNO 3 ) and the conversion of RO 2 to peroxides (Witte et al., 2011) . Ozone production efficiency (OPE) can be defined as the total number of ozone molecules produced when a molecule of NO x is oxidized and has a nonlinear relationship with concentrations of its precursors so that ozone concentrations depend on both the absolute and relative concentrations of NO x and VOCs (Duncan and Chameides, 1998; Jeon et al., 2014) .
Several studies (Choi et al. 2012; Duncan et al., 2010; Witte et al., 2011) used the FNR to determine the chemical sensitivity of ozone production (OP) by using models such as the Community Multiscale Air
Quality (CMAQ) model. They found that FNR < 1, an FNR between 1 and 2, and FNR > 2 can refer to VOC-sensitive, mixed, and NO x -sensitive regimes, respectively. The reason for selecting these thresholds is that in VOC-sensitive conditions, an increase (or a decrease) in VOC (NO x ) leads to an increase in ozone concentrations; in NO x -sensitive conditions, however, ozone increases as NO x levels increase.
Moreover, an FNR between 1 and 2 (mixed regime) reflects the transition between regimes, in which both NO x and VOCs can change the OP. In this study, we also used the FNR to investigate the chemical sensitivity of the OP to its precursors even though it was not feasible to use satellite images to determine the specific boundaries of the FNR describing VOC-sensitive, mixed, and NO x -sensitive regimes.
Moreover, when the column density of NO 2 is less than 1 × 10 15 molecules cm -2 , typical of regions remote to anthropogenic sources, calculating the FNR is not feasible (e.g., Choi et al. 2012; Martin et al., 2006; Russell et al., 2010) . As shown in Table S5 and Figure S8 , The FNR significantly increased in the BTH (75%) and Wuhan (398%) regions between 2019 and 2020. Despite increasing dramatically in BTH and Wuhan, the FNR did not do so in either the SMA (16%) or the TMA (20%). East China, especially J o u r n a l P r e -p r o o f Wuhan and BTH, imposed more stringent lockdown situations, reducing NO x and VOC emissions. As described in Sections 3.2 and 3.3., the NO 2 levels decreased by 83% in Wuhan and 54% in BTH in 2020;
formaldehyde levels, however, decreased by only 10% in Wuhan and 13% in BTH. This finding indicates that NO 2 was the main factor responsible for the change in the chemical regimes in the BTH and Wuhan regions. Figure S9 shows that values of the FNR in most parts of East China, especially in Wuhan, changed from below 1 to above 2, indicating that ozone production in most of these regions became less NO x saturated in February 2020. Interestingly, ground measurements showed that despite the reductions in NO 2 and formaldehyde concentrations in 2020, surface ozone increased in most parts of East China (Figure 8 ),
suggesting that the reductions in NO x and formaldehyde in February 2020 were not sufficiently high to decrease ozone concentrations. Studies (Gao et al., 2020; Ma et al., 2020; Zhu et al., 2017) have reported increases in surface ozone concentrations in recent years, underscoring the challenge that China has faced while attempting to reduce surface ozone. J o u r n a l P r e -p r o o f 
During the COVID-19 outbreak, there was a dramatic decrease in human activities, resulting in significant reductions in pollutant levels in the atmosphere. In this study, we used satellite remote sensing J o u r n a l P r e -p r o o f to investigate the impact of the COVID-19 outbreak on atmospheric concentrations of pollutants, including NO 2 , formaldehyde, SO 2 , and CO. In this regard, we compared levels of pollutants and the AOD in February 2019 and February 2020. Of the four pollutant concentrations, NO 2 concentrations decreased the most from 2019 to 2020: 54% in BTH, 83% in Wuhan, 33% in the SMA, and 19% in the TMA. Results showed a delay in NO 2 reductions in the SMA resulting from the delay in the COVID-19
outbreak and subsequent stay-at-home strategies in the SMA compared to the BTH and Wuhan. As expected, compared to the other three regions, Wuhan experienced the greatest reductions in pollutant levels with 83%, 11%, 71%, and 4% decreases in the column density of NO 2 , formaldehyde, SO 2 , and CO, respectively, in February 2020. The AOD also decreased by about 62% in Wuhan. Although SO 2 decreased dramatically in Wuhan, it remained relatively unchanged in BTH, due to the larger number of power plants and industrial sections, some of the main sources of SO 2 that remained open in BTH. All of the pollutants except for SO 2 in the SMA and the TMA decreased in 2020. The reason for the increase in SO 2 levels of 30% in the SMA and 243% in the TMA was the effects of transport from polluted upwind regions, which were much more polluted in 2020 than in 2019. Transport from upwind regions was also responsible for the 20% increase in AOD levels in the SMA Analyses showed that the mean FNR increased dramatically in BTH (75%) and Wuhan (398%) from 2019 to 2020, while the SMA and the TMA did not experience major changes, a moderate 16% increase in the SMA and 20% in the TMA. This increase in the FNR suggests that OP in all four regions, especially in BTH and Wuhan, became more sensitive to NO x in 2020. We also investigated surface ozone concentrations in eastern China. Despite the reductions in ozone precursors, NO x, and formaldehyde, the surface ozone levels in 2020 measured increases of nearly 20% in East China, 17% in BTH, and 50% in Wuhan, suggesting that reductions in NO x and formaldehyde in 2020 were not high enough to decrease ozone concentrations over East Asia. As investigations of meteorological data revealed that meteorological factors were not strongly correlated with the column density of NO 2 and the surface measurements of ozone, they were not the main factors responsible for changes in NO 2 and J o u r n a l P r e -p r o o f surface ozone levels. In addition, we assume that PM levels also decreased from 2019 to 2020 because of reductions in the atmospheric precursors of PM, including SO 2 , VOCs, and NO x .