One Sentence Summary: Chinese COVID-19 policies relate to fuel use.
In December 2019, a respiratory disease, coronavirus disease 2019 , emerged in Wuhan City, Hubei Province, China (1) . COVID-19 has since spread worldwide causing tens of thousands of deaths (2) . To combat the spread of COVID-19, the Chinese government sealed off several cities reporting large numbers of infected people, including Wuhan, starting January 23, 2020; this included halting public transportation and closing local businesses. These prevention efforts quickly expanded nationwide. The policy announcements and restrictions, applied at an unprecedented scale, have implications for the Chinese environment and the economy that we quantitatively evaluate in this paper. In particular, we use satellite nitrogen dioxide (NO2) measurements to monitor changes in fossil fuel usage, related to economic activity, over China following the outbreak of COVID-2019. Nitrogen oxides (NO + NO2 = NOx), emitted during high temperature combustion, are relatively short-lived in the atmosphere (lifetimes of the order of hours near the surface), and therefore remain relatively close to their sources (3) . NO2 tropospheric vertical column density (TVCD) retrieved from backscattered solar radiation, such as from the Ozone Monitoring Instrument (OMI; 4), has been widely used to monitor both long term and short-term changes in fuel consumption (5, 6) . OMI's successor, the Tropospheric Monitoring Instrument (TROPOMI; 7) offers a higher spatial resolution measurement of NO2 TVCD.
We observe substantial reductions of NO2 TVCD after the 2020 Lunar New Year (LNY) on January 25, 2020. Figure 1 shows 20-day averages of OMI NO2 TVCD before, during and after the 2020 LNY (hereafter referred to as the "pre", "peri" and "post" periods). An average reduction of 48% in NO2 TVCD over China is observed from pre to peri periods. Consistency in the trends of retrieved NO2 TVCD is found between OMI and its successor TROPOMI ( Figure   S1 ). A reduction in NO2 TVCD is typically observed during LNY because most Chinese factories shut down for the holiday and the traffic volumes decrease, resulting in a decrease in fuel consumption and thus NOx emissions. OMI NO2 TVCD shows an average pre to peri decline of 26% from data covering the 2015 to 2019 period (Fig. S2 ). Similarly, TROPOMI shows a reduction of 33% in 2019 (Fig. S3 ). This suggests that the observed reduction in 2020 far exceeds the typical holiday-related pre to peri period reduction. Consistent with the 2015-2019 data, the 2020 NO2 TVCD 7-day moving averages show a significant reduction during approximately the two weeks leading up to LNY and reach a minimum around LNY, consistent with the gradual shutdown of factories before the holiday (Fig. 2) . In prior years, a rebound of NO2 TVCD usually begins around 7 days after LNY, marking the end of the holiday season. OMI and TROPOMI (Fig. S4 ) NO2 TVCDs show similar temporal patterns prior to 2020 with a clear reduction before LNY and an increase shortly thereafter. However, while the 2020 data show similar initial declines in the week leading up to LNY, we do not observe the typical uptick in NO2 TVCDs starting the week after the LNY as in previous years (Fig. 2) . OMI (and TROPOMI) NO2 TVCDs show a longer period of low values near the minimum. Note that the 2020 data are generally lower than previous years, probably reflecting in part the effects of China's clean air policies that require installation of denitrification devices for all coal-fired power plants and cement plants (8) .
To rule out the possibility that the large NO2 TVCD decreases observed in 2020 may be driven by changes in the meteorological conditions affecting local NOx chemistry and NOx transport, we use Goddard Earth Observing System version 5 Chemistry-Climate Model (GEOS-CCM; 9) simulations with constant emissions. We find the simulated effects of meteorology on NO2 TVCD small as compared with the prolonged NO2 reduction we observe from the pre to peri period (Fig. S5 ). The simulation with constant emissions shows many areas with increases from the pre to peri periods (Fig. S6 ). This suggests that in many areas the actual decrease in NOx emissions may be larger than what is inferred from the observed NO2 TVCDs. (Fig. S8 ). This suggests that measures to reduce COVID-19 spread affected power generation as well as industrial production including steel, iron, and oil. Direct NO2 reductions from transportation are indicated by the visually reduced TROPOMI NO2 TVCDs along the China National Highways (Fig. S9 ).
We next explore how COVID-19 policy interventions (most of which happened to coincide with the 2020 LNY) are associated with reductions in NO2 TVCD. First, we consider the announcements the government made to the public (Table S1 ). Once the government publicly reports that a COVID-19 case has been confirmed in a province, the public in that province might choose to reduce their exposure to others (e.g., stay at home, work from home and/or travel less). In that case, we would expect a reduction in NO2 TVCD following the announcement of the first case in each province. This is indeed what we find, after taking previous years' NO2 TVCD and variation across provinces into account (see Eq. S1): following the report of the first case in each province, OMI NO2 TVCD declined by about 27% (coeff = -1.383, p = 0.002, Table 1 Col. 1).
The second policy intervention is more invasive: the government took decisive action to further reduce the spread of the virus by limiting the mobility of citizens and locking down entire provinces; on average, lockdowns occurred 3.7 days after the report of the first case. We would expect that a lockdown would be followed by a reduction in travel as well as business activity, which in turn should lead to reductions of NO2 TVCD. Our model (Eq. S2) shows that OMI NO2 TVCD reduces by 24% following the lockdowns (coeff = -1.134, p < 0.001, Table 1 Col. 2).
Finally, we consider the two policies jointly (Eq. S3). We find that both the announcement of the first case reported as well as the lockdown are associated with a reduction in NO2 TVCDs in each province (Table 1 Col. 3). These results suggest that the effect of the announcement is about as large (16%; coeff = -0.851, p = 0.043) as the effect of the lockdown (15%; coeff = -0.752, p < 0.001). All results are qualitatively similar using TROPOMI (Table S2) . Note. NO2 TVCD is based on OMI. We use a fixed-effects model (Eqs. S1-3) with first case announced and lockdown coded as binary indicator variables. We control for the average 2015-2019 OMI NO2 TVCDs to adjust for seasonal variation and include provinces' fixed-effects to adjust for geographical variation. The "Constant" term is the average province fixed-effect used as a baseline to compare the relative effect of the policy interventions. All standard errors (shown in parentheses) are clustered at the province level. * p < 0.05, ** p < 0.01, *** p < 0.001.
NO2 reductions are closely related to improvements in air quality (10) . Under normal circumstances, many Chinese cities have poor air quality that reduces life quality and expectancy (11) . During the COVID-19 crisis, NO2 pollution was additionally reduced by ~20% for a period of between 30 and 50 days. While temporary, these substantial reductions in air pollution may have positive health impact for lives in otherwise heavily polluted areas (12) . This unusual period offers a rare counterfactual of a potential society which uses substantially less fossil fuels and has lower mobility (13) .
While this research provides an early insight into the NO2 changes in China in early 2020, our findings are not without limitations. Because the relationship between NO2 TVCD and NOx emissions is not strictly linear, the analysis of NO2 TVCD provides a qualitative description of changes in NOx emissions. Accurately quantifying the changes in NOx emissions (14) is beyond the scope of this initial assessment.
Our results suggest that the announcement of the first case was followed by a reduction in NO2 emissions, with a further reduction following the actual lockdown. However, it is important to note that these results do not suggest that the mobility restrictions did not have a critical impact.
Indeed, recently published work suggests that the travel restrictions in China reduced the spread of the disease by up to 80% by mid-February, in particular internationally (15) . In line with our results is the finding that human mobility was reduced early on during the outbreak (16) 20) . For time series analysis, we further compute 7-day running averages to smooth out daily fluctuations in NO2 TVCD due to retrieval noise, including the effects of clouds, and influences of meteorology (wind-driven transport influences NO2 TVCDs).
We select facilities with reported NOx emissions > 5 Gg/yr (21) . 
For the policy evaluation, we make use of the timing of when the Chinese government first publicly reported that a person was infected with COVID-19, which occurred on several different dates across the country's provinces. The first public announcement of "viral pneumonia of unknown cause" in Wuhan occurred on January 3, 2020. Daily public health statements began on January 11, 2020, which included the new cases, deaths, and recoveries reported separately for each province. Of particular interest for our analysis are the times when the government announced the first case in each province (Table S1 ). We also use the exact timing when the government put restrictive mobility policies in place, in order to reduce the likelihood of transmission. The first such policy was put in place for Wuhan on January 23, 2020, followed by more restrictions for other provinces shortly after (Table S1) .
We conduct a statistical evaluation of the exact timing of the reduction in NO2 TVCDs.
While the 2020 Lunar New Year coincided roughly with the lockdown of most Chinese provinces, the government's policy actions actually took two forms and varied over time. The first policy action was public announcements of new cases in each province, while the second policy action was to restrict movement and order citizens to stay in-doors (which became known as "lockdown"). We explore the timing of these two potential candidates-announcements of new cases and restrictive mobility policies-to identify to what extent they are responsible for NO2 TVCD reductions. We take advantage of the temporal variation of these measures across the country.
To analyze the effects of these policies, we use fixed-effects models that predict tropospheric NO2 TVCD, controlling for previous years' NO2 TVCD as well as fixed effects for each province:
(,* = + (,* + *+,-+ + * + (,* (S1)
where z is the outcome variable (daily NO2 TVCD for the period from 4 weeks before LNY to 8 weeks after LNY), x is an indicator variable on and after the first case is announced on day t in for the TROPOMI data where prior data is only available for 2019), a is the average fixed effect across all provinces and v is the fixed effect of province p (relative to a), and e is an error term that is clustered at the province p.
To estimate the effect of the lockdown policy, we use the following fixed-effects model:
(,* = + (,* + *+,-+ + * + (,*
where y is an indicator variable for the lockdown of the province p starting on day t (which is 1 during the time of the lockdown; otherwise coded as 0), and all other variables are as defined above.
We use a similar fixed-effects model predicting the effect of both policies jointly:
(,* = + (,* + (,* + *+,-+ + * + (,*
where all variables are as previously specified. β, λ and δ are the derived coefficients of the model.
Using the above specified fixed-effect models enables us to estimate the effect of the policy precisely, as we hold constant province-specific variation as well as prior year variation in NO2.
Our primary analysis uses OMI data (Table 1 ) but our results are qualitatively unchanged if we use TROPOMI data (Table S2 ).
Meteorology Figure S6 shows the relative pre ( *+. ) to peri ( *.+, ) period differences of NO2 TVCD in 2020 for OMI ( Fig S6A) and GEOS-CCM simulations with constant emissions ( Fig   S6B) . We calculate the relative difference as ( We use the difference in relative changes ( Fig S6C) to represent the changes in NOx emissions assuming that the relationship between NO2 TVCDs on NOx emissions is linear.
Considering that natural sources of NOx are significantly smaller than anthropogenic sources and do not change rapidly over such short time, the differences are then presumed to be primarily due to changes in anthropogenic NOx emissions. Decreases are widely observed, implying actual decreases in NOx emissions. Note. NO2 TVCD is based on TROPOMI. We use a fixed-effects model (Eqs. S1-3) with first case announced and lockdown coded as binary indicator variables. We control for the previous year's NO2 TVCD to adjust for seasonal variation and include provinces' fixed-effects to adjust for geographical variation. The "Constant" term is the average province fixed-effect used as a baseline to compare the relative effect of the policy interventions. All standard errors (shown in parentheses) are clustered at the province level. * p < 0.05, ** p < 0.01, *** p < 0.001.