key: cord-0794888-dljjbqrh authors: Yuxiang, Zhang; Haixu, Bo; Zhe, Jiang; Yu, Wang; Yunfei, Fu; Bingwei, Cao; Xuewen, Wang; Jiaqi, Chen; Rui, Li title: Untangle contributions of meteorology condition and human mobility to tropospheric NO(2) in Chinese mainland during COVID-19 pandemic in early 2020 date: 2021-04-09 journal: Natl Sci Rev DOI: 10.1093/nsr/nwab061 sha: d9ef7757b58a99b062eb2f2e7f4f263c8bb6b6a1 doc_id: 794888 cord_uid: dljjbqrh In early 2020, unprecedented lockdowns and travel bans were implemented in Chinese mainland to stop the spread of novel coronavirus (COVID-19), which have led to large reduction of anthropogenic emissions. This provided a unique opportunity to isolate the effects from emission and meteorology on tropospheric nitrogen dioxide (NO(2)). Comparing the atmospheric NO(2) in 2020 to 2017, we found the changes of emission have led to a -49.3 ± 23.5% reduction, which was ∼12% more than satellite observed reduction of -37.8 ± 16.3%. The discrepancy was mainly due to the changes of meteorology which have contributed to an 8.1 ± 14.2% increase of NO(2). We also revealed that the emission induced reduction of NO(2) has significantly negative correlations to human mobility, particularly that inside the city. The intra-city migration index derived from Baidu Location-Based-Service can explain 40.4%±17.7% variance of the emission induced reduction of NO(2) in 29 megacities which each has a population of over 8 million in Chinese mainland. To curb the spread of the COVID-19, Chinese government implemented nation-wide strict control measures from the late January to March 2020 (1, 2) . Lockdowns were executed in cities and provinces, leading to a gradually stopped inter-city and inter-province traffic (1) . Inside cities and villages, strict self-quarantine have also been implemented. All people had to stay at home except for shopping living necessities or seeking medical treatment. Businesses and industries have suspended operation or reduced production largely. The intensive lockdown measures have led to dramatic decrease of human mobility (3, 4) . Nitrogen Dioxide (NO 2 ), as one of the most important air pollutants, is harmful to human respiratory system (5) (6) (7) and plays essential roles in the formation of acid rains, second order aerosols (8) and ozone (9) (10) (11) . The dominant sources of tropospheric NO 2 over east China are anthropogenic combustions in winter, among which the contributions from power generation, industry and transportations are about 19%, 42% and 35%, respectively (12) . The decrease of human mobility due to the lockdown measures is expected to have produced impacts on tropospheric NO 2 via affecting industry and transportation activities (13, 14) . Recent studies have reported satellite observed large drop of column NO 2 density during this period due to the COVID-19 quarantine (1, 13, 15) . Besides anthropogenic emissions, tropospheric NO 2 concentrations are also strongly modulated by changes in meteorological conditions (16) (17) (18) (19) . Changes in wind speed, atmosphere stability (related to temperature and pressure etc.), solar radiation and humidity from day to day can quickly change the atmospheric NO 2 densities (16, 20) . Temperature and humidity are crucial to the photochemical processes related to NO 2 (21) . Higher temperature and higher humidity can reduce the lifetime of NO 2 and accelerate the conversion of NO 2 to secondary nitrate aerosols (17, 19) and thus lead to a negative correlation with atmospheric NO 2 concentration in most places (16, 19) . Solar radiation is the key factor controlling the photodissociation rate of NO 2 (NO 2 NO+O), and can highly affect the lifetime of NO 2 (21, 22) . This is strongly supported by the observation of increased NO 2 concentration during the solar eclipse (22). In general, surface NO 2 concentration is found to decrease with increasing solar radiation (16, 21) . In addition, high wind speed and high planetary boundary layer height (PBLH) both favor the dispersion and dilution of air pollutants in the boundary layer of atmosphere (16, 17) and can reduce NO 2 concentration (21, 23, 24) . Anthropogenic emissions and meteorological conditions can both affect atmospheric NO 2 concentration, but their effects are often tangled. Although the reported literatures demonstrate the important influences of lockdowns on tropospheric NO 2 (13, 25) , the respective contributions from anthropogenic and meteorological processes are not clear. Chemical transport models are powerful to analyze the sources of atmospheric composition changes. However, the modelled results can be affected by potential uncertainties in the emission and chemistry processes. For example, Liu et al. (2018) shows that the modelled surface NO 2 over North China Plain is about 34% lower than surface measurement with GEOS-Chem model while is 26% higher than CMAQ model (26) . Recent studies suggest the possibility to constrain the observation-based anthropogenic and meteorological influences with statistical models to avoid the effects of potential uncertainties in model simulations (27) (28) (29) . During the early stage of COVID-19 pandemic, anthropogenic emissions in China were much lower than before (13, 15) , while the changes of meteorology condition in 2020 were expected to be smaller than changes in emissions. It provides an ideal test-bed to study the separate impacts of emission and meteorological changes on atmospheric NO 2 with statistical models. In this study, we investigated the effects of meteorology conditions and human mobility associated with COVID-19 quarantine on atmospheric NO 2 in China using a statistical model to represent the NO 2 (27) . The human mobility strengths, including migration and intra-city flow were quantified using Baidu Migration data (3, 30) . We focused on the month before (hereafter Month-01) and after (hereafter Month-02) the Chinese Spring Festival in 2017, 2018, 2019 and 2020 to take the holiday effect on human mobility into account. A statistical model of troposphere NO 2 According to the annual Report on the State of the Environment in China from 2015 to 2019 (http://english.mee.gov.cn/Resources/Reports/soe/), the mean NO 2 concentrations of the cities in China were relatively stable from 2017 to 2019. Before that, anthropogenic NOx emissions (normalized in 2010) were reduced by about 21% in 2012-2015 (7%/year) and about 6% in 2015-2017 (3%/year) (12) . The dramatic declines of anthropogenic NOx emissions in 2012-2015 as well as the subsequent slowdown of emission reductions were mainly driven by the installation of selective catalytic reduction (SCR) systems in utilities for coal-fired power plants (12) . Recent studies have revealed that the satellite observed column NO 2 density in China in 2020 Month-02 was much lower than that in 2019 (15) and concluded that this drop was attributed to the COVID-19 related city lockdowns and travel bans (1). However, it must be recognized that the atmospheric NO 2 concentration is also highly affected by meteorology conditions (15) . Assuming the real measurements of Atmospheric NO 2 (in logarithm) can be separated into two parts, NO 2 contributed by emission F (x, t) and by meteorology conditions G (x, t), we have the following function associated with geolocation (x) and time (t) (27) : The temporal and spatial variations of G (x, t) can be modelled using a simplified linear function of five key meteorology parameters as described in Data and Methods. We found these two assumptions resulted in good agreements between the observed and modelled tropospheric NO 2 , based on self-consistency check (using training data) and independent check (using independent data). Therefore, we have: It should be noted that the modelled emission term F (x) in Eq.2 is only a function of geolocation. In other words, its value keeps constant at given 0.5×0.5 degree box based on statistical regression. The detailed regression procedures and sensitivity tests are described in Data and Methods. The difference between satellite observations and modelling results can be expressed as: where the first term at the right hand of Eq.3 represents the error introduced by ignoring the temporal variations of emission. The second term Δ represents the modelling error of G (x, t). The performance of the model was analyzed using independent (from establishing the model) observations in 2017. The model successfully predicted the monthly mean atmospheric NO 2 in 2017 Month-02 ( Figure 1 ) with negligible bias in most areas in China. Even in the heaviest polluted areas in central and eastern China with NO 2 over 10×10 15 molec/cm 2 , the mean bias is only 4.3% comparing to satellite observations. On the other hand, the spatial correlation coefficients between model prediction and satellite NO 2 are as high as 0.97 (p<0.001). Similar results for 2018 and 2019 can be seen in Figure S4 . The model also works well at predicting daily NO 2 . At the scales of provinces ( Figure S5 ) and cities ( Figure S6 The above results demonstrated that the contribution of emission to the atmospheric NO 2 , in the same month of the adjacent years, can be estimated as a time-independent geolocation-based function. The spatial and temporal variations of meteorology effects can be modelled using the linear function of five selected key parameters. The modelling error is generally less than 5% based on validations in 2017. The underneath reason for the emission situation in 2017 was similar to that in 2018/2019, and the regression model captured the quantitative dependence of atmospheric NO 2 on meteorology conditions. Consequently, if the anthropogenic emissions in 2020 was similar to that in 2018/2019 (i.e., without the effects from COVID-19 quarantine), the model was expected to provide good prediction for tropospheric NO 2 in 2020. It should be noted that changes of column atmospheric NO 2 is not linearly associated with the emission due to the nonlinear effect from atmospheric chemistry. Based on the results from GEOS-Chem chemical transport model study ( Figure S17 ), we found broadly linear response of modelled tropospheric NO 2 columns to changes in anthropogenic NOx and VOCs emissions, e.g., 50% reduction of anthropogenic emissions results in about 45% reduction of tropospheric NO 2 columns. The model simulations suggest that the influence from nonlinear processes is small (about 5%). In 2020, although the NO 2 variations related to meteorology conditions could still be modelled with good accuracy, the emissions of NO 2 were significantly reduced due to the city lockdowns and travel bans. Therefore, the foundation of the statistical model describing the contribution of emission collapsed. The term of F (x, t) − F (x) in Eq.3 in 2020 became much larger than that in 2017. The model overestimated monthly mean NO 2 by 6 -9×10 15 molec/cm 2 in the heavily polluted areas in China ( Figure 1d ). Similar overestimation also could be seen from the time series of daily mean NO 2 in most polluted cities and provinces ( Figure S7 -S10) such as Tianjin, Shanghai, Shandong, Jiangsu, and Beijing. If we compared the satellite observation of atmospheric NO 2 in 2020 to that in 2017, we could decompose the difference into three isolated terms (31) : where the first term represents the emission-induced reductions in 2020; the second represents the meteorology induced variations; and the third represents the modelling error. Using real satellite observations and modelling results in 2020 and 2017, the map of the contributions (unit: %) from emission and meteorology (i.e., the above three terms) to the reduction of NO 2 is shown in Figure 2 . In most China's cities with monthly mean NO 2 over 3×10 15 molec/cm 2 , we found that emission-induced reductions in 2020 ( Figure 2b ) was larger than the satellite observed reduction (Figure 2a) , because the meteorology in 2020 led a net increase of NO 2 comparing to 2017 ( Figure 2c) . Overall, the OMI observed troposphere NO 2 decreased by -37.8+16.3% in 2020 comparing to that in 2017. Using the model mentioned above, we estimated that if the weather conditions in 2020 were the same as in 2017, the NO 2 columns should decrease by -49.3+23.5% because of the reduced anthropogenic emission, which means the emission-induced reduction of NO 2 due to the COVID-19 quarantine was actually higher than OMI actual observations. The meteorological conditions in 2020 did not favor the dilution and ventilation of air pollutants, and thus have led to an increase of NO 2 of 8.1+14.2%. Meanwhile, the modelling error of the above estimation is only -3.32+17.8% which is significantly smaller than the other two terms. The statistic model results are consistent with GEOS-Chem model simulations. As shown in found the reduction of PM2.5 simulated by WRF-CMAQ is smaller than the reduction of precursor emissions, also indicating the unfavorable meteorology (lower PBLH, WS and higher RH) for the dilution of the pollutants (35) . Therefore, the COVID-19 quarantine actually has caused a reduction of NO 2 larger than we saw from the satellite observation (i.e. a direct comparison of 2020 to 2017), but the weather effect has cancelled out some of the emission effect. The modelling error is significantly smaller than each of the two effects, particularly the mean value. This is the first time to isolate contributions of emission and weather to the satellite observed reduction of NO 2 in early 2020. Similar analyses using situations in 2018 and 2019 were given in Supplementary Material (Figure S11-S12). Overall, when comparing 2020 to 2018 and 2019, the emission induced reduction of NO 2 also was significantly larger than the satellite observed reduction due to the meteorology contribution. This confirmed the conclusion derived from the 2020-2017 comparison. Based on Eq.3, the difference between satellite observations and model prediction in 2020, i.e. logrNO (2020) = logNO ( , 2020) − logNO ( , 2020) mainly represents the emission related reduction of NO 2 (hereafter logrNO 2 ), which can be attributed to multiple factors including the prohibition of human mobility implemented by the government, the close of businesses consuming fusel fuel such as restaurants, hotels etc., the reduction of industrial production due to weakened domestic and international trades etc. It is hard to make a thorough survey to measure all of those factors in the current situation when COVID-19 remains a serious threaten to human health. (4). In Month-01shopping and visiting inside cities also increased for preparing the coming Spring Festival, and the intra-city index of C-index (blue curves) also was high before Jan 23 rd 2020. For the same reason, the C-index dropped after the lockdowns. The "Spring Festival Effect" also was shown in 2019 except that the C-index picked up quickly several days after the Spring Festival. Meanwhile, the emission related change of logrNO 2 (vertical bars in Figure 3 ) in 2020 just oscillated around zero in each day before Jan 23 rd . After that, it held positive values and was significantly larger than the standard variations. In the contrast, in 2019, the difference between satellite observation and model prediction of NO 2 , i.e. logrNO 2 , maintained small values during the whole period of Month-01 and Month-02, and the temporal variations were always within the range of standard deviation. The comparison between 2020 and 2019 confirmed that the emission changes in 2020 induced large reduction of NO 2 . To investigate the quantitative relationship between logrNO 2 and the human mobility indices, we presented the scatter plots of daily logrNO 2 to daily indices in 2019 and 2020 in cities each with population of over 8 million in the lower panel in Figure 3 . In 2019, there were almost no statistically significant correlations between logrNO 2 to all types of migration indices because the emission induced variations of NO 2 was very small. While in 2020, daily logrNO 2 in individual city or averaged in all cities both negatively correlated with those migration indices with P<0.001. The intra-city migration index C-index showed the strongest correlation and could explain 22.1% (i.e. the R 2 at individual city level) to 60.8% (the R 2 averaged in all megacities) variance of the logrNO 2 . The immigration index I-index could explain 13.0 to 36.0%; the emigration index E-index could explain 12.3 to 27.0%. A list of the explained variance of logrNO 2 by migration indices (from high to low) of all 29 megacities were given in Table 1 . In some of the cities in Southern China such as Guangzhou and Dongguan, C-index could explain as high as ~70% variance. In Mid and Eastern China, cities like Suzhou, Heze, and Xuzhou also showed explained variance of over 60% by the C-index. This indicates the human mobility inside the city is more important to affect the emission of NO 2 than the population flow toward (I-index) or out (E-index) of the city. Not just in those megacities, negative correlations between logrNO 2 and migration indices were also seen nation widely in the mainland of China in 2020. In Figure 4 , among cities with mean NO 2 over 3×10 15 China has paid great efforts with huge economy losses to stop the spread of COVID-19 in 2020 (1). The unprecedented lockdowns and travel bans have led to large reduction of anthropogenic emission of air pollutions (13) . There are several studies reported the reduction of air pollutants in Early 2020 in China and attributed that to the effect of COVID-19 quarantine without considering the effect of meteorology. In this study, we took the opportunity of this unintentionally conducted circumstance to investigate the isolated effects from emission and meteorology condition on atmospheric NO 2 , and the quantitative relationship between the reductions of NO 2 to human mobility using state-of-the-art satellite remote sensing product and location-service-based big data. We established a statistical model representing the column density of NO 2 as a function of only five key meteorology parameters with the assumption that emission was constant. Comparing to satellite observations in early 2017, the model predicted monthly mean NO 2 was only biased by 4.3% in the heaviest polluted areas in central and eastern China and showed spatial correlation coefficient of 0.97 (P<0.001). By using the statistical model, it was found that the travel bans and lockdowns of China in 2020 have resulted in the decrease of observed NO 2. Meanwhile, the changes of meteorological conditions, such as lower PBLH, lower solar radiation etc., have led to an increase of atmospheric NO 2 ( Figure 5 ). As shown in Figure 5 , comparing to 2017, the anthropogenic emission changes in early 2020 have led to a -49.3+23.5% reduction of atmospheric NO 2 , and meanwhile, the changes of meteorology conditions have led to an 8.1+14.2% increase. Consequently, the net reduction of NO 2 in 2020 comparing to that in 2017 was brought down to -37.8+16.3%. And that the modelling error was -3.3+17.8%. It is the first time to reveal that the COVID-19 quarantine caused reduction of atmospheric NO 2 actually was larger than what we saw from the satellite observations. In addition, the emission induced reduction of NO 2 shows statistically significant correlations to human mobility. Quantitatively, the migration index representing the movement inside the city has the highest explained variance among all indices: it can explain 40.4%+17.7% variance on average in 29 megacities each with population of over 8 million in the mainland of China. This study established a method to untangle the contribution of emission and meteorology condition to the reduction of atmosphere NO 2 in early 2020, and quantitatively assessed the effect of the city lockdowns and travel bans implemented by China's government in response to the COVID-19 on the tropospheric NO 2 reduction. This analysis may shed a light onto the parameterization of NO 2 emission related to human mobility, as well as the understanding of the effect of transportation on atmosphere NO 2 . The indices based on the Baidu Big Data are able to provide the daily information of human activities, and thus, can predict the change of NO 2 . In the future, the data can be used to modify the emission model and make the emission estimation more accurate (37, 38) . In this study, we established a simplified model (27) to describe the dependence of atmospheric NO 2 on emission and meteorology. Comparing to those physical and chemical transfer models, which require large source of computing time (26) and are affected by uncertainties in modelled physical or chemical processes, the approach shown in this work is fast and accurate with relative error less than 5% even in the heaviest polluted area in China, provided the emission does not change significantly. It demonstrates the reliability of statistic approaches to predict tropospheric NO 2 changes. The impacts of COVID-19 controls on tropospheric NO 2 have attracted substantial attentions. However, it remains a challenge to interpret the observed tropospheric NO 2 changes, particularly when considering the large influence of meteorological variabilities. This work shows that meteorological variabilities have resulted in about 20% enhancement of tropospheric NO 2 column over northern China in 2020. It is an important advance to improve our understanding about the responses of tropospheric NO 2 to perturbations in anthropogenic activity changes. We suggest more efforts to develop novel statistic-based approaches as important supplements to the chemical transport models, particularly to understand the sensitivity of NO 2 to various meteorological variables in order to provide more accurate predictions. We admit that the uncertainty in statistic model deserves further studies, in particular its dependence on the background of NO 2 concentration, as well as the sensitivity of NO 2 to each selected meteorology parameters. More meteorology parameters or more complicated functions to describe the dependence of atmospheric NO 2 on meteorology conditions deserve further studies as well. The standard product of tropospheric NO 2 column density retrieved from Ozone Monitoring Instrument (OMI) onboard Aura satellite (OMNO2, Level 2, version 003, available at https://disc.gsfc.nasa.gov/datasets/OMNO2_003/summary) was used in this study. Original orbit data were gridded into 0.25*0.25 degree to collocate with reanalysis data. To exclude the potential impacts from cloud contamination, only samples with cloud fraction <30% and NO 2 column concentration <50×10 15 molec/cm 2 were used in our study. Row anomaly issue was carefully treated by using the official quality flag of OMNO2 (see OMNO2 README file) and was based on the abnormal proportion of negative value in the data (if the ratio of negative value in an x-track was larger than 2%, all the data in the track were not used). The ECMWF atmospheric reanalysis data (ERA5, Single Level and Pressure Level) were used to provide meteorological parameters in this study. Original ERA5 data have a spatial resolution of 0.25*0.25 degree and a temporal resolution of one hour. Three migration indices derived from Baidu location-based services (LBS) were used to quantify the human mobility. They are proxies for the population moving flow in (immigration index, I-index), out of (emigration index, E-index) the city and inside the city (intra-city index, C-index). The data are operated by Baidu, Inc. and are available at https://qianxi.baidu.com/. The information is derived from billions of location requested per day using Baidu Map app with the permission of sharing from users. All of the proxies are not absolute number of travelers but proportional values. A multiple variable linear regression model was developed to quantify tropospheric nitrogen dioxide (NO 2 ) as a function of meteorological factors, which was inspired by the model described by de Foy and Schauer (2015) (27) and Seo J. et. al. (2018) (31) , using combined satellite observations and atmosphere reanalysis data. Based on our statistics on the satellite retrievals (refer to Figure S17 ) and the studies (28, 39) , the values of atmospheric column NO 2 are log-normally distributed. Therefore, we used log(NO 2 ) to do the multiple regression analysis so that we can scale to a normal distribution with zero mean and unit standard deviation. Contributions from emission source are assumed unchanging with time in this model. The tropospheric NO 2 is considered as a linear function of five key meteorological factors (25) , planetary boundary layer height (PBLH), solar radiation (SR), surface temperature (T), relative humidity (RH) and wind speed (WS). To ensure all variables having similar order of magnitude, a logarithm transformation is conducted on NO 2 column density, PBLH and SR. As a result, the regression model can be expressed as follows. 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