Space borne tropospheric nitrogen dioxide (NO 2 ) observations 1 from 2005-2020 over the Yangtze River Delta (YRD), China: 2 variabilities, implications, and drivers and Corresponding Indications at a Suburb Site in From MAX-

19 Nitrogen dioxide (NO 2 ) is mainly affected by local emission and meteorology rather than long- 20 range transport. Accurate acknowledge of its long-term variabilities and drivers are significant for 21 understanding the evolutions of economic and social development, anthropogenic emission, and the 22 effectiveness of pollution control measures on regional scale. In this study, we quantity the long- 23 term variabilities and the underlying drivers of NO 2 from 2005 to 2020 over the Yangtze River Delta 24 (YRD), one of the most densely populated and highly industrialized city clusters in China, using 25 OMI space borne observations and the multiple linear regression (MLR) model. We have compared 26 the space borne tropospheric results to the surface in-situ data, yielding correlation coefficients of 27 0.8 significant increases in anthropogenic NO 2 emission. The decreasing trends in NO 2 VCD trop from 42 2011 to 2020 over the YRD are mainly attributed to the stringent clean air measures which either 43 adjust high energy industrial structure toward low energy industrial structure or directly reduce 44 pollutant emissions from different industrial sectors.

effectiveness of pollution control measures on regional scale. In this study, we quantity the long-23 term variabilities and the underlying drivers of NO2 from 2005 to 2020 over the Yangtze River Delta 24 (YRD), one of the most densely populated and highly industrialized city clusters in China,using 25 OMI space borne observations and the multiple linear regression (MLR) model. We have compared 26 the space borne tropospheric results to the surface in-situ data, yielding correlation coefficients of 27 0.8 to 0.9 over all megacities within the YRD. As a result, the tropospheric NO2 column 28 measurements can be used as representatives of near-surface conditions, and we thus only use 29 ground-level meteorological data for MLR regression. The inter-annual variabilities of tropospheric 30 NO2 vertical column densities (NO2 VCDtrop) from 2005 to 2020 over the YRD can be divided into 31 two stages. The first stage was from 2005 to 2011, which showed overall increasing trends with a 32 wide range of (1.91 ± 1.50) to (6.70 ± 0.10) 10 14 molecules/cm 2 ·yr -1 (p<0.01) over the YRD. The 33 second stage was from 2011 to 2020, which showed over all decreasing trends of (-6.31 ± 0.71) to 34 (-11.01 ± 0.90)10 14 molecules/cm 2 ·yr -1 (p<0.01) over each of the megacities. reasonable accuracy. Typical space borne instruments include the SCIAMACHY, GOME, OMI, and 72 TROPOMI, which have been widely used in scientific investigations of global nitrogen cycle, O3 73 formation regime, and regional pollution & transport, quantification of NO2 emissions from biomass 74 burning regions, megacities, and industrial facilities, and validation of shipborne observations and 75 atmospheric chemical transport models (CTMs) ( and then it divides the resulting tropospheric NO2 SCDs by the tropospheric air mass factor (AMF). 142 The formulation for calculating NO2 VCDtrop is as follow: 143 where AMF is defined as the ratio of the SCD to the VCD (Solomon et al., 1987), 145 = (2) 146 The tropospheric AMF are calculated by NO2 profiles simulated by the Global Modeling 147 Initiative (GMI) chemistry transport model with the horizontal resolution of 1°  1.25° (Rotman et 148 al., 2001). Separation of stratospheric and tropospheric columns is achieved by the local analysis of 149 the stratospheric field over unpolluted areas (Bucsela et al., 2013 Hefei, within the YRD. The population, geolocation, the number of measurement site, and data 165 range of each city are summarized in Table 1. The number of measurement site in each city ranges 166 from 8 to 11, the altitude ranges from 3 to 50 m (above sea level, a.s.l.), and the population ranges 167 from 0.9 to 2.5 million. All ground level NO2 data at each station are measured by active differential 168 absorption ultraviolet (UV) analyzers. We use a data quality control method following previous 169 studies to remove unreliable NO2 data (Lu et  2021a). Specifically, we first convert all hourly measurements into Z scores, we then remove the 171 measurement if its Z score meets one of the following rules: (1) is larger or smaller than the 172 previous value −1 by 9 (| -−1 | > 9); (2) The absolute value of is greater than 4 (| | > 173 4); (3) the ratio of the Z value to the third-order center moving average is greater than 2 ( 2), where i represents the i th hourly measurement data. After removing OUTLIERS with above filter 175 criteria, we finally average NO2 data at all measurement sites in each city to form a city 176 representative NO2 dataset. 177

Meteorological fields 178
We The regression results represent the meteorology induced contributions to the variabilities 210 of NO2 VCDtrop. Since both soil and lighting NOx are meteorology dependent, the effects of soil and 211 lighting NOx on NO2 variability are also attributed to meteorology contribution. The difference 212 ′ between the monthly OMI NO2 VCDtrop anomalies and y calculated as equation (6)  213 represents the portion that cannot be explicitly explained by the meteorological influence.
By subtracting the meteorological influence from the total NO2 amounts, the ′ is referred to 216 as the aggregate contribution of anthropogenic emission. Positive and ′ indicate that 217 meteorology and anthropogenic emission cause NO2 VCDtrop above the reference value (i.e., the 16-218 year mean), respectively. In contrast, negative and ′ indicate that meteorology and 219 anthropogenic emission cause NO2 VCDtrop below the reference value, respectively. 220 Since the meteorological parameters listed in Table 2 differ in units and magnitudes, which 221 could lead to unstable performance of the model. Therefore, we normalized all meteorological 222 parameters via equation (7)  can be divided into two stages (Fig. 2b) We have followed the methodology of (Li et al., 2020)) and used the linear regression model 270 to estimate the inter annual trends of NO2 VCDtrop over the YRD (Table 3) and (-0.92 ± 0.26)10 14 molecules/cm 2 ·yr -1 (p<0.01), respectively. 279

Comparisons with the CNMEC data 327
In order to investigate if satellite column measurements can represent the near surface 328 variabilities, we have compared the OMI NO2 VCDtrop data over the 6 megacities within the YRD 329 with the ground level NO2 data provided by the CNMEC (Figure 4)

Implications and drivers 363
We incorporate the 11 meteorological parameters listed in Table 2 into the MLR model to fit 364 the time series of monthly averaged NO2 VCDtrop from 2005 to 2020 over the 6 megacities within 365 the YRD ( Figure S1). Correlation plots of the MLR regression results and the satellite tropospheric 366 NO2 data are shown in Figure 5. The results show that the MLR model can well reproduce the 367 seasonal variabilities of tropospheric NO2 VCDs over each city with correlation coefficients of 0.85 368 to 0.90. We separate the contributions of meteorology and anthropogenic emission to the NO2 369 variability over the 6 megacities with the methodology described in section 2.3. Figure 6 shows 370 monthly averaged tropospheric NO2 VCDs along with the meteorological-driven contributions and 371 the anthropogenic-driven contributions in each city. Figure 7 is the same as Figure 6, but the 372 statistics are based on annual average. 373

Drivers of seasonal cycles of NO2 VCDtrop 374
As shown in Figure 6 for  As shown in Figure S2,  February and some regional travel restrictions occasionally occurred in other seasons across China 416 due to COVID-19 disease. In the comparison, we removed all NO2 measurements in 2020 to 417 eliminate the influence of COVID-19. The monthly averaged NO2 VCDtrop from 2005 to 2019 along  418 with the meteorological contributions and the anthropogenic contributions in each city are shown in 419 Figure S4. Figure S5 and Figure S6 are the same as Figure 2 and Figure 3, respectively, but for 2011 420 to 2019. We obtained the same conclusion as that from Figure 6, indicating the drivers of seasonal 421 cycles of NO2 VCDtrop deduced above are consistent over years. these anomalies in meteorological contributions are highly correlated with temperature anomalies 437 ( Figure S7). As shown in Figure S8 and S9, the temperature in all cities is lower than the reference primary sector includes agriculture, forestry, animal husbandry, and fishery; The secondary industry 451 includes mining, manufacturing, power, heat, gas and water production and supply, and construction; 452 The tertiary industry, namely the service industry, refers to all industries excluded the primary 453 industry and the secondary industry. The secondary industry is more related to energy and fuel 454 consumptions, and it thus dominates the anthropogenic NO2 emission. Figure S10 shows the time 455 series of GDP over the YRD from 2005 to 2020 and Figure S11 is the same as Figure S10 but for 456 year-to-year increment, i.e., the increase in GDP at a given year relative to its previous year.  People's Republic of China, 2007China, , 2018