key: cord-1027220-9by6losd
authors: Zhu, Shengqiang; Poetzscher, James; Shen, Juanyong; Wang, Siyu; Wang, Peng; Zhang, Hongliang
title: Comprehensive Insights Into O(3) Changes During the COVID‐19 From O(3) Formation Regime and Atmospheric Oxidation Capacity
date: 2021-05-18
journal: Geophys Res Lett
DOI: 10.1029/2021gl093668
sha: 9b145de170cfaf14d0f7af6b81bd5675947baf13
doc_id: 1027220
cord_uid: 9by6losd

Economic activities and the associated emissions have significantly declined during the 2019 novel coronavirus (COVID‐19) pandemic, which has created a natural experiment to assess the impact of the emitted precursor control policy on ozone (O(3)) pollution. In this study, we utilized comprehensive satellite, ground‐level observations, and source‐oriented chemical transport modeling to investigate the O(3) variations during the COVID‐19 pandemic in China. Here, we found that the significant elevated O(3) in the North China Plain (40%) and Yangtze River Delta (35%) were mainly attributed to the enhanced atmospheric oxidation capacity (AOC) in these regions, associated with the meteorology and emission reduction during lockdown. Besides, O(3) formation regimes shifted from VOC‐limited regimes to NO(x)‐limited and transition regimes with the decline of NO(x) during lockdown. We suggest that future O(3) control policies should comprehensively consider the effects of AOC on the O(3) elevation and coordinated regulations of the O(3) precursor emissions.

such as SO 2 and CO also decreased. In contrast, during the same period, ozone (O 3 ) increased significantly with a nationwide increase of 47.3% Le et al., 2020; Y. B. Zhao et al., 2020) .

Besides the lockdown period, China experienced persistent O 3 pollutions in recent five years, especially in urban areas (Fang et al., 2019; K. Li, Jacob, et al., 2019; X. Lu et al., 2018) . O 3 is formed through nonlinear photochemical reactions of nitrogen oxides (NO x = NO + NO 2 ) and volatile organic compounds (VOCs; Liu et al., 2012) . The O 3 sensitivity regime, determined by the relative abundance of VOCs and NO x , plays a significant role in O 3 formation (L. Jin et al., 2008; K. D. Lu et al., 2010) . Previous studies have reported that the elevated O 3 during lockdowns was mainly attributed to the enhanced atmospheric oxidation capacity (AOC; Goldstein & Galbally, 2007; Le et al., 2020) , reflected by the levels of major oxidants such as hydroxyl radical (OH) and nitrate radical (NO 3 ; Mochida et al., 2003) . AOC was controlled both by emission and meteorological fields through nonlinear photochemistry (T. Feng et al., 2020) . Specifically, NO 2 is defined as the main sink of OH radical, as it reacts with OH to form nitric acid (HNO 3 ; Chang et al., 2018; Sadanaga et al., 2005) . During the lockdown, the drastic decreases in NO 2 levels increased OH concentration, which then reacted with VOCs, facilitating the formation of secondary pollutants . Unfortunately, the current understanding of AOC and secondary pollution is still limited, and the previous research methods are relatively simple, either ground-level observation or modeling methods (Feng et al., 2019; Sheehy et al., 2010; Shiu et al., 2007) . The COVID-19 lockdown provides an important opportunity to study the interaction between O 3 levels, O 3 formation regime and AOC attributed to emission and meteorology. In addition, the combination of ground-level observations, satellite retrievals and modeling methods offers a comprehensive analysis on O 3 formation.

In this study, we used ground-level and satellite data to identify changes in O 3 levels and its associated precursors (NO 2 and HCHO) during the COVID-19 lockdowns in China. The Community Multiscale Air Quality (CMAQ) model was also applied to analyze the characteristics of air quality in the same period. The roles of the O 3 sensitivity regime and AOC were also discussed to provide an in-depth explanation for the increase in O 3 . We found that O 3 elevations during the COVID-19 lockdown period in the NCP and YRD were mainly controlled by enhanced AOC, which was attributed to the reductions of anthropogenic emissions and meteorological variations. In contrast, O 3 decreased slightly in the Pearl River Delta (PRD). The results aim to formulate more effective emission control policies, particularly focused on reducing AOC to battle the persistent O 3 pollution in China.

TROPOMI products are available for free through the Copernicus Open Access Hub (https://scihub.copernicus.eu, last access: September 2020). In this study, we utilized two TROPOMI datasets, tropospheric NO 2 column number density, and tropospheric HCHO column number density, and re-projected them into the same domain as model simulations by using a Lambert projection. As suggested by the data provider, we have filtered the source data to remove pixels with quality assurance (QA) values less than 75% for tropospheric NO 2 column number density datasets and 50% for tropospheric HCHO column number density.

The emission reductions based on the bottom-up inventory model of Multi-resolution Emission Inventory for China (MEIC) have been estimated and validated in previous studies . The MEIC model consists of five emission sectors: industry, power, residential, transportation, and agriculture. The thermal power generation has decreased 8.9% in the January and February of 2020 than in 2019 while China has generated 1.7% more thermal power in January and February 2019 than in 2018. Such a difference in the growth rates between 2019 and 2020 was assumed to the impact of COVID-19 restrictions. The same technique has been implemented in the industrial sector. For the residential sector, the commercial activity level of boilers and stoves has been evaluated in the emission adjustment process. Meanwhile, national traffic volume data including on-road and off-road conditions have been adopted for transportation emission adjustment. The detailed information can be found in Table S1 and the emission reduction data proved to be reliable 

Three future emission scenarios including NO x reduction, VOC reduction, and NO x and VOC reduction scenarios have been designed in this study. In NO x reduction scenario, we further reduced the NO x emissions by 50% from the levels of the Lockdown scenario. In VOC reductions scenario, we further reduced the VOC emissions by 50% from the levels of the Lockdown scenario. And in NO x and VOC reduction scenarios, we further reduced both the NO x and VOC emissions by 50% from the levels of the Lockdown scenario.

A modified CMAQ model v5.0.2 with an expanded SAPRC-99 photochemical mechanism was applied to simulate the O 3 levels and track the sources of its precursors in China Ying & Krishnan, 2010; H. Zhang & Ying, 2011) . The time interval for which the simulation was conducted spanned from January 1 to March 31, comprising the Pre-lockdown (January 6 to 22), Lockdown (January 23 to February 29), and Post-lockdown (March 1 to 31) periods. Simulations for the same period in 2019, a control period during which there were no emission reductions due to the COVID-19 induced lockdowns, were also conducted. The model domain included China and its surrounding countries ( Figure S1 ), with a horizontal resolution of 36 × 36 km (127 × 197 grids). The vertical extent was ∼20 km from the surface and divided into 18 sigma layers with the first layer height at a height of ∼35 m from the surface. The detailed model validation can be found in the supporting information. (1)

Here, H i was each layer height acquired by meteorological simulation and a was the conversion factor of CMAQ modeling concentration and column concentration. In our study, the O 3 formation regimes were categorized into VOC-limited, NO x -limited and transition regimes based on the formaldehyde nitrogen concentrations ratio (R1) as shown in Equation 2 (X. M. Jin & Holloway, 2015; Tang et al., 2012) .

Here, we set a R1 < 1.0 as a VOC-limited regime, a R1 > 2.0 as a NO x -limited regime and a R1 between 1.0 and 2.0 as a transition regime (Duncan et al., 2010; Witte et al., 2011) . Also, we adopted two other O 3 formation regime indices R2 and R3 for comparison with R1. The detailed information about R2 and R3 could be found in the supporting information.

According to surface observation, changes in China's surface maximum daily 8 h (MDA8) O 3 show significant spatial variations from Pre-lockdown to Post-lockdown. During the Lockdown, O 3 levels increase in large areas throughout northern and central China compared to the Pre-lockdown, while they decreased in South China (Figure 1a) , consistent with previous studies Le et al., 2020; Y. B. Zhao et al., 2020) . The most prominent O 3 increase occurred in the NCP (Figure 1b) , with a mean MDA8 O 3 increase of 54% (from 24 to 37 ppb; Figure 1c ). In Baoding and Shijiazhuang (major cities in the NCP), O 3 increased by over 100%. Moreover, in the YRD, a noticeable MDA8 O 3 increase of 44% (from 26 to 38 ppb) was observed. During Post-lockdown, observed O 3 concentrations continued to increase in the NCP and YRD, partially due to the rising temperature ( Figure S2 ). O 3 variation is more complex in the PRD, however. In general, O 3 levels decrease from Pre-lockdown to Post-lockdown. But in Guangzhou, the most populated city of PRD, an increase in O 3 was observed. Considering the similar temperature levels between

Pre-lockdown and Lockdown over China, these variations are more related to the sudden reductions of O 3 precursors.

In addition, the changes of O 3 levels between the same periods of Pre-lockdown and Lockdown in 2019 were not as obvious as in 2020 ( Figure S3a ). Compared to the same period of Lockdown in 2019, heightened O 3 pollution was observed in the NCP and YRD during the Lockdown in 2020 ( Figure S3b ). Although O 3 precursors decreased drastically in these regions, mean MDA8 O 3 levels were 14%-19% higher than in 2019. In contrast, in the PRD, the mean MDA8 O 3 during the Lockdown of 2019 is close to (or even slightly higher) that in 2020, and the opposite of the trend observed in the other regions. O 3 levels were controlled by AOC, which was mainly attributed to emission and meteorological fields (T. Feng et al., 2020; K. H. Zhao et al., 2021) . The detailed meteorological conditions during three periods (Pre-lockdown, Lockdown, and Post-lockdown) in 2020 were presented in Figure S2 and Tables S6a and S6b . Furthermore, the sensitivity experiments were conducted to investigate the impacts of meteorology and emission reduction on O 3 elevation ( Figure S12 ). The results showed that both meteorology and emission reduction played important roles in O 3 elevation in NCP. The meteorology contributed more to daytime O 3 elevation and the emission reduction contributed more to the nighttime O 3 elevation in YRD due to the weaker NO-titration effects . Meanwhile, it is more critical to deeply understand the associations of O 3 formation regime, AOC and O 3 levels with the impacts of meteorology and emission reduction, which provided valuable O 3 control policies.

ZHU ET AL. 

Given that the ratio of HCHO to NO 2 determines the O 3 formation regimes, HCHO and NO 2 are considered the most important precursors of O 3 (X M Jin et al., 2017) . The satellite column data and CMAQ model have revealed significant reductions of NO 2 throughout much of China, especially in NCP and YRD regions ( Figure S4a ). According to the satellite data, NO 2 in the NCP, YRD, and PRD regions declined by 59.61%, 63.28%, and 44.03% during the Lockdown respectively. These reductions are mainly attributed to the significant decline of NO x emissions from industry, power, and transportation sectors illustrated by the source apportionment analysis (Table S7) .

However, no noticeable changes were observed in the HCHO concentration during the Lockdown. The spatial distribution of HCHO, similar to that of NO 2 , exhibits higher levels in southeast China, whereas in western China, due to the low anthropogenic VOCs emissions, the HCHO concentration is relatively low (Bo et al., 2008; M. Li, Zhang, et al., 2019) . The HCHO in the atmosphere is mainly formed through direct emissions from industrial and biogenic sectors and through secondary sources such as the oxidation reaction between VOCs and OH. During the Lockdown, emissions of HCHO and other VOCs declined significantly (−37%) in China (Table S1 ) and therefore might have reduced HCHO levels. However, the enhanced AOC during Lockdown likely promoted the formation of HCHO from secondary sources, offsetting the impact of the decline in HCHO emissions and explaining why HCHO levels remained relatively constant. Also, the background HCHO was relatively constant and contributed most to the total HCHO concentrations as shown in our source apportionment analysis (Table S7 ). The constant background HCHO levels can be mainly explained by the oxidation of methane, which has a long lifetime and relatively stable concentrations (Boeke et al., 2011; X. M. Jin & Holloway, 2015) .

In general, the O 3 sensitivity regimes in China shifted from VOC-limited to NO x -limited and transition categories during the Lockdown, as indicated by both satellite data and model simulations (Figure 2b and Table S8 ). The comparative experiments which used two other O 3 formation regime indices R2 and R3 also show the similar results (Figures S16a and S16b). During the Pre-lockdown period in 2020, the VOC-limited regime dominates in the NCP, YRD, and PRD regions due to the relative abundant NO x emissions from industry and transportation sectors, consistent with the previous studies (Xing et al., 2011) . However, during the Lockdown period, VOC-limited regimes shifted to NO x -limited and transition regimes in these regions. The percentage of NO x -limited regimes in the NCP, YRD, and PRD regions during the Lockdown period increased from 11%, 37%, and 31% to 56%, 65%, and 69%, respectively based on R1. These changes in the O 3 formation regime and NO 2 and HCHO concentrations in 2019 were not as obvious as in 2020 (Figure S5a) ; NO 2 and HCHO concentrations during the periods in 2019 that correspond to the Pre-lockdown and Lockdown periods in 2020 remained relatively constant compared with 2020, explaining the lack of the same remarkable variations in the O 3 formation regimes as in 2020 ( Figure S5b ). We have slightly overestimated the NO 2 and HCHO concentrations in keys regions (Figure 2a ), which may lead to uncertainties in the determination of the O 3 formation regime. In particular, the modeling results show the larger areas of VOC-limited and transition regimes compared to the satellite retrieval data (Figure 2b ). The differences in the simulated and satellite retrieved O 3 formation regime in 2019 are due to the uncertainties from both satellite data and CMAQ model ( Figure S5b ). Such NO 2 uncertainty originates from satellite errors in slant column retrieval, cloud and aerosol correction algorithm, surface albedo, and priori NO 2 profile shape (Dimitropoulou et al., 2020; Ialongo et al., 2020) . Besides, the uncertainty of chemical transport models is mainly due to emission inventories, associated with activity levels, emission source fraction, and emission factors Hu et al., 2017) . In general, our model performance was validated against ground-level observations (Tables S3 and S5 ) and similar O 3 formation regime distributions have been reported in previous studies (X. M. Jin & Holloway, 2015) . Also, the spatial ranges of O 3 formation regimes based on R1, R2, and R3 differ from each other in Figures S16a and S16b, which were mainly due to the uncertainties in the quantitative relationships of O 3 and its precursors (Liang et al., 2006) . In particular, O 3 formation regime indices are subject to many uncertainties, including deposition rates, aerosol interactions and case-to-case variations (Jimenez & Baldasano, 2004) . The uncertainty is relatively higher for indices R3 since H 2 O 2 is vulnerable to reaction rate and mechanism uncertainties as shown in Figure S16 . Generally, the conversion trends of the O 3 formation regime shift based on R1, R2, and R3 were consistent in these scenarios ( Figure S16 ), and the results of the O 3 formation regime distributions based on R1, R2, and R3 were relatively reliable compared to the previous studies (Jimenez & Baldasano, 2004) .

Consequently, O 3 levels increased in the NCP and YRD regions during the Lockdown period in 2020, with the pronounced reductions of NO x (X. M. Jin & Holloway, 2015) . Meanwhile, we were more concerned about the impacts of AOC on O 3 levels, which were associated closely with the shift of O 3 formation regime.

Our model simulations demonstrated significantly enhanced AOC in the NCP and YRD regions (Figure 3b and 3d), which is consistent with the variation of O 3 concentrations. HO x (OH and HO 2 ) radicals, the main daytime oxidant, increased significantly in central and northern China with the highest growth rates of 0.06 and 2.71 ppt for OH and HO 2 radical, respectively due to the relatively low levels of NO 2 , the primary HO x sink (Figures 3a and S6a) . Specifically, in the NCP, YRD, and PRD regions, the average increase in HO x was 0.79, 0.92, and 0.17 ppt, respectively. In both Baoding and Shijiazhuang, OH radicals have increased over 98% during the Lockdown (up to 0.019 and 0.021ppt, respectively). HO 2 radicals have increased over 580% (up to 0.92 and 0.95ppt, respectively) in these two cities. The rise in OH and HO 2 radicals could be the ZHU ET AL.

10.1029/2021GL093668 6 of 11 leading cause of the O 3 increase during the Lockdown period in the NCP and YRD regions given their strong association with O 3 production (Kentarchos & Roelofs, 2003; Ren et al., 2013; Q. Zhang et al., 2014) . The OH radicals oxidize VOCs to produce peroxy radicals such as HO 2 , which covert NO to NO 2 without consuming O 3 . Then the NO 2 produces O 3 through photolysis reactions, leading the O 3 accumulation as shown in the mechanism scheme ( Figure S7 ; Pollack et al., 2013; Tan et al., 2019) .

At the same time, the NO 3 radical, the primary nighttime oxidant, has increased significantly in the NCP and YRD regions during the Lockdown (averaged 0.49 and 0.29 ppt, respectively). In addition, NO 3 radicals have increased by 853% (up to 0.7 ppt) and 612% (up to 0.84 ppt) in Baoding and Shijiazhuang cities respectively during Lockdown. This phenomenon can be explained by the low levels of VOC and NO 2 , both of which serve as important sinks for the NO 3 radical (Lucas & Prinn, 2005 ; Figure S6a ). In contrast, in the PRD region, levels of the NO 3 radical declined (up to −0.21 ppt; Figure 3c ). Diurnal variations of OH, HO 2 , and NO 3 radicals also showed AOC was enhanced obviously in NCP and YRD regions ( Figures S8-S10) . The less enhancement of AOC are found in 2019 compared to 2020 during the same periods (Pre-lockdown and Lockdown), especially in the YRD ( Figure S11 ) and thus leading to fewer O 3 elevation. The sensitivity experiment results also indicated the important role of AOC associated with emission reductions and meteorology in O 3 elevation in 2020 (Figures S12-S15). Given the enhanced AOC, a significant increase in O 3 was observed in the NCP and YRD regions. In the PRD, however, the constant AOC was responsible for a slight decrease in O 3 . Importantly, in the NCP and YRD regions, the increase in O 3 enhanced the AOC due to local photochemistry (Asaf et al., 2009; Geyer et al., 2001) , creating a vicious cycle of heightening O 3 levels.

Significantly, our results of future emission scenarios showed the consistency between O 3 formation regime and AOC levels. Specifically, O 3 formation regimes were further shifted to NO x -limited and transition regimes in NCP and YRD regions in NO x reduction scenario compared to Lockdown ( Figure S16c ). Simultaneously, AOC levels were enhanced obviously in NCP and YRD regions, which caused the further ZHU ET AL.

10.1029/2021GL093668 7 of 11 O 3 elevation in these regions ( Figure S17 ). Our diurnal results also showed the elevation of O 3 and AOC in these two regions, especially in NCP ( Figure S18 ). The further elevation of O 3 and AOC could be explained by that there were still around half of areas categorized as VOC-limited and transition regimes in NCP and YRD regions during Lockdown (Table S8) , even though the O 3 formation regimes in many areas were shifted from VOC-limited to NO x -limited and transition regimes during that period. On the contrary, the levels of O 3 and AOC both decreased in VOC reduction scenario ( Figure S17 ). O 3 formation regimes were also shifted from NO x -limited and transition regimes to VOC-limited regimes. Meanwhile, a slight elevation of AOC and O 3 has been found in NCP regions in NO x and VOC reduction scenario, accompanied by a slight O 3 formation regime shift from VOC-limited regimes to NO x -limited and transition regimes.

In the future, we underscore the importance of a carefully tailed and balanced strategy of O 3 precursor emission to maintain steady AOC levels, which in turn control the O 3 concentrations. Specifically, we ought to adopt the synergistic control of NO x and VOC emissions in central and southern China as shown in Figure S17. Meanwhile, we should still pay more attention to the reduction of VOC emissions in NCP and YRD regions, in order to maintain the relatively low levels of AOC and O 3 . Arbitrary further reduction of NO x emissions beyond lockdown levels would not control the O 3 levels in NCP and YRD regions due to that there were still many areas belonging to VOC-limited and transition regimes.

Based on this analysis, we have devised a conceptual scheme to demonstrate the roles of the O 3 formation regime shift and enhanced AOC on O 3 level during the COVID-19 Lockdown period in China (Figure 4) . O 3 formation regime has shifted from VOC-limited to NO x -limited with the impact of dramatic emission reduction during Lockdown. Besides, AOC was enhanced significantly with the decline of NO x , which contributed to the O 3 elevation during Lockdown. Also, the importance of balanced emission control policies has been emphasized for reducing the O 3 pollution events in China according to the modeling of the future emission scenarios. Previous policies, which have focused on the arbitrary reduction of primary emission of NO x , SO 2 , and VOCs, need to be reconsidered as different regions have different O 3 sensitivity, and current policies might unintentionally enhance AOC in certain regions, thereby heightening ozone levels. With the effects of AOC enhancement accompanied by the O 3 formation regime shift, O 3 might be elevated in turn.

In the future, we recommend that O 3 control policies of emission reduction utilize knowledge regarding the associations of O 3 formation regime, AOC variations, and O 3 levels. Specifically, we believe emission control policies ought to ensure a balance between emitted NO x and VOCs to maintain stable O 3 formation regimes and thereby control O 3 emissions.

ZHU ET AL.

10.1029/2021GL093668 8 of 11 

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Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China

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The authors declare no conflicts of interest relevant to this study.

TROPOMI production data could be found online at (https://scihub.copernicus.eu, last access: January 2021). Ground-level observation data were publicly available at (https://doi.org/10.6084/m9.figshare.13524683.v1, last access: January 2021; https://doi.org/10.6084/m9.figshare.13524686.v1, last access: January 2021). Surface meteorological data could be found from National Climate Data Center (https://www.ncdc.noaa.gov/ data-access, last access: January 2021). The Multi-resolution Emission Inventory for China could be found at (http://www.meicmodel.org, last access: January 2021).

This work was supported by Institute of Eco-Chongming (ECNU-IEC-202001).