Volume 48, Issue 10 e2021GL093668

Research Letter

Free Access

Comprehensive Insights Into O₃ Changes During the COVID-19 From O₃ Formation Regime and Atmospheric Oxidation Capacity

Shengqiang Zhu,

Shengqiang Zhu

orcid.org/0000-0003-0593-2360

Department of Environmental Science and Engineering, Fudan University, Shanghai, China

Contribution: Data curation, Formal analysis, Investigation, Software, Visualization, Writing - original draft

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James Poetzscher,

James Poetzscher

orcid.org/0000-0001-6618-0719

Department of Environmental Science and Engineering, Fudan University, Shanghai, China

School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China

Contribution: Data curation, Formal analysis, Investigation, Writing - original draft

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Juanyong Shen,

Juanyong Shen

School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Contribution: Data curation, Investigation, Visualization, Writing - original draft

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Siyu Wang,

Siyu Wang

Department of Environmental Science and Engineering, Fudan University, Shanghai, China

Contribution: Data curation

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Peng Wang,

Corresponding Author

Peng Wang

[email protected]

orcid.org/0000-0002-9686-5337

Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China

Correspondence to:

P. Wang and H. Zhang,

[email protected];

[email protected]

Contribution: Conceptualization, Supervision, Writing - review & editing

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Hongliang Zhang,

Corresponding Author

Hongliang Zhang

[email protected]

orcid.org/0000-0002-1797-2311

Department of Environmental Science and Engineering, Fudan University, Shanghai, China

Institute of Eco-Chongming (IEC), Shanghai, China

Correspondence to:

P. Wang and H. Zhang,

[email protected];

[email protected]

Contribution: Conceptualization, Supervision, Writing - review & editing

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Shengqiang Zhu,

Shengqiang Zhu

orcid.org/0000-0003-0593-2360

Department of Environmental Science and Engineering, Fudan University, Shanghai, China

Contribution: Data curation, Formal analysis, Investigation, Software, Visualization, Writing - original draft

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James Poetzscher,

James Poetzscher

orcid.org/0000-0001-6618-0719

Department of Environmental Science and Engineering, Fudan University, Shanghai, China

School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China

Contribution: Data curation, Formal analysis, Investigation, Writing - original draft

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Juanyong Shen,

Juanyong Shen

School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Contribution: Data curation, Investigation, Visualization, Writing - original draft

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Siyu Wang,

Siyu Wang

Department of Environmental Science and Engineering, Fudan University, Shanghai, China

Contribution: Data curation

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Peng Wang,

Corresponding Author

Peng Wang

[email protected]

orcid.org/0000-0002-9686-5337

Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China

Correspondence to:

P. Wang and H. Zhang,

[email protected];

[email protected]

Contribution: Conceptualization, Supervision, Writing - review & editing

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Hongliang Zhang,

Corresponding Author

Hongliang Zhang

[email protected]

orcid.org/0000-0002-1797-2311

Department of Environmental Science and Engineering, Fudan University, Shanghai, China

Institute of Eco-Chongming (IEC), Shanghai, China

Correspondence to:

P. Wang and H. Zhang,

[email protected];

[email protected]

Contribution: Conceptualization, Supervision, Writing - review & editing

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First published: 04 May 2021

https://doi.org/10.1029/2021GL093668

Citations: 21

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Abstract

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 (O₃) pollution. In this study, we utilized comprehensive satellite, ground-level observations, and source-oriented chemical transport modeling to investigate the O₃ variations during the COVID-19 pandemic in China. Here, we found that the significant elevated O₃ 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, O₃ 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 O₃ control policies should comprehensively consider the effects of AOC on the O₃ elevation and coordinated regulations of the O₃ precursor emissions.

Key Points

O₃ formation regime shift from a VOC-limited to a NO_x-limited regime has been found in most areas of China
Enhanced atmospheric oxidation capacity (AOC) contributed to O₃ elevation during COVID-19 lockdown
O₃ control policies should consider from the comprehensive sights of O₃ formation regime and AOC in the future

Plain Language Summary

Severe ozone (O₃) pollution has been a public concern in China during the last decade. Due to the 2019 novel coronavirus (COVID-19) pandemic, China has implemented the strict restrictions, leading to the significant decline of economic activities and the associated emissions. However, the O₃ levels have been elevated in vast areas of China during the COVID-19 lockdown period. In this study, we applied the comprehensive satellite, ground-level observations, and source-oriented chemical transport modeling to investigate the O₃ elevation during the COVID-19 lockdown periods. Importantly, our results showed that O₃ has increased significantly in North China Plain (40%) and Yangtze River Delta (35%) during lockdown, which attributed to the enhanced atmospheric oxidation capacity (AOC) associated with the meteorology and emission reduction. Besides, we found the O₃ formation regimes shifted from VOC-limited to NO_x-limited due to the lower NO_x during lockdown. In the future, we suggest that O₃ control policies ought to ensure a balance between emitted NO_x and VOCs to maintain stable O₃ formation regimes and AOC levels and thereby control O₃ emissions.

1 Introduction

Beginning in January 2020, a novel coronavirus (COVID-19) began rapidly spreading throughout China, first in Wuhan, and then in other major cities (Hsiang et al., 2020; Tisdell, 2020). In response, to prevent the spread of COVID-19, the first lockdown was implemented on January 23, suspending nonessential traffic in Wuhan, the epicenter of the outbreak. Over the following days, major cities throughout China issued similar travel restrictions and stringent lockdown measures, affecting over half a billion residents. These restrictive measures resulted in substantial reductions in human activities, which consequently induced unprecedented decreases in anthropogenic emissions of air pollutants, especially from the industry and transportation sectors (Dai et al., 2020; Venter et al., 2020). In particular, during the lockdown, NO₂ levels in Eastern China were estimated to have decreased by ∼65% compared to the same period in 2019, mainly due to the reduction of vehicle emissions (Griffith et al., 2020; Huang et al., 2020). Similarly, other pollutants such as SO₂ and CO also decreased. In contrast, during the same period, ozone (O₃) increased significantly with a nationwide increase of 47.3% (Huang et al., 2020; Le et al., 2020; Y. B. Zhao et al., 2020).

Besides the lockdown period, China experienced persistent O₃ pollutions in recent five years, especially in urban areas (Fang et al., 2019; K. Li, Jacob, et al., 2019; X. Lu et al., 2018). O₃ is formed through nonlinear photochemical reactions of nitrogen oxides (NO_x = NO + NO₂) and volatile organic compounds (VOCs; Liu et al., 2012). The O₃ sensitivity regime, determined by the relative abundance of VOCs and NO_x, plays a significant role in O₃ formation (L. Jin et al., 2008; K. D. Lu et al., 2010). Previous studies have reported that the elevated O₃ 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 (NO₃; Mochida et al., 2003). AOC was controlled both by emission and meteorological fields through nonlinear photochemistry (T. Feng et al., 2020). Specifically, NO₂ is defined as the main sink of OH radical, as it reacts with OH to form nitric acid (HNO₃; Chang et al., 2018; Sadanaga et al., 2005). During the lockdown, the drastic decreases in NO₂ levels increased OH concentration, which then reacted with VOCs, facilitating the formation of secondary pollutants (Yang et al., 2020). 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 O₃ levels, O₃ 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 O₃ formation.

In this study, we used ground-level and satellite data to identify changes in O₃ levels and its associated precursors (NO₂ 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 O₃ sensitivity regime and AOC were also discussed to provide an in-depth explanation for the increase in O₃. We found that O₃ 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, O₃ 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 O₃ pollution in China.

2 Material and Methods

2.1 Satellite Data

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 NO₂ 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 NO₂ column number density datasets and 50% for tropospheric HCHO column number density.

2.2 The Emission Reductions Due to the COVID-19 Lockdown

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 (Huang et al., 2020). 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 (Huang et al., 2020).

2.3 Future Emission Scenarios

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.

2.4 The CMAQ Model Configuration

A modified CMAQ model v5.0.2 with an expanded SAPRC-99 photochemical mechanism was applied to simulate the O₃ levels and track the sources of its precursors in China (Wang et al., 2019, 2020; 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.

Specifically, NO₂ and HCHO concentrations ( $urn:x-wiley:00948276:media:grl62377:grl62377-math-0001$ ) from 17 vertical layers (with the highest layer height of ∼10 km) in the CMAQ model were added up to ascertain their tropospheric column concentrations ( $urn:x-wiley:00948276:media:grl62377:grl62377-math-0002$ ; (H. J. Eskes et al., 2020) as showed in Equation 1:

$urn:x-wiley:00948276:media:grl62377:grl62377-math-0003$ (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 O₃ 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).

$urn:x-wiley:00948276:media:grl62377:grl62377-math-0004$ (2)

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 O₃ formation regime indices R2 and R3 for comparison with R1. The detailed information about R2 and R3 could be found in the supporting information.

3 Results and Discussion

3.1 Significant O₃ Variations During the Lockdown

According to surface observation, changes in China's surface maximum daily 8 h (MDA8) O₃ show significant spatial variations from Pre-lockdown to Post-lockdown. During the Lockdown, O₃ 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 (Huang et al., 2020; Le et al., 2020; Y. B. Zhao et al., 2020). The most prominent O₃ increase occurred in the NCP (Figure 1b), with a mean MDA8 O₃ increase of 54% (from 24 to 37 ppb; Figure 1c). In Baoding and Shijiazhuang (major cities in the NCP), O₃ increased by over 100%. Moreover, in the YRD, a noticeable MDA8 O₃ increase of 44% (from 26 to 38 ppb) was observed. During Post-lockdown, observed O₃ concentrations continued to increase in the NCP and YRD, partially due to the rising temperature (Figure S2). O₃ variation is more complex in the PRD, however. In general, O₃ levels decrease from Pre-lockdown to Post-lockdown. But in Guangzhou, the most populated city of PRD, an increase in O₃ was observed. Considering the similar temperature levels between Pre-lockdown and Lockdown over China, these variations are more related to the sudden reductions of O₃ precursors.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

(a) Observed and CMAQ predicted surface MDA8 O₃ in China during Pre-lockdown and Lockdown periods. The dots represent the observed MDA8 O₃ values; SJZ, Shijiazhuang; BD, Baoding; GZ, Guangzhou; (b) Observed and CMAQ predicted MDA8 O₃ growth rate during Pre-lockdown and Lockdown periods (The growth rate was calculated by dividing MDA8 O₃ differences between 2020 and 2019 years by the absolute MDA8 O₃ values in 2019 as shown in Equation 8 in the supporting information); (c) Observed mean MDA8 O₃ in the NCP, YRD, and PRD regions. CMAQ, Community Multiscale Air Quality; MDA, maximum daily.

In addition, the changes of O₃ 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 O₃ pollution was observed in the NCP and YRD during the Lockdown in 2020 (Figure S3b). Although O₃ precursors decreased drastically in these regions, mean MDA8 O₃ levels were 14%–19% higher than in 2019. In contrast, in the PRD, the mean MDA8 O₃ 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. O₃ 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 O₃ elevation (Figure S12). The results showed that both meteorology and emission reduction played important roles in O₃ elevation in NCP. The meteorology contributed more to daytime O₃ elevation and the emission reduction contributed more to the nighttime O₃ elevation in YRD due to the weaker NO-titration effects (Huang et al., 2020). Meanwhile, it is more critical to deeply understand the associations of O₃ formation regime, AOC and O₃ levels with the impacts of meteorology and emission reduction, which provided valuable O₃ control policies.

3.2 Changes of O₃ Precursors and Formation Regimes

Given that the ratio of HCHO to NO₂ determines the O₃ formation regimes, HCHO and NO₂ are considered the most important precursors of O₃ (X M Jin et al., 2017). The satellite column data and CMAQ model have revealed significant reductions of NO₂ throughout much of China, especially in NCP and YRD regions (Figure S4a). According to the satellite data, NO₂ 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 NO₂, 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 (Huang et al., 2020) 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 O₃ 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 O₃ 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 O₃ formation regime and NO₂ and HCHO concentrations in 2019 were not as obvious as in 2020 (Figure S5a); NO₂ 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 O₃ formation regimes as in 2020 (Figure S5b). We have slightly overestimated the NO₂ and HCHO concentrations in keys regions (Figure 2a), which may lead to uncertainties in the determination of the O₃ 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 O₃ formation regime in 2019 are due to the uncertainties from both satellite data and CMAQ model (Figure S5b). Such NO₂ uncertainty originates from satellite errors in slant column retrieval, cloud and aerosol correction algorithm, surface albedo, and priori NO₂ 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 (Cheng et al., 2020; Hu et al., 2017). In general, our model performance was validated against ground-level observations (Tables S3 and S5) and similar O₃ formation regime distributions have been reported in previous studies (X. M. Jin & Holloway, 2015). Also, the spatial ranges of O₃ 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 O₃ and its precursors (Liang et al., 2006). In particular, O₃ 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 H₂O₂ is vulnerable to reaction rate and mechanism uncertainties as shown in Figure S16 (Sillman, 1995). Generally, the conversion trends of the O₃ formation regime shift based on R1, R2, and R3 were consistent in these scenarios (Figure S16), and the results of the O₃ formation regime distributions based on R1, R2, and R3 were relatively reliable compared to the previous studies (Jimenez & Baldasano, 2004).

Consequently, O₃ 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 O₃ levels, which were associated closely with the shift of O₃ formation regime.

3.3 The Dominating Role of the Enhanced AOC in O₃ Formation

Our model simulations demonstrated significantly enhanced AOC in the NCP and YRD regions (Figure 3b and 3d), which is consistent with the variation of O₃ concentrations. HO_x (OH and HO₂) 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 HO₂ radical, respectively due to the relatively low levels of NO₂, 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). HO₂ radicals have increased over 580% (up to 0.92 and 0.95ppt, respectively) in these two cities. The rise in OH and HO₂ radicals could be the leading cause of the O₃ increase during the Lockdown period in the NCP and YRD regions given their strong association with O₃ production (Kentarchos & Roelofs, 2003; Ren et al., 2013; Q. Zhang et al., 2014). The OH radicals oxidize VOCs to produce peroxy radicals such as HO₂, which covert NO to NO₂ without consuming O₃. Then the NO₂ produces O₃ through photolysis reactions, leading the O₃ accumulation as shown in the mechanism scheme (Figure S7; Pollack et al., 2013; Tan et al., 2019).

At the same time, the NO₃ 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, NO₃ 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 NO₂, both of which serve as important sinks for the NO₃ radical (Lucas & Prinn, 2005; Figure S6a). In contrast, in the PRD region, levels of the NO₃ radical declined (up to −0.21 ppt; Figure 3c). Diurnal variations of OH, HO₂, and NO₃ 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 O₃ elevation. The sensitivity experiment results also indicated the important role of AOC associated with emission reductions and meteorology in O₃ elevation in 2020 (Figures S12–S15). Given the enhanced AOC, a significant increase in O₃ was observed in the NCP and YRD regions. In the PRD, however, the constant AOC was responsible for a slight decrease in O₃. Importantly, in the NCP and YRD regions, the increase in O₃ enhanced the AOC due to local photochemistry (Asaf et al., 2009; Geyer et al., 2001), creating a vicious cycle of heightening O₃ levels.

Significantly, our results of future emission scenarios showed the consistency between O₃ formation regime and AOC levels. Specifically, O₃ 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 O₃ elevation in these regions (Figure S17). Our diurnal results also showed the elevation of O₃ and AOC in these two regions, especially in NCP (Figure S18). The further elevation of O₃ 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 O₃ 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 O₃ and AOC both decreased in VOC reduction scenario (Figure S17). O₃ formation regimes were also shifted from NO_x-limited and transition regimes to VOC-limited regimes. Meanwhile, a slight elevation of AOC and O₃ has been found in NCP regions in NO_x and VOC reduction scenario, accompanied by a slight O₃ 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 O₃ precursor emission to maintain steady AOC levels, which in turn control the O₃ 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 O₃. Arbitrary further reduction of NO_x emissions beyond lockdown levels would not control the O₃ levels in NCP and YRD regions due to that there were still many areas belonging to VOC-limited and transition regimes.

4 Conclusion and Implication

Based on this analysis, we have devised a conceptual scheme to demonstrate the roles of the O₃ formation regime shift and enhanced AOC on O₃ level during the COVID-19 Lockdown period in China (Figure 4). O₃ 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 O₃ elevation during Lockdown. Also, the importance of balanced emission control policies has been emphasized for reducing the O₃ 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, SO₂, and VOCs, need to be reconsidered as different regions have different O₃ sensitivity, and current policies might unintentionally enhance AOC in certain regions, thereby heightening ozone levels. With the effects of AOC enhancement accompanied by the O₃ formation regime shift, O₃ might be elevated in turn. In the future, we recommend that O₃ control policies of emission reduction utilize knowledge regarding the associations of O₃ formation regime, AOC variations, and O₃ levels. Specifically, we believe emission control policies ought to ensure a balance between emitted NO_x and VOCs to maintain stable O₃ formation regimes and thereby control O₃ emissions.

Acknowledgments

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

Conflict of Interest

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

Open Research

Data Availability Statement

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).

Supporting Information

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Citing Literature

Volume48, Issue10

28 May 2021

e2021GL093668

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SPECIAL COLLECTION: The COVID-19 Pandemic: Linking Health, Society, and Environment

Comprehensive Insights Into O₃ Changes During the COVID-19 From O₃ Formation Regime and Atmospheric Oxidation Capacity

Abstract

Key Points

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1 Introduction