Increasing but Variable Trend of Surface Ozone in the Yangtze River Delta Region of China

Introduction

Tropospheric ozone (O₃) is a critical secondary air pollutant featured with strong oxidation (Thompson, 1992), which is produced via complicated photochemical reactions with volatile organic compounds (VOCs) and nitrogen oxides (NO_x, x = 1 and 2) acting as its precursors (Seinfeld and Pandis, 2006; Wang et al., 2017). A high concentration of O₃ may degrade air quality and pose health risks to humans, such as respiratory illness and premature birth (Barry et al., 2019; Ekland et al., 2021; Guan et al., 2021). Moreover, a severe O₃ pollution episode leads to premature mortality as well. A rise of 10 μg m⁻³ in O₃ concentration would increase mortality by 2% (Lei et al., 2019). For the ecosystem, O₃ can impair plant production by getting into leaves through stomata (Ainsworth et al., 2012). Agricultural yields are also negatively impacted by O₃ with losses ranging from 6% to 15% for wheat products in China (Feng et al., 2015). At the same time, as an oxidant, the elevated O₃ also promotes the formation of secondary aerosols (Chen et al., 2020b; Feng et al., 2021). For example, the enhanced O₃ and secondary aerosols induced an unexpected haze event in Beijing during the COVID-19 lockdown (Huang et al., 2020; Le et al., 2020).

In 2013, China implemented the Air Pollution Prevention and Control Action Plan (APPCAP), which includes a series of measures for alleviating the current air pollution with particular attention to fine particulate matter (PM_2.5). The PM_2.5 pollution was, thus, effectively controlled with more than 30% of nationwide PM_2.5 reduced by the end of 2018 (Zhai et al., 2019). At the same time, O₃ concentration indicated an inverse trend. The average maximum daily average 8-h O₃ at the 90th percentile (MDA8-90) in 74 major cities increased by 20% from 2013 to 2017 in China. Major causes for the aggravated O₃ are summarized as follows. First, a decrease in NO_x and increase in VOC emissions (Zheng et al., 2018) facilitated the nonlinear production of O₃ in urban areas that belong to VOC-limited (Wang et al., 2017; Li et al., 2019c; Liu and Wang, 2020b). Second, enhanced radiation intensity and increased HO₂ radicals caused by the reduced PM_2.5 also posed a nonnegligible influence on O₃ pollution (Li et al., 2019a). Besides this, the meteorologically driven variability also contributed to the increase of O₃ (Liu and Wang, 2020a; Dang et al., 2021). From 2013 to 2017, humidity and wind speed drove an annual increase of 2.3 ppbv in O₃ over the Yangtze River Delta (YRD) region (Li et al., 2019b).

The rapid increase of O₃ in China since 2013 has been confirmed by many observational and model studies (Shen et al., 2019; Zhai et al., 2019; Chen et al., 2020c; Shen et al., 2020; Liu et al., 2021; Mousavinezhad et al., 2021). However, the upward trend has significant differences in different regions and different periods. Chen et al. (2021) revealed that annual O₃ concentration was in continuous increase across the YRD region during 2014–2019 but rose in volatility in the Pearl River Delta (PRD) region over the same period. In addition, the increment of O₃ in the YRD region is more obvious with a rate of 3.2 ppbv yr⁻¹ from 50.7 to 66.3 ppbv, which is approximately four times that in the PRD region.

In this study, we used multiple air quality observations in conjunction with a regional chemical model, weather research and forecasting coupled with chemistry (WRF-Chem) to analyze the O₃ pollution in the YRD region from 2014 to 2020 with the aim of exploring the interannual and regional variations of O₃ and understanding the chemical principles behind it. Our study differs from previous studies in that 1) we combined surface air quality and meteorology observations, satellite remote sensing observations, numerical model simulation and an indicator-based approach to uncover the increasing but variable pattern of O₃ in the YRD region, and 2) we further explored and compared the chemical mechanisms and driving factors in different regions and at different stages.

Materials and Methods

Observation

We adopted air quality monitoring measurements from 2014 to 2020 over the YRD region (Figure 1). Data was derived from the public website of the Ministry of Ecology and Environment (MEE) of China (http://106.37.208.233:20035/). MEE provides hourly in situ measurements of six criteria air pollutants, including PM_2.5, PM₁₀, NO₂, O₃, SO₂, and CO. A total of 127 sites from 26 cities in the YRD region were utilized for further analyses. Descriptions for the monitoring sites and cities can be found in Supplementary Table S1; Supplementary Figure S1. It is noted that the number of surface observational sites established by MEE continuously increased since 2013. We selected these sites with complete records during 2014–2020 for analyzing. Meanwhile, for measurements of the gaseous pollutants after September 2018, we converted the mass concentration under reference state into that under standard state in advance (https://www.mee.gov.cn/xxgk2018/xxgk/xxgk01/201808/t20180815_629602.html).

FIGURE 1

FIGURE 1. Simulation domains of the WRF-Chem model used in this study (left). Right panel illustrates the YRD region as well as the locations of 26 cities in the YRD region. Names of the 26 cities are also labeled with colorful text in this panel.

Moreover, we also obtained the hourly meteorological observation, including surface temperature, relative humidity, and wind speed from the open-source website (https://q-weather.info/) for assessing the meteorological conditions in the YRD region. Four meteorological stations located at the provincial cities of the YRD region were collected, including Shanghai, Nanjing, Hangzhou, and Hefei. Illustration of the meteorological stations used in this study is given in Supplementary Table S2; Supplementary Figure S1.

The WRF-Chem Model

WRF-Chem (Grell et al., 2005) is a regional air quality model developed by the National Center for Atmospheric Research (NCAR), Pacific Northwest National Laboratory (PNNL), and the National Oceanic and Atmospheric Administration (NOAA) of the United States. We applied WRF-Chem (v3.9.1) to simulate the summertime O₃ in July over the YRD region from 2014 to 2020. Each month-long simulation was spinned up for the last 2 days of the previous month. Spatially, two nested domains are configured (Figure 1). The parent domain covers the eastern China (100 × 100 grid cells) with a horizontal resolution of 18 km. The nested domain, whose horizontal resolution is 6 km, mainly includes the YRD region (145 × 166 grid cells). A total of 28 vertical layers extends from the ground to the height of 50 hPa. The bottom seven layers distribute below 1 km. The initial and boundary conditions of meteorological fields are determined by FNL reanalysis data sets by the National Centers for Environmental Prediction (NCEP) of the United States. Physical parameterization for describing subgrid processes, such as radiation, microphysics, and the surface layer are listed in Supplementary Table S3. For chemical species, the initial and boundary conditions are provided via the global chemical transport model MOZART (Emmons et al., 2010) and CAM-Chem (Emmons et al., 2020). Meanwhile, the Statewide Air Pollution Research Center (SAPRC99) mechanism is utilized for the gas phase reaction scheme in our study (Carter, 2000). For aerosols, we apply Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) as the aerosol parameterization (Zaveri et al., 2008).

Multi-resolution Emission Inventory for China (MEIC) developed by Tsinghua University was adopted as the anthropogenic emission (Li et al., 2017). MEIC summarizes the 0.25° × 0.25° gridded emissions of SO₂, NO_x, CO, CO₂, NH₃, VOCs, EC, OC, PM_2.5, and PM₁₀. All species are categorized into five sectors, i.e., agriculture, industry, power, transportation, and residential activities. Online biogenic emissions were calculated by Model of Emissions of Gases and Aerosols from Nature (MEGAN, v2.0.4), which comprehensively took landcover, meteorology, and atmospheric chemical compositions into consideration (Guenther et al., 2006). Biomass burning emissions were provided by the Fire Inventory from NCAR (FINN). Combined with satellite retrieval constrains, FINN can generate high spatio-temporal resolution products of biomass burning emissions (Wiedinmyer et al., 2011).

O₃ Formation Regime

The ratio of formaldehyde (HCHO) to NO_y was utilized as the indicator (i.e., HCHO/NO_y) for diagnosing the formation regime of O₃ (Hu, 2021). In this indicator, HCHO can track the contribution of VOCs (Cao et al., 2018), which is a transient oxidation product of many VOCs and is positively correlated with peroxyl radicals. NO_y describes the impacts of multiple reactive nitrogenous compounds, including NO, NO₂, HONO, HNO₃, HO₂NO₂, N₂O₅, NO₃, peroxy acetyl nitrate (PAN), and other organic nitrates. Reactive nitrogen species play a central role in determining the levels of O₃ and hydroxyl radicals in the troposphere. In Supplementary Figure S2, NO_y is mainly concentrated in cities along the Yangtze River with 7.2 ppbv on average. Considering that photochemistry is the most active in the presence of abundant radiation, we, thus, investigated the O₃ formation regime from 12:00 to 16:00 (local time, UTC + 8). When HCHO/NO_y is less than 0.3, the O₃ formation regime is considered to be VOC-limited. NO_x controls O₃ production if the ratio is more than 0.72. Otherwise, it is expected to be under a mixed-limited condition, sensitive to changes both in NO_x and VOCs. The threshold values were obtained by counting the distribution of the HCHO/NO_y for different O₃ formation regimes in the YRD, which allow the HCHO/NO_y sensitivity zone to be delineated in a way that is more consistent with the EKMA curve and accurately represents the sensitivity of O₃ to the precursors (Hu, 2021; Li, 2021).

Model Evaluation

Reliable simulation of meteorological parameters and air pollutants could be the crucial premise for analyzing complicated causes of O₃ pollution. Comparisons between observation versus simulation are concluded in Table 1. To statistically quantify the model performance, we applied two following statistical metrics, i.e., index of agreement (IOA, varies from 0 to 1) and mean fractional bias (MFB, varies from −2 to 2). IOA and MFB are calculated based on Eqs 1–2, where s, o, and N represent simulation, observation, and the number of samples, respectively.

$IOA=1-\frac{{\displaystyle \sum _{i=1}^{\mathit{N}}{\left(C_s-C_o\right)}^2}}{{\displaystyle \sum _{i=1}^N{\left(\left|C_s-\overline{C_o}\right|+\left|C_o-\overline{C_o}\right|\right)}^2}}(1)$

$MFB=\frac{1}{N}{\displaystyle \sum _{i=1}^{\mathit{N}}\frac{\left(C_o-C_m\right)}{\left(\frac{C_o+C_m}{2}\right)}}(2)$

TABLE 1

TABLE 1. Model performance in predicting meteorological parameters and air pollutants over the YRD region during the study period.

First, our model is capable of reproducing meteorological conditions, which is the key to simulate horizontal transportation, chemical transformation, and the removal of air pollutants. As shown in Table 1, the mean observed (simulated) temperature, relative humidity, and wind speed is averaged for 301.3 K (300.2 K), 79.1% (79.5%), and 2.9 m/s (3.1 m/s), respectively, in July from 2014 to 2020. Differences in terms of magnitudes between observation and simulation are quite small. All MFBs are less than 10% as well. IOAs for these meteorological parameters vary from 0.64 to 0.85, highlighting that our model has a good agreement with surface observations. The evaluation indicators of each city for each year can be found in the Supplementary Table S4. The city-level IOAs and MFBs varied in 0.6–0.9 and −0.1–0.3 except for the wind speed in 2016, which again confirmed the reasonable model performance.

Moreover, WRF-Chem reasonably reproduced the concentration levels of photochemical products. The mean observed summer O₃ and NO₂ concentrations are 51.8 and 12.6 ppbv averaged for 2014–2020. Simulations for these two species posed an overestimation by 28% and 12%, according to the hourly MFB index. Meanwhile, both hourly IOA values exceed 0.60, indicating that our model reasonably captures the temporal variations of O₃ and NO₂ as well. WRF-Chem also well simulates PM_2.5 with a predicted IOA of 0.59 as well as a MFB of –17%.

Supplementary Figures S3, S4 show scatterplots of simulations versus observations in daily MDA8 O₃ and NO₂ concentrations and their regression parameters in the 26 cities of the YRD region. The simulated O₃ demonstrates various model performance in different cities. The daily IOA of O₃ ranges from 0.55 (Hefei) to 0.83 (Shanghai) and the corresponding daily MFB poses a range from 13% (Shanghai) to 49% (Wuhu). Meanwhile, most of the fitted slopes of O₃ range from 0.9 to 1.3, further confirming that our model is reliable in reproducing concentrations of O₃. In addition, we verified the diurnal patterns of O₃ in all 26 cities of the YRD region as well. As shown in Supplementary Figure S5, both observation and simulation pose the same diurnal patterns with the peak O₃ concentration occurring between 12:00 and 16:00. The correlation coefficients (R) are more than 0.96, implying that diurnal variations of O₃ are successfully captured.

Results and Discussion

Observed O₃ Pollution in the YRD Region From 2014 to 2020

We first analyzed the observed spatio-temporal variations of six air pollutants over the YRD region from 2014 to 2020. As illustrated in Supplementary Figure S6, regional mean O₃ concentration is 107.1 μg m⁻³ in 2020, which increased by 18% compared with the concentration level in 2014. In contrast, concentrations of other five air pollutants sharply decreased over the study period. For example, the annual mean SO₂ shows a dramatic drop by 16.8 μg m⁻³ from 2014 to 2020 with a relative reduction of 68%. At the same time, both PM_2.5 and PM₁₀ concentrations are also reduced by more than 50%. It is precisely because of this decline that the dominant air pollutant in the YRD region has been changing from PM_2.5 to O₃. For NO₂, the variation trend generally showed a decrease with a rate of 0.8 μg m⁻³ yr⁻¹ even though an individual slight increase occurred in 2017. The changes of city-level pollutant concentrations are shown in Supplementary Figure S7. In addition, Figure 2; Supplementary Figures S8–S12 show the frequency distribution of O₃, NO₂, PM_2.5, PM₁₀, CO, and SO₂ daily concentrations, respectively, which again emphasize that the concentration distribution of O₃ is changing from a low to a high value region while other pollutants show an opposite trend.

FIGURE 2

FIGURE 2. Frequency distribution of the MDA8 O₃ concentration in the YRD region during the study period. Red bars represent the values of 2014. Blue bars indicate the values in the other year during 2015–2020. The colored shaded areas show different pollution levels according to the National Ambient Air Quality Standard of China.

Based on the above analysis, we further find that the upward trend of O₃ is significantly different at different stages. In general, the increasing trend of O₃ concentration during 2014–2017 is more obvious in contrast to the years 2018–2020. Annual mean O₃ rapidly increased by 4.9 μg m⁻³ yr⁻¹ (5.5% yr⁻¹) during the period of 2014–2017, while in 2018–2019, the increasing trend slowed down, with the increase rate dropped to 1.1 μg m⁻³ yr⁻¹. Even in 2020, O₃ in many YRD cities has decreased by 0.1–14.3 μg m⁻³ compared with 2019, mainly caused by the reduction of human activity and the more precipitation in 2020, especially in mobile sources and industry (Chen H. et al., 2020; Huang et al., 2021).

In addition, the upward trend of O₃ is also different in different regions of the YRD. As shown in Figure 3, the growth of O₃ in the Anhui Province is faster than that in other YRD regions. Especially during 2014–2017, the O₃ concentration in Anhui increased by a rate of 14.7 μg m⁻³ yr⁻¹—four times that in other provinces (i.e., Shanghai, Jiangsu, and Zhejiang, SJZ). Such trends can also be observed on the city scale (Supplementary Table S5; Supplementary Figure S7). Hefei doubled its O₃ concentration between 2014 and 2017 while, in Shanghai, Nanjing, and Hangzhou, the increase was 9.7%–14.3%. To highlight the distinctive variation of O₃ in Anhui, we further compared the frequency distributions of O₃ in Anhui with that in other regions from 2014 to 2020 (Figure 2). In 2014, the O₃ concentration in Anhui was mostly concentrated around 50 μg m⁻³ while the city-level O₃ concentration in SJZ distributed in a range of 14.8–256.7 μg m⁻³, and the average value was 80% higher than that in Anhui. However, after 2014, the O₃ in Anhui prominently increased with the frequency distribution rapidly moved to the high value scope (central at ∼70 μg m⁻³), and by 2018, it was close to other regions of YRD.

FIGURE 3

FIGURE 3. Monthly variations of SO₂, NO₂, PM_2.5, CO, O₃, and PM₁₀ concentrations over the YRD region in 2014 (filled dots). Also shown are the differences between 2014 and each of 2015–2020 (bars).

Driving Factors of Interannual Changes in O₃ From 2014 to 2020

We analyze the interannual variation of O₃ in the YRD region from 2014 to 2020 through two factors. One is the O₃ formation regime, and the other is the precursor (NO_x and VOCs) concentrations. The dual role of NO_x in O₃ generation results in the variation of O₃ formation regime. NO_x can either promote O₃ production through photochemical reactions with atomic oxygen (O), O₂, or mitigate O₃ formation by removing OH from the oxidation cycle. At the same time, ambient radical budget and weather conditions can also affect the O₃ generation regime (Li et al., 2019b; Liu and Wang, 2020a; Lin et al., 2020; Wang et al., 2020).

We employed HCHO/NO_y as an indicator to diagnose the formation regime of O₃. Figure 4 shows the spatial distributions of the indicator over the YRD region simulated by WRF-Chem. The surrounding areas of the Yangtze River (purple line) are mostly under VOC-limited, mainly due to the abundant NO_x emissions from industrial and shipping activities (An et al., 2021). In contrast, the south regions of YRD demonstrate a universal pattern of NO_x-limited. This could be attributed to the substantial biogenic VOCs emissions from the forest districts in Zhejiang province (Cao et al., 2021). Other YRD regions are mainly controlled by both NO_x and VOCs.

FIGURE 4

FIGURE 4. Spatial distribution of the indicator (HCHO/NO_y) for diagnosing the O₃ formation regime over the YRD region in the summertime. (A) indicates 2014–2017, and (B) indicates 2018–2020. Red, green, and blue shaded areas represent VOC-limited, mix-limited, and NO_x-limited condition, respectively.

To better understand the fate of O₃ formation regime, we primarily explore the emission characteristics of NO_x and VOCs during this period. As shown in Figure 5, the NO_x emission over the YRD region was continuously reduced by a rate of 3.5% yr⁻¹ from 2014 to 2020, which was largely benefited from the APPCAP (http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm). Unprecedented emission reductions due to COVID-19 in 2020 also enhance this trend (Wang G. et al., 2021; Li et al., 2021b; Hu et al., 2021).

FIGURE 5

FIGURE 5. The schematic diagram for illustrating changes of precursor emissions and concentrations, O₃ formation regime and O₃ concentration over the YRD region during the study period.

However, during 2014 and 2017, the VOC emissions in the YRD region were not effectively controlled and even increased by 0.8% yr⁻¹. After 2018, with the implementation of strict VOC control (http://www.gov.cn/zhengce/content/2018-07/03/content_5303158.htm), VOC emissions began to decline, and in 2020, the VOC emissions in the YRD region dropped by 15.4% compared with the highest level in 2017. The decline in aromatics propene/ethene emission from the petrochemical industry as well as gasoline evaporation and vehicular emissions is the main component of the reduction of VOCs emissions in 2020 (Wang M. et al., 2021; Qi et al., 2021). The distribution of emission variation also reveals similar trends with the whole region (Supplementary Figure S13). Emissions of NO_x in 2018–2020 decreased by 17.7%–50.8% compared to 2014–2017 in most regions of the YRD. VOCs emissions for 87.8% of YRD areas are reduced in 2018–2020 relative to 2014–2017.

With the changes in NO_x and VOCs emissions, the O₃ formation regime has also changed in response. In 2014–2017, most cities along the Yangtze River are in VOC-limited areas, and other regions are mainly under mix-limited except for the forested areas in southern Zhejiang. Since 2018, the original VOC-limited region has shifted to mix-limited (36%). It should be noted that there are still 15% areas controlled by VOC-limited over the YRD region.

In addition, the concentrations of NO_x and VOCs in the YRD region show a consistent trend as their emissions. As shown in Figure 5, the NO₂ concentration clearly demonstrates a continuous decreasing trend over the YRD region during 2014–2020. In term of VOCs, we employed the column density of HCHO via satellite retrievals to represent the interannual variations of VOCs (Li R. et al., 2021). The column density of HCHO shows an inverted “U” shape, which increases from 7.2 × 10¹⁵ molec cm² (2014) to 8.0 × 10¹⁵ molec·cm² (2017) and then drops to 7.4 × 10¹⁵ molec cm² (2020).

As a result of the changes of both O₃ formation regime and precursor concentrations, the O₃ concentration in the YRD region shows an increasing but variable trend during 2014–2020. Between 2014–2017, the declined NO₂ and the elevated VOCs, under the VOC-limited and mix-limited O₃ formation regime, led to a rapid increase of O₃. However, when it comes to 2018–2020, the VOCs began to slowly decline, and meanwhile, the formation regime shifted to more mix-limited so that the upward trend of O₃ has obviously slowed down and even appeared as a downward trend in 2020.

Explanation for Distinctive Variation of O₃ in Anhui

The main reason of the rapid growth of O₃ in Anhui, which is different from that in other regions, is the distinctive growth of NO_x during 2014–2017. The NO₂ in Anhui elevated with a rate of 2 μg m⁻³ yr⁻¹ while, in other YRD regions, it posed a remarkable decline of 1.1 μg m⁻³ yr⁻¹. At the same time, the O₃ formation regime in Anhui was mostly controlled by NO_x-limited (24%) or mix-limited conditions (65%). It is also different from other YRD regions where more than 27% areas were controlled by VOC-limited condition. The ambient non-VOC-limited condition is conducive to generation of O₃ when NO₂ increases, via enhancing the “NO_x cycle” (Wang et al., 2017). In addition, the VOCs concentration in Anhui also increased in 2014–2017, which had not yet received much attention over the early stage. It intensified the O₃ production as well. Since 2018, the concentration of O₃ in Anhui (130.6 μg m⁻³) reached the same level as other regions (122.9 μg m⁻³).

Discussion

Though the emission control strategy since 2018 is effective for slowing down the worsening O₃ pollution over the YRD region, there is still a long way to go to see continuous decline in O₃ concentration. Meanwhile, previous studies point out that the O₃ formation regime in other Chinese city clusters, such as the north China plain and PRD, are dominated by VOC-limited and changes toward mix-limited (Jiang et al., 2018; Wang et al., 2019). To address this issue, a more efficient synergistic measure should be taken to control both NO_x and VOCs (Li et al., 2019c; Huang et al., 2020). O₃ concentration can be quickly reduced with a reasonable reduction ratio between these two precursors according to its nonlinear formation regime. In addition, it is also complicated to conduct the reduction of VOCs emissions. A comprehensive source apportionment for detailed VOCs speciation is necessary for planning better control policies (Wu and Xie, 2017).

Meanwhile, the mitigation of O₃ that is focused on other seasons except for summertime is also proved to be meaningful. Previous case studies of air quality, such as research during COVID-19 or summit exhibited that O₃ could increase resulting from the weakened titration of NO_x due to the reduction of its emissions (Le et al., 2020; Li K. et al., 2021; Zhang et al., 2021b). The elevated O₃ further facilitated the production of secondary aerosols and even led to haze events (Chang et al., 2020; Zhang et al., 2021a; Wang N. et al., 2021). Thus, controlling O₃ concentration during other nonwarm seasons also helps to eliminate the negative environmental impacts by O₃ and PM_2.5.

Conclusion

In this study, we employed multiple air quality observations and WRF-Chem simulation to describe the increasing but variable trend of O₃ concentration in the YRD region from 2014 to 2020, and further revealed the main factors that caused such trends. During the period of 2014–2020, the annual average O₃ concentration in the YRD region was 101 μg m⁻³ with an increase from 93.4 μg m⁻³ in 2014 to 107 μg m⁻³ in 2020. The increase rate was 4.9 μg m⁻³ yr⁻¹ in the first 4 years, which was four times higher than that in 2018–2020. It should be noted that the O₃ concentration in Anhui significantly increased by 44.1 μg m⁻³ during 2014–2017, twofold higher than that in the YRD region (13.0 μg m⁻³). Since 2018, both Anhui and other regions in the YRD slowed down their increase in O₃.

We further analyzed the two main factors driving the interannual variation of O₃ in the YRD region, including precursor concentrations and the corresponding O₃ formation regime. With the help of the indicator HCHO/NO_y simulated by WRF-Chem, we concluded that the O₃ production regime along the Yangtze River has shifted from VOC-limited to mix-limited. In 2014–2017, most cities along the Yangtze River were in VOC-limited areas, and other regions were mainly under mix-limited. After 2018, 36% areas that belong to the VOC-limited condition shifted to a mix-limited condition. Therefore, the alleviative NO_x and elevated VOCs under a VOC- and mix-limited O₃ formation regime generally contributed to the cumulative rise in O₃ concentration. However, since 2018, the drop of both precursors under the mix-limited O₃ formation regime alleviated the worsening O₃ pollution. Our work is of importance to understand the current O₃ pollution in a megacity cluster of China and offers an investigative insight for further alleviating O₃ pollution in the future.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author Contributions

Data curation, model simulation, visualization, and writing-original draft preparation, KT and HZ; Supervision, funding acquisition, writing-review and editing, NL, HL, and JH; Data curation, WF. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (2019YFA0606804), the National Natural Science Foundation of China (41975171), the Major Research Plan of the National Social Science Foundation (18ZDA052).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2022.836191/full#supplementary-material

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