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Review

Holiday Effect of Ozone Pollution in China

by
Sijun Chen
1,
Jun Chen
1,
Jiangyong Li
1,2,
Daocheng Gong
1,2,3,* and
Boguang Wang
1,2,3
1
College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
2
Guangdong Provincial Observation and Research Station for Atmospheric Environment and Carbon Neutrality in Nanling Forests, Guangzhou 511443, China
3
Research Center on Low-Carbon Economy for Guangzhou Region, Jinan University, Guangzhou 510632, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(5), 559; https://doi.org/10.3390/atmos16050559
Submission received: 17 March 2025 / Revised: 1 May 2025 / Accepted: 2 May 2025 / Published: 7 May 2025
(This article belongs to the Special Issue Air Pollution in China (3rd Edition))
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Abstract

:
Over recent decades, China has achieved significant progress in reducing haze pollution, yet photochemical pollution, primarily characterized by elevated ozone (O3) levels, has intensified, posing severe ecological and public health risks. The “holiday effect”—periodic changes in human activities during holidays such as weekends and the Spring Festival—provides a critical lens to evaluate anthropogenic influences on O3 pollution, particularly under scenarios of reduced anthropogenic emissions driven by the carbon neutrality pledge. However, a comprehensive summary of this phenomenon remains lacking. This review systematically examines spatial–temporal variations and driving factors, including precursor emissions, atmospheric oxidation capacity (AOC), and meteorological conditions, of the holiday effects across China. Key findings reveal that reduced nitrogen monoxide during holidays weakens its titration effect on O3. Additionally, decreased particulate matter on holidays leads to improved atmospheric visibility and a corresponding enhancement in AOC and biogenic volatile organic compound emissions, all of which boost the photochemical formation capacity of O3. Furthermore, solar radiation and temperature positively correlate with O3 pollution during holidays, whereas rainfall and cloud cover suppress it. This review also provides suggestions for future research regarding mechanistic studies on photochemistry, machine learning for pollution drivers, radical roles in O3 formation, and health impact assessments.

1. Introduction

With rapid economic growth and the steady progress of industrialization and urbanization, air pollution in China has increasingly become a serious issue, particularly in densely populated megacity clusters such as the Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) regions [1,2]. The initiation of the state-initiative “Action Plan for Air Pollution Prevention and Control” (referred to as the National Ten Measures for Air) since 2013 resulted in a rapid reduction in heavy pollution and a significant improvement in air quality, with a general downward trend in the concentrations of key pollutants such as fine particulate matter (PM2.5), nitrogen oxides (NOx), and sulfur dioxide (SO2) [3,4]. However, photochemical pollution, primarily characterized by elevated ozone (O3) levels, has grown increasingly severe. O3 has emerged as the primary pollutant in many regions of China [5,6,7]. As the third most significant greenhouse gas in terms of radiative forcing, rising O3 concentrations directly impact climate change [8]. Moreover, O3 is highly oxidative and irritating, posing significant risks to human health [9]. Severe O3 pollution has been linked to increased health risks, including cardiovascular and respiratory diseases, hypertension, strokes, and chronic obstructive pulmonary disease [9]. Additionally, O3 can also harm vegetation, affecting plant growth and crop yield, posing a negative impact on ecosystems and food security [10]. Thus, a comprehensive investigation on the formation mechanisms, influencing factors, and trends of O3 pollution can provide a scientific basis for developing more effective pollution control and emission reduction strategies, ultimately protecting the ecological environment and public health [11].
Ground-level O3 formation is primarily driven by photochemical reactions involving NOx and volatile organic compounds (VOCs) [12]. As a secondary pollutant, O3 is generated through complex chemical processes and is influenced by many factors [12]. Studies have revealed that variations in O3 concentrations exhibit a distinct “Holiday Effect” whereby changes in human activity during holidays lead to significant shifts in precursor emissions (i.e., NOx and VOCs) [13,14]. Typically, these precursors tend to decrease during holidays due to reduced industrial activity and anthropogenic emissions. However, O3 concentrations often rise despite the reduction of its precursors. This phenomenon has spurred extensive studies on the weekend effect on O3 pollution [15,16]. Due to China’s traditional culture and diverse holiday customs, there are significant differences in human activities during holidays, which in turn affect traffic loads and industrial emissions. These factors exert a substantial influence on atmospheric chemistry and O3 formation. Investigating the holiday effect is therefore essential for developing more precise and effective control strategies [17].
The holiday effect not only impacts the precursor emissions but also varies across different regions [18]. Even within the same region, its manifestation can differ depending on temporal and meteorological conditions [17]. Two key mechanisms underpin the O3 holiday effect: the NO titration effect and the nonlinear relationship between O3 and its precursors. These mechanisms are pivotal in explaining the observed fluctuations in O3 levels during holidays compared to regular days [17]. Environmental factors such as solar radiation intensity and temperature can affect the rates of atmospheric chemical reactions [19], thus playing a crucial role in the formation and variations of O3 [20,21,22,23]. In conclusion, addressing O3 pollution requires a comprehensive understanding of its formation mechanisms, the influence of holidays, and the interplay of environmental and human factors.
Current studies on the holiday effect of O3 pollution primarily focus on developed urban agglomerations such as the BTH, the YRD, and the PRD, with particular attention given to weekends (WENDs) and the Spring Festival (SF, also known as the Chinese Lunar New Year) [24]. However, there is limited research on other holidays, such as May Day (MD), Chinese National Day (CND), Tomb Sweeping Day (TSD), and Mid-Autumn Festival (MAD). Furthermore, the impact of pandemic control measures on the holiday effect has yet to be fully explored. The holiday effect is a complex and intriguing phenomenon that is closely linked to human work patterns. As demonstrated by the 2023 cultural and tourism data (Table 1) from the Ministry of Culture and Tourism of China (https://www.mct.gov.cn (accessed on 10 April 2025)), holidays comprise a mere 8% of the total annual days, but contributed 32.5% of all tourist trips. This indicates a significant surge in tourism activities, abrupt increases in visits to scenic spots, and heightened traffic pressure during holiday periods. It not only highlights the profound influence of human activities on air quality but also offers valuable insights for improving urban air pollution forecasting, control strategies, and the formulation of targeted emission reduction measures [25].
Herein, we systematically reviewed available studies over the past two decades and summarized the temporal and spatial characteristics as well as the influencing factors of holiday-induced changes in O3 pollution across China. Recommendations and future research directions for mitigating the holiday effect of O3 pollution were also proposed. This review can help develop more effective O3 pollution control strategies in China, particularly under scenarios of reduced anthropogenic emissions driven by the carbon neutrality pledge.

2. Search Strategy

The holiday effect denotes the way people’s travel and vacation activities throughout holidays cause shifts in their modes of production, daily routines, and travel customs, consequently exerting a considerable impact on air quality [17]. In China, short legal holidays mainly include WENDs, New Year’s Day (NYD), TSD, Dragon Boat Festival (DBF), and MAD. Long legal holidays, varying from a minimum of one day up to a maximum of seven days, provide the context to examine the influence of the holiday effect on O3 pollution [17]. Each traditional festival carries distinct cultural significance and entails differing trip behaviors; generally, people opt to gather with family during SF, TSD, and MAD, while they tend to travel during CND and MD.
In this study, peer-reviewed scientific publications were retrieved from the CNKI and Web of Science (WOS). Our search query in the databases was ‘TS = (“Ozone” OR “O3” OR “Air pollution” OR “Volatile organic compounds” OR “VOCs” OR “Atmospheric pollution”) AND TS = (“Holiday effect” OR “Weekend” OR “holiday” OR “Vacation” OR “Leave” OR “Golden Week” OR “National Day” OR “Spring Festival” OR “Chinese New Year” OR “New Year” OR “May Day” OR “Labor Day” OR “International Workers’ Day” OR “New Year’s Day” OR “Tomb Sweeping Day” OR “Dragon Boat Festival” OR “Mid-Autumn Day”)’. “TS” represents the terms that were contained in titles, abstracts, and keywords. The selection of search terms underwent a rigorous iterative process: initial searches were conducted using core terms (“Ozone”, “O3 pollution”, “Air pollution”, “Holiday effect”, “Weekend”, “Spring Festival”), which helped identify relevant research. Subsequent analysis of these publications and their references further uncovered additional key terms. A total of 130 publications published in 2004–2025 were considered for bibliometric analysis in this study. Keyword co-occurrence analysis was conducted using VOSviewer (Figure 1), whereby the size of each keyword node illustrates its frequency of occurrence, and the spatial distance between two nodes quantifies their degree of association. Studies on the weekend effect of O3 are highly prevalent. Burst detection analysis of keywords was conducted using CiteSpace (Figure 2), revealing that recent studies on the holiday effect have shown a significant burst in intensity and high attention towards O3 pollution and the weekend effect.

3. Temporal Variations

3.1. Overall Characteristics

This study analyzed the interannual variations and regional distributions of the literature related to the “holiday effect” on O3 pollution in China over the past two decades (2004–2025), as illustrated in Figure 3. The findings reveal a noteworthy number of studies exploring the holiday effect during WENDs, SF, and CND, whereas investigations into other holidays, such as TSD, DBF, MD, and MAD, are comparatively sparse [26,27]. Moreover, studies have experienced a marked increase post 2013, with particularly prolific years identified as 2020, 2021, 2023, and 2024.
To provide a comprehensive overview of the temporal dynamics of O3 and its key precursors NO2 and CO during key holidays, as well as their comparison with non-holiday periods, publicly available data (https://quotsoft.net/air/ (accessed on 10 April 2025)) of hourly averaged concentrations from national monitoring stations over a decade spanning 2015 to 2024 were analyzed (Figure 4).
Holiday-specific mean concentrations during the past decade are summarized in Table 2, with the data derived from the 24 h average concentrations during the holiday periods. In addition, we also present the Pearson correlation coefficients (r) between NO2, CO, and O3 concentrations in different holidays from 2015 to 2024 (Figure 5). In the relationship between NO2 and O3 (Figure 5a), most holiday periods exhibit a negative correlation, especially in MAD. However, during the SF, a slight positive correlation between NO2 and O3 is observed. Regarding the relationship between CO and O3 (Figure 5b), the correlation during holidays is more complex, with some years exhibiting a positive correlation and others showing a negative correlation. For example, during CND, a positive correlation between CO and O3 is observed. In contrast, the correlation between CO and O3 is weaker on weekdays, while the correlation becomes significantly stronger during holidays. As indicated in Table 2, the concentrations of these pollutants exhibit significant variations across different holidays. For instance, during the SF, the average O3 concentration was 57.23 μg/m3, which is notably higher than that (34.34 μg/m3) in NYD. The NO2 concentrations also showed distinct differences, with SF at 19.68 μg/m3, much lower than the 82.70 μg/m3 observed during MD. Additionally, the mean CO concentration during SF was 0.97 mg/m3, which is higher than the 0.71 mg/m3 during MD. These differences in pollutant levels are likely influenced by reduced traffic and industrial activities during the holidays, as well as the specific behaviors associated with each holiday period. Through correlation analysis of holiday and workday periods, a general negative correlation between NO2, CO, and O3 was observed (Figure 5), with notable holiday effects during the summer–autumn seasons encompassing MD, DBF, MAD, and CND, as well as during the winter season (i.e., SF) (p < 0.05). During winter, NO2 and CO concentrations tend to be comparatively higher (Table 2), while O3 concentrations are relatively lower. Notably, only during MD do both NO2 and O3 concentrations reach elevated levels concurrently (p < 0.05). In contrast, for holidays such as NYD, SF, MAD, and CND, O3 concentrations exhibit inverse trends to those of NO2. When comparing O3 concentrations during WENDs and weekdays (WDAYs) across various seasons, it becomes evident that during the summers and autumns of many years (e.g., 2015, 2016, 2018, 2020, and 2021), O3 concentrations on WENDs were consistently higher than those on WDAYs (p < 0.05). Notably, 2015 and 2021 demonstrated a pronounced weekend effect throughout the year (p < 0.05).
The impact of holidays on O3 pollution varies by season. During the spring and summer holidays, the strong sunlight and higher temperatures, combined with reduced traffic and industrial activity, lead to a decrease in NOx concentrations, which weakens the titration effect of NO on O3, typically increasing O3 levels [14,17]. In contrast, during the autumn holidays, especially the CND, O3 pollution tends to be more severe due to increased human activity, including tourism, which raises the concentrations of NOx and VOCs from traffic and industrial emissions [5]. Winter holidays generally see lighter O3 pollution due to lower temperatures and weaker sunlight, although during the SF, the large amounts of NO2 and PM2.5 produced by fireworks cause a temporary increase in O3 concentrations [22]. The seasonal variations underscore the complex interplay of meteorological conditions, anthropogenic emissions, and temporal factors on O3 concentrations during specific holiday and non-holiday periods, with statistical tests confirming significant seasonal differences (p < 0.05).

3.2. Weekend Effect

The “weekend effect” of O3 pollution, a common phenomenon observed in numerous urban areas globally, is characterized by elevated O3 concentrations but relatively lower precursor concentrations on WENDs. Cleveland et al. (1974) first introduced the concept of the weekend effect [28], with subsequent observations in American cities such as Washington, Los Angeles [29,30], Tucson [31], California [32,33], and some European cities [34,35]. In recent years, with the accelerated urbanization in China, the cyclical fluctuations in air pollutants have also drawn significant attention. Studies across various regions consistently reveal that the concentrations of those pollutants, such as PM2.5, PM10, NO2, SO2, and CO, tend to be higher on WDAYs compared to WENDs, driven by reduced vehicle traffic and human activities on WENDs, while O3 exhibits higher levels on WENDs [36,37], highlighting a unique response pattern to these periodic shifts in urban activity.
To evaluate the presence of the “weekend effect”, the formula U = (CWENDs − CWDAYs)/CWDAYs is widely used [26,38,39], where U represents the percentage of the weekend effect, and CWENDs and CWDAYs denote the average O3 concentration on WENDs and WDAYs, respectively. If U > 0, it indicates higher O3 concentrations on WENDs compared to WDAYs, thereby implying a weekend effect [26,36,37,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68]. For example, the weekend effect of O3 in Liuzhou is characterized by consistently higher concentrations on weekends compared to weekdays throughout the year, with the most pronounced increase observed in autumn (+11.29%, 76.70 vs. 68.92 μg/m3, p < 0.05) and the smallest variation in summer (+0.74%, 56.18 vs. 55.77 μg/m3) [39]. If U < 0, it indicates lower WENDs O3 concentrations, suggesting a reverse weekend effect [27,50,69,70,71,72,73,74,75,76,77,78,79,80,81]. For example, in Tai’an, the weekend effect of O3 exhibits an inverse pattern characterized by significantly lower concentrations on weekends (−75.8%, 14.1 vs. 58.1 μg/m3, p < 0.05) [73]. When U = 0, it indicates that WENDs O3 concentrations are close to those on WDAYs, suggesting that the weekend effect is insignificant [77,82,83,84].
A prominent weekend effect was identified in Suzhou during the summers of 2016–2019 [85]. In this case, higher O3 concentrations were observed during the night and early morning on WENDs compared to WDAYs. In the Lin’an district of Hangzhou [86], a weekend effect was observed in the summer, while a reverse weekend effect occurred in the winter. In Shenzhen [87], a reverse weekend effect was noted during the summer, whereas Guangzhou exhibited a weekend effect during autumn [88]. This variation can be attributed to the control of different precursors in these regions, e.g., Guangzhou operates under a NOx-limited regime during the summer and a VOC-limited regime in the fall. Overall, the weekend effect of O3 pollution exhibits spatial (e.g., urban and suburban) and temporal (e.g., season) variations even within the same region [38,85,88,89,90,91,92].
Numerous studies [36,37,42,44,48,49,51,53,93] have demonstrated that lower anthropogenic emissions on WENDs weaken the titration effect of NO on O3, which leads to higher O3 concentrations. Furthermore, the accumulation scale and formation rate of O3 are often greater on WENDs than on WDAYs [44,51,93,94].

3.3. Spring Festival Effect

During the SF, noticeable alterations occur in various aspects of people’s lives, including production activities, daily routines, and travel patterns. Dai et al. (2021) [95] used machine learning methods to quantify the impact of meteorological factors on air quality data from 31 major cities in China. They found that during the 2020 SF, NO2 concentrations decreased by 14.1% and O3 concentrations increased by 6.6%, indicating that the activities during the SF had a significant effect on air quality. Commonly, this period is characterized by factory closures and employee vacations, leading to a substantial reduction in urban traffic congestion. In contrast, intercity travel experiences a surge at the beginning and end of the festival due to the homecoming and reworking wave [95]. This surge results in elevated traffic loads on highways, while the traffic burden on major urban roads diminishes [27,96,97]. Notably, massive fireworks and firecrackers throughout the SF release significant amounts of pollutants such as NO2, SO2, and PM2.5, thus exacerbating particulate pollution. This causes a decrease in atmospheric visibility, inhibits photochemical reactions, and markedly influences the O3 formation [98,99]. In addition, for VOCs that contribute to O3 formation potential (OFP), olefins are notably prevalent, significantly exceeding the levels of other VOC species, while aromatics and alkanes play a less significant role [100].
Numerous studies have identified a robust negative correlation between O3 and primary pollutants (e.g., SO2, NO, PM10, and PM2.5) during the SF [97,98,101,102,103,104]. Low-level O3 appears to align with the high peak period of SO2, which may be related to the SO2 conversion into secondary sulfate; namely, SO2 can be readily oxidized into sulfate by O3 or atmospheric free radicals [101]. Other studies propose that following the ignition of firecrackers, NO and SO2 are initially emitted into the atmosphere, which subsequently react with O3 to yield NO2 and SO3, respectively, leading to a decrease in O3 levels, while the emitted PM10 and PM2.5 contain reducible substances that further consume O3 in the atmosphere [98,99,105]. For example, during the SF in 2014 in Jinan, a valley city in NCP, the O3 concentration began to rise from midnight as the number of fireworks increased. However, during the peak of firework burning at night, the O3 concentration dropped sharply and returned to normal levels within 8 h [105]. Interestingly, Shen et al. (2016) [74] found that O3 concentrations during the SF are higher than those during non-SF periods, particularly at night. This night-time elevation may be attributed to the radiation generated by the large amount of bright light, with wavelengths below 240 nm, produced during the ignition of fireworks and firecrackers. This radiation decomposes O2 into O, thereby promoting the night-time generation of O3 [74]. In summary, O3 pollution during the SF is influenced by a complex interplay of factors, encompassing traditional customs and other contributing elements. This multifaceted influence underscores the need for comprehensive strategies to mitigate O3 pollution and associated air quality challenges during this period.

3.4. Chinese National Day Effect

Research on the holiday effects of O3 pollution in China has mainly focused on WENDs or the SF, with the important holiday of CND receiving comparatively less attention. CND occurs in autumn, a season that offers favorable meteorological conditions conducive to photochemical reactions. Furthermore, factory shutdowns during this period coincide with a surge in public travel [14], making CND a topic of considerable research significance. Studies by Su et al. (2017) [106] revealed that extensive human activities during holidays can, to some extent, exacerbate urban traffic burdens, thereby influencing urban air quality. During CND, the influx of tourists (e.g., Shantou City received 3.789 million visitors, with over 12,000 vehicles entering Nan’ao Island daily) and the resulting rise in traffic loads contributed to elevated concentrations of O3 precursors, which began increasing from the day before CND and remained persistently high throughout the period [107]. Moreover, the prevalence of clear skies during CND further enhances photochemical reactions. These factors cumulatively result in a notable increase in O3 levels during CND (Figure 4), with peak O3 concentrations closely aligning with intensified human activities [36,107].
Su et al. (2017) [106] highlighted that the variation in O3 during CND is quite complex. In cities within the BTH region, O3, NO2, and other key pollutants exhibit a single-peak pattern, with an initial increase followed by a decrease. However, in other cities, O3 shows an opposite trend to NO2, CO, and other gases, aligning more closely with the characteristics of weekend effects. Xu et al. (2017) [18] reported that the combined influence of intensified human activities, favorable weather conditions, and enhanced traffic emissions during CND makes alkenes the most sensitive precursors for O3 formation. Wang et al. (2022) [14] found that during CND, limiting industrial aromatic hydrocarbon emissions from solvent usage resulted in an O3 decrease of about 20%. Studies by Chen et al. (2023) [108,109] captured a downtrend of O3 levels across CND at a regional background site of PRD. Although anthropogenic contributions showed a holiday effect of “decreasing during CND and rebounding after CND”, biogenic contributions to O3 formation significantly enhanced during CDN. Notably, isoprene and its oxidation product formaldehyde consistently ranked among the top OFP contributors during both polluted and non-polluted periods, underscoring the significant role of biogenic volatile organic compounds (BVOCs) in O3 formation. Wang et al. (2016) [22] further emphasized that the sky conditions and meteorological patterns during CND, including fewer maritime air masses and more continental flows coupled with unfavorable dispersion conditions, collectively contribute to elevated O3 levels during CND. In conclusion, the marked increase in O3 during CND is primarily driven by amplified human activities and altered meteorological conditions, which warrants further in-depth investigations.

3.5. Pandemic Effects

In 2020, the coincidence of the pandemic lockdown with the SF brought about a temporary halt to factories and various industries, significantly reducing traffic loads in many regions (e.g., a 50% reduction in the YRD) [110,111,112]. This led to a significant decrease in NO2 emissions. However, several regions in China (such as the BTH) faced severe O3 pollution during this period [113]. The pandemic control measures inadvertently strengthened the O3 concentration during SF, making it much higher than that on WDAYs. After the COVID-19 lockdown began in 2020, the average NO2 concentration across 31 cities in China decreased by 29.5%, the average O3 concentration increased by 31.2%, and the average PM2.5 concentration decreased by 7.0% [95]. Many studies identified that during the pandemic, the substantial reduction in primary emissions weakened the titration effect of NO, while increased temperatures collectively contributed to a rise in O3 concentration [110,114,115,116,117,118]. This enhancement in O3 levels elevated the atmospheric oxidation capacity (AOC) and amplified photochemical reactions [96]. Wang et al. (2022) [119] further suggest that when O3 reaches a certain concentration, its impact on PM2.5 becomes more pronounced, indicating a synergistic pollution effect between O3 and PM2.5. In summary, the pandemic control measures during the SF in 2020 significantly influenced the traditional “Spring Festival effect” on air quality. Lin et al. (2023) [43] reveal that the COVID-19 lockdown in large cities might amplify the weekend effect of O3 pollution. During the COVID-19 pandemic, industrial and traffic emissions significantly decreased, resulting in a notable decline in NO2 levels and a noticeable rise in O3 concentrations. During the pandemic, the BTH region remained highly sensitive to VOCs and experienced severe smog episodes accompanied by significant O3 pollution [120]. Zhang (2023) [121] reveals that Shenyang city did not exhibit the weekend effect in the past few years, but during the COVID-19 control measures in 2020, the weekend effect was significantly amplified. During the pandemic lockdown, the average concentration of urban NOx was 49% lower than in previous years on WENDs (p < 0.05), and the lockdown also impacted O3 formation, with WENDs O3 concentrations in Wuhan during the lockdown period being 38% higher than on WDAYs [118]. Yu et al. (2022) [122] reported that the O3 concentrations in the PRD generally show higher levels during holidays than on WDAYs, with those on non-pandemic WDAYs being higher than those on pandemic WDAYs, but with those on non-pandemic holidays being lower than those on pandemic holidays. In summary, the COVID-19 lockdown significantly altered the conventional ‘Holiday Effect’ on air quality. Despite substantial emission reductions, decreased industrial activity, temperature variations, and increased particulate matter concentrations collectively drove O3 elevation. These findings underscore the necessity for comprehensive air pollution control strategies during exceptional events like pandemics—strategies that must account for both emission abatement and atmospheric chemistry dynamics.

4. Spatial Variations

Precursor emissions and the influencing factors exhibit significant spatial variability, resulting in distinct O3 characteristics across different cities. Within the same city, O3 pollution control measures may differ based on the time, leading to varying manifestations of the O3 “holiday effect” [89]. Consequently, tailored emission reduction strategies for precursors, based on local conditions, are essential. Inappropriate reduction ratios of precursors could even aggravate local O3 concentrations [123]. In China, studies on the holiday effect have been relatively scattered and primarily concentrated in regions with severe O3 pollution, such as the BTH, the YRD, and the PRD (Figure 3) [124]. In contrast, other regions, including the Chengdu–Chongqing Urban Agglomeration (CCUA), Central Liaoning Urban Agglomeration (CLUA), Shandong Peninsula Urban Agglomeration (SPUA), Wuhan Urban Agglomeration (WUA), Changsha–Zhuzhou–Xiangtan Urban Agglomeration (CZXUA), North Shanxi Urban Agglomeration (NSUA), West Coast Straits Urban Agglomeration (WCSUA), and Guanzhong–Shaanxi Urban Agglomeration (GSUA), have seen relatively limited research on this topic. Figure 6 shows the ten-year (2015–2024) average O3 concentration distribution on NYD, SF, TSD, MD, DBF, MAD, CND, WENDs, and WDAYs in China. The results showed that NYD consistently experienced the lowest O3 concentrations, with no O3 pollution observed. In contrast, the DBF exhibited the most severe O3 pollution, with numerous areas surpassing national standards (Level I) in 2017 and 2018. Significant O3 pollution was prevalent north of the Qinling–Huai River line, primarily concentrated in the BTH region and surrounding urban clusters, with the pollution peaking in 2017 and 2023. Additionally, the MD (2015–2018) and MAD (2021–2022) periods also face elevated O3 concentrations, following an overall east-high, west-low spatial distribution pattern.
Figure 7 illustrates the differential changes in O3 concentrations from 2015 to 2024 by calculating the relative percentage change in concentrations at each monitoring station. The results indicate that, compared to 2015, O3 concentrations increased significantly in several regions in 2024, especially during specific holiday periods. More precisely, during NYD, SF, and TSD, the increase in O3 concentrations was primarily concentrated in regions north of the Yangtze River. In contrast, during the MAD, a noticeable decrease in O3 concentrations was observed along the eastern coastal areas. Notably, during CND, the most significant increase in O3 concentrations occurred in the PRD, reflecting considerable variations in air quality during the holiday period.

4.1. Beijing–Tianjin–Hebei

The BTH region serves as a pivotal political and cultural center in China [125]. This region is densely populated with heavy industrial enterprises that predominantly rely on traditional energy such as coal, making it a major contributor to O3 precursor emissions, including vehicle exhaust, industrial discharges, and coal-fired power plant emissions [89]. In major cities like Beijing, vehicle emissions are the primary sources of NOx and VOCs [123]. Research on the holiday effect in the BTH has predominantly centered on Beijing, a city situated within a VOC-controlled region which consistently exhibits a pronounced holiday effect. Zhou et al. (2014) [126] employed the Community Multiscale Air Quality (CMAQ) model to investigate O3 formation in Tianjin and found that while NOx emissions suppress O3 formation, VOCs facilitate it, which is consistent with the primary mechanisms driving the O3 holiday effect. In regions with severe O3 pollution, the suppression effect of NO on O3 diminishes during holidays, which leads to a higher O3 accumulation efficiency and duration compared to regular workdays, thereby manifesting the holiday effect of O3 pollution [32,36,38,42,44,47,48,51,80,84,90,91,93,105,113,118,119].

4.2. Yangtze River Delta

The YRD region is one of the most economically dynamic and innovative regions in China [127]. The region’s unique geographical features contribute to spatial differences in O3 levels, with coastal cities generally experiencing higher O3 concentrations compared to their inland counterparts [17,21]. The holiday effect in the YRD is more complex compared to that in the BTH, with different holiday effects observed within [37,41,42,70,85,86,89,103] and neighboring this region [40,42,52,76]. Modeling studies by Zhao et al. (2004) [128] showed that meteorological conditions play a crucial role and affect the diversity of the holiday effect of O3 pollution. Fan et al. (2013) [86] further explored the seasonal variability in the O3 holiday effect, finding that it exhibits a weekend effect in summer and a reverse weekend effect in winter. These variations are influenced by changes in meteorological conditions and precursor emissions throughout the year. Moreover, the holiday effect in the same region can differ across years. For example, in Suzhou, He et al. (2023) [89] observed a weekend effect in 2017 and 2019, but a reverse weekend effect in 2016 and 2018. This temporal variability is closely linked to solar radiation levels: in 2017 and 2019, solar radiation on WENDs was significantly higher than on WDAYs, while in 2016 and 2018, WENDs solar radiation was substantially lower than that on WDAYs [89]. These findings suggest a positive correlation between higher solar radiation and increased O3 concentrations. Overall, the holiday effect in the YRD is multifaceted and highly variable; further studies are needed to elucidate the underlying mechanisms driving these effects and develop targeted strategies.

4.3. Pearl River Delta

The PRD is one of China’s three most developed urban agglomerations and serves as another critical economic hub, which is renowned for its high-tech industrial sectors (e.g., chemical and electronics manufacturing industries) [129]. The average O3 concentration in this region exhibits a fluctuating yet overall upward trend, which is primarily attributed to both local emissions and regional transport [5,9,130]. Distinct holiday effects have been reported in the PRD, such as weekend effects [42,88,92], reverse weekend effects [87], SF effects [131,132], and reverse holiday effects during the CND [5,14,107,108,109,133]. Zou et al. (2019) [88] reported that the titration of O3 and lower NOx levels on WENDs lead to faster O3 formation in autumn Guangzhou, resulting in a more pronounced weekend effect on morning O3 levels. Furthermore, during the dry season, a weekend effect is observed, whereas the wet season shows a reverse weekend effect [92]. The SF effect primarily relates to the COVID-19 control measures implemented in the PRD, where coordinated control measures still prevailed. Daytime O3 decreases and night-time increases are likely due to significant reductions in precursor emissions resulting from lockdown measures—with NOx emissions decreasing by 50% and VOCs by 46%—and the diminished titration effect of NOx at night. As shown in Figure 6 and Figure 7, O3 concentrations in the PRD during the CND showed a significant increase compared to those on WDAYs, with statistical tests confirming the difference (p < 0.05). During the CND, meteorological conditions in the PRD were highly conducive to O3 formation. The influx of tourists and increased vehicular activity raised emissions from the catering industry and transportation sectors, elevating precursor concentrations and exacerbating O3 pollution [107]. Severe near-surface O3 pollution linked to the horizontal transport of pollutants also occurred during the CND. For example, Shen et al. (2020) [5] reported that O3 pollution events during CND in PRD were primarily caused by O3 generated from upwind cities and transported to downwind areas. Overall, the O3 pollution in the PRD is significantly influenced by the holiday effect, with distinct pollution characteristics observed during different holidays [88,108,132]. Future strategies should differentiate control strategies for various holidays and enhance regional joint efforts to effectively address O3 pollution issues.

4.4. Other Regions

In addition to the BTH, YRD, and PRD region, studies on the holiday effect have also been conducted in several other areas across China, such as CCUA [26,38,49,58,59,83,134,135], CLUA [44,136], Xinjiang Urumqi Urban Agglomeration (XUUA) [45,137,138,139,140], GSUA [91,141,142], CZXUA [27,82,143,144,145], Anhui Province [94], Guangxi Province [39], Hainan Province [146], Heilongjiang Province [147], Hong Kong [39], and Qinghai Province [148]. For the SF effect, studies have been reported in Wuhan [94,115,118,134,149], NSUA [150,151], Shandong Province [97,152], Shanxi Province [151], and Henan Province [98,153]. By comparing and analyzing research progress from various regions, we can gain a more comprehensive understanding of the mechanisms and influencing factors behind the holiday effect of O3 pollution. This widespread phenomenon requires coordinated attention and collaborative strategies from all regions in China.

5. Influencing Factors

5.1. Precursors

Generally, surface O3 concentration demonstrates a strong correlation with changes in precursor concentrations [76]. The Relative Incremental Reactivity (RIR) method serves as an important indicator for evaluating the sensitivity of O3 formation to precursors [18]. Wang et al. (2022) [14] employed the AtChem2-MCM model to calculate the RIR values of precursors (NO, VOCs, CO) during different periods (holidays, post-holidays, and non-holidays), and revealed that only the RIR for NO was negative, indicating that an increase in NO concentration could enhance its titration effect on O3, thereby mitigating O3 pollution. The observed higher O3 concentrations on holidays compared to working days are primarily attributed to a decrease in the concentration of NO, which diminishes NO’s titration effect on O3 and leads to increased O3 levels [17,95].

5.1.1. O3 Formation Mechanisms

Ground-level O3 is a secondary pollutant formed through complex photochemical reactions involving NOx and VOCs under sunlight [110]. The primary pathway can be summarized as follows:
NO2 photolysis: NO2 + hν → NO + O, O + O2 → O3.
VOCs oxidation: VOCs + OH → RO2, RO2 + NO → NO2.
NO titration: O3 + NO → NO2 + O2.
The photolysis of NO2 contributes to the enhancement in O3 concentrations. However, when NO2 levels decrease, the associated reduction in NO concentrations may increase O3 concentrations. This process is dynamic and governed by the complex interactions between sunlight, NO, NO2, and O3. The photochemical characteristics of O3 fundamentally explain why O3 concentrations tend to increase during holidays despite reduced anthropogenic emissions [8,110].

5.1.2. NOx and VOCs

The ratio of VOCs to NOx can significantly influence the sensitivity of O3 to changes in these precursors [154]. The Empirical Kinetic Modeling Approach (EKMA) curve, derived from photochemical reaction modeling, simulates O3 formation to reveal the nonlinear relationship among O3 and its precursors VOCs and NOx, with its characteristic ridge line formed by connecting the turning points of O3 concentration isopleths delineating three distinct control regimes: a synergistic control regime near the ridge line, a VOC-controlled regime above it, and a NOx-controlled regime below it [154]. Specifically, if O3 formation occurs in a NOx-controlled regime, reducing NOx emissions will lead to a decrease in O3 concentrations. Conversely, if O3 formation is in a VOC-controlled regime, reducing VOC emissions can effectively lower O3 levels [40,47,48], with the reduction in NOx emissions leading to an increase in O3 concentrations [155]. The holiday effect introduces variability into this dynamic by causing temporary shutdowns of factories and shifts in travel patterns, which can lead to abrupt changes in NOx or VOC emissions [131]. These changes result in regular fluctuations in O3 concentrations, further highlighting the complexity of O3 formation and its sensitivity to precursor emissions. O3 formation sensitivity exhibits seasonal variations; i.e., in winter, most parts of China are likely in a VOC-controlled region, whereas in spring and summer, a greater number of regions transition into a NOx-controlled regime [156,157,158]. Numerous studies posit that the primary reason for the holiday effect lies in the lower concentration of NO on non-working days, which weakens NO’s reductive suppression of O3, thereby facilitating an increase in O3 concentrations [44,51,118]. The absence of a holiday effect in certain cases can be attributed to a higher NO2/NO ratio, which favors O3 formation [76]. Guo et al. (2022) [85] investigated the diurnal variations in O3 and identified a notable holiday effect, characterized by elevated O3 levels during the night and morning but decreased afternoon concentrations on holidays, which can be attributed to the lower NO2/NO ratios and reduced net O3 formation in the afternoon on holidays. The enhanced indoor activities in homes and hospitals during lockdowns notably increased the emissions of solvent-related VOCs [111,134]. In addition to a relative lack of HOx radicals (OH and HO2) [116], O3 formation lies in a VOC-controlled regime. Overall, both seasonal variations and changes in emissions between working and non-working days as well as reductions in traffic and increases in household activities during the pandemic can significantly influence O3 concentrations.

5.1.3. CO

The relationship between CO and O3 is complex. CO is a precursor for O3 formation in the troposphere, primarily through photochemical oxidation in the presence of NOx, which leads to O3 production [159]. The concentrations of NO2 and CO can, to some extent, reflect vehicle emissions around urban monitoring sites [160]. A study indicates that air pollutant variations in Metropolitan Lima should be attributed to vehicular traffic (for NO2 and CO) and ground-level solar radiation (for O3), while O3 is additionally influenced by nocturnal transport [161]. The concentration patterns exhibited a distinct “weekend effect”, with analytical results showing a negative correlation between CO and O3 but a positive correlation between CO and NO2—findings that closely align with the conclusions derived from Figure 5 [49,128,161]. Studies suggest that reduced traffic emissions during holidays may lead to lower CO concentrations, but their O3-promoting effect could be weakened by the simultaneous decline in NOx [160]. In contrast, an increase in CO promotes O3 formation, counteracting the holiday effect.

5.1.4. Primary Pollutants

Primary pollutants such as PM2.5, PM10, and SO2 can also influence the holiday effect of O3 pollution. Generally, lower concentrations of PM are associated with stronger solar radiation and enhanced photochemical reactions, both of which are favorable for the O3 formation [116,117]. Wang et al. (2020) [155] reported that, at lower concentrations of PM2.5, a positive correlation exists between PM2.5 and O3; however, at higher PM2.5 concentrations, this relationship becomes negative. Additionally, reductive substances present in PM10 and PM2.5 can consume O3 [98]. SO2 exhibits a negative correlation with O3 concentrations, and a decrease in SO2 can reduce its consumption of O3, thereby promoting an increase in O3 levels [98,101,118]. Typically, concentrations of PM2.5, PM10, and SO2 are higher on WDAYs compared to most of the holidays.

5.2. Atmospheric Oxidation Capacity

Generally, the concentrations of OH, nitrate (NO3) radicals, and Ox (Ox = O3 + NO2) can be used as the indicator of AOC, which also poses a significant impact on the holiday effect of O3 pollution [162]. Observations in Urumqi found that NOx concentrations were 9.7% lower on WENDs compared to WDAYs, while the average Ox concentration was 0.76% higher on WENDs, resulting in O3 concentrations being 3.5% higher on WENDs [137]. Similarly, Geng et al. (2008) [41] reported that a substantial reduction in NO2 led to an increase in OH concentrations, thereby enhancing the AOC on WENDs, which contributed to the occurrence of the O3 weekend effect. Yang et al. (2020) [163] analyzed the OFP of VOCs before, during, and after the CND and found that the top ten VOCs included a higher proportion of unsaturated species, which are more likely to react with OH to produce O3 [116]. Ma et al. (2022) [113] reported that during the pandemic lockdown period, a significant reduction in NOx emissions not only increased O3 formation but also enhanced night-time NO3 radical concentrations, thereby further strengthening the AOC and promoting the secondary formation of O3. Additionally, Huang et al. (2023) [134] found that a reduction in traffic flow led to a marked decrease in NOx emissions, while the reduction in VOCs was comparatively smaller. This imbalance between NOx and VOCs further increased the AOC in urban areas, exacerbating O3 pollution. Le et al. (2020) [116] and Zhao et al. (2022) [164] revealed that reductions in NOx were associated with increased urban O3 concentrations, which in turn enhanced the AOC and promoted the formation of sulfate and other secondary aerosols. These studies demonstrate that variations in NOx and VOC abundances can significantly affect the AOC, thereby influencing O3 production during different periods.

5.3. Meteorological Conditions

The holiday effect is also closely associated with the changes in meteorological conditions such as solar radiation, temperature, relative humidity (RH), rainfall, and cloud cover [26,37,52,89,165]. Studies indicate that solar radiation and O3 concentration exhibit a positive correlation. On WDAYs, decreased human activities on holidays lead to lower particulate concentrations, which improve atmospheric visibility and solar radiation intensity, as well as enhance the BVOC emissions, thereby contributing to distinct holiday effects [89]. Temperature and O3 also show a positive correlation, with higher temperatures generally increasing the BVOC emissions and the exceedance rate of O3. For RH, while O3 concentrations initially rise with increasing RH, they subsequently decrease as RH continues to increase. Generally, O3 pollution events become extremely rare when RH surpasses 80% [26,52,165]. O3 is negatively correlated with rainy days; on clear and dry days, O3 pollution tends to be more severe. During rainy and cloudy days, cloud cover weakens photochemical reactions, and precipitation can remove some O3 [26]. Tang et al. (2009) [37] reported that an increase in cloud cover can correspondingly reduce the intensity of the O3 weekend effect. Additionally, Qiu et al. (2018) [92] observed that the weekend effects vary between dry and wet seasons in southern China. For example, in Guangzhou, a weekend effect occurs during the dry season, whereas a reverse weekend effect is evident in the wet season. Overall, these studies highlight the significant influence of meteorological conditions on the holiday effects of O3 pollution.

5.4. Other Factors

Tan et al. (2013) [166] identified a strong correlation between the holiday effect and urbanization. Regions with higher urbanization rates, larger population densities, and greater traffic intensities tend to exhibit a more pronounced holiday effect. Nonetheless, as urbanization reaches a stable state, the intensity of the holiday effect typically diminishes. Generally, longer holidays are associated with a more pronounced holiday effect compared to shorter ones. While studies revealed that although the concentrations of NOx, CO, and VOC progressively decline with additional vacation days, the holiday effect does not appear to be directly influenced [17,166,167]. Overall, the relationship between the holiday effect, urbanization, and the duration of holidays warrants further investigation and analysis.

6. Summary and Outlook

This review summarizes two decades of studies on the holiday effect of O3 pollution in China, highlighting its spatiotemporal dynamics and influencing factors. Key findings reveal that reduced NO during holidays weakens its titration effect on O3. Additionally, decreased PM on holidays leads to improved atmospheric visibility and a corresponding enhancement in AOC, along with enhanced BVOC emissions, all of which boost the photochemical formation capacity of O3. Furthermore, solar radiation and temperature exhibit a positive correlation with O3 pollution during holidays, whereas rainfall and cloud cover suppress it. Key holidays like SF and CND exhibit distinct pollution patterns, with SF influenced by fireworks emissions and pandemic lockdowns, while CND is shaped by tourism surges and seasonal meteorology.
To develop effective O3 pollution control strategies, it is essential to consider a multidisciplinary approach that integrates factors such as regional disparities, precursor emissions, meteorological conditions, and the holiday effect. The dynamic nature of the O3-VOC-NOx relationship necessitates region-specific control strategies, which can be informed by the unique characteristics of the holiday effect in different regions. Such an approach allows for more tailored future emission reduction measures, rather than adopting a one-size-fits-all strategy.
To further advance our understanding and control strategy of O3 pollution during holidays, the following research priorities are recommended, which can refine China’s O3 mitigation strategies, balancing emission controls with regional socio-economic activities.
  • Elucidate photochemical pathways under varying holiday conditions, particularly the roles of radicals (e.g., OH, NO3), reactive VOCs (e.g., BVOCs, carbonyls), and other photochemical indicators (e.g., peroxyacetyl nitrate) in O3 formation.
  • Leverage machine learning to identify dominant factors (e.g., transport paths, heatwaves, biomass burning) during extreme O3 episodes in the holiday.
  • Further examine AOC’s role as a prerequisite for photochemical reactions.
  • Quantify cross-regional O3 contributions and vertical profiles of O3 and its precursors (i.e., VOCs and NOx), especially during long holidays like CND.
  • Assess acute and chronic health impacts of holiday O3 spikes on humans and their effects on ecology (e.g., crop yield, forest).

Author Contributions

Conceptualization, S.C. and J.C.; methodology, S.C. and D.G.; software, S.C.; validation, S.C. and D.G.; formal analysis, S.C. and J.L.; investigation, S.C. and J.C.; resources, S.C. and J.L.; data curation, S.C. and J.L.; writing—original draft preparation, S.C. and J.C.; writing—review and editing, S.C. and D.G.; visualization, S.C.; supervision, D.G.; project administration, D.G. and B.W.; funding acquisition, D.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) Projects (42121004, 42205105) and the Science and Technology Planning Project of Guangdong Province of China (2024B1212040006).

Data Availability Statement

The data set is available to the community and can be accessed by request from Daocheng Gong (dcgong@jnu.edu.cn).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AOCAtmospheric Oxidation Capacity
BTHBeijing–Tianjin–Hebei
CCUAChengdu–Chongqing Urban Agglomeration
CLUACentral Liaoning Urban Agglomeration
CMAQCommunity Multiscale Air Quality
CNDChinese National Day
CZXUAChangsha–Zhuzhou–Xiangtan Urban Agglomeration
EKMAEmpirical Kinetic Modeling Approach
GSUAGuanzhong–Shaanxi Urban Agglomeration
HO2Hydroperoxyl Radical
HOxOH + HO2
MADMid-Autumn Festival
MDMay Day
NOxNitrogen Oxides
NSUANorth Shanxi Urban Agglomeration
NYDNew Year’s Day
O3Ozone
OFPOzone Formation Potential
OHHydroxyl Radical
PM2.5Fine Particulate Matter
PRDPearl River Delta
RHRelative Humidity
RIRRelative Incremental Reactivity
SFSpring Festival
SO2Sulfur Dioxide
SPUAShandong Peninsula Urban Agglomeration
TSDTomb Sweeping Day
VOCsVolatile Organic Compounds
WCSUAWest Coast Straits Urban Agglomeration
WDAYsWeekday
WENDsWeekend
WOSWeb of Science
WUAWuhan Urban Agglomeration
XUUAXinjiang Urumqi Urban Agglomeration
YRDYangtze River Delta

References

  1. Lai, A.; Chen, X.; Liu, Y.; Jiang, M.; Liu, Y.; Wang, X.; Fan, Q. Characteristics of complex pollution with high concentrations of PM2.5 and O3 over the Pearl River Delta, China. J. Sun Yat-Sen Univ. (Nat. Sci. Ed.) 2018, 57, 30–36. [Google Scholar]
  2. Zhao, S.; Yin, D.; Yu, Y.; Kang, S.; Qin, D.; Dong, L. PM2.5 and O3 pollution during 2015–2019 over 367 Chinese cities: Spatiotemporal variations, meteorological and topographical impacts. Environ. Pollut. 2020, 264, 114694. [Google Scholar] [CrossRef]
  3. China’s State Council, Action Plan for Air Pollution Prevention and Control. 2013. Available online: https://www.gov.cn/gongbao/content/2013/content_2496394.htm (accessed on 23 April 2025).
  4. Shen, L.; Jacob, D.J.; Liu, X.; Huang, G.; Li, K.; Liao, H.; Wang, T. An evaluation of the ability of the Ozone Monitoring Instrument (OMI) to observe boundary layer ozone pollution across China: Application to 2005–2017 ozone trends. Atmos. Chem. Phys. 2019, 19, 6551–6560. [Google Scholar] [CrossRef]
  5. Shen, J.; Liu, Y.; Yan, P.; Wang, Y.; Pan, Z.; Chen, D. Analysis of ozone pollution process in Guangdong province around the National Day in 2018. Saf. Environ. Eng. 2020, 27, 75–80. [Google Scholar]
  6. Yan, H.; Zhang, W.; Hou, M.; Li, Y.; Gao, P. Sources and control area division of ozone pollution in cities at prefecture level and above in China. Environ. Sci. 2020, 41, 5215–5224. [Google Scholar]
  7. Yang, J.; Bu, D.; Liu, J.; Du, M. Research review on urban ozone pollution control in China. Environ. Sustain. Dev. 2020, 47, 86–90. [Google Scholar]
  8. Cao, B.; Yin, Z. Future atmospheric circulations benefit ozone pollution control in Beijing-Tianjin-Hebei with global warming. Sci. Total Environ. 2020, 743, 140645. [Google Scholar] [CrossRef]
  9. Wang, P.; Shen, J.; Xia, M.; Sun, S.; Zhang, Y. Unexpected enhancement of ozone exposure and health risks during National Day in China. Atmos. Chem. Phys. 2021, 21, 10347–10356. [Google Scholar] [CrossRef]
  10. Feng, Z.; Hu, E.; Wang, X.; Jiang, L.; Liu, X. Ground-level O3 pollution and its impacts on food crops in China: A review. Environ. Pollut. 2015, 199, 42–48. [Google Scholar] [CrossRef]
  11. Gao, A.; You, X.; Li, Z.; Liao, C.; Yin, Z.; Zhang, B.; Zhang, H. Health effects associated with ozone in China: A systematic review. Environ. Pollut. 2025, 199, 125642. [Google Scholar] [CrossRef]
  12. Zhang, Y.; Shao, K.; Tang, X. The study of urban photochemical smog pollution in China. Acta Sci. Nat. Univ. Peki. 1998, 34, 392–400. [Google Scholar]
  13. Hua, J.; Zhang, Y.; Foy, B.D.; Mei, X.; Shang, J.; Feng, C. Competing PM2.5 and NO2 holiday effects in the Beijing area vary locally due to differences in residential coal burning and traffic patterns. Sci. Total Environ. 2021, 750, 141575. [Google Scholar] [CrossRef]
  14. Wang, J.; Zhang, Y.; Wu, Z.; Luo, S.; Song, W.; Wang, X. Ozone episodes during and after the 2018 Chinese National Day holidays in Guangzhou: Implications for the control of precursor VOCs. J. Environ. Sci. 2022, 114, 322–333. [Google Scholar] [CrossRef] [PubMed]
  15. Zhao, M.; Zhang, Y.; Pei, C.; Chen, T.; Mu, J.; Liu, Y.; Wang, Y.; Wang, W.; Xue, L. Worsening ozone air pollution with reduced NOx and VOCs in the Pearl River Delta region in autumn 2019: Implications for national control policy in China. J. Environ. Manag. 2022, 324, 116327. [Google Scholar] [CrossRef]
  16. Zhao, Q.; Li, L.; Li, H. Research progress on surface ozone pollution in domestic and overseas. Adv. Environ. Sci. 2018, 31, 72–76. [Google Scholar]
  17. Chen, P.; Tan, P.; Chou, C.C.K.; Lin, Y.; Chen, W.; Shiu, C. Impacts of holiday characteristics and number of vacation days on “holiday effect” in Taipei: Implications on ozone control strategies. Atmos. Environ. 2019, 202, 357–369. [Google Scholar] [CrossRef]
  18. Xu, Z.; Huang, X.; Wei, N.; Chi, X.; Xu, Z.; Zheng, L.; Sun, P.; Ding, A. Influence of synoptic condition and holiday effects on VOCs and ozone production in the Yangtze River Delta region, China. Atmos. Environ. 2017, 168, 112–124. [Google Scholar] [CrossRef]
  19. Jiang, S.; Wang, K. Exploring the Holiday Effect on Air Temperatures. Sci. Rep. 2018, 8, 17943. [Google Scholar] [CrossRef]
  20. Liu, M.; Yue, Q.; Lv, J. Analysis and prediction of ozone concentration in urban and suburb of Shanghai. Environ. Sci. Technol. 2008, 21, 1–4. [Google Scholar]
  21. Liu, Z.; Xie, X.; Xie, M.; Wang, T. Spatio-temporal distribution of ozone pollution over Yangtze River Delta Region. J. Ecol. Rural Environ. 2016, 32, 445–450. [Google Scholar]
  22. Wang, Y.; Huang, X.; Pei, C.; Zhang, J. Spatial-temporal variations of ground-level ozone and preliminary analysis on the sensitivity of ozone formation to precursors in Guangzhou city. Saf. Environ. Eng. 2016, 23, 83–88. [Google Scholar]
  23. Yan, R.; Li, L.; An, J.; Huang, C. Simulation study of ozone generation and precursors control in Shanghai during summer time. Environ. Pollut. Control 2016, 38, 30–35+40. [Google Scholar]
  24. Ren, Y.; Wang, G.; Zhang, Q.; Tao, C.; Ji, S.; Wang, Q.; Wang, W. The impact of Chinese new year on air quality in north China based on machine learning. Atmos. Environ. 2025, 342, 120874. [Google Scholar] [CrossRef]
  25. Li, B. Application and research of weekend effect in air pollution. Jiangxi Chem. Ind. 2018, 29–31. [Google Scholar] [CrossRef]
  26. Cui, L.; Ni, C.; Wang, C.; Xie, Y.; Hu, S.; Gan, X. Temporal variation of ozone concentration and impact factors in Chengdu. Environ. Monit. China 2015, 31, 41–46. [Google Scholar]
  27. Zhou, G.; Song, B.; Luo, Y.; Bi, J. Holiday effects on ambient air quality in Changsha during 2016. Environ. Monit. China 2018, 34, 68–76. [Google Scholar]
  28. Cleveland, W.S.; Graedel, T.E.; Kleiner, B.; Warner, J.L. Sunday and workday variations in photochemical air pollutants in new jersey and new york. Science 1974, 186, 1037–1038. [Google Scholar] [CrossRef]
  29. Elkus, B.Z.; Wilson, K.R. Photochemical Air Pollution: Weekday-Weekend Differences. Atmos. Environ. 1977, 11, 509–515. [Google Scholar] [CrossRef]
  30. Levitt, S.B.; Chock, D.P. Weekday-Weekend Pollutant Studies of the Los Angeles Basin. J. Air Pollut. Control Assoc. 1976, 26, 1091–1092. [Google Scholar] [CrossRef]
  31. Diem, J.E. Comparisons of weekday–weekend ozone: Importance of biogenic volatile organic compound emissions in the semi-arid southwest USA. Atmos. Environ. 2000, 34, 3445–3451. [Google Scholar] [CrossRef]
  32. Altshuler, S.L.; Arcado, T.D.; Lawson, D.R. Weekday vs. weekend ambient ozone concentrations: Discussion and hypotheses with focus on northern California. J. Air Waste Manag. Assoc. 1995, 45, 967–972. [Google Scholar] [CrossRef]
  33. Qin, Y. Weekend/weekday differences of ozone, NOx, Co, VOCs, PM10 and the light scatter during ozone season in southern California. Atmos. Environ. 2004, 38, 3069–3087. [Google Scholar] [CrossRef]
  34. Brönnimann, S.; Neu, U. Weekend-weekday differences of near-surface ozone concentrations in Switzerland for different meteorological conditions. Atmos. Environ. 1997, 31, 1127–1135. [Google Scholar] [CrossRef]
  35. Pont, V.; Fontan, J. Comparison between weekend and weekday ozone concentration in large cities in France. Atmos. Environ. 2001, 35, 1527–1535. [Google Scholar] [CrossRef]
  36. Lei, Y.; Zhang, X.; Tang, Y.; Fan, G. Holiday effects on PM2.5 and other major pollutants in Beijing. Acta Sci. Circumstantiae 2015, 35, 1520–1528. [Google Scholar]
  37. Tang, W.; Zhao, C.; Geng, F.; Peng, L. Study on weekend effect of ozone in Shanghai region. Sci. Sin. 2009, 39, 99–105. [Google Scholar]
  38. Ruan, Y.; Li, L.; Huang, Z.; Zeng, S. Meteorological causes of ozone pollution in Sichuan and Chongqing in summer. J.Chengdu Univ. Inf. Technol. 2021, 36, 425–433. [Google Scholar]
  39. Wu, Q.; Zhang, L.; Chen, L.; Chen, F.; Peng, X. Analysis of air pollution characteristics and weekend effect of Liuzhou in 2019. Environ. Prot. Sci. 2020, 46, 138–143. [Google Scholar]
  40. Ding, J.; Shen, Y.; Chang, H.; Ma, C. Analysis of the concentrations characteristics of O3 and NO2 in Nantong. Guangdong Chem. Ind. 2014, 41, 149–150+155. [Google Scholar]
  41. Geng, F.; Tie, X.; Xu, J.; Zhou, G.; Peng, L.; Gao, W.; Tang, X.; Zhao, C. Characterizations of ozone, NOx and VOCs measured in Shanghai, China. Atmos. Environ. 2008, 42, 6873–6883. [Google Scholar] [CrossRef]
  42. Li, J.; Tie, X.; Cao, J. A preliminary study of PM2.5 weekend effect in typical cities. J. Earth Environ. 2015, 6, 224–230. [Google Scholar]
  43. Lin, Y.; Qin, B.; Duan, Z.; Peng, W.; Yuan, X. Characteristics and control measures of near-surface ozone pollution in China. Leather. Manuf. Environ. Technol. 2023, 4, 121–123. [Google Scholar]
  44. Liu, M.; Wang, C.; Hou, L.; Yu, X.; Lin, H. Spatial-temporal patterns and variation trend of ozone pollution in Shenyang. Environ. Monit. China 2017, 33, 126–131. [Google Scholar]
  45. Liu, X.; Zhong, Y.; He, Q.; Peng, Y.; Luo, X. The variation characteristics and influencing factors of surface ozone concentration in the taklimakan desert hinterland. J. Desert Res. 2013, 33, 626–633. [Google Scholar]
  46. Lv, H.; He, H.; Hao, Y. Variation characteristics and weekend effect of nitrogen oxides and ozone in Hong Kong Port. Chin. J. Environ. Eng. 2017, 11, 4196–4201. [Google Scholar]
  47. Li, M. Study on the Characteristics and Driving Factors of Ozone Pollutionin Shandong Province. Master’s Thesis, Shandong Normal University, Jinan, China, 2021. [Google Scholar]
  48. Shi, Y.; Xu, Y.; Wang, G.; Shi, L. Study of the “Weekend Effect” of O3, NOx and other pollutants in summer of Beijing. Environ. Sci. 2009, 30, 2832–2838. [Google Scholar]
  49. Jian, W. Study on “Weekend Effect” of air pollutants in Yubei district of Chongqing. J. Qinghai Environ. 2023, 33, 86–89+104. [Google Scholar]
  50. Wang, N.; Xu, J.; Pei, C.; Tang, R.; Zhou, D.; Chen, Y.; Li, M.; Deng, X.; Deng, T.; Huang, X.; et al. Air Quality During COVID-19 Lockdown in the Yangtze River Delta and the Pearl River Delta: Two Different Responsive Mechanisms to Emission Reductions in China. Environ. Sci. Technol. 2021, 55, 5721–5730. [Google Scholar] [CrossRef]
  51. Wang, Z.; Li, Y.; Chen, T.; Zhang, D.; Sun, F.; Wei, Q.; Dong, X.; Sun, R.; Huan, N.; Pan, L. Ground-level ozone in urban Beijing over a 1-year period: Temporal variations and relationship to atmospheric oxidation. Atmos. Res. 2015, 164–165, 110–117. [Google Scholar] [CrossRef]
  52. Wang, T.; Chen, M.; Zhou, M.; Li, J.; Wu, S.; Luo, M.; Xia, Z. Pollution characteristics and influence factors of air pollutants in Wuxi City. Environ. Pollut. Control 2015, 37, 74–78+95. [Google Scholar]
  53. Yin, Y.; Shan, W.; Ji, X.; You, L.; Su, Y. Characteristics of Atmospheric Ozone in the Urban Area of Ji’nan. Environ. Sci. 2006, 27, 2299–2302. [Google Scholar]
  54. Cui, M.; An, X.; Sun, Z.; Wang, B.; Wang, C.; Ren, W.; Li, Y. Characteristics and Meteorological Conditions of Ozone Pollution in Beijing. Ecol. Environ. Monit. 2019, 4, 25–35. [Google Scholar]
  55. Jiang, Y. Spatial and Temporal Distribution of NO2 Concentration Infive Provinces and One City in MID-Easter China Andanalysis of Influencing Factors. Master’s Thesis, Anhui Jianzhu University, Hefei, China, 2024. [Google Scholar]
  56. Zhang, L.; Wang, X.; Wang, Z.; Wei, Y.; Song, W.; Li, E. Ozone pollution characteristics of Shijiazhuang in 2014. Environ. Sci. Technol. 2017, 40, 1–8. [Google Scholar]
  57. Yang, J. Investigating the Light Absorption Properties of Black Carbon Aerosols in Shanghai’s Urban and Suburban Regions. Master’s Thesis, Shanghai Normal University, Shanghai, China, 2024. [Google Scholar]
  58. Luo, J.; Zhang, J.; Huang, Y.; Zhong, X.; Chen, Z.; Liu, C.; Shi, K. Multiple Fractal Difference Analysis of NO2-O3 Role in Urban and Suburban Areas. Environ. Sci. Technol. 2024, 47, 79–85. [Google Scholar]
  59. Zhang, Z.; Chen, S.; Ou, Z.; Zhang, Y.; Wang, X. Pollution characteristics and source apportionments of ambient NMHCs during summer in typical areas of the Chengdu-Chongqing Basin. Acta Sci. Circumstantiae 2024, 44, 35–47. [Google Scholar]
  60. Li, S.; Yu, D.; Shi, C.; Zhao, N.; Xu, X.; Lu, S. Correlation Analysis of PM2.5 and O3 in Different Types of Urban Forests in Beijing. Res. Environ. Sci. 2024, 37, 536–544. [Google Scholar]
  61. Zhang, J.; Chi, H.; Li, L. Analysis of variation characteristics of air quality in luohu district of Shenzhen in 2019. Environ. Eng. 2024, 42, 152–160. [Google Scholar]
  62. Tang, J. Regional Differences in Summer Ozone Response to Temperature and Weekend Effect in Beijing. Master’s Thesis, Chinese Research Academy of Environmental Sciences, Beijing, China, 2023. [Google Scholar]
  63. Zheng, Z. Fine-Scale Spatiotemporal Analysis of Ozone Formation Sensitivity in a Typical Industrial City of the North China Plain. Master’s Thesis, Chinese Research Academy of Environmental Sciences, Beijing, China, 2023. [Google Scholar]
  64. Shi, C. Correlation Study Between PM2.5 and Ozone (O3) in Beijing Urban Forests. Master’s Thesis, Shenyang Agricultural University, Shenyang, China, 2023. [Google Scholar]
  65. Zhang, X. Ground-Level Ozone Concentration Prediction in Beijing Using Stacking Model. Master’s Thesis, Shanxi University, Taiyuan, China, 2023. [Google Scholar]
  66. Liu, T. Exploring the Characteristics and Causes of Ozone Pollution Around Traffic Arteries: An Example of Roadside Monitoring Stations in Guangzhou. Master’s Thesis, Harbin Normal University, Harbin, China, 2023. [Google Scholar]
  67. Tang, J.; Tian, R.; Guo, W.; Zhang, Z.; Xu, J.; Liu, S.; Zhang, S.; Ma, L.; Ye, Z.; Huang, H. Quantitative relationship between near-surface ozone and air temperature over Beijing area in summer. Res. Environ. Sci. 2023, 36, 904–912. [Google Scholar]
  68. Zhang, G.; Sun, Y.; Xu, W.; Wu, L.; Duan, Y.; Liang, L.; Li, Y. Identifying the O3 chemical regime inferred from the weekly pattern of atmospheric O3, CO, NOx, and PM10: Five-year observations at a center urban site in Shanghai, China. Sci. Total Environ. 2023, 888, 164079. [Google Scholar] [CrossRef]
  69. An, J.; Hang, Y.; Zhu, B.; Wang, D. Observational study of ozone concentrations in northern suburb of Nanjing. Ecol. Environ. Sci. 2010, 19, 1383–1386. [Google Scholar]
  70. Huang, Z. Observational study of surface ozone and its precursors in the suburb of Shanghai during spring. Environ. Sci. Technol. 2011, 34, 87–89+99. [Google Scholar]
  71. Jia, P. Research on Deep Learning-Based Ozone Near-Term Forecasting Technology in the Yangtze River Delta Region. Master’s Thesis, Nanjing University of Information Science & Technology, Nanjing, China, 2021. [Google Scholar]
  72. Lin, J. Observational research on ozone and its precursors at Langqi island, Minjiang Estuary. Chem. Eng. Equip. 2017, 305–307. [Google Scholar] [CrossRef]
  73. Luan, Z.; Lu, H.; Li, T.; Chen, B. Characteristics and sensitivity analysis of near-surface atmospheric ozone pollution in Tai’an city in 2016 201. Meteorol. Environ. Sci. 2023, 46, 72–80. [Google Scholar]
  74. Shen, L.; Li, L.; Lv, S.; Zhang, X.; Wang, F.; Zhang, G. Impacts of fireworks on the atmospheric pollutant distributions during Spring Festival in Jiaxing. Acta. Sci. Circumstantiae 2016, 36, 1548–1557. [Google Scholar]
  75. Shen, L.; Wang, H.; Lu, S.; Li, L.; Zhang, X.; Zhang, G.; Wang, F. Weekend Effect in Distributions of Particulate Matters, Main Gas Pollutants and Aerosol Size in Spring of Jiaxing. Huanjing Kexue 2015, 36, 4348–4357. [Google Scholar]
  76. Wang, J.; An, J.; Shao, P.; Zou, J.; Lin, X.; Zhang, Y. Characteristic study on the “Weekend Effect” of atmospheric O3 in northern suburb of Nanjing. Environ. Sci. 2017, 38, 2256–2263. [Google Scholar]
  77. Yang, J.; Li, P.; Li, Y.; Feng, Q.; Li, J.; Han, Y. Characteristics of SO2, NO2 and O3 in the northern suburb of Nanjing. J. Meteorol. Environ. 2014, 30, 66–70. [Google Scholar]
  78. Xu, J.; Yang, H.; Li, G.; Ma, W.; Zhang, L.; Li, H.; Zhao, H. Variation characteristics of pollutants in ordos city and their relationship with meteorological factors. Inn. Mong. Sci. Technol. Econ. 2025, 121–125. [Google Scholar]
  79. Gao, W.; Zhou, L.; Zeng, Y.; Shen, J.; Gao, T.; Wang, C.; Liao, Q. Characteristics of O3 pollution and its response to environmental factors in Yueyang City. Geochimica 2024, 1–10. [Google Scholar] [CrossRef]
  80. Chen, L.; Ma, J.; Zhen, X.; Cao, Y. Variation characteristics and meteorological influencing factors of air pollution in Shanghai. J. Meteorol. Environ. 2017, 33, 59–67. [Google Scholar]
  81. Zhang, Y.; Wu, R.; Yang, X.; Yang, D.; Li, Y.; Wang, R.; Xu, H. Research on air pollution characteristics and influencing factors of typical urbanroad traffic densified monitoring stations. J. Environ. Eng. Technol. 2023, 13, 929–939. [Google Scholar]
  82. Guan, R.; Zheng, K.; Huang, Y. Analysis on weekend and holiday effects of air pollutants in Zhangjiajie city. J. Tongren Univ. 2018, 20, 81–85. [Google Scholar]
  83. Luo, J.; Huang, X.; Zhang, J.; Zhang, J.; Song, H. Study on the characteristics of air pollution and its holiday effect in Chengdu urban area. Sichuan Environ. 2018, 37, 21–26. [Google Scholar]
  84. Ma, B.; Chen, Z.; Liang, H. Analyse of characteristic of daily change in air pollutants’concentrations Shenzhen. Environ. Monit. China 2004, 47–54. [Google Scholar]
  85. Guo, Y.; Bao, Y.; Shen, L.; Zhu, Y. Spatial-temporal distribution characteristics and simulation analysis of subaerial ozone in Suzhou city. Environ. Sci. Technol. 2022, 45, 66–77. [Google Scholar]
  86. Fan, Y.; Fan, S.; Zhang, H.; Zu, F.; Meng, Q.; He, J. Characteristics of SO2, NO2, O3 volume fractions and their relationship with weather conditions at Linan in summer and winter. Trans. Atmos. Sci. 2013, 36, 121–128. [Google Scholar]
  87. Jingyi, X. Study on the Correlation and Synergistic Control of OVOCs in the Atmosphere and PM2.5 and O3 Pollution Characteristics. Master’s Thesis, Harbin Institute of Technology, Harbin, China, 2022. [Google Scholar]
  88. Zou, Y.; Charlesworth, E.; Yin, C.; Yan, X.; Deng, X.; Li, F. The weekday/weekend ozone differences induced by the emissions change during summer and autumn in Guangzhou, China. Atmos. Environ. 2019, 199, 114–126. [Google Scholar] [CrossRef]
  89. He, Y.; Lin, H.; Cao, S.; Song, J.; Su, W.; Xiong, Y. Characteristics of ozone pollution and high-impact meteorological factors in urban: A case study of Suzhou. Environ. Sci. 2023, 44, 85–93. [Google Scholar]
  90. Hu, Y.; Wang, J.; Xu, L.; Han, J. Analysis of meteorological conditions and temporal and spatial distribution characteristics of ground-level ozone pollution Ininner Mongolia. Environ. Monit. China 2022, 38, 65–72. [Google Scholar]
  91. Li, S.; Huang, Y.; Li, S.; Chen, M.; Cao, Z. Analysis on the seasonal difference of “Weekend Effect” of atmospheric O3 and its precursors in urban area of Xi’an. Environ. Pollut. Control 2019, 41, 1207–1210. [Google Scholar]
  92. Qiu, X.; Liang, G.; Zhang, J. Analysis of the weekend efect of air pollutants in the center of Guangzhou city. Guangzhou Environ. Sci. 2018, 33, 13–16. [Google Scholar]
  93. Wang, Z.; Li, Y.; Dong, X.; Sun, R.; Sun, N.; Pan, L. Analysis on weekend effect of air pollutants in urban atmosphere of Beijing. J. Univ. Chin. Acad. Sci. 2015, 32, 843–850. [Google Scholar]
  94. He, R.; Shan, L.; Tian, L.; Zhou, K.; Zhu, L. Evaluation of weekly cycle of air pollution in Anhui province. Trans. Atmos. Sci. 2021, 44, 363–370. [Google Scholar]
  95. Dai, Q.; Hou, L.; Liu, B.; Zhang, Y.; Song, C.; Shi, Z.; Hopke, P.K.; Feng, Y. Spring Festival and COVID-19 Lockdown: Disentangling PM sources in major Chinese cities. Geophys. Res. Lett. 2021, 48, e2021GL093403. [Google Scholar] [CrossRef]
  96. Javed, Z.; Wang, Y.; Xie, M.; Tanvir, A.; Rehman, A.; Ji, X.; Xing, C.; Shakoor, A.; Liu, C. Investigating the Impacts of the COVID-19 Lockdown on Trace Gases Using Ground-Based MAX-DOAS Observations in Nanjing, China. Remote Sens. 2020, 12, 3939. [Google Scholar] [CrossRef]
  97. Zhou, J.; Wu, Q. Characteristics of Spring Festival effect of atmospheric pollutants in Jinan city. J. Zhejiang Agric. Sci. 2018, 59, 338–342+347. [Google Scholar]
  98. Li, W.; Du, G.; Wang, M. Research on characteristics of the temporal and spatial variation of atmosphere pollutants concentrations during the Spring Festival from 2012 to 2014 in Zhengzhou. Meteorol. Environ. Sci. 2015, 38, 12–21. [Google Scholar]
  99. Zhou, Z.; Zhou, M.; Bao, Z.; Chen, Y.; Xu, Y. Analysis on impact of fireworks and firecrackers on air quality in county cities during Spring Festival. Environ. Sci. Manag. 2025, 50, 111–114. [Google Scholar]
  100. Chen, F.; Xian, Y.; Qi, M.; Qiao, Y. Characteristics of air pollution during the Spring Festival in Nanjing typical station. J. Qinghai Environ. 2018, 28, 184–188. [Google Scholar]
  101. Liu, W.; Hu, T.; Mao, Y.; Xu, A. Characteristics and origin analysis of air pollution during the Spring Festival in Linfen, Fenwei Plain. Environ. Sci. 2021, 42, 5122–5130. [Google Scholar]
  102. Liu, Y.; Ma, W.; Yin, S.; Li, C.; Xu, K.; Zhang, C.; Zhang, W.; Xue, Z.; Sun, Y.; Ji, D.; et al. Quantification of enhanced VOC emissions from fireworks. Environ. Pollut. 2022, 315, 120389. [Google Scholar] [CrossRef]
  103. Niu, Z.; Hu, T.; Kong, L.; Zhang, W.; Rao, P.; Ge, D. Air-pollutant mass concentration changes during COVID-19 pandemic in Shanghai, China. Air Qual. Atmos. Health 2021, 14, 523–532. [Google Scholar] [CrossRef] [PubMed]
  104. Li, M.; Kang, N.; Zhang, J.; Zhang, J.; Zhang, L. Analysis of air pollution status during the Spring Festival period in theYangtze River delta region from 2019 to 2022. Environ. Chem. 2025, 44, 903–915. [Google Scholar]
  105. Song, Y.; Wan, X.; Bai, S.; Guo, D.; Ren, C.; Zeng, Y.; Li, Y.; Li, X. The characteristics of air pollutants during two distinct episodes of fireworks burning in a valley city of north China. PLoS ONE 2017, 12, e0168297. [Google Scholar] [CrossRef] [PubMed]
  106. Su, X.; Feng, T.; Cao, J. Characteristics and formation mechanism of a heavy air pollution episode over Chinese cities during the National Day holiday in 2015. J. Earth Environ. 2017, 8, 25–36. [Google Scholar]
  107. Guo, W. Analysis of the continuous ozone pollution process during the National Day of 2018 in Shantou. Guangzhou Chem. Ind. 2020, 48, 111–115. [Google Scholar]
  108. Chen, J.; Gong, D.; Liao, T.; Zhou, Y. National Day effect and source analysis of atmospheric volatile organic compounds in the Pearl River Delta region. China Environ. Sci. 2023, 43, 3265–3280. [Google Scholar]
  109. Chen, J.; Liu, T.; Gong, D.; Li, J.; Chen, X.; Li, Q.; Liao, T.; Zhou, Y.; Zhang, T.; Wang, Y.; et al. Insight into decreased ozone formation across the Chinese National Day Holidays at a regional background site in the Pearl River Delta. Atmos. Environ. 2023, 315, 120142. [Google Scholar] [CrossRef]
  110. Javed, Z.; Tanvir, A.; Wang, Y.; Waqas, A.; Xie, M.; Abbas, A.; Sandhu, O.; Liu, C. Quantifying the impacts of COVID-19 lockdown and Spring Festival on air quality over Yangtze River Delta region. Atmosphere 2021, 12, 735. [Google Scholar] [CrossRef]
  111. Li, L.; Li, Q.; Huang, L.; Wang, Q.; Zhu, A.; Xu, J.; Liu, Z.; Li, H.; Shi, L.; Li, R.; et al. Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation. Sci. Total Environ. 2020, 732, 139282. [Google Scholar] [CrossRef]
  112. Wang, Y.; Yuan, Y.; Wang, Q.; Liu, C.; Zhi, Q.; Cao, J. Changes in air quality related to the control of coronavirus in China: Implications for traffic and industrial emissions. Sci. Total Environ. 2020, 731, 139133. [Google Scholar] [CrossRef] [PubMed]
  113. Ma, T.; Duan, F.; Ma, Y.; Zhang, Q.; Xu, Y.; Li, W.; Zhu, L.; He, K. Unbalanced emission reductions and adverse meteorological conditions facilitate the formation of secondary pollutants during the COVID-19 lockdown in Beijing. Sci. Total Environ. 2022, 838, 155970. [Google Scholar] [CrossRef] [PubMed]
  114. Fan, C.; Li, Y.; Guang, J.; Li, Z.; Elnashar, A. The impact of the control measures during the COVID-19 outbreak on air pollution in China. Remote Sens. 2020, 12, 1613. [Google Scholar] [CrossRef]
  115. Huang, X.; Ding, A.; Gao, J.; Zheng, B.; Zhou, D.; Qi, X.; Tang, R. Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. Natl. Sci. Rev. 2021, 8, nwaa137. [Google Scholar] [CrossRef] [PubMed]
  116. Le, T.; Wang, Y.; Liu, L.; Yang, J.; Yung, Y.L.; Li, G. Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science 2020, 369, 702–706. [Google Scholar] [CrossRef]
  117. Sicard, P.; Marco, A.D.; Agathokleous, E.; Feng, Z. Amplified ozone pollution in cities during the COVID-19 lockdown. Sci. Total Environ. 2020, 735, 139542. [Google Scholar] [CrossRef]
  118. Wang, Y.; Li, Z.; Wang, Q.; Jin, X.; Yan, P. Enhancement of secondary aerosol formation by reduced anthropogenic emissions during Spring Festival 2019 and enlightenment for regional PM2.5 control in Beijing. Atmos. Chem. Phys. 2021, 21, 915–926. [Google Scholar] [CrossRef]
  119. Wang, X.; Duan, W.; Zhu, J.; Wei, W.; Cheng, S.; Mao, S. Nonlinear influence of winter meteorology and precursor on PM2.5 based on mathematical and numerical models: A COVID-19 and Winter Olympics case study. Atmos. Environ. 2022, 278, 119072. [Google Scholar]
  120. Li, D.; Wu, Q.; Wang, H.; Xiao, H. The Spring Festival Effect: The change in NO2 column concentration in China caused by the migration of human activities. Atmos. Pollut. Res. 2021, 12, 101232. [Google Scholar] [CrossRef]
  121. Zhang, Y. Analysis of Ozone Pollution Characteristics in Shenyang During the Pandemic. Master’s Thesis, Liaoning University, Shenyang, China, 2023. [Google Scholar]
  122. Yu, X.; Yang, S.; Liu, N.; Zhang, Y. Amplification Effects of O3 Pollution in the Pearl River Delta During the Lockdown Period of COVID-19. Environ. Monit. China 2022, 38, 21–36. [Google Scholar]
  123. Tang, T.; Chang, E. Analysis of atmospheric ozone pollution characteristics and suggestions for control measures in the Beijing-Tianjin-Hebei region. Clean. World 2023, 39, 151–153. [Google Scholar]
  124. Li, M.; Zhang, Q.; Zheng, B.; Tong, D.; Lei, Y.; Liu, F. Persistent growth of anthropogenic non-methane volatile organic compound (NMVOC) emissions in China during 1990–2017: Drivers, speciation and ozone formation potential. Atmos. Chem. Phys. 2019, 19, 8897–8913. [Google Scholar] [CrossRef]
  125. Bai, Y.; Qin, X.; Zhao, X.; Sun, Z. Impact of urban development on natural comfort of humanliving environment in Beijing-Tianjin-Hebei region. Journal of Beijing. Norm. Univ. (Nat. Sci.) 2024, 60, 233–241. [Google Scholar]
  126. Zhou, Y.; Wang, Y.L.; Chen, L.; Wang, W.; Zhang, L.N.; Ji, S. Study of the relationship between O3 generation and its precursors NOx and VOCs emission in Tianjin. Environ. Monit. Forew. 2014, 6, 37–40+44. [Google Scholar]
  127. Ji, K. Research on the cross-regional joint prevention and control mechanism of environmental pollution in the Yangtze River Delta. J. Soc. Sci. Jiamusi Univ. 2025, 43, 40–43+48. [Google Scholar]
  128. Zhao, C.; Peng, L.; Sun, A.; Qin, Y.; Liu, H. Numerical modeling of tropospheric ozone over Yangtze Delta region. Acta Sci. Circumstantiae 2004, 24, 525–533. [Google Scholar]
  129. Fan, S.; Wang, H.; Lu, X.; He, G.; Cao, T. Progresses on ozone pollution in the Guangdong-Hong Kong-Macao Greater Bay Area since 1990s. Acta Sci. Circumstantiae 2023, 43, 1–18. [Google Scholar]
  130. Li, Y.; Lau, K.; Fung, J. Ozone source apportionment (OSAT) to differentiate local regional and super-regional source contributions in the Pearl River Delta region, China. J. Geophys. Res. Atmos. 2012, 117. [Google Scholar] [CrossRef]
  131. Zhao, W.; Fan, S.; Xie, W.; Sun, J. Influence of burning fireworks on air quality during the Spring Festival in the Pearl River delta. Environ. Sci. 2015, 36, 4358–4365. [Google Scholar]
  132. Zhu, Y.; Zheng, B.; Yang, C.; Tang, R.; Xu, J. Air quality changes and the driving factors in Guangdong Province during the COVID⁃19 pandemic. J. Nanjing Univ. (Nat. Sci.) 2021, 57, 1032–1041. [Google Scholar]
  133. Liao, M.; Gong, D.; Wang, S.; Liu, T.; Wang, H. Concentration characteristics and sources of PAN at Nanling background station during the National Day holidays. China Environ. Sci. 2021, 41, 2493–2503. [Google Scholar]
  134. Huang, F.; Wu, Q.; Wang, P. Population mobility and urban air quality: Causal inference and impact measurement. Sustainablity 2023, 15, 11591. [Google Scholar] [CrossRef]
  135. Tan, H.; Lin, Y.; Yao, Y.; Ma, K.; Fang, F. Spatiotemporal evolution and potential source areas of surface ozone in the Sichuan basin during 2015–2021. Res. Environ. Sci. 2023, 36, 660–672. [Google Scholar]
  136. Sun, N. Analysis of Spatiotemporal Patterns of Air Pollutants in Shenyang. Master’s Thesis, Shenyang University of Chemical, Shenyang, China, 2023. [Google Scholar]
  137. Fan, S.; Liu, N.; Zhang, F. Analysis of the air pollution characteristics trends and weekend effect in Urumqi. Environ. Prot. Xinjiang 2021, 43, 1–7. [Google Scholar]
  138. Wang, S.; Zhang, J. The ‘Holiday Effect’ of environmental air quality in Urumqi city. Arid Environ. Monit. 2025, 39, 21–33. [Google Scholar]
  139. Ma, Z.; Yan, J.; Yahefu, P.; Abbas, A. Characteristics and potential sources of surface O3 variation in Urumqi and Kashgar. Arid Zone Res. 2024, 41, 2004–2014. [Google Scholar]
  140. Lei, B. Analysis of Ozone Pollution Characteristics and Source Apportionment in Tumxuk City, Xinjiang. Master’s Thesis, Shihezi University, Shihezi, China, 2023. [Google Scholar]
  141. Yang, X.; Wang, Q.; Wang, L.; Liu, F.; Li, C.; Xu, G. Meteorological impacts, and potential source area analysis of ozone pollutionin Xi′an from 2014 to 2022. Res. Environ. Sci. 2025, 38, 486–496. [Google Scholar]
  142. Qiang, Y. Holiday Effect on Air Quality and the Impact of Control Measures in Xi’an. Master’s Thesis, Xi’an University of Architecture and Technology, Xi’an, China, 2023. [Google Scholar]
  143. Hua, R. Spatiotemporal Distribution Characteristics and Pollution Potential Modeling of Ozone in Changsha. Master’s Thesis, Chengdu University of Information Technology, Chengdu, China, 2023. [Google Scholar]
  144. Zhang, G. Analysis of Influencing Factors and Evolution Trends of PM2.5 and O3 Pollution in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration. Master’s Thesis, Hunan University of Science and Technology, Xiangtan, China, 2023. [Google Scholar]
  145. Bin, J. Integrated Quantification of PM2.5 and Ozone Pollution Using High Spatiotemporal Resolution Prediction Modeling. Master’s Thesis, Hunan University, Changsha, China, 2023. [Google Scholar]
  146. Xie, R.; Hu, S. Study on ozone pollution characteristics in Qionghai City from 2018 to 2021. Environ. Sci. Manag. 2023, 48, 40–45. [Google Scholar]
  147. Li, L.; Wang, L.; Liu, X.; Wang, K.; Xu, Y.; Li, S.; Jiang, J. Temporal and spatial distribution characteristics of ozone and its relationship with meteorological factors in Harbin. China Environ. Sci. Manag. 2020, 40, 1991–1999. [Google Scholar]
  148. Yan, X.; Cao, D. Analysis on the Influence of Temporal and Spatial Variation of Ozone and Meteorological Conditions in High Altitud Area of Haidong City. J. Qinghai Environ. 2022, 32, 180–184. [Google Scholar]
  149. Gbrimblecombe, P.; Lai, Y. Effect of fireworks, Chinese New Year and the COVID-19 lockdown on air pollution and public attitudes. Aerosol Air Qual. Res. 2020, 20, 2318–2331. [Google Scholar] [CrossRef]
  150. Yu, X.; Yang, A.; Chen, L.; Wang, Y.; Zhu, L.; Wang, W.; Wang, X.; Yan, S.; Chen, E. Spatiotemporal distributions and variations in summertime ozone photochemical production regimes over Shanxi. Environ. Sci. 2024, 45, 4432–4439. [Google Scholar]
  151. Geng, H.; Xuan, Y.; Cai, X.; Zhang, Y.; Zhou, H. Mass concentration variation and cluster analysis of urban air pollutants in Taiyuan, Shanxi Province during Chinese New Year of 2014. Acta Sci. Circumstantiae 2015, 35, 965–974. [Google Scholar]
  152. Wang, Y.; Wang, X.; Zhang, B.; Zhao, L.; Liu, Y.; Si, S.; Xue, L. Traffic conditions on typical roads in urban Jinan and the differentiated impacts on air quality. J. Shandong Univ. (Eng. Sci.) 2025, 55, 138–148. [Google Scholar]
  153. Yang, S.; Yang, Q.; Kong, H.; Wang, R.; Xu, W. Analysis of spatio-temporal characteristics of air pollution and its impact factors in Henan province during 2015–2021. Meteorol. Environ. Sci. 2024, 47, 24–33. [Google Scholar]
  154. Tang, X.; Zhang, Y.; Shao, M. Atmospheric Environmental Chemistry, 2nd ed.; Higher Education Press: Beijng, China, 2006; pp. 232–236. [Google Scholar]
  155. Wang, L.; Li, M.; Yu, S.; Chen, X.; Li, Z. Unexpected rise of ozone in urban and rural areas, and sulfur dioxide in rural areas during the coronavirus city lockdown in Hangzhou, China: Implications for air quality. Environ. Chem. Lett. 2020, 18, 1713–1723. [Google Scholar] [CrossRef]
  156. Li, Y.; Shi, G.; Chen, Z. Spatial and temporal distribution characteristics of ground-level nitrogen dioxide and ozone across China during 2015–2020. Environ. Res. Lett. 2021, 16, 124031. [Google Scholar] [CrossRef]
  157. Silver, B.; He, X.; Arnold, S.R.; Spracklen, D.V. The impact of COVID-19 control measures on air quality in China. Environ. Res. Lett. 2020, 15, 084021. [Google Scholar] [CrossRef]
  158. Zhang, L.; Mu, Y.; Cai, Y.; Chen, X. Analysis of pollution characteristics of atmospheric VOCs and their ozone formation potential in Nanjing. Admin. Technol. Environ. Monit. 2024, 36, 65–69. [Google Scholar]
  159. Zhang, L.; Jacob, D.J.; Bowman, K.W.; Logan, J.A.; Turquety, S.; Hudman, R.C.; Li, Q.; Beer, R.; Worden, H.M.; Worden, J.R.; et al. Ozone-CO correlations determined by the TES satellite instrument in continental outflow regions. Geophys. Res. Lett. 2006, 33. [Google Scholar] [CrossRef]
  160. Cao, L. Pollution Characteristics and Health Risk Assessment of Odor-Causing Factors from Multi-Component Volatile Organic Compounds in Nanjing’s Atmosphere. Master’s Thesis, Nanjing University of Information Science and Technology, Nanjing, China, 2024. [Google Scholar]
  161. Benites-Morales, O.F.; Pacsi-Valdivia, S.A. AnÁlisis Temporal De Los Contaminantes AtmosfÉricos (NO2, O3 TroposfÉrico Y Co) Y Su RelaciÓn Con La Temperatura Del Aire Y La RadiaciÓn Solar En Lima Metropolitana. Ecol. Apl. 2023, 22, 17–25. [Google Scholar] [CrossRef]
  162. Yang, Y.; Wang, Y.; Zhou, P.; Yao, D.; Ji, D.; Sun, J.; Wang, Y.; Zhao, S.; Huang, W.; Yang, S.; et al. Atmospheric reactivity and oxidation capacity during summer at a suburban site between Beijing and Tianjin. Atmos. Chem. Phys. 2020, 20, 8181–8200. [Google Scholar] [CrossRef]
  163. Yang, Y.; Wang, Y.; Yao, D.; Zhao, S. Significant decreases in the volatile organic compound concentration, atmospheric oxidation capacity and photochemical reactivity during the National Day holiday over a suburban site in the North China Plain. Environ. Pollut. 2020, 263, 114657. [Google Scholar] [CrossRef]
  164. Zhao, Z.; Sun, N.; Zhou, W.; Ma, S.; Li, X.; Li, M. Chemical compositions in winter PM2.5 in Changzhou of the Yangtze River Delta Region, China: Characteristics and atmospheric responses along with the different pollution levels. Front. Environ. Sci. 2022, 10, 907735. [Google Scholar] [CrossRef]
  165. Yan, W.; Liu, D.; Kang, Z.; Xie, Z.; Sun, Y. The characteristics of ozone pollution and its relationship with meteorological factors in Jiangsu. J. Meteorol. Sci. 2019, 39, 477–487. [Google Scholar]
  166. Tan, P.H.; Chou, C.; Chou, C.C.K. Impact of urbanization on the air pollution “holiday effect” in Taiwan. Atmos. Environ. 2013, 70, 361–375. [Google Scholar] [CrossRef]
  167. Tan, P.H.; Chou, C.; Liang, J.Y.; Chou, C.C.K.; Shiu, C.J. Air pollution “holiday effect” resulting from the Chinese New Year. Atmos. Environ. 2009, 43, 2114–2124. [Google Scholar] [CrossRef]
Atmosphere 16 00559 g001
Figure 1. Keyword co-occurrence analysis of holiday effect.
Figure 1. Keyword co-occurrence analysis of holiday effect.
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Figure 2. Keyword flashpoint diagrams.
Figure 2. Keyword flashpoint diagrams.
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Figure 3. The interannual variation and regional distribution of studies on the holiday effect of O3 pollution in China over the past two decades (2004~2025).
Figure 3. The interannual variation and regional distribution of studies on the holiday effect of O3 pollution in China over the past two decades (2004~2025).
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Figure 4. The interannual and seasonal variations in the concentrations of O3, NO2, and CO during holidays and non-holidays in China over the past decade (2015~2024).
Figure 4. The interannual and seasonal variations in the concentrations of O3, NO2, and CO during holidays and non-holidays in China over the past decade (2015~2024).
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Figure 5. The Pearson correlation coefficient (r) between the NO2 (a), CO (b), and O3 concentrations for various holidays in China over the past decade (2015–2024), with statistical significance at p < 0.05.
Figure 5. The Pearson correlation coefficient (r) between the NO2 (a), CO (b), and O3 concentrations for various holidays in China over the past decade (2015–2024), with statistical significance at p < 0.05.
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Figure 6. Ten-year (2015–2024) averaged O3 concentration distribution on (a) NYD, (b) SF, (c) TSD, (d) MD, (e) DBF, (f) MAD, (g) CND, (h) WENDs, and (i) WDAYs in China.
Figure 6. Ten-year (2015–2024) averaged O3 concentration distribution on (a) NYD, (b) SF, (c) TSD, (d) MD, (e) DBF, (f) MAD, (g) CND, (h) WENDs, and (i) WDAYs in China.
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Figure 7. O3 concentration changes from 2015 to 2024 on (a) NYD, (b) SF, (c) TSD, (d) MD, (e) DBF, (f) MAD, (g) CND, (h) WENDs, and (i) WDAYs in China (ΔC% = (C2024 − C2015)/C2015 × 100%).
Figure 7. O3 concentration changes from 2015 to 2024 on (a) NYD, (b) SF, (c) TSD, (d) MD, (e) DBF, (f) MAD, (g) CND, (h) WENDs, and (i) WDAYs in China (ΔC% = (C2024 − C2015)/C2015 × 100%).
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Table 1. Distribution and percentage of tourist trips during major Chinese holidays in 2023 (data available at https://www.mct.gov.cn (accessed on 10 April 2025)).
Table 1. Distribution and percentage of tourist trips during major Chinese holidays in 2023 (data available at https://www.mct.gov.cn (accessed on 10 April 2025)).
HolidayDuration (Days)Tourist Arrivals (Billion)Daily Average (Million)Tourist Trip Percentage (%)
NYD30.5317571.08
SF73.0844006.30
TSD30.247920.49
MD52.7454805.6
DBF31.0635332.17
MAD&CND88.2610,32516.89
Other33632.99981.867.46
Table 2. The average concentrations of O3, NO2, and CO during different holidays in China over the past decade (2015~2024).
Table 2. The average concentrations of O3, NO2, and CO during different holidays in China over the past decade (2015~2024).
HolidayO3 Mean
(μg/m3)		O3 Range
(μg/m3)		NO₂ Mean
(μg/m3)		NO₂ Range
(μg/m3)		CO Mean
(mg/m3)		CO Range
(mg/m3)
NYD34.3428.67–44.6044.7034.37–60.121.310.96–2.00
SF57.2350.22–66.1119.6815.04–27.890.970.71–1.33
TSD71.0658.49–89.1826.3017.40–38.260.760.60–1.03
MD82.7070.31–93.6824.9418.48–34.660.710.53–0.91
DBF86.9273.98–110.8020.4414.50–29.220.710.56–1.00
MAD68.3858.48–95.0021.8914.86–28.180.760.59–1.03
CND61.3052.23–74.2224.6517.32–33.570.730.53–1.00
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Chen, S.; Chen, J.; Li, J.; Gong, D.; Wang, B. Holiday Effect of Ozone Pollution in China. Atmosphere 2025, 16, 559. https://doi.org/10.3390/atmos16050559

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Chen S, Chen J, Li J, Gong D, Wang B. Holiday Effect of Ozone Pollution in China. Atmosphere. 2025; 16(5):559. https://doi.org/10.3390/atmos16050559

Chicago/Turabian Style

Chen, Sijun, Jun Chen, Jiangyong Li, Daocheng Gong, and Boguang Wang. 2025. "Holiday Effect of Ozone Pollution in China" Atmosphere 16, no. 5: 559. https://doi.org/10.3390/atmos16050559

APA Style

Chen, S., Chen, J., Li, J., Gong, D., & Wang, B. (2025). Holiday Effect of Ozone Pollution in China. Atmosphere, 16(5), 559. https://doi.org/10.3390/atmos16050559

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