Yasong Li1, Tijian Wang This email address is being protected from spambots. You need JavaScript enabled to view it.2, Qin’geng Wang1, Yawei Qu3, Hao Wu4, Min Xie2, Mengmeng Li2, Shu Li2, Bingliang Zhuang2 

1 School of the Environment, Nanjing University, Nanjing 210023, China
2 School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
3 College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211112, China
4 Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China


Received: April 5, 2023
Revised: July 5, 2023
Accepted: July 29, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.230060  


Cite this article:

Li, Y., Wang, T., Wang, Q., Qu, Y., Wu, H., Xie, M., Li, M., Li, S., Zhuang, B. (2023). Spatiotemporal Variations of PM2.5 and O3 Relationship during 2014–2021 in Eastern China. Aerosol Air Qual. Res. 23, 230060. https://doi.org/10.4209/aaqr.230060


HIGHLIGHTS

  • Spatial-temporal evolution of relationship between PM2.5 and O3 were investigated.
  • The PM2.5 level decreased significantly, and O3 increased first and then decreased.
  • The PM2.5-O3 correlation demonstrated noticeable spatial and seasonal variations.
  • The association between PM2.5 and O3 tended to become more positive.
 

ABSTRACT


In recent times, there has been a noteworthy reduction in the mass concentration of PM2.5 in the atmosphere of China. On the other hand, ozone (O3) pollution has intensified during this period. Conducting a comprehensive spatiotemporal analysis of the relationship between them is crucial to gain insights into their interaction mechanisms and developing regulation measures. We quantified the spatiotemporal variations of them in the Yangtze River Delta (YRD) utilizing long-term integrated data during 2014–2021 and examined the evolution of their relationship at different time scales through statistical analysis. Based on our investigations, it was found that the yearly average level of PM2.5 presented a consistent diminish, falling from 55.8 µg m3 (2014) to 31.9 µg m–3 (2021), while O3 increased by 7.2% during 2014–2017 but decreased by 1.9% in 2018–2021. Their relationship was mainly negative during 2015–2021, but more positive correlations were observed after 2015. These two pollutants displayed a positive correlation during the spring and summer seasons while demonstrating a negative correlation in the autumn and winter seasons. Spatially, Anhui was exhibiting more severe PM2.5 pollution, and Jiangsu was experiencing more pronounced O3 pollution. The negative correlation was mainly concentrated in inland areas, while a positive correlation mostly occurred in coastal cities. More negative correlations were observed in Anhui with higher PM2.5, and more positive correlations were found in Shanghai with lower PM2.5. Our study has revealed substantial spatiotemporal fluctuations in the level and correlation of the two pollutants. It was emphasized that due to the reduction of PM2.5 caused by strict pollution control policies in the last couple of years, the correlation between them tends to be more positive. These findings serve as a valuable reference for formulating a reasonable collaborative control scheme for these two pollutants.


Keywords: Yangtze River Delta, O3, Spatiotemporal variation, Correlation, PM2.5


1 INTRODUCTION


The deleterious impact of atmospheric pollutants on humans, crops, and climate is well documented (Sillmann et al., 2021; Pandya et al., 2022; Yadav et al., 2022; Ma et al., 2023). Among these pollutants, PM2.5 and O3 have been identified as major contributors. The atmospheric presence of volatile organic compounds (VOCs) and nitrogen oxides (NOx) can induce a chemical reaction with OH radicals under certain light conditions, resulting in the production of O3. This process also entails the oxidation of sulfur dioxide (SO2), NOx, and VOCs, producing secondary particulate matter. This phenomenon has been extensively studied in published literature (Wang et al., 2022).

Complex interaction exists between PM2.5 and O3: first, as an oxidant, O3 can improve the oxidation capacity and promote the oxidation of SO2, nitrogen dioxide (NO2), and nitrous pentoxide (N2O5). 

Additionally, O3 can enhance the oxidation of secondary organic aerosol (SOA) and expedite the formation of fresh ions, consequently influencing the levels of PM2.5 (Wang et al., 2016a; Chan et al., 2017; Li et al., 2017a). Second, the latest studies have demonstrated that PM2.5 has the potential to attenuate ground-level ultraviolet radiation through extinction, thus affecting the photolysis rate and changing the speed of O3 generation and consumption (Gao et al., 2020). Third, particles such as black carbon, sand dust, sea salt, etc., can serve as reaction surfaces for heterogeneous chemical reactions, promote the heterogeneous absorption and reaction process of O3, N2O5, NO2, SO2, etc., and thus change the concentration of them (Fenidel et al., 1995; Ramachandran, 2015; Li et al., 2017b; Li et al., 2019). Finally, particles can also affect atmospheric radiation through direct or indirect effects, which sequentially affect the transmission of pollutants and other substances, thus affecting the concentration of pollutants (Boynard et al., 2014; Xing et al., 2017; Wang et al., 2020a). These interactions exhibit three distinct patterns: positive correlation, non-correlation, and negative correlation.

Over the past few years, both PM2.5 and O3 environmental issues have been existing in YRD. While active efforts have been made to alleviate the pollution of PM2.5, the problem of deteriorating O3 pollution persists. Dai et al. (2021) reported an increase of 49.5% in the concentration of maximum daily 8-hour average O3 (MDA8 O3) in the YRD region from 2014 to 2019. Meanwhile, While the PM2.5 levels showed a significant decrease of 22.1%, they remained higher than those reported in other locations such as Zhuhai and Shenzhen (Zhang and Cao, 2015), as well as the United States and South Korea. In 2020, Wang et al. (2020b) conducted a spatiotemporal analysis of the two pollutants’ distribution across China. The findings indicate increased PM2.5 concentrations in the inland cities of the YRD, while central cities exhibited higher levels of O3. In several regions of China, including Beijing, YRD, Tianjin, and Hebei, the seasonal peak of PM2.5 concentration is generally observed during the winter months. Conversely, O3 pollution tends to be more severe in summer (Tao et al., 2017). This pattern of seasonal pollution is a well-documented phenomenon in China, as evidenced by numerous published studies (Latha and Badarinath, 2005; Tong et al., 2017; Mukta et al., 2020; Li et al., 2021; Luo et al., 2022).

Numerous studies have investigated the associations between the two pollutants. Ding et al. (2013) analyzed the levels of them in Nanjing for one year (August 2011–July 2012), and it was found that a positive correlation was observed between the secondary PM2.5 and O3 in summer, indicating that the high concentration of oxidant increased the transmutation of NOx and SO2. A study by Wang et al. (2014) observed a weak positive correlation in 31 provincial capitals from 2013 to 2014. Qiu et al. (2022) explored the spatiotemporal evolution of correlation over China. Their findings indicate a significant pattern of spatial and temporal distribution. The correlation in the southern regions was higher than in the northern regions. Additionally, a higher correlation was observed during the summer, whereas a lower correlation was found during the winter. Several examinations indicated that those two pollutants exhibit distinct correlations at daily and hourly time scales, possibly attributed to the varied associations between PM2.5 components and O3 (Weaver et al., 2009; Huang et al., 2021). Although some investigations have been implemented on the relationship between them, most of them focus on their relationship in a short period of about one year.

We aim to examine the pollution characteristics of these two pollutants over YRD over eight years from 2014 to 2021 by discussing the hourly concentrations and determining the spatiotemporal variation of the relationship between them across different time scales through correlation analysis. The data and methodology were outlined in Section 2. The pollution characteristics were discussed in Sections 3.1 and 3.2. In the present article, we have presented the spatial-temporal evolution of the correlation between the two variables in Sections 3.3 and 3.4, focusing on daily and hourly scales, respectively. Finally, we debated pertinent phenomena and offered our interpretation in Section 3.5.

 
2 METHODS


 
2.1 Study Region

The region depicted in Fig. 1, situated near the estuary of the Yangtze River in eastern China, is known as the YRD. It is among the areas in China that experience significant levels of PM2.5 and O3 pollutants. It includes Shanghai, as well as the provinces of Jiangsu (13 cities), Zhejiang (11 cities), and Anhui (16 cities).

 
Fig. 1. The geographic distributions of 41 cities with observed PM2.5 and O3 concentrations over YRD.Fig. 1. The geographic distributions of 41 cities with observed PM2.5 and O3 concentrations over YRD.


2.2 Data Sources

Eight-year data of 41 cities from May 2014 to December 2021, based upon PM2.5 and O3 hourly concentrations, were taken from the China National Environmental Monitoring Centre (CNEMC) (http://106.37.208.233:20035/). According to the Technical Specifications for Environmental Air Quality Assessment in China (Trial) (HJ 633 – 2013), We first carried out quality control on the monitoring data. Manual checks were performed during data processing to eliminate missing values and outliers. Following the Chinese National Ambient Air Quality Standard (NAAQS), when there are more than 20 hours of effective data in a day, calculate the daily mean concentration of PM2.5. When there are at least 6 hours of effective value every 8 hours, calculate the 8-hour moving average O3 concentration (Zhao et al., 2016).

 
2.3 Correlation Analysis

The present study computes the Spearman correlation coefficient (SCR) to examine the correlation between those two pollutants at both hourly and daily time scales. Hourly SCR is calculated from their hourly concentrations, whereas daily SCR is determined using the daily concentrations of PM2.5 and MDA8 O3. In contrast to the Pearson correlation coefficient, the SCR is not constrained by two conditions: (1) both groups of data should meet normal distribution or close to normal unimodal distribution; (2) both groups of data are continuous, and a monotone function can be used to describe the relationship between the two groups of data (Qiu et al., 2022). SCR < –0.1 indicate negatively correlated, SCR > 0.1 indicate positively correlated, and –0.1 < SCR < 0.1 indicate no statistical correlation between the two data groups.

 
3 RESULTS AND DISCUSSION


 
3.1 Characteristics of PM2.5 and O3


3.1.1 Spatiotemporal distribution

Fig. 2 illustrated the spatiotemporal patterns of average MDA8 O3 and PM2.5 concentrations across the YRD region from 2014 to 2021. The average PM2.5 concentration PM2.5 kept reducing during 2014–2021 (from 55.8 µg m–3 to 31.9 µg m–3). This symbolizes a decline of about 23.9 µg m–3 or 42.8%, and the annual decrease rate of regional PM2.5 concentrations was 6.1% yr–1. Compared to PM2.5, the MDA8 O3 exhibited an evident pattern of initial ascent followed by descent (Fig. 2(b)). Specifically, it rose steadily from 96.3 µg m–3 to 103.2 µg m–3 (2014–2017) and then decreased at an annual rate of 1.9% during 2018–2021 (from 101.2 µg m–3 to 95.5 µg m–3). Spatially, the spatial distribution of PM2.5 concentrations between 2014–2021 displayed a notable pattern, with elevated levels in the northern region and reduced levels in the southern region (Fig. 2(a)). Concerning individual provinces, from 2014 to 2021, the provinces of Anhui and Jiangsu exhibited extreme PM2.5 levels (Table 1), with their average concentrations being 49.3 µg m–3 and 48.2 µg m–3, respectively. Regarding MDA8 O3, Shanghai (in 2014, 2016–2017) and Jiangsu Province (in 2018–2021) exhibited the highest concentrations (Fig. 2(b)). Generally, the YRD region presently faces significant air pollution challenges, with the main problems being the high PM2.5 in Anhui Province and the high O3 in Jiangsu Province.

Fig. 2. Spatial distributions of (a) PM2.5 and (b) MDA8 O3concentrations in YRD during 2014–2021. The figure in the upper right corner signifies the annual regional mean concentration (µg m–3).Fig. 2. Spatial distributions of (a) PM2.5 and (b) MDA8 O3concentrations in YRD during 2014–2021. The figure in the upper right corner signifies the annual regional mean concentration (µg m–3).

Table 1. Annual average of PM2.5 and MDA8 O3 in various provinces in YRD from 2014 to 2021.

 
3.1.2 Seasonal variation

The seasonal variation of PM2.5 and MDA8 O3 from 2014 to 2021 was investigated in Fig. S1 and Fig. S5. The seasonal variation in PM2.5 concentration followed a "U"-shaped pattern, with the highest levels occurring during winter. This trend can be attributed to the combination of consistent weather patterns and heightened anthropogenic emissions. The heating requirement also contributed significantly to PM2.5 (Sun et al., 2013). The accumulation of PM2.5 is controlled by an assortment of atmospheric aspects, such as the height and temperature of the lower boundary layer, as well as solar radiation. These factors can combine during winter to create conditions that favor PM2.5 accumulation (Xiao et al., 2015). Conversely, the seasonal variation of MDA8 O3 concentrations showed an inverted "U" pattern, with higher O3 observed during the spring and summer. The observed pattern can primarily be attributed to the decreased sensitivity of O3 to radiation during the winter season (Wang and Fang, 2016).

Fig. 7 displayed the interannual seasonal variation in PM2.5 and MDA 8 O3 during 2014–2021, respectively. The concentrations of PM2.5 demonstrated a diminishing drift across all seasons, in line with the annual trend, with declining rates of 5.4% yr–1, 4.6% yr–1, 6.3% yr–1, and 4.3% yr–1 in spring, summer, autumn, and winter, respectively. On the contrary, the tendency of MDA8 O3 in spring, summer, and autumn was consistent with that of the entire year (increasing and then decreasing). At the same time, concentrations of MDA8 O3 continued to rise during winter, with annual growth rates of 2.7% yr–1 during 2014–2017 and 6.6% yr–1 during 2018–2021. These findings indicate the possibility of increased O3 pollution over YRD during winter.

 
3.1.3 Monthly variations

Fig. 3 illustrated the monthly variation in concentrations of those two pollutants. In this study, we observed higher PM2.5 from November to February, whereas MDA8 O3 levels were higher from April to October. This finding suggested that wintertime emission and weather conditions increase the likelihood of PM2.5 pollution (Cai et al., 2017; Zhang et al., 2018), whereas, in warmer months, the elevated temperature and increased radiation favor photochemical reaction and the generation of O3 (Wang et al., 2018; Wang et al., 2023). Notably, during June and July, there was a notable reduction in the concentration of both PM2.5 and MDA8 O3, as the YRD region has transitioned into the "plum rain" period starting from mid-June to mid-July, marked by increased precipitation. Cloud cover and increased rainfall impede solar radiation, resulting in a diminished magnitude of photochemical reactions and consequent reduction in O3 levels (Lu et al., 2019).

 Fig. 3. The monthly variation in (a) PM2.5 and (b) MDA8 O3.Fig. 3. The monthly variation in (a) PM2.5 and (b) MDA8 O3.

 
3.2 Excess Rate of PM2.5

In this study, we defined daily mean PM2.5 concentration exceeding 75 µg m–3 and MDA8 O3 concentration exceeding 160 µg m–3 as PM2.5 and O3 exceeding standards, respectively. It is important to note that these definitions align with prior research (Dai et al., 2021; Wang et al., 2023) and current air quality guidelines. The excess rate of PM2.5, MDA8 O3, and both PM2.5 and MDA8 O3 during 2014–2021 were presented in Table S1 and Fig. S2. The highest percentage of PM2.5 exceeding the standard was observed in Anhui province, with a rate of 17.5%, including 24.1% in Suzhou, 22.2% in Fuyang, and 21.2% in Bozhou (Fig. S2(a)). Jiangsu Province had the highest rate of MDA8 O3 pollution, with an exceedance rate of 12.5%, including 16.8% in Wuxi, 16.0% in Changzhou, and 14.7% in Yangzhou (Fig. S2(b)). The highest number of exceeding standard days for both PM2.5 and O3 (Fig. S2(c)) was observed in Yangzhou, Changzhou, and Wuxi (Sha et al., 2019). From 2014 to 2021, interannual changes in polluted days (Table S1) showed that most cities in the YRD experienced an initial increase and subsequent decrease in O3 pollution and a reduction in PM2.5 and co-polluted days. The findings underscore the intricate interplay between PM2.5 and O3 and the necessity of joint management of these contaminants.

 
3.3 Relationship between PM2.5 and O3


3.3.1 Temporal evolution

Fig. 4 and Fig. S3 depicted the interannual variation of PM2.5 and MDA8 O3 concentrations and their SCR in YRD from 2015 to 2021. The red, white, and blue denoted positive, no, and negative correlation, respectively. The results indicated that in 2015, the bulk of cities exhibited a significant negative correlation, with only Huaibei in Anhui Province demonstrating a weak positive correlation. After 2015, the negative correlation weakened, and a growing number of cities, such as Shanghai, Wenzhou, Taizhou, and Zhoushan, displayed positive or no correlation. These findings suggested a decline in the suppressive impact of PM2.5 on O3 and a possible increase in the oxidation of O3 on PM2.5. Moreover, during the study period, some cities shifted from positive to no correlation, such as Huangshan (2016–2017) and Huaibei (2015–2016). The proportion of cities exhibiting different correlation patterns between PM2.5 and O3 in YRD during 2015–2021 was presented in Fig. S4. Although the negative correlation remains prevailing, the proportion of positive correlations has increased since 2015. Furthermore, the correlation between the two pollutants may vary across different cities or even exhibit anti-correlation in different years, indicating that the effect of various interactions can change over time.

Fig. 4. The interannual variation of PM2.5 and MDA8 O3 concentrations and their SCR (Spearman correlation coefficient) in the YRD region from 2015 to 2021.Fig. 4. The interannual variation of PM2.5 and MDA8 O3 concentrations and their SCR (Spearman correlation coefficient) in the YRD region from 2015 to 2021.

We believe that the decrease in primary PM2.5 has significantly shaped the relationship between PM2.5 and O3 from 2015 to 2020. Numerous studies have highlighted the inhibitory effects of PM2.5 on O3 through various mechanisms, including direct influences on photolysis (Dickerson et al., 1997; Gao et al., 2023; Qu et al., 2023), heterogeneous reactions (Lou et al., 2014; Tan et al., 2020; Wu et al., 2020), radiation feedback (Qu et al., 2021; Yang et al., 2022), and more. To explore this further, we compared the anthropogenic emissions of SO2, NOx, carbon monoxide (CO), VOCs, PM2.5, and black carbon (BC) from 2015 to 2020 (Fig. 5 and Table 2). We obtained the data from the Multi-resolution Emission Inventory for China (MEIC) (http://meicmodel.org/?page_id=560) developed by Tsinghua University for the years 2015 to 2017 and referred to Zheng et al. (2021) for the years 2018 to 2020. Our analysis revealed substantial reductions in these emissions during this period. Specifically, SO2, NOx, CO, VOCs, PM2.5, and BC exhibited decreases of 56.2%, 20.6%, 19.7%, 14.9%, 37.7%, and 28.6%, respectively. The significant reduction in PM2.5 resulted in a weakened inhibitory effect of PM2.5 on O3, consistent with our study's observed weaker negative correlation between PM2.5 and O3. Additionally, previous research (Zhu et al., 2021) has indicated that the weakened radiation feedback effect, caused by the substantial decline in PM2.5 due to Coronavirus Disease 2019 (COVID-19) lockdown in 2020, further enhanced the increase of O3 levels.

Fig. 5. Changes in China's anthropogenic emissions (Tg) from 2015 to 2020. The species include (a) SO2, (b) NOx, (c) CO, (d) VOCs, (e) PM2.5, and (f) BC.Fig. 5. Changes in China's anthropogenic emissions (Tg) from 2015 to 2020. The species include (a) SO2, (b) NOx, (c) CO, (d) VOCs, (e) PM2.5, and (f) BC.

Table 2. Changes in China's anthropogenic emissions (Tg) from 2015 to 2020.

 
3.3.2 Spatial distribution

In this study, we present the spatial distribution of SCR between those two pollutants in the YRD region from 2015 to 2021, as depicted in Fig. 6. Additionally, we provide a tabular summary of the number of cities within the YRD region that exhibit varying PM2.5 and O3 relationships across different provinces from 2014 to 2021, as detailed in Table S2. Our findings reveal that negative correlations are prevalent in inland areas, including Anhui and Jiangsu, whereas positive correlations are predominantly observed in Shanghai and the coastal districts of Zhejiang. Coastal regions of Jiangsu show no significant correlation. Generally, the two pollutants were mostly uncorrelated or positively correlated below 30°N while mainly negatively correlated above 30°N. The negative correlation between them may be owing to the extinction effect and the heterogeneous chemical reaction on the surface of PM2.5 (Qu et al., 2023). The positive correlation in coastal areas may be thanks to the monsoon. The conversion of gas pollutants into new particles can be facilitated by several meteorological factors, including a maritime air mass, relatively stable wind speed and direction, and strong solar radiation (Perry and Hobbs, 1994). These factors have been reported to promote the production of secondary organic aerosols and other particle species in various locations worldwide (which increases PM2.5). Additionally, the monsoon can somewhat clean the suspended solids and other substances in the air, thus enhancing the surface solar radiation (which increases O3) (Perry and Hobbs, 1994).

Fig. 6. The geographic distribution of SCR (Spearman correlation coefficient) between PM2.5 and O3 during 2015–2021.Fig. 6. The geographic distribution of SCR (Spearman correlation coefficient) between PM2.5 and O3 during 2015–2021.

 
3.3.3 Seasonal differences

Fig. S5 and Fig. 7 illustrated the seasonal difference and interannual season variation of SCR between PM2.5 and O3 during 2014–2021. The results demonstrated a clear and strong positive correlation during the spring and summer, whereas, during autumn and winter, they were primarily irrelevant and negatively correlated. Fig. S6 illustrated the spatial distribution of the SCR over the YRD region from 2014 to 2021 across the four seasons. During the spring season between 2014 and 2021, a positive correlation was observed. However, in 2017, there were more cities where the correlation was either weak or negative. In summer, a strong positive correlation was observed every year. In autumn, the correlation gradually shifted from negative to positive. Despite the persistence of a negative correlation during winter, we witnessed a gradual increase in the number of cities where a positive correlation was detected for half of the study period. In brief, our study found a predominantly positive correlation between daily levels of those two pollutants in the YRD during the spring, summer, and autumn. While there was mainly a negative correlation during winter, the number of cities demonstrating a positive correlation has increased over time.

Fig. 7. The interannual variation of PM2.5 and MDA8 O3 concentrations and their SCR (Spearman correlation coefficient) in (a) spring, (b) summer, (c) autumn, and (d) winter seasons during 2014–2021.Fig. 7. The interannual variation of PM2.5 and MDA8 O3 concentrations and their SCR (Spearman correlation coefficient) in (a) spring, (b) summer, (c) autumn, and (d) winter seasons during 2014–2021.Fig. 7. The interannual variation of PM2.5 and MDA8 O3 concentrations and their SCR (Spearman correlation coefficient) in (a) spring, (b) summer, (c) autumn, and (d) winter seasons during 2014–2021.

 
3.4 Diurnal Variation of PM2.5 and O3 Relationship

The diurnal variation of the SCR, PM2.5, and O3 concentrations over YRD during 2014–2021 was shown in Fig. 8. The study revealed a diurnal pattern in the concentration of O3, with a singular peak during the day and lower levels at night. Conversely, PM2.5 exhibited an inverse pattern, with a single peak of reduced concentration during the day and elevated concentrations at night. The O3 peaked at 15:00 and gradually decreased until reaching the lowest value around 07:00 the following day. The afternoon peak in O3 was primarily ascribed to the delay in O3's response to solar radiation and temperature, as it takes time for the precursor to undergo complex photochemical reactions and generate O3. The elevated concentration of PM2.5 during the initial peak was attributed to the surge of anthropogenic particulate matter during this period, compounded by a shallow atmospheric mixing layer and the occurrence of a temperature inversion layer close to the ground, which created unfavorable dispersion conditions for pollutants.

Fig. 8. The diurnal variation of the SCR (Spearman correlation coefficient), PM2.5, and O3 concentrations over YRD during 2014–2021.Fig. 8. The diurnal variation of the SCR (Spearman correlation coefficient), PM2.5, and O3 concentrations over YRD during 2014–2021.Fig. 8. The diurnal variation of the SCR (Spearman correlation coefficient), PM2.5, and O3 concentrations over YRD during 2014–2021.

The study examined the hourly relationship between those two pollutants in the YRD region from 2014 to 2021. The results indicate a weak negative correlation, with a stronger negative correlation observed at night compared to the afternoon, as shown in Fig. 8. Further analysis of the spatial distribution of the PM2.5-O3 relationship across the YRD revealed that the negative correlation dominates at every hour of the day (Fig. S7). However, the strength of the negative correlation weakens during the day, with some coastal cities exhibiting a positive correlation between 13:00 to 17:00, including Wenzhou, Taizhou, and Zhoushan. The study suggests that the hourly relationship between PM2.5 and O3 in the YRD is primarily negative, with some temporal and spatial variations.

 
3.5 Discussions

Examining the spatiotemporal change in the association between PM2.5 and O3 is fundamental to comprehensively investigating their interaction mechanism. We quantified the correlation by analyzing the SCR of those two pollutants. Our findings revealed that the correlation exhibited low-high spatial-temporal distribution characteristics, with low values in the north, winter, and night and high values in the south, summer, and daytime. During the summer, PM2.5 and O3 showed a strong positive correlation due to intense sunlight promoting photochemistry, leading to O3 formation and accumulation, which facilitated the production of secondary components in PM2.5 (Li et al., 2012; Zhu et al., 2019; Yao et al., 2021). Moreover, the intense photochemical process contributed significantly to the development of secondary constituents within PM2.5. In contrast, in the winter, primary emissions appear to have a more substantial impact on PM2.5 than during the summer, thereby contributing to a heightened proportion of primary constituents in PM2.5. Additionally, the photochemical formation of O3 declines, resulting in opposite patterns in PM2.5 and O3 levels (Bian et al., 2012). Coastal areas are characterized by their proximity to the ocean, leading to elevated wind speeds and abundant moisture content. The high wind speed facilitates the dispersion and removal of pollutants, consequently influencing the concentration of pollutants. Additionally, moist water vapor can increase hygroscopic growth of aerosol, such as sea salt, resulting in stronger extinction ability (Qin et al., 2022; Wang et al., 2023). Consequently, the relationship between PM2.5 and O3, apart from emission sources, was influenced by natural disparities between coastal areas and inland cities, resulting in different spatial patterns in their correlations.

Regarding the interannual evolution of correlation, the relationship between the two pollutants in the YRD region exhibited a significant negative trend, but the strength of the correlation weakened after 2015. This trend was closely linked to precursor emissions, O3 generation mechanisms, and weather conditions (Wang et al., 2016b, 2017). Significantly, the observed negative correlation between these variables during nighttime hours was primarily attributed to the consumption of O3 resulting from heterogeneous chemical reactions occurring on the surface of PM2.5. Previous research has indicated that the nocturnal reaction of O3 and NO2 generates N2O5, which can be further hydrolyzed to facilitate the formation of fine nitrate particles (Wen et al., 2015). These particles contribute to an elevation in PM2.5 levels during nighttime in the North China region. During the daytime, elevated concentrations of O3 and highly oxidizing atmospheric conditions facilitated the production of secondary aerosols, thereby promoting the formation of new particles. However, PM2.5 can alter the radiation balance and photolysis rate by absorbing and scattering solar radiation during the daytime, thereby affecting O3 (Qu et al., 2023). As a result, the correlation between these two pollutants remains negative due to the interaction competition. Our survey highlights that as the PM2.5 level has rapidly declined in the YRD, their negative correlation has progressively attenuated and even shifted towards a positive association during the winter.

 
4 CONCLUSIONS


We explored the spatiotemporal fluctuations of PM2.5 and O3 over YRD, including 41 cities, based on hourly ground-level monitoring data for 2014–2021. The spatiotemporal evolution of the relationship between those two pollutants on daily and hourly scales in YRD was investigated. To conclude, the major points were summarized as follows:

During 2014–2021, the mean PM2.5 over YRD reduced on the average annual rate of –6.1% yr–1 (from 55.8 µg m–3 to 31.9 µg m–3). For O3, the regional MDA8 O3 showed an obvious increase of 7.2% in 2014–2017 and then reduced on an average annual rate by 1.9% yr–1 during 2018–2021. The concentration of PM2.5 exhibited significant pollution levels during the winter season, while O3 demonstrated substantial contamination during the summer. Spatially, from 2014 to 2021, there was a noticeable variation in PM2.5 levels, with higher in the northern and lower in the southern regions. Anhui province had relatively high levels of PM2.5 pollution, while Jiangsu province had higher levels of O3 pollution. Throughout the studied period, there was frequent co-pollution of PM2.5 and O3 in the YRD, with a more significant number of co-polluted days (more than 55 days) observed in certain cities in Jiangsu province, namely Yangzhou, Changzhou, Wuxi, and Zhenjiang.

The spatiotemporal variability of the relationship between PM2.5 and O3 during 2014–2021 was studied on two timescales. Our results indicated that the relationship between them during 2014–2021 was mainly negative correlation, but with the increase of years, especially after 2015, the negative correlation intensity weakens, and the proportion of positive correlation increases. Spatially, the strong negative correlation was primarily concentrated in inland areas, while the positive correlation was chiefly concentrated in Shanghai and Zhejiang coastal areas. Seasonally, PM2.5 and O3 revealed a positive correlation during the spring and summer. In contrast, during the autumn and winter, although they were primarily negatively correlated, the number of positively correlated cities increased. A weak negative correlation largely characterized the hourly association basis. However, in the afternoon, certain coastal cities exhibited a positive correlation. This finding is interesting as it suggests regional variations in the relationship, particularly in coastal areas. Further research is warranted to investigate the underlying factors contributing to this variability.

In conclusion, the present study provides novel insights into the spatiotemporal variability of the relationship between PM2.5 and O3 over the period of 2014–2021. Our results demonstrate that the concentration and correlation between these pollutants exhibit significant spatial and seasonal variations. Notably, we observe a tendency for the correlation to become more positive as the concentration of PM2.5 decreases. This trend may be attributed to the reduced inhibition of O3 by PM2.5 and their shared precursors (Shao et al., 2021), which may result in a more pronounced positive correlation between these two pollutants. These results enhance our comprehension of the complex interactions between these air pollutants in this region and may have ramifications for advancing more effective air pollution management strategies. However, it is worth noting that our conclusions are based solely on observational and analytical studies and do not incorporate any modeling research.

 
ACKNOWLEDGMENTS 


The National Key Basic Research & Development Program of China (2020YFA0607802, 2019YFC0214603), the National Natural Science Foundation of China (42077192, 41621005), the Creative talent exchange program for foreign experts in the Belt and Road countries, and the Emory University-Nanjing University Collaborative Research Grant provided support for this work.

 
ADDITIONAL INFORMATION AND DECLARATIONS



Disclaimer

The authors attest that they have no competing interests, whether financial or personal, that could have influenced the content or outcomes of this paper.


REFERENCES


  1. Bian, J.C., Pan, L.L., Paulik, L., Vomel, H., Chen, H.B., Lu, D.R. (2012). In situ water vapor and ozone measurements in Lhasa and Kunming during the Asian summer monsoon. Geophys. Res. Lett. 39, L19808. https://doi.org/10.1029/2012gl052996

  2. Boynard, A., Clerbaux, C., Clarisse, L., Safieddine, S., Pommier, M., Van Damme, M., Bauduin, S., Oudot, C., Hadji-Lazaro, J., Hurtmans, D., Coheur, P.F. (2014). dFirst simultaneous space measurements of atmospheric pollutants in the boundary layer from IASI: A case study in the North China Plain. Geophys. Res. Lett. 41, 645–651. https://doi.org/10.1002/2013gl058333

  3. Cai, W., Li, K., Liao, H., Wang, H., Wu, L. (2017). Weather conditions conducive to Beijing severe haze more frequent under climate change. Nat. Clim. Change 7, 257–262. https://doi.org/​10.1038/nclimate3249

  4. Chan, K.L., Wang, S.S., Liu, C., Zhou, B., Wenig, M.O., Saiz-Lopez, A. (2017). On the summertime air quality and related photochemical processes in the megacity Shanghai, China. Sci. Total Environ. 580, 974–983. https://doi.org/10.1016/j.scitotenv.2016.12.052

  5. Dai, H.B., Zhu, J., Liao, H., Li, J.D., Liang, M.X., Yang, Y., Yue, X. (2021). Co-occurrence of ozone and PM2.5 pollution in the Yangtze River Delta over 2013-2019: Spatiotemporal distribution and meteorological conditions. Atmos. Res. 249, 105363. https://doi.org/10.1016/j.atmosres.2020.​105363

  6. Dickerson, R., Kondragunta, S., Stenchikov, G., Civerolo, K., Doddridge, B., Holben, B. (1997). The impact of aerosols on solar ultraviolet radiation and photochemical smog. Science 278, 827–830. https://doi.org/10.1126/science.278.5339.82

  7. Ding, A.J., Fu, C.B., Yang, X.Q., Sun, J.N., Zheng, L.F., Xie, Y.N., Herrmann, E., Nie, W., Petaja, T., Kerminen, V.M., Kulmala, M. (2013). Ozone and fine particle in the western Yangtze River Delta: an overview of 1 yr data at the SORPES station. Atmos. Chem. Phys. 13, 5813–5830. https://doi.org/10.5194/acp-13-5813-2013

  8. Fenidel, W., Matter, D., Burtscher, H., Schmidt-Ott, A. (1995). Interaction between carbon or iron aerosol particles and ozone. Atmos. Environ. 29, 967–973. https://doi.org/10.1016/1352-2310(95)00038-Z

  9. Gao, J., Li, Y., Zhu, B., Hu, B., Wang, L., Bao, F. (2020). What have we missed when studying the impact of aerosols on surface ozone via changing photolysis rates? Atmos. Chem. Phys. 20, 10831–10844. https://doi.org/10.5194/acp-20-10831-2020

  10. Gao, J., Li, Y., Xie, Z., Wang, L., Hu, B., Bao, F. (2023). Which aerosol type dominate the impact of aerosols on ozone via changing photolysis rates? Sci. Total Environ. 854, 158580. https://doi.org/​10.1016/j.scitotenv.2022.158580

  11. Huang, L., Sun, J.J., Jin, L., Brown, N.J., Hu, J.L. (2021). Strategies to reduce PM2.5 and O3 together during late summer and early fall in San Joaquin Valley, California. Atmos. Res. 258, 105633. https://doi.org/10.1016/j.atmosres.2021.105633

  12. Latha, K.M., Badarinath, K. (2005). Seasonal variations of PM10 and PM2.5 particles loading over tropical urban environment. Int. J. Environ. Health Res. 15, 63–68. https://doi.org/10.1080/​09603120400018964

  13. Li, C., McLinden, C., Fioletov, V., Krotkov, N., Carn, S., Joiner, J., Streets, D., He, H., Ren, X.R., Li, Z.Q., Dickerson, R.R. (2017a). India is overtaking China as the world's largest emitter of anthropogenic sulfur dioxide. Sci. Rep. 7, 14304. https://doi.org/10.1038/s41598-017-14639-8

  14. Li, L., Chen, C.H., Huang, C., Huang, H.Y., Zhang, G.F., Wang, Y.J., Wang, H.L., Lou, S.R., Qiao, L.P., Zhou, M., Chen, M.H., Chen, Y.R., Streets, D.G., Fu, J.S., Jang, C.J. (2012). Process analysis of regional ozone formation over the Yangtze River Delta, China using the Community Multi-scale Air Quality modeling system. Atmos. Chem. Phys. 12, 10971–10987. https://doi.org/10.5194/​acp-12-10971-2012

  15. Li, M., Wang, T., Han, Y., Xie, M., Li, S., Zhuang, B., Chen, P. (2017b). Modeling of a severe dust event and its impacts on ozone photochemistry over the downstream Nanjing megacity of eastern China. Atmos. Environ. 160, 107–123. https://doi.org/10.1016/j.atmosenv.2017.04.010

  16. Li, M., Wang, T., Xie, M., Li, S., Zhuang, B., Huang, X., Chen, P., Zhao, M., Liu, J. (2019). Formation and evolution mechanisms for two extreme haze episodes in the Yangtze River Delta region of China during winter 2016. J. Geophys. Res. 124, 3607–3623. https://doi.org/10.1029/​2019JD030535

  17. Li, Y., Yin, S., Yu, S., Bai, L., Wang, X., Lu, X., Ma, S. (2021). Characteristics of ozone pollution and the sensitivity to precursors during early summer in central plain, China. J. Environ. Sci. 99, 354–368. https://doi.org/10.1016/j.jes.2020.06.021

  18. Lou, S., Liao, H., Zhu, B. (2014). Impacts of aerosols on surface-layer ozone concentrations in China through heterogeneous reactions and changes in photolysis rates. Atmos. Environ. 85, 123–138. https://doi.org/10.1016/j.atmosenv.2013.12.004

  19. Lu, H., Lyu, X., Cheng, H., Ling, Z., Guo, H. (2019). Overview on the spatial-temporal characteristics of the ozone formation regime in China. Environ. Sci. Processes Impacts 21, 916–929. https://doi.org/10.1039/C9EM00098D

  20. Luo, Y., Zhao, T., Yang, Y., Zong, L., Kumar, K.R., Wang, H., Meng, K., Zhang, L., Lu, S., Xin, Y. (2022). Seasonal changes in the recent decline of combined high PM2.5 and O3 pollution and associated chemical and meteorological drivers in the Beijing–Tianjin–Hebei region, China. Sci. Total Environ. 838, 156312. https://doi.org/10.1016/j.scitotenv.2022.156312

  21. Ma, D., Wang, T., Wu, H., Qu, Y., Liu, Jian, Liu, Jane, Li, S., Zhuang, B., Li, M., Xie, M. (2023). The effect of anthropogenic emission, meteorological factors, and carbon dioxide on the surface ozone increase in China from 2008 to 2018 during the East Asia summer monsoon season. Atmos. Chem. Phys. 23, 6525–6544. https://doi.org/10.5194/acp-23-6525-2023

  22. Mukta, T.A., Hoque, M.M.M., Sarker, M.E., Hossain, M.N., Biswas, G.K. (2020). Seasonal variations of gaseous air pollutants (SO2, NO2, O3, CO) and particulates (PM2.5, PM10) in Gazipur: an industrial city in Bangladesh. Adv. Environ. Technol. 6, 195–209. https://doi.org/10.22104/​AET.2021.4890.1320

  23. Pandya, S., Gadekallu, T.R., Maddikunta, P.K.R., Sharma, R. (2022). A study of the impacts of air pollution on the agricultural community and yield crops (Indian context). Sustainability 14, 13098. https://doi.org/10.3390/su142013098

  24. Perry, K.D., Hobbs, P.V. (1994). Further evidence for particle nucleation in clear air adjacent to marine cumulus clouds. J. Geophys. Res. 99, 22803–22818. https://doi.org/10.1029/94JD01926

  25. Qin, M., Hu, A., Mao, J., Li, X., Sheng, L., Sun, J., Li, J., Wang, X., Zhang, Y., Hu, J. (2022). PM2.5 and O3 relationships affected by the atmospheric oxidizing capacity in the Yangtze River Delta, China. Sci. Total Environ. 810, 152268. https://doi.org/10.1016/j.scitotenv.2021.152268

  26. Qiu, Y.T., Wu, Z.T., Shang, D.J. (2022). The temporal and spatial distribution of the correlation between PM2.5 and O3 contractions in the urban atmosphere of China. Chin Sci Bull 67, 2008–2017. https://doi.org/10.1360/TB-2021-0765 (in Chinese)

  27. Qu, Y., Voulgarakis, A., Wang, T., Kasoar, M., Wells, C., Yuan, C., Varma, S., Mansfield, L. (2021). A study of the effect of aerosols on surface ozone through meteorology feedbacks over China. Atmos. Chem. Phys. 21, 5705–5718. https://doi.org/10.5194/acp-21-5705-2021

  28. Qu, Y., Wang, T., Yuan, C., Wu, H., Gao, L., Huang, C., Li, Y., Li, M., Xie, M. (2023). The underlying mechanisms of PM2.5 and O3 synergistic pollution in East China: Photochemical and heterogeneous interactions. Sci. Total Environ. 873, 162434. https://doi.org/10.1016/j.scitotenv.​2023.162434

  29. Ramachandran, S. (2015). New directions: Mineral dust and ozone - Heterogeneous chemistry. Atmos. Environ. 106, 369–370. https://doi.org/10.1016/j.atmosenv.2015.01.078

  30. Sha, T., Ma, X.Y., Jia, H.L., van der A, R.J., Ding, J.Y., Zhang, Y.L., Chang, Y.H. (2019). Exploring the influence of two inventories on simulated air pollutants during winter over the Yangtze River Delta. Atmos. Environ. 206, 170–182. https://doi.org/10.1016/j.atmosenv.2019.03.006

  31. Shao, M., Wang, W., Yuan, B., Parrish, D.D., Li, X., Lu, K., Wu, L., Wang, X., Mo, Z., Yang, S., Peng, Y., Kuang, Y., Chen, W., Hu, M., Zeng, L., Su, H., Cheng, Y., Zheng, J., Zhang, Y. (2021). Quantifying the role of PM2.5 dropping in variations of ground-level ozone: Inter-comparison between Beijing and Los Angeles. Sci. Total Environ. 788, 147712. https://doi.org/10.1016/j.scitotenv.​2021.147712

  32. Sillmann, J., Aunan, K., Emberson, L., Büker, P., Van Oort, B., O’Neill, C., Otero, N., Pandey, D., Brisebois, A. (2021). Combined impacts of climate and air pollution on human health and agricultural productivity. Environ. Res. Lett. 16, 093004. https://doi.org/10.1088/1748-9326/​ac1df8

  33. Sun, Y.W., Zhou, X.H., Wai, K.M., Yuan, Q., Xu, Z., Zhou, S.Z., Qi, Q., Wang, W.X. (2013). Simultaneous measurement of particulate and gaseous pollutants in an urban city in North China Plain during the heating period: Implication of source contribution. Atmos. Res. 134, 24–34. https://doi.org/10.1016/j.atmosres.2013.07.011

  34. Tan, Z., Hofzumahaus, A., Lu, K., Brown, S.S., Holland, F., Huey, L.G., Kiendler-Scharr, A., Li, X., Liu, X., Ma, N. (2020). No evidence for a significant impact of heterogeneous chemistry on radical concentrations in the North China Plain in summer 2014. Environ. Sci. Technol. 54, 5973–5979. https://doi.org/10.1021/acs.est.0c00525

  35. Tao, J., Zhang, L.M., Cao, J.J., Zhang, R.J. (2017). A review of current knowledge concerning PM2.5 chemical composition, aerosol optical properties and their relationships across China. Atmos. Chem. Phys. 17, 9485–9518. https://doi.org/10.5194/acp-17-9485-2017

  36. Tong, L., Zhang, H.L., Yu, J., He, M.M., Xu, N.B., Zhang, J.J., Qian, F.Z., Feng, J.Y., Xiao, H. (2017). Characteristics of surface ozone and nitrogen oxides at urban, suburban and rural sites in Ningbo, China. Atmos. Res. 187, 57–68. https://doi.org/10.1016/j.atmosres.2016.12.006

  37. Wang, D.F., Zhou, B., Fu, Q.Y., Zhao, Q.B., Zhang, Q., Chen, J.M., Yang, X., Duan, Y.S., Li, J. (2016a). Intense secondary aerosol formation due to strong atmospheric photochemical reactions in summer: observations at a rural site in eastern Yangtze River Delta of China. Sci. Total Environ. 571, 1454–1466. https://doi.org/10.1016/j.scitotenv.2016.06.212

  38. Wang, L., Zhao, B., Zhang, Y., Hu, H. (2023). Correlation between surface PM2.5 and O3 in eastern China during 2015–2019: Spatiotemporal variations and meteorological impacts. Atmos. Environ. 294, 119520. https://doi.org/10.1016/j.atmosenv.2022.119520

  39. Wang, P., Qiao, X., Zhang, H. (2020a). Modeling PM2.5 and O3 with aerosol feedbacks using WRF/Chem over the Sichuan Basin, southwestern China. Chemosphere 254, 126735. https://doi.org/10.1016/j.chemosphere.2020.126735

  40. Wang, T., Tham, Y.J., Xue, L., Li, Q., Zha, Q., Wang, Z., Poon, S.C.N., Dubé, W.P., Blake, D.R., Louie, P.K.K., Luk, C.W.Y., Tsui, W., Brown, S.S. (2016b). Observations of nitryl chloride and modeling its source and effect on ozone in the planetary boundary layer of southern China. J. Geophys. Res. 121, 2476–2489. https://doi.org/10.1002/2015JD024556

  41. Wang, T., Xue, L.K., Brimblecombe, P., Lam, Y.F., Li, L., Zhang, L. (2017). Ozone pollution in China: A review of concentrations, meteorological influences, chemical precursors, and effects. Sci. Total Environ. 575, 1582–1596. https://doi.org/10.1016/j.scitotenv.2016.10.081

  42. Wang, T., Xue, L.K., Feng, Z.Z., Dai, J.N., Zhang, Y.N., Tan, Y. (2022). Ground-level ozone pollution in China: a synthesis of recent findings on influencing factors and impacts. Environ. Res. Lett. 17, 063003.https://doi.org/10.1088/1748-9326/ac69fe

  43. Wang, Y.G., Ying, Q., Hu, J.L., Zhang, H.L. (2014). Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013-2014. Environ. Int. 73, 413–422. https://doi.org/10.1016/j.envint.2014.08.016

  44. Wang, Y., Gao, W., Wang, S., Song, T., Gong, Z., Ji, D., Wang, L., Liu, Z., Tang, G., Huo, Y., Tian, S., Li, J., Li, M., Yang, Y., Chu, B., Petäjä, T., Kerminen, V.M., He, H., Hao, J., Kulmala, M., et al. (2020b). Contrasting trends of PM2.5 and surface-ozone concentrations in China from 2013 to 2017. Natl. Sci. Rev. 7, 1331–1339. https://doi.org/10.1093/nsr/nwaa032

  45. Wang, Y.Y., Du, H.Y., Xu, Y.Q., Lu, D.B., Wang, X.Y., Guo, Z.Y. (2018). Temporal and spatial variation relationship and influence factors on surface urban heat island and ozone pollution in the Yangtze River Delta, China. Sci. Total Environ. 631–632, 921–933. https://doi.org/10.1016/j.​scitotenv.2018.03.050

  46. Wang, Z.B., Fang, C.L. (2016). Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration. Chemosphere 148, 148–162. https://doi.org/10.1016/j.​chemosphere.2015.12.118

  47. Weaver, C.P., Liang, X.Z., Zhu, J., Adams, P.J., Amar, P., Avise, J., Caughey, M., Chen, J., Cohen, R.C., Cooter, E., Dawson, J.P., Gilliam, R., Gilliland, A., Goldstein, A.H., Grambsch, A., Grano, D., Guenther, A., Gustafson, W.I., Harley, R.A., He, S., et al. (2009). A preliminary synthesis of modeled climate change impacts on U.S. regional ozone concentrations. Bull. Amer. Meteor. Soc. 90, 1843–1864. https://doi.org/10.1175/2009BAMS2568.1

  48. Wen, L., Chen, J., Yang, L., Wang, X., Xu, C., Sui, X., Yao, L., Zhu, Y., Zhang, J., Zhu, T., Wang, W. (2015). Enhanced formation of fine particulate nitrate at a rural site on the North China Plain in summer: The important roles of ammonia and ozone. Atmos. Environ. 101, 294–302. https://doi.org/10.1016/j.atmosenv.2014.11.037

  49. Wu, J., Bei, N., Hu, B., Liu, S., Wang, Y., Shen, Z., Li, X., Liu, L., Wang, R., Liu, Z. (2020). Aerosol–photolysis interaction reduces particulate matter during wintertime haze events. Proc. Natl. Acad. Sci. U.S.A. 117, 9755–9761. https://doi.org/10.1073/pnas.1916775117

  50. Xiao, Q.Y., Ma, Z.W., Li, S.S., Liu, Y. (2015). The impact of winter heating on air pollution in China. PLoS One 10, e0117311. https://doi.org/10.1371/journal.pone.0117311

  51. Xing, C.Z., Liu, C., Wang, S.S., Chan, K.L., Gao, Y., Huang, X., Su, W.J., Zhang, C.X., Dong, Y.S., Fan, G.Q., Zhang, T.S., Chen, Z.Y., Hu, Q.H., Su, H., Xie, Z.Q., Liu, J.G. (2017). Observations of the vertical distributions of summertime atmospheric pollutants and the corresponding ozone production in Shanghai, China. Atmos. Chem. Phys. 17, 14275–14289. https://doi.org/10.5194/​acp-17-14275-2017

  52. Yadav, P., Usha, K., Singh, B. (2022). Air pollution mitigation and global dimming: a challenge to agriculture under changing climate, in: Climate Change and Crop Stress, Elsevier, pp. 271–298. https://doi.org/10.1016/B978-0-12-816091-6.00015-8

  53. Yang, H., Chen, L., Liao, H., Zhu, J., Wang, W., Li, X. (2022). Impacts of aerosol–photolysis interaction and aerosol–radiation feedback on surface-layer ozone in North China during multi-pollutant air pollution episodes. Atmos. Chem. Phys. 22, 4101–4116. https://doi.org/10.5194/​acp-22-4101-2022

  54. Yao, Y.J., Wang, M.Y., Zeng, C.C. (2021). Study on the pollution characteristics and interaction between PM2.5 and O3 at different types of sites in Guangzhou. China Environ. Sci. 41, 4495–4506. (in Chinese)

  55. Zhang, Q.Q., Ma, Q., Zhao, B., Liu, X.Y., Wang, Y.X., Jia, B.X., Zhang, X.Y. (2018). Winter haze over North China Plain from 2009 to 2016: Influence of emission and meteorology. Environ. Pollut. 242, 1308–1318. https://doi.org/10.1016/j.envpol.2018.08.019

  56. Zhang, Y.L., Cao, F. (2015). Fine particulate matter (PM2.5) in China at a city level. Sci. Rep. 5, 14884. https://doi.org/10.1038/srep14884

  57. Zhao, S.P., Yu, Y., Yin, D.Y., He, J.J., Liu, N., Qu, J.J., Xiao, J.H. (2016). Annual and diurnal variations of gaseous and particulate pollutants in 31 provincial capital cities based on in situ air quality monitoring data from China National Environmental Monitoring Center. Environ. Int. 86, 92–106. https://doi.org/10.1016/j.envint.2015.11.003

  58. Zheng, B., Zhang, Q., Geng, G., Chen, C., Shi, Q., Cui, M., Lei, Y., He, K. (2021). Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020. Earth Syst. Sci. Data 13, 2895–2907. https://doi.org/10.5194/essd-13-2895-2021

  59. Zhu, J., Chen, L., Liao, H., Dang, R.J. (2019). Correlations between PM2.5 and ozone over China and associated underlying reasons. Atmosphere 10, 352. https://doi.org/10.3390/atmos10070352

  60. Zhu, J., Chen, L., Liao, H., Yang, H., Yang, Y., Yue, X. (2021). Enhanced PM2.5 decreases and O3 increases in China during COVID-19 lockdown by aerosol-radiation feedback. Geophys. Res. Lett. 48, e2020GL090260. https://doi.org/10.1029/2020GL090260


Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

7.3
2022CiteScore
 
 
77st percentile
Powered by
Scopus
 
   SCImago Journal & Country Rank

2022 Impact Factor: 4.0
5-Year Impact Factor: 3.4

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.