The Characteristics of PM_2.5 and O₃ Synergistic Pollution in the Sichuan Basin Urban Agglomeration

Abstract

The synergistic pollution of fine particulate matter (PM_2.5) and ozone (O₃) has become one of the major factors affecting ambient air quality. Due to the unique geographical location of the Sichuan Basin, air pollution is more likely to occur. To assess the synergistic pollution status of PM_2.5 and O₃ in the Sichuan Basin, this study analyzed time series analysis, correlation analysis, and interaction analysis of PM_2.5 and O₃ based on hourly data from national monitoring stations in the Sichuan Basin from 2015 to 2024. Additionally, the approximate envelope method (AEM) was used to estimate the secondary PM_2.5 concentration. The results showed the following: Chongqing, Zigong, Luzhou, Chengdu, and Deyang experienced severe pollution. From 2015 to 2018, these cities showed high pollution levels. Since 2019, such high levels of pollution have not been observed; during the PM_2.5 pollution period (November to January of the following year), PM_2.5 and O₃-8h exhibited a negative correlation. During the O₃-8 pollution period (May to August), PM_2.5 and O₃-8h showed a positive correlation; secondary PM_2.5 increased with the intensity of photochemical reactions, while the concentration of primary PM_2.5 showed little change compared to secondary PM_2.5. Secondary PM_2.5 concentrations peaked around 8:00–12:00 and reached a trough between 16:00 and 20:00 in all five cities; during the PM_2.5 pollution period, the trend of O₃ in the five cities was consistent. Ozone concentration showed a distinct single-peak daily variation under different PM_2.5 pollution levels. As PM_2.5 concentration increased, the peak O₃ concentration decreased, and the valley concentration became lower. In different seasons, the increase in PM_2.5 concentration can both enhance and suppress the concentration of O₃. The enhanced atmospheric photochemical activity level promotes the formation of secondary components in particles. This achievement can provide a reference for the coordinated control and improvement of air quality in the Sichuan Basin.

1. Introduction

Since the implementation of the “Air Pollution Prevention and Control Action Plan” in 2013, the concentration of ambient PM_2.5 in China has significantly decreased. However, tropospheric O₃ has remained on a steady upward trend [1,2,3,4]. To further strengthen air pollution control in the country, the Ministry of Ecology and Environment issued the “Three-Year Action Plan to Win the Blue Sky Defense War” in 2018. During the 2018–2020 period, PM_2.5 levels continued to decline [5,6], and O₃ saw a nationwide decrease for the first time in 2020. However, an anomaly was observed in the first half of 2022, and the air pollution situation in China remains severe.

In recent years, the issue of synergistic pollution between PM_2.5 and O₃ has attracted widespread attention. There are complex interactions between the two pollutions, including photochemical reactions and heterogeneous reactions [7,8,9]. O₃, due to its strong oxidizing nature, stimulates the formation of secondary components in PM_2.5, while PM_2.5 affects O₃ concentrations by altering photolysis rates, cloud optical thickness, and heterogeneous reaction processes [10,11,12,13]. Many scholars have studied the pollution characteristics and interactions between PM_2.5 and O₃. Lee et al. [14] found that a high positive correlation between O₃ and PM_2.5 was observed in summer (June to August), whereas the correlation was negative in winter (January to February and December). Yang et al. [15] also found that in the Xinglong area, O₃ and PM_2.5 concentrations showed a certain positive correlation in summer. From this, it can be inferred that during the high-temperature period of summer when atmospheric oxidizing capacity increases, PM_2.5 in the atmosphere undergoes a series of chemical reactions with O₃ and other substances under sunlight during the photoaging process. This generates secondary PM_2.5, further increasing the mass concentration and complexity of PM_2.5. Zhu et al. [16] showed that in most regions and seasons in China, the concentration of PM_2.5 is positively correlated with O₃ concentration, but in winter, this correlation is mainly negative in North China. In winter, the solar radiation is weak, and the photolysis rate decreases, which suppresses photochemical reactions, such as the key reactions for O₃ formation. Since the photoaging process relies on sunlight-driven chemical reactions, the impact of photoaging on PM_2.5 is weaker in winter. This indirectly reduces the generation of O₃. Xiao et al. [17] reported that from 2013 to 2019, there were 94 days of PM_2.5-O₃ compound pollution in Tianjin, showing a yearly declining trend. It was also observed that during the periods of compound pollution days from 2013 to 2019, when the hourly concentration of PM_2.5 was between 75 μg/m³ and 85 μg/m³, the peak hourly concentration of O₃ appeared within the range of 301–326 μg/m³.

PM_2.5 can be categorized into primary PM_2.5 and secondary PM_2.5. Primary PM_2.5 is directly emitted into the atmosphere from various sources, while secondary PM_2.5 is not directly emitted but is formed in the atmosphere through chemical reactions. The main processes include the conversion of gases (sulfur dioxide (SO₂), nitrogen oxides (NO_x), and volatile organic compounds (VOCs) into particulate matter (aerosols), such as sulfates (SO₄²⁻), nitrates (NO₃⁻), and organic aerosols [18]. Chang et al. [19] analyzed the daily variation patterns of PM10 in Taipei City under different daily maximum 8 hr averages (MDA8) from 1994 to 2003. To evaluate the formation of secondary aerosols under different ozone levels, they established the “CO tracer method,” using CO as a tracer for primary aerosols and MDA8 as an indicator of photochemical activity. Jia et al. [20] analyzed the air pollutant measurement data from Nanjing, East China, from 2013 to 2015, and found that the enhancement of atmospheric oxidation could promote the formation of secondary particles, with secondary particles contributing up to 26.76% to the environmental PM_2.5 levels. Du et al. [21] analyzed the concentration and variation characteristics of secondary PM_2.5 under different PM_2.5 pollution levels in Chengdu during the winters of 2015–2018, based on air quality monitoring data. The Approximate Envelope Method (AEM) was used to estimate the secondary PM_2.5 concentrations. The results showed that under light pollution, moderate pollution, and heavy pollution conditions, secondary PM_2.5 concentrations accounted for 56.3%, 63.6%, and 67.4% of the total PM_2.5, respectively. The secondary PM_2.5 concentration increased year by year, while the primary PM_2.5 concentration showed a significant decreasing trend. Liu et al. [22] utilized AEM to estimate secondary PM_2.5 concentrations in the Beijing–Tianjin–Hebei region for varying degrees of composite pollution from 2015 to 2022. The results showed that as the intensity of compound pollution increased, the proportion of secondary PM_2.5 also significantly rose. These studies indicate that the “CO tracer method” has become an effective way to estimate secondary PM_2.5 concentrations. Although many scholars have calculated secondary PM_2.5 in different cities, studies on the entire year and continuous years in some cities of the Sichuan Basin are still limited.

The Sichuan Basin region has unique geographical characteristics, with high mountains surrounding it and a flat central area, which leads to poor air circulation and makes it prone to stable weather conditions. These conditions facilitate the accumulation of pollutants, resulting in severe PM_2.5 and O₃ pollution and the presence of synergistic pollution [23,24,25]. Therefore, this study utilizes environmental air quality monitoring data from the Sichuan Basin between 2015 and 2024 to analyze the pollution characteristics and interactions of PM_2.5 and O₃, and to estimate the concentrations and changing trends of primary and secondary PM_2.5.

2. Materials and Methods

2.1. Data Sources

This study selected hourly datasets for CO, PM_2.5, and O₃-8h (daily maximum 8 h average) from 22 cities in the Sichuan Basin from 2015 to 2024. The number of available national control monitoring sites in the Sichuan Basin from 2015 to 2024 is 126, 121, 129, 128, 126, 127, 149, 156, 152, 154, respectively. The data were processed using R programming language. The data were sourced from the National Urban Air Quality Real-time Release Platform (https://air.cnemc.cn:18014/ (accessed on 1 October 2024)). The unit for CO is mg/m³, and the units for PM_2.5 and O₃-8h are μg/m³. The daily average for PM_2.5 and CO refers to the arithmetic mean of the 24 h average concentration of a day, the monthly average refers to the arithmetic mean of daily average concentrations over a month, the seasonal average refers to the arithmetic mean of daily average concentrations over a season, and the annual average refers to the arithmetic mean of daily average concentrations over a year (since there is no annual average in the environmental air quality standard, the O₃ daily maximum 8 h average is used to assess O₃ pollution status) [26]. The secondary standard concentrations for PM_2.5 on a daily and annual basis are 75 μg/m³ and 35 μg/m³, respectively. The O₃ daily maximum 8 h average is the maximum of all 8 h rolling average concentrations from 8:00 to 24:00 within a natural day (MDA8) [27], and the seasonal and annual averages are evaluated using the 90th percentile of the O₃ daily maximum 8 h average. According to extensive literature review and the selection criteria outlined in the “Environmental Air Quality Standards” (GB 3095-2012) and “Technical Regulation on Ambient Air Quality Assessment (Trial Implementation)” (HJ 663-2013), quality control was conducted as follows: (1) Null values and records with PM_2.5 > 900 μg/m³ in the original data were marked as missing. (2) For calculating the daily average at a station, if a station’s data for the entire day contains fewer than 20 data points, that day’s data were considered invalid and removed; for calculating the monthly average, there must be at least 27 daily averages in a month (with at least 25 daily averages in February); for calculating the annual average, there must be at least 324 daily averages within a year. The spatial distribution map of the Sichuan Basin is shown in Figure 1:

2.2. Pollution Interval Classification

Referring to the “Technical Regulation on Ambient Air Quality Assessment (Trial Implementation) (HJ 663-2013)” and the “Environmental Air Quality Standards (GB 3095-2012)”, the pollution intervals for PM_2.5 and O₃-8h are classified into 12 intervals, as shown in

Table 1. The PM_2.5 and O₃-8h pollution intervals are divided into 12 ranges.

Numbers	Intervals
1	PM2.5 ≤ 35 µg/m3 and O3-8h ≤ 100 µg/m3
2	PM2.5 ≤ 35 µg/m3 and 100 µg/m3 < O3-8h ≤ 160 µg/m3
3	PM2.5 ≤ 35 µg/m3 and 160 µg/m3 < O3-8h ≤ 200 µg/m3
4	PM2.5 ≤ 35 µg/m3 and O3-8h > 200 µg/m3
5	35 µg/m3 < PM2.5 ≤ 75 µg/m3 and O3-8h ≤ 100 µg/m3
6	35 µg/m3 < PM2.5 ≤ 75 µg/m3 and 100 µg/m3 < O3-8h ≤ 160 µg/m3
7	35 µg/m3 < PM2.5 ≤ 75 µg/m3 and 160 µg/m3 < O3-8h ≤ 200 µg/m3
8	35 µg/m3 < PM2.5 ≤ 75 µg/m3 and O3-8h > 200 µg/m3
9	PM2.5 > 75 µg/m3 and O3-8h ≤ 100 µg/m3
10	PM2.5 > 75 µg/m3 and 100 µg/m3 < O3-8h ≤ 160 µg/m3
11	PM2.5 > 75 µg/m3 and 160 µg/m3 < O3-8h ≤ 200 µg/m3
12	PM2.5 > 75 µg/m3 and O3-8h > 200 μg/m3

. MDA8 is used as an indicator of photochemical activity levels [28], which can be divided into four grades: low photochemical level (MDA8 ≤ 100 µg·m⁻³), light photochemical level (100 µg·m⁻³ < MDA8 ≤ 160 µg·m⁻³), moderate photochemical level (160 µg·m⁻³ < MDA8 ≤ 200 µg·m⁻³), and heavy photochemical level (MDA8 > 200 µg·m⁻³). The annual evaluation of O₃ is based on the 90th percentile of MDA8 (MDA8-90).

2.3. AEM

This study uses MDA8 as an indicator of photochemical activity to estimate the formation of secondary PM_2.5 under different photochemical conditions. Meanwhile, CO is used as a tracer for primary PM_2.5 emissions, assuming that the structure of these primary emission sources remains largely unchanged [19]. By analyzing these data, we aim to better understand the trends in secondary PM_2.5 changes and their relationship with photochemical reactions. Under this assumption, the larger the ratio of PM_2.5 concentration to CO concentration, the greater the proportion of secondary components in PM_2.5 [29]. When the MDA8 at a monitoring station is ≤ 100 µg·m⁻³ (low photochemical level), the observed PM_2.5 concentration is considered to be primary PM_2.5. When the MDA8 at a monitoring station is >100 µg·m⁻³ (including light, moderate, and heavy photochemical levels), the CO concentration at that station under such photochemical conditions is multiplied by the PM_2.5/CO ratio at the light photochemical level to estimate the primary PM_2.5 concentration under those photochemical conditions. Since the emission characteristics may vary at different times, an envelope estimation is performed every 3 h. CO concentrations are grouped into ranges of 0.5–0.7, 0.7–0.9, 0.9–1.1, 1.1–1.3, 1.3–1.5, and 1.5–1.7 mg/m³. For each group, the three smallest PM_2.5 concentrations and their corresponding CO concentrations are averaged, and the average ratio is taken as the Rp for that 3 h period [21].

$$\begin{gathered} {\left({PM}_{2.5}\right)}_{P,X,h}={CO}_{X,h}\times {\left(R_P\right)}_h \\ {\left({PM}_{2.5}\right)}_{S,X,h}={\left({PM}_{2.5}\right)}_{O,X,h}-{\left({PM}_{2.5}\right)}_{P,X,h} \end{gathered}$$

In the equation: h represents the hour, X represents the light, moderate, and heavy chemical reaction levels, ${\left({PM}_{2.5}\right)}_P$ is the primary PM_2.5 concentration, ${\left({PM}_{2.5}\right)}_S$ is the secondary PM_2.5 concentration, and ${\left({PM}_{2.5}\right)}_O$ is the observed data value.

3. Results and Discussion

3.1. Annual Variation Trends of PM_2.5 and O₃-8h Pollution

According to the PM_2.5 and O₃-8h pollution concentrations in five cities in the Sichuan Basin, the pollution is divided into 12 intervals as shown in Figure 2. The figures for the other 17 cities (Figures S1–S17) are provided in the attachment. Among these 22 cities, Chongqing, Zigong, Luzhou, Chengdu, and Deyang experience more severe pollution, which is similar to the findings of Gao et al. [30]. High concentration areas of PM_2.5 and O₃ in the Sichuan Basin are located in Chengdu, Deyang, Zigong, Dazhou, and the Chengdu Plain, centered around Chengdu. Aba Tibetan and Qiang Autonomous Prefecture and Ganzi Tibetan Autonomous Prefecture have the lightest pollution, with the highest number of days in the concentration range of PM_2.5 ≤ 35 μg/m³ and O₃ ≤ 100 μg/m³. This is consistent with the findings of Zhao et al. [31]. This is mainly due to the mountainous terrain, forests, and grasslands in these two regions, with relatively low population density, located far from heavy industrial zones and large cities, thus being less affected by pollution caused by industrialization. A common characteristic of Chongqing, Zigong, Luzhou, Chengdu, and Deyang is the higher number of days with pollution in the concentration range of PM_2.5 > 75 μg/m³ and 160 < O₃ ≤ 200 μg/m³; PM_2.5 > 75 μg/m³ and O₃ > 200 μg/m³ from 2015 to 2018. Since 2019, such high levels of pollution have not been observed, which is closely related to the “Three-Year Action Plan to Win the Blue Sky Defense War” issued in 2018. In Chengdu, the concentration range of PM_2.5 > 75 μg/m³ and O₃ ≤ 100 μg/m³ showed a consistent decrease from 2015 to 2024, indicating significant improvement in PM_2.5 levels. However, O₃ concentrations did not show improvement, which is consistent with the findings of Dun et al. [32].

3.2. Correlation Between PM_2.5 and O₃-8h

In Figure 3a, the correlation between PM_2.5 and O₃-8h during PM_2.5 pollution periods (November to January) is shown, with a negative correlation (r = −0.18, passing the significance test at the 0.05 level). This finding is consistent with the results of Kang et al. [33]. The primary reasons for this phenomenon relate to two aspects: first, the accumulation of PM_2.5 concentrations can absorb solar radiation reaching the Earth’s surface, and the lower temperatures in winter slow down photochemical reactions, leading to reduced O₃ formation [34]; on the other hand, in cold winter conditions, if pollution sources release excessive NO, these NO molecules will react with O₃ in the air, generating NO₂ and other byproducts, which significantly reduces O₃ concentrations [35].

In Figure 3b, the correlation between PM_2.5 and O₃-8h during O₃ pollution periods (May to August) is depicted, showing a positive correlation (r = 0.38, passing the significance test at the 0.05 level). This indicates that during the O₃ pollution period (May to August), PM_2.5 and O₃ co-pollution are in the same increasing and decreasing phase. Compared to the PM_2.5 pollution period, the correlation during O₃ pollution periods is stronger and is positive, which is consistent with the findings of Zhang et al. [36]. Their study showed that from 2013 to 2017, Xuzhou experienced positive correlations between PM_2.5 and O₃ during summer monsoon seasons and negative correlations during winter monsoon seasons. Jia et al. [20] also studied the correlation between PM_2.5 and O₃ in Nanjing and found a positive correlation between PM_2.5 and O₃ during the summer (R = 0.40 moderate correlation). This is mainly because during the O₃ pollution period, high temperatures combined with large-scale emissions of precursor substances such as VOCs and NOx trigger intense photochemical reactions. This not only increases the level of O₃ in the air but also enhances the overall oxidative capacity of the atmosphere, creating an environment conducive to the formation of secondary PM_2.5 particles, which also helps to increase O₃ concentrations, ultimately leading to the synergistic increase in both PM_2.5 and O₃ concentrations [37].

In winter, the high PM_2.5 concentrations in the Sichuan Basin are mainly caused by the following factors: 1. Basin Topography: The Sichuan Basin is surrounded by mountains, forming a relatively closed terrain structure that easily leads to the accumulation of pollutants within the basin, making it difficult for them to disperse. 2. Industrial Emissions: SO₂ and NOx emitted from industrial activities chemically react in the atmosphere to form sulfates and nitrates, significantly increasing the concentration of PM_2.5. 3. Coal-fired Power Plants and Industrial Boilers: In the southern Sichuan region, coal-fired power plants and industrial boilers make particularly significant contributions to PM_2.5 levels. 4. Vehicle Exhaust Emissions: Vehicle exhaust is one of the important sources of PM_2.5. Nitrogen oxides (NO_x) and volatile organic compounds (VOCs) in exhaust fumes rapidly convert into secondary particles under stable and high-humidity conditions. The main sources of high O₃ concentrations in the Sichuan Basin during summer are as follows: 1. High Temperature: In summer, higher temperatures and intense solar radiation promote the occurrence of photochemical reactions, accelerating the formation of ozone. 2. Precursor Emissions: Nitrogen oxides (NO_x) and volatile organic compounds (VOCs) serve as precursors that generate ozone through complex photochemical reactions. These precursors are emitted from various sources, such as vehicle exhaust, industrial emissions, and natural emissions. 3. Regional Transport: Pollutants in the Chengdu Plain may spread to surrounding cities, while pollutants from southern Sichuan and northeastern Sichuan regions may also be transported into the Chengdu Plain. Due to the topographical characteristics of the Sichuan Basin, the wind speed in this region is relatively low, with average wind speeds from 2015 to 2024 ranging from 2.0 m/s, 2.0 m/s, 2.0 m/s, 2.2 m/s, 2.2 m/s, 2.0 m/s, 2.1 m/s, 2.2 m/s, 2.3 m/s, 2.3 m/s. Therefore, wind speed has a limited impact on air pollution.

3.3. The Effect of O₃ on PM_2.5

Figure 4 illustrates the daily variations in primary and secondary PM_2.5 in five major polluted cities within the Sichuan Basin from 2015 to 2024 under different photochemical levels. As shown in Figure 4 and

Table 2. Primary and secondary PM_2.5 concentrations (μg/m³) in five cities from 2015 to 2024 under different photochemical levels.

Cities	Different O3 Photochemical Levels	PM2.5	Primary PM2.5	Secondary PM2.5
Chongqing	Light photochemical level	31.23	18.94	12.29
	Moderate photochemical level	35.73	20.13	15.60
	Heavy photochemical level	33.78	19.05	14.74
Chengdu	Light photochemical level	37.13	20.86	16.28
	Moderate photochemical level	38.12	21.49	16.63
	Heavy photochemical level	45.10	23.31	21.79
Zigong	Light photochemical level	45.44	23.87	21.57
	Moderate photochemical level	52.99	27.53	25.46
	Heavy photochemical level	62.07	29.30	32.77
Luzhou	Light photochemical level	37.45	25.46	11.99
	Moderate photochemical level	45.24	28.69	16.55
	Heavy photochemical level	42.91	25.28	17.63
Deyang	Light photochemical level	34.59	21.42	13.16
	Moderate photochemical level	37.62	22.23	15.39
	Heavy photochemical level	44.71	23.80	20.94

, the concentrations of secondary PM_2.5 in all five cities increase with the intensity of photochemical reactions, which is consistent with the findings of Liu et al. [38], indicating that enhanced atmospheric oxidizing capacity can promote the formation of secondary PM_2.5. Under four different photochemical conditions, while the concentration of primary PM_2.5 does not change significantly relative to secondary PM_2.5 as photochemical reaction intensity increases, there is a significant increase in secondary PM_2.5 concentration. Specifically, at high photochemical levels compared to low photochemical levels, secondary PM_2.5 concentrations are 1.2 times, 1.3 times, 1.5 times, 1.5 times, and 1.6 times higher in Chongqing, Chengdu, Zigong, Luzhou, and Deyang, respectively. However, primary PM_2.5 concentrations exhibit more pronounced fluctuations as photochemical reaction intensity increases. Under different photochemical levels, Zigong shows the highest concentrations of both primary and secondary PM_2.5, while Chongqing exhibits the lowest concentrations for both. The secondary PM_2.5 concentrations in the five cities tend to peak between 8:00 and 12:00 and reach a trough between 16:00 and 20:00, which is consistent with the findings of Du et al. [21], who observed that from 2015 to 2018, the secondary PM_2.5 concentration in Chengdu reached its maximum from morning to noon under different PM_2.5 pollution conditions. The strong sunlight in the morning to noon promotes photochemical reactions, leading to an increase in secondary PM_2.5 concentrations, while the lack of sunlight in the afternoon causes a gradual decrease in secondary PM_2.5 concentrations.

Figure 5 shows the contribution ratios of primary and secondary PM_2.5 in five cities from 2015 to 2024. The general trend observed in these five cities is that the concentration of primary PM_2.5 has been increasing year by year, while the concentration of secondary PM_2.5 has been decreasing. Moreover, in most years, the concentration of primary PM_2.5 is higher than that of secondary PM_2.5. This phenomenon is mainly associated with emission sources such as vehicle exhaust, industrial production, construction dust, and coal-fired boilers within urban areas. Between 2015 and 2018, Zigong, Chongqing, Chengdu, and Deyang experienced higher concentrations of secondary PM_2.5 compared to primary PM_2.5. However, from 2019 to 2024, the concentration of primary PM_2.5 surpassed that of secondary PM_2.5. This shift can be attributed to China’s later efforts in controlling gaseous precursors in the atmosphere (such as sulfur dioxide, nitrogen oxides, and volatile organic compounds), which has led to a decrease in the formation efficiency of secondary PM_2.5 [39].

3.4. The Effect of PM_2.5 on O₃

Figure 6 shows the daily variation of O₃ concentrations under different PM_2.5 pollution levels during PM_2.5 pollution periods (November to January of the following year). Under various PM_2.5 concentration ranges, the five cities exhibit a clear unimodal structure in O₃ concentrations. O₃ concentrations are lowest between 8:00 a.m. and 12:00 due to lower sun angles and relatively cooler temperatures, which slow down the rate of ozone formation. As solar radiation intensity increases and temperatures rise, coupled with increased vehicle exhaust emissions during evening rush hours, O₃ concentrations begin to increase, peaking between 16:00 p.m. and 20:00 p.m. before gradually decreasing. This observation is similar to the findings of Liu et al. [38]. Among the five cities, as PM_2.5 concentrations increase, peak O₃ concentrations decrease, and trough concentrations become even lower. This phenomenon is attributed to high PM_2.5 concentrations inhibiting the production of O₃ [34].

4. Conclusions

This study mainly analyzes the pollution characteristics and interactions of PM_2.5 and O₃ in the Sichuan Basin from 2015 to 2024 and estimates the primary and secondary PM_2.5 concentrations. The main conclusions are as follows: (1) According to the PM_2.5 and O₃-8h pollution concentrations in 22 cities of the Sichuan Basin, the pollution is divided into 12 intervals. Among these cities, Chongqing, Zigong, Luzhou, Chengdu, and Deyang experience more severe pollution. From 2015 to 2018, these cities showed higher pollution levels (PM_2.5 > 75 μg/m³ and 160 < O₃ ≤ 200 μg/m³; PM_2.5 > 75 μg/m³ and O₃ > 200 μg/m³), and such high pollution levels have not occurred since 2019. In Chengdu, the concentration range of PM_2.5 > 75 μg/m³ and O₃ ≤ 100 μg/m³ showed a consistent decrease from 2015 to 2024. (2) During the PM_2.5 pollution period (November to January), PM_2.5 and O₃-8h exhibited a negative correlation. During the O₃-8h pollution period (May to August), PM_2.5 and O₃-8h showed a positive correlation. (3) The secondary PM_2.5 concentration increases with the intensity of photochemical reactions, while the primary PM_2.5 concentration shows little change compared to the secondary PM_2.5 concentration, only exhibiting more pronounced fluctuations as the intensity of photochemical reactions increases. Under different photochemical levels, Zigong has the highest concentrations of both primary and secondary PM_2.5, while Chongqing has the lowest concentrations for both. In all five cities, the secondary PM_2.5 concentration peaks around 8:00–12:00 and reaches a trough between 16:00 and 20:00. The general trend of the contribution ratio of primary to secondary PM_2.5 in the five cities is that the contribution ratio of primary PM_2.5 increases year by year, while the contribution ratio of secondary PM_2.5 decreases year by year. In most years, the concentration of primary PM_2.5 is higher than that of secondary PM_2.5. From 2015 to 2018, in Zigong, Chongqing, Chengdu, and Deyang, the secondary PM_2.5 concentration was higher than the primary PM_2.5 concentration, but from 2019 to 2024, the primary PM_2.5 concentration was higher than the secondary PM_2.5 concentration. (4) During the PM_2.5 pollution period (November to January), the trend in the five cities is consistent. Under different PM_2.5 pollution levels, the daily variation of O₃ concentration shows a distinct unimodal pattern, with the lowest O₃ concentration occurring between 8:00 and 12:00 and the peak occurring between 16:00 and 20:00, after which it gradually decreases. As the PM_2.5 concentration increases, the peak O₃ concentration decreases, and the valley concentration becomes lower.

This study investigated the long-term synergistic pollution of PM_2.5 and O₃ and their interaction effects in the Sichuan Basin, achieving certain results. However, there are still areas for improvement. This study only used the CO tracer method to estimate the primary and secondary PM_2.5 concentrations, which is not comprehensive enough. In the future, relevant instruments can be used to monitor the concentration and component analysis of secondary PM_2.5, and the estimated secondary PM_2.5 can be compared with the monitored secondary PM_2.5 concentrations.