Introduction

Over the past few decades, the state and characteristics of atmospheric pollution in China have undergone a gradual transformation from singular sources of pollution, such as coal smoke and petrochemical emissions, towards a more intricate form of atmospheric pollution1. Notably, traditional pollutants such as sulfur dioxide (SO2) and total suspended particulate (TSP) have been effectively controlled. However, the rapid increase in the number of motor vehicles has led to a continuous rise in nitrogen oxides (NOx) emissions2, resulting in a progressively severe regional air pollution characterized by high concentrations of fine particulate matter (PM2.5) and ground-level ozone (O3)3. This trend is particularly pronounced in economically developed regions such as the Beijing-Tianjin-Hebei region (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)4,5.

Among the primary air pollutants, PM2.5 and O3 are recognized as pivotal contributors to atmospheric compound pollution6. PM2.5, defined as particulate matter with an aerodynamic diameter of 2.5 µm or less, originates from diverse sources, encompassing both natural and anthropogenic sources7. As a secondary pollutant, O3 is generated through the photochemical reaction of precursor pollutants such as NOx and volatile organic compounds (VOCs) under sunlight exposure. These precursor emissions predominantly stem from industrial processes, vehicular exhaust, and various anthropogenic activities8. As of 2018, the annual mean PM2.5 and maximum daily 8 h average concentrations of O3 (MDA8 O3) in China remained relatively high, with concentrations recorded at 38.4 μg m−3 and 95.8 μg m−3 respectively9. Against the background of global climate change, atmospheric pollution attributed to PM2.5 and O3 have emerged as a significant environmental and public health concern, given its profound impact on air quality, human health, and the global environment10,11,12.

PM2.5 and O3 pose significant risks to human health, as they can irritate the respiratory tract, leading to symptoms such as coughing, wheezing, and shortness of breath13. These pollutants also have the potential to exacerbate preexisting respiratory conditions such as asthma and chronic obstructive pulmonary disease13. Moreover, they can penetrate deep into the lungs, causing inflammation and resulting in lung damage. Prolonged exposure to PM2.5 and O3 can lead to decreased lung function over time. Furthermore, epidemiological studies have highlighted the association between PM2.5 and O3 exposure and cardiovascular issues. These pollutants can enter the bloodstream, contributing to the development of heart diseases, including heart attacks, strokes, and hypertension14. Utilizing observed PM2.5 data, Maji et al.15 reported that in China, PM2.5-related hospital admissions due to respiratory and cardiovascular diseases in 2016 were 610,000 (95% CI 370,000–860,000) and 360,000 (95% CI 200,000–520,000), respectively. Additionally, the total morbidity estimates for asthma attack, chronic bronchitis, and emergency hospital admissions were 1,000,000 (95% CI 700,000–1,280,000), 990,000 (95% CI 500,000–1,440,000), and 120,000 (95% CI 60,000–180,000), respectively15. In a separate study, Zhao et al16 employed meta-analysis method techniques to estimate O3-related health effects across China in 2018. Their findings revealed that the total number of all-cause, cardiovascular, and respiratory deaths attributable to O3 were 178,529 (95% CI 90,584–346,912), 118,842 (95% CI 40,787–192,507), and 38,178 (95% CI 0–80,159), respectively.

In recent decades, the Chinese government has demonstrated a steadfast commitment to monitoring and mitigating air pollution, instituting a series of policies and measures aimed at enhancing air quality. Previous studies have indicated that the successful implementation of the “Air Pollution Prevention and Control Action Plan” since 2013 has resulted in a decline in PM2.5 levels across China, with a reduction rate of 3.4 μg m−3 per year, particularly notable in regions such as BTH, central China, and northeast China had larger declines17. However, there has been a notable upward trend in the national average O3 concentration, showing an annual increase of 3.4 μg m−3 per year, with more pronounced increases observed certain regions, including PRD17. It is noteworthy that while PM2.5 levels have decreased nationally, no significant change has been observed in the PRD, suggesting that PM2.5 pollution in this area may still be at high levels9,17. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA), encompassing nine cities in the PRD, Hong Kong, and Macao, stands as one of China's most economically robust regions. Despite its economic strength, the GBA lags behind other global Greater Bay Areas in terms of air quality18. Moreover, there has been increasing attention on atmospheric compound pollution characterized by PM2.5 and O3 in this region. To date, no comprehensive studies have been conducted to assess their impact on human health19.

Given the above concerns, the aim of this study is to examine the spatial distribution and temporal trends of PM2.5 and O3 in the GBA from 2015 to 2019, and to quantify their impact on human health.

Data and methods

Data source

This study utilized monthly PM2.5 and MDA8 O3 data from nine cities in the PRD, Hong Kong, and Macao spanning from 2015 to 2019. These data were obtained from two sources: https://quotsoft.net/air/ and the monitoring results reports of the Guangdong-Hong Kong-Macao Pearl River Delta Regional Air Quality Monitoring Network (http://gdee.gd.gov.cn/kqjc/index.html). The combined network comprises 61 air quality automatic monitoring stations, distributed throughout the GBA, including 11 in Guangzhou, 11 in Shenzhen, 8 in Foshan, 4 in Zhuhai and 4 in Jiangmen 4 in Zhaoqing, 5 in Huizhou, 4 in Zhongshan, 5 in Dongguan, 4 in Hong Kong, and 1 in Macao, as illustrated in Fig. 1. The annual mean concentrations of PM2.5 and MDA8 O3 per station were calculated by averaging the monthly mean values for all months of the year. Subsequently, the annual averaged concentrations for each city were determined based on all stations in this city.

Figure 1
figure 1

Distribution of air quality monitoring sites in the GBA (The map was generated by ArcGIS 10.7 https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources).

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Moreover, population data for the nine cities in the PRD, Hong Kong, and Macao for each year were obtained from the statistical yearbooks of the Guangdong Provincial Bureau of Statistics (http://stats.gd.gov.cn/gdtjnj/), the Hong Kong Census and Statistics Department (https://www.censtatd.gov.hk/sc/), and the Macao Census and Statistics Department (https://www.dsec.gov.mo/zh-MO/).

Health impact assessment

Many epidemiological studies on air pollution rely on Health Impact Assessment (HIA), a widely employed method for quantify the potential effects of various air pollutants, including PM2.5, PM10, SO2, NO2, CO, and O3, on human health13. HIA involves determining the health risk for an individual when the concentration of a particular air pollutant exceeds a certain threshold, typically calculated based on the exposure–response coefficient (β). It's worth noting that the β value in assessing the health risks associated with long-term exposure to air pollution is typically calculated through epidemiological studies. These studies often analyze extensive population data, including individuals exposed to varying levels of air pollutants and their health outcomes, such as the number of people with cardiovascular or respiratory diseases. By statistically analyzing these data, the association between air pollution exposure and health issues can be determined. The β value is a crucial parameter derived from this association analysis, representing the magnitude of the impact on health risks per unit increase in air pollutant concentration. For PM2.5, the β value indicates the relative increase in health risks for each unit increase in PM2.5 concentration. For instance, if a study finds that for every 10 μg m−3 increase in PM2.5 concentration, the incidence of cardiovascular diseases increases by 20%, the corresponding β value would be 0.2. Thus, establishing the exposure–response relationship between air pollutants and mortality is crucial for conducting HIA. Through extensive literature review and and meta-analysis, previous studies have identified associations between PM2.5 and premature deaths. Specifically, an annual mean increase of 1 μg m−3 in PM2.5 concentration was found to correspond to 0.34%, 0.07%, and 0.11% in all-cause, cardiovascular, and respiratory premature deaths, respectively20,21. Similarly, for MDA8 O3, each 1 μg m−3 rise in its concentration was associated with increases of 0.10% in non-accidental mortality, 0.15% in cardiovascular mortality, and 0.20% in respiratory mortality22. It's important to note that the β values used in this study to assess human health risks due to PM2.5 and O3 are derived from the above these studies. Referring to the study from Zhao et al.16, PM2.5 and MDA8 O3 indicators were employed to estimate premature deaths across three health endpoints attributed to long-term exposure to PM2.5 and O3. The calculation formulas are as follows:

$${\text{RR}} = \exp \, \left[ {\beta \times \left( {{\text{C}} - {\text{C}}_{0} } \right)} \right]$$
(1)
$${\text{E}} = \left[ {\left( {{\text{RR}} - {1}} \right)/{\text{RR}}} \right] \times {\text{P}} \times {\text{F}}_{{\text{p}}} = \left[ {{1} - {\text{exp}}^{{ - \beta \times ({\text{C }} - {\text{ C}}0)}} } \right] \times {\text{P}} \times {\text{F}}_{{\text{p}}}$$
(2)

Here, C represents the annual average concentration of PM2.5 and MDA8 O3, while C0 denotes the safety threshold. If the concentration exceeds C0, it signifies potential health risks. The C0 values for PM2.5 and MDA8 O3 are set at 10 μg m−3 and 26.7 ppb, respectively, based on the study by Kuerban et al.23. β represents the percentage increase in health effects associated with a 1 μg m−3 increase in PM2.5 and MDA8 O3 concentration. As previously mentioned, β values for all-cause and cardiovascular mortality attributed to PM2.5 are 0.34% (95% CI 0.08–0.50%) and 0.07% (95% CI 0.04–0.09%), respectively20,21. For MDA8 O3, corresponding β values are 0.10% (95% CI 0.05–0.20%) and 0.15% (95% CI 0.05–0.29%), respectively22. The β values of respiratory mortality are 0.11% (95% CI 0.00–0.22%) for PM2.5 and 0.20% (95% CI 0.00%, 0.43%) for MDA8 O322,24. RR represents the relative risk, while P denotes the exposed population of each city. Fp denotes the mortality rate for three health endpoints. According to the study by Liao et al.25 on municipal-level mortality rates, where detailed information regarding the Fp of each city from 2006 to 2012 was provided. Considering the minimal fluctuation in Fp values each year, we utilized the average Fp value from their study covering the years 2006 to 2016 as the Fp value for calculating the health impacts of PM2.5 and O3 during 2015–2019 in this study, as shown in Table 1. E represents the number of deaths related to PM2.5 and O3.

Table 1 The Fp value for all-cause, cardiovascular, and respiratory in each city of GBA.
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Results and discussion

Spatiotemporal distribution and monthly variation of PM2.5 and MDA8 O3

Figure 2 indicates the spatial pattern and average concentrations of PM2.5 and MDA8 O3 across various cities in the GBA over the five-year period. Overall, there was a gradual decrease in PM2.5 concentration in each city from 2015 to 2019. Additionally, the spatial distribution of PM2.5 remained consistent each year, exhibiting a pattern of decrease from northwest to southeast, which was consistent with previous findings by Lin et al.26 and Miao et al.27 utilizing satellite remote sensing technology. The highest PM2.5 concentration was recorded in Zhaoqing (31.8–40.4 μg m−3), followed by Foshan (29.8–39.6 μg m−3) and Dongguan (31.9–37.1 μg m−3). This phenomenon could be attributed to the fact that these cities are inland and the presence of mountains obstructs the dispersion of PM2.59. The concentrations in square brackets represent the maximum and minimum values of PM2.5 during 2015–2019. However, coastal cities benefit from ocean breezes, which facilitate the dispersion and dilution of PM2.5. Furthermore, higher levels of precipitation aids in the deposition of PM2.5, thereby reducing its concentration in the air. Therefore, the concentration of PM2.5 in coastal cities such as Hong Kong (18.9–27.0 μg m−3), Macao (17.4–29.3 μg m−3), Shenzhen (24.1–29.8 μg m−3), and Huizhou (24.8–29.5 μg m−3) was relatively low. A similar phenomenon was also observed in the study by Fang et al.18.

Figure 2
figure 2figure 2

Spatio-temporal distribution of PM2.5 and MDA8 O3 in each city from 2015 to 2019 (The map was generated by ArcGIS 10.7 https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources).

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MDA8 O3 presented the opposite spatiotemporal distribution compared to PM2.5, with its concentration generally increasing in each city during the period between 2015 and 2019. Spatially, MDA8 O3 concentration exhibited a decreasing pattern from east to west. Higher concentrations were observed in Dongguan (92.3–110.7 μg m−3), foshan (81.5–99.8 μg m−3), and Jiangmen (73.2–104.2 μg m−3), whereas some cities like Hong Kong (77.5–88.8 μg m−3) and Shenzhen (80.3–93.8 μg m−3) had lower concentrations. This disparity may be attributed to higher temperatures, stronger photochemical reactions, and larger emissions of O3 precursors such as NOx and VOCs from ships and ports in coastal cities10.

To investigate the seasonal variations of PM2.5 and O3, Fig. 3 illustrates their monthly average concentrations over the five-year period. While PM2.5 exhibits minor fluctuations across different months and years, its monthly pattern generally resembles a “V” shape, with higher concentrations in winter (Dec., Jan. and Feb.) and autumn (Sep., Oct. and Nov.), and lower concentrations in summer (Jun., Jul. and Aug.) and spring (Mar., Apr. and May.). The highest concentration of PM2.5 occurs during winter, which was attributed to increased anthropogenic emissions and unfavorable meteorological conditions17. Lower temperatures, reduced light intensity, shorter sunshine duration, and stable atmospheric stratification in winter facilitate the formation of a strong and persistent inversion layer. This inhibits the diffusion and dilution of PM2.5, leading to its continuous accumulation in the air and frequent heavy pollution events9. PM2.5 levels begin to decline from January, reaching their lowest point in June, and gradually increase thereafter until December. During summer, PM2.5 concentrations remain low due to factors such as intense solar radiation, strong atmospheric convection, and a thinner temperature inversion layer, which collectively enhance air ventilation and PM2.5 dilution. Additionally, summer weather is typically rainy, and the wet deposition of particulate matter, along with cleaner air brought by marine monsoon, contributes to the removal of PM2.511.

Figure 3
figure 3

Monthly variation characteristics of PM2.5 and MDA8 O3 in the GBA.

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Studies have suggested that the concentration of O3 in southern cities was significantly higher than that in northern cities in China9. In northern cities, the monthly variation of MDA8 O3 formed an inverted V shape, with the highest concentration occurring around June8. Conversely, in southern cities, it exhibited a distinctive M-shaped pattern, peaking in May–June and then gradually decreasing, with a second peak in September-October8,9, which is consistent with the findings of this study. Regarding seasons, higher MDA8 O3 levels were observed in autumn, followed by summer, spring, and winter. Surface O3 is primarily produced through the photochemical reaction of precursors, a process whose rate is influenced by various meteorological conditions, including temperature, solar radiation, relative humidity, and precipitation10. Typically, during summer, characterized by high temperatures, ample sunshine, and dry air, the photochemical reaction of O3 precursors intensifies, facilitating the formation of O3. However, our study reveals a noteworthy finding: the peak MDA8 O3 concentration occurred in September during autumn, rather than in summer. This was attributed to the frequent precipitation in summer, which effectively inhibited the production of O32. The lowest MDA8 O3 concentrations were observed in winter. Firstly, colder temperatures and weaker sunlight reduce the occurrence of photochemical reactions that generate O3. Additionally, atmospheric stability in winter hinders the mixing and dispersion of O3. Moreover, emissions of O3 precursors like VOCs from plants may decrease in winter, further limiting O3 formation. Overall, these factors contribute to lower O3 concentrations during the winter months10.

Change trends of PM2.5 and MDA8 O3 during 2015–2019

Figure 4 illustrates the trend analysis of the two pollutants in the GBA and its corresponding cities. The annual average PM2.5 concentrations in this area from 2015 to 2019 were 33.1 μg m−3, 30.6 μg m−3, 32.4 μg m−3, 27.7 μg m−3, and 26.1 μg m−3, respectively, indicating an overall downward trend. Linear fitting based on the average concentration of each year over the five-year period revealed a decline rate for PM2.5 in this region of 1.7 μg m−3/year. Among the eleven cities analyzed, Macao (-2.8 μg m−3/year), Foshan (-2.5 μg m−3/year), Guangzhou (-2.1 μg m−3/year), Jiangmen (-2.0 μg m−3/year), and Hong Kong (-1.9 μg m−3/year) exhibited higher decline rates over the five-year period. Conversely, Huizhou (-0.6 μg m−3/year) and Dongguan (-1.0 μg m−3/year) experienced lower declines. By 2019, although the annual average PM2.5 concentration of all cities in the GBA fell below the level-2 Chinese Ambient Air Quality Standard (CAAQS, GB3095-2012) threshold of 35 μg m−3, none of the cities had yet achieved the Grade I annual standards (15 μg m−3) specified in the CAAQS. Thus, PM2.5 pollution remains a significant concern in this region, necessitating the implementation of more stringent air pollution control measures to enhance air quality.

Figure 4
figure 4

Linear change trend of PM2.5 and MDA8 O3 in five years (The map on the right was generated by ArcGIS 10.7 https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources).

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Contrary to PM2.5, MDA8 O3 in the GBA has generally exhibited an upward trend over the past five years, with concentration of 83.8 μg m−3 in 2015, 84.6 μg m−3 in 2016, 92.4 μg m−3 in 2017, 89.4 μg m−3 in 2018, and 97.6 μg m−3 in 2019. The average rise of MDA8 O3 over the period 2015–2019 was 3.2 μg m−3/year. Notably, the upward trend was particularly evident in eight cities, except for Zhaoqing (+ 1.0 μg m−3/year), Huizhou (+ 1.3 μg m−3/year), and Dongguan (+ 1.8 μg m−3/year). Specifically, significant increases were observed in Jiangmen (+ 6.9 μg m−3/year), Zhongshan (+ 5.8 μg m−3/year), Guangzhou (+ 4.5 μg m−3/year), and Foshan (+ 4.0 μg m−3/year). Consistent with our findings, previous studies have also indicated a shift in China's main air pollutant from PM2.5 to O3 since 201319,28. Indeed, there exists a correlation between the overall decline in PM2.5 and the rise in O3. Li et al.19 demonstrated that a key factor contributing to the increase in summer O3 in the North China Plain during 2013–2017 was the decrease in PM2.5, which enhanced surface solar radiation and facilitated atmospheric photochemical reactions, thereby exacerbating O3 pollution. However, these relationships are not purely causal, as the increase in O3 is complex and influenced by various factors such as meteorological conditions, emission sources, and chemical reactions17. Consequently, while future efforts should prioritize controlling PM2.5, the government should also closely control O3 pollution in this region.

Numerous studies have documented changes in PM2.5 and O3 pollution across various regions of China in recent years. In Beijing, for the period 2014–2018, PM2.5 levels were observed to decrease while MDA8 O3 levels were on the rise, with rates of change measured at 7.4 μg m−3/year and 1.3 μg m−3/year, respectively29. Similarly, in the YRD region during the same period, Zhao et al.17 reported a decline in PM2.5 and an increase in MDA8 O3 by 3.1 μg m−3/year and 3.6 μg m−3/year, respectively. In the BTH region, the rates of change were even more pronounced at 7.1 μg m−3/year for PM2.5 decrease and 5.4 μg m−3/year for MDA8 O3 increase. Contrastingly, in the PRD region, PM2.5 exhibited a decreasing trend of 2.2 μg m−3/year from 2015 to 2020, while MDA8 O3 increased by 1.8 μg m−3/year30. While the decline rate of PM2.5 in the other 9 cities except Macao and Foshan was much lower than that observed in Beijing, YRD, and BTH regions, and the rising rate of MDA8 O3 was comparatively lower than in the YRD and BTH regions, it is noteworthy that MDA8 O3 levels in the GBA still reached nearly 100 μg m−3 in 2019, significantly exceeding the national average level. These findings underscore the imperative for further improvements in PM2.5 and O3 levels in the GBA.

Health impact assessment of PM2.5 and O3

Since the establishment of air quality monitoring stations in 2013, previous studies have extensively assessed the health impacts of air pollution across China15,16,17,22,23,24. A study conducted by Kuerban et al.23 found the numbers of premature deaths, cardiovascular diseases, respiratory diseases, and chronic bronchitis attributed to long-time PM2.5 exposure in China for the year 2018 were 334,118, 70,983, 109,327, and 228,855, respectively. They decreased by 23%, 25%, 27%, and 27%, respectively, compared to 2015, reflecting China's achievements in controlling health risks from PM2.5 in recent years. Regarding long-term exposure to O3 in 2019, predictions indicated that health impacts estimates on all-cause mortality, respiratory mortality, and cardiovascular mortality were 181,000 (95% CI 91,500–352,000), 33,800 (95% CI 0–71,400), and 112,000 (95% CI 38,100–214,000), respectively, which increased by 53%, 55%, and 53%, respectively, compared to the year 201522. While these studies have significantly contributed to our understanding of PM2.5 and O3 health risk assessment in China, it's essential to acknowledge that they uniformly applied the same mortality rate (Fp) for health endpoints across all cities in China, potentially reducing the reliability of the evaluation results. Given the differences in Fp values for health endpoints across different cities, the utilization of municipal-level Fp values for health endpoints in this study could yield a more accurate health risk estimate compared to previous studies.

Table 2 shows that the total PM2.5-related all-cause mortality, cardiovascular diseases, and respiratory diseases in the GBA in 2019 were 21,113 (95% CI 4968–31,048), 1333 (95% CI 762–1714), and 1424 (95% CI 0–2848), respectively, indicating decreases of 27.6%, 28.0%, and 28.4%, respectively, compared to 2015. At the municipal level, the highest percentage decrease in PM2.5-attributed all-cause mortality from 2015 to 2019 was observed in Macao (60.9%), followed by Hong Kong (46.6%) and Foshan (31.2%), suggesting that the control of PM2.5 has brought better health benefits for them.. Health effects associated with PM2.5 mainly depend on PM2.5 concentrations and population size. As a result, certain cities with high levels of PM2.5 and population density, such as Foshan [all-cause deaths (AD): 2963–4367; cardiovascular deaths (CD): 179–267; respiratory deaths (RD): 190–282], and Dongguan [AD: 3385–4147; CD: 217–268; RD: 198–244], have exhibited a significant number of deaths (see Fig. 5). Note that the range in brackets indicates the number of deaths from 2015 to 2019. Although Zhaoqing [AD: 1921–2648; CD: 101–141; RD: 173–240] had the highest concentration of PM2.5 among all cities, its population is relatively small compared to others, resulting in a lower number of deaths caused by PM2.5. Similarly, while Guangzhou's [AD: 5523–7901; CD: 373–540; RD: 432–624] PM2.5 concentration is not exceptionally high, its population exceeds 16 million, making it the city with the greatest health risk from PM2.5 exposure. Furthermore, despite Shenzhen's [AD: 869–1208; CD: 69–96; RD: 69–96] larger population, its PM2.5 pollution levels are relatively lower, resulting in reduced health risks related to PM2.5 exposure.

Table 2 Estimation of the impact of PM2.5 and O3 on human health in the GBA.
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Figure 5
figure 5

The estimated PM2.5-related health impacts in various cities during 2015–2019.

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On the contrary, the all-cause, cardiovascular and respiratory-related deaths due to long-time O3 exposure increased by 45.9% from 11,161 (95% CI 5581–22,323) to 16,286 (95% CI 8143–32,572), 46.2% from 5009 (95% CI 1670–9685) to 7321 (95% CI 2440–14,155), and 44.2% from 4379 (95% CI 0–9414) to 6314 (95% CI 0–13,576), respectively, during 2015–2019 (Table 2). Spatially, a significant percentage increase (> 30%) in all-cause deaths were observed in Guangzhou (61.3%), Jiangmen (41.6%), and Foshan (33.0%), highlighting the urgent need for implementing O3 control measures in these cities. However, the increases in the mortality burden of diseases attributable to O3 were not significant in Macao (1.0%) and Zhuhai (3.1%). Similar to PM2.5, Guangzhou [AD: 2423–3941; CD: 1170–1894; RD: 1114–1796], Dongguan [AD: 1799–2628; CD: 821–1194; RD: 616–891], Foshan [AD: 1266–2074; CD: 546–890; RD: 477–774], etc. had a higher number of O3-related deaths due to their large population, as shown in Fig. 6. Although Shenzhen had a larger population, its O3 concentration was lower, so it had only 495–738 AD, 282–419 CD, and 233–345 RD, because its Fp values and O3 concentration were very low. In addition, Jiangmen [AD: 665–1682; CD: 301–756; RD: 223–555] had relatively high O3 levels despite its small population, resulting in a comparatively high O3 risk. Conversely, Hong Kong[AD: 956–1394; CD: 347–504; RD: 355–515], with a larger population but the lowest O3 concentration among all cities, also experienced higher risks associated with O3.

Figure 6
figure 6

The estimated O3-related health impacts in various cities during 2015–2019.

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Exposure–response coefficient (β) and safety threshold (C0) are critical factors in air pollution risk assessment. In China, due to the absence of comprehensive cohort studies on long-term exposure to PM2.5 and O3, there is no uniform determination of β values20. Consequently, different epidemiological studies have employed varying β values, leading to differing estimations of health risks31,32,33. For example, Feng et al.34 and Zhang et al.12 used different β values to estimate total mortality attributed to PM2.5 in China for 2015, reporting 1,130,000 and 1,850,000 deaths, respectively. Additionally, Zhang et al.24 utilized β values from Zhang et al.21 and Yin et al.14 to calculate 205,800 (95% CI 176,200–240,000) and 121,500 (95% CI 66,200–176,221) AD caused by O3 exposure across China in 2015, respectively. Currently, there is no theoretical explanation for C0. Regarding C0 for PM2.5, the World Health Organization (WHO) has recommended a reference concentration of 10 μg m−314,35. Regarding the O3 threshold value for short-term exposure, currently, there is no theoretical explanation. The WHO has recommended 35 ppb (70 μg m−3) as the baseline level of O322, while a threshold of 100 μg m−3 has been deemed safe for public health by both the CAAQS Grade I and WHO23. In previous studies on the health risks of long-term O3 exposure, epidemiological studies have confirmed that a threshold value of 26.7 ppb has the highest correlation with disease mortality22. Therefore, this study utilized it to evaluate the health risks of long-term O3 exposure. When employing the same β and C0 values as used in our study, Kuerban et al.23 estimated that the total AD attributed to PM2.5 in the 9 cities of the PRD were 20,306 and 18,877 in 2015 and 2018, respectively, which were lower than our evaluation results. However, their estimates for both CD and RD were notably higher than ours. A similar discrepancy was observed when comparing the estimation of human health risks caused by O3 in our study with the results of Zhao et al.16. One potential explanation for this disparity between these studies is that our study utilized city-level FP values, whereas their studies employed the national average FP value for each city16.

It is imperative to acknowledge that this study entails certain uncertainties. On the one hand, the acquisition of mortality data for various cities presents challenges, leading us to rely on the average Fp values reported by Liao et al.25 for the years 2006–2012 to assess the health impacts of PM2.5 and O3 in GBA from 2015 to 2019. Despite the minimal annual fluctuations in Fp values per city, they could still impact the estimation results of this study. On the other hand, the current distribution of air quality monitoring stations predominantly focuses on urban areas within the GBA and is limited in number. In this study, the annual average concentrations of PM2.5 and O3 for each city were determined by averaging the data from all monitoring stations within that city, which could also affect the accuracy of the assessment results to some extent. While spatial interpolation techniques offer insights into the spatial distribution of pollutant concentrations, their efficacy is constrained by the scarcity of monitoring stations. Additionally, the absence of crucial data such as the number of disease-related deaths across different hospitals and the spatial distribution of cases significantly impacts the estimation of health mortality. In summary, to more accurately assess the impact of atmospheric pollution on human health, it is necessary for future research to establish more air quality monitoring stations in the region. Additionally, the utilization of more precise disease data can help mitigate this uncertainty.

Conclusions

This is the first study to comprehensively assess combined pollution characterized by PM2.5 and O3 and its potential health impacts in the GBA. We observed a decline in PM2.5 and a rise in MDA8 O3 during 2015–2019, with a decline rate for PM2.5 of 1.7 μg m−3/year and a rise rate for MDA8 O3 of 3.2 μg m−3/year. The significant decrease in PM2.5, particularly in Macao, Foshan, Guangzhou, and Jiangmen, highlights the efforts of these cities in controlling PM2.5 in recent years. On the other hand, Jiangmen exhibited the highest increase in MDA8 O3, followed by Zhongshan and Guangzhou, indicating the urgent need to implement measures to prevent O3 pollution in these regions in the future. Compared to 2015, the estimated number of AD, CD, and RD in 2019 caused by PM2.5 decreased by 27.6%, 28.0%, and 28.4%, respectively. In contrast, those caused by O3 increased by 45.9%, 46.2%, and 44.2%, respectively. These findings indicate that the health benefits resulting from improvements in PM2.5 might be offset by the health risks associated with increased O3 levels if insufficient attention is given to O3 control in the future. Thus, it is urgent to implement coordinated control of PM2.5 and O3 in the GBA.