This study focuses on China, including 31 provinces in the Chinese mainland, excluding Hongkong, Macau, Taiwan, and Hainan. Based on the social, natural, economic, and human environment, these 31 regions were further categorized into seven geo-administrative regions, including North China (Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia), South China (Guangdong, Guangxi, Hainan), East China (Shanghai, Anhui, Fujian, Jiangsu, Jiangxi, Shandong, Zhejiang), Central China (Henan, Hunan, Hubei), Southwest China (Yunnan, Guizhou, Sichuan, Chongqing, Tibet), Northwest China (Shaanxi, Gansu, Ningxia, Qinghai, Xinjiang), and Northeast China (Heilongjiang, Jilin, Liaoning) (Figure 1).

The daily maximum 8-h O₃ concentration (MDA8) reanalysis dataset of 10 × 10 km from January 1, 2013, to December 1, 2018, is from the tracking air pollution in China (http://tapdata.org/). The dataset is based on a machine learning algorithm and multi-data information fusion inversion. Its comprehensive construction combines ground monitoring data, satellite remote sensing data, high-resolution emission inventory data, air quality model simulation, and other multi-source data, which greatly improves the spatial and temporal accuracy of the data inversion results compared with the previous air quality reanalysis data (15). The daily O₃ concentrations in 360 prefecture-level cities in China during the study period were obtained from the China National Environmental Monitoring Center (http://www.cnemc.cn/sssj/). In order to reduce the error in the calculation of the health risk model, we calculated the 90th percentile concentration of the MDA8 O₃ concentration from the interannual scale based on the daily MDA8 O₃ concentration as the threshold.

The population size (Pop), the proportion of secondary industry to GDP (S_GDP), disposable income per capita (P_GDP), and soot emissions (Dust) for 360 prefecture-level cities in China during the study period were obtained from the China Statistical Yearbook (http://www.stats.gov.cn/tjsj/ndsj/#). The nitrogen oxide (NOx) and volatile organic compound (VOC) emissions were obtained from the China Multiscale Emissions Inventory Model (http://meicmodel.org/). The 1 × 1 km spatial resolution population data were obtained from the World pop dataset (https://www.worldpop.org/).

The daily meteorological data were obtained from the China Meteorological Data Network (http://data.cma.cn/) during the study period. The meteorological data obtained in this study mainly include air temperature (Tem, °C), sea level pressure (Pa, Pa), relative humidity (Hum, %), 2-m mean wind speed (WS, m/s), 1-h precipitation (Pre, mm), and 10-min mean visibility (Vis, m).

Trend analysis is usually used for the analysis of temporal dynamics of air pollutants to explore the interannual rate of pollutant changes (16). In this paper, the rate of change of O₃ concentrations in China from 2013 to 2018 was analyzed based on the trend analysis method, which is calculated as Equation (1):

Where O₃ indicates the O₃ concentration of each cell; n indicates the time span, here the time span is 6; and i is the time unit.

Previous studies have shown that significant heterogeneity in the spatial distribution of air quality concentrations and population density leads to major spatial differences in the exposure risks of populations to air quality (17). In addition, health risks due to exposure to pollutants are usually defined as a function of the multiplication of population density and pollutant concentration (18). Although the exposure risk intensity in the area can be quantified to some extent, it cannot distinguish the severity of the local area relative to the whole. To address this issue, we introduced a model for the relative exposure risk of the population attributable to O₃ exposure, as shown in Equation (2), which can evaluate the exposure status in each pixel of the cells (19):

where R_i indicates the risk of population exposure in grid cell i; P_i indicates the number of exposed populations in grid cell i; C_i indicates the O₃ concentration in grid cell i, and n indicates the total number of grid cells in the study area. To better reflect the spatial difference of relative population exposure risk, we categorized the population exposure risk as extremely low risk, low risk, lower risk, higher risk, high risk, and extremely high risk by using the reclassification method in ArcGIS10.8 software. The corresponding exposure risk values are R_i = 0, 0 < R_i ≤ 1, 1 < R_i ≤ 2, 2 < R_i ≤ 3, 3 < R_i≤ 5 and R_i> 5, respectively. A higher value of R indicates a higher exposure risk.

In this study, a standard damage function was applied to estimate the population of premature deaths from respiratory diseases due to O₃ exposure. The specific equations are shown in Equations (3) and (4), and the relationships shown in the following equations have been extensively applied in previous studies (14, 20, 21).

where RR is the relative risk; (RR−1)/RR is the attributable fraction; x_i is the O₃ concentration in a city i or grid i; x₀ is the threshold concentration; β is the exposure-response coefficient, which represents the additional health risk associated with an increase in unit O₃ concentration (22, 23); ΔM is the number of premature deaths of respiratory diseases attributable to exposure to the O₃ environment; y₀ is the baseline mortality rate of respiratory diseases, and pop is the number of the exposed population. In this study, the mortality rate of respiratory diseases was obtained from the National Bureau of Statistics, where the crude mortality rate of respiratory diseases y₀ (1/100,000) in urban China from 2013 to 2018 was, 76.61, 74.17, 73.36, 69.03, 67.20 and 68.02, respectively. β values in this study were obtained from Shang et al. (24), per 10 μg/m³ with a value of 0.48% (95% CL: 0.38%, 0.58%). Song et al. (25) concluded that the exposure-response coefficients obtained from a meta-analysis by Shang et al. (24) based on a 33–time series and case-crossover study conducted could to some extent reflect the health risks attributed to air pollution in China. Meanwhile, which has widely been used in several past studies for China (26, 27).

Compared with the classical geographically weighted regression model (GWR), the MGWR model was a flexible regression model (28). Each regression coefficient was obtained based on local regression, and the bandwidth is specific. In addition, the GWR model uses weighted least squares in the fitting operation, while the MGWR model was equivalent to a generalized additive model (GAM), which could perform regression analysis on spatial variables with linear or non-linear relationships, and was also an effective tool for dealing with various complex non-linear relationships of spatial variables (29). Assuming that there are n observations, for observation i ∈ {1,2,3,…, n} at location (U_i, V_i), the MGWR were calculated as follows (30):

where y_i is the response variable O₃ concentration, β₀(U_i, V_i) is the intercept, X_ij is the j_th predictor variable i, β_bwj(U_i, V_i) is the j_th coefficient, bwj in β_bwj indicates the bandwidth used for calibration of the jth conditional relationship, ε_i is the error term. In addition, the spatial kernel function type selected during the model operation is bisquare, the bandwidth search type is golden, and the model parameter initialization type takes GWR estimation as the initial estimation model.

This study used the trend analysis method, spatial autocorrelation model, population exposure risk model, exposure-response function, and MGWR model to analyze the spatial-temporal pattern, exposure risk, health risk, and driving factors of O₃ concentration in China from 2013 to 2018. Firstly, we use the trend analysis method and spatial autocorrelation model to explore the changing trend and spatial-temporal distribution of O₃ concentration in China. Secondly, we selected the population exposure risk model and exposure-response function to investigate the population exposure risk and health risk attributed to O₃ pollution, and discussed their temporal and spatial correlation characteristics. Finally, we use the MGWR model to reveal the dominant factors of spatial distribution difference of O₃ concentration in China. Additionally, in this study we used O₃ concentration reanalysis data at 10 × 10 km resolution and population raster data at 1 × 1 km resolution to investigate the exposure risks and health risks attributed to O₃ pollution. To spatially match the 10 × 10 km O₃ concentration reanalysis data, we used the aggregation module of ArcGIS10.6 software to quantitatively change the spatial resolution of the 1 km × 1 km population data. During the aggregation calculation, the output image element cell size was set to 10 × 10 km, i.e., 0.01° × 0.01°, and the nearest neighbor assignment method was selected for the aggregation technique. Figure 2 shows the research framework of this paper.

Figure 3 shows the temporal and spatial distribution and changing trend of the annual average concentration of MDA8 (AMDA8, O₃) from 2013 to 2017 in China. From 2013 to 2018, the annual average O₃ concentrations in China were 110.75, 108.21, 111.13, 115.57, 120.49, and 115.95 μg/m³, respectively, and changed at a rate of 1.84 μg/m³/yr increase (Figure 3H). From a spatial and temporal perspective, the highest annual average O₃ concentrations were found in central China in 2013, 2015, and 2016, with annual average O₃ concentrations of 121.87, 118.84, and 122.78 μg/m³, respectively. The highest annual average O₃ concentrations in 2014, 2017, and 2018 were all found in East China, with annual average O₃ concentrations of 116.98, 135.03, and 137.91 μg/m³, respectively. In comparison, the lowest O₃ concentration in 2013 occurred in the Northeast region (98.33 μg/m³), the lowest O₃ concentrations from 2014 to 2017 occurred in the Southwest region of China (90.86, 94.43, 99.20, and 104.27 μg/m³), and the lowest O₃ concentration in 2018 occurred in the Northwest region (103.44 μg/m³) (Figures 3A–G). Since 2013, 89.62% of China's territory has experienced a significant increase in annual average O₃ concentrations, with 2.73% of the regions experiencing an average rate of change in annual average O₃ concentrations exceeding 5.00 μg/m³/yr. However, the rate of variation of O₃ concentration varies from region to region has strong spatial variability. The rate of change of O₃ concentration in the Central Plains urban agglomeration is the most variable in terms of the country, with its O₃ concentration change rate exceeding 4.0 μg/m³/yr. In contrast, the rate of change of O₃ concentration in the Chengdu-Chongqing urban agglomeration (−0.3 ± 1.0 μg/m³/yr), Southwest China (−0.5 ± 1.1 μg/m³/yr) and South China (−1.0 ± 1.4 μg/m³/yr) decreases significantly (Figure 3G).

Figure 4 represents the spatial clustering characteristics of the rate of variation of O₃ concentration at county-level units in China from 2013 to 2018. The results show that the global Moran's I index is significant at the 1% level, indicating a consistent and enhanced positive spatial autocorrelation in the rate of variation of O₃ concentration (Figure 4A). The results of the hot spot analysis show that there is a significant hot spot (HH) region for O₃ concentration growth rate, which is mainly contiguous and focused in Shaanxi, Shanxi, central Inner Mongolia, Beijing–Tianjin–Hebei (BTH), southwest Liaoning, central Henan, eastern Hubei, Anhui, Jiangsu, and Shandong in China, which are the regions with the strongest O₃ growth rate in China. In addition, we found a significant cold spot area (LL) covering a large part of China (about 90% of the territory). These regions are mainly located in northeastern, southern, southwestern, eastern, and northwestern China, where the growth rate of O₃ concentration is relatively low and even decreasing regions are observed (Figures 4B, C). The standard deviation ellipsometric analysis evaluated the overall variations in the spatial pattern of O₃ concentration growth rate from 2013 to 2018 in China (Figure 4D). It can be found that the regions with significantly increased O₃ concentration growth rates are mainly concentrated in BTH, Shanxi, Shandong, Jiangsu, Jiangxi, Anhui, Hubei, Henan, and Shaanxi in China. This result also indicates that the above-mentioned regions are the primary contributors of O₃ during the whole study period in China. Meanwhile, the center of the median growth rate of O₃ concentration is located north of the standard deviation ellipse arithmetic center, indicating that the growth rate of surface O₃ concentration is greater in northern China than in southern China.

Overall, the total population exposed to O₃ > 160 μg/m³ increased from 1.2% in 2013 to 28.9% in 2018, compared to a decrease in the population exposed to O₃ < 160 μg/m³ from 7.2% in 2013 to 3.6% in 2018 (Figure 5). Figures 6, 7 represents the spatial pattern of exposure risk levels attributed to O₃ pollution in 2013, 2015, and 2018. We found that most regions have remained at low (52.89–55.73%) or extremely low (19.48–20.48%) O₃ exposure risk levels over three time periods in China. From a temporal perspective, only 4.83% of the territory of the country was at high exposure to O₃ pollution in 2013, and this percentage increased to 6.45 and 7.19% in 2015 and 2018, respectively. Similarly, the area of the territory exposed to extremely high risk also exhibits a marked increasing trend, from 7.61% in 2013 to 9.62% in 2015 and further to 11.35% in 2018 (Figures 7A–C). Spatially, the distribution patterns of O₃ exposure risk levels were similar for the three time periods of 2013, 2015, and 2018 in China. With the rapid increase of O₃ concentration in the North China Plain, the high exposure risk level regions of BTH and YRD have been continuous, which constitute a high O₃ exposure risk level aggregation area including the Bohai Rim, YRD, Pearl River Delta (PRD), Shanxi and Guanzhong Plain urban clusters. Spatially, the distribution patterns of O₃ exposure risk levels were similar for the three time periods of 2013, 2015, and 2018 in China. In contrast, the extremely low risk and lower risk areas of O₃ pollution are widely distributed in China, which is mainly located in most regions of northwest, southwest, and northeast in China (Figures 7D–F).

Figure 8 indicates the spatial and temporal distribution of premature deaths from respiratory diseases attributable to O₃ exposure from 2013 to 2018. Overall, there was an average of over 24,000 premature deaths from respiratory diseases due to O₃ exposure per year in China from 2013 to 2018, and the growth rate fluctuated at 1,178 cases per year (p < 0.05). Specifically, the number of premature deaths attributable to O₃ exposure increased from 236,200 in 2013 to 272,300 in 2018, an increase of 36,100 cases compared to 2013. Spatially, the regions with <500 cases of premature death due to O₃ exposure are mainly located in Tibet, Qinghai, east of Xinjiang, west of Sichuan, west of Inner Mongolia, Liaoning, and Heilongjiang; the regions with more than 500 cases are mainly located in the region east of Hu line, mainly including most of eastern China and western Xinjiang, central Inner Mongolia, southern Gansu, most of southern China, most of northern China, and Liaoning in northeast China. The regions with more than 1,000 cases are mainly located in BTH, Sichuan–Chongqing region, Fenwei Plain, East China Plain, Jianghan Plain, Yangtze River Delta, and Pearl River Delta region. Meanwhile, the regions with more than 1,000 cases of premature death due to O₃ exposur e are further expanding over time.

Multicollinearity refers to the distortion of model estimates due to significant correlations between the independent variables in the linear modeling regression process. Therefore, before conducting model regression analysis, to test whether there is multicollinearity between each explanatory variable, we use variance inflation factor (VIF) to test the multicollinearity problem between each explanatory variable, and previous studies have shown that when VIF ≥ 10, it indicates that there is a serious multicollinearity problem between the dependent variable and the independent variable. multicollinearity problem, which should be removed from the actual model operation. The collinearity test in this study was performed in SPSS 25.0 software and the results of the analysis showed that the range of VIF values for all explanatory variables was 1.000–9.765, which indicates that there was no cointegration between the dependent and independent variables. Table 1 indicates the diagnostic information of the MGWR model for the socioeconomic and meteorological factors. In terms of the number of valid parameters, the goodness–of–fit R² for the responses of socioeconomic and meteorological factors to O₃ concentrations are 0.861 and 0.799, respectively, and the residual sum of squares (RSS) is 136.297 and 136.51 μg/m³, respectively, with the absolute values of the deficit information criterion (AIC) and the log-likelihood value (Log-likelihood) < 5,000. These regression results indicate that MGWR uses fewer parameters to obtain regression results that are closer to the true values and can be fully used to assess the relationship between O₃ pollution and socioeconomic and meteorological factors.

Figure 9 indicates the spatial distribution of regression coefficients of socio-economic factors. The high values (>0.27) of regression coefficients for the total population are mainly located in North and East China, where the total population is significantly and positively correlated with its corresponding O₃ concentration. The influence of the share of secondary industry on surface O₃ in East and North China is significantly higher than that in other regions, and its regression coefficient exceeds 0.08. We also find that over 80% of the regional disposable income per capita is positively correlated with O₃, with regression coefficients ranging from 0.07 to 0.36. In contrast, Guangdong, Shandong, and Northeast provinces show a significant negative correlation between disposable income per capita and O₃, with regression coefficients, were below −0.02. The industrial dust emissions in Sichuan and Chongqing are significantly (p < 0.001) positively correlated with the corresponding O₃ concentration with a regression coefficient > 0.52, while industrial dust emissions in cities located in East China are significantly (p < 0.01) negatively correlated with the corresponding O₃ concentration with a regression coefficient ranging from −0.53 to −0.25, where O₃ concentrations in cities located in eastern Jiangsu and Anhui provinces are more affected by the negative correlation of industrial dust emissions. The NOx emissions were significantly and positively correlated with O₃ concentrations in Central China, East China, South China, Sichuan and Chongqing, and parts of Southwest and Northwest China (p < 0.05), with regression coefficients ranging from 0.60 to 1.26. There was a significant (p < 0.01) negative correlation between VOCs emissions and O₃ concentrations in Hubei, Jiangxi, Zhejiang, Anhui, Jiangsu, Shanghai, Guangdong, Fujian, and Guangxi cities with regression coefficients ranging from −0.53 to −0.35.

Figure 10 shows the spatial differences in the effects of various meteorological factors on O₃ concentration. It can be found that the temperature of cities in North, East, and Northeast China showed a significant (p < 0.05) positive correlation with O₃ concentration, with regression coefficients ranging from 0.23 to 0.49. The relative humidity was negatively correlated with O₃ concentration in all cities during the study period. Among them, cities in Heilongjiang, Jilin, Liaoning, Beijing, Tianjin, north-central Hebei, northwestern Shanxi, western Inner Mongolia, and northwestern Ningxia and northern Shaanxi showed a weak negative correlation between relative humidity and O₃ concentration with a non-significant (p > 0.05) regression coefficient of < −0.07. In contrast, cities in southern Zhejiang, southern Anhui, Jiangxi, central Hubei, Hunan, Chongqing, Guizhou, Yunnan, and cities in Fujian, Guangdong, and Guangxi regions showed a significant (p < 0.01) strong negative correlation between relative humidity (Hum) and its corresponding O₃ concentration with regression coefficients ranging from −0.18 to −0.15. Wind speed (WS) showed a significant (p < 0.05) negative correlation with O₃ concentrations in Heilongjiang, Jilin, Liaoning, Guangxi, southern Henan, Hubei, eastern Shandong, Jiangsu, Shanghai, Zhejiang, Sichuan and Chongqing regions, and northern Shanxi, with regression coefficients ranging from −0.02 to −0.06. It is particularly noteworthy that cities in BTH, southwestern Shanxi, northern Henan, central Shaanxi, Ningxia, southern Gansu, western Shandong, and Anhui have a significant positive correlation between their wind speed and O₃ concentration with regression coefficients >0.45. For air pressure, cities located in northern China showed a significant (p < 0.05) negative correlation between air pressure (Pa) and O₃ concentration, with regression coefficients ranging from −3.6 × 10⁻³ to −1.3 × 10⁻³. Precipitation showed a significant (p < 0.05) positive correlation with O₃ concentration in Heilongjiang, Jilin, South China, Guangxi, and Guangdong, with regression coefficients ranging from 3.93 to 19.21, while other regions showed negative correlations. Visibility was positively correlated with O₃ concentration in all cities.

The results of the spatial and temporal pattern analysis of O₃ concentrations show that East, Central, and North China are the regions with the highest growth of O₃ concentrations in China from 2013 to 2018, which is mainly attributed to the huge amount of anthropogenic emissions. The areas of East, Central, and North China are one of the most densely populated and industrially developed regions in China, and the massive industrial activities, transportation, and human activities result in the emission of large amounts of O₃ precursors. In contrast, Southwest and South China are the regions with the largest decreases in O₃ concentrations in China. Previous studies have found that Southwest and Northwest China are located in high-latitude regions, and their corresponding atmospheric vertical exchange and photochemical reactions are stronger due to the special topography and intense solar radiation compared to inland regions, resulting in higher background values of O₃ concentrations in these regions (9, 31). However, the extent of the influence of solar radiation on O₃ in southwest and southern China is significantly weaker than the influence of anthropogenic emissions compared to the dramatic increase in O₃ concentrations due to strong anthropogenic emissions in East, Central, and North China (32).

There are strong spatial variations in the influence of different drivers on O₃. Relative to lower population density regions, a larger population size implies more energy consumption and pollution emissions, meanwhile, it also further compresses the green area of cities, leading to a significant reduction in the ability of cities to mitigate air pollution, which better explains why the positive correlation between population size and O₃ concentration is significantly higher in densely populated northern and eastern China than in other regions (21, 33). Previous studies have shown that industrial emissions are the predominant source of air pollution (34). Our study found that the share of secondary industry in GDP had a significant positive correlation with O₃ concentration, especially in central China, and eastern China, where industrial production is dominant, and the contribution of urban industrial production to O₃ concentration is stronger than in other regions. At the urban scale, the formation of O₃ concentrations depends on the VOCs–NOx ratio (35). In general, the higher the NOx emissions in cities, the lower the VOCs–NOx ratio. For example, the formation of O₃ in some cities located in Central and Northern China is often limited by VOCs (36, 37). In these cities, the reduction of VOCs emissions decreases the formation of O3, but the reduction of NOx emissions increases the formation of O₃. This chemical reaction tends to depend on the amount of VOCs and NOx emissions; the larger the emissions the more intense their reaction and the larger the O₃ emissions generated (38). In addition, industrial dust emissions indirectly affect solar radiation intensity by affecting atmospheric visibility, which further contributes to the O₃ photochemical reaction rate (39).

Temperature is an important ambient condition for photochemical reactions, and higher temperatures can promote the rapid production of O₃ concentration, therefore, temperature and O₃ concentration are mostly positively correlated, especially in cities in Northern, Eastern, and Northeastern China where the solar temperature is higher in the warm season (40). The wind speed has a diffusion and transport effect on pollutants in the atmosphere. For example, O₃ concentrations in cities in Northeast, South, Central, and East China, and Sichuan and Chongqing regions showed a significant (p < 0.05) negative correlation with wind speed. However, our results found a significant positive correlation between O₃ concentration and wind speed in most cities located in the North China Plain. Li et al. (41) attributed the significant positive correlation between O₃ concentration and wind speed in the North China Plain region to the influence of warm-season burning winds, especially from June to August each year, when the burning winds blow from the mountains to the northern and western parts of the North China Plain, bringing dry the hot air further leads to a higher temperature in the region, which accelerates the photochemical reaction of O₃ production to some extent. Relative humidity has a negative correlation with O₃ concentration. Previous studies have shown that water vapor can not only absorb and release energy through changes in the aqueous phase but also undergo internal reactions, especially when controlling for other influencing factors, higher relative humidity leads to higher water vapor saturation, resulting in easy removal of O₃ and its precursors and lower O₃ concentrations (42). In addition, water vapor can reduce solar ultraviolet radiation through extinction mechanisms, thus affecting photochemical reactions and O₃ concentrations (43).

In summary, O₃ pollution in China is gradually increasing, and more and more of China's population is exposed to high O₃ concentration pollution. Scientific and effective reduction of O₃ concentration exposure levels in China is crucial to reduce population exposure risks (44). Under these circumstances, this study proposes policy recommendations on how to reduce O₃ concentrations in Chinese cities from the perspective of the drivers affecting the spatial distribution of O₃ and epidemiology. For O₃ pollution areas dominated by O₃ precursors (e.g., NOx, VOCs, and CO), the authorities can ensure that their emissions comply with government regulations by optimizing the industrial structure and reducing the emissions of O₃ precursors. Meanwhile, the governmental department should focus on the synergistic management of PM_2.5 and O₃ compound pollution. Research shows that NOx is not only an important precursor for O₃ generation but also an important precursor for PM_2.5 (45). Therefore, strengthening the NOx deep regulation and emission reduction is a key step to promote synergistic control. Furthermore, the O₃ abatement measures in the future should pay attention to different seasonal O₃ control measures and strengthen regional cooperation for O₃ pollution prevention.

For O₃ pollution regions dominated by meteorological factors, the department should forecast the variation of O₃ concentration due to the change of meteorological factors promptly, meanwhile develop a detailed O₃ pollution early warning program to reduce the risk of public exposure and explore a sustainable development path for O₃ pollution management in China. From an epidemiological perspective, to protect public health and improve the status of O₃ pollution, it is crucial to establish studies of health effects attributed to O₃ exposure from a national perspective. In addition, it is important for relevant government departments to establish a mechanism to revise the National Ambient Air Quality Standards (NAAQS) for regulatory assessment and health risk prediction of future O₃ air quality standards in China (46).

Surface O₃ distribution has strong spatial and temporal heterogeneity, and there are significant differences in O₃ concentrations with time scales. This study only focused on the interannual spatial variability characteristics of O₃ concentrations, neglecting the seasonal variability of O₃ concentration changes. Furthermore, due to the lack of basic research data and inadequate research methods, this study only focused on the number of premature respiratory deaths attributed to O₃ pollution in the assessment of health risks attributed to O₃ pollution, neglecting the all-cause premature death group. Additionally, using the same exposure risk coefficient (β) may lead to spatial errors in the estimated health risks due to significant spatial differences in O₃ exposure levels. For example, Wang et al. (21) estimated the population of premature deaths from respiratory diseases caused by O₃ pollution between 2013 and 2017 in China using the method of Turner et al. (47), and their results found an average of 186,000 deaths from respiratory diseases due to O₃ pollution during the study period. This is slightly lower compared to our findings. A primary reason for this is that our study and Wang et al. (21) used different exposure response coefficients and critical thresholds. In addition, the interpolation of O₃ concentrations at large scales of pollution can also cause large errors in the assessment results. Therefore, in the future, we hope to conduct a detailed and comprehensive analysis of seasonal differences in O₃ pollution and all-cause health risks in China by utilizing more detailed surface O₃ monitoring data and meta-analysis methods. To provide a scientific basis for the improvement of O₃ pollution in China.