To analyze air pollution, we evaluated the annual concentration of five common ambient pollutants at the nation-wide level and in five areas. At the national level, the mean concentrations of all ambient air pollutants did not exceed the standard concentration. Furthermore, the concentrations of some pollutants (PM10: 55.27 µg/m3, NO2: 0.0323 ppm, CO: 0.5312 ppm, SO2: 0.0049 ppm) in the Area 1 (metropolitan region) were greater than those in the other four areas. However, the concentration of O3 showed an opposite trend than other pollutants. Air quality standards were reported on an annual basis (PM10, 50 µg/m3; NO2, 0.03 ppm; SO2, 0.02 ppm) and every 8 hours (CO, 9 ppm; O3, 0.06 ppm). The concentration of PM10 in Area 1 was statistically higher than the air quality standard in Korea (P < 0.05), whereas the concentration of NO2 in Area 1 was insignificantly higher than the standard level (P = 0.06) (Table 2).
To compare the differences between the air pollution in Area 1 and that in the others (Area 2, 3, 4, and 5) temporally, the average annual concentrations of PM10 were evaluated. The concentration of PM10 in Area 1 was significantly higher than that of Areas 2 to 5 (all P < 0.05), which showed similar trends (Supplementary Fig. 1). Baseline characteristics with spatially visualized distribution of PM10 concentration and mortality rates because of circulatory and respiratory diseases are presented as per the five sub-divided areas (Supplementary Fig. 2). The mean concentration of PM10 in Area 1 (55.27 µg/m3) was the highest whereas that in Area 3 (45.27 µg/m3) was the lowest. The highest annual mortality rate because of circulatory diseases was in Area 2 (105.5 per 100,000), whereas the lowest mortality rate was in Area 1 (83.1 per 100,000). In contrast, mortality rate because of respiratory diseases was the highest in Area 5 (35.85 per 100,000) and the lowest in Area 1 (8.85 per 100,000) (Supplementary Fig. 2, Supplementary Table 1).
To evaluate the spatial associations between mortality rates and air pollutants, Pearson correlation analysis was performed at the national level and in the five clustered areas (Table 3). The Shapiro-Wilk test showed that all variables were normally distributed. Correlation tests were performed using the total mortality rates and sex-based mortality rates separately. However, the correlation coefficients for men and women were not significantly different (Supplementary Table 2). The nation-wide analysis showed that mortality rates of circulatory diseases (IHD, HD, and CVD) were significantly correlated with PM10 and SO2 concentrations; however, except HD and PM10 were not found to be correlated. Only O3 levels were positively correlated with mortality due to respiratory diseases, whereas the other pollutants presented a negative correlation except with CLRD. In Area 1, PM10 and SO2 showed stronger correlations with mortality due to circulatory diseases as well as CLRD compared with their associations at the national level. Pearson’s r of PM10 and mortality rates because of IHD, HD, CVD, and CLRD were 0.420, 0.313, 0.596, and 0.523, respectively. (Table 3)
The mortality rates because of HD, IHD, CVD, and CLRD were significantly positively correlated with PM10 concentrations at the national level, which led to investigation regarding the associations between PM10 and mortality rates at the district-level. Figure 1 showed the spatial distribution of Pearson’s coefficients of these associations. Districts are marked in gray did not show statistical significance. Figure 1C shows the most remarkable map with significant correlations between PM10 concentrations and mortality from CVD in Area 1 (Table 3).
The adjusted R2 values from the multivariable linear regression models are presented in Table 4. To estimate the effect of PM10 concentrations on mortality rates in Area 1, several regression models were examined including control variables: smoking rate, education level, and population density. The univariate regression with only PM10 showed a 35.4% variance in mortality rates for CVD, while each of smoking rate, education, and population density showed rates of 26.8%, 31.1%, and 0.9%, respectively (adjusted R2). In Table 4, the multivariable regression model including PM10, smoking rate, education level, and population density to predict mortality rate of CVD shows the highest adjusted R2 of 0.532.