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Air pollution is a major environmental risk factor affecting health worldwide. According to the World Health Organization, more than 4 million people die prematurely every year due to outdoor air pollution(1). The relationship between fine particulate matter (PM2.5) and mortality has been evaluated worldwide, in China, and in multiple-city studies (2-4). Evidence is accumulating showing regional differences in health response to air pollution. For example, the impact of PM2.5 on mortality varies greatly by country, region, and climate characteristics. Hebei Province’s capital, Shijiazhuang, is situated in the heart of the North China Plain and the Beijing-Tianjin-Hebei regional city cluster, and is one of the most polluted cities in China (5). We analyzed the most recent and longest time series data available, spanning the years 2015 to 2020, to explore the relation between PM2.5 and cause-specific mortality and to identify PM2.5-related sensitive illnesses and vulnerable populations. We determined the shapes of PM2.5 exposure-response curves and explore how PM2.5 and its health risks have changed in recent years in Shijiazhuang through environmental pollution control measures such as the “Blue Sky Protection Campaign,” improvements in energy, heating, transportation and land use, and improvements in polluting small enterprises.
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Table 1 showed mortality, PM2.5, and meteorological data and daily average counts of non-accidental (ALL), cardiovascular (CVD), and respiratory (RESP) deaths. During the study period, there were averages of 35 ALL, 19 CVD, and 5 RESP deaths per day. Among the 76,859 ALL deaths, there were 41,473 (54.0%) CVD deaths and 9,955 (13.0%) RESP deaths. Daily concentration of PM2.5 ranged from 6.3 μg/m3 to 625.3 μg/m3, and the annual-mean concentration was 77.6 μg/m3. There were 767 days (35% of the study period days) in which PM2.5 concentration was over 75 μg/m3, the national second ambient air quality standard in China. As shown in Supplementary Table S1, PM2.5 pollution was lower during 2018 to 2020 compared with 2015 to 2017, as the PM2.5 average concentration decreased from 91.1 μg/m3 to 64.1 μg/m3, while the maximum concentration decreased from 625.3 μg/m3 to 355.0 μg/m3 and the number of days exceeding the national standard decreased from 480 to 287 days.
Variable Mean (SD) Min P25 P50 P75 Max Daily mortality ALL 35 (10) 12 28 34 41 107 CVD 19 (7) 4 14 18 23 67 RESP 5 (3) 0 3 4 6 31 Air pollutant (μg/m3) PM2.5 77.6 (67.9) 6.3 35.0 56.0 94.5 625.3 Weather conditions Average temperature (℃) 14.8 (10.8) −10.2 4.6 16.0 24.6 33.7 Relative humidity (%) 55.5 (20.3) 7 39 55 72 100 Abbreviations: ALL=total non-accidental mortality from all causes; CVD=cardiovascular disease; RESP=respiratory disease; PM2.5=particulate matter with an aerodynamic diameter less than or equal to 2.5 μm; SD=standard deviation; Min=Minimum; P25=the 25th percentile; P50=the median; P75=the 75th percentile; Max=Maximum. Table 1. Daily mortality, PM2.5, and meteorological data in Shijiazhuang, 2015–2020.
As shown in Figure 1, the delayed effects of PM2.5 on ALL mortality were statistically significant for lag1, lag2, lag01, lag02, lag03, and lag04; the largest delayed effects estimates were for lag01, in which a 10 μg/m3 increase in PM2.5 was associated with an increment in ALL deaths of 0.47% (95% CI: 0.24%, 0.70%). For CVD mortality, the delayed effects of PM2.5 were statistically significantly for lag1, lag2, lag01, lag02, lag03, lag04, and lag05; the largest delayed effects estimates were also for lag01, with a 10 μg/m3 increase in PM2.5 corresponding to a 0.49% (95% CI: 0.19%, 0.79%) increment in death. For RESP mortality, in single-day lag models, significant associations were limited to the first day after PM2.5 exposure, with a 10 μg/m3 increase in PM2.5 corresponding to a 0.78% (95% CI: 0.33%, 1.23%) increment in death. When PM2.5 exposures were lagged over multiple days, the associations were strongest for exposures during lag01 (Estimates: 0.72%, 95% CI: 0.22%, 1.23%).
Figure 1.Percent changes (95% CI) in daily cause-specific mortality per 10 μg/m3 increase in PM2.5 concentrations using different lag days; (A) ALL mortality, (B) CVD mortality, (C) RESP mortality.
Abbreviations: ALL=total non-accidental death; CVD=cardiovascular disease; RESP=respiratory disease; PM2.5=particulate matter with an aerodynamic diameter less than or equal to 2.5 μm.For Figure 2A and 2B, the exposure-response curves for ALL and CVD showed increasing trends. When PM2.5 concentrations were lower than 120 μg/m3 or higher than 300 μg/m3, slopes of curves showed marked increases. When PM2.5 concentrations were between 120 μg/m3 and 300 μg/m3, slope was flat. In Figure 2C, the exposure-response curve for respiratory mortality was nearly linear and positive. When PM2.5 concentration was over 300 μg/m3, confidence intervals were wider than when PM2.5 concentration was less than 300 μg/m3.
Figure 2.Exposure-response curves for associations of daily PM2.5 concentrations (lag 01) with (A) ALL mortality, (B) CVD mortality, and (C) RESP mortality.
Notes: The y-axis can be interpreted as the relative change from the mean effect of PM2.5 on mortality. The solid lines represent mean estimates, and the shaded areas represent 95% confidence intervals. Abbreviations: ALL=total non-accidental death; CVD=cardiovascular disease; RESP=respiratory disease; PM2.5=particulate matter with an aerodynamic diameter less than or equal to 2.5 μm.As shown in Table 2, compared with 2015–2017, during 2018–2020, the effect of PM2.5 on ALL mortality was larger, and the estimated effect value changed from 0.50% to 0.63%, but the difference was not statistically significant. The effect on CVD mortality was slightly less and not statistically significantly different. The effect of PM2.5 on RESP mortality was significantly less and was statistically significantly different. The association between PM2.5 and total mortality varied by demographic characteristics. Throughout the 2015–2020 study period, an increase in PM2.5 of 10 μg/m3 corresponded to a 0.53% increment in deaths of males and a 0.39% increment in deaths of females. The 5–64-year-old group and ≥65-year-old-group had similar mortality associations. The association between PM2.5 and total mortality was a 0.51% increment for people with lower educational achievement and a 0.37% increment for those with higher educational achievement. There were no statistically significant differences among sex, age, and education in stratified analyses.
Different groups 2015–2020 (Lag01) 2015–2017 (Lag01) 2018–2020 (Lag0) Cause-Specific mortality ALL 0.47 (0.24, 0.70) 0.50 (0.23, 0.78) 0.63 (0.26, 1.01) CVD 0.49 (0.19, 0.79) 0.65 (0.28, 1.01) 0.60 (0.12, 1.08) RESP 0.72 (0.22, 1.23) 0.94 (0.32, 1.56) −0.15 (−0.99, 0.70)* Sex Man 0.53 (0.25, 0.81) 0.58 (0.25, 0.91) 0.60 (0.12, 1.08) Woman 0.39 (0.07, 0.70) 0.39 (0.01, 0.78) 0.69 (0.18, 1.20) Age (years) 5–64 0.51 (0.09, 0.93) 0.63 (0.13, 1.14) 0.54 (−0.16, 1.25) ≥65 0.47 (0.22, 0.72) 0.47 (0.17, 0.78) 0.67 (0.27, 1.07) Education level Low 0.51 (0.25, 0.78) 0.57 (0.25, 0.89) 0.67 (0.23, 1.10) High 0.37 (0.02, 0.72) 0.37 (−0.07, 0.81) 0.56 (−0.01, 1.12) Notes: Educational level: low, ≤9 years of education; high, >9 years of education.
Abbreviations: ALL=total non-accidental mortality from all causes; CVD=cardiovascular disease; RESP=respiratory disease.
* P<0.05 vs. 2015–2017.Table 2. Percent change (95% CI) in daily cause-specific mortality and total non-accidental mortality stratified by sex, age, and educational level per 10 μg/m3 increase in concentration of PM2.5 in different time periods.
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