Ambient Ozone Pollution and Daily Mortality: A Nationwide Study in 272 Chinese Cities

Abstract

Background: Few large multicity studies have been conducted in developing countries to address the acute health effects of atmospheric ozone pollution.

Objective: We explored the associations between ozone and daily cause-specific mortality in China.

Methods: We performed a nationwide time-series analysis in 272 representative Chinese cities between 2013 and 2015. We used distributed lag models and over-dispersed generalized linear models to estimate the cumulative effects of ozone (lagged over 0-3 d) on mortality in each city, and we used hierarchical Bayesian models to combine the city-specific estimates. Regional, seasonal, and demographic heterogeneity were evaluated by meta-regression.

Results: At the national-average level, a 10-μg/m³ increase in 8-h maximum ozone concentration was associated with 0.24% [95% posterior interval (PI): 0.13%, 0.35%], 0.27% (95% PI: 0.10%, 0.44%), 0.60% (95% PI: 0.08%, 1.11%), 0.24% (95% PI: 0.02%, 0.46%), and 0.29% (95% PI: 0.07%, 0.50%) higher daily mortality from all nonaccidental causes, cardiovascular diseases, hypertension, coronary diseases, and stroke, respectively. Associations between ozone and daily mortality due to respiratory and chronic obstructive pulmonary disease specifically were positive but imprecise and nonsignificant. There were no statistically significant differences in associations between ozone and nonaccidental mortality according to region, season, age, sex, or educational attainment.

Conclusions: Our findings provide robust evidence of higher nonaccidental and cardiovascular mortality in association with short-term exposure to ambient ozone in China. https://doi.org/10.1289/EHP1849.

Figure 1.

National-average percentage difference (posterior mean and 95% posterior intervals) in daily total mortality per $10{-\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ increase in ozone concentration in 272 Chinese cities during single-day lags (lag 0, 1, 2, 3), multiple-day averaging lags (lag 0–1, 0–2, 0–3), and cumulative lags based on a polynomial distributed lag model (PDLM 0–3, 0–6, 0–9). Estimates were generated using over-dispersed generalized linear models adjusted for calendar day [natural cubic spline with 7 degrees of freedom (df)], day of the week, temperature (cross-basis function for temperature lagged for 0–13 d from distributed lag nonlinear model), and humidity (lag 0, natural smooth function, 3 df) to estimate city-specific associations that were combined using hierarchical Bayesian models.