Estimating Spatiotemporal Variation in Ambient Ozone Exposure during 2013–2017 Using a Data-Fusion Model

Abstract

Abstract Since 2013, clean-air actions in China have reduced ambient concentrations of PM2.5. However, recent studies suggest that ground surface O3 concentrations increased over the same period. To understand the shift in air pollutants and to comprehensively evaluate their impacts on health, a spatiotemporal model for O3 is required for exposure assessment. This study presents a data-fusion algorithm for O3 estimation that combines in situ observations, satellite remote sensing measurements, and model results from the community multiscale air quality model. Performance of the algorithm for O3 estimation was evaluated by five-fold cross-validation. The estimates are highly correlated with the in situ observations of the maximum daily 8 h averaged O3 (R2 = 0.70). The mean modeling error (measured using the root-mean-squared error) is 26 μg/m3, which accounts for 29% of the mean level. We also found that satellite O3 played a key role to improve model performance, particularly during warm months. The estimates were further used to illustrate spatiotemporal variation in O3 during 2013–2017 for the whole country. In contrast to the reduced trend of PM2.5, we found that the population-weighted O3 mean increased from 86 μg/m3 in 2013 to 95 μg/m3 in 2017, with a rate of 2.07 (95% CI: 1.65, 2.48) μg/m3 per year at the national level. This increased trend in O3 suggests that it is becoming an important contributor to the burden of diseases attributable to air pollutants in China. The developed method and the results generated from this study can be used to support future health-related studies in China.