In China, ozone pollution shows an increasing trend and becomes the primary air pollutant in warm seasons. Leveraging the air quality monitoring network, a random forest model is developed to predict the daily maximum 8-h average ozone concentrations ([O3]MDA8) across China in 2015 for human exposure assessment. This model captures the observed spatiotemporal variations of [O3]MDA8 by using the data of meteorology, elevation, and recent-year emission inventories (cross-validation R2 = 0.69 and RMSE = 26 μg/m3). Compared with chemical transport models that require a plenty of variables and expensive computation, the random forest model shows comparable or higher predictive performance based on only a handful of readily-available variables at much lower computational cost. The nationwide population-weighted [O3]MDA8 is predicted to be 84 ± 23 μg/m3 annually, with the highest seasonal mean in the summer (103 ± 8 μg/m3). The summer [O3]MDA8 is predicted to be the highest in North China (125 ± 17 μg/m3). Approximately 58% of the population lives in areas with more than 100 nonattainment days ([O3]MDA8>100 μg/m3), and 12% of the population are exposed to [O3]MDA8>160 μg/m3 (WHO Interim Target 1) for more than 30 days. As the most populous zones in China, the Beijing-Tianjin Metro, Yangtze River Delta, Pearl River Delta, and Sichuan Basin are predicted to be at 154, 141, 124, and 98 nonattainment days, respectively. Effective controls of O3 pollution are urgently needed for the highly-populated zones, especially the Beijing-Tianjin Metro with seasonal [O3]MDA8 of 140 ± 29 μg/m3 in summer. To the best of the authors' knowledge, this study is the first statistical modeling work of ambient O3 for China at the national level. This timely and extensively validated [O3]MDA8 dataset is valuable for refining epidemiological analyses on O3 pollution in China.
Keywords: China; Human exposure; Machine learning; Ozone pollution; Spatiotemporal distributions.
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