Satellite-Based Long-Term Spatiotemporal Patterns of Surface Ozone Concentrations in China: 2005–2019

BACKGROUND: Although short-term ozone ([Formula: see text]) exposure has been associated with a series of adverse health outcomes, research on the health effects of chronic [Formula: see text] exposure is still limited, especially in developing countries because of the lack of long-term exposure estimates. OBJECTIVES: The present study aimed to estimate the spatiotemporal distribution of monthly mean daily maximum 8-h average [Formula: see text] concentrations in China from 2005 to 2019 at a 0.05° spatial resolution. METHODS: We developed a machine learning model with a satellite-derived boundary-layer [Formula: see text] column, [Formula: see text] precursors, meteorological conditions, land-use information, and proxies of anthropogenic emissions as predictors. RESULTS: The random, spatial, and temporal cross-validation [Formula: see text] of our model were 0.87, 0.86, and 0.76, respectively. Model-predicted spatial distribution of ground-level [Formula: see text] concentrations showed significant differences across seasons. The highest summer peak of [Formula: see text] occurred in the North China Plain, whereas southern regions were the most polluted in winter. Most large urban centers showed elevated [Formula: see text] levels, but their surrounding suburban areas may have even higher [Formula: see text] concentrations owing to nitrogen oxides titration. The annual trend of [Formula: see text] concentrations fluctuated over 2005–2013, but a significant nationwide increase was observed afterward. DISCUSSION: The present model had enhanced performance in predicting ground-level [Formula: see text] concentrations in China. This national data set of [Formula: see text] concentrations would facilitate epidemiological studies to investigate the long-term health effect of [Formula: see text] in China. Our results also highlight the importance of controlling [Formula: see text] in China’s next round of the Air Pollution Prevention and Control Action Plan. https://doi.org/10.1289/EHP9406