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

Abstract

Background:Although short-term ozone () exposure has been associated with a series of adverse health outcomes, research on the health effects of chronic 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 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 column, precursors, meteorological conditions, land-use information, and proxies of anthropogenic emissions as predictors.Results:The random, spatial, and temporal cross-validation of our model were 0.87, 0.86, and 0.76, respectively. Model-predicted spatial distribution of ground-level concentrations showed significant differences across seasons. The highest summer peak of occurred in the North China Plain, whereas southern regions were the most polluted in winter. Most large urban centers showed elevated levels, but their surrounding suburban areas may have even higher concentrations owing to nitrogen oxides titration. The annual trend of concentrations fluctuated over 2005–2013, but a significant nationwide increase was observed afterward.Discussion:The present model had enhanced performance in predicting ground-level concentrations in China. This national data set of concentrations would facilitate epidemiological studies to investigate the long-term health effect of in China. Our results also highlight the importance of controlling in China’s next round of the Air Pollution Prevention and Control Action Plan. https://doi.org/10.1289/EHP9406