Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China

Ozone (O 3 ) is an important trace and greenhouse gas in the atmosphere, posing a threat to the ecological environment and human health at the ground level. Large-scale and long-term studies of O 3 pollution in China are few due to highly limited direct ground and satellite measurements. This study offers a new perspective to estimate ground-level O 3 from solar radiation intensity and surface temperature by employing an extended ensemble learning of the space-time extremely randomized trees (STET) model, together with ground-based observations, remote sensing products , atmospheric reanalysis, and an emission inventory . A full-coverage (100%), high-resolution (10 km) and high-quality daily maximum 8-h average (MDA8) ground-level O 3 dataset covering China (called ChinaHighO 3 ) from 2013 to 2020 was generated. Our MDA8 O 3 estimates (predictions) are reliable, with an average out-of-sample (out-of-station) coefficient of determination of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m 3 in China. The unique advantage of the full coverage of our dataset allowed us to accurately capture a short-term severe O 3 pollution exposure event that took place from 23 April to 8 May in 2020. Also, a rapid increase and recovery of O 3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. Trends in O 3 concentration showed an average growth rate of 2.49 μg/m 3 /yr ( p < 0.001) from 2013 to 2020, along with the continuous expansion of polluted areas exceeding the daily O 3 standard (i.e., MDA8 O 3 = 160 μg/m 3 ). Summertime O 3 concentrations and the probability of occurrence of daily O 3 pollution have significantly increased since 2015, especially in the North China Plain and the main air pollution transmission belt (i.e., the “2 + 26” cities). However, a decline in both was seen in 2020, mainly due to the coordinated control of air pollution and ongoing COVID-19 effects. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.