ABSTRACT
Ozone (O₃) 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 O₃ pollution in China are few due to highly limited direct ground and satellite measurements. This study offers a new perspective to estimate ground-level O₃ 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 O₃ dataset covering China (called ChinaHighO₃) from 2013 to 2020 was generated. Our MDA8 O₃ 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/m³ in China. The unique advantage of the full coverage of our dataset allowed us to accurately capture a short-term severe O₃ pollution exposure event that took place from 23 April to 8 May in 2020. Also, a rapid increase and recovery of O₃ concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. Trends in O₃ concentration showed an average growth rate of 2.49 μg/m³/yr (p < 0.001) from 2013 to 2020, along with the continuous expansion of polluted areas exceeding the daily O₃ standard (i.e., MDA8 O₃ = 160 μg/m³). Summertime O₃ concentrations and the probability of occurrence of daily O₃ 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.
ABSTRACT
Gaseous pollutants at the ground level seriously threaten the urban air quality environment and public health. There are few estimates of gaseous pollutants that are spatially and temporally resolved and continuous across China. This study takes advantage of big data and artificial-intelligence technologies to generate seamless daily maps of three major ambient pollutant gases, i.e., NO2, SO2, and CO, across China from 2013 to 2020 at a uniform spatial resolution of 10 km. Cross-validation between our estimates and ground observations illustrated a high data quality on a daily basis for surface NO2, SO2, and CO concentrations, with mean coefficients of determination (root-mean-square errors) of 0.84 (7.99 µg m-3), 0.84 (10.7 µg m-3), and 0.80 (0.29 mg m-3), respectively. We found that the COVID-19 lockdown had sustained impacts on gaseous pollutants, where surface CO recovered to its normal level in China on around the 34th day after the Lunar New Year, while surface SO2 and NO2 rebounded more than 2 times slower due to more CO emissions from residents' increased indoor cooking and atmospheric oxidation capacity. Surface NO2, SO2, and CO reached their peak annual concentrations of 21.3 ± 8.8 µg m-3, 23.1 ± 13.3 µg m-3, and 1.01 ± 0.29 mg m-3 in 2013, then continuously declined over time by 12 %, 55 %, and 17 %, respectively, until 2020. The declining rates were more prominent from 2013 to 2017 due to the sharper reductions in anthropogenic emissions but have slowed down in recent years. Nevertheless, people still suffer from high-frequency risk exposure to surface NO2 in eastern China, while surface SO2 and CO have almost reached the World Health Organization (WHO) recommended short-term air quality guidelines (AQG) level since 2018, benefiting from the implemented stricter "ultra-low” emission standards. This reconstructed dataset of surface gaseous pollutants will benefit future (especially short-term) air pollution and environmental health-related studies.
ABSTRACT
Ozone (O3) 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 O3 pollution in China are few due to highly limited direct ground and satellite measurements. This study offers a new perspective to estimate ground-level O3 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 O3 dataset covering China (called ChinaHighO3) from 2013 to 2020 was generated. Our MDA8 O3 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/m3 in China. The unique advantage of the full coverage of our dataset allowed us to accurately capture a short-term severe O3 pollution exposure event that took place from 23 April to 8 May in 2020. Also, a rapid increase and recovery of O3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. Trends in O3 concentration showed an average growth rate of 2.49 μg/m3/yr (p < 0.001) from 2013 to 2020, along with the continuous expansion of polluted areas exceeding the daily O3 standard (i.e., MDA8 O3 = 160 μg/m3). Summertime O3 concentrations and the probability of occurrence of daily O3 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.