Improved 1 km resolution PM 2.5 estimates across China using enhanced space–time extremely randomized trees

Wei, Jing; Li, Zhanqing; Cribb, Maureen; Huang, Wei; Xue, Wenhao; Sun, Lin; Guo, Jianping; Peng, Yiran; Li, Jing; Lyapustin, Alexei; Liu, Lei; Wu, Hao; Song, Yimeng

Fine particulate matter with aerodynamic diameters inline-formula≤2.5inline-formulaµm (PMinline-formula2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PMinline-formula2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PMinline-formula2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PMinline-formula2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PMinline-formula2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (inline-formulaR2) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) inline-formulaµg minline-formula−3, a small mean absolute error of 6.69 (7.15) inline-formulaµg minline-formula−3 and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PMinline-formula2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PMinline-formula2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly inline-formulaR2=0.80), which can be used to estimate historical PMinline-formula2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PMinline-formula2.5 dataset across mainland China (i.e., ChinaHighPMinline-formula2.5), important for air pollution studies focused on urban areas.

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Wei, Jing / Li, Zhanqing / Cribb, Maureen / et al: Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees. 2020. Copernicus Publications.

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