Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees

Abstract

Fine particulate matter with aerodynamic diameters ≤ 2:5 μm (PM2:5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM2:5 concentrations has made intensive use of satellitederived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM2:5 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 PM2:5 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 PM2:5 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 (R2) of 0.89 (0.88), a low rootmean-square error of 10.33 (10.93) μgm-3, a small mean absolute error of 6.69 (7.15) μgm-3 and a small mean relative error of 21.28% (23.69 %). In particular, the model captured well the PM2:5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM2:5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R2 D 0:80), which can be used to estimate historical PM2:5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM2:5 dataset across mainland China (i.e., ChinaHighPM2:5), important for air pollution studies focused on urban areas.