Machine learning-based generation of high-resolution 3D full-coverage aerosol distribution data over China using multisource data

Aerosol pollution significantly influences the interaction between solar radiation and the earth's atmosphere and seriously threatens human health. Numerous studies have applied machine learning models such as Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) to estimate aerosol-related parameters, including aerosol optical depth and particulate matter concentrations (e.g., PM 2.5 ). However, current aerosol products primarily provide horizontal or spatially discontinuous vertical data, lacking comprehensive three-dimensional (3D) coverage. To address this gap, we developed the XGBoost-LightGBM-Wavelet (XLW) model, integrating XGBoost, LightGBM, and wavelet transforms to merge multisource data. This approach, for the first time, produced high-resolution, three-dimensional, full-coverage aerosol distribution data for China in 2015. The model outputs a dataset of aerosol spatial distribution with a horizontal resolution of 0.05°, and 167 layers within 10 km in the vertical direction. The XLW model demonstrates excellent predictive ability, effectively filling gaps in aerosol distribution. It enhances signal continuity and strengthens lower-layer signals, closely matching ground LiDAR observations and providing a more accurate representation compared to the Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) data. The dataset accurately reveals the 3D distribution of aerosols, which is meaningful for a comprehensive study of aerosol distribution at different altitudes in various regions. At 300 m height above ground level, the most polluted regions are the North China Plain and the Yangtze River Delta region, with an average aerosol extinction coefficient (AEC) of 0.34 and 0.40 km −1 , respectively. As the height increases to 1 km, the average AEC notably decreases to 0.23 and 0.24 km −1 in the North China Plain and the Yangtze River Delta. By 3 km, aerosol distribution becomes sparse over most regions of China. For the vertical variations of aerosol distributions in typical cities, in the North China Plain and Yangtze River Delta, aerosol concentrations consistently decrease from the near-surface to 4 km. However, in the Pearl River Delta, aerosol concentrations decrease consistently from 0 to 2 km, with relatively stable between 2 and 3 km. Above 4 km, aerosol concentrations are nearly negligible in all typical cities. The XLW model can accurately produce a high-resolution, 3D, full-coverage aerosol spatial distribution dataset, which is vital for conducting thorough studies on aerosol transport, aerosol radiative effects, and climate change. • First obtained the 3D full-coverage aerosol extinction coefficient dataset. • The XLW model can efficiently and accurately retrieve 3D full-coverage data. • There are significant horizontal and vertical differences in aerosol distribution. • 3D data can support multi-scale research at urban, regional, and national levels.