The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China

Abstract

Respirable particles with aerodynamic diameters ≤ 10 µm (PM₁₀) have important impacts on the atmospheric environment and human health. Available PM₁₀ datasets have coarse spatial resolutions, limiting their applications, especially at the city level. A tree-based ensemble learning model, which accounts for spatiotemporal information (i.e., space-time extremely randomized trees, denoted as the STET model), is designed to estimate near-surface PM₁₀ concentrations. The 1-km resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product and auxiliary factors, including meteorology, land-use cover, surface elevation, population distribution, and pollutant emissions, are used in the STET model to generate the high-resolution (1 km) and high-quality PM₁₀ dataset for China (i.e., ChinaHighPM₁₀) from 2015 to 2019. The product has an out-of-sample (out-of-station) cross-validation coefficient of determination (CV-R²) of 0.86 (0.82) and a root-mean-square error (RMSE) of 24.28 (27.07) μg/m³, outperforming most widely used models from previous related studies. High levels of PM₁₀ concentration occurred in northwest China (e.g., the Tarim Basin) and the Northern China Plain. Overall, PM₁₀ concentrations had a significant declining trend of 5.81 μg/m³ per year (p < 0.001) over the past five years in China, especially in three key urban agglomerations. The ChinaHighPM₁₀ dataset is potentially useful for future small- and medium-scale air pollution studies by virtue of its higher spatial resolution and overall accuracy.

Keywords: 1-km resolution; AOD; ChinaHighPM(10); PM(10); Remote sensing.