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Respirable particles with aerodynamic diameters ≤ 10 µm (PM1 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 (M
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