China 1 km Hourly Real-Time Air Pollutant Grid Dataset (2015-present)

It is generated by the data-driven air quality assimilation framework, which integrates neural process theory and meta-learning, synergistically fusing multi-source data including ground-based in-situ measurements from over 2,000 national monitoring stations, satellite remote sensing retrievals (Himawari-8, MODIS, TROPOMI, etc.), ERA5/CAMS meteorological fields, MEIC emission inventories, and geographic auxiliary data (DEM, land use, population density, NDVI).The dataset is rigorously validated using an independent 2025 test set, with coefficients of determination (R²) of 0.91 (PM₂.₅), 0.93 (PM₁₀), 0.85 (NO₂), 0.95 (O₃), 0.58 (SO₂), and 0.69 (CO), and root mean square error (RMSE) reduced by 59.2%–83.5% compared with CAMS analysis products.

It captures fine-grained spatiotemporal variations, long-range cross-regional transport, and interspecies coupling characteristics of air pollutants, supporting long-term evolution analysis, emission source tracing, public health risk assessment, regional pollution governance evaluation, and air quality forecasting research.Owing to the extremely large volume of the full-resolution dataset, only the 10 km resolution monthly average data and 1 km resolution annual average data have been uploaded.