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High spatial resolution PM2.5 data covering a long time period are urgentlyneeded to support population exposure assessment and refined air qualitymanagement. In this study, we provided complete-coverage PM2.5 predictions witha 1-km spatial resolution from 2000 to the present under the Tracking AirPollution in China (TAP, http://tapdata.org.cn/) framework. To support highspatial resolution modelling, we collected PM2.5 measurements from bothnational and local monitoring stations. To correctly reflect the temporalvariations in land cover characteristics that affected the local variations inPM2.5, we constructed continuous annual geoinformation datasets, including theroad maps and ensemble gridded population maps, in China from 2000 to 2021. Wealso examined various model structures and predictor combinations to balancethe computational cost and model performance. The final model fused 10-km TAPPM2.5 predictions from our previous work, 1-km satellite aerosol optical depthretrievals and land use parameters with a random forest model. Our annual modelhad an out-of-bag R2 ranging between 0.80 and 0.84, and our hindcast model hada by-year cross-validation R2 of 0.76. This open-access 1-km resolution PM2.5data product with complete coverage successfully revealed the local-scalespatial variations in PM2.5 and could benefit environmental studies andpolicy-making.

Spatiotemporal continuous estimates of daily 1-km PM2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework