  High spatial resolution PM2.5 data covering a long time period are urgently
needed to support population exposure assessment and refined air quality
management. In this study, we provided complete-coverage PM2.5 predictions with
a 1-km spatial resolution from 2000 to the present under the Tracking Air
Pollution in China (TAP, http://tapdata.org.cn/) framework. To support high
spatial resolution modelling, we collected PM2.5 measurements from both
national and local monitoring stations. To correctly reflect the temporal
variations in land cover characteristics that affected the local variations in
PM2.5, we constructed continuous annual geoinformation datasets, including the
road maps and ensemble gridded population maps, in China from 2000 to 2021. We
also examined various model structures and predictor combinations to balance
the computational cost and model performance. The final model fused 10-km TAP
PM2.5 predictions from our previous work, 1-km satellite aerosol optical depth
retrievals and land use parameters with a random forest model. Our annual model
had an out-of-bag R2 ranging between 0.80 and 0.84, and our hindcast model had
a by-year cross-validation R2 of 0.76. This open-access 1-km resolution PM2.5
data product with complete coverage successfully revealed the local-scale
spatial variations in PM2.5 and could benefit environmental studies and
policy-making.
