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

TL;DR

This study developed high-resolution, continuous daily PM2.5 estimates at 1-km spatial resolution across China from 2000 to present, supporting detailed exposure assessment and policy-making.

Contribution

It introduces a novel spatiotemporal modeling framework combining satellite data, land use, and ensemble methods for long-term high-resolution PM2.5 mapping in China.

Findings

Achieved annual model R2 of 0.80-0.84

Hindcast model R2 of 0.76

Provided complete 1-km PM2.5 coverage from 2000 to present

Abstract

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.