Preprints
https://doi.org/10.48550/arXiv.2203.07591
https://doi.org/10.48550/arXiv.2203.07591
 
07 Jun 2022
07 Jun 2022
Status: this preprint is open for discussion.

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

Qingyang Xiao1, Guannan Geng1, Shigan Liu2, Jiajun Liu1, Xia Meng3, and Qiang Zhang2 Qingyang Xiao et al.
  • 1State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
  • 2Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
  • 3School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China

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.

Qingyang Xiao et al.

Status: open (until 20 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-211', Anonymous Referee #1, 15 Jun 2022 reply

Qingyang Xiao et al.

Qingyang Xiao et al.

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Short summary
We provided complete coverage PM2.5 concentration at a 1-km resolution from 2000 to the present that carefully considered the significant changes in land-use characteristics in China. This high-resolution PM2.5 data sucessfully revealed the local scale PM2.5 variations. We noticed changes in PM2.5 spatial patterns in association with the clean air policies that the pollution hotspots have transferred from urban centers to rural regions with limited air quality monitoring.