环境科学
空气质量指数
土地覆盖
人口
空气污染
气象学
地理信息系统
时间分辨率
气溶胶
卫星
污染
遥感
土地利用
地理
化学
有机化学
社会学
人口学
土木工程
航空航天工程
工程类
物理
生物
量子力学
生态学
作者
Qingyang Xiao,Guannan Geng,Shigan Liu,Jiajun Liu,Xia Meng,Qiang Zhang
标识
DOI:10.5194/acp-22-13229-2022
摘要
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/, last access: 3 October 2022) framework. To support high spatial resolution modeling, 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 policymaking.
科研通智能强力驱动
Strongly Powered by AbleSci AI