Tracking Air Pollution in China: Near Real-Time PM 2.5 Retrievals from Multisource Data Fusion

作者：Guannan Geng, Qingyang Xiao, Shigan Liu, Xiaodong Liu, Jing Cheng, Yixuan Zheng, Tao Xue, Dan Tong, Bo Zheng, Yiran Peng, Xiaomeng Huang, Kebin He, Qiang Zhang · 发表于：Environmental Science & Technology · 年份：2021 · DOI：10.1021/acs.est.1c01863 · 被引用次数：594 · 研究领域：Air Quality and Health Impacts、Air Quality Monitoring and Forecasting、Atmospheric chemistry and aerosols

High Resolution Image Download MS PowerPoint Slide Air pollution has altered the Earth’s radiation balance, disturbed the ecosystem, and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant data set with timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://tapdata.org.cn/ ) that combines information from multiple data sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, and other ancillary data such as meteorological fields, land use data, population, and elevation. Daily full-coverage PM 2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM 2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R 2 of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM 2.5 data allow us to track the day-to-day variations in PM 2.5 concentrations over China in a timely manner. The long-term records of PM 2.5 da...