Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multiple Data Sources
Guannan Geng, Qingyang Xiao, Shigan Liu, Xiaodong Liu, Jing Cheng,, Yixuan Zheng, Dan Tong, Bo Zheng, Yiran Peng, Xiaomeng Huang, Kebin He and, Qiang Zhang

TL;DR
This paper introduces TAP, a near real-time, high-resolution PM2.5 dataset for China, integrating multiple data sources and advanced machine learning to support research and policy with timely, comprehensive air pollution data.
Contribution
It develops the first near real-time, full-coverage PM2.5 dataset for China using a novel two-stage machine learning approach combining diverse data sources.
Findings
Achieved an R2 of 0.83 in PM2.5 estimation.
Improved high pollution level predictions and gap filling.
Enabled daily tracking of PM2.5 variations across China.
Abstract
Air pollution has altered the Earth radiation balance, disturbed the ecosystem and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant dataset 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, tapdata.org) that combines information from multiple data sources, including ground measurements, satellite retrievals, dynamically updated emission inventories, operational chemical transport model simulations and other ancillary data. Daily full-coverage PM2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling…
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Taxonomy
TopicsAir Quality and Health Impacts · Air Quality Monitoring and Forecasting · Atmospheric chemistry and aerosols
