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Air pollution has altered the Earth radiation balance, disturbed theecosystem and increased human morbidity and mortality. Accordingly, afull-coverage high-resolution air pollutant dataset with timely updates andhistorical long-term records is essential to support both research andenvironmental management. Here, for the first time, we develop a near real-timeair pollutant database known as Tracking Air Pollution in China (TAP,tapdata.org) that combines information from multiple data sources, includingground measurements, satellite retrievals, dynamically updated emissioninventories, operational chemical transport model simulations and otherancillary data. Daily full-coverage PM2.5 data at a spatial resolution of 10 kmis our first near real-time product. The TAP PM2.5 is estimated based on atwo-stage machine learning model coupled with the synthetic minorityoversampling technique and a tree-based gap-filling method. Our model has anaveraged out-of-bag cross-validation R2 of 0.83 for different years, which iscomparable to those of other studies, but improves its performance at highpollution levels and fills the gaps in missing AOD on daily scale. The fullcoverage and near real-time updates of the daily PM2.5 data allow us to trackthe day-to-day variations in PM2.5 concentrations over China in a timelymanner. The long-term records of PM2.5 data since 2000 will also support policyassessments and health impact studies. The TAP PM2.5 data are publiclyavailable through our website for sharing with the research and policycommunities.

Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multiple Data Sources