  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 technique and a tree-based gap-filling method. Our model has an
averaged out-of-bag cross-validation R2 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 PM2.5 data allow us to track
the day-to-day variations in PM2.5 concentrations over China in a timely
manner. The long-term records of PM2.5 data since 2000 will also support policy
assessments and health impact studies. The TAP PM2.5 data are publicly
available through our website for sharing with the research and policy
communities.
