  Air quality impacts people's health and daily life, affects the sensitive
ecosystems, and even restrains a country's development. By collecting and
processing the time series data of Air Quality Index (AQI) of 363 cities of
China from Jan. 2015 to Mar. 2019, we dedicated to characterize the universal
patterns, the clustering and correlation of air quality of different cities by
using the methods of complex network and time series analysis. The main results
are as follows: 1) The Air Quality Network of China (AQNC) is constructed by
using the Planar Maximally Filtered Graph (PMFG) method. The geographical
distances on the correlation of air quality of different cities have been
studied, it is found that 100 km is a critical distance for strong correlation.
2) Seven communities of AQNC have been detected, and their patterns have been
analyzed by taking into account the Hurst exponent and climate environment, it
is shown that the seven communities are reasonable, and they are significantly
influenced by the climate factors, such as monsoon, precipitation, geographical
regions, etc. 3) The motifs of air quality time series of seven communities
have been investigated by the visibility graph, for some communities, the
evolutionary patterns of the motifs are a bit stable, and they have the
long-term memory effects. While for others, there are no stable patterns.
