  Air pollution is a crucial issue affecting human health and livelihoods, as
well as one of the barriers to economic and social growth. Forecasting air
quality has become an increasingly important endeavor with significant social
impacts, especially in emerging countries like China. In this paper, we present
a novel Transformer architecture termed AirFormer to collectively predict
nationwide air quality in China, with an unprecedented fine spatial granularity
covering thousands of locations. AirFormer decouples the learning process into
two stages -- 1) a bottom-up deterministic stage that contains two new types of
self-attention mechanisms to efficiently learn spatio-temporal representations;
2) a top-down stochastic stage with latent variables to capture the intrinsic
uncertainty of air quality data. We evaluate AirFormer with 4-year data from
1,085 stations in the Chinese Mainland. Compared to the state-of-the-art model,
AirFormer reduces prediction errors by 5%~8% on 72-hour future predictions. Our
source code is available at https://github.com/yoshall/airformer.
