  Accurately forecasting air quality is critical to protecting general public
from lung and heart diseases. This is a challenging task due to the complicated
interactions among distinct pollution sources and various other influencing
factors. Existing air quality forecasting methods cannot effectively model the
diffusion processes of air pollutants between cities and monitoring stations,
which may suddenly deteriorate the air quality of a region. In this paper, we
propose HighAir, i.e., a hierarchical graph neural network-based air quality
forecasting method, which adopts an encoder-decoder architecture and considers
complex air quality influencing factors, e.g., weather and land usage.
Specifically, we construct a city-level graph and station-level graphs from a
hierarchical perspective, which can consider city-level and station-level
patterns, respectively. We design two strategies, i.e., upper delivery and
lower updating, to implement the inter-level interactions, and introduce
message passing mechanism to implement the intra-level interactions. We
dynamically adjust edge weights based on wind direction to model the
correlations between dynamic factors and air quality. We compare HighAir with
the state-of-the-art air quality forecasting methods on the dataset of Yangtze
River Delta city group, which covers 10 major cities within 61,500 km2. The
experimental results show that HighAir significantly outperforms other methods.
