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Accurately forecasting air quality is critical to protecting general publicfrom lung and heart diseases. This is a challenging task due to the complicatedinteractions among distinct pollution sources and various other influencingfactors. Existing air quality forecasting methods cannot effectively model thediffusion processes of air pollutants between cities and monitoring stations,which may suddenly deteriorate the air quality of a region. In this paper, wepropose HighAir, i.e., a hierarchical graph neural network-based air qualityforecasting method, which adopts an encoder-decoder architecture and considerscomplex air quality influencing factors, e.g., weather and land usage.Specifically, we construct a city-level graph and station-level graphs from ahierarchical perspective, which can consider city-level and station-levelpatterns, respectively. We design two strategies, i.e., upper delivery andlower updating, to implement the inter-level interactions, and introducemessage passing mechanism to implement the intra-level interactions. Wedynamically adjust edge weights based on wind direction to model thecorrelations between dynamic factors and air quality. We compare HighAir withthe state-of-the-art air quality forecasting methods on the dataset of YangtzeRiver Delta city group, which covers 10 major cities within 61,500 km2. Theexperimental results show that HighAir significantly outperforms other methods.

HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method