DSGNN: A Dual-View Supergrid-Aware Graph Neural Network for Regional Air
  Quality Estimation
Xin Zhang Ling Chen Xing Tang Hongyu Shi
Air quality estimation can provide air quality for target regions without air
quality stations, which is useful for the public. Existing air quality
estimation methods divide the study area into disjointed grid regions, and
apply 2D convolution to model the spatial dependencies of adjacent grid regions
based on the first law of geography, failing to model the spatial dependencies
of distant grid regions. To this end, we propose a Dual-view Supergrid-aware
Graph Neural Network (DSGNN) for regional air quality estimation, which can
model the spatial dependencies of distant grid regions from dual views (i.e.,
satellite-derived aerosol optical depth (AOD) and meteorology). Specifically,
images are utilized to represent the regional data (i.e., AOD data and
meteorology data). The dual-view supergrid learning module is introduced to
generate supergrids in a parameterized way. Based on the dual-view supergrids,
the dual-view implicit correlation encoding module is introduced to learn the
correlations between pairwise supergrids. In addition, the dual-view message
passing network is introduced to implement the information interaction on the
supergrid graphs and images. Extensive experiments on two real-world datasets
demonstrate that DSGNN achieves the state-of-the-art performances on the air
quality estimation task, outperforming the best baseline by an average of
19.64% in MAE.