  When predicting PM2.5 concentrations, it is necessary to consider complex
information sources since the concentrations are influenced by various factors
within a long period. In this paper, we identify a set of critical domain
knowledge for PM2.5 forecasting and develop a novel graph based model,
PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world
dataset, we validate the effectiveness of the proposed model and examine its
abilities of capturing both fine-grained and long-term influences in PM2.5
process. The proposed PM2.5-GNN has also been deployed online to provide free
forecasting service.
