## 2 Related Work  
### Air Pollution Prediction  
Currently, air pollution prediction methods can be generally classified into three categories: contained deterministic, statistical, and deep learning methods. Deterministic methods adopt mathematical expressions, meteorological theories, and the physical-chemical reactions of air pollution to simulate the transformation and diffusion processes of pollutants [18]. Common methods include the weather research and forecasting model [19]. However, deterministic methods need to utilize a lot of prior knowledge, which limits their prediction performance. To address this limitation, statistical methods have been proposed, which employ principles of statistics to discovery the relationships between external factors (e.g., meteorological data) and air pollution. Statistical methods can be divided into two categories: classical statistical methods and shallow machine learning methods. Specifically, classical statistical methods include the historical average (HA) [20]. For instance, HA [20] was developed to forecast air quality in Delhi. Unfortunately, these methods are based on linear assumptions, which impacts their prediction precision in a nonlinear problem like air pollution forecasting. In light of these factors, shallow machine learning methods (e.g., support vector regression (SVR) [21]) were proposed. Nonetheless, these methods fail to consider spatio-temporal correlations in air pollution data.  
With the advent of deep learning technology [22], the potential of this technology for extracting spatio-temporal features is gradually being explored. Many researchers have adopted deep-learning-based methods for air pollution prediction [23; 24; 25]. For instance, [PERSON] et al. proposed a convolutional neural network and long short-term memory (CNN-LSTM) [24] to predict ozone concentrations over 24 h. Specifically, a convolutional neural network (CNN) was proposed to extract spatial dependencies. Long short-term memory (LSTM) was used to capture temporal dependencies. A CNN can only model the road network in a standard form, e.g., 2D-matrix. Despite the benefits of convolutional operations in mining spatial dependencies between neighboring sites, there are some crucial spatial dependencies, such as the relationships between distant sensors, that are challenging to express in the standard form. In view of this, graph neural networks (e.g., graph attention network (GAT)) have attracted a lot of attention because of their ability to represent a sensor network using graph theory. This can completely preserve the original spatial relationships in the sensor network. For example, [PERSON] et al. [25] leveraged a graph attention network and long short-term memory (GAT-LSTM) to predict \(PM_{2.5}\) over 24 h in Beijing. In particular, spatial relationships between nodes in the graph were extracted by GAT, which introduced a spatial attention inspired by the transformer [26] model. LSTM was adopted to capture temporal dependencies. Although these models achieve accurate predictions, they are trained and predicted on a central cloud. This can put computational pressure on the server. Meanwhile, the security risk increases. Moreover, these methods fail to fuse external factors in an effective manner.