## 2 Related Work  
Air pollution prediction has become a popular research topic in recent years. Researchers have proposed several methods to fulfill the objectives. In this section, we introduce the previous works of air quality forecasting, fine-grained air quality estimation, and the combination of air quality forecasting and fine-grained air quality estimation.  
* **Air Quality Forecasting:** Previous studies have proposed many data-driven approaches for air quality modeling. For example, the work [7] introduced autoregressive integrated moving average (ARIMA), which is a famous and widely adopted data-driven model for air pollution forecast at monitoring stations. In [8], the authors proposed a traditional physical-based numerical model that used historical air pollution data [9] to formulate the urban pollution process and pollutant dispersion. Moreover, studies [3; 4; 17] used the combination of spatial and temporal features to model air quality forecasting. Recently, big data and neural network techniques have improved the traditional methods and achieved state-of-the-art performance in urban air quality forecasting [10; 11]. Graph convolution neural network (GCN) has become particularly popular in many works. The GCN-based air quality prediction takes monitoring stations as nodes of the graph and uses static features to calculate the node embedding to forecast the predicted values [13].
* **Fine-Grained Estimation:** Several studies have proposed to use basic interpolation methods such as spatial averaging, nearest neighbor, inversed distance weighting (IDW), and kriging to solve the fine-grained estimation of air quality for locations without monitoring stations [18; 19]. In addition, the work [5] proposed an affinity graph framework to infer real-time air quality of a location without a monitoring station, and recommend the best locations for new monitoring stations. As air quality forecasting, several studies have investigated interpolation methods using the combination of spatial and temporal features [3; 6; 20]. In addition, a neural network with an attention mechanism [12] has become popular for estimating the air quality of a region without monitoring stations.
* **Joint Air Quality Forecasting and Fine-Grained Estimation:** Few studies have attempted to achieve both air quality forecasting and fine-grained estimation. The work [15] used the combination of GCN and LSTM networks to learn the spatial-temporal correlation and then predict these two targets. The work in [16] proposed a hybrid model to perform feature selection, air quality forecasting, and fine-grained estimation jointly in one single model. Moreover, the studies in [14; 21] show that using air quality forecasting and fine-grained estimating as multi-task learning can improve the generalization performance by leveraging the commonalities and differences between these two tasks.  
Although previous studies [14; 16] have proposed methods to jointly model air quality forecasting and fine-grained estimation and enhance the performance, there is still much for improvement. For example, these methods do not solve long-term prediction challenges in regions without sufficient historical data or monitoring stations. To solve this problem, we propose a model that can capture the complex spatial and temporal interaction between air pollution and geographical data, and then predict the air quality in places without sufficient historical data.