# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
## 2 Related Work  
[PERSON] et al., [PERSON] et al., [PERSON] et al., and [PERSON] et al. highlighted the relationship between meteorology, season, and air quality [2; 3; 7; 9]. However, the relationship between weather features and air quality seems to be different in different areas. [PERSON] et al. mentioned that temperature and air quality show a positive correlation in summer, but [PERSON] et al. obtained the opposite results in North China. [PERSON] et al. and [PERSON] et al. showed that human activity, such as vehicle exhausts and industrial exhaust, is the main source of PM2.5; however, some human activity is beneficial to air quality. [PERSON] et al.  
Figure 1: Real-time map of monitored PM2.5 ([[https://airbox.edimaxcloud.com/](https://airbox.edimaxcloud.com/)]([https://airbox.edimaxcloud.com/](https://airbox.edimaxcloud.com/)) (accessed on 27 May 2021)).  
studied streets with or without bushes, and found that the air quality of streets with bushes was better than streets without bushes [5]. In addition to the points above, there are still lots of factors that could affect air quality. Traffic structures, wildfires, and even government regulations could have an impact on air quality [24; 25; 26; 27; 28; 29; 30]. As stated above, factors that could affect air quality are diverse and complex.  
Based on the collected references, air quality analysis issues are classified into real time prediction and future forecasting. The literature on these two topics emphasized the importance of feature selection and has proposed some methods based on feature combinations. The more complete the features are, the more accurate the model is. The first category focuses on real-time air quality prediction in locations without air quality stations using features and air quality data. The majority of references propose an air quality prediction algorithm with multiple features, such as meteorological, traffic, human mobility, road networks, points of interest, and time [9; 13; 31; 32]. [PERSON] et al. analyzed the characteristics of air pollution using pattern mining, and found that air pollution is serious in autumn and winter [33]. There are some approaches that predict air quality using social networks [12; 14; 15; 16; 17]. These works predict air quality by mining key words in public posts and photos from users on social networks.  
Another method is future air quality forecasting by features. People are more interested in future air quality forecasting than real-time air quality inference. A great deal of research has tried to forecast air quality using multiple features [4; 6; 8; 10]. The authors in above works collected meteorology, traffic flow, human mobility, points of interest, road networks, and city data to forecast air quality. [PERSON] et al. used pattern mining and the Bayesian Gaussian model to analyze where air pollution comes from [34]. [PERSON] and [PERSON] also used a Bayesian approach to forecast air quality in relation to wildfire events [35]. [PERSON] et al. and [PERSON] et al. forecasted air quality using several models [36; 37]. They analyzed the distribution of different sources, including chemical pollution, atmospheric composition, and ground emissions. As deep learning and machine learning are becoming more popular, some research has tried to solve this issue using machine learning and deep learning. [PERSON] et al. used some learning models to forecast PM2.5 [11; 18; 19; 20; 21]. The results showed that the deep learning approach outperforms most models. Almost all the references mentioned that the most important factor affecting air quality forecasting is the features. Feature information is necessary in forecasting air quality. Natural factor and human factor analyses of air pollution composition are beneficial in forecasting air quality. There are other features that remain unknown. In this research, temporal and spatial features are prepared for PM2.5 forecasting.