###### Abstract  
Air pollution in cities is a severe and worrying problem because it causes threats to economic development and health. Furthermore, with the development of industry and technology, rapid population growth, and the massive expansion of cities, the total amount of pollution emissions continue to increase. Hence, observing and predicting the air quality index (AQI), which measures fatal pollutants to humans, has become more and more critical in recent years. However, there are insufficient air quality monitoring stations for AQI observation because the construction and maintenance costs are too high. In addition, finding an available and suitable place for monitoring stations in cities with high population density is difficult. This study proposes a spatial-temporal model to predict the long-term AQI in a city without monitoring stations. Our model calculates the spatial-temporal correlation between station and region using an attention mechanism and leverages the distance information between all existing monitoring stations and target regions to enhance the effectiveness of the attention structure. Furthermore, we design a hybrid predictor that can effectively combine the time-dependent and time-independent predictors using the dynamic weighted sum. Finally, the experimental results show that the proposed model outperforms all the baseline models. In addition, the ablation study confirms the effectiveness of the proposed structures. air quality prediction; deep learning; spatial-temporal attention +
Footnote †: journal: Medical Imaging, 2019  
because of various complicated factors, such as meteorological effects, surrounding land usages, and the daily behaviors of people. Moreover, sudden changes and accidents might affect the air quality as well and thus make it difficult for a general air quality prediction model to accurately forecast the future. For example, an industrial accident such as a fire in a factory might cause the concentration of pollutants to increase rapidly. Conversely, the outbreak of widespread diseases like COVID-19 [2] might decrease pollution emissions because of substantially reduced human activities. In general, air quality prediction in cities has focused on two conditions. One is to predict future air pollution in areas with monitoring stations, known as the air pollution forecasting problem. This problem has been widely discussed and has had many high-performance solutions in recent research [3,4]. The other condition is to estimate air pollution in areas without monitoring stations in the current hour, also known as fine-grained air pollution estimation [5,6]. The second condition is also more challenging because there is no sufficient labeled data to train an effective module [5]. In the real world, monitoring stations are usually insufficient and sparsely distributed in a large city because of the high construction and maintenance costs. In addition, suitable places in densely populated cities are rare and unavailable. As a result, people have to analyze and predict the current fine-grained AQI with limited historical data. Traditionally, researchers [7, 8, 9] used historical air pollution data crawled from established monitoring stations to predict air quality. However, with the increasing amount of open data and sensors, people can collect more heterogeneous and dynamic data highly correlated with air pollutants. The studies [10,11] utilized these data and developed deep-learning-based models that outperform the traditional methods in air quality modeling. However, deep learning-based methods were still unable to accurately predict fine-grained air quality throughout a city because of the scarcity of air pollution monitoring stations and the incompleteness of historical data. Therefore, recently, it has become popular to discuss how existing monitoring stations can predict future air pollution levels and assess the current air pollution levels in minuscule urban areas.