# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 1 Introduction  
Air pollution is a serious environmental problem that is attracting increasing attention worldwide [1]. With the rapid development of the Chinese economy and the acceleration of industrialization, urban air pollution is getting worse. As one of the main pollutants in the air, fine particulate matter (PM2.5) contains a large amount of toxic and harmful substances due to its small particle size. It not only stays in the atmosphere for a long time, but also has a long transport distance, resulting in a decrease in air visibility, seriously affecting our living environment and physical health. In response to it, the Chinese government established air quality monitoring stations in most cities,to detect PM\({}_{2.5}\) and other air pollutant concentrations in real time [2]. However, it is inevitable for the government to bear a significant financial burden because of expensive equipment [3,4]. In addition to monitoring, there is a rising demand for the prediction of future air quality. Obviously, the prediction of real-time and future PM\({}_{2.5}\) concentration is essential for air pollution control and the prevention of health issues caused by air pollution. With the development of machine learning in recent years, artificial neural network (ANN), support vector regression (SVR), and other methods were successfully applied to the prediction of air pollutant concentration. [PERSON] et al. [5] used the spatial features of roads, factories, and parks in the prediction area to predict the concentration of PM\({}_{10}\) and NO\({}_{2}\). [PERSON] et al. [6] used SVR to predict the PM\({}_{2.5}\) concentration of a target station using observation data from the surrounding monitoring stations. Although all these aforementioned methods made use of the spatial features that affect the concentrations of pollutants, the temporal correlation of air pollutants and the time-delay characteristics of PM\({}_{2.5}\) were not considered. Due to the dynamic nature of relevant atmospheric environments, the recurrent neural network (RNN) is especially suitable to simulate the temporal evolution of air pollutant distributions because RNNs can handle arbitrary sequences of inputs, thereby guaranteeing the capacity to learn temporal sequences [7]. [PERSON] et al. [8] used meteorological data to predict PM\({}_{2.5}\) concentration using an RNN. [PERSON] et al. [9] combined random forest (RF) and an RNN to analyze and forecast the next 24-h PM\({}_{2.5}\) concentration of air pollutants in Hangzhou, China. When there is a long time lag in the traditional RNN, however, it may suffer from problems such as gradient disappearance and gradient explosion [10]. These RNN-based methods do not take full advantage of spatial features either. Additionally, the states of the feature formation at different times will also have different effects on future PM concentrations. The existing studies did not consider the effects of feature states of the past different times on air pollutants, but only extracted the temporal correlation features of historical data. To tackle the aforementioned problems, we propose an attention-based convolutional neural network (CNN)-long short-term memory (LSTM) model, AC-LSTM, for predicting the PM\({}_{2.5}\) concentrations over the next 24 h. The proposed AC-LSTM model comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network [10], and attention-based network. As a representative network of RNN, the LSTM network overcomes the defect of gradient disappearance and gradient explosion of the traditional RNN due to its special cell structure [10]. It can capture the spatiotemporal correlation and interdependence of air quality-related time-series data at the same time. The joint one-dimensional CNN aims to extract spatiotemporal features from air quality data and local spatial correlation features of PM\({}_{2.5}\) concentrations among air monitoring stations.