# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 5 Conclusions and Future Work  
In this paper, we propose an attention-based CNN-LSTM model to predict urban PM\({}_{2.5}\) concentrations over the next 24 h. By taking the pollutant concentration in air quality data, meteorological data, and PM\({}_{2.5}\) concentrations in adjacent monitoring stations as the input, the model can learn the spatiotemporal correlation and long-term dependence of PM\({}_{2.5}\) concentrations. At the same time, the attention mechanism can capture the importance degrees of different feature states based on past time and further improve the prediction accuracy of the model. The experimental results show that the AC-LSTM model improved performance in the multiscale prediction tasks. Several main conclusions of this paper can be highlighted as follows:  
1. Through the analysis of air quality data, PM\({}_{2.5}\) concentration has a strong spatiotemporal correlation. Due to the air flow, PM\({}_{2.5}\) concentration in the predicted area can be easily affected by the PM\({}_{2.5}\) concentrations of the adjacent monitoring stations. As PM\({}_{2.5}\) stays in the air for a long time, the past feature states also affect future PM\({}_{2.5}\) concentration. This motivated the design of a spatiotemporal model for effective prediction of PM\({}_{2.5}\) concentrations;
2. The experimental results indicate that, in addition to using only the pollutant concentrations of the air monitoring stations, adding the meteorological data and the PM\({}_{2.5}\) concentrations of the adjacent monitoring stations can improve the prediction accuracy of the model, especially for prediction tasks on time scales over one hour;
3. The proposed AC-LSTM model can be applied to multiscale predictors at different time gaps. When compared with the traditional machine learning methods, such as SVR, MLP, and RFR, its prediction accuracy was improved significantly, especially in predicting the PM\({}_{2.5}\) concentrations over the gap of one hour. In comparison with deep learning methods, such as simple RNN, LSTM, and CNN-LSTM, AC-LSTM produced improved prediction with lower MAE and RMSE measures due to the introduced attention mechanism in the LSTM model.  
Although the proposed model can support the multiscale prediction of PM\({}_{2.5}\) concentrations in the temporal domain, in the future, we will also explore its expansion for multiscale prediction in the spatial domain. In addition, the model will also be extended for predicting other pollutants. Last but not least, sensing data, especially satellite data, will also be utilized for large-scale prediction of the PM\({}_{2.5}\) concentrations and other pollutants for early warning of air pollution and the protection of people's health.  
Methodology, S.L., Y.Y., and X.X.; software, S.L. and L.G.; supervision, G.X. and J.R.; writing--original draft, S.L.; writing--review and editing, G.X. and J.R. All authors read and agreed to the published version of the manuscript.  
This work was supported in part by the Shanxi International Cooperation Project (No. 201803D421039), the Key Research and Development Plan of Shanxi Province (No. 201703D111027), the Key Research and Development Plan of Shanxi Province (No. 201703D111023), and The Hundreds Talent Program of Shanxi Province, China.  
The authors declare no conflict of interest.  
Figure A4: The results of all models in the 19-h PM\({}_{2.5}\) prediction: (**a**) SVR, RFR, and MLP; (**b**) simple RNN, LSTM, CNN–LSTM, and AC-LSTM.