# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 4 Conclusions  
In recent years, predicting the PM2.5 concentration has attracted the attention of many scholars, especially those who are committed to environmental protection. Coupled with the improvement of urban air pollution prediction and control management, many air quality monitoring stations are deployed in many cities. How to effectively use the data collected by these monitoring stations and improve urban air quality is an important issue. In this paper, an intelligent PM2.5 concentration prediction model CNN-BiLSTM-Attention is proposed. Taking Beijing as a research case, this model was applied to a dataset of hours from January 2013 to February 2017 in Shunyi District, Beijing. Results show that:  
1. For a model based on deep learning, the parameters adjustment of the network framework is inevitable. In this paper, a large number of experiments and tuning tools are used to determine the parameters. 2. The performance of the hybrid CNN-BiLSTM-Attention model proposed in this paper is better than the traditional models and machine learning models used to predict PM2.5 concentration. Additionally, it is better than the integration of the two models based on CNN and LSTM. This is due to the attention mechanism that can capture the degree of influence of the feature states at different times on the PM2.5 concentration. The attention-based layer can automatically weight the past feature states. 3. The short-term (48 h) and long-term (72 h, 96 h, 120 h, and 144 h) predictions of the models carried out in this paper show that the prediction performance is the best in the next 48 h, with MAE, RMAE and R2 being 2.366 \(\upmu\)g/m\({}^{3}\), 3.065 \(\upmu\)g/m\({}^{3}\) and 0.960, respectively. Additionally, the CNN-BiLSTM-Attention model predicts the next 144 h is still better than other models' predictions for the next 48 h. Therefore, this hybrid model has good generalization ability and is also conducive to long-term dependence feature extraction. The proposed CNN-BiLSTM-Attention model is an intelligent PM2.5 concentration prediction model based on the analysis and modeling of historical air quality data. It can help environmental protection agencies implement some measures to strengthen environmental protection. Meanwhile, it provides a reference for the measures taken by the transportation-related departments to reduce related gas emissions. The model established in this paper is closely related to reality, deeply analyzes and discusses PM2.5 issues, establishes a corresponding model, and analyzes the prediction accuracy so that the model has good versatility and generalization. It can also be used to predict other pollutants. With the large-scale deployment of air quality monitoring stations, the prediction model in this paper has potential for application. However, since air quality monitoring stations have only been deployed in recent years, the limitation of the amount of data may affect the training of the model. In the future, as more air quality monitoring stations are deployed, there will be longer periods of data to optimize the prediction model. In addition, the PM2.5 concentration is spatially related. In the future, PM2.5 concentration data from surrounding monitoring stations and related factors will be taken into consideration to further improve the prediction accuracy of the model. Conceptualization, J.Z. and B.R. ; methodology, T.L. and Y.P. ; validation, Y.P. ; formal analysis, J.Z. ; investigation, Y.P. ; resources, Y.P. ; data curation, B.R. ; writing--original draft preparation, J.Z. and Y.P. ; writing--review and editing, T.L. and B.R. ; visualization, Y.P. ; supervision, T.L. and J.Z. ; project administration, T.L. ; funding acquisition, J.Z. and T.L. All authors have read and agreed to the published version of the manuscript.