## 5 Conclusions and Future Work  
In this paper, based on historical air quality data and meteorological data, an improved integrated deep neural network method based on an attention mechanism is proposed to predict PM\({}_{2.5}\) concentration within 24 h. Due to the spread of air pollution, air quality at one site may be affected by other sites. Pearson correlation coefficient is used to learn the spatial correlation between sites and select neighboring sites. The exogenous series correlation method is used to learn the correlation between the exogenous series of other sites and the central site collection. In order to evaluate the performance of DSTP-FC model, PM\({}_{2.5}\) concentration of Beijing central site is predicted. Compared with other models, the proposed model provides better prediction results based on RMSE and MAE indicators. This study can draw several useful findings: (1) compared with LSTM, LSTM-FC and CNN-LSTM, DA-RNN and DSTP-FC models with attention mechanism show better prediction performance; (2) Compared with the MSE loss function, sudden changes in PM\({}_{2.5}\) concentration can be better predicted when using the DILATE loss function; (3) The target series of the central site is also affected by exogenous series of other sites.  
In our future work: (1) More factors should be considered, such as the sudden emission of air pollutants in the factory, so the law of PM\({}_{2.5}\) concentration change can be better studied and the sudden change of PM\({}_{2.5}\) concentration can be accurately predicted; (2) The LSTM structure is used as one of the basic units in the attention module of the model, and other RNN structures can be tried; (3) In order to verify the universality of the proposed model, the monitoring sites' data of other cities should be used for experiments.  
Funding acquisition, P.S.; Project administration, J.N.; Writing--original draft, X.F.; Writing--review and editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the National Natural Science Foundation of China (61873086, 61801169) and the Applied Basic Research Programs of Changzhou (CJ20200061).  
**Informed Consent Statement**: Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time Methods**} & \multicolumn{2}{c}{**1–6 h**} & \multicolumn{2}{c}{**7–12 h**} & \multicolumn{2}{c}{**13–24 h**} \\ \cline{2-7}  & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline DSTP-FC-MAE & 35.36 & 21.03 & 48.13 & 34.66 & 54.03 & 43.25 \\ DSTP-FC-MSE & 34.55 & 20.42 & **44.17** & **31.93** & 52.79 & 39.46 \\ DSTP-FC-DILATE & **32.51** & **19.50** & 45.22 & 32.22 & **51.45** & **37.04** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of prediction accuracy of PM\({}_{2.5}\) concentration using different loss functions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**1–6 h**} & \multicolumn{2}{c}{**7–12 h**} & \multicolumn{2}{c}{**13–24 h**} \\ \cline{2-7}  & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline Cross validation & 33.67 & 22.20 & 42.39 & 32.15 & 49.90 & 38.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental results of the cross validation.  
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [[http://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data](http://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data)]([http://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data](http://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data)), accessed on 20 September 2019.  
The authors declare no conflict of interest.