###### Abstract
Deep learning models include convolutional neural networks (CNN) [20], backpropagation neural networks (BPNN) [21], recurrent neural networks (RNN) [22], gated recurrent units (GRU) [23], long short-term memory neural networks (LSTM) [24], and bidirectional long short-term memory neural networks (Bi LSTM) [25], which have been applied to forecasting of PM\({}_{2.5}\) concentration. However, the prediction performance of the above deep learning models has improved to some extent. However, when the problem becomes complex, the prediction accuracy may be limited by the structure of a single network model. [26]. The hybrid deep learning model has several different network structures to better quantify complex data and create a better fit for changes in PM\({}_{2.5}\) concentration. Common hybrid deep learning models include LSTM fully-connected networks (LSTM-FC) [27], CNN-LSTM [28], attention-based CNN-LSTM (AC-LSTM) [29], and EEMD-GRNN model [30]. The above model forecasts PM\({}_{2.5}\) concentration based on relevant historical data, such as pollutant data (e.g., PM\({}_{10}\), SO\({}_{2}\), CO) and meteorological data (e.g., dew point temperature, air pressure, wind direction). Moreover, PM\({}_{2.5}\) concentration is a diffusion problem with spatial correlation. [31]. However, most studies focus on forecasting air quality at a single station with its historical data rather than the prediction of spatial correlation in neighboring regions. Consequently, the above model has three major issues. Firstly, it is challenging for the above model to thoroughly extract the spatial characteristics of the pollutant data, which makes it vulnerable to issues with feature information loss and decreased model predictive power. Secondly, it is difficult to extract the geographical and temporal correlation aspects of meteorological and pollutant data between several stations using the above approach. Finally, it is hard to extract the pollutant data's long-term dependency due to the above model's simplistic structure. To solve the above problem, we construct a hybrid deep learning model (FPHFA) based on the attention mechanism. The reasons are as follows. 1. Our model uses multi-channel 1D CNNs to process data from neighboring sites (i.e., pollutant data and meteorological data) to predict pollutant concentrations at the target site. This fully extracts the spatial characteristics among the stations and captures the spatiotemporal characteristics of the pollutant data and meteorological data. 2. The attention mechanism, as a lightweight module, does not consume too many resources of the computer. The attention mechanism matches the corresponding weights to the time series at different moments and concentrates the information that is more effective for prediction at different moments, thus improving the final prediction results. 3. Bi LSTM, as the prediction output layer, is more suitable for processing long time series spatiotemporal big data. Bi LSTM effectively utilizes the input forward and backward feature information to fully capture the long time series variation pattern of pollutant concentration. In this paper, in order to make more accurate predictions of future PM\({}_{2,5}\) concentrations in the target city, the following objectives should be achieved. (1) Efficient use of historical pollutant data and meteorological data from sites within the city; (2) In-depth extraction of spatial characteristics between sites; (3) Accurate realization of long-term prediction of pollutant concentrations at target sites. The remainder of the piece is organized as follows. The pollutant concentration prediction model's overall structure is described in Section 2, along with a thorough description of each component of the model. In Section 3 of the paper, the research area, the empirical data, and the methods for processing the experimental data are all given. The experimental findings from the experimental analysis are presented in Section 4. The experimental findings are given in Section 5, along with a discussion.