###### Abstract  
Air pollution has become a critical factor affecting the health of human beings. Forecasting the trend of air pollutants will be of considerable help to public health, including improving early-warning systems. The article designs a novel hybrid deep learning framework FPHFA (FPHFA is the abbreviation of the title of this paper) for PM\({}_{2.5}\) concentration forecasting is proposed, which learns spatially correlated features and long-term dependencies of time series data related to PM\({}_{2.5}\). Owing to the complex nonlinear dynamic and spatial features of pollutant data, the FPHFFA model combines multi-channel one-dimensional convolutional neural networks, bi-directional long short-term memory neural networks, and attention mechanisms for the first time. Multi-channel 1D CNNs are applied to capture trend features between some sites and overall spatial characteristics of PM\({}_{2.5}\) concentration, Bi LSTMs are used to learn the temporal correlation of PM\({}_{2.5}\) concentration, and the attention mechanism is used to focus more effective information at different moments. We carried out experimental evaluations using the Beijing dataset, and the outcomes show that our proposed model can effectively handle PM\({}_{2.5}\) concentration prediction with satisfactory accuracy. For the prediction task from 1 to 12 h, our proposed prediction model performs well. The FPHFA also achieves satisfactory results for prediction tasks from 13 to 96 h.  
air pollution; PM\({}_{2.5}\) concentration; deep learning; attention mechanism +
Footnote †: journal: Applied  
Quality Modeling System (CMAQ) [7], the Nested Air Quality Prediction Modeling System (NAQPMS) [8], and the Weather Research and Forecasting Model with Chemistry (WRF-Chem) [9]. Although chemical transport models comprehensively consider the physical and chemical processes affecting the change of atmospheric pollutant concentration, their input data, such as emission sources and meteorological fields, are uncertain, and the models are computationally intensive and take a long time to compute [10]. Compared with chemical transport models, the approach of the statistical model is simple, efficient, and widely applicable. It learns and analyzes historical data, explores the intrinsic characteristics of the data, and gives more reasonable forecasting for the future based on the current state. There are two main types of statistical models: machine learning and deep learning [11]. Machine learning mainly relies on regression forecasting in statistics, combining trends in air quality and other influencing factors to achieve PM\({}_{2.5}\) concentration. Common models available for PM\({}_{2.5}\) concentration prediction include random forest (RF) models [12], autoregressive moving average (ARMA) models [13], support vector regression (SVR) [14], and linear regression (LR) models [15]. Changes in PM\({}_{2.5}\) concentration are strongly impacted by multiple factors like weather, traffic, and pollution sources, but the simple structure of machine learning models and the weak level of generalization of the models make it difficult to accurately represent the nonlinear, non-smooth process of PM\({}_{2.5}\) changes. Compared to traditional machine learning models, deep learning models have also been adopted in the area of PM\({}_{2.5}\) concentration forecasting due to their ability to obtain a more robust nonlinear fit to the data by a deeper number of hidden layers and effective training with a large volume of data. Deep learning has demonstrated improved performance in temporal prediction to date, particularly in image identification [16], natural language processing (NLP) [17], the electricity sector [18], and prediction using historical data [19] (including the field of air pollutant concentration prediction).