Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM_2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM_2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM_2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM_2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM_2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R²) = 0.74; root mean square error (RMSE) = 18.96 μg/m³) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks.

Air pollution is a significant urban issue, with practical applications for pollution control, urban environmental management planning, and urban construction. However, owing to the complexity and differences in spatiotemporal changes for various types of pollution, it is challenging to establish a framework that can capture the spatiotemporal correlations of different types of air pollution and obtain high prediction accuracy. In this paper, we proposed a deep learning framework suitable for predicting various air pollutants: a graph convolutional temporal sliding long short-term memory (GT-LSTM) model. The hybrid integrated model combines graph convolutional networks and long short-term networks based on a strategy with temporal sliding. Herein, the graph convolution networks gather neighbor information for spatial dependency modeling based on the spatial adjacency matrices of different pollutants and the graph convolution operator with parameter sharing. LSTM networks with a temporal sliding strategy are used to learn dynamic air pollution changes for temporal dependency modeling. The framework was applied to predict the average concentrations of PM_2.5, PM₁₀, O₃, CO, SO₂, and NO₂ in the Bejing-Tianjin-Hebei (BTH) region for the next 24 hours. Experiments demonstrated that the proposed GT-LSTM model could extract high-level spatiotemporal features and achieve higher accuracy and stability than state-of-the-art baselines. Advancement in this methodology can assist in providing decision support capabilities to mitigate air quality issues.