An Approach to Spatiotemporal Air Quality Prediction Integrating SwinLSTM and Kriging Methods

Air pollution has become a major environmental issue, posing severe threats to human health and ecosystems. Accurately predicting future regional air quality is crucial for effective air pollution control and management strategies. This study proposes a novel deep learning-based approach. First, Kriging interpolation was applied to meteorological indicators such as temperature, humidity, and wind speed, as well as climate-altering gas indicators like CO2, SO2, and NO2 recorded at monitoring stations to obtain their spatial distributions over the entire region. Subsequently, a long short-term memory neural network (SwinLSTM) incorporating Swin Transformer feature extraction was employed to learn the correlations from regional meteorological data and historical air quality records. This model overcomes the limitation of traditional CNNs by capturing long-range spatial dependencies when processing two-dimensional meteorological data through its sliding window attention mechanism. Ultimately, it outputs air quality predictions in both spatial and temporal dimensions. This study collected data from 29 stations across four cities surrounding China’s Dongting Lake for experimentation. Predictions for PM2.5 and PM10 levels over the entire lake area were made for 1, 6, and 24 h. The results demonstrate that the proposed SwinLSTM architecture significantly outperforms the current mainstream ConvLSTM architecture, with an average R-squared improvement of 5%, establishing a new state-of-the-art model for spatiotemporal air quality prediction.