Air pollutant prediction based on a attention mechanism model of the Yangtze River Delta region in frequent heatwaves.

Heatwaves pose significant threats to urban environments, affecting both ecological systems and public health, primarily through the exacerbation of air pollution. Accurate prediction of air pollutant concentrations during heatwave periods is crucial for authorities to develop timely prevention and control strategies. Thus, we developed the 1D-CNN-BiLSTM-attention model, specifically designed to account for the unique data characteristics associated with heatwave conditions. Our model leverages an attention mechanism to enhance its ability to learn and predict air pollutant behavior during heatwaves. Across six scenario-based experiments, the model demonstrated high predictive accuracy, achieving a MAPE of 2.93 %. The model integrates meteorological indicators such as temperature, humidity, wind speed, cloud cover, and precipitation, extending its predictive capability across a spatial range of 150 km. In experiments testing the model's applicability to three typical city types in the Yangtze River Delta region, the results confirmed its effectiveness in predicting air pollutants. These findings highlight the model's usefulness for studying air pollution during urban heatwave periods on a regional scale, demonstrating its robustness and reliability under varying weather conditions. • Developed an 1D-CNN-BiLSTM-attention model that integrates feature learning on both temporal and spatial aspects of the data. • The attention mechanism enhances the adaptability of urban air pollution models to handle specific weather conditions. • Discussed the forecast performance under the combination of input weather and the difference of input range is discussed. [ABSTRACT FROM AUTHOR]