An attention-based CNN model integrating observational and simulation data for high-resolution spatial estimation of urban air quality

Machine learning, especially deep learning, can outperform traditional atmospheric models in air quality assessment, offering enhanced efficiency and accuracy without relying on detailed emission inventories and atmospheric chemical mechanisms. Despite their predictive power, deep learning models often grapple with the perception of being “black boxes” due to their intricate architectures. Here, we develop an attention-based convolutional neural network (CNN-attention) model that incorporates observational data, the parallelized large-eddy-simulation model (PALM), and urban morphology data for high-resolution spatial estimation of urban air quality. Our findings indicate that the CNN-attention model outperforms traditional CNN with higher accuracy and efficiency, achieving R² = 0.987 and root mean square error (RMSE) = 0.15 mg/m³, while significantly reducing training time and memory usage. Compared to traditional machine learning models, the CNN exhibits higher R² values and lower RMSE, showcasing its adeptness at capturing complex nonlinear patterns. The inclusion of attention layer further improves the model's performance by dynamically assigning attention scores to key features, enabling the model to focus on areas of critical emissions and distinctive urban features such as highways, arterial roads, intersections, and dense building clusters. This approach also reveals fluid dynamical principles, highlighting the significant disparities in pollutant concentration across roadways caused by atmospheric turbulence, and the distinct plume formations influenced by land use and topography. When applied to various urban settings, the CNN-attention model exhibits superior generalizability and transferability. This study provides valuable scientific insights and technical support for urban planning, air quality management, and exposure risk evaluation.