Daily high-resolution PM2.5 mapping using spatiotemporal CNN-transformer-KAN model

• A novel CTKNet was proposed for PM 2.5 estimation. • First application of the KAN in air pollution modeling. • Spatiotemporally continuous AOD data is utilized for high-resolution PM 2.5 estimation. • Spatial-temporal PM 2.5 trends are revealed to support air quality policy and planning. Daily high-resolution mapping of fine particulate matter (PM 2.5 ) is critical for air quality monitoring and public health. However, current methods struggle to achieve high accuracy over large spatial and temporal scales due to limitations in modeling complex spatiotemporal dependencies. This study proposed a novel hybrid deep learning model—CNN-Transformer-KAN Network (CTKNet)—which utilizes Convolutional Neural Networks (CNN) to capture spatial features, Transformers for capturing long-range dependencies, and the Kolmogorov–Arnold Network (KAN) for nonlinear representation learning. Utilizing spatially continuous satellite aerosol optical depth (AOD) data and other multi-source spatiotemporal inputs, CTKNet estimated daily PM 2.5 at a spatial resolution of 1 km across China for the period 2015–2020, marking the first application of KAN in PM 2.5 estimation. It outperformed existing models, achieving a high cross-validation coefficient of determination (R 2 ) of 0.95 (sample-based), 0.90 (station-based), and 0.78 (time-based), and corresponding RMSEs of 8.13, 11.03, and 17.76 µg/m 3 . Yearly sample-based cross-validation R 2 values ranged from 0.91 to 0.96 with RMSEs below 11.74 µg/m 3 , while seasonal R 2 values ranged from 0.82 to 0.89 with RMSEs below 22.14 µg/m 3 . Analysis reveals a significant decline in PM 2.5 nationwide, especially in eastern and central China. Seasonal peaks occur in winter, with minima in summer, influenced by meteorology. Spatially, PM 2.5 is highest in eastern and northern regions; urban agglomerations like Beijing–Tianjin–Hebei (BTH) show severe pollution, while Pearl River Delta (PRD) exhibits the lowest levels due to favorable conditions. CTKNet also holds promise for other fine-scale environmental mapping tasks using multi-source spatiotemporal data.