The spatiotemporal mechanism of surface compound ozone and heat (SCOH) potential risk across urban China

• Spatiotemporal characteristics of large-scale surface compound ozone and heat (SCOH). • SCOH has the ability to spread in urban region. • The influence of different factors on SCOH was detected by machine learning. • The complex risk of SCOH can be mitigated by compact living environment. The synergistic effects of large-scale surface compound ozone and heat (SCOH) present a more extensive and persistent risk to population exposure and environmental safety compared to isolated extreme heat or ozone events. Quantifying the spatiotemporal mechanisms and diffusivity of SCOH in urban areas is therefore critical for risk mitigation. This study integrates the air pollutants spatiotemporal dataset named Multiple Air Pollutants dataset (MuAP) and surface heat datasets to map the 1 km-scale time delay correlation between surface ozone and heat. Combining BayesConvLightGBM and SHapley Additive exPlanations (SHAP), the quantitative influence of urban factors such as building/canopy height and road length on SCOH in predominant urban is examined through scene analysis and diffusion potential analysis. The results show that SCOH has significant temporal and spatial distribution characteristics. Based on more effective spatiotemporal response BayesConvLightGBM modeling of SCOH (The BayesConvLightGBM’s R 2 is 0.03–0.07 higher than LightGBM), we found that buildings, roads, and trees have the ability to significantly affect SCOH in urban, locally or globally. Meanwhile, more compact planning of urban areas will help reduce the complex risk of SCOH. Even so, it is still important to be aware of the risk of exposure of SCOH to the population at a range of 4 km or more during a 30-day time delay period. This study deepens the quantification of nonlinear interactions between urban infrastructure and SCOH propagation, the understanding of surface compound ozone and heat, and strengthens key elements and quantification approaches using optimized machine learning. This is of great significance to explain the spatiotemporal response of SCOH, and provides an important reference for the study of compound exposure.