Real-time mapping of gapless 24-hour surface PM₁₀ in China

Large-scale mapping of surface coarse particulate matter (PM₁₀) concentration remains a key focus for air quality monitoring. Satellite aerosol optical depth (AOD)-based data fusion approaches decouple the non-linear AOD-PM₁₀ relationship, enabling high-resolution PM₁₀ data acquisition, but are limited by spatial incompleteness and the absence of nighttime data. Here, a gridded visibility-based real-time surface PM₁₀ retrieval (RT-SPMR) framework for China is introduced, addressing the gap in seamless hourly PM₁₀ data within the 24-hour cycle. This framework utilizes multisource data inputs and dynamically updated machine-learning models to produce 6.25-km gridded 24-hour PM₁₀ data. Cross-validation showed that the RT-SPMR model's daily retrieval accuracy surpassed prior studies. Additionally, through rolling iterative validation experiments, the model exhibited strong generalization capability and stability, demonstrating its suitability for operational deployment. Taking a record-breaking dust storm as an example, the model proved effective in tracking the fine-scale evolution of the dust intrusion process, especially in under-observed areas. Consequently, the operational RT-SPMR framework provides comprehensive real-time capability for monitoring PM₁₀ pollution in China, and has the potential to improve the accuracy of dust storm forecasting models by enhancing the PM₁₀ initial field.