Abstract
Abstract 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 nonlinear 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 potential for improving the accuracy of dust storm forecasting models by enhancing the PM10 initial field.
Published Version
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