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Real-time mapping of gapless 24-hour surface PM10 in China
Xutao Zhang1, Ke Gui1, Hengheng Zhao1
1State Key Laboratory of Severe Weather and Key Laboratory of Atmospheric Chemistry of China Meteorological Administration, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
National Science Review
|March 21, 2025
Summary
A new real-time surface particulate matter (PM10) retrieval framework provides seamless hourly PM10 data for China. This visibility-based model enhances air quality monitoring and dust storm forecasting accuracy.
Area of Science:
- Environmental Science
- Atmospheric Science
- Remote Sensing
Background:
- Accurate mapping of surface coarse particulate matter (PM10) is crucial for air quality monitoring.
- Existing satellite aerosol optical depth (AOD) methods struggle with spatial gaps and lack of nighttime data.
Purpose of the Study:
- To introduce a novel real-time surface PM10 retrieval (RT-SPMR) framework for China.
- To address the need for seamless, 24-hour PM10 data acquisition.
Main Methods:
- Developed a gridded visibility-based framework utilizing multisource data.
- Employed dynamically updated machine-learning models for hourly PM10 data generation at 6.25-km resolution.
- Validated the model through cross-validation and rolling iterative experiments.
Main Results:
- The RT-SPMR model achieved superior daily retrieval accuracy compared to previous studies.
- Demonstrated strong generalization capability and stability for operational use.
- Effectively tracked fine-scale dust storm evolution, including in under-observed regions.
Conclusions:
- The operational RT-SPMR framework offers comprehensive real-time PM10 pollution monitoring for China.
- The model has the potential to improve dust storm forecasting by enhancing the PM10 initial field.
