First close insight into global daily gapless 1 km PM 2.5 pollution, variability, and health impact.

This research-oriented project focuses on the evaluation and implementation support for modeling high-resolution, gapless particulate matter (PM2.5) concentrations at a global scale. Utilizing advanced machine learning algorithms and big data integration, the methodology reconstructs daily 1 km spatial resolution air quality profiles from 2017 to 2022. The framework addresses the critical challenge of missing satellite observations due to cloud cover, providing a continuous spatiotemporal dataset for environmental analysis. Through systematic data processing, the project analyzes the disparities in exposure risks across urban, rural, developed, and developing regions. Additionally, it evaluates the impact of major global events, such as COVID-19 lockdowns, and natural episodes like biomass burning, on air quality dynamics

and associated public health risks. The proposed research direction assists academic researchers in structuring robust validation protocols, optimizing predictive models, and analyzing environmental exposure trends. By offering technical guidance on spatial statistics and machine learning workflows, this project facilitates a deeper understanding of localized pollution variability and supports the development of targeted pollution control strategies.

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