Ground-Level Near Real-Time Modeling for PM2.5 Pollution Prediction
Air pollution is a worldwide public health threat that can cause or exacerbate many illnesses, including respiratory disease, cardiovascular disease, and some cancers. However, epidemiological studies and public health decision-making are stymied by the inability to assess pollution exposure impacts in near real time. To address this, developing accurate digital twins of environmental pollutants will enable timely data-driven analytics - a crucial step in modernizing health policy and decision-making. Although other models predict and analyze fine particulate matter exposure, they often rely on modeled input data sources and data streams that are not regularly updated. Another challenge s...
| Authors | Zachary R. Fox, Janet O. Agbaje, Dakotah Maguire et al. |
|---|---|
| Primary category | stat.AP |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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