aceMachine Learning Predictions of PM<sub>2.5</sub> and Their Applications to Exposure and Health Assessment in the CONUS
Atmospheric fine particulate matter (PM 2.5 ) is one of the most important risk factors for various respiratory and cardiovascular diseases. We develop a two-phase data-driven framework that uses geostationary Aerosol Optical Depth (AOD) retrievals to predict hourly PM 2.5 at 0.01° × 0.01° resolution and support exposure assessment across multiple spatial and temporal contexts. We use a U-Net-like partial convolutional neural network framework inspired by image inpainting to address missing Aerosol Optical Depth…
A partial convolutional neural network and random forest produce hourly 0.01-degree PM2.5 estimates that track ground monitors, enabling exposure and mortality assessment and revealing higher exposure among minority groups.
Satellite observation gaps remain substantial in less densely monitored rural regions, which constrains characterization of exposure events there.
Evidence
- Peer-reviewedGeoHealth2026-10-08
How should this claim be treated?
Truvace Impact Record TRV-2026-1611, v1: “Machine Learning Predictions of PM<sub>2.5</sub> and Their Applications to Exposure and Health Assessment in the CONUS.” Truvace, 2026-10-16. /record/TRV-2026-1611 (accessed at citation time). sha256 ed350642d4b932ef…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1611 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.