ArticlebioRxiv : the preprint server for biology2026
Estimating Daily Taxon-specific Tree Pollen at a 1-km Resolution in Atlanta, GA from 2020 to 2024.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
While tree pollen is a major trigger of allergic respiratory conditions and different taxa exhibit varying allergenic potentials, the lack of high-resolution, taxon-specific exposure metrics have limited our ability to identify which local pollen taxa are primarily responsible for respiratory illness. Traditional pollen monitoring networks, which have an intermittent sampling schedule, are not ideal for examining the delayed effects of pollen exposure due to the missing days. In this study, we developed a modeling framework integrating atmospheric dispersion effects, taxa-specific phenology, and machine learning to predict daily counts of 13 tree taxa in the five-county Metro Atlanta area, Georgia at a 1-km resolution from 2020 to 2024. Machine learning model performance was validated with daily pollen counts collected by a multi-site monitoring network equipped with automated pollen sensors. Findings showed that
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