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Estimating Daily Taxon-specific Tree Pollen at a 1-km Resolution in Atlanta, GA from 2020 to 2024
Xueying Zhang1,2, Wenhao Wang3, Yohei Saburi3
1Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX.
Biorxiv : the Preprint Server for Biology
|June 4, 2026
Summary
This study developed a new model to predict daily tree pollen counts for 13 types in Atlanta. Birch and Oak pollens were most accurately predicted, offering better data for allergy research.
Area of Science:
- Environmental science
- Allergy research
- Computational biology
Background:
- Tree pollen is a major cause of respiratory allergies.
- Current pollen monitoring lacks the resolution to identify specific allergenic taxa.
- Gaps in traditional monitoring hinder studies on delayed pollen exposure effects.
Purpose of the Study:
- To develop a high-resolution, taxon-specific model for predicting daily tree pollen counts.
- To create gapless exposure metrics for investigating spatial and temporal pollen variability.
- To support epidemiological studies on pollen-related respiratory conditions.
Main Methods:
- Integrated atmospheric dispersion, taxa-specific phenology, and machine learning.
- Predicted daily counts for 13 tree taxa at 1-km resolution in Metro Atlanta (2020-2024).
- Validated model performance against automated pollen sensor data.
Main Results:
- Machine learning models achieved high predictive performance for pollen counts.
- Betula (birch) and Quercus (oak) pollens showed the best predictive accuracy (R2 0.69-0.92).
- Generated 1-km resolution, gapless daily pollen exposure data.
Conclusions:
- The modeling framework provides accurate, high-resolution pollen exposure data.
- This data can identify urban pollen hotspots and aid epidemiological research.
- Enables a better understanding of specific tree pollens' impact on respiratory health.
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