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A High-Resolution, Single-Grain, In Vivo Pollen Hydration Bioassay for Arabidopsis thaliana
Published on: June 30, 2023
Spatial and temporal modeling of daily pollen concentrations
Curt T Dellavalle1, Elizabeth W Triche, Michelle L Bell
1School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA. curt.dellavalle@gmail.com
International Journal of Biometeorology
|February 19, 2011
Summary
Spatial models can accurately estimate pollen counts in areas lacking monitoring stations. These models, using kriging and regression with weather/land data, provide reliable pollen concentration data for allergy sufferers and researchers.
Area of Science:
- Environmental science
- Biometeorology
- Allergen monitoring
Background:
- Accurate pollen counts are crucial for allergy sufferers, medical professionals, and researchers.
- Existing pollen monitoring stations are scarce, and their spatial reliability is often unknown.
- There is a need for reliable methods to estimate pollen concentrations in unmonitored locations.
Purpose of the Study:
- To develop and compare spatial models for estimating daily pollen concentrations in areas without monitoring stations.
- To assess the reliability of spatial interpolation and regression-based models for pollen estimation.
- To evaluate the impact of weather and land-cover data on pollen concentration predictions.
Main Methods:
- Utilized daily pollen count data (Acer, Quercus, tree, grass, weed) from 14 stations (2003-2006) in the northeastern/mid-Atlantic US.
- Applied ordinary kriging for spatial interpolation of pollen counts.
- Developed mixed-effects and generalized estimating equations incorporating daily/seasonal weather, pollen season, and land-cover data.
Main Results:
- Ordinary kriging showed good agreement with observed counts for Acer, Quercus, grass, and weed pollen, and tree pollen when peak periods were excluded.
- Longitudinal models also demonstrated good agreement with observed counts, except at the extremes of pollen distributions.
- Spatial interpolation and regression models provide reliable daily pollen estimates, particularly outside of peak pollen periods.
Conclusions:
- Spatial modeling techniques, including kriging and regression with weather/land-cover data, can reliably estimate daily pollen concentrations in unmonitored areas.
- These methods are valuable for allergy sufferers, public health, and environmental research where monitoring infrastructure is limited.
- Model accuracy is high for most conditions, with slight deviations noted during extremely high pollen events.
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