Observations from the USA National Phenology Network can be leveraged to model airborne pollen
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Final Accepted Manuscript
Author
Katz, Daniel S. W.Vogt, Elizabeth
Manangan, Arie
Brown, Claudia L.
Dalan, Dan
Zhu, Kai
Song, Yiluan
Crimmins, Theresa M.
Affiliation
Mel and Enid Zuckerman College of Public Health, University of ArizonaUSA National Phenology Network, School of Natural Resources and Environment, University of Arizona
Issue Date
2022-12-23
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Springer Science and Business Media LLCCitation
Katz, D. S. W., Vogt, E., Manangan, A., Brown, C. L., Dalan, D., Zhu, K., Song, Y., & Crimmins, T. M. (2022). Observations from the USA National Phenology Network can be leveraged to model airborne pollen. Aerobiologia.Journal
AerobiologiaRights
© The Author(s), under exclusive licence to Springer Nature B.V. 2022.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
The USA National Phenology Network (USA-NPN) hosts the largest volunteer-contributed collection of plant phenology observations in the USA. The potential contributions of these spatially and temporally explicit observations of flowers and pollen cones to the field of aerobiology remain largely unexplored. Here, we introduce this freely available dataset and demonstrate its prospective applications for modeling airborne pollen in a case study. Specifically, we compare the timing of 4265 observations of flowering for oak (Quercus) trees in the eastern USA to winter–spring temperatures. We then use this relationship to predict the day of peak flowering at 15 pollen monitoring stations in 15 years and compare the predicted day of peak flowering to the peak day of measured pollen (n = 111 station-years). There was a strong association between winter–spring temperature and the presence of open flowers (r2 = 0.66, p < 0.0001) and the predicted peak flowering was strongly correlated with peak airborne pollen concentrations (r2 = 0.81, p < 0.0001). These results demonstrate the potential for the USA-NPN’s phenological observations to underpin source-based models of airborne pollen. We also highlight opportunities for leveraging and enhancing this near real-time dataset for aerobiological applications.Note
12 month embargo; published: 23 December 2022ISSN
0393-5965EISSN
1573-3025Version
Final accepted manuscriptSponsors
National Science Foundationae974a485f413a2113503eed53cd6c53
10.1007/s10453-022-09774-3
