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    Observations from the USA National Phenology Network can be leveraged to model airborne pollen

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    Name:
    NAB_NPN_short_comm_221011_to_s ...
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    Format:
    PDF
    Description:
    Final Accepted Manuscript
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    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 Arizona
    USA National Phenology Network, School of Natural Resources and Environment, University of Arizona
    Issue Date
    2022-12-23
    Keywords
    Citizen science
    Flowers
    Open data
    Phenological models
    Pollen cones
    
    Metadata
    Show full item record
    Publisher
    Springer Science and Business Media LLC
    Citation
    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
    Aerobiologia
    Rights
    © 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 2022
    ISSN
    0393-5965
    EISSN
    1573-3025
    DOI
    10.1007/s10453-022-09774-3
    Version
    Final accepted manuscript
    Sponsors
    National Science Foundation
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10453-022-09774-3
    Scopus Count
    Collections
    UA Faculty Publications

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