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Spatial and temporal modeling of daily pollen concentrations

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Abstract

Accurate assessments of pollen counts are valuable to allergy sufferers, the medical industry, and health researchers; however, monitoring stations do not exist in most areas. In addition, the degree of spatial reliability provided by the limited number of monitoring stations is poorly understood. We developed and compared spatial models to estimate pollen concentrations in locations without monitoring stations. Daily Acer, Quercus, and overall tree, grass, and weed pollen counts, in grains/m3, were obtained from 14 aeroallergen monitoring stations located in the northeastern and mid-Atlantic region of the United States from 2003 to 2006. Pollen counts were spatially interpolated using ordinary kriging. Mixed effects and generalized estimating equations incorporating daily and seasonal weather characteristics, pollen season characteristics and land-cover information were also developed to estimate daily pollen concentrations. We then compared observed values from a monitoring station to model estimates for that location. Observed counts and kriging estimates for tree pollen differed (p = 0.04), but not when peak periods were removed (p = 0.29). No differences between observed and kriging estimates of Acer (p = 0.46), Quercus (p = 0.24), grass (p = 0.31) or weed pollen (p = 0.29) were found. Estimates from longitudinal models also demonstrated good agreement with observed counts, except for the extremes of pollen distributions. Our results demonstrate that spatial interpolation techniques as well as regression methods incorporating both weather and land-cover characteristics can provide reliable estimates of daily pollen concentrations in areas where monitors do not exist for all but periods of extremely high pollen.

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Acknowledgements

We thank the National Allergy Bureau, and specifically Jerome Schultz, for providing daily ambient pollen measurements used in this study.

Ethical standards

This research was conducted without human or animal subjects and did not violate any legal or ethical standards.

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Authors and Affiliations

  1. School of Forestry and Environmental Studies, Yale University, 195 Prospect Street, New Haven, CT, 06511, USA

    Curt T. DellaValle & Michelle L. Bell

  2. Department of Community Health/Epidemiology, Brown University School of Medicine, Box G-S121-2, Providence, RI, 02912, USA

    Elizabeth W. Triche

Authors
  1. Curt T. DellaValle
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  2. Elizabeth W. Triche
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  3. Michelle L. Bell
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Correspondence to Curt T. DellaValle.

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DellaValle, C.T., Triche, E.W. & Bell, M.L. Spatial and temporal modeling of daily pollen concentrations. Int J Biometeorol 56, 183–194 (2012). https://doi.org/10.1007/s00484-011-0412-y

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  • DOI: https://doi.org/10.1007/s00484-011-0412-y

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