Spatiotemporal modelling of airborne birch and grass pollen concentration across Switzerland: A comparison of statistical, machine learning and ensemble methods.

Journal: Environmental research
PMID: 39305973

Abstract

BACKGROUND: Statistical and machine learning models are commonly used to estimate spatial and temporal variability in exposure to environmental stressors, supporting epidemiological studies. We aimed to compare the performances, strengths and limitations of six different algorithms in the retrospective spatiotemporal modeling of daily birch and grass pollen concentrations at a spatial resolution of 1 km across Switzerland.

Authors

  • Behzad Valipour Shokouhi
    Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland.
  • Kees de Hoogh
    Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland; University of Basel, Petersplatz 1, Postfach 4001 Basel, Switzerland. Electronic address: c.dehoogh@swisstph.ch.
  • Regula Gehrig
    Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland.
  • Marloes Eeftens
    Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland. Electronic address: marloes.eeftens@swisstph.ch.

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