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Maya-Manzano, J.M. ; Smith, M.* ; Markey, E.* ; Hourihane Clancy, J.* ; Sodeau, J.* ; O'Connor, D.J.*

Recent developments in monitoring and modelling airborne pollen, a review.

Grana 60, 1-19 (2020)
Postprint DOI
Open Access Green
Public awareness of the rising importance of allergies and other respiratory diseases has led to increased scientific effort to accurately and rapidly monitor and predict pollen, fungal spores and other bioaerosols in our atmosphere. An important driving force for the increased social and scientific concern is the realisation that climate change will increasingly have an impact on worldwide bioaerosol distributions and subsequent human health. In this review we examine new developments in monitoring of atmospheric pollen as well as observation and source-orientated modelling techniques. The results of a Scopus (R) search for scientific publications conducted with the terms 'Pollen allergy' and 'Pollen forecast' included in the title, abstract or keywords show that the number of such articles published has increased year on year. The 12 most important allergenic pollen taxa in Europe as defined by COST Action ES0603 were ranked in terms of the most 'popular' for model-based forecasting and for forecasting method used.Betula, Poaceae andAmbrosiaare the most forecast taxa. Traditional regression and phenological models (including temperature sum and chilling models) are the most used modelling methods, but it is notable that there are a large number of new modelling techniques being explored. In particular, it appears that Machine Learning techniques have become more popular and led to better results than more traditional observation-orientated models such as regression and time-series analyses.
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Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
1.015
0.000
6
7
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Aerobiology ; Phenology ; Aeroallergen ; Pollen Forecasting ; Real-time Pollen Monitoring Networks ; Machine Learning; Multimodel Ensemble Simulations; Olea-europaea L.; Birch Pollen; Ambrosia Pollen; Poaceae Pollen; Betula Pollen; Meteorological Parameters; Phenological Models; Temporal Variations; Aerosol-particles
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 0017-3134
e-ISSN 1651-2049
Zeitschrift Grana
Quellenangaben Band: 60, Heft: 1, Seiten: 1-19 Artikelnummer: , Supplement: ,
Verlag Taylor & Francis
Verlagsort London [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Allergy Research (IAF)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Allergy
PSP-Element(e) G-505400-001
Förderungen Technological University Dublin (Dublin, Ireland)
Met Eireann
DOI 10.1080/00173134.2020.1769176
WOS ID WOS:000547831300001
Erfassungsdatum 2020-10-06
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