Recent developments in monitoring and modelling airborne pollen, a review

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dc.contributor.authorMaya-Manzano, Jose Maríaen
dc.contributor.authorSmith, Matten
dc.contributor.authorMarkey, Emmaen
dc.contributor.authorHourihane Clancy, Jerryen
dc.contributor.authorSodeau, Johnen
dc.contributor.authorO´Connor, David J.en
dc.contributor.funderEnvironmental Protection Agencyen
dc.contributor.funderMet Éireannen
dc.contributor.funderIrish Research Councilen
dc.date.accessioned2023-05-25T11:03:24Z
dc.date.available2023-05-25T11:03:24Z
dc.date.issued2020-07-07en
dc.description.abstractPublic 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® 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 and Ambrosia are 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.en
dc.description.sponsorshipIrish Research Council (GOIPG/2019/4195)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMaya-Manzano, J. M., Smith, M., Markey, E., Hourihane Clancy, J., Sodeau, J. and O'Connor, D. J. (2020) 'Recent developments in monitoring and modelling airborne pollen, a review', Grana, 60(1), pp.1-19. doi: 10.1080/00173134.2020.1769176en
dc.identifier.doi10.1080/00173134.2020.1769176en
dc.identifier.eissn1651-2049en
dc.identifier.endpage19en
dc.identifier.issn0017-3134en
dc.identifier.issued1en
dc.identifier.journaltitleGranaen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/14513
dc.identifier.volume60en
dc.language.isoenen
dc.publisherTaylor & Francis Groupen
dc.rights© 2020, Taylor & Francis. All rights reserved. This is an Accepted Manuscript of an article published by Taylor & Francis on 7 July 2020 in Grana, available online: https://doi.org/10.1080/00173134.2020.1769176en
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectAerobiologyen
dc.subjectPhenologyen
dc.subjectAeroallergenen
dc.subjectPollen forecastingen
dc.subjectReal-time pollen monitoring networksen
dc.subjectMachine learningen
dc.titleRecent developments in monitoring and modelling airborne pollen, a reviewen
dc.typeArticle (peer-reviewed)en
oaire.citation.issue1en
oaire.citation.volume60en
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