Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Understanding hourly patterns of Olea pollen concentrations as tool for the environmental impact assessment.
Sci. Total Environ. 736:139363 (2020)
Bioinformatics clustering application for mining of a large set of olive pollen aerobiological data to describe the daily distribution of Olea pollen concentration. The study was performed with hourly pollen concentrations measured during 8 years (2011-2018) in Extremadura (Spain). Olea pollen season by quartiles of the pollen integral in preseason (Qt: 0%-25%), in-season (Q2 and Q3: 25%-75%) and postseason (Q4: 75%-100%). Days with pollen concentrations above 100 grains/m(3) were clustered according to the daily distribution of the concentrations. The factors affecting the prevalence of the different clusters were analyzed: distance to olive groves and the moment during the pollen season and the meteorology. During the season, the highest hourly concentrations during the day where between 12:00 and 14:00, while during the preseason the highest hourly concentrations were detected in the afternoon and evening hours. In the postseason the pollen concentrations were more homogeneously distributed during 9-16 h. The representation shows a well-defined hourly pattern during the season, but a more heterogeneous distribution during the preseason and postseason. The cluster dendrogram shows that all the days could be clustered in 6 groups: most of the clusters shows the daily peaks between 11:00 and 15:00 with a smooth curve (Cluster 1 and 3) or with a strong peak (2 and 5). Days included in duster 9 shows an earlier peak in the morning (before 9:00). On the other hand, cluster 6 shows a peak in the afternoon, after 15:00. Hourly concentrations show a sharper pattern during the season, with the peak during the hours close to the emission. Out of the season, when pollen is expected to come from farther distances, the hourly peak is located later from the emission time of the trees. Significant factors for predicting the hourly pattern were wind speed and direction and the distance to the olive groves. (C) 2020 Elsevier B.V. All rights reserved.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
Altmetric
6.551
1.809
5
7
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Olea Pollen ; Hourly Data ; Aerobiology ; Clustering ; Neural Networks; Airborne Pollen; Allergenic Pollen; Olive-pollen; Big Data; Quercus Pollen; Betula Pollen; South Spain; Rhinitis; Climate; Trends
Sprache
englisch
Veröffentlichungsjahr
2020
HGF-Berichtsjahr
2020
ISSN (print) / ISBN
0048-9697
e-ISSN
1879-1026
Zeitschrift
Science of the Total Environment, The
Quellenangaben
Band: 736,
Artikelnummer: 139363
Verlag
Elsevier
Verlagsort
Radarweg 29, 1043 Nx Amsterdam, Netherlands
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
WOS ID
WOS:000542087400002
Scopus ID
85085566747
Erfassungsdatum
2020-06-16