Poisson, negative binomial, and zero-inflated negative binomial regression models for predicting daily airborne pollen concentration levels in Sinop (Türkiye)

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dc.contributor.authorYigiter, Ayten
dc.contributor.authorDemir, Cemile Cansi
dc.contributor.authorHamurkaroglu, Canan
dc.contributor.authorOzler, Hulya
dc.contributor.authorKaplan, Ayse
dc.contributor.authorDanacioglu, Nazan
dc.contributor.authorKaplan, Sumeyra Sezer
dc.date.accessioned2026-04-25T14:19:48Z
dc.date.available2026-04-25T14:19:48Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractPollen, produced during the flowering period of plants, especially anemogamous plants that produce high volumes of pollen, poses a risk to individuals with pollen allergies when it is present in the atmosphere. Meteorological factors are known to affect the duration, distribution, and amount of pollen in the air. The remarkable increase in allergic cases in recent years has led to many studies investigating the relationship between pollen and spores that cause allergies and meteorological factors in T & uuml;rkiye as well as in the world. In this study, meteorological factors and their influence on pollen concentrations in the air were examined for the Sinop region in northern T & uuml;rkiye. First, descriptive statistics for pollen obtained from plant taxa were obtained and interpreted. Precipitation, humidity, temperature, and wind speed were considered as meteorological parameters, and the effects of these variables on pollen counts and their annual changes were modelled using Poisson, negative binomial, and zero-inflated negative binomial (ZINB) regression models. The estimation results for all pollen taxa were then discussed. In the models obtained for each pollen type, the statistical significance of the independent variables such as temperature, precipitation, relative humidity, wind speed, time, and lag 1 was found to be different according to the pollen type.
dc.identifier.doi10.1007/s10661-025-14871-0
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue1
dc.identifier.orcid0000-0002-1596-2996
dc.identifier.orcid0000-0002-4998-5926
dc.identifier.pmid41385146
dc.identifier.scopus2-s2.0-105024685122
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s10661-025-14871-0
dc.identifier.urihttps://hdl.handle.net/11486/8193
dc.identifier.volume198
dc.identifier.wosWOS:001638714700003
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironmental Monitoring and Assessment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260420
dc.subjectPoisson regression
dc.subjectNegative binomial regression
dc.subjectZero-inflated negative binomial regression
dc.subjectPollen
dc.subjectMeteorological conditions
dc.titlePoisson, negative binomial, and zero-inflated negative binomial regression models for predicting daily airborne pollen concentration levels in Sinop (Türkiye)
dc.typeArticle

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