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Logistic regression models for predicting daily airborne Alternaria and Cladosporium concentration levels in Catalonia (NE Spain)

dc.creatorVélez-Pereira A.M.
dc.creatorDe Linares C.
dc.creatorCanela M.-A.
dc.creatorBelmonte J.
dc.date.accessioned2020-03-26T16:32:32Z
dc.date.available2020-03-26T16:32:32Z
dc.date.issued2019
dc.identifier.citationInternational Journal of Biometeorology; Vol. 63, Núm. 12; pp. 1541-1553
dc.identifier.issn00207128
dc.identifier.urihttps://hdl.handle.net/20.500.12585/8873
dc.description.abstractAlternaria and Cladosporium are the most common airborne fungal spores responsible for health problems, as well as for crop pathologies. The study of their behavior in the air is a necessary step for establishing control and prevention measures. The aim of this paper is to develop a logistic regression model for predicting the daily concentrations of airborne Alternaria and Cladosporium fungal spores from meteorological variables. To perform the logistic regression analysis, the concentration levels are binarized using concentration thresholds. The fungal spore data have been obtained at eight aerobiological monitoring stations of the Aerobiological Network of Catalonia (NE Spain). The meteorological data used were the maximum and minimum daily temperatures and daily rainfall provided by the meteorological services. The relationship between the meteorological variables and the fungal spore levels has been modeled by means of logistic regression equations, using data from the period 1995–2012. Values from years 2013–2014 were used for validation. In the case of Alternaria, three equations for predicting the presence and the exceedance of the thresholds 10 and 30 spores/m3 have been established. For Cladosporium, four equations for the thresholds 200, 500, 1000, and 1500 spores/m3 have been established. The temperature and cumulative rainfall in the last 3 days showed a positive correlation with airborne fungal spore levels, while the rain on the same day had a negative correlation. Sensitivity and specificity were calculated to measure the predictive power of the model, showing a reasonable percentage of correct predictions (ranging from 48 to 99%). The simple equations proposed allow us to forecast the levels of fungal spores that will be in the air the next day, using only the maximum and minimum temperatures and rainfall values provided by weather forecasting services. © 2019, ISB.eng
dc.description.sponsorshipMinisterio de Ciencia y Tecnología, MICYT: CGL2012-39523-C02-01/CLI European Commission, EC: ENV4-CT98-0755 CSD 2007_00067 GRACCIE 2005SGR00519, 2009SGR1102, 2017SGR1692, 2014SGR1274 CGL2012-39523-C02-01, CGL2005-07543/CLI, REN2001-10659-CO3-01, AMB97-0457-CO7-021, CTM2017-89565-C2-1-P, BOS2002-03474, GGL2006-12648-CO3-02, CGL2009-11205 Federación Española de Enfermedades Raras, FEDER
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer New York LLC
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85072154915&doi=10.1007%2fs00484-019-01767-1&partnerID=40&md5=196567149daaaa95ffd04f7f8129792f
dc.titleLogistic regression models for predicting daily airborne Alternaria and Cladosporium concentration levels in Catalonia (NE Spain)
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datacite.rightshttp://purl.org/coar/access_right/c_16ec
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1007/s00484-019-01767-1
dc.subject.keywordsFungal spore daily concentration levels
dc.subject.keywordsFungal spore dispersal patterns
dc.subject.keywordsPrediction model
dc.subject.keywordsThreshold
dc.subject.keywordsAlternaria
dc.subject.keywordsCladosporium
dc.subject.keywordsFungus spore
dc.subject.keywordsMicrobiology
dc.subject.keywordsSeason
dc.subject.keywordsSpain
dc.subject.keywordsStatistical model
dc.subject.keywordsAir Microbiology
dc.subject.keywordsAlternaria
dc.subject.keywordsCladosporium
dc.subject.keywordsLogistic Models
dc.subject.keywordsSeasons
dc.subject.keywordsSpain
dc.subject.keywordsSpores, Fungal
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.description.notesThis work was supported by the Spanish Ministry of Science and Technology through the project “CGL2012-39523-C02-01/CLI” and by the Administrative Department of Science, Technology and Innovation-COLCIENCIAS (Colombia) through the doctoral fellowship to Andrés M. Vélez-Pereira.
dc.description.notesThe authors wish to thank the entities and projects that make possible to obtain the database used in this study: Laboratorios LETI S.A.; Servei Meteorològic de Catalunya; Diputacions de Barcelona, Girona and Tarragona; SCAIC; SEAIC; Stallergenes Iberica; J. Uriach y Cia; European Commission for “ENV4-CT98-0755”; Spanish Ministry of Science and Technology I+D+I for “AMB97-0457-CO7-021,” “REN2001-10659-CO3-01,” “BOS2002-03474,” “CGL2004-21166-E,” “CGL2005-07543/CLI,” “GGL2006-12648-CO3-02,” “CGL2009-11205,” “CGL2012-39523-C02-01,” “CTM2017-89565-C2-1-P,” FEDER “A way to build Europe,” and CONSOLIDER CSD 2007_00067 GRACCIE; and Catalan Government AGAUR for “2005SGR00519,” “2009SGR1102,” “2014SGR1274,” and “2017SGR1692.” This work is contributing to the ICTA “Unit of Excellence” (MinECo, MDM2015-0552). The authors wish to thank the anonymous referees for careful reading and very helpful comments that resulted in an overall improvement of the paper.
dc.type.spaArtículo
dc.identifier.orcid55817731200
dc.identifier.orcid13407293800
dc.identifier.orcid6701707061
dc.identifier.orcid7005080602


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