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Estimation of particulate matter contributions from desert outbreaks in mediterranean countries (2015–2018) using the time series clustering method

dc.contributor.authorGómez Losada, Álvaro
dc.contributor.authorPires, José Carlos M.
dc.date.accessioned2024-03-06T11:26:40Z
dc.date.available2024-03-06T11:26:40Z
dc.date.issued2020-12-23
dc.identifier.citationGómez-Losada Á, Pires JCM. Estimation of Particulate Matter Contributions from Desert Outbreaks in Mediterranean Countries (2015–2018) Using the Time Series Clustering Method. Atmosphere. 2021; 12(1):5. https://doi.org/10.3390/atmos12010005es
dc.identifier.issn2073-4433
dc.identifier.urihttps://hdl.handle.net/20.500.12412/5425
dc.description.abstractNorth African dust intrusions can contribute to exceedances of the European PM10 and PM2.5 limit values and World Health Organisation standards, diminishing air quality, and increased mortality and morbidity at higher concentrations. In this study, the contribution of North African dust in Mediterranean countries was estimated using the time series clustering method. This method combines the non-parametric approach of Hidden Markov Models for studying time series, and the definition of different air pollution profiles (regimes of concentration). Using this approach, PM10 and PM2.5 time series obtained at background monitoring stations from seven countries were analysed from 2015 to 2018. The average characteristic contributions to PM10 were estimated as 11.6 ± 10.3 µg·m−3 (Bosnia and Herzegovina), 8.8 ± 7.5 µg·m−3 (Spain), 7.0 ± 6.2 µg·m−3 (France), 8.1 ± 5.9 µg·m−3 (Croatia), 7.5 ± 5.5 µg·m−3 (Italy), 8.1 ± 7.0 µg·m−3 (Portugal), and 17.0 ± 9.8 µg·m−3 (Turkey). For PM2.5, estimated contributions were 4.1 ± 3.5 µg·m−3 (Spain), 6.0 ± 4.8 µg·m−3 (France), 9.1 ± 6.4 µg·m−3 (Croatia), 5.2 ± 3.8 µg·m−3 (Italy), 6.0 ± 4.4 µg·m−3 (Portugal), and 9.0 ± 5.6 µg·m−3 (Turkey). The observed PM2.5/PM10 ratios were between 0.36 and 0.69, and their seasonal variation was characterised, presenting higher values in colder months. Principal component analysis enabled the association of background sites based on their estimated PM10 and PM2.5 pollution profiles.es
dc.language.isoenges
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleEstimation of particulate matter contributions from desert outbreaks in mediterranean countries (2015–2018) using the time series clustering methodes
dc.typearticlees
dc.identifier.doi10.3390/atmos12010005
dc.issue.number1es
dc.journal.titleAtmospherees
dc.relation.projectIDThis work was financially supported by Base Funding—UIDB/00511/2020 of the Laboratory for Process Engineering, Environment, Biotechnology and Energy—LEPABE—funded by national funds through the FCT/MCTES (PIDDAC). J.C.M. Pires acknowledges the FCT Investigator 2015 Programme (IF/01341/2015).es
dc.rights.accessRightsopenAccesses
dc.subject.keywordAfrican dustes
dc.subject.keywordAir pollutiones
dc.subject.keywordHidden Markov modelses
dc.subject.keywordParticulate matteres
dc.subject.keywordPM2.5/PM10 ratioes
dc.subject.keywordPrincipal component analysises
dc.volume.number12es


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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