Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia

Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorization (PMF) model. This approach can estimate the factor-related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmen...

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Authors: Vlachou, Athanasia, Tobler, Anna, Lamkaddam, Houssni, Canonaco, Francesco, Daellenbach, Kaspar Rudolf, Jaffrezo, Jean Luc, Minguillón, María Cruz, Maasikmets, Marek, Teinemaa, Erik, Baltensperger, Urs, El-Haddad, Imad, Preávôt, Andreá S.H.
Format: article
Status:Published version
Publication Date:2019
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/200320
Online Access:http://hdl.handle.net/10261/200320
Access Level:Open access
Keyword:Particulate matter
Haze
Water-soluble ions
Aerosols
Estonia
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spelling Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in EstoniaVlachou, AthanasiaTobler, AnnaLamkaddam, HoussniCanonaco, FrancescoDaellenbach, Kaspar RudolfJaffrezo, Jean LucMinguillón, María CruzMaasikmets, MarekTeinemaa, ErikBaltensperger, UrsEl-Haddad, ImadPreávôt, Andreá S.H.Particulate matterHazeWater-soluble ionsAerosolsEstoniaBootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorization (PMF) model. This approach can estimate the factor-related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions, though, can be challenging, and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit suboptimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter samples with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water-soluble organic aerosol mass spectra have provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia: Tallinn (urban), Tartu (suburban) and Kohtla-Järve (KJ; industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu and accounting for 73 % ± 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % ± 8 % and 11 % ± 5 %, respectively), and two other primary OA types lower in mass. A sulfur-containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle, and an oil OA was connected to the oil shale industries in KJ prevailing at this site that comprises 36 % ± 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behavior: a winter oxygenated OA dominated in winter (36 % ± 14 % for KJ, 25 % ± 9 % for Tallinn and 13 % ± 5 % for Tartu) and was correlated with benzoic and phthalic acid, implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % ± 5 % in KJ, 41 % ± 7 % in Tallinn and 35 % ± 7 % in Tartu) and exhibited high correlations with oxidation products of a-pinene-like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA), suggesting a biogenic origin.Acknowledgements. This work was funded by the Estonian–Swiss cooperation program “Enforcement of the surveillance network of the Estonian air quality: Determination of origin of fine particles in Estonia”. María Cruz Minguillón acknowledges the Ramón y Ca-jal Fellowship awarded by the Spanish Ministry of Economy, Industry and Competitiveness. The Labex OSUG@2020 (ANR-10-LABX-56) provided the funding for part of the analytical equipment at Institut des Géosciences de l’Environnement (IGE; France). We also acknowledge the contribution of the COST Action CA16109 COLOSSAL.Peer reviewedCopernicus PublicationsMinguillón, María Cruz [0000-0002-5464-0391]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202019info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/200320reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.5194/acp-19-7279-2019Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2003202026-05-22T06:33:51Z
dc.title.none.fl_str_mv Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
title Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
spellingShingle Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
Vlachou, Athanasia
Particulate matter
Haze
Water-soluble ions
Aerosols
Estonia
title_short Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
title_full Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
title_fullStr Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
title_full_unstemmed Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
title_sort Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
dc.creator.none.fl_str_mv Vlachou, Athanasia
Tobler, Anna
Lamkaddam, Houssni
Canonaco, Francesco
Daellenbach, Kaspar Rudolf
Jaffrezo, Jean Luc
Minguillón, María Cruz
Maasikmets, Marek
Teinemaa, Erik
Baltensperger, Urs
El-Haddad, Imad
Preávôt, Andreá S.H.
author Vlachou, Athanasia
author_facet Vlachou, Athanasia
Tobler, Anna
Lamkaddam, Houssni
Canonaco, Francesco
Daellenbach, Kaspar Rudolf
Jaffrezo, Jean Luc
Minguillón, María Cruz
Maasikmets, Marek
Teinemaa, Erik
Baltensperger, Urs
El-Haddad, Imad
Preávôt, Andreá S.H.
author_role author
author2 Tobler, Anna
Lamkaddam, Houssni
Canonaco, Francesco
Daellenbach, Kaspar Rudolf
Jaffrezo, Jean Luc
Minguillón, María Cruz
Maasikmets, Marek
Teinemaa, Erik
Baltensperger, Urs
El-Haddad, Imad
Preávôt, Andreá S.H.
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Minguillón, María Cruz [0000-0002-5464-0391]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Particulate matter
Haze
Water-soluble ions
Aerosols
Estonia
topic Particulate matter
Haze
Water-soluble ions
Aerosols
Estonia
description Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorization (PMF) model. This approach can estimate the factor-related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions, though, can be challenging, and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit suboptimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter samples with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water-soluble organic aerosol mass spectra have provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia: Tallinn (urban), Tartu (suburban) and Kohtla-Järve (KJ; industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu and accounting for 73 % ± 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % ± 8 % and 11 % ± 5 %, respectively), and two other primary OA types lower in mass. A sulfur-containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle, and an oil OA was connected to the oil shale industries in KJ prevailing at this site that comprises 36 % ± 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behavior: a winter oxygenated OA dominated in winter (36 % ± 14 % for KJ, 25 % ± 9 % for Tallinn and 13 % ± 5 % for Tartu) and was correlated with benzoic and phthalic acid, implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % ± 5 % in KJ, 41 % ± 7 % in Tallinn and 35 % ± 7 % in Tartu) and exhibited high correlations with oxidation products of a-pinene-like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA), suggesting a biogenic origin.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/200320
url http://hdl.handle.net/10261/200320
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.5194/acp-19-7279-2019
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Copernicus Publications
publisher.none.fl_str_mv Copernicus Publications
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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