Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment

This study examines the consistency between the chemical composition of source profiles retrieved by positive matrix factorization (PMF), which is based on bulk chemical analysis, and the composition of a large data set of individual particles from real-world environmental samples. Since PMF derives...

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Autores: Manousakas, M., Rausch, J., Jaramillo-Vogel, D., Schneider-Beltran, K. S., Alastuey, Andrés, Jaffrezo, J. L., Uzu, G., Perseguers, S., Schnidrig, N., Prevot, A. S.H., Daellenbach, K. R.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/399104
Acceso en línea:http://hdl.handle.net/10261/399104
https://api.elsevier.com/content/abstract/scopus_id/105014253603
Access Level:acceso abierto
Palabra clave:Source apportionment
Automated single particle analysis
Bulk analysis
Machine-learning
PMF
http://metadata.un.org/sdg/9
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/11
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Make cities and human settlements inclusive, safe, resilient and sustainable
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spelling Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionmentManousakas, M.Rausch, J.Jaramillo-Vogel, D.Schneider-Beltran, K. S.Alastuey, AndrésJaffrezo, J. L.Uzu, G.Perseguers, S.Schnidrig, N.Prevot, A. S.H.Daellenbach, K. R.Source apportionmentAutomated single particle analysisBulk analysisMachine-learningPMFhttp://metadata.un.org/sdg/9http://metadata.un.org/sdg/3http://metadata.un.org/sdg/11Ensure healthy lives and promote well-being for all at all agesBuild resilient infrastructure, promote inclusive and sustainable industrialization and foster innovationMake cities and human settlements inclusive, safe, resilient and sustainableThis study examines the consistency between the chemical composition of source profiles retrieved by positive matrix factorization (PMF), which is based on bulk chemical analysis, and the composition of a large data set of individual particles from real-world environmental samples. Since PMF derives source profiles from the average chemical composition of many particles, it is crucial to assess how well these profiles reflect the actual composition of particles originating from individual sources. To address this, we compare PMF-based source apportionment of coarse particulate matter (PM<inf>coarse</inf>) with Automated Single-Particle Analysis (ASPA) using Scanning Electron Microscopy (SEM) coupled with Energy Dispersive X-ray Spectroscopy (EDX) and a machine-learning based particle classification. Both methods identified at least four major PM<inf>coarse</inf> sources—mineral dust, non-exhaust vehicle emissions, biological particles, and road salt—across urban and rural environments in Switzerland. The elemental composition of these sources determined by PMF was compared with ASPA-derived compositions of analogous particle types. The results indicate that while PMF effectively captures key source characteristics, single-particle analysis provides a more detailed representation of source-specific chemical compositions alongside morpho-textural features. ASPA also facilitated the identification and quantification of elements not detected in bulk analysis, such as oxygen and silica, improving overall PM characterization. A sensitivity test using a single-location subset demonstrated that incorporating ASPA-derived profiles into PMF enhances source differentiation, particularly for small data sets. These findings demonstrate the utility of single-particle analysis as an independent approach for constraining and validating the chemical composition of source profiles, thereby providing a means to enhance and validate source apportionment outcomes derived from bulk analysis methods such as PMF.The co-authors at IGE are really grateful for all people on the Air O Sol analytical plateau at IGE for the chemical analyses of this overall series of filter samples.Peer reviewedElsevier0000-0003-1111-0881Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/399104https://api.elsevier.com/content/abstract/scopus_id/105014253603reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésAtmospheric Environment Xhttps://doi.org/10.1016/j.aeaoa.2025.100363Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3991042026-05-22T06:33:51Z
dc.title.none.fl_str_mv Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment
title Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment
spellingShingle Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment
Manousakas, M.
Source apportionment
Automated single particle analysis
Bulk analysis
Machine-learning
PMF
http://metadata.un.org/sdg/9
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/11
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Make cities and human settlements inclusive, safe, resilient and sustainable
title_short Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment
title_full Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment
title_fullStr Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment
title_full_unstemmed Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment
title_sort Comparison of PM source profiles identified by different techniques and the potential of utilizing single-particle analysis data in source apportionment
dc.creator.none.fl_str_mv Manousakas, M.
Rausch, J.
Jaramillo-Vogel, D.
Schneider-Beltran, K. S.
Alastuey, Andrés
Jaffrezo, J. L.
Uzu, G.
Perseguers, S.
Schnidrig, N.
Prevot, A. S.H.
Daellenbach, K. R.
author Manousakas, M.
author_facet Manousakas, M.
Rausch, J.
Jaramillo-Vogel, D.
Schneider-Beltran, K. S.
Alastuey, Andrés
Jaffrezo, J. L.
Uzu, G.
Perseguers, S.
Schnidrig, N.
Prevot, A. S.H.
Daellenbach, K. R.
author_role author
author2 Rausch, J.
Jaramillo-Vogel, D.
Schneider-Beltran, K. S.
Alastuey, Andrés
Jaffrezo, J. L.
Uzu, G.
Perseguers, S.
Schnidrig, N.
Prevot, A. S.H.
Daellenbach, K. R.
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv 0000-0003-1111-0881
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Source apportionment
Automated single particle analysis
Bulk analysis
Machine-learning
PMF
http://metadata.un.org/sdg/9
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/11
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Make cities and human settlements inclusive, safe, resilient and sustainable
topic Source apportionment
Automated single particle analysis
Bulk analysis
Machine-learning
PMF
http://metadata.un.org/sdg/9
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/11
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Make cities and human settlements inclusive, safe, resilient and sustainable
description This study examines the consistency between the chemical composition of source profiles retrieved by positive matrix factorization (PMF), which is based on bulk chemical analysis, and the composition of a large data set of individual particles from real-world environmental samples. Since PMF derives source profiles from the average chemical composition of many particles, it is crucial to assess how well these profiles reflect the actual composition of particles originating from individual sources. To address this, we compare PMF-based source apportionment of coarse particulate matter (PM<inf>coarse</inf>) with Automated Single-Particle Analysis (ASPA) using Scanning Electron Microscopy (SEM) coupled with Energy Dispersive X-ray Spectroscopy (EDX) and a machine-learning based particle classification. Both methods identified at least four major PM<inf>coarse</inf> sources—mineral dust, non-exhaust vehicle emissions, biological particles, and road salt—across urban and rural environments in Switzerland. The elemental composition of these sources determined by PMF was compared with ASPA-derived compositions of analogous particle types. The results indicate that while PMF effectively captures key source characteristics, single-particle analysis provides a more detailed representation of source-specific chemical compositions alongside morpho-textural features. ASPA also facilitated the identification and quantification of elements not detected in bulk analysis, such as oxygen and silica, improving overall PM characterization. A sensitivity test using a single-location subset demonstrated that incorporating ASPA-derived profiles into PMF enhances source differentiation, particularly for small data sets. These findings demonstrate the utility of single-particle analysis as an independent approach for constraining and validating the chemical composition of source profiles, thereby providing a means to enhance and validate source apportionment outcomes derived from bulk analysis methods such as PMF.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
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/10261/399104
https://api.elsevier.com/content/abstract/scopus_id/105014253603
url http://hdl.handle.net/10261/399104
https://api.elsevier.com/content/abstract/scopus_id/105014253603
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Atmospheric Environment X
https://doi.org/10.1016/j.aeaoa.2025.100363
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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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