Source apportionment for contaminated soils using multivariate statistical methods

The application of statistical techniques for the recognition and identification of contamination sources has become an increasingly important tool. The chemical compositions of soil samples collected in the Puchuncaví Valley (Chile) provide a dataset suitable for the application of source apportion...

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Detalles Bibliográficos
Autores: Parra, Sonnia, Bravo, Manuel A., Quiroz, Waldo, Moreno, Teresa, Karanasiou, Angeliki, Font, Oriol, Vidal, Víctor, Cereceda-Balic, Francisco
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2014
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/344937
Acceso en línea:http://hdl.handle.net/10261/344937
https://api.elsevier.com/content/abstract/scopus_id/84906501702
Access Level:acceso abierto
Palabra clave:Soil contamination
Emission sources
Positive matrix factorization
http://metadata.un.org/sdg/11
Make cities and human settlements inclusive, safe, resilient and sustainable
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spelling Source apportionment for contaminated soils using multivariate statistical methodsParra, SonniaBravo, Manuel A.Quiroz, WaldoMoreno, TeresaKaranasiou, AngelikiFont, OriolVidal, VíctorCereceda-Balic, FranciscoSoil contaminationEmission sourcesPositive matrix factorizationhttp://metadata.un.org/sdg/11Make cities and human settlements inclusive, safe, resilient and sustainableThe application of statistical techniques for the recognition and identification of contamination sources has become an increasingly important tool. The chemical compositions of soil samples collected in the Puchuncaví Valley (Chile) provide a dataset suitable for the application of source apportionment techniques such as positive matrix factorization (PMF) and principal component analysis (PCA) with varimax rotation. PMF allowed the identification of the chemical profile and the relative contribution of three interpretable factors related to three contamination sources. Combining these results with a PCA analysis successfully showed that the main source of pollution emits Cu, Zn, As, Se, Mo, Sn, Sb and Pb. Therefore, the use of source profiles for contaminated soils shows much promise both for incorporating well-established knowledge about pollution sources and as a tool for incremental, exploratory data analysis. © 2014 Elsevier B.V.The authors acknowledge Dirección de Investigación y Estudios Avanzados (VIREA-PUCV) for the postdoctoral research fellowship and also gratefully thank Peter Wentzell, Ph.D for the data analysis using the algorithm MCR-WALS. This work was also supported by the Agencia Española de Cooperación Internacional al Desarrollo (project A1/037813/11, Spain) and the international firm AES-GENER, Chile.Peer reviewedElsevierConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242014info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/344937https://api.elsevier.com/content/abstract/scopus_id/84906501702reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésChemometrics and Intelligent Laboratory Systemshttps://doi.org/10.1016/j.chemolab.2014.08.003Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3449372026-05-22T06:33:51Z
dc.title.none.fl_str_mv Source apportionment for contaminated soils using multivariate statistical methods
title Source apportionment for contaminated soils using multivariate statistical methods
spellingShingle Source apportionment for contaminated soils using multivariate statistical methods
Parra, Sonnia
Soil contamination
Emission sources
Positive matrix factorization
http://metadata.un.org/sdg/11
Make cities and human settlements inclusive, safe, resilient and sustainable
title_short Source apportionment for contaminated soils using multivariate statistical methods
title_full Source apportionment for contaminated soils using multivariate statistical methods
title_fullStr Source apportionment for contaminated soils using multivariate statistical methods
title_full_unstemmed Source apportionment for contaminated soils using multivariate statistical methods
title_sort Source apportionment for contaminated soils using multivariate statistical methods
dc.creator.none.fl_str_mv Parra, Sonnia
Bravo, Manuel A.
Quiroz, Waldo
Moreno, Teresa
Karanasiou, Angeliki
Font, Oriol
Vidal, Víctor
Cereceda-Balic, Francisco
author Parra, Sonnia
author_facet Parra, Sonnia
Bravo, Manuel A.
Quiroz, Waldo
Moreno, Teresa
Karanasiou, Angeliki
Font, Oriol
Vidal, Víctor
Cereceda-Balic, Francisco
author_role author
author2 Bravo, Manuel A.
Quiroz, Waldo
Moreno, Teresa
Karanasiou, Angeliki
Font, Oriol
Vidal, Víctor
Cereceda-Balic, Francisco
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Soil contamination
Emission sources
Positive matrix factorization
http://metadata.un.org/sdg/11
Make cities and human settlements inclusive, safe, resilient and sustainable
topic Soil contamination
Emission sources
Positive matrix factorization
http://metadata.un.org/sdg/11
Make cities and human settlements inclusive, safe, resilient and sustainable
description The application of statistical techniques for the recognition and identification of contamination sources has become an increasingly important tool. The chemical compositions of soil samples collected in the Puchuncaví Valley (Chile) provide a dataset suitable for the application of source apportionment techniques such as positive matrix factorization (PMF) and principal component analysis (PCA) with varimax rotation. PMF allowed the identification of the chemical profile and the relative contribution of three interpretable factors related to three contamination sources. Combining these results with a PCA analysis successfully showed that the main source of pollution emits Cu, Zn, As, Se, Mo, Sn, Sb and Pb. Therefore, the use of source profiles for contaminated soils shows much promise both for incorporating well-established knowledge about pollution sources and as a tool for incremental, exploratory data analysis. © 2014 Elsevier B.V.
publishDate 2014
dc.date.none.fl_str_mv 2014
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/344937
https://api.elsevier.com/content/abstract/scopus_id/84906501702
url http://hdl.handle.net/10261/344937
https://api.elsevier.com/content/abstract/scopus_id/84906501702
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Chemometrics and Intelligent Laboratory Systems
https://doi.org/10.1016/j.chemolab.2014.08.003
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
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