Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers

Autores
Jurado Zavaleta, Marcelo A.; Alcaraz, Mirta Raquel; Peñaloza, Lidia Guadalupe; Boemo, Analía; Cardozo, Ana; Tarcaya, Gerardo; Azcarate, Silvana Mariela; Goicoechea, Hector Casimiro
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In Argentina, both surface and ground water are used for a diverse priority purposes, such as drinking and basic hygiene, but they are also utilized as receivers of different types of industrial and urban and suburban effluents that affect their natural composition. This activity accompanied by the increase of the population and climate changes have activated the alarms of organism water management forced to implement strict quality controls previous to its use. In this work, a systematic evaluation of a set of physicochemical and biological parameters measured in 19 sampling sites during the period 2017–2019 is presented. Principal component analysis (PCA) and matrix augmentation-PCA (MA-PCA) were applied as exploratory analysis tools to visualize and interpret the information contained in the dataset. Both studies allowed to detect the relevant variables and to differentiate the samples based on pollution areas. These models led to similar conclusions; nonetheless, MA-PCA provided a more straightforward overview of the spatiotemporal variation of the samples in comparison to classical PCA. Finally, a significant and sensitive discriminant model (93% non-error rate) was developed to analyze and predict the self-depuration of the rivers. The excellent predictive ability achieved by this model makes its application suitable for the monitoring of the water quality.
Fil: Jurado Zavaleta, Marcelo A.. Universidad Nacional de Salta; Argentina
Fil: Alcaraz, Mirta Raquel. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Peñaloza, Lidia Guadalupe. Universidad Nacional de Salta; Argentina
Fil: Boemo, Analía. Universidad Nacional de Salta; Argentina
Fil: Cardozo, Ana. No especifíca;
Fil: Tarcaya, Gerardo. No especifíca;
Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
ARGENTINA RIVERS
CHEMOMETRIC MODELING
SELF-DEPURATION MONITORING
SOURCE POLLUTION
SURFACE WATER QUALITY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/151046
Acceder
id CONICETDig_6965ef256c7b2d1773c407c40da60592
oai_identifier_str oai:ri.conicet.gov.ar:11336/151046
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina riversJurado Zavaleta, Marcelo A.Alcaraz, Mirta RaquelPeñaloza, Lidia GuadalupeBoemo, AnalíaCardozo, AnaTarcaya, GerardoAzcarate, Silvana MarielaGoicoechea, Hector CasimiroARGENTINA RIVERSCHEMOMETRIC MODELINGSELF-DEPURATION MONITORINGSOURCE POLLUTIONSURFACE WATER QUALITYhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1In Argentina, both surface and ground water are used for a diverse priority purposes, such as drinking and basic hygiene, but they are also utilized as receivers of different types of industrial and urban and suburban effluents that affect their natural composition. This activity accompanied by the increase of the population and climate changes have activated the alarms of organism water management forced to implement strict quality controls previous to its use. In this work, a systematic evaluation of a set of physicochemical and biological parameters measured in 19 sampling sites during the period 2017–2019 is presented. Principal component analysis (PCA) and matrix augmentation-PCA (MA-PCA) were applied as exploratory analysis tools to visualize and interpret the information contained in the dataset. Both studies allowed to detect the relevant variables and to differentiate the samples based on pollution areas. These models led to similar conclusions; nonetheless, MA-PCA provided a more straightforward overview of the spatiotemporal variation of the samples in comparison to classical PCA. Finally, a significant and sensitive discriminant model (93% non-error rate) was developed to analyze and predict the self-depuration of the rivers. The excellent predictive ability achieved by this model makes its application suitable for the monitoring of the water quality.Fil: Jurado Zavaleta, Marcelo A.. Universidad Nacional de Salta; ArgentinaFil: Alcaraz, Mirta Raquel. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Peñaloza, Lidia Guadalupe. Universidad Nacional de Salta; ArgentinaFil: Boemo, Analía. Universidad Nacional de Salta; ArgentinaFil: Cardozo, Ana. No especifíca;Fil: Tarcaya, Gerardo. No especifíca;Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; ArgentinaFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Science2021-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/151046Jurado Zavaleta, Marcelo A.; Alcaraz, Mirta Raquel; Peñaloza, Lidia Guadalupe; Boemo, Analía; Cardozo, Ana; et al.; Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers; Elsevier Science; Microchemical Journal; 162; 3-2021; 1-400026-265XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0026265X20337838info:eu-repo/semantics/altIdentifier/doi/10.1016/j.microc.2020.105841info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:34:40Zoai:ri.conicet.gov.ar:11336/151046instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:34:40.388CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers
title Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers
spellingShingle Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers
Jurado Zavaleta, Marcelo A.
ARGENTINA RIVERS
CHEMOMETRIC MODELING
SELF-DEPURATION MONITORING
SOURCE POLLUTION
SURFACE WATER QUALITY
title_short Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers
title_full Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers
title_fullStr Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers
title_full_unstemmed Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers
title_sort Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers
dc.creator.none.fl_str_mv Jurado Zavaleta, Marcelo A.
Alcaraz, Mirta Raquel
Peñaloza, Lidia Guadalupe
Boemo, Analía
Cardozo, Ana
Tarcaya, Gerardo
Azcarate, Silvana Mariela
Goicoechea, Hector Casimiro
author Jurado Zavaleta, Marcelo A.
author_facet Jurado Zavaleta, Marcelo A.
Alcaraz, Mirta Raquel
Peñaloza, Lidia Guadalupe
Boemo, Analía
Cardozo, Ana
Tarcaya, Gerardo
Azcarate, Silvana Mariela
Goicoechea, Hector Casimiro
author_role author
author2 Alcaraz, Mirta Raquel
Peñaloza, Lidia Guadalupe
Boemo, Analía
Cardozo, Ana
Tarcaya, Gerardo
Azcarate, Silvana Mariela
Goicoechea, Hector Casimiro
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ARGENTINA RIVERS
CHEMOMETRIC MODELING
SELF-DEPURATION MONITORING
SOURCE POLLUTION
SURFACE WATER QUALITY
topic ARGENTINA RIVERS
CHEMOMETRIC MODELING
SELF-DEPURATION MONITORING
SOURCE POLLUTION
SURFACE WATER QUALITY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In Argentina, both surface and ground water are used for a diverse priority purposes, such as drinking and basic hygiene, but they are also utilized as receivers of different types of industrial and urban and suburban effluents that affect their natural composition. This activity accompanied by the increase of the population and climate changes have activated the alarms of organism water management forced to implement strict quality controls previous to its use. In this work, a systematic evaluation of a set of physicochemical and biological parameters measured in 19 sampling sites during the period 2017–2019 is presented. Principal component analysis (PCA) and matrix augmentation-PCA (MA-PCA) were applied as exploratory analysis tools to visualize and interpret the information contained in the dataset. Both studies allowed to detect the relevant variables and to differentiate the samples based on pollution areas. These models led to similar conclusions; nonetheless, MA-PCA provided a more straightforward overview of the spatiotemporal variation of the samples in comparison to classical PCA. Finally, a significant and sensitive discriminant model (93% non-error rate) was developed to analyze and predict the self-depuration of the rivers. The excellent predictive ability achieved by this model makes its application suitable for the monitoring of the water quality.
Fil: Jurado Zavaleta, Marcelo A.. Universidad Nacional de Salta; Argentina
Fil: Alcaraz, Mirta Raquel. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Peñaloza, Lidia Guadalupe. Universidad Nacional de Salta; Argentina
Fil: Boemo, Analía. Universidad Nacional de Salta; Argentina
Fil: Cardozo, Ana. No especifíca;
Fil: Tarcaya, Gerardo. No especifíca;
Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description In Argentina, both surface and ground water are used for a diverse priority purposes, such as drinking and basic hygiene, but they are also utilized as receivers of different types of industrial and urban and suburban effluents that affect their natural composition. This activity accompanied by the increase of the population and climate changes have activated the alarms of organism water management forced to implement strict quality controls previous to its use. In this work, a systematic evaluation of a set of physicochemical and biological parameters measured in 19 sampling sites during the period 2017–2019 is presented. Principal component analysis (PCA) and matrix augmentation-PCA (MA-PCA) were applied as exploratory analysis tools to visualize and interpret the information contained in the dataset. Both studies allowed to detect the relevant variables and to differentiate the samples based on pollution areas. These models led to similar conclusions; nonetheless, MA-PCA provided a more straightforward overview of the spatiotemporal variation of the samples in comparison to classical PCA. Finally, a significant and sensitive discriminant model (93% non-error rate) was developed to analyze and predict the self-depuration of the rivers. The excellent predictive ability achieved by this model makes its application suitable for the monitoring of the water quality.
publishDate 2021
dc.date.none.fl_str_mv 2021-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/151046
Jurado Zavaleta, Marcelo A.; Alcaraz, Mirta Raquel; Peñaloza, Lidia Guadalupe; Boemo, Analía; Cardozo, Ana; et al.; Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers; Elsevier Science; Microchemical Journal; 162; 3-2021; 1-40
0026-265X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/151046
identifier_str_mv Jurado Zavaleta, Marcelo A.; Alcaraz, Mirta Raquel; Peñaloza, Lidia Guadalupe; Boemo, Analía; Cardozo, Ana; et al.; Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers; Elsevier Science; Microchemical Journal; 162; 3-2021; 1-40
0026-265X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0026265X20337838
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.microc.2020.105841
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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