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dc.contributor.author
Jurado Zavaleta, Marcelo A.  
dc.contributor.author
Alcaraz, Mirta Raquel  
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Peñaloza, Lidia Guadalupe  
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Boemo, Analía  
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Cardozo, Ana  
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Tarcaya, Gerardo  
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Azcarate, Silvana Mariela  
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Goicoechea, Hector Casimiro  
dc.date.available
2022-02-01T11:16:38Z  
dc.date.issued
2021-03  
dc.identifier.citation
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  
dc.identifier.issn
0026-265X  
dc.identifier.uri
http://hdl.handle.net/11336/151046  
dc.description.abstract
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.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier Science  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/  
dc.subject
ARGENTINA RIVERS  
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CHEMOMETRIC MODELING  
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SELF-DEPURATION MONITORING  
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SOURCE POLLUTION  
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SURFACE WATER QUALITY  
dc.subject.classification
Química Analítica  
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Ciencias Químicas  
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CIENCIAS NATURALES Y EXACTAS  
dc.title
Chemometric modeling for spatiotemporal characterization and self-depuration monitoring of surface water assessing the pollution sources impact of northern Argentina rivers  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2021-09-07T14:05:03Z  
dc.journal.volume
162  
dc.journal.pagination
1-40  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Amsterdam  
dc.description.fil
Fil: Jurado Zavaleta, Marcelo A.. Universidad Nacional de Salta; Argentina  
dc.description.fil
Fil: Alcaraz, Mirta Raquel. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Peñaloza, Lidia Guadalupe. Universidad Nacional de Salta; Argentina  
dc.description.fil
Fil: Boemo, Analía. Universidad Nacional de Salta; Argentina  
dc.description.fil
Fil: Cardozo, Ana. No especifíca;  
dc.description.fil
Fil: Tarcaya, Gerardo. No especifíca;  
dc.description.fil
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  
dc.description.fil
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.journal.title
Microchemical Journal  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0026265X20337838  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.microc.2020.105841