Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, Brazil

Detalhes bibliográficos
Autor(a) principal: Sahoo, Prafulla Kumar
Data de Publicação: 2019
Outros Autores: Felix Guimaraes, Jose Tasso, Martins Souza-Filho, Pedro Walfir, Powell, Mike A., Silva, Marcio Sousa da, Moraes, Aline Mamede, Alves, Ronnie, Leite, Alessandro Saba, Nascimento Junior, Wilson, Rodrigues, Tarcisio Magevski, Costa, Vladimir Eliodoro [UNESP], Dall'Agnol, Roberto
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.catena.2018.12.003
http://hdl.handle.net/11449/185468
Resumo: Statistical evaluation applied to geochemical data of upland lake sediments and their catchment basins rocks from Serra dos Carajas was used to identify geochemical signatures associated with underlying processes, sediment provenances, and source-sink relationship. The lakes are Violao, Amendoim and Tres Irmas - TI1, TI2 and TI3. A centred log-ratio transformation (clr) was used prior to multivariate analyses in order to eliminate closure issues in compositional data. Due to the similarity between delta N-15 values and organic sources (mainly from C3 plants), the three lakes were clustered together. Violao Lake receives largest organic contribution from autochthonous sources, such as siliceous sponge spicules and algae, except for it shallower portion (WNW extension), which is more similar to TI2, having low delta N-15 values that are similar to the isotopic signature of upland swamps. The upper continental crust (UCC) normalization pattern shows that sediments are mainly enriched in Fe, P and Se, which is closely related to the catchment lithology. The distribution of elements in TI2 is significantly different from the other lakes, because it is dominated by organic carbon, while the other lakes are a mix of detritus and organic carbon. Factor Analysis (FA) using clr-transformed data distinguishes several geochemical assemblages in the sediments, with the major detritic groups being similar to catchment basin laterites: the Ti-Zr-Hf-Nb-Y-HREEs group corresponds to resistant minerals which remained stable during lateritization; the LREEs group reflects mobilization and reprecipitation by REE bearing minerals; and the Al-V-Cr-Sc association reflects metavolcanic rock. The Fe-P-Mo-As-Zn cluster in the sediments is attributed to Fe-oxyhydroxide precipitation, while TOC-SO3-Hg-Se group is controlled by organic matter. Principal Component Analysis (PCA) further indicates that detritic lake sediments are not directly derived from the parent rocks, but from weathered crusts, mainly ferruginous laterites and soils, which is consistent with their elements ratios.
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spelling Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, BrazilMultivariate statisticsGeochemical processesUpland lakesSedimentsAmazoniaSerra dos CarajasStatistical evaluation applied to geochemical data of upland lake sediments and their catchment basins rocks from Serra dos Carajas was used to identify geochemical signatures associated with underlying processes, sediment provenances, and source-sink relationship. The lakes are Violao, Amendoim and Tres Irmas - TI1, TI2 and TI3. A centred log-ratio transformation (clr) was used prior to multivariate analyses in order to eliminate closure issues in compositional data. Due to the similarity between delta N-15 values and organic sources (mainly from C3 plants), the three lakes were clustered together. Violao Lake receives largest organic contribution from autochthonous sources, such as siliceous sponge spicules and algae, except for it shallower portion (WNW extension), which is more similar to TI2, having low delta N-15 values that are similar to the isotopic signature of upland swamps. The upper continental crust (UCC) normalization pattern shows that sediments are mainly enriched in Fe, P and Se, which is closely related to the catchment lithology. The distribution of elements in TI2 is significantly different from the other lakes, because it is dominated by organic carbon, while the other lakes are a mix of detritus and organic carbon. Factor Analysis (FA) using clr-transformed data distinguishes several geochemical assemblages in the sediments, with the major detritic groups being similar to catchment basin laterites: the Ti-Zr-Hf-Nb-Y-HREEs group corresponds to resistant minerals which remained stable during lateritization; the LREEs group reflects mobilization and reprecipitation by REE bearing minerals; and the Al-V-Cr-Sc association reflects metavolcanic rock. The Fe-P-Mo-As-Zn cluster in the sediments is attributed to Fe-oxyhydroxide precipitation, while TOC-SO3-Hg-Se group is controlled by organic matter. Principal Component Analysis (PCA) further indicates that detritic lake sediments are not directly derived from the parent rocks, but from weathered crusts, mainly ferruginous laterites and soils, which is consistent with their elements ratios.Vale Institute of TechnologyConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Inst Tecnol Vale, Rua Boaventura da Silva 955, BR-66055090 Belem, Para, BrazilUniv Fed Para, Inst Geociencias, Programa Posgrad Geol & Geoquim, Av Augusto Correa 1 Guama, BR-66075110 Belem, Para, BrazilGeocon Environm Consulting, London, ON N6G 3H9, CanadaGerencia Meio Ambiente Minas Carajas, Dept Ferrosos Norte, Estr Raymundo Mascarenhas,S-N Mina N4, Parauapebas, Para, BrazilUniv Estadual Paulista, Inst Biociencias, Ctr Isotopos Estaveis, Rua Prof Dr Antonio Celso Wagner Zanin 250, BR-18618689 Botucatu, SP, BrazilUniv Estadual Paulista, Inst Biociencias, Ctr Isotopos Estaveis, Rua Prof Dr Antonio Celso Wagner Zanin 250, BR-18618689 Botucatu, SP, BrazilCNPq: 479182/2012-4CNPq: 442088/2014-0CNPq: 306108/2014-3CNPq: 302839/2016-0CNPq: 306450/2013-5Elsevier B.V.Inst Tecnol ValeUniv Fed ParaGeocon Environm ConsultingGerencia Meio Ambiente Minas CarajasUniversidade Estadual Paulista (Unesp)Sahoo, Prafulla KumarFelix Guimaraes, Jose TassoMartins Souza-Filho, Pedro WalfirPowell, Mike A.Silva, Marcio Sousa daMoraes, Aline MamedeAlves, RonnieLeite, Alessandro SabaNascimento Junior, WilsonRodrigues, Tarcisio MagevskiCosta, Vladimir Eliodoro [UNESP]Dall'Agnol, Roberto2019-10-04T12:35:41Z2019-10-04T12:35:41Z2019-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article47-62http://dx.doi.org/10.1016/j.catena.2018.12.003Catena. Amsterdam: Elsevier Science Bv, v. 175, p. 47-62, 2019.0341-8162http://hdl.handle.net/11449/18546810.1016/j.catena.2018.12.003WOS:000459358500006Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCatenainfo:eu-repo/semantics/openAccess2024-04-11T17:47:18Zoai:repositorio.unesp.br:11449/185468Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-11T17:47:18Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, Brazil
title Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, Brazil
spellingShingle Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, Brazil
Sahoo, Prafulla Kumar
Multivariate statistics
Geochemical processes
Upland lakes
Sediments
Amazonia
Serra dos Carajas
title_short Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, Brazil
title_full Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, Brazil
title_fullStr Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, Brazil
title_full_unstemmed Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, Brazil
title_sort Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, Brazil
author Sahoo, Prafulla Kumar
author_facet Sahoo, Prafulla Kumar
Felix Guimaraes, Jose Tasso
Martins Souza-Filho, Pedro Walfir
Powell, Mike A.
Silva, Marcio Sousa da
Moraes, Aline Mamede
Alves, Ronnie
Leite, Alessandro Saba
Nascimento Junior, Wilson
Rodrigues, Tarcisio Magevski
Costa, Vladimir Eliodoro [UNESP]
Dall'Agnol, Roberto
author_role author
author2 Felix Guimaraes, Jose Tasso
Martins Souza-Filho, Pedro Walfir
Powell, Mike A.
Silva, Marcio Sousa da
Moraes, Aline Mamede
Alves, Ronnie
Leite, Alessandro Saba
Nascimento Junior, Wilson
Rodrigues, Tarcisio Magevski
Costa, Vladimir Eliodoro [UNESP]
Dall'Agnol, Roberto
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Inst Tecnol Vale
Univ Fed Para
Geocon Environm Consulting
Gerencia Meio Ambiente Minas Carajas
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Sahoo, Prafulla Kumar
Felix Guimaraes, Jose Tasso
Martins Souza-Filho, Pedro Walfir
Powell, Mike A.
Silva, Marcio Sousa da
Moraes, Aline Mamede
Alves, Ronnie
Leite, Alessandro Saba
Nascimento Junior, Wilson
Rodrigues, Tarcisio Magevski
Costa, Vladimir Eliodoro [UNESP]
Dall'Agnol, Roberto
dc.subject.por.fl_str_mv Multivariate statistics
Geochemical processes
Upland lakes
Sediments
Amazonia
Serra dos Carajas
topic Multivariate statistics
Geochemical processes
Upland lakes
Sediments
Amazonia
Serra dos Carajas
description Statistical evaluation applied to geochemical data of upland lake sediments and their catchment basins rocks from Serra dos Carajas was used to identify geochemical signatures associated with underlying processes, sediment provenances, and source-sink relationship. The lakes are Violao, Amendoim and Tres Irmas - TI1, TI2 and TI3. A centred log-ratio transformation (clr) was used prior to multivariate analyses in order to eliminate closure issues in compositional data. Due to the similarity between delta N-15 values and organic sources (mainly from C3 plants), the three lakes were clustered together. Violao Lake receives largest organic contribution from autochthonous sources, such as siliceous sponge spicules and algae, except for it shallower portion (WNW extension), which is more similar to TI2, having low delta N-15 values that are similar to the isotopic signature of upland swamps. The upper continental crust (UCC) normalization pattern shows that sediments are mainly enriched in Fe, P and Se, which is closely related to the catchment lithology. The distribution of elements in TI2 is significantly different from the other lakes, because it is dominated by organic carbon, while the other lakes are a mix of detritus and organic carbon. Factor Analysis (FA) using clr-transformed data distinguishes several geochemical assemblages in the sediments, with the major detritic groups being similar to catchment basin laterites: the Ti-Zr-Hf-Nb-Y-HREEs group corresponds to resistant minerals which remained stable during lateritization; the LREEs group reflects mobilization and reprecipitation by REE bearing minerals; and the Al-V-Cr-Sc association reflects metavolcanic rock. The Fe-P-Mo-As-Zn cluster in the sediments is attributed to Fe-oxyhydroxide precipitation, while TOC-SO3-Hg-Se group is controlled by organic matter. Principal Component Analysis (PCA) further indicates that detritic lake sediments are not directly derived from the parent rocks, but from weathered crusts, mainly ferruginous laterites and soils, which is consistent with their elements ratios.
publishDate 2019
dc.date.none.fl_str_mv 2019-10-04T12:35:41Z
2019-10-04T12:35:41Z
2019-04-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.catena.2018.12.003
Catena. Amsterdam: Elsevier Science Bv, v. 175, p. 47-62, 2019.
0341-8162
http://hdl.handle.net/11449/185468
10.1016/j.catena.2018.12.003
WOS:000459358500006
url http://dx.doi.org/10.1016/j.catena.2018.12.003
http://hdl.handle.net/11449/185468
identifier_str_mv Catena. Amsterdam: Elsevier Science Bv, v. 175, p. 47-62, 2019.
0341-8162
10.1016/j.catena.2018.12.003
WOS:000459358500006
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Catena
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 47-62
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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