Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.11/6046 |
Resumo: | When considering complex scenarios involving several attributes, such as in environmental characterization, a clearer picture of reality can be achieved through the dimensional reduction of data. In this context, maps facilitate the visualization of spatial patterns of contaminant distribution and the identification of enriched areas. A set, of 15 Potentially Toxic Elements (PTEs) – (As, Ba, Cd, Co, Cr, Cu, Hg,Mo, Ni, Pb, Sb, Se, Tl, V, and Zn), was measured in soil, collected in Langreo's municipality (80 km2), Spain. Relative enrichment (RE) is introduced here to refer to the proportion of elements present in a given context. Indeed, a novel approach is provided for research into PTE fate. This method involves studying the variability of PTE proportions throughout the study area, thereby allowing the identification of dissemination trends. Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the elements analyzedmake up the entirety of the soil. However, in geochemical studies the analyzed elements are just a fraction of the total soil composition. Therefore, considering compositional data is pivotal. The spatial characterization of PTEs considering raw and compositional data together allowed a broad discussion about, not only the PTEs concentration's distribution but also to reckon possible trends of relative enrichment (RE). Transformations to open closed data are widely used for this purpose. Spatial patterns have an indubitable interest. In this study, the Centered Log-ratio transformation (clr) was used, followed by its back-transformation, to build a set of compositional data that, combined with raw data, allowed to establish the sources of the PTEs and trends of spatial dissemination. |
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Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soilsSoil pollutionPTEsCompositional dataOrdinary krigingLocal G-clusteringRelative enrichmentWhen considering complex scenarios involving several attributes, such as in environmental characterization, a clearer picture of reality can be achieved through the dimensional reduction of data. In this context, maps facilitate the visualization of spatial patterns of contaminant distribution and the identification of enriched areas. A set, of 15 Potentially Toxic Elements (PTEs) – (As, Ba, Cd, Co, Cr, Cu, Hg,Mo, Ni, Pb, Sb, Se, Tl, V, and Zn), was measured in soil, collected in Langreo's municipality (80 km2), Spain. Relative enrichment (RE) is introduced here to refer to the proportion of elements present in a given context. Indeed, a novel approach is provided for research into PTE fate. This method involves studying the variability of PTE proportions throughout the study area, thereby allowing the identification of dissemination trends. Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the elements analyzedmake up the entirety of the soil. However, in geochemical studies the analyzed elements are just a fraction of the total soil composition. Therefore, considering compositional data is pivotal. The spatial characterization of PTEs considering raw and compositional data together allowed a broad discussion about, not only the PTEs concentration's distribution but also to reckon possible trends of relative enrichment (RE). Transformations to open closed data are widely used for this purpose. Spatial patterns have an indubitable interest. In this study, the Centered Log-ratio transformation (clr) was used, followed by its back-transformation, to build a set of compositional data that, combined with raw data, allowed to establish the sources of the PTEs and trends of spatial dissemination.ElsevierRepositório Científico do Instituto Politécnico de Castelo BrancoBoente, C.Albuquerque, M.T.D.Fernández-Braña, A.Gerassis, S.Sierra, C.Gallego, J.R.2020-08-31T00:30:10Z20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/6046engBOENTE, C. [et al.] (2018) - Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils. Science of The Total Environment. ISSN 0048-9697. Vol. 631–632, p. 1117-11260048-969710.1016/j.scitotenv.2018.03.048info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-13T01:46:56Zoai:repositorio.ipcb.pt:10400.11/6046Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:36:45.435168Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils |
title |
Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils |
spellingShingle |
Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils Boente, C. Soil pollution PTEs Compositional data Ordinary kriging Local G-clustering Relative enrichment |
title_short |
Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils |
title_full |
Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils |
title_fullStr |
Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils |
title_full_unstemmed |
Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils |
title_sort |
Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils |
author |
Boente, C. |
author_facet |
Boente, C. Albuquerque, M.T.D. Fernández-Braña, A. Gerassis, S. Sierra, C. Gallego, J.R. |
author_role |
author |
author2 |
Albuquerque, M.T.D. Fernández-Braña, A. Gerassis, S. Sierra, C. Gallego, J.R. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico de Castelo Branco |
dc.contributor.author.fl_str_mv |
Boente, C. Albuquerque, M.T.D. Fernández-Braña, A. Gerassis, S. Sierra, C. Gallego, J.R. |
dc.subject.por.fl_str_mv |
Soil pollution PTEs Compositional data Ordinary kriging Local G-clustering Relative enrichment |
topic |
Soil pollution PTEs Compositional data Ordinary kriging Local G-clustering Relative enrichment |
description |
When considering complex scenarios involving several attributes, such as in environmental characterization, a clearer picture of reality can be achieved through the dimensional reduction of data. In this context, maps facilitate the visualization of spatial patterns of contaminant distribution and the identification of enriched areas. A set, of 15 Potentially Toxic Elements (PTEs) – (As, Ba, Cd, Co, Cr, Cu, Hg,Mo, Ni, Pb, Sb, Se, Tl, V, and Zn), was measured in soil, collected in Langreo's municipality (80 km2), Spain. Relative enrichment (RE) is introduced here to refer to the proportion of elements present in a given context. Indeed, a novel approach is provided for research into PTE fate. This method involves studying the variability of PTE proportions throughout the study area, thereby allowing the identification of dissemination trends. Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the elements analyzedmake up the entirety of the soil. However, in geochemical studies the analyzed elements are just a fraction of the total soil composition. Therefore, considering compositional data is pivotal. The spatial characterization of PTEs considering raw and compositional data together allowed a broad discussion about, not only the PTEs concentration's distribution but also to reckon possible trends of relative enrichment (RE). Transformations to open closed data are widely used for this purpose. Spatial patterns have an indubitable interest. In this study, the Centered Log-ratio transformation (clr) was used, followed by its back-transformation, to build a set of compositional data that, combined with raw data, allowed to establish the sources of the PTEs and trends of spatial dissemination. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 2018-01-01T00:00:00Z 2020-08-31T00:30:10Z |
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://hdl.handle.net/10400.11/6046 |
url |
http://hdl.handle.net/10400.11/6046 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
BOENTE, C. [et al.] (2018) - Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils. Science of The Total Environment. ISSN 0048-9697. Vol. 631–632, p. 1117-1126 0048-9697 10.1016/j.scitotenv.2018.03.048 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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