Local versus regional soil screening levels to identify potentially polluted areas
Autor(a) principal: | |
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Data de Publicação: | 2019 |
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/10400.11/6446 |
Resumo: | Soil screening levels (SSLs) are reference threshold values required by environmental laws, established based on soil geochemical background data from often-extensive sampling areas. Such areas are often inappropriate for interpreting the true risk of pollution in small areas, since they overlook local factors (e.g., geology, industry, and traffic), which are unfeasible to encompass in large-scale samplings. To solve this issue, the calculation of local SSLs is proposed herein, performed on amajor scale closer to the area of interest. To exemplify this proposal, a soil sampling campaign was performed in the Municipality of Langreo, one of the most industrialized areas in the Principality of Asturias (northwestern Spain). Sampling allowed the measurement of local soil screening levels for several inorganic contaminants. Afterwards, a soil pollution index was calculated, referred to both regional and local thresholds, to assess the degree of contamination. Both pollution indicators were subjected to a methodology based on a Bayesian network analysis, followed by a stochastic sequential Gaussian simulation approach. The methodologies used showed differences in the identification of potentially polluted areas depending on the soil screening levels (regional or local) used. It was concluded that, in urban/industrial cores, local soil screening levels facilitate the identification of polluted areas and also reduce the uncertainty associated with sampling density and diffuse contamination. Thus, the use of local levels circumvents false-positive areas that would be classified as polluted were regional soil screening levels to be used. |
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Local versus regional soil screening levels to identify potentially polluted areasSoil pollutionPotentially toxic elementsSoil screening levelsGeostatisticsMachine learningSoil screening levels (SSLs) are reference threshold values required by environmental laws, established based on soil geochemical background data from often-extensive sampling areas. Such areas are often inappropriate for interpreting the true risk of pollution in small areas, since they overlook local factors (e.g., geology, industry, and traffic), which are unfeasible to encompass in large-scale samplings. To solve this issue, the calculation of local SSLs is proposed herein, performed on amajor scale closer to the area of interest. To exemplify this proposal, a soil sampling campaign was performed in the Municipality of Langreo, one of the most industrialized areas in the Principality of Asturias (northwestern Spain). Sampling allowed the measurement of local soil screening levels for several inorganic contaminants. Afterwards, a soil pollution index was calculated, referred to both regional and local thresholds, to assess the degree of contamination. Both pollution indicators were subjected to a methodology based on a Bayesian network analysis, followed by a stochastic sequential Gaussian simulation approach. The methodologies used showed differences in the identification of potentially polluted areas depending on the soil screening levels (regional or local) used. It was concluded that, in urban/industrial cores, local soil screening levels facilitate the identification of polluted areas and also reduce the uncertainty associated with sampling density and diffuse contamination. Thus, the use of local levels circumvents false-positive areas that would be classified as polluted were regional soil screening levels to be used.SpringerRepositório Científico do Instituto Politécnico de Castelo BrancoBoente, C.Gerassis, S.Albuquerque, M.T.D.Taboada, J.Gallego, J. R.2020-03-31T00:30:23Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/6446engBOENTE, C. [et al.] (2019) - Local versus regional soil screening levels to identify potentially polluted areas. Mathematical Geosciences. ISSN 1874-8953. https://doi.org/10.1007/s11004-019-09792-x1874-895310.1007/s11004-019-09792-xinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-25T01:47:17Zoai:repositorio.ipcb.pt:10400.11/6446Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:37:05.394227Repositório 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 |
Local versus regional soil screening levels to identify potentially polluted areas |
title |
Local versus regional soil screening levels to identify potentially polluted areas |
spellingShingle |
Local versus regional soil screening levels to identify potentially polluted areas Boente, C. Soil pollution Potentially toxic elements Soil screening levels Geostatistics Machine learning |
title_short |
Local versus regional soil screening levels to identify potentially polluted areas |
title_full |
Local versus regional soil screening levels to identify potentially polluted areas |
title_fullStr |
Local versus regional soil screening levels to identify potentially polluted areas |
title_full_unstemmed |
Local versus regional soil screening levels to identify potentially polluted areas |
title_sort |
Local versus regional soil screening levels to identify potentially polluted areas |
author |
Boente, C. |
author_facet |
Boente, C. Gerassis, S. Albuquerque, M.T.D. Taboada, J. Gallego, J. R. |
author_role |
author |
author2 |
Gerassis, S. Albuquerque, M.T.D. Taboada, J. Gallego, J. R. |
author2_role |
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. Gerassis, S. Albuquerque, M.T.D. Taboada, J. Gallego, J. R. |
dc.subject.por.fl_str_mv |
Soil pollution Potentially toxic elements Soil screening levels Geostatistics Machine learning |
topic |
Soil pollution Potentially toxic elements Soil screening levels Geostatistics Machine learning |
description |
Soil screening levels (SSLs) are reference threshold values required by environmental laws, established based on soil geochemical background data from often-extensive sampling areas. Such areas are often inappropriate for interpreting the true risk of pollution in small areas, since they overlook local factors (e.g., geology, industry, and traffic), which are unfeasible to encompass in large-scale samplings. To solve this issue, the calculation of local SSLs is proposed herein, performed on amajor scale closer to the area of interest. To exemplify this proposal, a soil sampling campaign was performed in the Municipality of Langreo, one of the most industrialized areas in the Principality of Asturias (northwestern Spain). Sampling allowed the measurement of local soil screening levels for several inorganic contaminants. Afterwards, a soil pollution index was calculated, referred to both regional and local thresholds, to assess the degree of contamination. Both pollution indicators were subjected to a methodology based on a Bayesian network analysis, followed by a stochastic sequential Gaussian simulation approach. The methodologies used showed differences in the identification of potentially polluted areas depending on the soil screening levels (regional or local) used. It was concluded that, in urban/industrial cores, local soil screening levels facilitate the identification of polluted areas and also reduce the uncertainty associated with sampling density and diffuse contamination. Thus, the use of local levels circumvents false-positive areas that would be classified as polluted were regional soil screening levels to be used. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z 2020-03-31T00:30:23Z |
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/10400.11/6446 |
url |
http://hdl.handle.net/10400.11/6446 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
BOENTE, C. [et al.] (2019) - Local versus regional soil screening levels to identify potentially polluted areas. Mathematical Geosciences. ISSN 1874-8953. https://doi.org/10.1007/s11004-019-09792-x 1874-8953 10.1007/s11004-019-09792-x |
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info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
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 |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799130832597680128 |