Compositional baseline assessments to address soil pollution: An application in Langreo, Spain
Potentially Toxic Elements (PTEs) are contaminants with high toxicity and complex geochemical behaviour and, therefore, high PTEs contents in soil may affect ecosystems and/or human health. However, before addressing the measurement of soil pollution, it is necessary to understand what is meant by p...
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Veröffentlicht in: | The Science of the total environment 2022-03, Vol.812, p.152383-152383, Article 152383 |
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description | Potentially Toxic Elements (PTEs) are contaminants with high toxicity and complex geochemical behaviour and, therefore, high PTEs contents in soil may affect ecosystems and/or human health. However, before addressing the measurement of soil pollution, it is necessary to understand what is meant by pollution-free soil. Often, this background, or pollution baseline, is undefined or only partially known. Since the concentration of chemical elements is compositional, as the attributes vary together, here we present a novel approach to build compositional indicators based on Compositional Data (CoDa) principles. The steps of this new methodology are: 1) Exploratory data analysis through variation matrix, biplots or CoDa dendrograms; 2) Selection of geological background in terms of a trimmed subsample that can be assumed as non-pollutant; 3) Computing the spread Aitchison distance from each sample point to the trimmed sample; 4) Performing a compositional balance able to predict the Aitchison distance computed in step 3.Identifying a compositional balance, including pollutant and non-pollutant elements, with sparsity and simplicity as properties, is crucial for the construction of a Compositional Pollution Indicator (CI). Here we explored a database of 150 soil samples and 37 chemical elements from the contaminated region of Langreo, Northwestern Spain. There were obtained three Cis: the first two using elements obtained through CoDa analysis, and the third one selecting a list of pollutants and non-pollutants based on expert knowledge and previous studies. The three indicators went through a Stochastic Sequential Gaussian simulation. The results of the 100 computed simulations are summarized through mean image maps and probability maps of exceeding a given threshold, thus allowing characterization of the spatial distribution and variability of the CIs. A better understanding of the trends of relative enrichment and PTEs fate is discussed.
[Display omitted]
•A novel method to define a baseline for non-polluted soils is proposed.•A method to build compositional indicators to address soil pollution is proposed.•Indicators obtained through compositional balances complement expert's criteria.•Sequential Gaussian Simulations offer a proper visualization of the indicators. |
doi_str_mv | 10.1016/j.scitotenv.2021.152383 |
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[Display omitted]
•A novel method to define a baseline for non-polluted soils is proposed.•A method to build compositional indicators to address soil pollution is proposed.•Indicators obtained through compositional balances complement expert's criteria.•Sequential Gaussian Simulations offer a proper visualization of the indicators.</description><identifier>ISSN: 0048-9697</identifier><identifier>EISSN: 1879-1026</identifier><identifier>DOI: 10.1016/j.scitotenv.2021.152383</identifier><identifier>PMID: 34952083</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Compositional indicators ; Ecosystem ; Environmental Monitoring ; Environmental Pollution ; Humans ; Metals, Heavy - analysis ; Potentially toxic elements ; Risk Assessment ; Sequential Gaussian simulation ; Soil ; Soil Pollutants - analysis ; Soil pollution ; Spain</subject><ispartof>The Science of the total environment, 2022-03, Vol.812, p.152383-152383, Article 152383</ispartof><rights>2021 Elsevier B.V.</rights><rights>Copyright © 2021 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c399t-e55d1a91d04ad0fc53b6f9578a044e2db8d8d2b07cb70ecfada7dd745b00e743</citedby><cites>FETCH-LOGICAL-c399t-e55d1a91d04ad0fc53b6f9578a044e2db8d8d2b07cb70ecfada7dd745b00e743</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.scitotenv.2021.152383$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34952083$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Boente, C.</creatorcontrib><creatorcontrib>Albuquerque, M.T.D.</creatorcontrib><creatorcontrib>Gallego, J.R.</creatorcontrib><creatorcontrib>Pawlowsky-Glahn, V.</creatorcontrib><creatorcontrib>Egozcue, J.J.</creatorcontrib><title>Compositional baseline assessments to address soil pollution: An application in Langreo, Spain</title><title>The Science of the total environment</title><addtitle>Sci Total Environ</addtitle><description>Potentially Toxic Elements (PTEs) are contaminants with high toxicity and complex geochemical behaviour and, therefore, high PTEs contents in soil may affect ecosystems and/or human health. However, before addressing the measurement of soil pollution, it is necessary to understand what is meant by pollution-free soil. Often, this background, or pollution baseline, is undefined or only partially known. Since the concentration of chemical elements is compositional, as the attributes vary together, here we present a novel approach to build compositional indicators based on Compositional Data (CoDa) principles. The steps of this new methodology are: 1) Exploratory data analysis through variation matrix, biplots or CoDa dendrograms; 2) Selection of geological background in terms of a trimmed subsample that can be assumed as non-pollutant; 3) Computing the spread Aitchison distance from each sample point to the trimmed sample; 4) Performing a compositional balance able to predict the Aitchison distance computed in step 3.Identifying a compositional balance, including pollutant and non-pollutant elements, with sparsity and simplicity as properties, is crucial for the construction of a Compositional Pollution Indicator (CI). Here we explored a database of 150 soil samples and 37 chemical elements from the contaminated region of Langreo, Northwestern Spain. There were obtained three Cis: the first two using elements obtained through CoDa analysis, and the third one selecting a list of pollutants and non-pollutants based on expert knowledge and previous studies. The three indicators went through a Stochastic Sequential Gaussian simulation. The results of the 100 computed simulations are summarized through mean image maps and probability maps of exceeding a given threshold, thus allowing characterization of the spatial distribution and variability of the CIs. A better understanding of the trends of relative enrichment and PTEs fate is discussed.
[Display omitted]
•A novel method to define a baseline for non-polluted soils is proposed.•A method to build compositional indicators to address soil pollution is proposed.•Indicators obtained through compositional balances complement expert's criteria.•Sequential Gaussian Simulations offer a proper visualization of the indicators.</description><subject>Compositional indicators</subject><subject>Ecosystem</subject><subject>Environmental Monitoring</subject><subject>Environmental Pollution</subject><subject>Humans</subject><subject>Metals, Heavy - analysis</subject><subject>Potentially toxic elements</subject><subject>Risk Assessment</subject><subject>Sequential Gaussian simulation</subject><subject>Soil</subject><subject>Soil Pollutants - analysis</subject><subject>Soil pollution</subject><subject>Spain</subject><issn>0048-9697</issn><issn>1879-1026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkE1r3DAQhkVJabbb_oVUxx7irSRLlt3bsqQfsNBDc66QpXHQIkuuxhvov4_NprlmLsPA-8E8hHzibMcZb76cdujCnGdIjzvBBN9xJeq2fkM2vNVdxZlorsiGMdlWXdPpa_Ie8cSW0S1_R65r2SnB2npD_hzyOGUMc8jJRtpbhBgSUIsIiCOkGemcqfW-LDfFHCKdcozn1fCV7hO10xSDs-tNQ6JHmx4K5Fv6e7IhfSBvBxsRPj7vLbn_dnd_-FEdf33_edgfK1d33VyBUp7bjnsmrWeDU3XfDJ3SrWVSgvB961sveqZdrxm4wXqrvddS9YyBlvWWfL7ETiX_PQPOZgzoIEabIJ_RiIZLIRlXapHqi9SVjFhgMFMJoy3_DGdmZWtO5oWtWdmaC9vFefNccu5H8C--_zAXwf4igOXTxwBlDYLkwIcCbjY-h1dLngALyZFE</recordid><startdate>20220315</startdate><enddate>20220315</enddate><creator>Boente, C.</creator><creator>Albuquerque, M.T.D.</creator><creator>Gallego, J.R.</creator><creator>Pawlowsky-Glahn, V.</creator><creator>Egozcue, J.J.</creator><general>Elsevier B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20220315</creationdate><title>Compositional baseline assessments to address soil pollution: An application in Langreo, Spain</title><author>Boente, C. ; Albuquerque, M.T.D. ; Gallego, J.R. ; Pawlowsky-Glahn, V. ; Egozcue, J.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-e55d1a91d04ad0fc53b6f9578a044e2db8d8d2b07cb70ecfada7dd745b00e743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Compositional indicators</topic><topic>Ecosystem</topic><topic>Environmental Monitoring</topic><topic>Environmental Pollution</topic><topic>Humans</topic><topic>Metals, Heavy - analysis</topic><topic>Potentially toxic elements</topic><topic>Risk Assessment</topic><topic>Sequential Gaussian simulation</topic><topic>Soil</topic><topic>Soil Pollutants - analysis</topic><topic>Soil pollution</topic><topic>Spain</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Boente, C.</creatorcontrib><creatorcontrib>Albuquerque, M.T.D.</creatorcontrib><creatorcontrib>Gallego, J.R.</creatorcontrib><creatorcontrib>Pawlowsky-Glahn, V.</creatorcontrib><creatorcontrib>Egozcue, J.J.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>The Science of the total environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Boente, C.</au><au>Albuquerque, M.T.D.</au><au>Gallego, J.R.</au><au>Pawlowsky-Glahn, V.</au><au>Egozcue, J.J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Compositional baseline assessments to address soil pollution: An application in Langreo, Spain</atitle><jtitle>The Science of the total environment</jtitle><addtitle>Sci Total Environ</addtitle><date>2022-03-15</date><risdate>2022</risdate><volume>812</volume><spage>152383</spage><epage>152383</epage><pages>152383-152383</pages><artnum>152383</artnum><issn>0048-9697</issn><eissn>1879-1026</eissn><abstract>Potentially Toxic Elements (PTEs) are contaminants with high toxicity and complex geochemical behaviour and, therefore, high PTEs contents in soil may affect ecosystems and/or human health. However, before addressing the measurement of soil pollution, it is necessary to understand what is meant by pollution-free soil. Often, this background, or pollution baseline, is undefined or only partially known. Since the concentration of chemical elements is compositional, as the attributes vary together, here we present a novel approach to build compositional indicators based on Compositional Data (CoDa) principles. The steps of this new methodology are: 1) Exploratory data analysis through variation matrix, biplots or CoDa dendrograms; 2) Selection of geological background in terms of a trimmed subsample that can be assumed as non-pollutant; 3) Computing the spread Aitchison distance from each sample point to the trimmed sample; 4) Performing a compositional balance able to predict the Aitchison distance computed in step 3.Identifying a compositional balance, including pollutant and non-pollutant elements, with sparsity and simplicity as properties, is crucial for the construction of a Compositional Pollution Indicator (CI). Here we explored a database of 150 soil samples and 37 chemical elements from the contaminated region of Langreo, Northwestern Spain. There were obtained three Cis: the first two using elements obtained through CoDa analysis, and the third one selecting a list of pollutants and non-pollutants based on expert knowledge and previous studies. The three indicators went through a Stochastic Sequential Gaussian simulation. The results of the 100 computed simulations are summarized through mean image maps and probability maps of exceeding a given threshold, thus allowing characterization of the spatial distribution and variability of the CIs. A better understanding of the trends of relative enrichment and PTEs fate is discussed.
[Display omitted]
•A novel method to define a baseline for non-polluted soils is proposed.•A method to build compositional indicators to address soil pollution is proposed.•Indicators obtained through compositional balances complement expert's criteria.•Sequential Gaussian Simulations offer a proper visualization of the indicators.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>34952083</pmid><doi>10.1016/j.scitotenv.2021.152383</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Compositional indicators Ecosystem Environmental Monitoring Environmental Pollution Humans Metals, Heavy - analysis Potentially toxic elements Risk Assessment Sequential Gaussian simulation Soil Soil Pollutants - analysis Soil pollution Spain |
title | Compositional baseline assessments to address soil pollution: An application in Langreo, Spain |
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