Approach to spatialize local to long-range atmospheric metal input (Cd, Cu, Hg, Pb) in epiphytic lichens over a meso-scale area (Pyrénées-Atlantiques, southwestern France).
Identifieur interne : 000084 ( PubMed/Corpus ); précédent : 000083; suivant : 000085Approach to spatialize local to long-range atmospheric metal input (Cd, Cu, Hg, Pb) in epiphytic lichens over a meso-scale area (Pyrénées-Atlantiques, southwestern France).
Auteurs : Julien P G. Barre ; Gaëlle Deletraz ; Jérôme Frayret ; Hervé Pinaly ; Olivier F X. Donard ; David AmourouxSource :
- Environmental science and pollution research international [ 1614-7499 ] ; 2015.
English descriptors
- KwdEn :
- Air Pollutants (analysis), Air Pollutants (pharmacokinetics), Atmosphere (chemistry), Environmental Monitoring (methods), Environmental Monitoring (statistics & numerical data), France, Geographic Information Systems, Lichens (metabolism), Linear Models, Mass Spectrometry, Metals, Heavy (analysis), Metals, Heavy (pharmacokinetics), Socioeconomic Factors, Spectrophotometry, Atomic, Statistics, Nonparametric.
- MESH :
- chemical , analysis : Air Pollutants, Metals, Heavy.
- chemical , pharmacokinetics : Air Pollutants, Metals, Heavy.
- geographic : France.
- chemistry : Atmosphere.
- metabolism : Lichens.
- methods : Environmental Monitoring.
- statistics & numerical data : Environmental Monitoring.
- Geographic Information Systems, Linear Models, Mass Spectrometry, Socioeconomic Factors, Spectrophotometry, Atomic, Statistics, Nonparametric.
Abstract
Geographically based investigations into atmospheric bio-monitoring usually provide information on concentration or occurrence data and spatial trends of specific contaminants over a specified study area. In this work, an original approach based on geographic information system (GIS) was used to establish metal contents (Hg, Cu, Pb, and Cd) in epiphytic lichens from 90 locations as atmospheric bio-monitors over a meso-scale area (Pyrénées-Atlantiques, southwestern France). This approach allows the integration of the heterogeneity of the territory and optimization of the sampling sites based on both socioeconomical and geophysical parameters (hereafter defined as urban, industrial, agricultural, and forested areas). The sampling strategy was first evaluated in several sites (n = 15) over different seasons and years in order to follow the temporal variability of the atmospheric metal input in lichens. The results demonstrate that concentration ranges remain constant over different sampling periods in "rural" areas (agricultural and forested). Higher variability is observed in the "anthropized" urban and industrial areas in relation to local atmospheric inputs. In this context, metal concentrations in lichens over the whole study show that (1) Hg and Cd are homogeneous over the whole territory (0.14 ± 0.04 and 0.38 ± 0.26 mg/kg, respectively), whereas (2) Cu and Pb are more concentrated in "anthropized" areas (9.3 and 11.9 mg/kg, respectively) than in "rural" ones (6.8 and 6.0 mg/kg, respectively) (Kruskall-Wallis, K(Cu) = 13.7 and K(Pb) = 9.7, p < 0.00001). They also showed a significant local enrichment for all metals in many locations in the Pays Basque (West) mainly due to metal and steel industrial activities. This confirms the local contribution of this contamination source over a wider geographic scale. A multiple linear regression model was applied to give an integrated spatialization of the data. This showed significant relationships for Pb and Cu (adjusted r (2) of 0.39 and 0.45, respectively), especially with regards to variables such as industry and road densities (source factors) and elevation or water balance (remote factors). These results show that an integrated GIS-based sampling strategy can improve biomonitoring data distribution and allows better differentiation of local and long-range contamination.
DOI: 10.1007/s11356-014-3990-5
PubMed: 25561253
Links to Exploration step
pubmed:25561253Le document en format XML
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Barre</name><affiliation><nlm:affiliation>Laboratoire de Chimie Analytique Bio-Inorganique et Environnement (LCABIE), Institut des Sciences Analytiques et de Physico-Chimie pour l'Environnement et les Matériaux (IPREM), UMR 5254 CNRS, Université de Pau et des Pays de l'Adour, Pau, France, julien.barre@univ-pau.fr.</nlm:affiliation></affiliation></author><author><name sortKey="Deletraz, Gaelle" sort="Deletraz, Gaelle" uniqKey="Deletraz G" first="Gaëlle" last="Deletraz">Gaëlle Deletraz</name></author><author><name sortKey="Frayret, Jerome" sort="Frayret, Jerome" uniqKey="Frayret J" first="Jérôme" last="Frayret">Jérôme Frayret</name></author><author><name sortKey="Pinaly, Herve" sort="Pinaly, Herve" uniqKey="Pinaly H" first="Hervé" last="Pinaly">Hervé Pinaly</name></author><author><name sortKey="Donard, Olivier F X" sort="Donard, Olivier F X" uniqKey="Donard O" first="Olivier F X" last="Donard">Olivier F X. Donard</name></author><author><name sortKey="Amouroux, David" sort="Amouroux, David" uniqKey="Amouroux D" first="David" last="Amouroux">David Amouroux</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2015">2015</date><idno type="RBID">pubmed:25561253</idno><idno type="pmid">25561253</idno><idno type="doi">10.1007/s11356-014-3990-5</idno><idno type="wicri:Area/PubMed/Corpus">000084</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000084</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Approach to spatialize local to long-range atmospheric metal input (Cd, Cu, Hg, Pb) in epiphytic lichens over a meso-scale area (Pyrénées-Atlantiques, southwestern France).</title><author><name sortKey="Barre, Julien P G" sort="Barre, Julien P G" uniqKey="Barre J" first="Julien P G" last="Barre">Julien P G. Barre</name><affiliation><nlm:affiliation>Laboratoire de Chimie Analytique Bio-Inorganique et Environnement (LCABIE), Institut des Sciences Analytiques et de Physico-Chimie pour l'Environnement et les Matériaux (IPREM), UMR 5254 CNRS, Université de Pau et des Pays de l'Adour, Pau, France, julien.barre@univ-pau.fr.</nlm:affiliation></affiliation></author><author><name sortKey="Deletraz, Gaelle" sort="Deletraz, Gaelle" uniqKey="Deletraz G" first="Gaëlle" last="Deletraz">Gaëlle Deletraz</name></author><author><name sortKey="Frayret, Jerome" sort="Frayret, Jerome" uniqKey="Frayret J" first="Jérôme" last="Frayret">Jérôme Frayret</name></author><author><name sortKey="Pinaly, Herve" sort="Pinaly, Herve" uniqKey="Pinaly H" first="Hervé" last="Pinaly">Hervé Pinaly</name></author><author><name sortKey="Donard, Olivier F X" sort="Donard, Olivier F X" uniqKey="Donard O" first="Olivier F X" last="Donard">Olivier F X. Donard</name></author><author><name sortKey="Amouroux, David" sort="Amouroux, David" uniqKey="Amouroux D" first="David" last="Amouroux">David Amouroux</name></author></analytic><series><title level="j">Environmental science and pollution research international</title><idno type="eISSN">1614-7499</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Air Pollutants (analysis)</term><term>Air Pollutants (pharmacokinetics)</term><term>Atmosphere (chemistry)</term><term>Environmental Monitoring (methods)</term><term>Environmental Monitoring (statistics & numerical data)</term><term>France</term><term>Geographic Information Systems</term><term>Lichens (metabolism)</term><term>Linear Models</term><term>Mass Spectrometry</term><term>Metals, Heavy (analysis)</term><term>Metals, Heavy (pharmacokinetics)</term><term>Socioeconomic Factors</term><term>Spectrophotometry, Atomic</term><term>Statistics, Nonparametric</term></keywords><keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Air Pollutants</term><term>Metals, Heavy</term></keywords><keywords scheme="MESH" type="chemical" qualifier="pharmacokinetics" xml:lang="en"><term>Air Pollutants</term><term>Metals, Heavy</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>France</term></keywords><keywords scheme="MESH" qualifier="chemistry" xml:lang="en"><term>Atmosphere</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Lichens</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Environmental Monitoring</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Environmental Monitoring</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Geographic Information Systems</term><term>Linear Models</term><term>Mass Spectrometry</term><term>Socioeconomic Factors</term><term>Spectrophotometry, Atomic</term><term>Statistics, Nonparametric</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Geographically based investigations into atmospheric bio-monitoring usually provide information on concentration or occurrence data and spatial trends of specific contaminants over a specified study area. In this work, an original approach based on geographic information system (GIS) was used to establish metal contents (Hg, Cu, Pb, and Cd) in epiphytic lichens from 90 locations as atmospheric bio-monitors over a meso-scale area (Pyrénées-Atlantiques, southwestern France). This approach allows the integration of the heterogeneity of the territory and optimization of the sampling sites based on both socioeconomical and geophysical parameters (hereafter defined as urban, industrial, agricultural, and forested areas). The sampling strategy was first evaluated in several sites (n = 15) over different seasons and years in order to follow the temporal variability of the atmospheric metal input in lichens. The results demonstrate that concentration ranges remain constant over different sampling periods in "rural" areas (agricultural and forested). Higher variability is observed in the "anthropized" urban and industrial areas in relation to local atmospheric inputs. In this context, metal concentrations in lichens over the whole study show that (1) Hg and Cd are homogeneous over the whole territory (0.14 ± 0.04 and 0.38 ± 0.26 mg/kg, respectively), whereas (2) Cu and Pb are more concentrated in "anthropized" areas (9.3 and 11.9 mg/kg, respectively) than in "rural" ones (6.8 and 6.0 mg/kg, respectively) (Kruskall-Wallis, K(Cu) = 13.7 and K(Pb) = 9.7, p < 0.00001). They also showed a significant local enrichment for all metals in many locations in the Pays Basque (West) mainly due to metal and steel industrial activities. This confirms the local contribution of this contamination source over a wider geographic scale. A multiple linear regression model was applied to give an integrated spatialization of the data. This showed significant relationships for Pb and Cu (adjusted r (2) of 0.39 and 0.45, respectively), especially with regards to variables such as industry and road densities (source factors) and elevation or water balance (remote factors). These results show that an integrated GIS-based sampling strategy can improve biomonitoring data distribution and allows better differentiation of local and long-range contamination.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25561253</PMID><DateCreated><Year>2015</Year><Month>05</Month><Day>24</Day></DateCreated><DateCompleted><Year>2015</Year><Month>08</Month><Day>21</Day></DateCompleted><DateRevised><Year>2015</Year><Month>05</Month><Day>24</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1614-7499</ISSN><JournalIssue CitedMedium="Internet"><Volume>22</Volume><Issue>11</Issue><PubDate><Year>2015</Year><Month>Jun</Month></PubDate></JournalIssue><Title>Environmental science and pollution research international</Title><ISOAbbreviation>Environ Sci Pollut Res Int</ISOAbbreviation></Journal><ArticleTitle>Approach to spatialize local to long-range atmospheric metal input (Cd, Cu, Hg, Pb) in epiphytic lichens over a meso-scale area (Pyrénées-Atlantiques, southwestern France).</ArticleTitle><Pagination><MedlinePgn>8536-48</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s11356-014-3990-5</ELocationID><Abstract><AbstractText>Geographically based investigations into atmospheric bio-monitoring usually provide information on concentration or occurrence data and spatial trends of specific contaminants over a specified study area. In this work, an original approach based on geographic information system (GIS) was used to establish metal contents (Hg, Cu, Pb, and Cd) in epiphytic lichens from 90 locations as atmospheric bio-monitors over a meso-scale area (Pyrénées-Atlantiques, southwestern France). This approach allows the integration of the heterogeneity of the territory and optimization of the sampling sites based on both socioeconomical and geophysical parameters (hereafter defined as urban, industrial, agricultural, and forested areas). The sampling strategy was first evaluated in several sites (n = 15) over different seasons and years in order to follow the temporal variability of the atmospheric metal input in lichens. The results demonstrate that concentration ranges remain constant over different sampling periods in "rural" areas (agricultural and forested). Higher variability is observed in the "anthropized" urban and industrial areas in relation to local atmospheric inputs. In this context, metal concentrations in lichens over the whole study show that (1) Hg and Cd are homogeneous over the whole territory (0.14 ± 0.04 and 0.38 ± 0.26 mg/kg, respectively), whereas (2) Cu and Pb are more concentrated in "anthropized" areas (9.3 and 11.9 mg/kg, respectively) than in "rural" ones (6.8 and 6.0 mg/kg, respectively) (Kruskall-Wallis, K(Cu) = 13.7 and K(Pb) = 9.7, p < 0.00001). They also showed a significant local enrichment for all metals in many locations in the Pays Basque (West) mainly due to metal and steel industrial activities. This confirms the local contribution of this contamination source over a wider geographic scale. A multiple linear regression model was applied to give an integrated spatialization of the data. This showed significant relationships for Pb and Cu (adjusted r (2) of 0.39 and 0.45, respectively), especially with regards to variables such as industry and road densities (source factors) and elevation or water balance (remote factors). These results show that an integrated GIS-based sampling strategy can improve biomonitoring data distribution and allows better differentiation of local and long-range contamination.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Barre</LastName><ForeName>Julien P G</ForeName><Initials>JP</Initials><AffiliationInfo><Affiliation>Laboratoire de Chimie Analytique Bio-Inorganique et Environnement (LCABIE), Institut des Sciences Analytiques et de Physico-Chimie pour l'Environnement et les Matériaux (IPREM), UMR 5254 CNRS, Université de Pau et des Pays de l'Adour, Pau, France, julien.barre@univ-pau.fr.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Deletraz</LastName><ForeName>Gaëlle</ForeName><Initials>G</Initials></Author><Author ValidYN="Y"><LastName>Frayret</LastName><ForeName>Jérôme</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Pinaly</LastName><ForeName>Hervé</ForeName><Initials>H</Initials></Author><Author ValidYN="Y"><LastName>Donard</LastName><ForeName>Olivier F X</ForeName><Initials>OF</Initials></Author><Author ValidYN="Y"><LastName>Amouroux</LastName><ForeName>David</ForeName><Initials>D</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>01</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>Germany</Country><MedlineTA>Environ Sci Pollut Res Int</MedlineTA><NlmUniqueID>9441769</NlmUniqueID><ISSNLinking>0944-1344</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000393">Air Pollutants</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D019216">Metals, Heavy</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000393" MajorTopicYN="N">Air Pollutants</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName><QualifierName UI="Q000493" MajorTopicYN="N">pharmacokinetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D001272" MajorTopicYN="N">Atmosphere</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="N">Environmental Monitoring</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005602" MajorTopicYN="N" Type="Geographic">France</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D040362" MajorTopicYN="N">Geographic Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008011" MajorTopicYN="N">Lichens</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016014" MajorTopicYN="N">Linear Models</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013058" MajorTopicYN="N">Mass Spectrometry</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019216" MajorTopicYN="N">Metals, Heavy</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName><QualifierName UI="Q000493" MajorTopicYN="N">pharmacokinetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012959" MajorTopicYN="N">Socioeconomic Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013054" MajorTopicYN="N">Spectrophotometry, Atomic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018709" MajorTopicYN="N">Statistics, Nonparametric</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year><Month>08</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>12</Month><Day>12</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>1</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>1</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>8</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25561253</ArticleId><ArticleId IdType="doi">10.1007/s11356-014-3990-5</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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