Applying geostatistics to identification of spatial patterns of fecal contamination in a mussel farming area (Havre de la Vanlée, France)
Identifieur interne : 001056 ( Istex/Corpus ); précédent : 001055; suivant : 001057Applying geostatistics to identification of spatial patterns of fecal contamination in a mussel farming area (Havre de la Vanlée, France)
Auteurs : Benoît Beliaeff ; Marie-Laure CochardSource :
- Water Research [ 0043-1354 ] ; 1995.
Abstract
Microbiological quality of shellfish production areas along the French coast is assessed through quantification of fecal coliforms, commonly used as indicators of fecal pollution. Their concentration is measured in filter-feeding molluscs, presumed to integrate highly fluctuating quantities of these germs from the surrounding filtrated water. In the context of a bacteriological monitoring network, knowledge of contamination spatial structures may help for further optimal sampling designs. This study aims at applying geostatistical techniques to describe and characterize the spatial structure of the fecal contamination in mussels (Mytilus edulis) over a given production area (Havre de la Vanlée, France), located in macrotidal waters. In comparison with common interpolation methods, the kriging estimator, a basic tool in geostatistics, presents the major advantage of providing unbiased estimates with known and minimum variances. It thus appears to be the most powerful procedure to produce contour maps for the concentration estimates and their standard deviations. Differences in mean concentration between the two conducted surveys are compared with differences in concentration found in continental inputs. Although two surveys are not sufficient to assess accurately spatio-temporal variabilities, the use of kriging reveals high spatial heterogeneity, with different structures and meteorological conditions from one survey to the other. This leads to questioning the validity of a monitoring based on a few sampling stations over a large area sampled monthly to estimate a mean bacteriological level.
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DOI: 10.1016/0043-1354(94)00294-H
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Their concentration is measured in filter-feeding molluscs, presumed to integrate highly fluctuating quantities of these germs from the surrounding filtrated water. In the context of a bacteriological monitoring network, knowledge of contamination spatial structures may help for further optimal sampling designs. This study aims at applying geostatistical techniques to describe and characterize the spatial structure of the fecal contamination in mussels (Mytilus edulis) over a given production area (Havre de la Vanlée, France), located in macrotidal waters. In comparison with common interpolation methods, the kriging estimator, a basic tool in geostatistics, presents the major advantage of providing unbiased estimates with known and minimum variances. It thus appears to be the most powerful procedure to produce contour maps for the concentration estimates and their standard deviations. Differences in mean concentration between the two conducted surveys are compared with differences in concentration found in continental inputs. Although two surveys are not sufficient to assess accurately spatio-temporal variabilities, the use of kriging reveals high spatial heterogeneity, with different structures and meteorological conditions from one survey to the other. 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Differences in mean concentration between the two conducted surveys are compared with differences in concentration found in continental inputs. Although two surveys are not sufficient to assess accurately spatio-temporal variabilities, the use of kriging reveals high spatial heterogeneity, with different structures and meteorological conditions from one survey to the other. This leads to questioning the validity of a monitoring based on a few sampling stations over a large area sampled monthly to estimate a mean bacteriological level.</p></abstract><textClass><keywords scheme="keyword"><list><head>Keywords</head><item><term>bacteriological monitoring</term></item><item><term>fecal coliforms</term></item><item><term>mussel beds</term></item><item><term>systematic sampling</term></item><item><term>spatial structure</term></item><item><term>kriging</term></item><item><term>interpolation</term></item><item><term>mapping</term></item><item><term>sampling optimization</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1995">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><original>false</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/F4889CDDB97491CF0712141AF29D5411C45C525D/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla"><item-info><jid>WR</jid><aid>9400294H</aid><ce:pii>0043-1354(94)00294-H</ce:pii><ce:doi>10.1016/0043-1354(94)00294-H</ce:doi><ce:copyright type="unknown" year="1995"></ce:copyright></item-info><head><ce:title>Applying geostatistics to identification of spatial patterns of fecal contamination in a mussel farming area (Havre de la Vanlée, France)</ce:title><ce:author-group><ce:author><ce:given-name>Benoît</ce:given-name><ce:surname>Beliaeff</ce:surname><ce:cross-ref refid="COR1"><ce:sup>∗</ce:sup></ce:cross-ref><ce:cross-ref refid="AFF1"><ce:sup>1</ce:sup></ce:cross-ref></ce:author><ce:author><ce:given-name>Marie-Laure</ce:given-name><ce:surname>Cochard</ce:surname><ce:cross-ref refid="AFF2"><ce:sup>2</ce:sup></ce:cross-ref></ce:author><ce:affiliation id="AFF1"><ce:label>1</ce:label><ce:textfn>IFREMER, Rue de l'île d'Yeu, BP 1049, 44037 Nantes Cedex 01 France</ce:textfn></ce:affiliation><ce:affiliation id="AFF2"><ce:label>2</ce:label><ce:textfn>IFREMER, BP 70, 29280, Plouzané, France</ce:textfn></ce:affiliation><ce:correspondence id="COR1"><ce:label>∗</ce:label><ce:text>Author to whom all correspondence should be addressed. Present address: NOAA N/ORCA21, SSMC4 1305 East-West Highway, Silver Spring, MD 20910, U.S.A.</ce:text></ce:correspondence></ce:author-group><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>Microbiological quality of shellfish production areas along the French coast is assessed through quantification of fecal coliforms, commonly used as indicators of fecal pollution. Their concentration is measured in filter-feeding molluscs, presumed to integrate highly fluctuating quantities of these germs from the surrounding filtrated water. In the context of a bacteriological monitoring network, knowledge of contamination spatial structures may help for further optimal sampling designs. This study aims at applying geostatistical techniques to describe and characterize the spatial structure of the fecal contamination in mussels (<ce:italic>Mytilus edulis</ce:italic>) over a given production area (Havre de la Vanlée, France), located in macrotidal waters. In comparison with common interpolation methods, the kriging estimator, a basic tool in geostatistics, presents the major advantage of providing unbiased estimates with known and minimum variances. It thus appears to be the most powerful procedure to produce contour maps for the concentration estimates and their standard deviations. Differences in mean concentration between the two conducted surveys are compared with differences in concentration found in continental inputs. Although two surveys are not sufficient to assess accurately spatio-temporal variabilities, the use of kriging reveals high spatial heterogeneity, with different structures and meteorological conditions from one survey to the other. This leads to questioning the validity of a monitoring based on a few sampling stations over a large area sampled monthly to estimate a mean bacteriological level.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>bacteriological monitoring</ce:text></ce:keyword><ce:keyword><ce:text>fecal coliforms</ce:text></ce:keyword><ce:keyword><ce:text>mussel beds</ce:text></ce:keyword><ce:keyword><ce:text>systematic sampling</ce:text></ce:keyword><ce:keyword><ce:text>spatial structure</ce:text></ce:keyword><ce:keyword><ce:text>kriging</ce:text></ce:keyword><ce:keyword><ce:text>interpolation</ce:text></ce:keyword><ce:keyword><ce:text>mapping</ce:text></ce:keyword><ce:keyword><ce:text>sampling optimization</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Applying geostatistics to identification of spatial patterns of fecal contamination in a mussel farming area (Havre de la Vanlée, France)</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Applying geostatistics to identification of spatial patterns of fecal contamination in a mussel farming area (Havre de la Vanlée, France)</title></titleInfo><name type="personal"><namePart type="given">Benoît</namePart><namePart type="family">Beliaeff</namePart><affiliation>IFREMER, Rue de l'île d'Yeu, BP 1049, 44037 Nantes Cedex 01 France</affiliation><description>Author to whom all correspondence should be addressed. 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Their concentration is measured in filter-feeding molluscs, presumed to integrate highly fluctuating quantities of these germs from the surrounding filtrated water. In the context of a bacteriological monitoring network, knowledge of contamination spatial structures may help for further optimal sampling designs. This study aims at applying geostatistical techniques to describe and characterize the spatial structure of the fecal contamination in mussels (Mytilus edulis) over a given production area (Havre de la Vanlée, France), located in macrotidal waters. In comparison with common interpolation methods, the kriging estimator, a basic tool in geostatistics, presents the major advantage of providing unbiased estimates with known and minimum variances. It thus appears to be the most powerful procedure to produce contour maps for the concentration estimates and their standard deviations. 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This leads to questioning the validity of a monitoring based on a few sampling stations over a large area sampled monthly to estimate a mean bacteriological level.</abstract><subject><genre>Keywords</genre><topic>bacteriological monitoring</topic><topic>fecal coliforms</topic><topic>mussel beds</topic><topic>systematic sampling</topic><topic>spatial structure</topic><topic>kriging</topic><topic>interpolation</topic><topic>mapping</topic><topic>sampling optimization</topic></subject><relatedItem type="host"><titleInfo><title>Water Research</title></titleInfo><titleInfo type="abbreviated"><title>WR</title></titleInfo><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">199506</dateIssued></originInfo><identifier type="ISSN">0043-1354</identifier><identifier type="PII">S0043-1354(00)X0014-0</identifier><part><date>199506</date><detail type="volume"><number>29</number><caption>vol.</caption></detail><detail type="issue"><number>6</number><caption>no.</caption></detail><extent unit="issue pages"><start>1427</start><end>1622</end></extent><extent unit="pages"><start>1541</start><end>1548</end></extent></part></relatedItem><identifier type="istex">F4889CDDB97491CF0712141AF29D5411C45C525D</identifier><identifier type="DOI">10.1016/0043-1354(94)00294-H</identifier><identifier type="PII">0043-1354(94)00294-H</identifier><recordInfo><recordContentSource>ELSEVIER</recordContentSource></recordInfo></mods></metadata><enrichments><istex:catWosTEI uri="https://api.istex.fr/document/F4889CDDB97491CF0712141AF29D5411C45C525D/enrichments/catWos"><teiHeader><profileDesc><textClass><classCode scheme="WOS">ENGINEERING, CIVIL</classCode><classCode scheme="WOS">ENGINEERING, ENVIRONMENTAL</classCode><classCode scheme="WOS">ENVIRONMENTAL SCIENCES</classCode><classCode scheme="WOS">WATER RESOURCES</classCode></textClass></profileDesc></teiHeader></istex:catWosTEI></enrichments></istex></record>Pour manipuler ce document sous Unix (Dilib)
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