Assessing groundwater vulnerability to agrichemical contamination in the Midwest US
Identifieur interne : 000D03 ( Istex/Corpus ); précédent : 000D02; suivant : 000D04Assessing groundwater vulnerability to agrichemical contamination in the Midwest US
Auteurs : Michael R. Burkart ; Dana W. Kolpin ; David E. JamesSource :
- Water Science and Technology [ 0273-1223 ] ; 1999.
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Abstract
Agrichemicals (herbicides and nitrate) are significant sources of diffuse pollution to groundwater. Indirect methods are needed to assess the potential for groundwater contamination by diffuse sources because groundwater monitoring is too costly to adequately define the geographic extent of contamination at a regional or national scale. This paper presents examples of the application of statistical, overlay and index, and process-based modeling methods for groundwater vulnerability assessments to a variety of data from the Midwest U.S. The principles for vulnerability assessment include both intrinsic (pedologic, climatologic, and hydrogeologic factors) and specific (contaminant and other anthropogenic factors) vulnerability of a location. Statistical methods use the frequency of contaminant occurrence, contaminant concentration, or contamination probability as a response variable. Statistical assessments are useful for defining the relations among explanatory and response variables whether they define intrinsic or specific vulnerability. Multivariate statistical analyses are useful for ranking variables critical to estimating water quality responses of interest. Overlay and index methods involve intersecting maps of intrinsic and specific vulnerability properties and indexing the variables by applying appropriate weights. Deterministic models use process-based equations to simulate contaminant transport and are distinguished from the other methods in their potential to predict contaminant transport in both space and time. An example of a one-dimensional leaching model linked to a geographic information system (GIS) to define a regional metamodel for contamination in the Midwest is included.
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DOI: 10.1016/S0273-1223(99)00042-6
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Burkart</name><affiliation><mods:affiliation>National Soil Tilth Laboratory, 2150 Pammel Drive, Ames, IA 50011, USA</mods:affiliation></affiliation></author><author><name sortKey="Kolpin, Dana W" sort="Kolpin, Dana W" uniqKey="Kolpin D" first="Dana W." last="Kolpin">Dana W. Kolpin</name><affiliation><mods:affiliation>US Geological Survey, 400 South Clinton Street, Iowa City, IA 52244, USA</mods:affiliation></affiliation></author><author><name sortKey="James, David E" sort="James, David E" uniqKey="James D" first="David E." last="James">David E. James</name><affiliation><mods:affiliation>National Soil Tilth Laboratory, 2150 Pammel Drive, Ames, IA 50011, USA</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Water Science and Technology</title><title level="j" type="abbrev">JWST</title><idno type="ISSN">0273-1223</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1999">1999</date><biblScope unit="volume">39</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="103">103</biblScope><biblScope unit="page" to="112">112</biblScope></imprint><idno type="ISSN">0273-1223</idno></series><idno type="istex">7D440E897E4745001A0BA6E71DF3142C83DB57E5</idno><idno type="DOI">10.1016/S0273-1223(99)00042-6</idno><idno type="PII">S0273-1223(99)00042-6</idno><idno type="ArticleID">99000426</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0273-1223</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>GIS</term><term>Groundwater</term><term>agriculture</term><term>aquifers</term><term>nitrate</term><term>pesticides</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Agrichemicals (herbicides and nitrate) are significant sources of diffuse pollution to groundwater. Indirect methods are needed to assess the potential for groundwater contamination by diffuse sources because groundwater monitoring is too costly to adequately define the geographic extent of contamination at a regional or national scale. This paper presents examples of the application of statistical, overlay and index, and process-based modeling methods for groundwater vulnerability assessments to a variety of data from the Midwest U.S. The principles for vulnerability assessment include both intrinsic (pedologic, climatologic, and hydrogeologic factors) and specific (contaminant and other anthropogenic factors) vulnerability of a location. Statistical methods use the frequency of contaminant occurrence, contaminant concentration, or contamination probability as a response variable. Statistical assessments are useful for defining the relations among explanatory and response variables whether they define intrinsic or specific vulnerability. Multivariate statistical analyses are useful for ranking variables critical to estimating water quality responses of interest. Overlay and index methods involve intersecting maps of intrinsic and specific vulnerability properties and indexing the variables by applying appropriate weights. Deterministic models use process-based equations to simulate contaminant transport and are distinguished from the other methods in their potential to predict contaminant transport in both space and time. An example of a one-dimensional leaching model linked to a geographic information system (GIS) to define a regional metamodel for contamination in the Midwest is included.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>Michael R. Burkart</name><affiliations><json:string>National Soil Tilth Laboratory, 2150 Pammel Drive, Ames, IA 50011, USA</json:string></affiliations></json:item><json:item><name>Dana W. 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Indirect methods are needed to assess the potential for groundwater contamination by diffuse sources because groundwater monitoring is too costly to adequately define the geographic extent of contamination at a regional or national scale. This paper presents examples of the application of statistical, overlay and index, and process-based modeling methods for groundwater vulnerability assessments to a variety of data from the Midwest U.S. The principles for vulnerability assessment include both intrinsic (pedologic, climatologic, and hydrogeologic factors) and specific (contaminant and other anthropogenic factors) vulnerability of a location. Statistical methods use the frequency of contaminant occurrence, contaminant concentration, or contamination probability as a response variable. Statistical assessments are useful for defining the relations among explanatory and response variables whether they define intrinsic or specific vulnerability. Multivariate statistical analyses are useful for ranking variables critical to estimating water quality responses of interest. Overlay and index methods involve intersecting maps of intrinsic and specific vulnerability properties and indexing the variables by applying appropriate weights. Deterministic models use process-based equations to simulate contaminant transport and are distinguished from the other methods in their potential to predict contaminant transport in both space and time. 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Indirect methods are needed to assess the potential for groundwater contamination by diffuse sources because groundwater monitoring is too costly to adequately define the geographic extent of contamination at a regional or national scale. This paper presents examples of the application of statistical, overlay and index, and process-based modeling methods for groundwater vulnerability assessments to a variety of data from the Midwest U.S. The principles for vulnerability assessment include both intrinsic (pedologic, climatologic, and hydrogeologic factors) and specific (contaminant and other anthropogenic factors) vulnerability of a location. Statistical methods use the frequency of contaminant occurrence, contaminant concentration, or contamination probability as a response variable. Statistical assessments are useful for defining the relations among explanatory and response variables whether they define intrinsic or specific vulnerability. Multivariate statistical analyses are useful for ranking variables critical to estimating water quality responses of interest. Overlay and index methods involve intersecting maps of intrinsic and specific vulnerability properties and indexing the variables by applying appropriate weights. Deterministic models use process-based equations to simulate contaminant transport and are distinguished from the other methods in their potential to predict contaminant transport in both space and time. An example of a one-dimensional leaching model linked to a geographic information system (GIS) to define a regional metamodel for contamination in the Midwest is included.</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Keywords</head><item><term>Groundwater</term></item><item><term>nitrate</term></item><item><term>pesticides</term></item><item><term>aquifers</term></item><item><term>agriculture</term></item><item><term>GIS</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1999">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/7D440E897E4745001A0BA6E71DF3142C83DB57E5/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier doc found" wicri:toSee="Elsevier, no converted or simple article"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" </istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 5.1.0//EN//XML" URI="art510.dtd" name="istex:docType"></istex:docType><istex:document><article version="5.1" xml:lang="en" docsubtype="fla"><item-info><jid>JWST</jid><aid>99000426</aid><ce:pii>S0273-1223(99)00042-6</ce:pii><ce:doi>10.1016/S0273-1223(99)00042-6</ce:doi><ce:copyright type="other" year="1999">International Association on Water Quality</ce:copyright></item-info><head><ce:title>Assessing groundwater vulnerability to agrichemical contamination in the Midwest US</ce:title><ce:author-group><ce:author><ce:given-name>Michael R.</ce:given-name><ce:surname>Burkart</ce:surname><ce:cross-ref refid="aff1">*</ce:cross-ref></ce:author><ce:author><ce:given-name>Dana W.</ce:given-name><ce:surname>Kolpin</ce:surname><ce:cross-ref refid="aff2">**</ce:cross-ref></ce:author><ce:author><ce:given-name>David E.</ce:given-name><ce:surname>James</ce:surname><ce:cross-ref refid="aff1">*</ce:cross-ref></ce:author><ce:affiliation id="aff1"><ce:label>*</ce:label><ce:textfn>National Soil Tilth Laboratory, 2150 Pammel Drive, Ames, IA 50011, USA</ce:textfn></ce:affiliation><ce:affiliation id="aff2"><ce:label>**</ce:label><ce:textfn>US Geological Survey, 400 South Clinton Street, Iowa City, IA 52244, USA</ce:textfn></ce:affiliation></ce:author-group><ce:abstract id="ab1"><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para id="SP0005">Agrichemicals (herbicides and nitrate) are significant sources of diffuse pollution to groundwater. Indirect methods are needed to assess the potential for groundwater contamination by diffuse sources because groundwater monitoring is too costly to adequately define the geographic extent of contamination at a regional or national scale. This paper presents examples of the application of statistical, overlay and index, and process-based modeling methods for groundwater vulnerability assessments to a variety of data from the Midwest U.S. The principles for vulnerability assessment include both intrinsic (pedologic, climatologic, and hydrogeologic factors) and specific (contaminant and other anthropogenic factors) vulnerability of a location. Statistical methods use the frequency of contaminant occurrence, contaminant concentration, or contamination probability as a response variable. Statistical assessments are useful for defining the relations among explanatory and response variables whether they define intrinsic or specific vulnerability. Multivariate statistical analyses are useful for ranking variables critical to estimating water quality responses of interest. Overlay and index methods involve intersecting maps of intrinsic and specific vulnerability properties and indexing the variables by applying appropriate weights. Deterministic models use process-based equations to simulate contaminant transport and are distinguished from the other methods in their potential to predict contaminant transport in both space and time. An example of a one-dimensional leaching model linked to a geographic information system (GIS) to define a regional metamodel for contamination in the Midwest is included.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="keyword"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Groundwater</ce:text></ce:keyword><ce:keyword><ce:text>nitrate</ce:text></ce:keyword><ce:keyword><ce:text>pesticides</ce:text></ce:keyword><ce:keyword><ce:text>aquifers</ce:text></ce:keyword><ce:keyword><ce:text>agriculture</ce:text></ce:keyword><ce:keyword><ce:text>GIS</ce:text></ce:keyword></ce:keywords></head><tail><ce:bibliography id="R0005"><ce:section-title>References</ce:section-title><ce:bibliography-sec id="RS0005"><ce:bib-reference id="bib1"><ce:label>Aller et al., 1987</ce:label><sb:reference><sb:contribution><sb:authors><sb:author><ce:given-name>L.</ce:given-name><ce:surname>Aller</ce:surname></sb:author><sb:author><ce:given-name>T.</ce:given-name><ce:surname>Bennett</ce:surname></sb:author><sb:author><ce:given-name>J.H.</ce:given-name><ce:surname>Lehr</ce:surname></sb:author><sb:author><ce:given-name>R.J.</ce:given-name><ce:surname>Petty</ce:surname></sb:author><sb:author><ce:given-name>G.</ce:given-name><ce:surname>Hackett</ce:surname></sb:author></sb:authors><sb:title><sb:maintitle>DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings</sb:maintitle></sb:title></sb:contribution><sb:host><sb:edited-book><sb:title><sb:maintitle>EPA-600/2-87-035</sb:maintitle></sb:title><sb:date>1987</sb:date><sb:publisher><sb:name>U.S. Environmental Protection Agency</sb:name><sb:location>Ada, Oklahoma</sb:location></sb:publisher></sb:edited-book></sb:host></sb:reference></ce:bib-reference><ce:bib-reference id="bib2"><ce:label>Biradar and Rayburn, 1995</ce:label><sb:reference><sb:contribution><sb:authors><sb:author><ce:given-name>D.P.</ce:given-name><ce:surname>Biradar</ce:surname></sb:author><sb:author><ce:given-name>A.L.</ce:given-name><ce:surname>Rayburn</ce:surname></sb:author></sb:authors><sb:title><sb:maintitle>Chromosomal damage induced by herbicide contamination at concentrations observed in public water supplies</sb:maintitle></sb:title></sb:contribution><sb:host><sb:issue><sb:series><sb:title><sb:maintitle>J. 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Indirect methods are needed to assess the potential for groundwater contamination by diffuse sources because groundwater monitoring is too costly to adequately define the geographic extent of contamination at a regional or national scale. This paper presents examples of the application of statistical, overlay and index, and process-based modeling methods for groundwater vulnerability assessments to a variety of data from the Midwest U.S. The principles for vulnerability assessment include both intrinsic (pedologic, climatologic, and hydrogeologic factors) and specific (contaminant and other anthropogenic factors) vulnerability of a location. Statistical methods use the frequency of contaminant occurrence, contaminant concentration, or contamination probability as a response variable. Statistical assessments are useful for defining the relations among explanatory and response variables whether they define intrinsic or specific vulnerability. Multivariate statistical analyses are useful for ranking variables critical to estimating water quality responses of interest. Overlay and index methods involve intersecting maps of intrinsic and specific vulnerability properties and indexing the variables by applying appropriate weights. Deterministic models use process-based equations to simulate contaminant transport and are distinguished from the other methods in their potential to predict contaminant transport in both space and time. An example of a one-dimensional leaching model linked to a geographic information system (GIS) to define a regional metamodel for contamination in the Midwest is included.</abstract><subject lang="en"><genre>Keywords</genre><topic>Groundwater</topic><topic>nitrate</topic><topic>pesticides</topic><topic>aquifers</topic><topic>agriculture</topic><topic>GIS</topic></subject><relatedItem type="host"><titleInfo><title>Water Science and Technology</title></titleInfo><titleInfo type="abbreviated"><title>JWST</title></titleInfo><name type="conference"><namePart>Integrated Management of Water Quality: The Role of Agricultural Diffuse Pollution Sources, Teolo, Padua, Italy</namePart><namePart type="date">19970511</namePart><namePart type="date">19970516</namePart></name><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">1999</dateIssued></originInfo><identifier type="ISSN">0273-1223</identifier><identifier type="PII">S0273-1223(00)X0003-0</identifier><part><date>1999</date><detail type="issue"><title>Integrated Management of Water Quality: The Role of Agricultural Diffuse Pollution Sources, Teolo, Padua, Italy</title></detail><detail type="volume"><number>39</number><caption>vol.</caption></detail><detail type="issue"><number>3</number><caption>no.</caption></detail><extent unit="issue pages"><start>1</start><end>157</end></extent><extent unit="pages"><start>103</start><end>112</end></extent></part></relatedItem><identifier type="istex">7D440E897E4745001A0BA6E71DF3142C83DB57E5</identifier><identifier type="DOI">10.1016/S0273-1223(99)00042-6</identifier><identifier type="PII">S0273-1223(99)00042-6</identifier><identifier type="ArticleID">99000426</identifier><accessCondition type="use and reproduction" contentType="copyright">©1999 International Association on Water Quality</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>International Association on Water Quality, ©1999</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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|étape= Corpus
|type= RBID
|clé= ISTEX:7D440E897E4745001A0BA6E71DF3142C83DB57E5
|texte= Assessing groundwater vulnerability to agrichemical contamination in the Midwest US
}}
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