Development and evaluation of a decision-supporting model for identifying the source location of microbial intrusions in real gravity sewer systems.
Identifieur interne : 000435 ( Ncbi/Merge ); précédent : 000434; suivant : 000436Development and evaluation of a decision-supporting model for identifying the source location of microbial intrusions in real gravity sewer systems.
Auteurs : Minyoung Kim [Corée du Sud] ; Christopher Y. Choi ; Charles P. GerbaSource :
- Water research [ 1879-2448 ] ; 2013.
English descriptors
- KwdEn :
- MESH :
- geographic : Arizona.
- analysis : Water Pollution.
- isolation & purification : Escherichia coli.
- Decision Support Techniques, Drainage, Sanitary, Environmental Monitoring, Geography, Gravitation, Models, Theoretical, Water Microbiology.
Abstract
Assuming a scenario of a hypothetical pathogenic outbreak, we aimed this study at developing a decision-support model for identifying the location of the pathogenic intrusion as a means of facilitating rapid detection and efficient containment. The developed model was applied to a real sewer system (the Campbell wash basin in Tucson, AZ) in order to validate its feasibility. The basin under investigation was divided into 14 sub-basins. The geometric information associated with the sewer network was digitized using GIS (Geological Information System) and imported into an urban sewer network simulation model to generate microbial breakthrough curves at the outlet. A pre-defined amount of Escherichia coli (E. coli), which is an indicator of fecal coliform bacteria, was hypothetically introduced into 56 manholes (four in each sub-basin, chosen at random), and a total of 56 breakthrough curves of E. coli were generated using the simulation model at the outlet. Transport patterns were classified depending upon the location of the injection site (manhole), various known characteristics (peak concentration and time, pipe length, travel time, etc.) extracted from each E. coli breakthrough curve and the layout of sewer network. Using this information, we back-predicted the injection location once an E. coli intrusion was detected at a monitoring site using Artificial Neural Networks (ANNs). The results showed that ANNs identified the location of the injection sites with 57% accuracy; ANNs correctly recognized eight out of fourteen expressions with relying on data from a single detection sensor. Increasing the available sensors within the basin significantly improved the accuracy of the simulation results (from 57% to 100%).
DOI: 10.1016/j.watres.2013.04.018
PubMed: 23770478
Links toward previous steps (curation, corpus...)
- to stream PubMed, to step Corpus: 000168
- to stream PubMed, to step Curation: 000168
- to stream PubMed, to step Checkpoint: 000168
Links to Exploration step
pubmed:23770478Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Development and evaluation of a decision-supporting model for identifying the source location of microbial intrusions in real gravity sewer systems.</title><author><name sortKey="Kim, Minyoung" sort="Kim, Minyoung" uniqKey="Kim M" first="Minyoung" last="Kim">Minyoung Kim</name><affiliation wicri:level="1"><nlm:affiliation>National Academy of Agricultural Science, Rural Development Administration, 249 Seodun-dong, Gwonson-gu, Suwon 441-707, Republic of Korea. mykim75@korea.kr</nlm:affiliation><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>National Academy of Agricultural Science, Rural Development Administration, 249 Seodun-dong, Gwonson-gu, Suwon 441-707</wicri:regionArea><wicri:noRegion>Suwon 441-707</wicri:noRegion></affiliation></author><author><name sortKey="Choi, Christopher Y" sort="Choi, Christopher Y" uniqKey="Choi C" first="Christopher Y" last="Choi">Christopher Y. Choi</name></author><author><name sortKey="Gerba, Charles P" sort="Gerba, Charles P" uniqKey="Gerba C" first="Charles P" last="Gerba">Charles P. Gerba</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="RBID">pubmed:23770478</idno><idno type="pmid">23770478</idno><idno type="doi">10.1016/j.watres.2013.04.018</idno><idno type="wicri:Area/PubMed/Corpus">000168</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000168</idno><idno type="wicri:Area/PubMed/Curation">000168</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000168</idno><idno type="wicri:Area/PubMed/Checkpoint">000168</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000168</idno><idno type="wicri:Area/Ncbi/Merge">000435</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Development and evaluation of a decision-supporting model for identifying the source location of microbial intrusions in real gravity sewer systems.</title><author><name sortKey="Kim, Minyoung" sort="Kim, Minyoung" uniqKey="Kim M" first="Minyoung" last="Kim">Minyoung Kim</name><affiliation wicri:level="1"><nlm:affiliation>National Academy of Agricultural Science, Rural Development Administration, 249 Seodun-dong, Gwonson-gu, Suwon 441-707, Republic of Korea. mykim75@korea.kr</nlm:affiliation><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>National Academy of Agricultural Science, Rural Development Administration, 249 Seodun-dong, Gwonson-gu, Suwon 441-707</wicri:regionArea><wicri:noRegion>Suwon 441-707</wicri:noRegion></affiliation></author><author><name sortKey="Choi, Christopher Y" sort="Choi, Christopher Y" uniqKey="Choi C" first="Christopher Y" last="Choi">Christopher Y. Choi</name></author><author><name sortKey="Gerba, Charles P" sort="Gerba, Charles P" uniqKey="Gerba C" first="Charles P" last="Gerba">Charles P. Gerba</name></author></analytic><series><title level="j">Water research</title><idno type="eISSN">1879-2448</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Arizona</term><term>Decision Support Techniques</term><term>Drainage, Sanitary</term><term>Environmental Monitoring</term><term>Escherichia coli (isolation & purification)</term><term>Geography</term><term>Gravitation</term><term>Models, Theoretical</term><term>Water Microbiology</term><term>Water Pollution (analysis)</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>Arizona</term></keywords><keywords scheme="MESH" qualifier="analysis" xml:lang="en"><term>Water Pollution</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Escherichia coli</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Decision Support Techniques</term><term>Drainage, Sanitary</term><term>Environmental Monitoring</term><term>Geography</term><term>Gravitation</term><term>Models, Theoretical</term><term>Water Microbiology</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Assuming a scenario of a hypothetical pathogenic outbreak, we aimed this study at developing a decision-support model for identifying the location of the pathogenic intrusion as a means of facilitating rapid detection and efficient containment. The developed model was applied to a real sewer system (the Campbell wash basin in Tucson, AZ) in order to validate its feasibility. The basin under investigation was divided into 14 sub-basins. The geometric information associated with the sewer network was digitized using GIS (Geological Information System) and imported into an urban sewer network simulation model to generate microbial breakthrough curves at the outlet. A pre-defined amount of Escherichia coli (E. coli), which is an indicator of fecal coliform bacteria, was hypothetically introduced into 56 manholes (four in each sub-basin, chosen at random), and a total of 56 breakthrough curves of E. coli were generated using the simulation model at the outlet. Transport patterns were classified depending upon the location of the injection site (manhole), various known characteristics (peak concentration and time, pipe length, travel time, etc.) extracted from each E. coli breakthrough curve and the layout of sewer network. Using this information, we back-predicted the injection location once an E. coli intrusion was detected at a monitoring site using Artificial Neural Networks (ANNs). The results showed that ANNs identified the location of the injection sites with 57% accuracy; ANNs correctly recognized eight out of fourteen expressions with relying on data from a single detection sensor. Increasing the available sensors within the basin significantly improved the accuracy of the simulation results (from 57% to 100%).</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23770478</PMID><DateCreated><Year>2013</Year><Month>08</Month><Day>05</Day></DateCreated><DateCompleted><Year>2014</Year><Month>02</Month><Day>25</Day></DateCompleted><DateRevised><Year>2013</Year><Month>08</Month><Day>05</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1879-2448</ISSN><JournalIssue CitedMedium="Internet"><Volume>47</Volume><Issue>13</Issue><PubDate><Year>2013</Year><Month>Sep</Month><Day>01</Day></PubDate></JournalIssue><Title>Water research</Title><ISOAbbreviation>Water Res.</ISOAbbreviation></Journal><ArticleTitle>Development and evaluation of a decision-supporting model for identifying the source location of microbial intrusions in real gravity sewer systems.</ArticleTitle><Pagination><MedlinePgn>4630-8</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.watres.2013.04.018</ELocationID><ELocationID EIdType="pii" ValidYN="Y">S0043-1354(13)00347-3</ELocationID><Abstract><AbstractText>Assuming a scenario of a hypothetical pathogenic outbreak, we aimed this study at developing a decision-support model for identifying the location of the pathogenic intrusion as a means of facilitating rapid detection and efficient containment. The developed model was applied to a real sewer system (the Campbell wash basin in Tucson, AZ) in order to validate its feasibility. The basin under investigation was divided into 14 sub-basins. The geometric information associated with the sewer network was digitized using GIS (Geological Information System) and imported into an urban sewer network simulation model to generate microbial breakthrough curves at the outlet. A pre-defined amount of Escherichia coli (E. coli), which is an indicator of fecal coliform bacteria, was hypothetically introduced into 56 manholes (four in each sub-basin, chosen at random), and a total of 56 breakthrough curves of E. coli were generated using the simulation model at the outlet. Transport patterns were classified depending upon the location of the injection site (manhole), various known characteristics (peak concentration and time, pipe length, travel time, etc.) extracted from each E. coli breakthrough curve and the layout of sewer network. Using this information, we back-predicted the injection location once an E. coli intrusion was detected at a monitoring site using Artificial Neural Networks (ANNs). The results showed that ANNs identified the location of the injection sites with 57% accuracy; ANNs correctly recognized eight out of fourteen expressions with relying on data from a single detection sensor. Increasing the available sensors within the basin significantly improved the accuracy of the simulation results (from 57% to 100%).</AbstractText><CopyrightInformation>Copyright © 2013 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kim</LastName><ForeName>Minyoung</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>National Academy of Agricultural Science, Rural Development Administration, 249 Seodun-dong, Gwonson-gu, Suwon 441-707, Republic of Korea. mykim75@korea.kr</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Choi</LastName><ForeName>Christopher Y</ForeName><Initials>CY</Initials></Author><Author ValidYN="Y"><LastName>Gerba</LastName><ForeName>Charles P</ForeName><Initials>CP</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>2013</Year><Month>04</Month><Day>22</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Water Res</MedlineTA><NlmUniqueID>0105072</NlmUniqueID><ISSNLinking>0043-1354</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D001130" MajorTopicYN="N" Type="Geographic">Arizona</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003661" MajorTopicYN="Y">Decision Support Techniques</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004323" MajorTopicYN="Y">Drainage, Sanitary</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="Y">Environmental Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004926" MajorTopicYN="N">Escherichia coli</DescriptorName><QualifierName UI="Q000302" MajorTopicYN="Y">isolation & purification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005843" MajorTopicYN="N">Geography</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006112" MajorTopicYN="Y">Gravitation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="Y">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014871" MajorTopicYN="Y">Water Microbiology</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014876" MajorTopicYN="N">Water Pollution</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Artificial Neural Networks</Keyword><Keyword MajorTopicYN="N">Microbial intrusion</Keyword><Keyword MajorTopicYN="N">Sewer system</Keyword><Keyword MajorTopicYN="N">Source identification</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2012</Year><Month>08</Month><Day>02</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2013</Year><Month>04</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2013</Year><Month>04</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>6</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>6</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>2</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">23770478</ArticleId><ArticleId IdType="pii">S0043-1354(13)00347-3</ArticleId><ArticleId IdType="doi">10.1016/j.watres.2013.04.018</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Corée du Sud</li></country></list><tree><noCountry><name sortKey="Choi, Christopher Y" sort="Choi, Christopher Y" uniqKey="Choi C" first="Christopher Y" last="Choi">Christopher Y. Choi</name><name sortKey="Gerba, Charles P" sort="Gerba, Charles P" uniqKey="Gerba C" first="Charles P" last="Gerba">Charles P. Gerba</name></noCountry><country name="Corée du Sud"><noRegion><name sortKey="Kim, Minyoung" sort="Kim, Minyoung" uniqKey="Kim M" first="Minyoung" last="Kim">Minyoung Kim</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Agronomie/explor/SisAgriV1/Data/Ncbi/Merge
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000435 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Ncbi/Merge/biblio.hfd -nk 000435 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Agronomie
|area= SisAgriV1
|flux= Ncbi
|étape= Merge
|type= RBID
|clé= pubmed:23770478
|texte= Development and evaluation of a decision-supporting model for identifying the source location of microbial intrusions in real gravity sewer systems.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Ncbi/Merge/RBID.i -Sk "pubmed:23770478" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Ncbi/Merge/biblio.hfd \
| NlmPubMed2Wicri -a SisAgriV1
| This area was generated with Dilib version V0.6.28. |  |