Distribution System Contamination Events: Exposure, Influence, and Sensitivity
This paper presents a two-part investigation of the response of municipal water distribution systems to contamination events. In Part I of the investigation, a contamination event was modeled as a steady 6-h injection of a soluble conservative substance into a single node. The injection was repeated...
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Veröffentlicht in: | Journal of water resources planning and management 2006-07, Vol.132 (4), p.283-292 |
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description | This paper presents a two-part investigation of the response of municipal water distribution systems to contamination events. In Part I of the investigation, a contamination event was modeled as a steady
6-h
injection of a soluble conservative substance into a single node. The injection was repeated node by node at all 89 nodes in the pipe network. In each case, the fraction of the population at risk of contaminant exposure was estimated at the end of a
72-h
simulation period. A dimensionless exposure index (EI) was introduced as a simple global measure of network response, ranging from
EI=0
(no consumers are exposed) to
EI=100%
(all consumers are at risk of exposure). Simulation results were used to construct a zone of influence (ZOI) map, which categorizes network injection nodes on the basis of their potential to expose downstream consumers. In Part II of the investigation, a generalized sensitivity analysis was performed to determine the sensitivity of network response to four dynamic network variables (base demand, storage capacity, injection mass, and injection duration). Latin hypercube sampling was used to set up 1,152 contamination event simulations at two injection nodes. Both nodes were selected on the basis of their exposure potential (one high, one low) as determined from the ZOI map. Based on the Kolmogorov-Smirnov
d
statistic, exposure levels in the example network were found to be most sensitive to variations in base demand and injection mass. Tank storage capacity was important in certain cases, while injection duration tended to be least important. |
doi_str_mv | 10.1061/(ASCE)0733-9496(2006)132:4(283) |
format | Article |
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6-h
injection of a soluble conservative substance into a single node. The injection was repeated node by node at all 89 nodes in the pipe network. In each case, the fraction of the population at risk of contaminant exposure was estimated at the end of a
72-h
simulation period. A dimensionless exposure index (EI) was introduced as a simple global measure of network response, ranging from
EI=0
(no consumers are exposed) to
EI=100%
(all consumers are at risk of exposure). Simulation results were used to construct a zone of influence (ZOI) map, which categorizes network injection nodes on the basis of their potential to expose downstream consumers. In Part II of the investigation, a generalized sensitivity analysis was performed to determine the sensitivity of network response to four dynamic network variables (base demand, storage capacity, injection mass, and injection duration). Latin hypercube sampling was used to set up 1,152 contamination event simulations at two injection nodes. Both nodes were selected on the basis of their exposure potential (one high, one low) as determined from the ZOI map. Based on the Kolmogorov-Smirnov
d
statistic, exposure levels in the example network were found to be most sensitive to variations in base demand and injection mass. Tank storage capacity was important in certain cases, while injection duration tended to be least important.</description><identifier>ISSN: 0733-9496</identifier><identifier>EISSN: 1943-5452</identifier><identifier>DOI: 10.1061/(ASCE)0733-9496(2006)132:4(283)</identifier><language>eng</language><publisher>American Society of Civil Engineers</publisher><subject>TECHNICAL PAPERS</subject><ispartof>Journal of water resources planning and management, 2006-07, Vol.132 (4), p.283-292</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a414t-a9ebb8069de8d8b68b2df4781bbe46d80bde9575ccbb251c12c4586ffcb1021f3</citedby><cites>FETCH-LOGICAL-a414t-a9ebb8069de8d8b68b2df4781bbe46d80bde9575ccbb251c12c4586ffcb1021f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttp://ascelibrary.org/doi/pdf/10.1061/(ASCE)0733-9496(2006)132:4(283)$$EPDF$$P50$$Gasce$$H</linktopdf><linktohtml>$$Uhttp://ascelibrary.org/doi/abs/10.1061/(ASCE)0733-9496(2006)132:4(283)$$EHTML$$P50$$Gasce$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,76162,76170</link.rule.ids></links><search><creatorcontrib>Khanal, Nabin</creatorcontrib><creatorcontrib>Buchberger, Steven G</creatorcontrib><creatorcontrib>McKenna, Sean A</creatorcontrib><title>Distribution System Contamination Events: Exposure, Influence, and Sensitivity</title><title>Journal of water resources planning and management</title><description>This paper presents a two-part investigation of the response of municipal water distribution systems to contamination events. In Part I of the investigation, a contamination event was modeled as a steady
6-h
injection of a soluble conservative substance into a single node. The injection was repeated node by node at all 89 nodes in the pipe network. In each case, the fraction of the population at risk of contaminant exposure was estimated at the end of a
72-h
simulation period. A dimensionless exposure index (EI) was introduced as a simple global measure of network response, ranging from
EI=0
(no consumers are exposed) to
EI=100%
(all consumers are at risk of exposure). Simulation results were used to construct a zone of influence (ZOI) map, which categorizes network injection nodes on the basis of their potential to expose downstream consumers. In Part II of the investigation, a generalized sensitivity analysis was performed to determine the sensitivity of network response to four dynamic network variables (base demand, storage capacity, injection mass, and injection duration). Latin hypercube sampling was used to set up 1,152 contamination event simulations at two injection nodes. Both nodes were selected on the basis of their exposure potential (one high, one low) as determined from the ZOI map. Based on the Kolmogorov-Smirnov
d
statistic, exposure levels in the example network were found to be most sensitive to variations in base demand and injection mass. Tank storage capacity was important in certain cases, while injection duration tended to be least important.</description><subject>TECHNICAL PAPERS</subject><issn>0733-9496</issn><issn>1943-5452</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNqFkV9LwzAUxYMoOKffoU-6gdX8a5vuQRi16mDow_Q5JG0KGV06k3S4b2_qZI8KF-7l8ruHyzkA3CB4h2CK7ifzVVFOYUZInNM8nWAI0ykieEYnmJHpCRihnJI4oQk-BaMjdw4unFtDCDOY4BF4fdTOWy17rzsTrfbOq01UdMaLjTbiZ1nulPFuFpVf2871Vt1GC9O0vTJVGIWpo5UyTnu9035_Cc4a0Tp19dvH4OOpfC9e4uXb86KYL2NBEfWxyJWUDKZ5rVjNZMokrhuaMSSlomnNoKxVnmRJVUmJE1QhXNGEpU1TSQQxasgYXB90t7b77JXzfKNdpdpWGNX1jmOGCMpT9C-IcpxlJNS_YHiPEZgE8OEAVrZzzqqGb63eCLvnCPIhGc6HZPjgOB8c50MyPCTDafiLBIHZQUAEfb7uemuCVcfrv4-_Ac49lJs</recordid><startdate>20060701</startdate><enddate>20060701</enddate><creator>Khanal, Nabin</creator><creator>Buchberger, Steven G</creator><creator>McKenna, Sean A</creator><general>American Society of Civil Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>7QH</scope><scope>7TV</scope><scope>7UA</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20060701</creationdate><title>Distribution System Contamination Events: Exposure, Influence, and Sensitivity</title><author>Khanal, Nabin ; Buchberger, Steven G ; McKenna, Sean A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a414t-a9ebb8069de8d8b68b2df4781bbe46d80bde9575ccbb251c12c4586ffcb1021f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>TECHNICAL PAPERS</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khanal, Nabin</creatorcontrib><creatorcontrib>Buchberger, Steven G</creatorcontrib><creatorcontrib>McKenna, Sean A</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>Aqualine</collection><collection>Pollution Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of water resources planning and management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khanal, Nabin</au><au>Buchberger, Steven G</au><au>McKenna, Sean A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distribution System Contamination Events: Exposure, Influence, and Sensitivity</atitle><jtitle>Journal of water resources planning and management</jtitle><date>2006-07-01</date><risdate>2006</risdate><volume>132</volume><issue>4</issue><spage>283</spage><epage>292</epage><pages>283-292</pages><issn>0733-9496</issn><eissn>1943-5452</eissn><abstract>This paper presents a two-part investigation of the response of municipal water distribution systems to contamination events. In Part I of the investigation, a contamination event was modeled as a steady
6-h
injection of a soluble conservative substance into a single node. The injection was repeated node by node at all 89 nodes in the pipe network. In each case, the fraction of the population at risk of contaminant exposure was estimated at the end of a
72-h
simulation period. A dimensionless exposure index (EI) was introduced as a simple global measure of network response, ranging from
EI=0
(no consumers are exposed) to
EI=100%
(all consumers are at risk of exposure). Simulation results were used to construct a zone of influence (ZOI) map, which categorizes network injection nodes on the basis of their potential to expose downstream consumers. In Part II of the investigation, a generalized sensitivity analysis was performed to determine the sensitivity of network response to four dynamic network variables (base demand, storage capacity, injection mass, and injection duration). Latin hypercube sampling was used to set up 1,152 contamination event simulations at two injection nodes. Both nodes were selected on the basis of their exposure potential (one high, one low) as determined from the ZOI map. Based on the Kolmogorov-Smirnov
d
statistic, exposure levels in the example network were found to be most sensitive to variations in base demand and injection mass. Tank storage capacity was important in certain cases, while injection duration tended to be least important.</abstract><pub>American Society of Civil Engineers</pub><doi>10.1061/(ASCE)0733-9496(2006)132:4(283)</doi><tpages>10</tpages></addata></record> |
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source | American Society of Civil Engineers:NESLI2:Journals:2014 |
subjects | TECHNICAL PAPERS |
title | Distribution System Contamination Events: Exposure, Influence, and Sensitivity |
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