Trace Element Analysis, Model-Based Clustering and Flushing to Prevent Drinking Water Contamination in Public Schools

Detalhes bibliográficos
Autor(a) principal: Carter,Jake A.
Data de Publicação: 2019
Outros Autores: Jones,Bradley T., Donati,George L.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Journal of the Brazilian Chemical Society (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532019000300462
Resumo: Drinking water samples taken from cafeteria sinks and water fountains in each of the 76 schools in the Winston-Salem/Forsyth County Schools (WSFCS) district (North Carolina, United States) were analyzed by inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) to determine As, Cd, Cr, Cu, Pb, Sb, Se and Tl. All samples from currently active schools tested below the maximum contaminant level (MCL) set for each element. Model-based clustering was employed to identify schools more prone to drinking water contamination. This multivariate approach may be used in a prevention program that can be tailored to specific school districts, with each school tested at a frequency compatible with its contamination risk level. Water flow stagnation during the summer break results in higher elemental concentrations in school drinking water, but a simple 5-60 min flushing procedure significantly reduces the contamination levels.
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spelling Trace Element Analysis, Model-Based Clustering and Flushing to Prevent Drinking Water Contamination in Public Schoolsdrinking water contaminationICP-MSmultivariate analysiswater flow stagnationpreventive flushingDrinking water samples taken from cafeteria sinks and water fountains in each of the 76 schools in the Winston-Salem/Forsyth County Schools (WSFCS) district (North Carolina, United States) were analyzed by inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) to determine As, Cd, Cr, Cu, Pb, Sb, Se and Tl. All samples from currently active schools tested below the maximum contaminant level (MCL) set for each element. Model-based clustering was employed to identify schools more prone to drinking water contamination. This multivariate approach may be used in a prevention program that can be tailored to specific school districts, with each school tested at a frequency compatible with its contamination risk level. Water flow stagnation during the summer break results in higher elemental concentrations in school drinking water, but a simple 5-60 min flushing procedure significantly reduces the contamination levels.Sociedade Brasileira de Química2019-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532019000300462Journal of the Brazilian Chemical Society v.30 n.3 2019reponame:Journal of the Brazilian Chemical Society (Online)instname:Sociedade Brasileira de Química (SBQ)instacron:SBQ10.21577/0103-5053.20180199info:eu-repo/semantics/openAccessCarter,Jake A.Jones,Bradley T.Donati,George L.eng2019-02-14T00:00:00Zoai:scielo:S0103-50532019000300462Revistahttp://jbcs.sbq.org.brONGhttps://old.scielo.br/oai/scielo-oai.php||office@jbcs.sbq.org.br1678-47900103-5053opendoar:2019-02-14T00:00Journal of the Brazilian Chemical Society (Online) - Sociedade Brasileira de Química (SBQ)false
dc.title.none.fl_str_mv Trace Element Analysis, Model-Based Clustering and Flushing to Prevent Drinking Water Contamination in Public Schools
title Trace Element Analysis, Model-Based Clustering and Flushing to Prevent Drinking Water Contamination in Public Schools
spellingShingle Trace Element Analysis, Model-Based Clustering and Flushing to Prevent Drinking Water Contamination in Public Schools
Carter,Jake A.
drinking water contamination
ICP-MS
multivariate analysis
water flow stagnation
preventive flushing
title_short Trace Element Analysis, Model-Based Clustering and Flushing to Prevent Drinking Water Contamination in Public Schools
title_full Trace Element Analysis, Model-Based Clustering and Flushing to Prevent Drinking Water Contamination in Public Schools
title_fullStr Trace Element Analysis, Model-Based Clustering and Flushing to Prevent Drinking Water Contamination in Public Schools
title_full_unstemmed Trace Element Analysis, Model-Based Clustering and Flushing to Prevent Drinking Water Contamination in Public Schools
title_sort Trace Element Analysis, Model-Based Clustering and Flushing to Prevent Drinking Water Contamination in Public Schools
author Carter,Jake A.
author_facet Carter,Jake A.
Jones,Bradley T.
Donati,George L.
author_role author
author2 Jones,Bradley T.
Donati,George L.
author2_role author
author
dc.contributor.author.fl_str_mv Carter,Jake A.
Jones,Bradley T.
Donati,George L.
dc.subject.por.fl_str_mv drinking water contamination
ICP-MS
multivariate analysis
water flow stagnation
preventive flushing
topic drinking water contamination
ICP-MS
multivariate analysis
water flow stagnation
preventive flushing
description Drinking water samples taken from cafeteria sinks and water fountains in each of the 76 schools in the Winston-Salem/Forsyth County Schools (WSFCS) district (North Carolina, United States) were analyzed by inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) to determine As, Cd, Cr, Cu, Pb, Sb, Se and Tl. All samples from currently active schools tested below the maximum contaminant level (MCL) set for each element. Model-based clustering was employed to identify schools more prone to drinking water contamination. This multivariate approach may be used in a prevention program that can be tailored to specific school districts, with each school tested at a frequency compatible with its contamination risk level. Water flow stagnation during the summer break results in higher elemental concentrations in school drinking water, but a simple 5-60 min flushing procedure significantly reduces the contamination levels.
publishDate 2019
dc.date.none.fl_str_mv 2019-03-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532019000300462
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532019000300462
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.21577/0103-5053.20180199
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Química
publisher.none.fl_str_mv Sociedade Brasileira de Química
dc.source.none.fl_str_mv Journal of the Brazilian Chemical Society v.30 n.3 2019
reponame:Journal of the Brazilian Chemical Society (Online)
instname:Sociedade Brasileira de Química (SBQ)
instacron:SBQ
instname_str Sociedade Brasileira de Química (SBQ)
instacron_str SBQ
institution SBQ
reponame_str Journal of the Brazilian Chemical Society (Online)
collection Journal of the Brazilian Chemical Society (Online)
repository.name.fl_str_mv Journal of the Brazilian Chemical Society (Online) - Sociedade Brasileira de Química (SBQ)
repository.mail.fl_str_mv ||office@jbcs.sbq.org.br
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