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dc.contributor.authorArnot, Jon A
dc.contributor.authorBrown, Trevor N.
dc.contributor.authorWania, Frank
dc.contributor.authorBreivik, Knut
dc.contributor.authorMcLachlan, Michael S.
dc.date.accessioned2016-04-05T11:07:58Z
dc.date.accessioned2016-11-08T14:12:58Z
dc.date.available2016-04-05T11:07:58Z
dc.date.available2016-11-08T14:12:58Z
dc.date.issued2012
dc.identifier.citationArnot, J.A., Brown, T.N., Wania, F., Breivik, K., McLachlan, M.S. (2012). Prioritizing chemicals and data requirements for screening level exposure and risk assessment. Environmental Health Perspectives, 120, 1565-1570. doi:10.1289/ehp.1205355nb_NO
dc.identifier.issn0091-6765
dc.identifier.urihttp://hdl.handle.net/11250/2420166
dc.description-nb_NO
dc.description.abstractBackground: Scientists and regulatory agencies strive to identify chemicals that may cause harmful effects to humans and the environment; however, prioritization is challenging because of the large number of chemicals requiring evaluation and limited data and resources. Objectives: We aimed to prioritize chemicals for exposure and exposure potential and obtain a quantitative perspective on research needs to better address uncertainty in screening assessments. Methods: We used a multimedia mass balance model to prioritize > 12,000 organic chemicals using four far-field human exposure metrics. The propagation of variance (uncertainty) in key chemical information used as model input for calculating exposure metrics was quantified. Results: Modeled human concentrations and intake rates span approximately 17 and 15 orders of magnitude, respectively. Estimates of exposure potential using human concentrations and a unit emission rate span approximately 13 orders of magnitude, and intake fractions span 7 orders of magnitude. The actual chemical emission rate contributes the greatest variance (uncertainty) in exposure estimates. The human biotransformation half-life is the second greatest source of uncertainty in estimated concentrations. In general, biotransformation and biodegradation half-lives are greater sources of uncertainty in modeled exposure and exposure potential than chemical partition coefficients. Conclusions: Mechanistic exposure modeling is suitable for screening and prioritizing large numbers of chemicals. By including uncertainty analysis and uncertainty in chemical information in the exposure estimates, these methods can help identify and address the important sources of uncertainty in human exposure and risk assessment in a systematic manner.nb_NO
dc.language.isoengnb_NO
dc.relation.urihttp://ehp.niehs.nih.gov/2012/11/1205355/
dc.titlePrioritizing Chemicals and Data Requirements for Screening-Level Exposure and Risk Assessmentnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.date.updated2016-04-05T11:07:58Z
dc.source.pagenumber1565-1570nb_NO
dc.source.volume120nb_NO
dc.source.journalEnvironmental Health Perspectivesnb_NO
dc.identifier.doi10.1289/ehp.1205355
dc.identifier.cristin946951
dc.relation.projectNorges forskningsråd: 196191nb_NO


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