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Uncertain numbers and uncertainty in the selection of input distributions--consequences for a probabilistic risk assessment of contaminated land.
University of Kalmar, School of Pure and Applied Natural Sciences.
University of Kalmar, School of Pure and Applied Natural Sciences.
University of Kalmar, School of Pure and Applied Natural Sciences.ORCID iD: 0000-0001-9382-9296
2006 (English)In: Risk Analysis, ISSN 0272-4332, E-ISSN 1539-6924, Vol. 26, no 5, p. 1363-1375Article in journal (Refereed) Published
Abstract [en]

Risks from exposure to contaminated land are often assessed with the aid of mathematical models. The current probabilistic approach is a considerable improvement on previous deterministic risk assessment practices, in that it attempts to characterize uncertainty and variability. However, some inputs continue to be assigned as precise numbers, while others are characterized as precise probability distributions. Such precision is hard to justify, and we show in this article how rounding errors and distribution assumptions can affect an exposure assessment. The outcome of traditional deterministic point estimates and Monte Carlo simulations were compared to probability bounds analyses. Assigning all scalars as imprecise numbers (intervals prescribed by significant digits) added uncertainty to the deterministic point estimate of about one order of magnitude. Similarly, representing probability distributions as probability boxes added several orders of magnitude to the uncertainty of the probabilistic estimate. This indicates that the size of the uncertainty in such assessments is actually much greater than currently reported. The article suggests that full disclosure of the uncertainty may facilitate decision making in opening up a negotiation window. In the risk analysis process, it is also an ethical obligation to clarify the boundary between the scientific and social domains.

Place, publisher, year, edition, pages
New York: Plenum Press , 2006. Vol. 26, no 5, p. 1363-1375
Keyword [en]
Distribution assumptions, Imprecise numbers, Interval analysis, Monte Carlo analysis, Probability bounds analysis
National Category
Environmental Sciences
Research subject
Environmental Science, Environmental Chemistry
Identifiers
URN: urn:nbn:se:hik:diva-1487DOI: 10.1111/j.1539-6924.2006.00808.xPubMedID: 17054537OAI: oai:DiVA.org:hik-1487DiVA, id: diva2:213756
Available from: 2009-04-28 Created: 2009-04-28 Last updated: 2017-12-13Bibliographically approved

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Bergbäck, BoÖberg, Tomas

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CiteExportLink to record
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  • apa
  • harvard1
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