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  • 1.
    Filipsson, Monika
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Öberg, Tomas
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Bergbäck, Bo
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Variability and uncertainty in Swedish exposure factors for use in quantitative exposure assessments2011In: Risk Analysis, ISSN 0272-4332, E-ISSN 1539-6924, Vol. 31, no 1, p. 108-119Article in journal (Refereed)
    Abstract [en]

    Information of exposure factors used in quantitative risk assessments has previously been compiled and reported for U.S. and European populations. However, due to the advancement of science and knowledge, these reports are in continuous need of updating with new data. Equally important is the change over time of many exposure factors related to both physiological characteristics and human behavior. Body weight, skin surface, time use, and dietary habits are some of the most obvious examples covered here. A wealth of data is available from literature not primarily gathered for the purpose of risk assessment. Here we review a number of key exposure factors and compare these factors between northern Europe—here represented by Sweden—and the United States. Many previous compilations of exposure factor data focus on interindividual variability and variability between sexes and age groups, while uncertainty is mainly dealt with in a qualitative way. In this article variability is assessed along with uncertainty. As estimates of central tendency and interindividual variability, mean, standard deviation, skewness, kurtosis, and multiple percentiles were calculated, while uncertainty was characterized using 95% confidence intervals for these parameters. The presented statistics are appropriate for use in deterministic analyses using point estimates for each input parameter as well as in probabilistic assessments.

  • 2.
    Iqbal, Muhammad Sarfraz
    et al.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Öberg, Tomas
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Description and Propagation of Uncertainty in Input Parameters in Environmental Fate Models2013In: Risk Analysis, ISSN 0272-4332, E-ISSN 1539-6924, Vol. 33, no 7, p. 1353-66Article in journal (Refereed)
    Abstract [en]

    Today, chemical risk and safety assessments rely heavily on the estimation of environmental fate by models. The key compound-related properties in such models describe partitioning and reactivity. Uncertainty in determining these properties can be separated into random and systematic (incompleteness) components, requiring different types of representation. Here, we evaluate two approaches that are suitable to treat also systematic errors, fuzzy arithmetic, and probability bounds analysis. When a best estimate (mode) and a range can be computed for an input parameter, then it is possible to characterize the uncertainty with a triangular fuzzy number (possibility distribution) or a corresponding probability box bound by two uniform distributions. We use a five-compartment Level I fugacity model and reported empirical data from the literature for three well-known environmental pollutants (benzene, pyrene, and DDT) as illustrative cases for this evaluation. Propagation of uncertainty by discrete probability calculus or interval arithmetic can be done at a low computational cost and gives maximum flexibility in applying different approaches. Our evaluation suggests that the difference between fuzzy arithmetic and probability bounds analysis is small, at least for this specific case. The fuzzy arithmetic approach can, however, be regarded as less conservative than probability bounds analysis if the assumption of independence is removed. Both approaches are sensitive to repeated parameters that may inflate the uncertainty estimate. Uncertainty described by probability boxes was therefore also propagated through the model by Monte Carlo simulation to show how this problem can be avoided.

  • 3.
    Sander, Per
    et al.
    University of Kalmar, School of Pure and Applied Natural Sciences.
    Bergbäck, Bo
    University of Kalmar, School of Pure and Applied Natural Sciences.
    Öberg, Tomas
    University of Kalmar, School of Pure and Applied Natural Sciences.
    Uncertain numbers and uncertainty in the selection of input distributions--consequences for a probabilistic risk assessment of contaminated land.2006In: Risk Analysis, ISSN 0272-4332, E-ISSN 1539-6924, Vol. 26, no 5, p. 1363-1375Article in journal (Refereed)
    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.

  • 4.
    Öberg, Tomas
    University of Kalmar, School of Pure and Applied Natural Sciences.
    Book Review:: Risk Management in Post-Trust Societies2006In: Risk Analysis, ISSN 0272-4332, E-ISSN 1539-6924, Vol. 26, no 3, p. 859-861Article, book review (Other academic)
  • 5.
    Öberg, Tomas
    University of Kalmar, School of Pure and Applied Natural Sciences.
    Book Review:: Risks and Decisions for Conservation and Environmental Management2005In: Risk Analysis, ISSN 0272-4332, E-ISSN 1539-6924, Vol. 25, no 6, p. 1685-1687Article, book review (Other academic)
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