Evaluating and expressing the propagation of uncertainty in chemical fate and bioaccumulation models

Authors

  • Matthew MacLeod,

    1. Canadian Environmental Modelling Centre, Environmental and Resource Studies, Trent University, Peterborough, Ontario K9J 7B8, Canada
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  • Alison J. Fraser,

    1. Canadian Environmental Modelling Centre, Environmental and Resource Studies, Trent University, Peterborough, Ontario K9J 7B8, Canada
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  • Don Mackay

    Corresponding author
    1. Canadian Environmental Modelling Centre, Environmental and Resource Studies, Trent University, Peterborough, Ontario K9J 7B8, Canada
    • Canadian Environmental Modelling Centre, Environmental and Resource Studies, Trent University, Peterborough, Ontario K9J 7B8, Canada
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Abstract

First-order analytical sensitivity and uncertainty analysis for environmental chemical fate models is described and applied to a regional contaminant fate model and a food web bioaccumulation model. By assuming linear relationships between inputs and outputs, independence, and log-normal distributions of input variables, a relationship between uncertainty in input parameters and uncertainty in output parameters can be derived, yielding results that are consistent with a Monte Carlo analysis with similar input assumptions. A graphical technique is devised for interpreting and communicating uncertainty propagation as a function of variance in input parameters and model sensitivity. The suggested approach is less calculationally intensive than Monte Carlo analysis and is appropriate for preliminary assessment of uncertainty when models are applied to generic environments or to large geographic areas or when detailed parameterization of input uncertainties is unwarranted or impossible. This approach is particularly useful as a starting point for identification of sensitive model inputs at the early stages of applying a generic contaminant fate model to a specific environmental scenario, as a tool to support refinements of the model and the uncertainty analysis for site-specific scenarios, or for examining defined end points. The analysis identifies those input parameters that contribute significantly to uncertainty in outputs, enabling attention to be focused on defining median values and more appropriate distributions to describe these variables.

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