Abstract
A reliable characterisation of uncertainties can aid uncertainty identification during environmental risk assessments (ERAs). However, typologies can be implemented inconsistently, causing uncertainties to go unidentified. We present an approach based on nine structured elicitations, in which subject-matter experts, for pesticide risks to surface water organisms, validate and assess three dimensions of uncertainty: its level (the severity of uncertainty, ranging from determinism to ignorance); nature (whether the uncertainty is epistemic or aleatory); and location (the data source or area in which the uncertainty arises). Risk characterisation contains the highest median levels of uncertainty, associated with estimating, aggregating and evaluating the magnitude of risks. Regarding the locations in which uncertainty is manifest, data uncertainty is dominant in problem formulation, exposure assessment and effects assessment. The comprehensive description of uncertainty described will enable risk analysts to prioritise the required phases, groups of tasks, or individual tasks within a risk analysis according to the highest levels of uncertainty, the potential for uncertainty to be reduced or quantified, or the types of location-based uncertainty, thus aiding uncertainty prioritisation during environmental risk assessments. In turn, it is expected to inform investment in uncertainty reduction or targeted risk management action.
Highlights
IntroductionThis paper introduces an approach to aid uncertainty identification and prioritisation within environmental risk assessment (ERA). Uncertainty in environmental systems is investigated through its constituent dimensions (Walker et al, 2003); namely, its location (where the uncertainty is manifest in the various stages of a risk assessment), its nature (due to the incompleteness of knowledge or the inherent variability of natural systems), and its level (the severity of the uncertainty, ranging from determinism to ignorance)
Implementation resulted in generic environmental risk assessment (ERA) template, version 3 (Fig. 1); organised by its phases, sub-phases, groups of tasks, and individual tasks (Table 1)
A reliable characterisation of potential uncertainties is critical for ERAs
Summary
This paper introduces an approach to aid uncertainty identification and prioritisation within ERAs. Uncertainty in environmental systems is investigated through its constituent dimensions (Walker et al, 2003); namely, its location (where the uncertainty is manifest in the various stages of a risk assessment), its nature (due to the incompleteness of knowledge or the inherent variability of natural systems), and its level (the severity of the uncertainty, ranging from determinism to ignorance). The nature of the uncertainty dictates the extent to which it can be managed; knowledge-based uncertainties can be quantified, reduced, and potentially removed; whilst those that reflect the randomness of natural processes can only be quantified (Kelly and Campbell, 2000). In order to manage uncertainty effectively, it is essential that reasonable attempts to identify all dimensions are made (Walker et al, 2003; Janssen et al, 2003; Refsgaard et al, 2007; Knol et al, 2009)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
