Conscious worst case definition for risk assessment, part I: a knowledge mapping approach for defining most critical risk factors in integrative risk management of chemicals and nanomaterials.
This paper helps bridge the gap between scientists and other stakeholders in the areas of human and environmental risk management of chemicals and engineered nanomaterials. This connection is needed due to the evolution of stakeholder awareness and scientific progress related to human and environmental health which involves complex methodological demands on risk management. At the same time, the available scientific knowledge is also becoming more scattered across multiple scientific disciplines. Hence, the understanding of potentially risky situations is increasingly multifaceted, which again challenges risk assessors in terms of giving the 'right' relative priority to the multitude of contributing risk factors. A critical issue is therefore to develop procedures that can identify and evaluate worst case risk conditions which may be input to risk level predictions. Therefore, this paper suggests a conceptual modelling procedure that is able to define appropriate worst case conditions in complex risk management. The result of the analysis is an assembly of system models, denoted the Worst Case Definition (WCD) model, to set up and evaluate the conditions of multi-dimensional risk identification and risk quantification. The model can help optimize risk assessment planning by initial screening level analyses and guiding quantitative assessment in relation to knowledge needs for better decision support concerning environmental and human health protection or risk reduction. The WCD model facilitates the evaluation of fundamental uncertainty using knowledge mapping principles and techniques in a way that can improve a complete uncertainty analysis. Ultimately, the WCD is applicable for describing risk contributing factors in relation to many different types of risk management problems since it transparently and effectively handles assumptions and definitions and allows the integration of different forms of knowledge, thereby supporting the inclusion of multifaceted risk components in cumulative risk management.
Sørensen
Peter B
PB
Department of Terrestrial Ecology, National Environmental Research Institute, University of Aarhus, Vejlsøvej 25, P.O. Box 314, DK-8600 Silkeborg, Denmark. pbs@dmu.dk
Thomsen
Marianne
M
Assmuth
Timo
T
Grieger
Khara D
KD
Baun
Anders
A
D004781:Environmental Exposure / Q000517:prevention & control / Q000706:statistics & numerical data; D004784:Environmental Monitoring; D004785:Environmental Pollutants / Q000633:toxicity*; D004787:Environmental Pollution / Q000706:statistics & numerical data; D006801:Humans; D007287:Inorganic Chemicals / Q000633:toxicity*; D008962:Models, Theoretical; D049329:Nanostructures / Q000633:toxicity*; D009930:Organic Chemicals / Q000633:toxicity*; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012308:Risk Management / Q000379:methods*
D004785:Environmental Pollutants; D007287:Inorganic Chemicals; D009930:Organic Chemicals