Evaluating uncertainties in an integrated approach for chemical risk assessment under REACH: more certain decisions?

Abstract

This thesis concentrates on uncertainty and variability in the risk assessment methodology for industrial chemicals as applied within the current regulatory framework for industrial chemicals in Europe, REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals). The methodological approaches discussed address the risk assessment for both humans and the environment. The aim of this thesis is to investigate in what way the scientific process of risk assessment can improve decision-making knowing that uncertainties are inherently linked to risk assessment. An important element of this decision-making is consideration of precautionary measures in the event of reasonable grounds for concern of a potential risk that cannot, or not in time, be determined with sufficient certainty. The investigation is built on two frameworks: the IPCS/WHO framework for Integrated Risk Assessment (IRA) and the framework for Uncertainty Management of Walker et al. (2003). The framework of Integrated Risk Assessment was developed to improve the quality and efficiency of the assessment of risks of adverse effects on human health and the environment from chemicals, physical factors, and other environmental stressors and to provide more complete and coherent inputs to the decision making process. The underlying idea is that both the scientific discussion and the regulatory responses can benefit from a more integrated, interdisciplinary approach leading to sharing of information, decreased uncertainties and fully informed decisions. The Uncertainty Management framework closely parallels the stages in the Integrated Risk Assessment scheme and is considered very useful for highlighting different types of uncertainty in risk assessment. The main, general conclusion of this thesis is that both the process and the methodology of risk assessment as a decision-support tool under REACH can be improved. The process can be improved by the introduction of an IRA framework with a strong uncertainty management component (IRA+). The methodology can be improved by a tiered approach for uncertainty analysis, starting with simple deterministic approaches and, if necessary, classification and prioritisation of uncertainties and probabilistic approaches. Qualitative and quantitative tools for uncertainty assessment were shown to be available. If process and methodology follow the direction shown, decision-support will be more transparent, will lead to less communication problems and will improve the trust between various parties involved. Decisions which fully take into account the uncertainties in the assessments performed, including the influence of divergent opinions and assumptions of experts and stakeholders, will be better informed and will lead to transparent decisions which can be communicated in a clear way.