In spite of the great effort in the area of environmental risk assessment of chemicals, a critical amount of uncertainty is still a reality due to high complexity of the governing risk problem. Unexpected adverse effects may thus take place event though the risk assessment predicts no-risk. Some chemicals are even lacking risk assessment due to a priori judgment based on risk screening methods that bypass further risk assessment activity. It is thus advisable to set up a safety net under the risk assessment of chemicals and this poster suggests a supporting paradigm using risk indicators. The purpose is to develop a novel approach for risk indicators that can support risk minimization. Risk indicators can identify risk reduction and thus yield a time trend analysis of the progress in risk level. Another option is to evaluate the current condition in relation to a reference condition defined as "good praxis" base on e.g. guidelines in order to assess the potential risk minimization. Monitoring activities can also benefit from risk indicators to help focusing on the right parameters to measure and on the right time and position in space. Adverse effects must be a consequence of known or unknown governing risk factors and the risk indicators need to reflect so many of such risk factors as possible in order to be a fine meshed safety net. A new approach is suggested where the basic concept of risk assessment are imbedded including several risk indicators. This poster sets up the paradigm that governs the method: HotSpot Driven Aggregation of Risk Indicators (HoSDARI) as described in Sørensen et al. (2008). The purpose of the risk assessment is to protect a defined target that has some value for protection. The target has a number of realizations each defined as a Protection Unit (PU). If the target is humans then every human that are covered by the risk assessment activity (e.g. all inhabitants in EU) will be a single PU or if the target is lakes then every single lake is a PU. The principle of the paradigm is to identify all, or at least a representative fraction, of PUs and to set up risk indicator values for each of them that can help to find hotspots in risk level. The focus is xenobiotics, but the methodology is useful for many other risk-related problems.

References

Sørensen, P.B.; Brüggemann, R; Thomsen M.; Gyldenkærne S.; Kjær C (2008). Aggregation of risk indicators for time trend analysis and risk minimization. In prep