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Mechanistic Effect Models for the Ecological Risk Assessment of Chemicals (CREAM)
Start date: Sep 1, 2009, End date: Jul 31, 2014 PROJECT  FINISHED 

There is widespread concern about how production and use of chemicals affect the environment. Yet food production and benefits of chemical products are vital for the functioning of European societies. In order to ensure sustainable use, EU regulations require extensive risk assessment before a chemical is approved for use. Current risk assessments focus on risk at the level of individual organisms, but according to EU directives the protection goal aims at achieving sustainable populations. Population-level effects depend not only on exposure and toxicity, but also on important ecological factors that are impossible to fully address empirically. Mechanistic effect models (MEMs) enable the integration of these factors, thus increasing the ecological relevance of risk assessments as well as providing vital understanding of how chemicals interact with ecosystems. Such understanding is crucial for improving risk mitigation strategies and ecosystem management. So far, however, regulators and industry have lacked understanding of the potential benefits that MEMs can deliver, and academics have been inconsistent in the approaches applied. This has led to scepticism about models, preventing a wider use of MEMs in risk assessment. Examples clearly demonstrating the power of MEMs for risk assessment are urgently needed, and industry, academia and regulatory authorities across Europe need scientists that are trained in both MEMs and regulatory risk assessment. CREAM will develop and experimentally validate a suite of MEMs for organisms relevant for chemical risk assessments. The consortium includes the main sectors involved (industry, academia, regulators) and will formulate Good Modelling Practice that will be followed in all individual projects, thus leading to consistency and transparency. CREAM will provide world class training for the next generation of ecological modellers, emphasizing transparency and rigorous model evaluation as core elements of the modelling process.
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