OUTLINE The EU FP6 NoMiracle project Results & Examples: Exposure Effects Risk Assessment Conclusions

• BACKGROUND Emissions are reduced and environmental quality improves, but some environmental health issues persist or are on the rise: Childhood brain cancer Allergies Infertility Autism, attention deficit, hyperactivity disorders Biodiversity decreases Could exposure to cumulative stressors play a role?

THE CALL Integrated Risk Assessment Methodologies Realistic Exposure • THE CALL Integrated Risk Assessment Methodologies Realistic Exposure Effects of combined exposures (including non- chemicals) Substances that provoke specific interactions Cover different regions in Europe Outdoor and indoor exposure Cover environmental and human health scientists Exposure to chemicals in products Precautionary principle

NoMiracle – objectives I • NoMiracle – objectives I To develop new methods for assessing the cumulative risks from combined exposures to several stressors including mixtures of chemical and physical/biological agents 2. To achieve more effective integration of the risk analysis of environmental and human health effects 3. To improve our understanding of complex exposure situations and develop adequate tools for sound exposure assessment 4. To develop a research framework for the description and interpretation of cumulative exposure and effect

NoMiracle – objectives II • NoMiracle – objectives II 5. To quantify, characterise and reduce uncertainty in current risk assessment methodologies, e.g. by improvement of the scientific basis for setting safety factors 6. To develop assessment methods which take into account geographical, ecological, social and cultural differences in risk concepts and risk perceptions across Europe 7. To improve the provisions for the application of the precautionary principle and to promote its operational integration with evidence-based assessment methodologies

Duration: 1 November 2004 – 30 October 2009 • NoMiracle – Details Duration: 1 November 2004 – 30 October 2009 Partners: 38 institutes from 17 European states Total Budget: 17 mio € EC contribution: 10 mio €

The NoMiracle Consortium SYKE ITM LHASA Ltd ULANC WRc WU AU APINI RU UCAM NERC VU LIMCO Alterra KCL KCL UA RWTHA UFZ UJAG ETC LEMTEC RIVM USOUTH SYMLOG EKUT DIA NIHP USALZ EPFL ETH UMFT LMC JRC UNIMIB DISAV ENVI UAVR CSIC URV

NoMiracle – Organization

EXPOSURE Compound-Matrix Interactions • EXPOSURE Compound-Matrix Interactions Accessibility and Chemical Activity Metabolic Fate Region-Specific Environmental Fate

COMPOUND-MATRIX INTERACTIONS Hypothesis: Bioconcentration Factor (BCF) can be predicted more reliably with Kmw than with Kow Experimental Kmw determination Theoretical model for prediction of Kmw Optimized model to predict Kmw Compound descriptors Phase parameters If Kmw is a better predictor for the BCF than Kow, the challenge is to develop good methods/models to measure/predict Kmw. These results demonstrate that we are able to predict Kmw quite accurately based on molecular descriptors.

CHEMICAL ACTIVITY I Bioavailability has traditionally been quantified using (freely dissolved) concentrations Partition coefficients (KP values or BCFs) can be difficult to quantify, especially for substances with Kow>6 Measurement techniques have been developed within the NoMiracle project to measure chemical activity It has been shown that substances with Kow>6 contribute to baseline toxicity Chemical activity seems to be a good dose metric for mixtures showing baseline toxicity

CHEMICAL ACTIVITY II

REGION-SPECIFIC ENVIRONMENTAL FATE MAPPE model

EFFECTS Meta Analysis of Mixture Toxicity Data • EFFECTS Meta Analysis of Mixture Toxicity Data Mechanistic Framework for Mixture Toxicity Toxicokinetic Interactions Experimental Methods for Mechanistic Mixture Studies (micro-arrays, metabolomics, proteomics) Immunotoxicity assay for mixtures Chemicals and Natural Stressors The DEBtox model for mixtures

META-ANALYSIS OF TOXICITY DATA Similar joint action Dissimilar joint action Interaction absent Simple similar action or concentration addition Independent action or response addition Interaction present Complex similar action Dependent similar action NoMiracle database: 322 mixture test results 65% of the studies showed interactions (antagonism, synergism, dose level deviation, dose ratio deviation) Data can be used to derive a safety factor for mixture toxicity

MECHANISTIC FRAMEWORK A significant part of the work (and budget) in NoMiracle focused on effect studies: Studies on interactions in the uptake & elimination of chemicals (e.g. Nickel and Chlorpyriffos); Studies on metabolic interactions, such as the impact of substances on the metabolism of other substances; Studies on interactions at the target site, i.e. by developing molecular techniques (micro-arrays, metabolomics and proteomics) to establish the mode(s) of action of single and multiple chemical exposures. Although these studies resulted in valuable scientific insights, which add to the general body of knowledge on mixture effects. However, it will take time before the general implications for risk assessment practice will become clear. The ultimate goal (prediction of mixture effects based on the molecular structure and concentration levels in the mixture) has not yet been realized.

CHEMICALS AND NATURAL STRESSORS META-ANALYSIS: 150 data sets heat, cold, desiccation, oxygen depletion, pathogens, immunomodulatory factors > 50% synergistic interactions Are current laboratory studies sufficiently representative for field conditions? Dendrobaena octaedra Ambient temperature (°C, 8 days) following 28 days cold acclimation at 0°C in Cu spiked soil (95% credibility interval) Bindesbøl et al. 2009: ET&C 28: 2341–2347 We managed to show the relevance of including potential interaction effects between chemical and natural stressors. However, we could not propose general methods to account for the impact of natural stressors on chemical stress (and vice versa).

The DEBMIXTOX MODEL Organism DEB parameters toxicity parameters Metabolic Processes (maintenance, growth, repro) DEB parameters (energy budget) External Concentration A in time Internal Concentration A in time Internal Concentration B in time kinetic parameters (e.g. elimination and uptake) Effect points in time (survival, movement, repro) toxicity parameters (e.g. NEC, hazard rate) External Concentration B in time Advantages: mechanistic framework toxicity is considered a process in time limited number of model parameters extrapolation over species and time

DEBMIXTOX RESULTS Predictions: Westland Green Houses Predictions: survival was correctly predicted in 19 out of 20 cases mortality was correctly predicted in 15 out of 17 cases 92% correct predictions! Mixture: 14 PAHs 13 biphenyls 8 metals 36 pesticides 10 nutrients/salst 22 env. parameters Baas et al., 2009 ES&T 43: 6064-6069

RISK ASSESSMENT Identification of Fundamental Uncertainties • RISK ASSESSMENT Identification of Fundamental Uncertainties Human and Ecological Uncertainty Factors Probabilistic Risk Assessment Individual-based Exposure Models Trait-based Vulnerability Assessment Risk Perception and Management Risk Maps for Mixtures Master Cases

ECOLOGICAL UNCERTAINTY FACTORS Database with NOECs 5,2 4.1 4.9 3,7 0.3 1.5 0.9 9.9 15.4 14.6 12.0 11.5 10.2 HC5 Ratio = AFHC5 Resampling Lowest More conservative Current Assessment Factor of 10 for small datasets (n=3) is conservative Less conservative

PROBABILISTIC RISK ASSESSMENT What are the benefits / disadvantages of performing an extra ecotoxicity test? Anthracene: 50% probability €8.8-14 million could be saved long-term ecotoxicity test costs €20-40.000

INDIVIDUAL-BASED EXPOSURE

RISK PERCEPTION AND MANAGEMENT The variation in opinions of experts (on complex risks) is comparable to that of the general public The general public does not (yet) have a particularly strong opinion on cumulative risks

RISK MAPS

Urbania (a hypothetical city) MASTER CASE Urbania (a hypothetical city) PM10 (outdoor & indoor) 4 VOCs (outdoor & indoor) 6 Pesticides (food) 3 Target Groups Children (0-6) Working Àdults (18-64) Elderly (65 and older)

GENERAL CONCLUSIONS EC: develop focused calls • GENERAL CONCLUSIONS EC: develop focused calls Exposure assessment can be improved using concepts like Kmw, chemical activity and spatially explicit fate models There is sufficient experimental data to develop a safety factor for mixture assessment More mechanistic studies on mixture toxicity are required (experimental & modeling) Time is a crucial factor in (mixture) toxicity Probabilistic approaches can make risk assessment and management more efficient Cumulative risk assessment requires an receptor- oriented approach (individual/ecosystem)