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Session 2 - Prof. A.M.J Ragas (AD)
The document discusses uncertainties in modeling human and ecological risks from chemicals, nanomaterials, and microplastics in the environment. Current risk management is reactive, substance-by-substance, and resource intensive. Modeling risks considers sources, emission, fate, exposure, and effects, but faces many uncertainties. For chemicals, better use and spatially explicit emissions data is needed. Fate modeling requires improved degradation data, especially for ionizing compounds. Effect modeling needs more data on mixtures, genomics, and untested substances. For nanomaterials and microplastics, long-term fate and effects are particularly uncertain due to degradation questions and lack of data.
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Session 2 - Prof. A.M.J Ragas (AD)
- 1. Uncertainties in modellinghuman and ecological risks of chemicals, nanomaterials and (micro)plastics Ad M.J. Ragas Department of Environmental Science, Radboud University, Nijmegen, The Netherlands Department of Science, Open Universiteit, Heerlen, The Netherlands OECD Workshop on Managing Contaminants of Emerging Concern in Surface Waters: Scientific developments and cost-effective policy responses Paris, 5 February 2018
- 2. Risks of chemicals,nanomaterials and (micro)plastics Current management: • Reactive • Substance-per-substance • Resource intensive • New substances
- 3. Modelling aquatic risksof chemicals, nanomaterials and (micro)plastics SOURCES • Consumers • Agriculture • Industry • Etc. EMISSION • Point source • Diffuse FATE • Partitioning • Degradation • Bioaccumu- lation EXPOSURE • Spatial variation • Behaviour EFFECTS • Human • Ecosystems • Economy
- 4. Example: modelling pharmaceuticalsin the environment National Consumption Data Substance Properties Water & Sediment Concentrations MODEL
- 5. Uncertainties in modellingaquatic risks of chemicals SOURCES • New substances • Product composition • Spatially explicit use data EMISSION • Linking use to emission FATE • Ionising compounds • Biodegradation • Secondary effects (e.g. Antibiotic Resistance) EXPOSURE • Exotic exposure routes (e.g., vultures & bees) EFFECTS • Untested substances => (eco)toxico- genomics? • Mixture effects • Interaction with other stressors
- 6. Uncertainties in modellingaquatic risks of chemicals SOURCES • New substances • Product composition • Spatially explicit use data EMISSION • Linking use to emission FATE • Ionising compounds • Biodegradation • Secondary effects (e.g. Antibiotic Resistance) EXPOSURE • Exotic exposure routes (e.g., vultures & bees) EFFECTS • Untested substances => (eco)toxico- genomics? • Mixture effects • Interaction with other stressors
- 7. Uncertainties in modellingaquatic risks of nanomaterials SOURCES • New materials • Specification of products and materials • (Spatially explicit) use data EMISSION • Linking use to emission FATE • Partitioning (colloid chemistry) • Dissolution • Aggregation • Detection • Long-term fate EFFECTS • Toxicity testing protocols • Physical effects • Long-term effects • Detection SimpleBox4Nano EXPOSURE • Exposure metric: weight, surface area, other?
- 8. Uncertainties in modellingaquatic risks of nanomaterials SOURCES • New materials • Specification of products and materials • (Spatially explicit) use data EMISSION • Linking use to emission FATE • Partitioning (colloid chemistry) • Dissolution • Aggregation • Detection • Long-term fate EFFECTS • Toxicity testing protocols • Physical effects • Long-term effects • Detection SimpleBox4Nano EXPOSURE • Exposure metric: weight, surface area, other?
- 9. Uncertainties in modellingaquatic risks of (micro)plastics SOURCES • Macroplastics • Cosmetics • Tyres • Synthetic textiles • ???? EMISSION • Linking use to emission FATE • Degradation macro to micro • Dispersal behaviour in relation to specific weight • Vehicle? • Long-term fate • Deep seas • Detection EFFECTS • Physical effects • Indirect effects • Long-term effects • Detection EXPOSURE • Spatial variation
- 10. Uncertainties in modellingaquatic risks of (micro)plastics SOURCES • Macroplastics • Cosmetics • Tyres • Synthetic textiles • ???? EMISSION • Linking use to emission FATE • Degradation macro to micro • Dispersal behaviour in relation to specific weight • Vehicle? • Long-term fate • Deep seas • Detection EFFECTS • Physical effects • Indirect effects • Long-term effects • Detection EXPOSURE • Spatial variation
- 11. Key messages • Weneed to get more grip on product composition and (spatially explicit) use data in order to produce reliable emission estimates • In order to improve the prediction of the fate of chemicals in the environment we need to focus on degradation data and the behaviour of ionizing compounds • In order to improve the prediction of effects of chemicals we need to focus on Quantitative Structure Activity Relationships (QSARs), (eco)toxicogenomics and mixture effects • The two most important questions for (micro)plastics and nanomaterials are long-term fate (degradation) and effects