Background, Aim and Scope. In 2005 a comprehensive comparison of LCIA toxicity characterisation models was initiated by the UNEP-SETAC Life Cycle Initiative, directly involving the model developers of CalTOX, IMPACT 2002, USES-LCA, BETR, EDIP, WATSON, and EcoSense. In this paper we describe this model-comparison process and its results-in particular the scientific consensus model developed by the model developers. The main objectives of this effort were (i) to identify specific sources of differences between the models' results and structure, (ii) to detect the indispensable model components, and (iii) to build a scientific consensus model from them, representing recommended practice. Methods. A chemical test set of 45 organics covering a wide range of property combinations was selected for this purpose. All models used this set. In three workshops, the model comparison participants identified key fate, exposure and effect issues via comparison of the final characterisation factors and selected intermediate outputs for fate, human exposure and toxic effects for the test set applied to all models. Results. Through this process, we were able to reduce inter-model variation from an initial range of up to 13 orders of magnitude down to no more than 2 orders of magnitude for any substance. This led to the development of USEtox, a scientific consensus model that contains only the most influential model elements. These were, for example, process formulations accounting for intermittent rain, defining a closed or open system environment, or nesting an urban box in a continental box. Discussion. The precision of the new characterisation factors (CFs) is within a factor of 100-1000 for human health and 10-100 for freshwater ecotoxicity of all other models compared to 12 orders of magnitude variation between the CFs of each model respectively. The achieved reduction of inter-model variability by up to 11 orders of magnitude is a significant improvement.Conclusions. USEtox provides a parsimonious and transparent tool for human health and ecosystem CF estimates. Based on a referenced database, it has now been used to calculate CFs for several thousand substances and forms the basis of the recommendations from UNEP-SETAC's Life Cycle Initiative regarding characterization of toxic impacts in Life Cycle Assessment. Recommendations and Perspectives. We provide both recommended and interim (not recommended and to be used with caution) characterisation factors for human health and freshwater ecotoxicity impacts. After a process of consensus building among stakeholders on a broad scale as well as several improvements regarding a wider and easier applicability of the model, USEtox will become available to practitioners for the calculation of further CFs. Keywords: Consensus model, life cycle impact assessment, LCIA, characterization modelling, comparison, harmonisation, human exposure, toxic impact, human toxicity, freshwater ecotoxicity, comparative impact assessment, characterization factors 1 Background, Aim and Scope In ...

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Screening level models for environmental assessment of engineered nanoparticles (ENP) are not generally available. Here, we present SimpleBox4Nano (SB4N) as the first model of this type, assess its validity, and evaluate it by comparisons with a known material flow model. SB4N expresses ENP transport and concentrations in and across air, rain, surface waters, soil, and sediment, accounting for nanospecific processes such as aggregation, attachment, and dissolution. The model solves simultaneous mass balance equations (MBE) using simple matrix algebra. The MBEs link all concentrations and transfer processes using first-order rate constants for all processes known to be relevant for ENPs. The first-order rate constants are obtained from the literature. The output of SB4N is mass concentrations of ENPs as free dispersive species, heteroaggregates with natural colloids, and larger natural particles in each compartment in time and at steady state. Known scenario studies for Switzerland were used to demonstrate the impact of the transport processes included in SB4N on the prediction of environmental concentrations. We argue that SB4N-predicted environmental concentrations are useful as background concentrations in environmental risk assessment.

Cerium dioxide nanoparticles (CeO2 NPs) are increasingly being used as a catalyst in the automotive industry. Consequently, increasing amounts of CeO2 NPs are expected to enter the environment where their fate in and potential impacts are unknown. In this paper we describe the fate and effects of CeO2 NPs of three different sizes (14, 20, and 29 nm) in aquatic toxicity tests. In each standard test medium (pH 7.4) the CeO2 nanoparticles aggregated (mean aggregate size approximately 400 nm). Four test organisms covering three different trophic levels were investigated, i.e., the unicellular green alga Pseudokirchneriella subcapitata, two crustaceans: Daphnia magna and Thamnocephalus platyurus, and embryos of Danio rerio. No acute toxicity was observed for the two crustaceans and D. rerio embryos, up to test concentrations of 1000, 5000, and 200 mg/L, respectively. In contrast, significant chronic toxicity to P. subcapitata with 10% effect concentrations (EC10s) between 2.6 and 5.4 mg/L was observed. Food shortage resulted in chronic toxicity to D. magna, for wich EC10s of > or = 8.8 and < or = 20.0 mg/L were established. Chronic toxicity was found to increase with decreasing nominal particle diameter and the difference in toxicity could be explained by the difference in surface area. Using the data set, PNEC(aquatic)S > or = 0.052 and < or = 0.108 mg/L were derived. Further experiments were performed to explain the observed toxicity to the most sensitive organism, i.e., P. subcapitata. Toxicity could not be related to a direct effect of dissolved Ce or CeO2 NP uptake or adsorption, nor to an indirect effect of nutrient depletion (by sorption to NPs) or physical light restriction (through shading by the NPs). However, observed clustering of NPs around algal cells may locally cause a direct or indirect effect.