Microplastics have been detected in freshwaters all over the world in almost all samples, and ecotoxicological studies have shown adverse effects of microplastics on organisms. However, no risk assessment of microplastics has been performed specifically in freshwater so far. The aim of the present study was therefore to review all exposure and ecotoxicity data available for microplastics in freshwaters and to perform a preliminary probabilistic risk assessment. The exposure probability distribution was based on 391 concentrations measured in Asia, Europe, and North America. Because exposure data are mainly available in particle number-based metrics but results from hazard studies are mostly mass-based, the hazard results were converted into particle number concentrations. A statistical analysis of the hazard data showed that there was no significant influence of particle shape or type of polymer on the no-observed-effect concentration. The predicted-no-effect concentration (PNEC) was calculated as the fifth percentile of the probabilistic species sensitivity distribution, based on 53 values from 14 freshwater species, to have a mode of 7.4 Â 10 5 particles Á m À3 (25th and 75th quantiles of 6.1 Â 10 5 and 1.3 Â 10 6 particles Á m À3 , respectively). The exposure probability distribution was divided by the PNEC probability distribution to calculate risk characterization ratios (RCRs), with modes of 1.3 Â 10 À6 in North America, 3.3 Â 10 À6 in Europe, and 4.6 Â 10 À3 in Asia. Probability distributions associated with the RCRs showed that ecological risks cannot be entirely excluded in Asia, where 0.4% of the RCR values were above 1. Environ Toxicol Chem 2019;38:436-447. C 2018

Most existing models for assessing the releases of engineered nanomaterials (ENMs) into the environment are based on the assumption that ENMs remain in their pristine forms during their whole life cycle. It is known, however, that this is not always the case as ENMs are often embedded into solid matrices during manufacturing and can undergo physical or chemical transformations during their life cycle, e.g. upon release to wastewater. In this work, we present a method for systematically assessing the forms in which nano-Ag and nano-TiO flow through their life cycle (i.e. production, manufacturing, use and disposal) to their points of release to air, soil and surface water. Input data on the forms of released ENMs were probability distributions based on peer-reviewed literature. Release data were incorporated into a probabilistic material flow analysis model to quantify the proportions of ENMs in product-embedded, matrix-embedded, pristine, transformed and dissolved forms in all technical and environmental compartments into which they flow, at the European scale. Releases of nano-Ag to surface water and soil were modelled to occur primarily in transformed forms (Q25 and Q75 of 34-58% and 78-86%, respectively, with means of 53% and 82%), while releases to air were mostly in pristine and matrix-embedded forms (38-46% and 36-44%, respectively, with means of 42% and 40%). In contrast, nano-TiO releases to air, soil and water were estimated to be predominantly in pristine form (75-85%, 90-95%, 96-98%, respectively, with means of 80%, 91% and 97%). The distributions of ENM releases between forms developed here will improve the representativeness and appropriateness of input data for environmental fate modelling and risk assessment of ENMs.

The calculation of a species sensitivity distribution (SSD) is a commonly accepted approach to derive the predicted no‐effect concentration (PNEC) of a substance in the context of environmental risk assessment. The SSD approach usually is data demanding and incorporates a large number of ecotoxicological values from different experimental studies. The probabilistic SSD (PSSD) approach is able to fully consider the variability between different exposure conditions and material types, which is of great importance when constructing an SSD for any chemical, especially for nanomaterials. The aim of our work was to further develop the PSSD approach by implementing methods to better consider the uncertainty and variability of the input data. We incorporated probabilistic elements to consider the uncertainty associated with uncertainty factors by using probability distributions instead of single values. The new PSSD method (named “PSSD+”) computes 10 000 PSSDs based on a Monte Carlo routine. For each PSSD calculated, the hazardous concentration for 5% of species (HC5) was extracted to provide a PNEC distribution based on all data available and their associated uncertainty. The PSSD+ approach also includes the option to consider a species weighting according to a typically constituted biome. We applied this PSSD+ approach to a previously published data set on C nanotubes and Ag nanoparticles. The evaluation of the uncertainty factor distributions and species weighting have shown that the proposed PSSD method is robust with respect to the calculation of the PNEC value. Furthermore, we demonstrated that the PSSD+ can handle both small and more comprehensive data sets because the PNEC distributions are a close representation of the data available. Finally, the sensitivity testing toward data set variations showed that the maximum variation of the mean PNEC was of a factor of about 2, so that the method is relatively insensitive to missing data points as long as the most sensitive species is included. Integr Environ Assess Manag 2020;16:211–222. © 2019 SETAC

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