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In the context of the EU GRACIOUS project, we propose a novel procedure for similarity assessment and grouping of nanomaterials. This methodology is based on the (1) Arsinh transformation function for scalar properties, (2) full curve shape comparison by application of a modified Kolmogorov-Smirnov metric for bivariate properties, (3) Ordered Weighted Average (OWA) aggregation-based grouping distance, and (4) hierarchical clustering. The approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. To facilitate the application of the proposed methodology, a software script was developed by using the R programming language which is currently under migration to a web tool. The presented approach was tested against a dataset, derived from literature review, related to immobilisation of Daphnia magna and reporting information on several nanomaterials., Comment: Accepted for publication

Nanotechnology is a so-called key-emerging technology that opens a new world of technological innovation. The novelty of engineered nanomaterials (ENMs) raises concern over their possible adverse effect to man and the environment. Thereupon, risk assessors are challenged with ever decreasing times-to-market of nano-enabled products. Combined with the perception that it is impossible to extensively test all new nanoforms, there is growing awareness that alternative assessment approaches need to be developed and validated to enable efficient and transparent risk assessment of ENMs. Associated with this awareness, there is the need to use existing data on similar ENMs as efficiently as possible, which highlights the need of developing alternative approaches to fate and hazard assessment like predictive modelling, grouping of ENMs, and read across of data towards similar ENMs. In this contribution, an overview is given of the current state of the art with regard to categorization of ENMs and the perspectives for implementation in future risk assessment. It is concluded that the qualitative approaches to grouping and categorization that have already been developed are to be substantiated, and additional quantification of the current sets of rules-of-thumb based approaches is a key priority for the near future. Most of all, the key question of what actually drives the fate and effects of (complex) particles is yet to be answered in enough detail, with a key role foreseen for the surface reactivity of particles as modulated by the chemical composition of the inner and outer core of particles. When it comes to environmental categorization of ENMs we currently are in a descriptive rather than in a predictive mode., Comment: 10 pages, 5 figures, This article is one of a collection of articles about the categorization of nanomaterials, generated by research and workshop discussions under the FutureNanoNeeds project funded by EU FP7