Material-specific properties applied to an environmental risk assessment of engineered nanomaterials – implications on grouping and read-across concepts

Abstract

Engineered nanomaterials (ENMs) are intentionally designed in different nano-forms of the same parent material in order to meet application requirements. Different grouping and read-across concepts are proposed to streamline risk assessments by pooling nano-forms in one category. Environmental grouping concepts still are in their infancy and mainly focus on grouping by hazard categories. Complete risk assessments require data on environmental release and exposure not only for ENMs but also for their nano-forms. The key requirement is to identify and to distinguish the production volumes of the ENMs regarding nano-form-specific applications. The aim of our work was to evaluate whether such a grouping is possible with the available data and which influence it has on the environmental risk assessment of ENMs. A functionality-driven approach was applied to match the material-specific property (i.e. crystal form/morphology) with the functions employed in the applications. We demonstrate that for nano-TiO₂, carbon nanotubes (CNTs), and nano-Al₂O₃ the total production volume can be allocated to specific nano-forms based on their functionalities. The differentiated assessments result in a variation of the predicted environmental concentrations for anatase vs. rutile nano-TiO₂, single-wall vs. multi-wall CNTs and α- vs. γ-nano-Al₂O₃ by a factor of 2 to 13. Additionally, the nano-form-specific predicted no-effect concentrations for these ENMs were derived. The risk quotients for all nano-forms indicated no immediate risk in freshwaters. Our results suggest that grouping and read-across concepts should include both a nano-form release potential for estimating the environmental exposure and separately consider the nano-forms in environmental risk assessments.

Keywords:

• Nano-form
• risk assessment
• grouping
• read-across
• predicted environmental concentration
• predicted no-effect concentration

Acknowledgements

The authors acknowledge Delphine Kawecki's and Véronique Adam's help in the development of the codes necessary for this model.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was financed by caLIBRAte which has received funding from the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement 686239.