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SUNDS probabilistic human health risk assessment methodology and its application to organic pigment used in the automotive industry

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Author: Pizzol, L. · Hristozov, D. · Zabeo, A. · Gianpietro, B. · Wohlleben, W. · Koivisto, A.J. · Jensen, K.A. · Fransman, W. · Stone, V. · Marcomini, A.
Type:article
Date:2019
Source:NanoImpact, 13, 26-36
Identifier: 844061
doi: doi:10.1016/j.impact.2018.12.001
Keywords: Health · Artificial intelligence · Automotive industry · Decision support systems · Extrapolation · Health · Health risks · Life cycle · Nanostructured materials · Nanotechnology · Risk perception · Uncertainty analysis · Engineered nanomaterials · Human health risk assessment · Organic red pigments · Probabilistic approaches · Product-life-cycle · Risk communication · Risk management strategies · Sustainable nanotechnologies · Risk assessment · Food and Nutrition · Healthy Living · Life · RAPID - Risk Analysis for Products in Development · ELSS - Earth, Life and Social Sciences

Abstract

The increasing use of engineered nanomaterials (ENMs) in nano-enabled products (NEPs) has raised societal concerns about their possible health and ecological implications. To ensure a high level of human and environmental protection it is essential to properly estimate the risks of these new materials and to develop adequate risk management strategies. To this end, we propose a quantitative Human Health Risk Assessment (HHRA) methodology, which was developed in the European Seventh Framework research project SUN (Sustainable Nanotechnologies) and implemented in the web-based SUN Decision Support System (SUNDS). One of the major strengths of this probabilistic approach as compared to its deterministic alternatives is its ability to clearly communicate the uncertainties in the estimated risks in order to support better risk communication for more objective decision making by industries and regulators. To demonstrate this methodology, we applied it in a real case study involving a nanoscale organic red pigment used in the automotive industry. Our analysis clearly showed that the main source of uncertainty was the extrapolation from (sub)acute in vivo toxicity data to long-term risk. This extrapolation was necessary due to a lack of (sub)chronic in vivo studies for the investigated nanomaterial. Despite the high uncertainty in the final results due to the conservative assumptions made in the risks assessment, the estimated risks are acceptable for all investigated exposure scenarios along the product lifecycle.