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A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks
Finbarr Murphy 1 , Barry Sheehan 1 , Martin Mullins 1 , Hans Bouwmeester, Hans J. P. Marvin 2 , Yamine Bouzembrak 2 , Anna Luisa Costa 3 , Rasel Das 4 , Vicki Stone 5 , Syed A. M. Tofail 6
1  Kemmy Business School, University of Limerick, Limerick, Ireland
2  RIKILT Wageningen University & Research (WR), Akkermaalsbos 2, 6708 PD Wageningen, The Netherlands
3  ISTEC-CNR, Via Granarolo, 64, I-48018 Faenza, RA, Italy
4  Nanotechnology and Catalysis Research Center, University of Malaya, 50603 Kuala Lumpur, Malaysia
5  Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, UK
6  Department of Physics, and Bernal Institute, University of Limerick, Limerick, Ireland

Published: 15 November 2016 by Springer Nature in Nanoscale Research Letters
Springer Nature, Volume 11; 10.1186/s11671-016-1724-y
Abstract: While control banding has been identified as a suitable framework for the evaluation and the determination of potential human health risks associated with exposure to nanomaterials (NMs), the approach currently lacks any implementation that enjoys widespread support. Large inconsistencies in characterisation data, toxicological measurements and exposure scenarios make it difficult to map and compare the risk associated with NMs based on physicochemical data, concentration and exposure route. Here we demonstrate the use of Bayesian networks as a reliable tool for NM risk estimation. This tool is tractable, accessible and scalable. Most importantly, it captures a broad span of data types, from complete, high quality data sets through to data sets with missing data and/or values with a relatively high spread of probability distribution. The tool is able to learn iteratively in order to further refine forecasts as the quality of data available improves. We demonstrate how this risk measurement approach works on NMs with varying degrees of risk potential, namely, carbon nanotubes, silver and titanium dioxide. The results afford even non-experts an accurate picture of the occupational risk probabilities associated with these NMs and, in doing so, demonstrated how NM risk can be evaluated into a tractable, quantitative risk comparator.
Keywords: Control Banding, Risk assessment, Bayesian
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