Issue 12, 2017

Bayesian network as a support tool for rapid query of the environmental multimedia distribution of nanomaterials

Abstract

An approach is presented describing the development of a Bayesian network (BN) based tool for rapid assessment of the distribution of engineered nanomaterials (ENMs). The methodology was demonstrated via the construction of a BN model for estimating the exposure concentrations of nanomaterials (BN-nanoExpo) based on simulation data derived from a mechanistic multimedia compartmental fate and transport model. The results of simulations of the distribution of six ENMs in eight different regions were generated for a broad range of geographical and meteorological parameters as well as ENM release rates to the air, water and soil major compartments. Test cases with the constructed BN-nanoExpo demonstrated the capability of the BN based model to portray a wide range of simulation results that can be obtained with the mechanistic fate and transport model. Moreover, BN-nanoExpo is shown to be a suitable tool for estimating both ENM concentrations and release rates given partial information, while also enabling assessment of the impact of uncertainties in input data on the predicted outcomes. The results of the current study suggest that there is merit in exploring the utility of the approach to more complex models, which would provide decision makers with powerful tools for rapid assessment of the behavior of nanomaterials in the environment.

Graphical abstract: Bayesian network as a support tool for rapid query of the environmental multimedia distribution of nanomaterials

Supplementary files

Article information

Article type
Paper
Submitted
02 Nov 2016
Accepted
23 Feb 2017
First published
27 Feb 2017

Nanoscale, 2017,9, 4162-4174

Bayesian network as a support tool for rapid query of the environmental multimedia distribution of nanomaterials

M. Bilal, H. Liu, R. Liu and Y. Cohen, Nanoscale, 2017, 9, 4162 DOI: 10.1039/C6NR08583K

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements