The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation.

Published

Journal Article

We describe the use of Bayesian networks as a tool for nanomaterial risk forecasting and develop a baseline probabilistic model that incorporates nanoparticle specific characteristics and environmental parameters, along with elements of exposure potential, hazard, and risk related to nanomaterials. The baseline model, FINE (Forecasting the Impacts of Nanomaterials in the Environment), was developed using expert elicitation techniques. The Bayesian nature of FINE allows for updating as new data become available, a critical feature for forecasting risk in the context of nanomaterials. The specific case of silver nanoparticles (AgNPs) in aquatic environments is presented here (FINE(AgNP)). The results of this study show that Bayesian networks provide a robust method for formally incorporating expert judgments into a probabilistic measure of exposure and risk to nanoparticles, particularly when other knowledge bases may be lacking. The model is easily adapted and updated as additional experimental data and other information on nanoparticle behavior in the environment become available. The baseline model suggests that, within the bounds of uncertainty as currently quantified, nanosilver may pose the greatest potential risk as these particles accumulate in aquatic sediments.

Full Text

Duke Authors

Cited Authors

  • Money, ES; Reckhow, KH; Wiesner, MR

Published Date

  • June 2012

Published In

Volume / Issue

  • 426 /

Start / End Page

  • 436 - 445

PubMed ID

  • 22521099

Pubmed Central ID

  • 22521099

Electronic International Standard Serial Number (EISSN)

  • 1879-1026

International Standard Serial Number (ISSN)

  • 0048-9697

Digital Object Identifier (DOI)

  • 10.1016/j.scitotenv.2012.03.064

Language

  • eng