Skip to main content
Journal cover image

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

Publication ,  Journal Article
Money, ES; Reckhow, KH; Wiesner, MR
Published in: The Science of the total environment
June 2012

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.

Duke Scholars

Published In

The Science of the total environment

DOI

EISSN

1879-1026

ISSN

0048-9697

Publication Date

June 2012

Volume

426

Start / End Page

436 / 445

Related Subject Headings

  • Water Pollution, Chemical
  • Risk Assessment
  • Models, Statistical
  • Models, Chemical
  • Metal Nanoparticles
  • Forecasting
  • Environmental Sciences
  • Environmental Monitoring
  • Bayes Theorem
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Money, E. S., Reckhow, K. H., & Wiesner, M. R. (2012). The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation. The Science of the Total Environment, 426, 436–445. https://doi.org/10.1016/j.scitotenv.2012.03.064
Money, Eric S., Kenneth H. Reckhow, and Mark R. Wiesner. “The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation.The Science of the Total Environment 426 (June 2012): 436–45. https://doi.org/10.1016/j.scitotenv.2012.03.064.
Money ES, Reckhow KH, Wiesner MR. The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation. The Science of the total environment. 2012 Jun;426:436–45.
Money, Eric S., et al. “The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation.The Science of the Total Environment, vol. 426, June 2012, pp. 436–45. Epmc, doi:10.1016/j.scitotenv.2012.03.064.
Money ES, Reckhow KH, Wiesner MR. The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation. The Science of the total environment. 2012 Jun;426:436–445.
Journal cover image

Published In

The Science of the total environment

DOI

EISSN

1879-1026

ISSN

0048-9697

Publication Date

June 2012

Volume

426

Start / End Page

436 / 445

Related Subject Headings

  • Water Pollution, Chemical
  • Risk Assessment
  • Models, Statistical
  • Models, Chemical
  • Metal Nanoparticles
  • Forecasting
  • Environmental Sciences
  • Environmental Monitoring
  • Bayes Theorem