Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver.


Journal Article

The adaptive nature of the Forecasting the Impacts of Nanomaterials in the Environment (FINE) Bayesian network is explored. We create an updated FINE model (FINEAgNP-2) for predicting aquatic exposure concentrations of silver nanoparticles (AgNP) by combining the expert-based parameters from the baseline model established in previous work with literature data related to particle behavior, exposure, and nano-ecotoxicology via parameter learning. We validate the AgNP forecast from the updated model using mesocosm-scale field data and determine the sensitivity of several key variables to changes in environmental conditions, particle characteristics, and particle fate. Results show that the prediction accuracy of the FINEAgNP-2 model increased approximately 70% over the baseline model, with an error rate of only 20%, suggesting that FINE is a reliable tool to predict aquatic concentrations of nano-silver. Sensitivity analysis suggests that fractal dimension, particle diameter, conductivity, time, and particle fate have the most influence on aquatic exposure given the current knowledge; however, numerous knowledge gaps can be identified to suggest further research efforts that will reduce the uncertainty in subsequent exposure and risk forecasts.

Full Text

Duke Authors

Cited Authors

  • Money, ES; Barton, LE; Dawson, J; Reckhow, KH; Wiesner, MR

Published Date

  • March 2014

Published In

Volume / Issue

  • 473-474 /

Start / End Page

  • 685 - 691

PubMed ID

  • 24412914

Pubmed Central ID

  • 24412914

Electronic International Standard Serial Number (EISSN)

  • 1879-1026

International Standard Serial Number (ISSN)

  • 0048-9697

Digital Object Identifier (DOI)

  • 10.1016/j.scitotenv.2013.12.100


  • eng