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

• Published in: The Science of the total environment 2014-03, Vol.473-474, p.685-691
• Main Authors: Money, Eric S., Barton, Lauren E., Dawson, Joseph, Reckhow, Kenneth H., Wiesner, Mark R.
• Format: Article
• Language: English
• Subjects: Aquatic exposure; Bayes Theorem; Bayesian analysis; Bayesian networks; Environmental Monitoring; Exposure; Forecasting; Mathematical models; Mesocosms; Metal Nanoparticles - analysis; Nano-silver; Nanomaterials; Nanoparticles; Nanostructure; Risk Assessment; Sensitivity analysis; Sensitivity and Specificity; Silver - analysis; Validation; Water Pollutants, Chemical - analysis; Water Pollution, Chemical - statistics & numerical data
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2013.12.100

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. •We validate a probabilistic model for nano-silver exposure.•We use mesocosm field data to verify model estimates.•The FINE Bayesian network reliably predicts nano water concentrations.•Significant physicochemical characteristics are identified to reduce uncertainty.