• Journal Article

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

Citation

Money, E., Barton, L. E., Dawson, J., Reckhow, K. H., & Wiesner, M. R. (2014). Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver. Science of the Total Environment, 473-474, 685-691. DOI: 10.1016/j.scitotenv.2013.12.100

Abstract

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