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

作者：Money Eric S^*; Barton Lauren E; Dawson Joseph; Reckhow Kenneth H; Wiesner Mark R

来源：Science of the Total Environment, 2014, 473: 685-691.

DOI：10.1016/j.scitotenv2013.12.100

• Nano-silver
• Bayesian networks
• Aquatic exposure
• Validation
• Sensitivity analysis
• Mesocosms

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.

• 出版日期2014-3-1