title: Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver.
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.
authors.affiliation: Center for the Environmental Implications of NanoTechnology (CEINT), Duke University, P.O. Box 90287, Durham, NC 27708-0827, USA; Dept. of Civil and Environmental Engineering, Pratt School of Engineering, Duke University, P.O. Box 90287, Durham, NC 27708-0827, USA. Electronic address: emoney@rti.org.
authors.forename: Eric S
authors.initials: ES
authors.lastname: Money
authors.affiliation: Center for the Environmental Implications of NanoTechnology (CEINT), Duke University, P.O. Box 90287, Durham, NC 27708-0827, USA; Dept. of Civil and Environmental Engineering, Pratt School of Engineering, Duke University, P.O. Box 90287, Durham, NC 27708-0827, USA.
authors.forename: Lauren E
authors.initials: LE
authors.lastname: Barton
authors.affiliation: Dept. of Biology, Oberlin College, 119 Woodland St., Oberlin, OH 44074, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Dawson
authors.affiliation: Center for the Environmental Implications of NanoTechnology (CEINT), Duke University, P.O. Box 90287, Durham, NC 27708-0827, USA; Nicholas School of the Environment, Duke University, P.O. Box 90328, Durham, NC 27708, USA.
authors.forename: Kenneth H
authors.initials: KH
authors.lastname: Reckhow
authors.affiliation: Center for the Environmental Implications of NanoTechnology (CEINT), Duke University, P.O. Box 90287, Durham, NC 27708-0827, USA; Dept. of Civil and Environmental Engineering, Pratt School of Engineering, Duke University, P.O. Box 90287, Durham, NC 27708-0827, USA.
authors.forename: Mark R
authors.initials: MR
authors.lastname: Wiesner
mesh_terms: D001499:Bayes Theorem; D004784:Environmental Monitoring; D053768:Metal Nanoparticles / Q000032:analysis*; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D012834:Silver / Q000032:analysis*; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data*
chemical_list: D014874:Water Pollutants, Chemical; D012834:Silver
keywords: Aquatic exposure; Bayesian networks; Mesocosms; Nano-silver; Sensitivity analysis; Validation