The use of Bayesian networks for nanoparticle risk forecasting: Model formulation and baseline evaluation

• Published in: The Science of the total environment 2012-06, Vol.426, p.436-445
• Main Authors: Money, Eric S., Reckhow, Kenneth H., Wiesner, Mark R.
• Format: Article
• Language: English
• Subjects: Air. Soil. Water. Waste. Feeding; Animal, plant and microbial ecology; Applied ecology; Applied sciences; Bayes Theorem; Bayesian analysis; Bayesian networks; Biological and medical sciences; Ecological risk; Ecotoxicology, biological effects of pollution; Environment. Living conditions; Environmental Monitoring; Exact sciences and technology; Expert elicitation; Forecasting; Fundamental and applied biological sciences. Psychology; General aspects; Global environmental pollution; Knowledge bases (artificial intelligence); Mathematical models; Medical sciences; Metal Nanoparticles - statistics & numerical data; Models, Chemical; Models, Statistical; Nano-silver; Nanocomposites; Nanomaterials; Nanoparticles; Nanostructure; Pollution; Probabilistic risk forecasting; Public health. Hygiene; Public health. Hygiene-occupational medicine; Risk; Risk Assessment - methods; Water Pollution, Chemical - statistics & numerical data
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2012.03.064

We describe the use of Bayesian networks as a tool for nanomaterial risk forecasting and develop a baseline probabilistic model that incorporates nanoparticle specific characteristics and environmental parameters, along with elements of exposure potential, hazard, and risk related to nanomaterials. The baseline model, FINE (Forecasting the Impacts of Nanomaterials in the Environment), was developed using expert elicitation techniques. The Bayesian nature of FINE allows for updating as new data become available, a critical feature for forecasting risk in the context of nanomaterials. The specific case of silver nanoparticles (AgNPs) in aquatic environments is presented here (FINEAgNP). The results of this study show that Bayesian networks provide a robust method for formally incorporating expert judgments into a probabilistic measure of exposure and risk to nanoparticles, particularly when other knowledge bases may be lacking. The model is easily adapted and updated as additional experimental data and other information on nanoparticle behavior in the environment become available. The baseline model suggests that, within the bounds of uncertainty as currently quantified, nanosilver may pose the greatest potential risk as these particles accumulate in aquatic sediments. ► We develop a probabilistic model for nanosilver risk in aquatic environments. ► Expert elicitation can be used to develop a baseline risk model under uncertainty. ► The framework combines particle behavior, exposure, and hazard into a single model. ► The model will be able to adapt to experimental data and multiple knowledge bases. ► Bayesian networks are a flexible tool for nanoparticle risk forecasting.