The development and use of the computational models for environmental impact assessment (EIA) metrics on Engineered Nanomaterials (ENMs) is a significant but necessary undertaking for the construction of a rationale platform in support of risk management. However, EIA requires a computational/modeling platform that is efficient, flexible, adaptive and functional even when confronted with partial/noisy information on ENM properties. The increased use of ENMs and research efforts on their potential environmental impact has resulted in significant advancement of models and tools to assess the toxicity, environmental releases and exposure concentrations of ENMs such that tiered approach to risk assessment can be pursued. However, it is first necessary to properly integrate data concerning ENMs physicochemical properties, dose-response/toxicity information, and ENMs release and exposure estimates. In addition, it is important to consider both data and modeling uncertainties that can pause a significant challenge for knowledge extraction. Given the above challenges and needs, a probabilistic Impact Assessment platform (NanoEIA) was developed making use of Bayesian Networks as a support tool for multi-criteria decision analysis for risk management and safe-by-design ENMs. The proposed NanoEIA platform provides a compact visual representation of the joint distributions of variables and utilizes the information from various nanoinformatics tools such as release rates, exposure and toxicity to define suitable risk metrics. The probabilistic inference approach of NanoEIA is helpful in tracking uncertainties supporting powerful causal as well as influential reasoning techniques. Due to data scarcity, the present approach also incorporates expert elicitation to construct the structure that visually divides the overall representation into the categories of physicochemical properties, exposure, toxicity and risk. Test cases of the proposed approach were developed for CuO and TiO2 nanoparticles in order to demonstrate an approach to NanoEIA that can serve to support decision analysis in a broader context of ensuring sustainable nanotechnology.

Probabilistic nanoinformatics modeling platform for assessing the potential environmental impact of engineered nanomaterials

248th National Meeting (2014)

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