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  • Title: Possibilities and limitations of modeling environmental exposure to engineered nanomaterials by probabilistic material flow analysis.
    Author: Gottschalk F, Sonderer T, Scholz RW, Nowack B.
    Journal: Environ Toxicol Chem; 2010 May; 29(5):1036-48. PubMed ID: 20821538.
    Abstract:
    Information on environmental concentrations is needed to assess the risks that engineered nanomaterials (ENM) may pose to the environment. In this study, predicted environmental concentrations (PEC) were modeled for nano-TiO2, carbon nanotubes (CNT) and nano-Ag for Switzerland. Based on a life-cycle perspective, the model considered as input parameters the production volumes of the ENMs, the manufacturing and consumption quantities of products containing those materials, and the fate and pathways of ENMs in natural and technical environments. Faced with a distinct scarcity of data, we used a probabilistic material flow analysis model, treating all parameters as probability distributions. The modeling included Monte Carlo and Markov Chain Monte Carlo simulations as well as a sensitivity and uncertainty analysis. The PEC values of the ENMs in the different environmental compartments vary widely due to different ENM production volumes and different life cycles of the nanoproducts. The use of ENM in products with high water relevance leads to higher water and sediment concentrations for nano-TiO2 and nano-Ag, compared to CNTs, where smaller amounts of ENM reach the aquatic compartments. This study also presents a sensitivity analysis and a comprehensive discussion of the uncertainties of the simulation results and the limitations of the used approach. To estimate potential risks, the PEC values were compared to the predicted-no-effect concentrations (PNEC) derived from published data. The risk quotients (PEC/PNEC) for nano-TiO2 and nano-Ag were larger than one for treated wastewater and much smaller for all other environmental compartments (e.g., water, sediments, soils). We conclude that probabilistic modeling is very useful for predicting environmental concentrations of ENMs given the current lack of substantiated data.
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