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Linking Exposure and Kinetic Bioaccumulation Models for Metallic Engineered Nanomaterials in Freshwater Ecosystems

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posted on 07.09.2018, 00:00 by Kendra L. Garner, Yuwei Qin, Stefano Cucurachi, Sangwon Suh, Arturo A. Keller
We developed a model (nanoBio) to simulate long-term kinetic bioaccumulation of metallic engineered nanomaterials (ENMs) across trophic levels within a freshwater aquatic ecosystem based on current understanding of environmental and biological fate. Seven species were chosen to understand exposure pathways, accumulation through trophic levels, and the potential for biomagnification. Uptake, elimination, and dissolution of the ENM are the only processes modeled, though different routes and rates are accounted for with each species. We explored the bioaccumulation of nCuO, nTiO2, and nZnO. nanoBio estimates the potential range in average body concentration across populations. Estimated bioconcentrations ranged from 1.7 × 10–8 pg nCuO g–1 for Selenastrum capricornutum to 27 μg nTiO2 g–1 for Oncorhynchus mykiss. The highest overall biomagnification was predicted for nTiO2 within the highest trophic level species. ENM dissolution decreases total biomagnification; however, the released metal ions may still cause toxicity. nanoBio results serve to (1) highlight trophic levels at potentially higher risk of bioaccumulation; (2) temporal patterns that influence peaks in concentration; (3) processes which require more experimental data to reduce uncertainty. Based on a sensitivity analysis, the most significant parameters to the variability in estimates include uptake rates from multiple exposure routes and assimilation efficiency, which has a substantial impact on biomagnification. Better understanding of the mechanisms and processes that impact bioaccumulation through targeted laboratory testing will greatly improve the predictive accuracy of nanoBio. We should stress the conditional nature of the rate constants used in this study, because the environment, the biology, and the toxicity itself can alter these parameter values over time. The model also can be used to guide testing protocols to determine key parameter values that influence bioaccumulation.

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