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Issue 4, 2016
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Multimedia environmental fate and speciation of engineered nanoparticles: a probabilistic modeling approach

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Abstract

The robustness of novel multimedia fate models in environmental exposure estimation of engineered nanoparticles (ENPs) remains unclear, because of uncertainties in the emission, physicochemical properties and natural variability in environmental systems. Here, we evaluate the uncertainty in predicted environmental concentrations (PECs) by using the SimpleBox4nano (SB4N) model. Monte Carlo (MC) simulations were performed on the environmental fate, concentrations and speciation of nano-CeO2, -TiO2 and -ZnO. Realistic distributions of uncertainty and variability were applied for all of SB4N's input and model parameter values. Environmental distribution over air, water, soil and sediment as well as nanomaterial speciation across natural colloid and coarse particles appeared to be similar for nano-CeO2, -TiO2 and -ZnO. ENPs in the atmosphere were effectively removed by deposition. ENPs in the water column were removed through hetero-aggregation–sedimentation with natural particles. ENPs accumulated in soil by attachment to grains. The sources of uncertainty and variability driving variation in PECs, which was identified in Spearman rank analysis, were related to production, emission, compartment volumes, and removal by dissolution or advection and appeared to be similar for the three ENPs. The variation in speciation within environmental compartments was influenced most by the physicochemical properties of the ENP and by model parameters that relate to the compartment of interest.

Graphical abstract: Multimedia environmental fate and speciation of engineered nanoparticles: a probabilistic modeling approach

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Publication details

The article was received on 22 Mar 2016, accepted on 23 May 2016 and first published on 24 May 2016


Article type: Paper
DOI: 10.1039/C6EN00081A
Environ. Sci.: Nano, 2016,3, 715-727
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    Multimedia environmental fate and speciation of engineered nanoparticles: a probabilistic modeling approach

    J. A. J. Meesters, J. T. K. Quik, A. A. Koelmans, A. J. Hendriks and D. van de Meent, Environ. Sci.: Nano, 2016, 3, 715
    DOI: 10.1039/C6EN00081A

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