Dynamic probabilistic material flow analysis of nano-SiO2, nano iron oxides, nano-CeO2, nano-Al2O3, and quantum dots in seven European regions

Dynamic probabilistic material flow analysis of nano-SiO2, nano iron oxides, nano-CeO2,... Static environmental exposure assessment models based on material flow analysis (MFA) have previously been used to estimate flows of engineered nanomaterials (ENMs) to the environment. However, such models do not account for changes in the system behavior over time. Dynamic MFA used in this study includes the time-dependent development of the modelling system by considering accumulation of ENMs in stocks and the environment, and the dynamic release of ENMs from nano-products. In addition, this study also included regional variations in population, waste management systems, and environmental compartments, which subsequently influence the environmental release and concentrations of ENMs. We have estimated the flows and release concentrations of nano-SiO2, nano-iron oxides, nano-CeO2, nano-Al2O3, and quantum dots in the EU and six geographical sub-regions in Europe (Central Europe, Northern Europe, Southern Europe, Eastern Europe, South-eastern Europe, and Switzerland). The model predicts that a large amount of ENMs are accumulated in stocks (not considering further transformation). For example, in the EU 2040 Mt of nano-SiO2 are stored in the in-use stock, 80,400 tonnes have been accumulated in sediments and 65,600 tonnes in natural and urban soil from 1990 to 2014. The magnitude of flows in waste management processes in different regions varies because of differences in waste handling. For example, concentrations in landfilled waste are lowest in South-eastern Europe due to dilution by the high amount of landfilled waste in the region. The flows predicted in this work can serve as improved input data for mechanistic environmental fate models and risk assessment studies compared to previous estimates using static models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Pollution Elsevier

Dynamic probabilistic material flow analysis of nano-SiO2, nano iron oxides, nano-CeO2, nano-Al2O3, and quantum dots in seven European regions

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0269-7491
D.O.I.
10.1016/j.envpol.2018.01.004
Publisher site
See Article on Publisher Site

Abstract

Static environmental exposure assessment models based on material flow analysis (MFA) have previously been used to estimate flows of engineered nanomaterials (ENMs) to the environment. However, such models do not account for changes in the system behavior over time. Dynamic MFA used in this study includes the time-dependent development of the modelling system by considering accumulation of ENMs in stocks and the environment, and the dynamic release of ENMs from nano-products. In addition, this study also included regional variations in population, waste management systems, and environmental compartments, which subsequently influence the environmental release and concentrations of ENMs. We have estimated the flows and release concentrations of nano-SiO2, nano-iron oxides, nano-CeO2, nano-Al2O3, and quantum dots in the EU and six geographical sub-regions in Europe (Central Europe, Northern Europe, Southern Europe, Eastern Europe, South-eastern Europe, and Switzerland). The model predicts that a large amount of ENMs are accumulated in stocks (not considering further transformation). For example, in the EU 2040 Mt of nano-SiO2 are stored in the in-use stock, 80,400 tonnes have been accumulated in sediments and 65,600 tonnes in natural and urban soil from 1990 to 2014. The magnitude of flows in waste management processes in different regions varies because of differences in waste handling. For example, concentrations in landfilled waste are lowest in South-eastern Europe due to dilution by the high amount of landfilled waste in the region. The flows predicted in this work can serve as improved input data for mechanistic environmental fate models and risk assessment studies compared to previous estimates using static models.

Journal

Environmental PollutionElsevier

Published: Apr 1, 2018

References

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