ReviewA critical review of engineered nanomaterial release data: Are current data useful for material flow modeling?

- We review the literature on release of nanomaterials from products.
- We analyze whether the data can be exploited by release models.
- We provide recommendations for the release assessment and release modelling literature.

Material flow analysis (MFA) is a useful tool to predict the flows of engineered nanomaterials (ENM) to the environment. The quantification of release factors is a crucial part of MFA modeling. In the last years an increasing amount of literature on release of ENM from materials and products has been published. The purpose of this review is to analyze the strategies implemented by MFA models to include these release data, in particular to derive transfer coefficients (TC). Our scope was focused on those articles that analyzed the release from applications readily available in the market in settings that resemble average use conditions. Current MFA studies rely to a large extent on extrapolations, authors' assumptions, expert opinions and other informal sources of data to parameterize the models. We were able to qualitatively assess the following aspects of the release literature: (i) the initial characterization of ENM provided, (ii) quantitative information on the mass of ENM released and its characterization, (iii) description of transformation reactions and (iv) assessment of the factors determining release. Although the literature on ENM release is growing, coverage of exposure scenarios is still limited; only 20% of the ENMs used industrially and 36% of the product categories involved have been investigated in release studies and only few relevant release scenarios have been described. Furthermore, the information provided is rather incomplete concerning descriptions and characterizations of ENMs and the released materials. Our results show that both the development of methods to define the TCs and of protocols to enhance assessment of ENM release from nano-applications will contribute to increase the exploitability of the data provided for MFA models. The suggestions we provide in this article will likely contribute to an improved exposure modeling by providing ENM release estimates closer to reality.

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