학술논문

A comprehensive framework for evaluating the environmental health and safety implications of engineered nanomaterials.
Document Type
Article
Source
Critical Reviews in Toxicology. Oct2017, Vol. 47 Issue 9, p767-814. 44p. 10 Diagrams, 4 Charts.
Subject
*NANOPARTICLE toxicity
*ENVIRONMENTAL toxicology
*ENVIRONMENTAL health
*OXIDATIVE stress
Language
ISSN
1040-8444
Abstract
Engineered nanomaterials (ENM) are a growing aspect of the global economy, and their safe and sustainable development, use, and eventual disposal requires the capability to forecast and avoid potential problems. This review provides a framework to evaluate the health and safety implications of ENM releases into the environment, including purposeful releases such as for antimicrobial sprays or nano-enabled pesticides, and inadvertent releases as a consequence of other intended applications. Considerations encompass product life cycles, environmental media, exposed populations, and possible adverse outcomes. This framework is presented as a series of compartmental flow diagrams that serve as a basis to help derive future quantitative predictive models, guide research, and support development of tools for making risk-based decisions. After use, ENM are not expected to remain in their original form due to reactivity and/or propensity for hetero-agglomeration in environmental media. Therefore, emphasis is placed on characterizing ENM as they occur in environmental or biological matrices. In addition, predicting the activity of ENM in the environment is difficult due to the multiple dynamic interactions between the physical/chemical aspects of ENM and similarly complex environmental conditions. Others have proposed the use of simple predictive functional assays as an intermediate step to address the challenge of using physical/chemical properties to predict environmental fate and behavior of ENM. The nodes and interactions of the framework presented here reflect phase transitions that could be targets for development of such assays to estimate kinetic reaction rates and simplify model predictions. Application, refinement, and demonstration of this framework, along with an associated knowledgebase that includes targeted functional assay data, will allow betterde novopredictions of potential exposures and adverse outcomes. [ABSTRACT FROM AUTHOR]