Getting to Green - Research Progress on Environmental Health and Safety Aspects of Engineered Nanomaterials - page 54

characterizing their structure, composition, and purity—substantial barriers to studying their effects on
health and the environment. Those barriers reflect the lack of information on the starting composition and
structure of the materials and the lack of knowledge of their history.
 Mechanisms and incentives for collecting information . Information management plans and
appropriate research infrastructure are needed to create a process for collecting information on
nanomaterial production and uses along the value chain.
Steps to Ensure Progress Toward Characterizing Commercial Sources of Nanomaterials
 Greater investment in research at the interface between the physical sciences, social
sciences, and business. A full understanding of potential risks along the value chain requires broad
and multidisciplinary expertise that will bridge physical and social sciences and engage the commercial
sector. The critical topics include trends in nanomaterial production, value-chain analysis, and human
behavior in relation to use of products that contain ENMs and the potential for exposures along the value
chain and throughout the life cycle. An improved understanding of those factors is needed as a starting
point for modeling nanomaterial exposure along the value chain.
MODEL DEVELOPMENT
A key outcome of the integration of data and information contained in the knowledge commons is
development of a suite of models. The models allow the application of new methods and instruments that
reflect thinking regarding hypothesis testing and assessment. Such models may be used to predict
physical characteristics of ENMs, outcomes of toxicity testing, and exposure potential in complex
systems. In their initial forms, the models represent working assumptions that are refined with additional
data. As confidence in a model increases, validation studies that involve comparisons of model outputs
with results from experimental systems that use benchmark or unknown ENMs can be conducted. The
process of data integration and model formulation and validation informs risk assessment. Given adequate
knowledge, refined and validated models allow prediction of potential hazards associated with exposure
to ENMs throughout their life cycle and value chain.
Mechanistic models should provide the greatest long-term benefit to the EHS nanotechnology
research community with regard to anticipating risks. However identifying the critical elements of
nanomaterial-environment and nanomaterial-biota interactions is a significant undertaking and will take
time to develop. There is a near term need to predict behaviors of nanomaterials in relevant environmental
and biologic matrices. Empirical predictive models that are parameterized appropriately (for example,
partition coefficients between nanomaterials and bacteria in wastewater treatment plants or approximate
dissolution rates and half times in specific media) may be sufficient to approximate behaviors of ENMs in
selected matrices. The forms of these predictive models, their parameters, and appropriate assays to
measure the values for these parameters in selected environmental and biologic media are still needed
(Hou et al. 2013; Westerhoff and Nowack 2013).
As described in Chapter 3, several indicators of research progress involve successful model
development. They include qualitative and quantitative models to characterize the origins and releases of
ENMs into the environment. The ability to address potential releases, transformations, environmental
concentrations, and exposures was highlighted previously. Efforts appear to be focused on specific release
points and routes of exposures (see examples in Chapter 3); progress in this indicator is considered to be
minimal (red in Table 3-1). Progress is hampered by a lack of information on ENMs in the value chain for
particular ENM-containing products and a lack of data from experimental studies to inform modeling
efforts on fate and transport in the environment. In another research indicator in Chapter 3, progress
toward the use of experimental research results in initial modeling efforts for predicting ENM behavior in
complex biologic and environmental settings was considered minimal (red). Because ENM behavior will
be influenced by the characteristics of the material and the properties of the system into which it is