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Leanne Marie Gilbertson is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Pittsburgh. After receiving her bachelor's degree in chemistry from Hamilton College in 2007, Dr. Gilbertson was a secondary school teacher for several years. She earned her PhD in environmental engineering from Yale University in 2014 with support from a NSF Graduate Research Fellowship and an EPA STAR Fellowship and remained at Yale as a postdoctoral associate prior to starting at Pitt. Dr. Gilbertson's research aims to inform sustainable design of emerging materials and products to ensure the safe realization of novel technologies.
Benjamin Alex Wender is a PhD candidate in the School of Sustainable Engineering and the Built Environment at Arizona State University (ASU) where he researches life cycle assessment (LCA) for emerging technologies. After receiving a bachelor's degree in physics from Hampshire College in 2008, he was awarded a Center for Nanotechnology in Society-ASU Engineering Fellowship in 2011 to research the intersection of participatory governance and LCA. Most recently he was awarded an EPA STAR fellowship in 2014 to develop life cycle-based tools that aid decision makers in exploring environmental uncertainties early in technology development to support responsible innovation practices.
Julie Beth Zimmerman is a Professor jointly appointed to the Department of Chemical and Environmental Engineering and the School of Forestry and Environment at Yale University. Dr. Zimmerman is the Associate Director for Research at the Yale Center for Green Chemistry and Green Engineering. Her research interests focus broadly on green chemistry and green engineering with a specific focus on renewable chemicals, materials, and fuels, sustainable water treatment, and sustainability assessments of emerging technologies. Further, to enhance the likelihood of successful implementation of these next generation designs, Dr. Zimmerman studies the effectiveness and impediments of current and potential policies developed to advance sustainability.
Matthew Eckelman is an Assistant Professor at Northeastern University in Civil and Environmental Engineering, with secondary appointments in Chemical Engineering and Public Policy. His research interests include energy and emissions modeling, life cycle assessment, and environmental analysis of novel materials. Dr. Eckelman consults regularly on sustainability-related projects with a range of businesses, non-profit institutions, and government agencies, and has served on panels at the U.S. National Academies and the National Institute for Standards and Technology on sustainable materials and chemical life cycle issues.
Comprehensive life cycle impact assessment (LCIA) of engineered nanomaterials (ENMs) and nano-enabled products requires quantification of impacts associated with conventional chemical and ENM releases. However few published assessments to date address nano-scale emissions, which precludes use of LCIA to identify the most significant environmental and human health ‘hot spots’. This frontier review summarizes recent advancements and challenges in LCIA for ENMs, focusing on human and ecotoxicity impact assessment models, and identifies recent nano-specific environment, health, and safety literature with promise to inform LCIA model development. Throughout, the manuscript calls for closer collaboration between experimental investigation and modeling research such that experimental data collection is prioritized according to the greatest life cycle uncertainties and modeling needs.
The historical focus on designing for function without a complementary focus on hazard has led to the unintended environmental and human health consequences of widely utilized substances such as asbestos and dichlorodiphenyltrichloroethane (DDT), motivating a more proactive and comprehensive approach to evaluating emerging chemicals, materials, and products. The potential widespread use of engineered nanomaterials (ENMs) and nano-enabled products for applications in diverse sectors (e.g., health care, consumer products, electronics, national defense, and environmental remediation) is coupled with concern over adverse impacts upon exposure to humans and the environment. Releases of ENMs can occur at multiple stages along the life cycle of a nano-enabled product, for example as uncontrolled emissions during ENM synthesis, wear-and-tear during use, or from waste management facilities processing nano-wastes and nano-enabled products. The chemical and physical form of the emissions varies along these points, as does the potential for human or ecological exposures, which necessitates a life cycle perspective when approaching issues of holistically designing safer nanomaterials. In addition to the potential adverse impacts of emitted ENMs themselves, there are concerns from non-nano emissions associated with nano-enabled products. For example, the formation and potential release of harmful polyaromatic hydrocarbons and volatile organic compounds during carbon nanotube (CNT) synthesis.1,2 As such, an approach that is pro-active, life-cycle based, and uses multiple criteria is necessary to identify potential unintended consequences and contribute to responsible development of ENMs and nano-enabled products.
Life cycle assessment (LCA) is one such approach that has been recommended by the National Nanotechnology Initiative4 and the National Research Council3,4 and is increasingly applied for ENMs and nano-enabled products. LCA, widely used in the chemical and product manufacturing sectors, is a systems-level methodology for evaluating environmental and human health impacts associated with a product or process. LCA methods have been prescribed in a series of international standards,5,6 and consist of four steps: (1) goal and scope definition, where the unit of analysis and system boundary of the study is established; (2) life cycle inventory (LCI) modeling, which accounts for each discrete energy and material input and emission across the life cycle of the product – including activities such as mining, processing of primary materials, manufacturing, use, transportation, and disposal; (3) life cycle impact assessment (LCIA), which uses coupled fate-exposure-effect models to translate the mass of each emission in the LCI into a quantified measure of potential environmental and/or human health impacts using so-called characterization factors (CFs); and (4) interpretation of results. LCA is a multi-criteria assessment tool, as separate CFs are applied for each substance and across a variety of impact categories such as global warming potential, ozone depletion, human toxicity and ecotoxicity, as presented in Fig. 1.
Fig. 1 Schematic representation of the life cycle stages and overview of the life cycle assessment (LCA). Life cycle inventory analysis quantifies resource use and emissions across the product life cycle and mid-point impact assessment uses characterization factors to convert LCI entries into environmental and human heath damages. Dashed lines in the schematic represent active research areas in need of greater coordination between experimental and modeling efforts as it related to nanomaterials and nano-enabled products. eq. = equivalents. CTUeco = comparative toxicity units for ecotoxicity.
Recent reviews summarize the accomplishments and critical challenges encountered in the application of LCA to the study of ENMs and nano-enabled products.7–9 These reviews draw several important conclusions including: 1) the majority of nano LCA studies to date are cradle-to-gate and do not include use or end-of-life considerations and 2) ENM releases are not commonly considered at any stage, which is in part due to the lack of inventory data and characterization factors for ENMs, as well as the significant uncertainty in use- and end-of-life stages;7–9 3) initial incorporation of this critical information, as it becomes available, can be facilitated by using existing tools and experimental data;9 4) many nano LCAs are constrained to mass-based functional units, which is inappropriate for quantifying product functionality comparisons as is standard in LCA;8 and 5) given the previously identified shortcomings, much can be learned from qualitative or screening-level assessments.7,9 This is not an unusual state of affairs for the assessment of emerging technologies,10 as environmental modeling tools are routinely adapted to incorporate new substances and experimental data.
Despite the progress in nano LCA described above, the capabilities of the current life cycle impact assessment models remain inapplicable in a comprehensive and universal manner to ENMs and the products they enable. Experimental studies pertaining to ENM transport, fate and transformations in the environment, occupational safety, and nanotoxicology continue to advance such that significant data and expertise are available to inform LCIA. Nonetheless, ENM-specific impact assessment models or CFs are not included in any commercial or publically available LCIA packages. Given the large number of ENMs, release scenarios, surface modifications, and possible permutations of these characteristics, there exists a need to prioritize data collection30 and improve model parsimony. Table 1 compiles prominent life cycle concerns associated with several commercially relevant ENM classes and calls attention to their similarities and differences pertinent to LCIA. The final column in Table 1 suggests those midpoint impact categories most relevant to the given ENM class based on the product categories, potential for release, exposure routes, transformations, and mechanisms of biological activity. It becomes clear that certain impact categories are more relevant for certain ENM classes and life cycle stages as indicated by the frequency of appearance throughout the table (e.g., human and ecotoxicity categories).
The need for environmental research prioritization is not unique to ENMs and nano-enabled products, but rather is shared by other emerging technologies. Meaningful inclusion of ENM releases, fate, exposure, and effects in LCIA models can be accelerated through coordinated efforts between experimentalists and life cycle modelers. Specifically, cooperative efforts early in experimental design can tailor data collection toward the greatest modeling uncertainties while fostering development of innovative modeling approaches. In particular, there is a need for sensitivity analyses of LCIA models to identify which parameters are most influential to model results and then integrating these data needs into experimental design to narrow specific uncertainty ranges. These data needs and modeling advances are discussed first as they relate to ENM releases (treated as environmental emissions in the life cycle inventory) and second to development of nano-specific characterization factors (life cycle impact assessment). While this review identifies many nano-LCIA experimental and modeling challenges, it cautions attempts to create nano-specific models that are overly detailed and have limited utility for risk modeling or decision making. Rather, coordination between both experimental and modeling approaches can enable iterative sensitivity analyses, direct data sharing that can identify which uncertainties are significant while others may be revealed as low priorities for further investigation, and inform experimental designs and priorities according to the greatest life cycle uncertainties.
Incorporation of ENM properties, namely shape and size, in LC inventory modeling was recently recommended in addition to mass and chemical composition44 that are currently considered for conventional chemicals. This approach stops short of tracking changes in ENM morphology and physiochemical properties when released from different products and life cycle stages – for example the loss or gain of surface functional groups – that will influence their fate, exposure, and toxicity potentials. This is problematic, as LCI modeling of conventional chemicals sums the total mass of each emitted material across all life cycle stages, implicitly assuming that releases from manufacturing, use, and end-of-life are environmentally equivalent. Thus, experimental efforts to quantify life cycle ENM releases should prioritize commercially-relevant nano-enabled products and explore the extent to which environmental residence times, bioavailability, and toxicity change across the life cycle or with different surface modifications. This can inform LCI modeling by identifying which ENM emissions and release scenarios are suitably different to necessitate distinct LCI entries and those that may be grouped into one entry with minimal increases in uncertainty.
3. Effect factor (EF), which represents the aggregated toxicological response of multiple organisms or humans upon exposure to a known dose.
Experimental data and mechanistically appropriate models are required to calculate each of these factors and to reduce the high ENM parameter and model uncertainty. In the following sections the relatively small but growing body of literature advancing LCIA of ENMs is reviewed, with a focus on published methodological improvements arising from the sustained interest in the environmental impacts of ENMs.
While there is significant uncertainty surrounding current ENM fate modeling approaches, experimentalists can expedite resolution that is more appropriate for a given class of ENMs. Future LCIA model development should assess the sensitivity of CF results to those mechanisms relevant to ENMs, including removal, stabilization and transformations for separate classes of ENMs since their behavior can be markedly different (e.g., carbonaceous vs. nanocellulose vs. metal and metal oxide ENMs). Those physicochemical properties that most determine fate for each ENM class should then be required in specifying ENM emissions in the life cycle inventory. In addition, adjustments made to existing models, such as USEtox, should aim to include these nano-relevant removal pathways, including aggregation and settling.
While there remain several challenges for insoluble ENMs, there is increased evidence for a distinct difference in the magnitude of toxicity of released ions upon dissolution of nanomaterials compared to the parent nanomaterial, and is particularly true for silver nanoparticles (with enhanced surface area to volume ratio and thus, more surface atoms, nanomaterials undergo dissolution more rapidly than their compared to their bulk counterparts).103,109 A recent meta-analysis comparing the ecotoxicity of three soluble ENMs – nano Ag, CuO, and ZnO – to their ionic counterparts found that the ENMs displayed reduced toxicity in the majority of studies and exceed the ionic form only in worst case scenarios.110 Thus, studies that assume a fixed percentage ionic release from ENMs18,111 and rely on existing EFs and CFs for ionic metal potentially overestimate toxicity impacts. To this end, there is the opportunity for enhanced resolution of environmental and human health impact evaluation of ENMs. With the currently available data it is possible to develop novel or update current effect models that will elucidate relevant obstacles and therefore, be able to direct future toxicological data acquisition.
The numerous data gaps, high uncertainty in experimental protocols and published parameter estimates, and rapid evolution of modeling approaches leaves development of robust LCI data and ENM-specific impact assessment models an ongoing endeavor. The possible permutations resulting from the large number of ENMs, associated surface modifications, and release scenarios creates a need for research prioritization that can be facilitated through greater coordination between modeling and experimental approaches. The early efforts reviewed herein point to innovative LCIA modeling approaches that, in the absence of clear mechanistic understanding, have combined probabilistic uncertainty modeling with scenario development to produce actionable results. In outlining where recent experimental advances can inform modifications to CF calculation for ENMs this review identifies several specific recommendations for experimentalists and LCA modelers to coordinate research agenda to streamline progress toward responsible development of nano-enabled products.
1) It is of critical importance to include ENM releases in nano LCA studies – despite uncertainties of current models and data – to assess the relative magnitude of ENM emissions within broader life cycle impacts.
2) Rather than adopting one fate modeling approach (e.g., either partitioning or colloidal), LCIA method developers should evaluate the sensitivity of FF and CF results to the choice of fate model as a way to prioritize further experimental investigation.
3) Robust impact assessments rely on relevant information being included in the life cycle inventory. Size and morphology have been recommended in specifying ENM emissions. In addition to identifying ENM attributes that govern property-hazard relationships, we recommend a consensus process around which ENM attributes are important in determining ENM fate and subsequent inclusion of these attributes during the life cycle inventory stage.
4) Not all life cycle impact categories are of equal concern when considering direct environmental and human health impacts of ENMs, especially under different release scenarios. As such, it is suggested to build consensus regarding priority categories and release scenarios for which nano-specific characterization factors will most improve understanding of these impacts.
5) Experimental investigations should follow an analytical sequence that considers first, the potential and likelihood of ENM release at each life cycle stage, the transport and fate of the released ENM (in parent, transformed, and/or complex matrix form), exposure of the released ENM (including appropriate dosimetry considerations), and finally, the effect (adverse or otherwise) caused by exposure to the delivered dose.
6) LCIA of ENM-enabled products requires more detailed characterization of ENMs as they are released from products (e.g., aged, transformed, composites) and overtime, as opposed to the raw or pristine forms.
LMG, BAW, and JBZ acknowledge the generous support of the U.S. Environmental Protection Agency (EPA) Assistance Agreement no. RD83558001-0. BAW acknowledges support from the EPA Science to Achieve Results program through grant no. FP917643 and the National Science Foundation (NSF) through grant no. ECCS-1140190 and grant no. SES-0937591. MJE recognizes the support from the NSF through award SNM-1120329. This work has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication.
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† Contributed equally to this work.

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