Modeling Approaches for Characterizing and Evaluating

Critical Review pubs.acs.org/est

Modeling Approaches for Characterizing and Evaluating Environmental Exposure to Engineered Nanomaterials in Support of Risk-Based Decision Making Christine Ogilvie Hendren,†,∥,* Michael Lowry,† Khara D. Grieger,† Eric S. Money,§,∥ John M. Johnston,‡ Mark R. Wiesner,§,∥ and Stephen M. Beaulieu† †

RTI International, 3040 Cornwallis Road, Research Triangle Park, North Carolina 27709, United States Ecosystems Research Division, Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency, 960 College Station Road, Athens, Georgia 30605, United States § Department of Civil and Environmental Engineering, Duke University, 121 Hudson Hall, Durham, North Carolina 27708, United States ∥ Center for the Environmental Implications of NanoTechnology (CEINT), PO Box 90287, Duke University, Durham, North Carolina 27708-0287, United States ‡

S Supporting Information *

ABSTRACT: As the use of engineered nanomaterials becomes more prevalent, the likelihood of unintended exposure to these materials also increases. Given the current scarcity of experimental data regarding fate, transport, and bioavailability, determining potential environmental exposure to these materials requires an in depth analysis of modeling techniques that can be used in both the near- and long-term. Here, we provide a critical review of traditional and emerging exposure modeling approaches to highlight the challenges that scientists and decision-makers face when developing environmental exposure and risk assessments for nanomaterials. We find that accounting for nanospecific properties, overcoming data gaps, realizing model limitations, and handling uncertainty are key to developing informative and reliable environmental exposure and risk assessments for engineered nanomaterials. We find methods suited to recognizing and addressing significant uncertainty to be most appropriate for near-term environmental exposure modeling, given the current state of information and the current insufficiency of established deterministic models to address environmental exposure to engineered nanomaterials.

1. INTRODUCTION

of environmental health and safety research into ENMs is to inform risk assessments and that risk is a product of both hazard and exposure, the importance of understanding environmental exposure to these materials through their fate and transport behavior is paramount to characterizing, and ultimately managing, their potential risk. The need and appreciation for environmental exposure research is increasing. Multiple organizations and researchers involved with advancing nanoenvironmental health and safety (nanoEHS) issues have specifically called for additional investigation of the potential for environmental exposure to ENMs.5−9 Three recent workshops held by EPA’s National Center for Environmental Assessment identified fate and

The introduction of engineered nanomaterials (ENMs) to the natural environment is inevitable with the increasing ubiquity of nanotechnology in applications ranging from industry to consumer products. As these novel materials are produced and utilized across an increasingly broad range of applications, there is potential across all stages of the value chain for their release to the environment and for possible unknown effects.1−3 Maximizing potential benefits of nanotechnology, while minimizing unintended negative consequences, ultimately depends upon understanding and managing possible related impacts. In the past decade, a number of studies have started to investigate the potential health, environmental, and safety impacts of ENMs. A Spring 2012 review of the International Council on Nanotechnology (ICON) database of nanorelated publications, however, reveals that, of all related environmental health and safety publications, approximately 90% represent toxicity and effects research while only 10% involve environmental fate and transport studies.4 Given that a main purpose © 2013 American Chemical Society

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July 8, 2012 November 21, 2012 January 7, 2013 January 8, 2013 dx.doi.org/10.1021/es302749u | Environ. Sci. Technol. 2013, 47, 1190−1205

Environmental Science & Technology

Critical Review

mental exposure assessment. In this analysis, specific attention is paid to capturing the complexity of ENM transport characteristics and associated data gaps, the variability in ENM types and properties, and limitations of current modeling approaches. We consider the goal of ENM environmental exposure modeling to be (i) to support assessments of risks that may be posed by ENMs and (ii) to support subsequent decisions to mitigate those risks. In proposing next steps for advancing the field, we address not only how environmental exposure models could be adapted or developed for ENMs, but also other methods that could be used to support evaluations of environmental exposure to ENMs. Therefore, the scope of the methodologies included in this review is not limited to strictly quantitative environmental exposure models but also encompasses other methods that could be used to support nano environmental exposure assessment in a qualitative or directional sense. The term “ENMs” used in this paper includes engineered nanoparticles, nanorods, nanoplates, and other nanostructured surfaces.21 This review of literature published through Spring 2012, did not explicitly consider ENMs that may be contained within polymers or other composite matrices. There have been very few studies to date that explicitly investigate environmental exposures to matrix-bound ENMs.22−24 While there is also a dearth of data on release rates of unbound particles, it was important to avoid complication such as knowledge of the initial mass of ENMs included in a solid matrix in addition to their release rate. Therefore we focused on the more straightforward release of particles directly to the environment. Additional modeling requirements, such as understanding the release potential of ENMs from these matrices and their distribution within products, may be needed; therefore, a standalone review for these types of materials is suggested. Additionally, since the design of environmental exposure models is influenced by the types of information required to support regulations, it should be noted that this review focused on U.S. regulatory literature.

transport research to be among the highest priority issues for characterizing environmental impacts of ENMs.10−12 Exposure assessment is the process of measuring and predicting the magnitude, frequency, and duration of contact between a potentially harmful agent and a target population.13 Estimating potential exposures requires prediction of the fate and transport behavior of materials that can result in such contact. Traditional exposure science seeks to represent the processes and flows of materials within an ecosystem, with the goal of predicting potential effects associated with the introduction of a physical, chemical, or biological stressor.14 In the case of ENMs, exposure assessment must account for the influence of their novel properties and the myriad scientific uncertainties. Predictive modeling of ENM behavior in the environment requires an understanding of the (1) potential sources of ENMs; (2) distribution of ENMs after release into the environment; and (3) transformations and persistence of ENMs in the environment.15,16 Though challenging to quantify, environmental release estimates are emerging based on approximate magnitudes of ENM production.17 However, significant measurement, detection, and modeling challenges make current estimations of ENM fate highly uncertain; reliable predictive models can only be developed if basic information needs for ENMs are met, including chemical-physical properties, environmental behavior, and relevant human health and ecological receptor characteristics. Given that much of this information is incomplete and absent for many ENMs, predictive exposure modeling will remain highly uncertain for the foreseeable future. Therefore, alternative evaluation methods are needed that explicitly address uncertainty and have the potential to represent environmental exposures in a qualitative or relative sense. In the absence of predictive environmental exposure models that generate quantitative results, the risk assessors and risk managers who would otherwise be consumers of such quantitative results will need methodologies to enable interim decisions on prioritizing risk management and risk research as it pertains to environmental exposure. For this practical reason, this review also addresses emerging methods that address environmental exposure potential in the absence of direct quantitative predictions. Multiple recent guidance documents on transport, fate, and exposure assessment have concluded that relatively simple and low-resolution models that produce more timely outputs with significant uncertainty may be required to support decisions in the near-term.14,18−20 In response, several scientists, researchers, and organizations have submitted tools and frameworks to meet the challenges that ENMs present to traditional environmental exposure and risk assessment. These include adaptive management and evaluation frameworks, decision support and prioritization tools, and Bayesian approaches, as reviewed in Section 5. Though these emerging methods are not limited to quantitative environmental exposure modeling, they are included because of their potential to inform decisions for which environmental exposure predictions are important supporting data. In light of these issues, the aims of this review are to (i) provide a targeted appraisal of current adaptable environmental fate and transport modeling approaches for ENMs, and of emerging approaches for evaluating ENM environmental exposure potential, (ii) identify key environmental exposure modeling challenges that must be addressed to respond robustly to the challenges posed by ENMs, and (iii) propose next steps for advancing the field of nanomaterial environ-

2. MATERIALS AND METHODS This analysis was based on a thorough literature review for ENMs, which included screening the scientific literature, that is, published and peer-reviewed scientific journal articles, publicly available reports and documents pertaining to potential environmental exposure of ENMs, as well as environmental exposure modeling approaches. Readers are also referred to the EPA State-of-the-Science Report on Predictive Models and Modeling Approaches for Characterizing and Evaluating Exposure to Nanomaterials (SOTS)18 for additional information. We acknowledge that the number of potential environmental exposure models is so extensive that a meaningful review can only distill available information from a focused subset of the literature. Other comprehensive reviews regarding environmental exposure of ENMs can be found in Stone et al. (2010), Boxall et al. (2007), Mueller and Nowack (2008), Gottschalk et al. (2011), and Gottschalk and Nowack (2011).25−29 Existing models that have been or could be applied to ENM environmental exposure were evaluated according to their relevance and applicability for predicting ENM transport. We focused on aquatic and terrestrial systems, including transport in surface water, sediments, subsurface (groundwater transport in porous media), and soils. Air models were not covered in this review. We also considered biological uptake and multimedia 1191

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though some models (e.g., EPA’s MINTEQA2) and approaches (e.g., DLVO theory) may be useful in modeling ENMs, these need to be modified and validated to ensure they adequately represent the chemical properties and/or transformation processes relevant to ENMs.20 Thus, while most established models may still provide insight, their potential application is relatively limited for ENM risk assessment purposes.32 Chemical properties required by traditional risk assessment models are inadequate for predicting the environmental transport of ENMs. Because the chemical and physical properties of ENMs are strongly related to the processes that control movement, robust chemical fate and transport predictors, such as water solubility, octanol−water partition coefficient, and vapor pressure, are not as important as properties such as particle size, surface charge, and surface potential.20 Metcalfe et al. (2009) proposed analogous characteristics that might be used for environmental fate and transport modeling of ENMs versus conventional compounds, such as ease of dispersal instead of solubility.33 The current review further supports the consensus that existing environmental fate and transport models must be adapted before being applied reliably to predict the environmental behavior of ENMs. Information is presented here on existing models that had been adapted or were suitable for adaptation to ENMs.

models. Many approaches reviewed are relevant to particular classes of ENMs, notably metals; however, complete evaluation of these approaches can require additional modeling tools such as geochemical speciation modeling, which were outside the scope of this review. Detailed reviews of models and approaches that show promise for ENM transport modeling can also be found in the SOTS report.18 A model evaluation framework was implemented to provide a systematic approach for reviewing and summarizing information. Review categories are consistent with the National Research Council paper, Models in Environmental Regulatory Decision Making.30 These categories include purpose and scope, background and history, complexity, whether and how uncertainty is accounted for in the model, model availability and usability, and applicability of the model to ENM behavior. Several categories of environmental fate and transport models were reviewed with respect to their potential for evaluating ENMs. The categories, some of which can overlap, were based primarily on NRC’s review of regulatory modeling practices at EPA.30 For example, porous media transport mechanisms may be important both for sediments in a surface water model and a groundwater transport model. Nevertheless, model categorization in this review is consistent with the expected structure and scope of environmental fate models. The categories included surface water, subsurface (groundwater and its surrounding porous media), biological uptake, and multimedia models. Within each of these categories, three types were evaluated: (i) models specific to ENMs, (ii) established and widely used regulatory models that could potentially be modified for application to ENMs (e.g., by adding nanospecific parameters or adding modeling components important to describe ENM behavior), and (iii) other models with potential application to ENMs in their present form or with limited modifications (e.g., colloid transport models). Models specifically designed for describing ENM environmental fate and transport could already be employed in evaluations. Of the models that need adaptation, it is important to understand which already have established user communities and acceptance in support of regulatory decisions and which are potentially informative but not yet embedded in the U.S. regulatory process.

4. MODELS FOR CURRENT OR POTENTIAL APPLICATION TO ENM ENVIRONMENTAL EXPOSURE It has been noted that “the exposure assessor’s toolbox is not empty”.34 Although novel methods are needed to update existing modeling approaches to accommodate ENMs, it is important to first evaluate which parts of the wheel need reinventing and which do not. 4.1. Surface Water Models of ENMs. Many traditional environmental fate models of chemicals in aquatic systems consider important processes including dissolution, volatilization, adsorption, biological uptake, photolysis, hydrolysis, and biodegradation. Less common are models that consider aggregation, attachment (often used interchangeably with adsorption in nanoparticle and colloid literature), and sedimentation, all of which are critical to understanding and predicting the environmental fate of ENMs. Models predicting ENM behavior in aquatic environments should account for the processes related to particulates in natural systems. Evaluation of surface water fate and transport models focuses heavily on accounting for these key processes. This review identified three surface water models that have been developed and/or applied specifically to evaluate ENM fate in the environment. Overall these models do incorporate many of the key processes describing nanoparticle behavior but lack explicit consideration of uncertainty. Of these, Mackay et al. (2006) developed a stochastic probability model predicting the environmental stability of nanoparticle suspensions in aqueous solutions and the associated uncertainty.35 Boncagni et al. (2009) implemented an experimental study of the exchange of titanium dioxide nanoparticles between streams and streambed sediments, evaluating the degree of aggregation and sedimentation under a range of conditions (pH and water flow velocity).36 The results were then interpreted with colloid models by Packman et al. (2000).37 Koelmans et al. (2009) carried out a compartmental modeling analysis of mass transfer between surface water and sediments, considering particulate

3. ENMS AND THEIR ENVIRONMENTAL TRANSPORT AND FATE As with environmental exposure modeling of traditional chemicals, the approaches to modeling ENMs will depend on characteristics of the materials themselves, as well as the processes to which they are subjected upon introduction to the environment (e.g., Nowack et al. 2012).16 The complex interactions and feedback loops between ENMs and the environmental systems they encounter will also play important roles in determining their fate, transport, and ultimate impacts. ENM-specific properties and processes are briefly reviewed in the Supporting Information because their incorporation into current and novel environmental exposure models will determine the success in applying such models to risk characterization and forecasting. In general, conventional models for chemical environmental fate and exposure assessment are not directly applicable in their current forms for ENMs.31 For example, the Estimation Programs Interface Suite (EPI Suite) has little applicability to ENMs because it is based on equilibrium partition coefficients and does not consider the behavior of particulates.20 Even 1192



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Table 1. Summary of Potential Surface Water Models and Their Use in ENM Exposure Assessments

established and widely used regulatory models can in theory be adapted for ENMs. Table 1 summarizes the strengths and limitations of the surface water models described above. 4.3. Subsurface Models of ENMs. Many traditional environmental fate models of chemicals in terrestrial systems consider processes such as dissolution, volatilization, adsorption to organic and inorganic matter, biological uptake, photolysis, hydrolysis, and biodegradation. Less common are models that address processes of aggregation, attachment (a term widely used in nanoparticle literature to encompass adsorption behaviors as well as more general surface adhesion between ENMs and other surfaces not described by adsorption theory), and sedimentation, all of which are critical to understanding and predicting the environmental fate of ENMs. Because models predicting ENM behavior in terrestrial environments must account for the processes related to particulates in natural systems, this review (of subsurface fate and transport models) focuses on whether they account for these key processes. Some established modeling approaches show promise for modeling subsurface ENMs, particularly colloid transport models.31 To date, only a few subsurface models have been developed and/or applied specifically to evaluate ENM fate in the environment. Similar to the case of ENM-specific surface water models, these models tend to account for some important processes for nanomaterial behavior (e.g., attachment) and in some cases are even well validated with experimental data, but again, they do not account explicitly for uncertainty and require significant assumptions with respect to input parameters. Tosco and Sethi (2009) developed a 1D model called MNM1D (micro and nanoparticle transport model in porous media) which considers constant or transient hydrochemical parameters (e.g., ionic strength) and describes attachment and detachment phenomena, while accounting for multiple attachment sites (www.polito.it/groundwater/

transport processes. Their model estimated steady-state concentrations of carbon-based nanoparticles by accounting for processes of sedimentation, aggregation, degradation, and burial in deeper sediment layers.38 4.2. Regulatory Surface Water Models. Several established surface water models have been used in risk assessments to support EPA regulatory programs.30 Although they represent only a subset of available models, they are among the most widely applied and representative of typical risk assessment modeling practice, particularly by EPA. Three moderately complex conceptual models were selected for review that consider some of the key processes characterizing particulate transport in aqueous systems, including sedimentation, resuspension, and particulate advection. The Hydrological Simulation Program−FORTRAN (HSPF) is utilized to simulate watershed hydrology and water quality. HSPF adopts a basin-scale approach by incorporating pollutant source models and fate and transport in one-dimensional stream channels. Another model, Water quality Analysis Simulation Program (WASP), provides a dynamic compartment-modeling approach for aquatic systems including the water column and underlying sediments. It can be applied in 1D, 2D, and 3D modes for a variety of pollutant types, including particulates, and has been used to evaluate eutrophication, phosphorus loading, bacterial contamination, as well as PCB, VOC, and heavy metal pollution. QUAL2K is a relatively simple model for simulating flow and water quality in rivers and streams. A wide range of chemical and biological pollutants within a river can be modeled, accounting for many physical−chemical processes and conditions. One potential limitation of these models is their lumped parameter formulation. Also, the applicability of these models for predicting ENM behavior is significantly limited by lack of both process knowledge and available empirical data characterizing ENMs. Nevertheless, such 1193



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Table 2. Summary of Potential Sub-Surface Models and Their Use in ENM Exposure Assessments

software).39 Ju and Fan (2009) developed a 1D nanoparticle transport model for enhanced oil recovery (EOR) applications, which involves injecting polysilicon nanoparticles to change the solid matrix from an oil-wet to a water-wet system.40 Li et al. (2008) developed a model to evaluate the transport of fullerene (C60) nanoparticles, accounting for nonequilibrium attachment kinetics and predicting maximum retention capacity based on flow velocity, nanoparticle size, and mean grain size of the porous medium.41 Liu et al. (2009) modeled experimental transport results for engineered multiwalled carbon nanotubes (MWCNTs), using a ID model.42 Cullen et al. (2010) simulated the transport of nanofullerenes (C60) and MWCNTs with a 2D finite element model based on classical colloid filtration theory (CFT), modified with a maximum

retention capacity term.43 Tian et al. (2010) also simulated retention, transport, and remobilization of two ENMs (Ag nanoparticles and CNTs) in saturated porous media.44 Furthermore, Chen et al. (2010) review several methods for quantifying the stability and properties related to the fate and bioavailability of nanomaterials (specifically CNTs and C60).45 4.4. Regulatory Subsurface Models. Several subsurface models have been used in risk assessments to support EPA regulatory programs. As with surface water models, this subset of models is widely used and representative of typical risk assessment modeling as practiced by the EPA. None of the three evaluated models account for key processes of ENM subsurface transport, including aggregation, attachment, and porous media filtering. All would thus need modification to 1194



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includes several optional mechanisms of colloid transport as documented in Šimůnek et al. (2006).60 Table 2 summarizes the strengths and limitations of the subsurface models described above. 4.6. Multimedia Models of ENMs. Multimedia models treat environmental media (e.g., surface water, groundwater, atmosphere, biota, etc.) as an integrated system, synthesizing information about chemical partitioning, reaction, and intermedia transport. Multimedia models have been used to estimate regional and global contaminant migration based on mass balance relationships as well as contaminant transport at local scales, including risk assessments of point contamination sources.61 Many multimedia models estimate the transport of material through environmental and biological compartments during the life cycle of a chemical, with the goal of estimating predicted environmental concentrations (PECs). Potential hazards, as well as predicted no-effect concentrations (PNECs), are addressed so that a risk quotient (PECs/ PNECs) can be calculated. Risk managers can determine which chemicals are of greater risk based on the risk quotient (i.e., risk quotient >1). Sensitivity and uncertainty analysis are also useful in developing intervention strategies for the chemicals associated with higher risk. We mention this in the current context of assessing environmental exposure models because in the absence of the ability to directly predict environmental exposure, it may be necessary to rely on other novel methods to derive information to support the types of environmental exposure-related risk management decisions that would ideally be provided by direct calculation of a risk quotient. Several multimedia models have been developed and/or applied specifically for evaluating ENM fate in the environment. Boxall et al. (2007a) developed a deterministic model by deriving dilution equations to predict environmental concentrations of ENMs in surface water, sludge, and soil.26 This model determines the PECs of specific ENMs after a life cycle that includes production, use, emission, and disposal. It requires estimates and data for parameters such as concentration of the ENM within the product, daily usage of the product, fraction of the ENM removed during sewage treatment, and sludge application rates. Uncertainty is introduced when data are not available for necessary parameters. Uncertainty is evaluated by allowing certain parameters (e.g., concentration of ENM within the product) to vary to calculate a range of PECs. This discrete type of variation is distinct from the probabilistic methods discussed under emerging approaches. Blaser et al. (2008) modeled the emissions of silver from biocidal nanosilver products. The model estimates silver emissions, assesses fate and estimates the PECs of silver in a river system. PNECs are estimated through critical evaluation of available toxicity data for environmentally relevant forms of silver and characterized for risk. Simplifying assumptions were made given the current state of data, such as neglecting emissions from production or solid waste, as well as the removal of marine environments from the system. This model would therefore need to be modified to capture the broader life cycle of ENMs.62 Mueller and Nowack (2008) presented a model to address the quantities of ENMs released into the environment from a life-cycle perspective. Using material flow analysis, three types of nanoparticles were studied: nanosilver, nanotitanium oxide, and carbon nanotubes. The model incorporated estimated worldwide production, particle release from products, and flow coefficients within compartments selected for the model. Different life cycles of the three products generated varied results for the PECs. These

simulate ENM transport. However, some researchers have simulated colloid-facilitated transport using conventional porous media transport models. For example, Contardi et al. (2001) used a transport model for natural colloids developed by Vilks et al. (1998), accounting only for advection, dispersion, sorption, and decay.46,47 They recognized that the model did not account for colloid behavior explicitly; however, they utilized an approximate approach to decrease the degree of sorption to account for colloid-facilitated transport. This approach utilizes a lumped-parameter approach to simulate complex processes; if such an approximate approach is effective for simulating ENMs, the regulatory models described in this section may have potential. However, given the unique and complex behaviors exhibited by ENMs in porous media, it seems unlikely that this approach would reliably predict under a broad range of conditions. PRZM (Pesticide Root Zone Model) has been used extensively to evaluate the fate of pesticides in agricultural settings with a variably saturated flow and transport model of the deeper unsaturated zone, accounting for processes of advection, dispersion, sorption, biodegradation, surface runoff, and sediment erosion. It includes a Monte Carlo pre- and postprocessor that supports probabilistic simulations.48 MODFLOW is a modular 3D finite-difference groundwater flow model and is one of the most widely used groundwater flow and transport models capable of simulating coupled groundwater/surface water systems, solute transport, variable-density and unsaturated-zone flow, aquifer-system compaction and land subsidence, parameter estimation, and groundwater management.48,49 BIOPLUME is a 2D finite-difference model utilized to simulate processes of natural attenuation of organic contaminants in groundwater and considers processes of advection, dispersion, sorption, and biodegradation.48,50 4.5. Other Subsurface Models Based on Colloid Theory. A few models developed for simulating colloid transport in porous media appear to have potential for addressing subsurface ENMs, although not developed specifically for that purpose. A strength in this area is that several of these models include onboard sensitivity analyses to determine which parameters have the greatest influence on exposure; however, none of the models specifically address ENMs. Corapcioglu and Choi (1996) developed a 1D model describing colloid transport in unsaturated porous media with four phases (aqueous, air, solid matrix, and colloid), concluding that the air−water interface could strongly limit colloid transport due to colloid attachment to the interface itself.51 Johnson et al. (2007) incorporated geochemical heterogeneity and random sequential deposition dynamics.52 Building on Ryan et al. (1999), Sun et al. (2001) developed a 2D colloid transport model for heterogeneous porous media.53,54 Bradford and Toride (2007) attempted to account for non-CFT behavior (behavior that does not conform to CFT) by using a conventional advection-dispersion equation model with firstorder kinetic deposition and release,55 allowing some parameters to vary stochastically. Bekhit and Hassan (2005) developed a 2D colloid transport model accounting for facilitated and retarded colloid transport.56 Moridis et al. (2003) utilized the TOUGH2 (Pruess, 1991) model to develop 3D simulations of a proposed nuclear waste disposal facility and associated colloid transport.57,58 This accounted for colloid transport using the EOS9nT module (Moridis et al., 1999).59 HYDRUS is a software package simulating water, heat, and solute movement in 2- and 3D variably saturated media and 1195



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Table 3. Summary of Potential Multi-Media Models and Their Use in ENM Exposure Assessments

were then compared to the PNECs specific to each material to estimate potential risk. In a continuation of this work, Gottschalk et al. (2010a) developed a probabilistic material flow analysis (PMFA) to calculate distributions of PECs and PNECs in a system of 11 compartments. The study used PMFA to address the lack of data on environmental fate, exposure, emission, and transformation characteristics of ENMs. This stochastic approach allows the model to represent uncertainties based on estimated input parameters. The authors suggested that using Monte Carlo simulations and Markov Chain Monte Carlo modeling is appropriate for estimating PECs when faced with limited data.27 4.7. Regulatory Multimedia Models. Currently accepted approaches for multimedia modeling exist within the regulatory community and, as such, are relevant to ENM characterization. In their current form, none of the models provide a comprehensive solution for estimating the fate of ENMs in the environment. Many of the associated submodels do not account for key processes of particulate transport in the environment such as aggregation, attachment, settling, and porous media filtering. In addition, these traditional multimedia models are strongly reliant on chemical property estimation tools (e.g., QSARs) developed for chemicals other than ENMs.

Some frameworks are highly abstracted, describing only mass transfer between environmental compartments using simple mass transfer functions rather than mechanistic formulations that would account explicitly for underlying processes. Such highly abstracted multimedia models may be useful for screening evaluations of ENM transport. Multimedia models specific to ENMs discussed in the previous section fall within this category; however, parametrization of such models generally requires empirical data currently unavailable for ENMs. 3MRA (Multimedia, Multipathway, and Multireceptor Risk Assessment) is a suite of 17 environmental fate and transport models originally designed to support the Hazardous Waste Identification Rule (HWIR). TRIM (Total Risk Integrated Methodology) is a collection of multiple models (e.g., fugacitybased models, simple air quality models, and human exposure models) to assess multimedia health and ecological risk deterministically for hazardous air pollutants. The RESRAD (Residual Radioactivity Models) family of codes is a set of components that allow fully interoperable probabilistic multimedia risk assessment, support multimedia modeling and provide capabilities for sensitivity and uncertainty analysis. CalTOX (California Total Exposure Model for Hazardous 1196



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Currently, no models or modeling approaches have been applied to ENMs or, because of theoretical underpinnings, could readily predict the bioaccumulation of ENMs in aquatic biota. Each approach is summarized, therefore, with a mention of its potential relevance and applicability to ENM bioaccumulation. From the plethora of published research on the development and validation of models that predict the uptake and accumulation of conventional chemicals, several articles and reports provide an excellent overview of methods and the uncertainties associated with predictive bioaccumulation modeling.69−72 4.8.1. QSAR Models. The earliest approaches to predict chemical concentrations in aquatic organisms were based on the relationship between an organism’s Bioconcentration Factor (BCF) and the log of the n-octanol−water partition coefficient (log Kow). As pointed out by Nichols et al. (2009), a curvilinear relationship is obtained when plotting the BCF versus the log Kow up to a log Kow value of roughly 6. The use of log Kow as a predictor of bioconcentration potential is based on partitioning of hydrophobic organic chemicals into the lipid tissue of animals; however, it is reasonable to assume that partitioning mechanisms for many ENMs are very different from the mechanism for conventional organic chemicals. Because ENMs often behave as particles rather than chemicals, in some cases the parameters and theories applied to conventional chemicals will be invalid for ENMs. Thus, QSAR approaches based on the log KOW are unlikely to produce reliable predictions without extensive study into the actual mechanisms that drive partition of ENMs. Similarly, fugacity-based bioaccumulation models will need extensive study on the chemical potential, or “escapability”, of ENMs before they can be applied for environmental exposure assessments of these materials.73,74 4.8.2. Mass Balance Models. These models predict bioaccumulation in various organs and tissues by calculating the net result of processes associated with chemical uptake (e.g., the amount of water that passes across the gill) and elimination. Such models conceptualize the animal as one or more compartments, and contaminant concentration in each is a function of the processes that affect the throughput of the contaminant mass.71 In this approach, a mass of chemical enters a compartment as a parent compound that is not biotransformed, a fraction of the chemical remains in the box (i.e., accumulation), and the remaining mass of chemical leaves the box (i.e., elimination, depuration). A mass balance approach approximates steady-state conditions, and supporting studies must demonstrate that steady state has been achieved to provide reliable data for model validation. Improvements are needed to better represent ADME processes for conventional chemicals, and metabolism has long been a significant source of uncertainty for hydrophobic chemicals.69 Given the hydrophobicity of certain classes of ENMs, such as fullerenes, significant research may be required before a mass balance approach may be applied reliably to ENMs. Mass balance approaches may simplify the ADME paradigm, however, by eliminating processes that are irrelevant to ENMs (e.g., certain elimination mechanisms may not be relevant for ENMs that bind strongly to cellular proteins). 4.8.3. Food Web Bioaccumulation Models. While mass balance models are strictly designed to predict uptake and accumulation from aqueous exposure, food web models account for both food and gill exposure. These models can be limited to aquatic food webs or extended to terrestrial

Waste Sites) is an Excel-based fugacity model for multimedia risk assessment. Due to its relative simplicity, this model has been incorporated into life-cycle assessment models (e.g., TRACI). The CalTOX model has been used primarily to assess contaminated soils; however, it has been adapted for multiple purposes including the support of risk ranking schemes and life cycle assessments. The California Exposure Modeling Research Center at Berkeley has an active program that involves continuing development of this model (http://eetd.lbl.gov/ ie/ERA/). ARAMS (Army Risk Assessment Modeling System) is a multimedia risk assessment tool that addresses human health and the ecological risks associated with military-relevant compounds (MRCs). It is applicable to any setting with contaminated sources or media, however. ARAMS, which uses FRAMES (Framework for Risk Analysis of Multi-Media Environmental Systems), as with 3MRA, to integrate environmental models and databases, considers temporal and spatial distribution of contaminants and lends itself to sensitivity and uncertainty analyses. It also has functional links to multiple existing databases, such as the Integrated Risk Information System (IRIS), Health Effects Assessment Summary Table (HEAST), Environmental Residue Effects Database (ERED), and BSAF (http://el.erdc.usace.army.mil/arams/). Finally, Mend-Tox (Modeling the Multimedia Environmental Distribution for Toxics) is a relatively new class of multimedia models built from the Integrated Spatial-Multimedia-Compartmental Models (ISMCM) that include well-mixed and spatial components integrated through physical boundary conditions.63,64 Mend-Tox calculates intermedia transport fluxes and predicts the concentrations of contaminants in various environmental compartments based on a number of physical chemical characteristics that would have to be evaluated for their appropriateness in predicting ENM behavior, but this type of model may offer an alternative to the FRAMES-based approaches mentioned above. Table 3 summarizes the strengths and limitations of the multimedia models described above. 4.8. Bioaccumulation Models. Although the primary focus of this review is fate and transport modeling, bioaccumulation of ENMs may, for some materials, represent a significant exposure pathway. It should be noted that there is considerable research (and attendant models) available to estimate tissue concentrations from organic chemical exposure. Furthermore, advances in our ability to predict uptake and accumulation of metals have occurred in the past few years. Several types of mathematical modeling approaches have been developed and used to predict exposure concentrations in biota, especially in aquatic systems. They can be classified as (1) quantitative structure−activity models (QSARs), (2) mass balance models, or (3) food web bioaccumulation models. Distinctions are certainly blurred because QSAR elements are found in all bioaccumulation models. Similarly, kinetics (i.e., absorption, distribution, metabolism, and elimination [ADME]) tend to be represented in most models, often using the log of the octanol−water partition coefficient as a surrogate. Nevertheless, it is useful to organize predictive bioaccumulation models into these three categories. Regarding ENMs specifically, there have been several recent bioaccumulation studies in organisms ranging from daphnia to largemouth bass; however, most of the studies have examined bioaccumulation from a direct ecotoxicity perspective, with little attention given to modeling.65−68 Therefore, there is a tremendous need for modeling approaches in this area. 1197



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needed now to aid in decision-making. In the absence of reliable quantitative and qualitative data or evaluation methods, alternative approaches must be developed for near-term decision-making. Several such approaches have been implemented in recent years, some of which are reviewed below.83

organisms that consume aquatic biota, addressing multiple trophic levels simultaneously. As suggested above, food web models are also based on kinetics principles (i.e., ADME) and require parameters related to chemistry (e.g., log Kow), ecology (e.g., nonlipid organic carbon in detritus, food web structure), and physiology (e.g., diffusion resistance for uptake) for useful predictions.70 This strongly suggests that, for food web models to be considered for ENMs, substantial research will be needed across all trophic levels. Although these models may not be immediately useful in predicting tissue concentrations in aquatic ecosystems for environmental exposure assessment of ENMs, the theoretical bases of their approaches have been validated over many years and, in general, model performance has been adequate for estimating environmental exposures in support of risk management decisions. That food web models have been used successfully for a variety of organic chemicals and metals (e.g., Bioaccumulation and Aquatic System Simulator, or BASS) suggests significant potential for further development to represent processes specific to ENM behavior in aquatic ecosystems.75 Studies emerging in the areas of ENM biouptake and trophic transfer may support the development of ENM-specific food web models.76,77 Zhu et al. (2010a) calculated BCFs (bioconcentration factor, the ratio of a contaminant concentration in an organism to its concentration in water) for daphnia at two different TiO2 exposure levels.68 Similarly, Zhang et al. (2007) calculated whole body BCFs for Cadmium and TiO2 in carp but also provided BCFs for gills, skin and muscle tissue and internal organs.78 Unrine et al. (2010) investigated the role of particle size on bioavailability of nanocopper to earthworms (Eisenia fetida), concluding that Cu NPs may enter terrestrial food chains from soil.79 Judy et al. (2011) demonstrated trophic transfer and biomagnification of gold NPs in a terrestrial food chain.80 Such studies are important in the development of empirical models that characterize ENM partitioning to organisms via food and gill exposure. For each class and type (e.g., functionalized) of ENM, generally applicable partitioning data form the basis of knowledge from which to develop robust predictors of EMN partitioning and exposure in natural systems (i.e., analogous to octanol−water coefficient). Currently used bioaccumulation models could be adapted for ENM exposure assessment as model partitioning parameters, for whole body accumulation as well as aqueous, lipid and protein fractions become available. 4.9. Shortcomings of Existing Environmental Exposure Models. While there are many potential strengths of the previously described models to predict the behavior of ENMs, most must be revised to take into account specific properties of ENMs. As seen in Table 1, many of the models do not specifically account for ENM properties and behavior, with the exception of a few (e.g., MNM1D).41 Few of the models handle data gaps and uncertainty well, posing a significant limitation for application to ENMs. Other risk evaluation approaches, perhaps less physically descriptive and detailed, will be important in the interim to enable decisions. A critical issue with developing environmental exposure models for ENMs, including fate and transport models and their subsequent validation, is the relatively long time required to gain knowledge upon which to make decisions versus the rapid development of nanotechnology. 81 A regulatory process based on quantitative risk assessment utilizing fate and transport models will be an inherently slow one. Given that reliable, mechanistic ENM risk assessment may not be available for years or even decades,82 screening tools are

5. EMERGING APPROACHES Many of the current and potential models outlined above are most useful when data are plentiful, or at least existent, and when there is a general consensus on the physicochemical and mechanistic processes that determine environmental exposure. While our scientific understanding of these processes related to ENMs is increasing, it is not yet at the stage where robust or comprehensive environmental exposure assessments of ENMs are feasible, which limits the completion of traditional risk assessments. This is due not only to a lack of knowledge specific to ENM fate and transport but also to a lack of knowledge about how much knowledge is lacking. In other words, overall uncertainty is not well characterized.84 With the dearth of data for many aspects of ENMs, any environmental exposure assessment must explicitly address scientific uncertainty to serve as a basis for near-term decisionmaking and long-term environmental health planning. Though conventional exposure assessments are needed, the state-of-thescience requires near-term solutions to identify potential exposure routes, estimates of environmental exposure concentrations, and the effects these exposures may have on human and ecological health. There are several alternatives for ENM environmental exposure assessment that account for uncertainties explicitly, providing risk managers with more robust information that goes beyond traditional information pathways to incorporate new knowledge bases that already exist for ENMs. The approaches in development are organized here into three categories: adaptive management and evaluation frameworks; multicriteria decision analysis (MCDA); and probabilistic approaches, including Bayesian networks (BayesNets) and Monte Carlo simulations. The potential implementation of these approaches to support ENM environmental exposure assessments, differences from traditional assessment, and challenges and limitations are discussed. 5.1. Adaptive Management and Evaluation Frameworks. Adaptive management techniques are based on the principle that multiple strategies can deal with a system of uncertainties and outcomes. These techniques have been applied to environmental decision-making for more than two decades and were initially conceived to deal with uncertainties in ecological systems and renewable resources.85,86 As Gregory et al. (2006) describe, adaptive frameworks recognize uncertainty and propose management alternatives that can be tested and refined over time.87 Sound adaptive management strategies require estimated responses under different scenarios, discrimination among competing hypotheses related to those responses, and explicit ways to link learning from experimental tests with management decisions. Both passive and active management strategies can be included in adaptive frameworks; passive frameworks use management to pursue particular objectives with unintentional learning, while active adaptive management uses management explicitly to learn and update knowledge that reduces uncertainty in future decisions.88 In the context of environmental exposure assessments for ENMs, adaptive management and evaluation frameworks have potential because they may incorporate unconventional information such as qualitative or subjective data, or proxy 1198



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ENM environmental exposure risks is Multi-Criteria Decision Analysis (MCDA). Similar to adaptive evaluation frameworks in their ability to incorporate nontraditional information pathways, MCDA frameworks inform decisions by ranking and prioritizing data generated through combining experimental data and stakeholder involvement. The process develops a conceptual model of the system and has decision alternatives for a framework that can collect, store, and process an array of information used in transparent decision-making.90 Possible outputs from an MCDA include ranking of options, identifying a single optimum alternative, or describing acceptable and unacceptable alternatives. Kiker et al. (2005) reviewed environmental applications of MCDA, ranging from site prioritization and remediation to impact assessment and resource management.91−94 More complex variants of MCDA, such as multiattribute utility theory (MAUT) and analytical hierarchical processes (AHP), use scoring rules to compare alternatives to decisions based on maximizing defined utility functions.90,95 While these methods are not direct replacements for predictive environmental exposure modeling, they do qualitatively incorporate considerations of environmental exposure. The criteria utilized to rank various decision alternatives can be (as seen in examples reviewed here) parameters that describe or predict potential exposure behavior of ENMs. Linkov et al. (2007) first explored MCDA techniques to prioritize ENMs with respect to risk assessment based on specific management objectives. ENM risk was weighed against social and economic values, along with associated risks and benefits, taking into account stakeholder preferences. This study used AHP to derive relative weights for the criteria as input by decision makers. The approach did not account for nanoparticle-specific properties though, and the uncertainty in the weighting criteria was not explicit.95 A later study by Linkov et al. (2009) incorporated additional elements into the decision-making process with MCDA, including physical and chemical properties and life cycle considerations. In the latter study, alternative ENMs were ranked to prioritize materials that needed further study based on risk potential. Uncertainty was addressed by incorporating Monte Carlo simulations to assess a wide range of parameter values and criteria weights. The result was a risk categorization of materials from extreme to very low risk.96 Many of the criteria upon which these risk scores were based (agglomeration, reactivity/charge, critical functional groups, contaminant dissociation, bioavailability potential, bioaccumulation potential, and toxic potential) have direct relationships to environmental exposure modeling. Although they were not incorporated directly into a predictive algorithm within this MCDA application, the inclusion of factors such as agglomeration, contaminant dissociation, and bioavailability potential, for example, illustrate that qualitative knowledge of ENM behavior specifically relevant to environmental exposure can be being utilized as it becomes available.96 To date, Linkov et al. (2007; 2009), Seager and Linkov (2008), Tervonen et al. (2009), and Canis et al. (2010) have applied MCDA for risk prioritization of ENMs or ENM manufacturing processes.96−100 Similar to other general, adaptive evaluation frameworks, MCDA can combine nontraditional information with more traditional data. For example, expert judgment on a range of potential values for an input parameter can be used in place of field data. As with many expert-based systems, a limitation of current MCDA models for ENMs is their reliance on decision-makers

data from similar materials, to guide management decisions. To date, however, there have been limited studies exploring these techniques for ENM environmental exposure. For example, Hansen et al. (2008) introduced a categorization framework for human exposure to ENMs, based on consumer products and the location and concentration of ENM within those products. The framework classifies products into different exposure categories by calculating potential exposure concentrations based on the product’s use. Uncertainty is incorporated with worst-case scenarios or estimates of information when data gaps are present. This model is currently limited to human exposure but could be expanded to other exposure routes or environmental concentrations. It does not take into account many chemical and physical properties of the ENMs, except the form of particle present (e.g., nanoparticle embedded in a solid matrix, nanostructured surfaces, etc.) and its physical location within a product. This framework serves as an adaptive management model since it relies upon currently available information (i.e., location of the ENM in the product) to provide information for risk estimates and may be adapted with more information from the product’s manufacturer about exposure potential.89 Metcalfe et al. (2009) proposed a conceptual exposure framework that includes other environmental pathways than the Hansen et al. (2008) framework. The Metcalfe et al. framework is called the Strategic Management and Assessment of Risks and Toxicity of Engineered Nanomaterials (SMARTEN). SMARTEN queries risk managers in an iterative decisionmaking process that incorporates knowledge related to nanoparticle-specific behaviors and properties. The authors frame their methodology around prioritizing ENMs to reduce risks prior to manufacturing. This prioritization must account for many physical and chemical properties that are unique to ENMs and considers unconventional pathways and hazard end points. The model has the flexibility to be refined as new information becomes available on the fate, transport, and effects of ENMs in various media. In the absence of currently available environmental exposure data, SMARTEN employs probability distributions, based initially on expert elicitation, to “allow us to account for the full spectrum of possible events in the particles’ lifetime”, addressing the need to consider a range of potential environmental exposure scenarios. Treatment of uncertainty by this framework is only tangentially described, so more explicit details are necessary.33 Both of these adaptive evaluation frameworks are able to incorporate nontraditional information into the environmental exposure and risk assessment process for ENMs. As illustrated by the lack of studies on this subject, however, applicability of this framework to various ENMs and across multiple decision end points still must be examined. SMARTEN, in particular, attempts to incorporate nanospecific characteristics (e.g., coating, dissolution capability, surface charge, aggregation potential) into the assessment process, at least at the conceptual level. The advantage of these frameworks is their flexibility to be refined as new knowledge is gained. There are also limitations, particularly (1) they lack clearly described procedures to implement adaptive frameworks for ENM environmental exposure assessment for reproducibility and generalization across materials and media; and (2) there has been no explicit validation for these frameworks from laboratory tests or other experimental data. 5.2. Multi-Criteria Decision Analysis. An alternative approach recently examined for decision support regarding 1199



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5.3.2. Bayesian Networks. Another probabilistic tool, Bayesian networks, was recently introduced in ENM environmental exposure assessments. Bayesian networks (BayesNets, also known as Bayesian belief networks or probability networks) are directed, acyclic graphs representing interconnected variables. They are a subset of more general influence diagrams that include utility and decision nodes. Outcome (“child”) variables are defined by their conditional dependence on one or more “parent” variables. Variables are defined by conditional probability tables and represented by a calculated probability distribution across states, based on the condition of the influencing variables. BayesNets have several features related to adaptive evaluation frameworks and quantitative assessments that differ from many traditional exposure assessment models. First, they are adaptable in that both the model structure and parametrization can be refined as knowledge changes. Second, they are updateable, containing robust mathematical algorithms to incorporate new scientific data for automatically updating the model parameters and probability distributions. Third, BayesNets are decomposable since complex systems can be treated as a single model or broken into submodels to facilitate better understanding of particular components. Fourth, BayesNets are integrated and can accept a variety of nontraditional knowledge bases such as expert judgment, mechanistic or physical relationships, simulation data, and experimental data. Finally, BayesNets are testable, facilitating efficient sensitivity analysis, prediction and scenario testing, and can be used as a diagnostic tool. Like adaptive management and MCDA, BayesNets can be used for risk ranking and research prioritization. Bayesian approaches are increasingly applied to complex environmental systems.104,105 There have also been limited studies on applying BayesNets for ecological risk assessment and modeling.106 Relevant to ENMs, Morgan introduced using influence diagrams to inform the risk management of nanoparticles; however, she did not go beyond a conceptual design.107 A more recent study by Money et al. (2012) expanded BayesNets as a quantitative tool for forecasting environmental exposure and risk of ENMs.108 These authors introduced the Forecasting the Impacts of Nanomaterials in the Environment (FINE) model. FINE attempts to incorporate a suite of parametersnanoparticle characteristics, environmental conditions, nanoparticle behavior, environmental exposure, hazard, and riskinto a single model to inform full-scale ecological risk assessments for nanoparticles. The dynamic baseline model is based on expert elicitation, with the ability to become more data-driven as new scientific evidence becomes available. It also is intended to be generalizable across many different nanoparticles, with techniques that can be applied for human exposure assessments. BayesNets are promising for taking nontraditional information about nanoparticles and incorporating it into a quantitative framework that informs environmental exposure and risk assessments. One challenge to using BayesNets in this context is balancing complexity with data and assessment needs. Parameterization of complex networks can be difficult, and oversimplification can lead to biased analyses.108 Additionally, relying on expert elicitation can create an initial model that is uninformative. Fortunately, the adaptive nature of BayesNets allows information to be updated iteratively, filling knowledge gaps and reducing uncertainty. Overall, these alternatives share the ability to acknowledge new information pathways unique to ENMs that more

to set weights and define parameters, which may lead to domain bias during the analysis. Spatial and temporal scales are not currently considered and the environmental fate of ENMs is not predicted, both of which are important determinants of potential environmental exposure. Although these limitations exist, MCDA overall can provide near-term guidance on the relative potential exposures of ENMs, to inform researchers and risk managers on possible future action in full-scale environmental exposure and risk assessments. 5.3. Probabilistic Approaches. While adaptive management and MCDA frameworks can incorporate probabilities into the analysis, a fully probabilistic approach for ENM environmental exposure assessment aims to characterize the uncertainty that exists in characterizing ENM environmental exposure risk quantitatively. Two probabilistic methods, Bayesian networks and, more generally, Monte Carlo analysis, have addressed both uncertainty and the need for near-term, quantitative assessments of potential ENM environmental exposure to facilitate risk characterization. 5.3.1. Monte Carlo Analysis. Monte Carlo simulations take random samples from a given probability distribution representing a set of parameters defining a system. Thousands of samples can be taken for each variable in the model, resulting in many thousands of possible outcomes. The results are then analyzed to determine the probability (likelihood) that a particular outcome will be observed. Monte Carlo methods have been employed for environmental exposure assessments by the USEPA and applied to environmental assessments in air, water, and soil.101,102 The methodology can be applied to many types of modelsit simply requires defining input variables and predicted output variables in terms of probability functions rather than discrete values. While it is acknowledged that several of the mechanistic models described herein for current or potential application to ENMs also utilize probabilistic simulation, this method is highlighted here given the importance of its broad potential to explicitly incorporate uncertainty in the form of probability distributions. Linkov et al. (2009) incorporated Monte Carlo simulations into their MCDA analysis for ranking ENM risks as a function of parameters representing environmental exposure potential as well as toxic potential.96 Gottschalk et al. (2010) used Monte Carlo in a PMFA to predict environmental concentrations of several ENMs via the mass balance approach.28 An advantage of probabilistic simulations over traditional deterministic exposure assessments is their ability to calculate an environmental exposure estimate in the context of a range of exposure scenarios. The probability distribution provides more information on the likelihood of a particular concentration being observed, as well as better representation of uncertainty. For example, instead of estimating the concentration of an ENM in a given environmental compartment, these models might estimate, with 90% confidence, that the ENM concentration in that compartment would not exceed a certain value. As with many modeling techniques, Monte Carlo is subject to underlying assumptions rooted in some form of expert judgment and is extremely sensitive to input distributions. Data requirements, validation, assumptions of independence among parameters, and selection of probability distributions to describe these parameters can hinder applying these techniques to environmental exposure and risk assessment. Incremental gains in information may come with large uncertainties.103 Future work is needed to investigate the correlation between parameters in all the models described here. 1200



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any limitations and uncertainties that may exist in the field-level data to provide the most informative results that can be used to compare models and select the most appropriate model for the task. To date, challenges still exist in detecting and analyzing nanomaterials in the field; therefore, proper validation of existing (or novel) models is difficult due to a lack of data. However, mesocosm level data sets have begun to emerge that can be used for validation purposes while waiting for larger scale field studies to develop.113−116 The status of environmental exposure models for ENMs suggests that, based on current data and exposure assessment methods, mechanistic and quantitative health and ecological risk assessments may still not yet be possible. Therefore, we conclude the most currently applicable methods for ENM environmental exposure assessments are those that account for uncertainty and allow for flexibility in the types of information incorporated. We recommend the adoption of such methods, reviewed here as emerging methods, to support informed decisions concerning environmental exposure to ENMs that would otherwise ideally be supported with quantitative environmental exposure predictions. Currently, it is more appropriate to support decisions using qualitative or probabilistic exposure potentials with well-characterized and acknowledged uncertainty. As reviewed here, a number of alternatives may be useful given the challenges that environmental exposure models and assessments currently face, including adaptive management and evaluation frameworks (e.g., categorization framework proposed by Hansen et al. (2008), SMARTEN, proposed by Metcalfe et al. (2009), decision support and prioritization tools (i.e., MCDA), and probabilistic approaches such as FINE, proposed by Money et al. (2012)).33,89,108 While most of these approaches are still in development and documented applications are few, their use in the near-term may be beneficial, considering the limitations of traditional environmental exposure assessments that were previously developed for other substances and subsequently applied to ENMs. The next step for these studies to illustrate their applicability fully for ENM environmental exposure assessment would be to formally incorporate scientific data on particle characteristics and behavior in various media in order to update findings and provide model validation when quantitative predictions are made. In light of this information, the focus of environmental exposure modeling for ENMs must concurrently update both traditional and unconventional methods of environmental exposure forecasting. Updating traditional modeling methods to represent the complexity of ENM specific parameters will be important to enabling full risk assessments of these emerging materials. This is especially important in a field with a high level of scientific uncertainty, and reliance on detailed methods of predicting environmental exposure concentrations will not produce timely results. Second, it is recommended that ENM environmental exposure science grow deliberately through investigation of the highest value areas in environmental exposure modeling and data generation to support modeling with a value of information approach. The focus should be on enabling nearterm decision-making by incorporating methods such as probabilistic modeling and sensitivity analysis, then allowing decision-makers to apply values (e.g., to define acceptable risk levels) in risk-screening and research prioritization. At the same time, nanorisk researchers should not abandon traditional methods. Rather, the community needs to continue developing

traditional exposure models may overlook. They also recognize the need to quantify uncertainty surrounding ENM environmental exposure to better inform decisionmakers on current knowledge and to provide tools for near-term, quantitative assessments. Many of these approaches have been implemented in other aspects of environmental science and assessment (i.e., MCDA, adaptive management, BayesNets). Thus far their application to ENMs is limited. This is similar to applying other alternative risk analysis frameworks for ENMs which have been proposed by international organizations, institutions, and scientists.83 Further studies that move beyond conceptual design into more quantitative assessments are needed, and those by Hansen et al. (2008), Linkov et al. (2009), and Money et al. (2012) have pushed in that direction, albeit from different perspectives.89,96,108 There are still limitations and challenges to these approaches, such as generalizability, reproducibility, data acquisition, and validation, which are key aspects that need to be addressed as applications of these techniques increase. Most likely, it will be a combination of results from these and other approaches that ultimately leads to better-informed environmental exposure and risk assessment for ENMs.

6. DISCUSSION While there is a clear need to gather environmental exposure data for ENMs including fate, transport, and behavior data, it is also evident that robust environmental exposure models are needed. Without such models, the ability to complete reliable environmental exposure assessments and subsequent risk assessments for ENMs is seriously undermined. To date, there have been a number of environmental exposure models that were developed for other substances and then applied to ENMs, many of which are reviewed in this paper. In order for these models to be applicable to ENMs they must specifically account for chemical−physical properties unique to ENMs. They must also provide robust methodologies to quantify uncertainty because of the numerous knowledge gaps that still exist surrounding ENM environmental exposure. In this review of more than 30 exposure models, significant expert knowledge in models developed for other contaminants is evident. On the other hand, we found that only a small fraction accounted for the unique properties of ENMs (i.e., Mackay et al. 2006, Tosco and Sethi 2009, Li et al. 2008).35,39,41 Furthermore, only a handful addressed associated uncertainty and data gaps (Mackay et al. 2006, Boxall et al. 2007a, Blaser et al. 2008, Gottschalk et al. 2009, 2010a, 2010b, RESRAD, ARAMS).26,28,35,62,109,110 In addition, most were unable to account for specific properties of ENMs that may influence environmental processes including persistence and/or transformation, and almost none could account for relevant health and ecological end points. As mentioned previously, these parameters are extremely important to include in any comprehensive environmental exposure model for ENMs and directly impact environmental exposure assessment. One of the biggest challenges in developing and implementing exposure assessment models for engineered nanomaterials is the ability to validate these models with field exposure data. The first step in this process will be developing analytical techniques that can identify the presence of nanomaterials in the environment and distinguish them from naturally occurring counterparts.111,112 Second, the data collected must correspond to the appropriate input parameters of potential exposure models, which should increasingly include ENM-appropriate descriptors. Finally, validation techniques will need to address 1201



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review policies and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

robust models that deal with uncertainty while sufficient data is generated. Third, the modeling community must improve communication of research needs, since it is imperative for researchers to be informed about environmental exposure data needs and contexts. Exposure science represents a nexus of many disciplines. However, institutional barriers make collaboration a challenge. For example, hazard researchers have a responsibility to dosimetry and toxicology specialists who utilize the information on realistic doses. Fourth, to capitalize on the complementary skills and knowledge base that drives risk assessment of ENMs, the environmental exposure modeling community must interact with its various disciplines to answer questions that allow the best near-term decisions to be made. Clear communication of the hierarchy of informational needs is an important first step. The infrastructure of academic nanotechnology investment seems to acknowledge the importance of integrated, crossdisciplinary approaches. For instance, the National Science Foundation has funded nanorisk research centers and collaborations, including CEINT and UC CEIN. On an international scale, the European commission has funded a number of projects focusing on nanorisk, and the OECD has developed the Working Party on Manufactured Nanomaterials to respond to some of these research needs. A symbiotic relationship is needed between nanohazard and nanoenvironmental exposure researchers, enabling them to contribute to each other’s research needs with a value of information perspective. Researchers investigating environmental exposure to ENMs should proactively seek input from those predicting likely releases and predicting toxic effects. Researchers predicting toxic effects should, in turn, look to emerging exposure literature for environmentally relevant exposure levels. Such interaction is in keeping with the National Nanotechnology Initiative Research Strategy and the National Research Council’s recent report, Research Strategy for Environmental, Health, and Safety (EHS) Aspects of Engineered Nanomaterials (ENMs).6 More holistic understanding of the decision-enabling value of data being generated will allow research in basic science to start with the end in mind.

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ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*Phone: 919-660-5193; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We greatly appreciate the contributions of Evan Bowles and Kyle Beaulieu in the development of this manuscript; Fran Rauschenberg is also thanked for technical editing. This work was supported in part by the National Science Foundation (NSF) and the U.S. Environmental Protection Agency (EPA) under NSF Cooperative Agreement EF-0830093, Center for the Environmental Implications of NanoTechnology (CEINT). This paper has been reviewed in accordance with the US Environmental Protection Agency’s peer and administrative 1202



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