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Risk assessment strategies as nanomaterials transition into commercial applications
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Springer Science+Business Media B.V. (This will be the copyright line in the final PDF)
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Journal of Nanoparticle Research
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Olson
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Mira S.
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Department of Civil, Architectural and Environmental Engineering
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Drexel University
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Philadelphia, PA, 19104, USA
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[email protected]
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Gurian
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Patrick L.
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Department of Civil, Architectural and Environmental Engineering
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Drexel University
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Philadelphia, PA, 19104, USA
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Abstract
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16 May 2011 21 February 2012
Commercial applications of nanomaterials are rapidly emerging in the marketplace. The environmental and human health risks of many nanomaterials remain unknown, and prioritizing how to efficiently assess their risks is essential. As nanomaterials are incorporated into a broader range of commercial products, their potential for environmental release and human exposure not only increases, but also becomes more difficult to model accurately. Emphasis may first be placed on estimating potential environmental exposure based on pertinent physical properties of the nanomaterials. Given that the greatest potential for global environmental impacts results from nanomaterials that are both persistent and toxic, this paper advocates screening first for persistence since it is easier to assess than toxicity. For materials that show potential for persistence, a higher burden of proof of their non-toxicity is suggested before they enter the commercial marketplace whereas a lower burden of proof may be acceptable for nanomaterials that are less persistent. Nanoparticles - Risk assessment - Environment - Persistence - Toxicity - Nanotechnology transfer
Journal: 11051 Article: 786
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J Nanopart Res (2012) 14:786 DOI 10.1007/s11051-012-0786-8
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RESEARCH PAPER
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Mira S. Olson • Patrick L. Gurian
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Received: 16 May 2011 / Accepted: 21 February 2012 Ó Springer Science+Business Media B.V. 2012
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Abstract Commercial applications of nanomaterials are rapidly emerging in the marketplace. The environmental and human health risks of many nanomaterials remain unknown, and prioritizing how to efficiently assess their risks is essential. As nanomaterials are incorporated into a broader range of commercial products, their potential for environmental release and human exposure not only increases, but also becomes more difficult to model accurately. Emphasis may first be placed on estimating potential environmental exposure based on pertinent physical properties of the nanomaterials. Given that the greatest potential for global environmental impacts results from nanomaterials that are both persistent and toxic, this paper advocates screening first for persistence since it is easier to assess than toxicity. For materials that show potential for persistence, a higher burden of proof of their non-toxicity is suggested before they enter the commercial marketplace whereas a lower burden of proof may be acceptable for nanomaterials that are less persistent.
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Keywords Nanoparticles Risk assessment Environment Persistence Toxicity Nanotechnology transfer
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M. S. Olson (&) P. L. Gurian Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA 19104, USA e-mail:
[email protected]
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Introduction
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Risk assessment strategies as nanomaterials transition into commercial applications
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Commercialization of nanotechnology continues to grow at a consistent and rapid pace. Following over 20 years of research, both basic and applied, the Project on Emerging Nanotechnologies now identifies over 1,300 nanotechnology-enabled commercial products available worldwide, with a projected inventory of 3,400 by the year 2020 (Woodrow Wilson Institute 2011). Manufactured nanomaterials are being incorporated into products at a rate that outpaces research and regulation to protect against their potential impacts on human health and the environment (Dunphy Guzman et al. 2006; Royal Commission on Environmental Pollution 2008; Alvarez et al. 2009). These products span a wide range of consumer product categories, and are enabled by many nanomaterials. More than 50% of available nanotechnology-enabled products include nanosilver, followed by carbon (including fullerenes), titanium (including titanium dioxide), silica, zinc (including zinc oxide), and gold (Woodrow Wilson Institute 2011). The U.S. EPA advocates an environmental risk assessment approach to nanotechnology, including hazard identification, dose–response assessment, exposure assessment, and risk characterization. This approach requires detailed research and education on nanoparticle sources, characterization, environmental fate and transport, environmental detection and analysis, potential releases, human exposure scenarios, and toxicity (Savage and Wentsel 2008). During the
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initial stages of nanotechnology research and development, exposure to nanomaterials was the highest in occupational settings. Human heath risks were the greatest for researchers and workers, and assessments of risk were reasonably confined to the lab or workplace, beginning with occupational exposure models. Despite the comparatively simple modeling environment, a recent report from the NANEX project (van Tongeren 2010) reported that the results from two exposure models developed to predict occupational exposure to manufactured nanomaterials [STOFFENMANAGER (Fransman 2011) and ECOTOC TRA (ECETOC 2005)] matched poorly with measured results and are therefore not recommended for risk assessment. Now that the development and commercialization of nanoproducts have increased, environmental exposure is more likely and assessing the risks of engineered nanomaterials to human and ecosystem health is of pressing concern to scientists, governments, and policy-makers (Owen and Handy 2007; Grieger et al. 2011). In addition to the health and environmental exposures from the nanomaterials themselves, as these technologies are commercialized, the manufacturing processes used to produce the materials have their own impacts, due to conventional pollutant emissions. Robichaud et al. (2005) investigated the impacts of industrial production of five common nanomaterials and concluded that these manufacturing processes used to produce these materials presented relatively low environmental risks compared with a number of conventional manufacturing processes. In this paper the emphasis is on the nanomaterials themselves, rather than on the production processes, but it must be kept in mind that lifecycle environmental risks are not only limited to exposures to the nanomaterials, but also to pollutants emitted in the process of manufacturing the nanomaterials. There are a number of factors that make a comprehensive risk assessment of nanomaterials prohibitively time and resource intensive. Among these are the (1) sheer volume of nanomaterials, nanotechnology-based consumer products, and potential exposure pathways; (2) diversity of nanomaterial physicochemical properties and the associated variability in toxicity, persistence and exposure potential; and (3) complexity of nanomaterial behavior in environmental media and their subsequent property changes from the original product/material due to agglomeration,
etc. In addition, current knowledge gaps, including limitations in analytical methods necessary to detect trace concentrations of nanoparticles in the environment and lack of data on the toxicity, fate, reactivity, and transport of nanomaterials in the environment, make a full assessment of the environmental health risks of nanotechnology challenging (Alvarez et al. 2009; Wiesner et al. 2009; Grieger et al. 2011). Thus, there is a need to prioritize among different materials and devote more attention to those having the potential for more serious and widespread impacts. In this paper, we first discuss considerations affecting the environmental fate of nanomaterials and examine exposure-modeling options. Then we discuss existing risk assessment frameworks for nanomaterials. The use of simple compartment equilibrium approaches is advocated based on the limited number of parameters required, and the focus that these models provide on large-scale effects that, if present, would have the most severe consequences. We discuss the parameters required for this approach and advocate that more readily measured properties be used to prioritize the attention allocated to determining less readily measured properties.
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Environmental fate of nanomaterials
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Available data indicate that nanoparticles released to water or air aggregate to at least some degree, and that these aggregates display characteristics that vary greatly from their parent forms (Boxall et al. 2007). In addition, some nanomaterials are functionalized, which also affects their behavior. Hansen et al. (2008) provide a framework to identify which characteristics are relevant to nanomaterial toxicity based on whether (1) the material is nanostructured (subdivided into bulk and multiphase materials); (2) the nanostructure is present on the surface of the material [subdivided into (a) surface nanostructure is the same material as bulk material, (b) a film of nanoscale thickness is present, and (c) a nanostructured film]; or (3) the material contains nanostructured particles (subdivided into a) particles in liquids, b) particles in solids, and c) airborne particles). For example, the size distribution of a nanomaterial is identified as being relevant to all categories of nanomaterials except bulk materials (Category 1a above). The authors present a summary of the literature indicating that in many cases the
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required information is not available. For example, only 17% of studies of C60 reported the size distribution employed. They conclude that more systematic characterization of nanomaterials is necessary to improve hazard identification efforts for nanomaterials. For many nanoparticles, size distribution changes over time due to aggregation and their subsequent effect on nanoparticle properties and fate is complicated by the effect of environmental conditions (pH, ionic strength, and electrolytes) on aggregation potential. Boxall et al. (2007) present a review of available information on the fate of several nanoparticle types in water systems, soil, and air. It is unclear whether the nanoscale form of metallic nanoparticles presents an environmental risk due to properties of the nanoparticles themselves, or if they merely present a greater surface area of the bulk form of the metal, and thereby a larger percentage of atoms that may convert to the soluble metal ion, with its own mechanisms of toxicity (Luoma 2008; Chinnapongse et al. 2011). For rapidly dissolving nanomaterials like ZnO, evidence suggests that toxicity from ZnO nanomaterials is not significantly different than from bulk ZnO or soluble Zn2? (Fairbairn et al. 2011). This is not true for all nanomaterials, and so it is critically important to identify the form of the released nanomaterial as it exists in the environment.
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Exposure assessment methods
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Nano-enabled products include cosmetics, sporting goods, clothing, textiles, personal care products, electronics, paint, and many others. Consumers of these products will have exposure to nanomaterials through intended use of the nano-enabled products. However, for persistent compounds, commercialization of nanotechnology rapidly expands the population being exposed to nanomaterials, reaching beyond the consumers of the nano-enabled products and into the general population. For persistent compounds, the population incurring risk is no longer limited to those receiving benefits from the technology, as such materials are eventually released into the environment over time. In contrast to occupational or intended-use exposure scenarios, it becomes much more difficult to predict the release dynamics of nanomaterials into the environment to determine environmental exposure scenarios.
Environmental exposure assessments require understanding of the source dynamics and discharge flux of nanomaterials into various environmental media, the leaching behavior of nanomaterials from commercial products during production, storage, use and disposal, environmental fate of nanomaterials, and material flow through disposal via waste incineration plants, landfills, and wastewater treatment plants (Alvarez et al. 2009). Environmental transport modeling can be conducted in widely varying degrees of granularity, ranging from computational fluid dynamic (CFD) approaches to dispersion, to idealized analytical representations of diffusion (Gaussian Plume models etc.), to models that assume complete dispersion and seek only to quantify the relative concentrations of the material in different environmental media (air, water, soil, and biota). To be accurate, these models should also incorporate the uncertainties inherent in the current state of knowledge on nanomaterial release rates, reactivity, and complex fate and interaction in the environment. Detailed dispersion models that account for specific processes may be appropriate for specialized circumstances with high exposures, such as manufacturing, but efforts to expand these models to incorporate the range of potential exposures associated with the full lifecycle of a commercial produce can rapidly become so complex as to make realistic modeling infeasible. In place of specific process models, one can substitute the simplified, idealized models of partitioning behavior among different environmental compartments. Mackay and co-workers (Mackay et al. 2003; Harvey et al. 2007) used this partitioning approach to develop a risk assessment framework for organic chemicals that was recently applied by Mueller and Nowack (2008) for the first quantitative environmental risk assessment of engineered nanoparticles. They approximate environmental inputs of nanomaterials using estimated worldwide production volumes, allocation of production volume to specific consumer product categories, predicted particle release from products, and flow coefficients within environmental compartments (Mueller and Nowack 2008). Boxall et al. (2007) compare potential exposure models ranging from complex CFD models to simpler dispersion models, and then propose a framework for applying simplistic algorithms to estimate environmental concentrations of nanomaterials in environmental media based on specific release scenarios. While being useful for
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setting upper bounds for exposure concentrations, these models do not consider particle-specific properties and environmental fate. For new compounds such as nanomaterials, there is often a lack of data on the parameters necessary to inform even the simplest environmental dispersion models. Gottschalk et al. (2010a, b) present a stochastic method for predicting environmental concentrations of substances with a lack of data on environmental fate, exposure, emission and transmission characteristics, and apply their model to study environmental concentrations of a number of different nanoparticles. Their framework uses material flow analysis and probability distributions of transfer coefficients to derive probability distributions of environmental concentrations throughout the entire compound life cycle, including not only environmental compartments but also technical compartments, such as production, manufacturing of products using the compound, product use, recycling, disposal, and pollutant fate in waste treatment facilities. This is an important contribution; however the data necessary to inform even those models relying on probabilistic techniques are still often lacking, and existing probabilistic models rely on information on the parent nanomaterial rather than on the form it assumes in the environment. As a result, uniform distributions of parameters must still be assumed, and appropriate bounds must be estimated.
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Assessment of risk based on nanomaterial properties
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There are a number of risk analysis frameworks that have been proposed for nanomaterials. Grieger et al. (2011) evaluated these frameworks and found that, although most frameworks were flexible for multiple nanomaterials and decision contexts, few were applicable outside of occupational settings or small-scale environmental release scenarios, and few had been tested on a wide range of nanomaterials. Traditional risk assessment frameworks used to evaluate the environmental hazard and risk associated with commercial chemicals are based on a number of criteria including persistence, bioaccumulation, bioavailability, toxicity, and potential for long range transport (Mackay et al. 2003). For nanomaterials, Tervonen et al. (2009) propose that these factors are dependent on five extrinsic
properties of the nanomaterial, including agglomeration and aggregation, reactivity, critical functional groups, particle size, and contaminant dissociation. Burello and Worth (2011) discuss new applications of quantitative structure–activity relationship (QSAR) methods to develop predictive models of nanomaterial reactivity based on their physical properties. Puzyn et al. (2010) developed a QSAR model that accurately predicts the cytotoxicity of metal oxide nanoparticles based on a single explanatory variable, the enthalpy of formation of a gaseous cation of the form of the metal present in the metal oxide. Zuin et al. (2011) identify a range of different physiological impacts of nanomaterials (effect on macrophages, effect on hepatocytes, effect on blood platelets, etc.) and assess the weight of evidence for these different concerns for a set of five nanomaterials (carbon black, single-walled nanotubes, C60, and positively and negatively charged quantum dots). Quantum dots were found to present the greatest concerns. The authors noted that while their analysis assumes that all indicators were equally important, multicriteria decision-making approaches might refine this by weighting indicators based on their importance. Tervonen et al. (2009) used stochastic multicriteria acceptability analysis (SMAA-TRI) to classify various nanomaterials into risk categories based on the current knowledge of their physico-chemical characteristics and expected environmental impact throughout the product life cycle. Hansen et al. (2008) assessed 580 products employing nanomaterials and identified the potential for human exposure based on where the nanostructured material occurs in the material (e.g., airborne nanoparticles have greater potential for exposure than the nanoparticles embedded in solids). Most products fell into the category in which human exposure would be expected, but quantitative exposure assessments generally could not be completed due to a lack of information on the concentration of nanomaterials in the product. Thus, despite these advances in frameworks and assessment methodologies, a comprehensive characterization of predictive properties for all nanomaterials is far from complete.
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Strategy for evaluating nanomaterials
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These different frameworks are all not only potentially useful but also require a variety of data inputs. The
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reality is that with the rate of commercialization of nanomaterials increasing rapidly, it will be difficult to fill these frameworks with appropriate information. Given these limitations, we propose a screening system for identifying those nanomaterials having the greatest potential for widespread damage. In some sense, this approach can be seen as a shift from ‘‘failsafe’’ approaches (i.e., proper handling of materials in a controlled environment) that are feasible when applications are small scale to ‘‘safe-fail’’ approaches needed for commercialization. A widely commercialized product will be used in such a wide variety of environments and will inevitably be mishandled and misused, and so one must assure that even under these conditions, the product will not cause any harm to human health or the environment. The risk management approach shifts from specific scenario analysis to a general consideration of whether the properties of the material give it the potential to cause widespread adverse impacts. This line of reasoning is supportive of the use of relatively simple equilibrium partitioning models that view the environment in a highly idealized form as a series of uniformly mixed compartments (air, soil, water, biota, etc.). Such models will miss important localized exposures and risks; instead, they are aimed at capturing large-scale effects that, if present, would have the most severe consequences. Identifying even the minimal information required for equilibrium partitioning models may be difficult for many nanomaterials. Information on toxicity is particularly difficult to assess. While some types of toxicity are non-specific (e.g., polar narcosis), many others are the result of highly specific interactions between inhibitors and receptors of specific species (e.g., DDT’s impact on bird egg shells). Some general tests are available. For example the Ames test measures carcinogenic potential by observing the potential of a chemical to induce mutations in bacteria. A positive result on this test may be considered to provide basis for concern but a negative result would not be proof of non-toxicity, as the test would be expected to miss many specific mechanisms of toxicity. Similarly the QSAR model of Puzyn et al. (2010) appears to be very successful at describing cytotoxicity effects of metallic nanoparticles because the relative potential of the metal ions to induce oxidative stress, a general mechanisms of toxicity, can be related to the stability of their crystalline form (with the more stable forms having lower toxicity due to
their being less prone to released from the crystal lattice). Toxicity due to more specific interactions between materials and receptors would not likely be captured by this approach. Animal dosing trials may capture a wider range of toxic effects, but these are expensive and difficult to conduct, and become more expensive as larger sample sizes are used to detect lower levels of toxic effects. These dosing studies may need to be reserved for the materials of the greatest concern. In contrast to toxicity, persistence is relatively easy to assess. The potential to accumulate in biota is generally assessed through a surrogate measure, the octanol–water partition coefficient. Persistence in the air and water can be assessed through microcosm experiments. While specific challenges certainly exist, such as quantitating low concentrations of nanomaterials and understanding how nanomaterials agglomerate, transform, and react with environmental media, in general, these challenges are expected to be less serious than the challenges associated with measuring toxicity. For this reason, it is proposed that initial characterization efforts focus on persistence. For a chemical with limited potential to persist in the environment, there is still a potential for the local environment to be adversely affected; however, there is little potential for the type of global systematic damage associated with persistent compounds, such as DDT and PCBs. For such materials, one might reasonably accept a lower standard of evidence for non-toxicity, such as screening analyses with bacteria and cell cultures or small sample animal trials. For persistent chemicals, the standard of evidence would need to be higher to address toxicity concerns, including larger numbers of animals and a wider variety of species. Table 1 provides a framework for comparing different materials based on these attributes of toxicity and persistence, and provides examples from the chemical industry. Most materials are in the lower right-hand corner, presenting neither toxicity nor persistence concerns. Materials in the lower left-hand corner present localized concerns but with less potential for global impacts due to their lack of persistence. These materials would have the potential for constant exposure if they are continually produced, but this exposure would be limited to the local environments of their production, use, and disposal. The consequences in those local environments are of
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Table 1 Classification system for materials based on persistence and toxicity Harder to assess
Persistent
Ecosystem wide concerns: ban or limited use (PCBs)
Long-term impact unknown: limit environmental releases (brominated flame retardants)
Non-persistent
Local concerns: scenario-specific precautions to reduce exposure (benzene)
Little need for regulation (many chemicals)
concern and certainly merit risk assessment efforts [e.g., the exposure scenario modeling of Hansen et al. (2008)] but would be prioritized below potential global threats. Materials in the upper left-hand corner are of the greatest concern as they are both toxic and persistent. They are generally subject to bans or tightly regulated to prevent environmental releases. Materials in the upper right-hand corner are persistent but not highly toxic. One may argue that these do not present concerns, if the dose response relationship is such that persistent levels are not associated with adverse outcomes. Nevertheless, the persistence of these compounds may be sufficient that global levels are observed to rise over a period of time creating questions as to whether thresholds for impacts might be reached. Such compounds present a difficult regulatory choice as materials can never be proved to be completely non-toxic. In fact, the brominated flame retardants listed as an example have provoked considerable concerns over their widespread occurrence and potential for subtle adverse impacts. In Table 2, we begin to assess how different nanomaterials compare on the attributes of toxicity and persistence. Assignments were made based on the literature review and expert estimates, but are not meant to be definitive. All metallic nanoparticles are classified as persistent reflecting the fact the metals will not be destroyed by chemical transformations that they may undergo in the environment. Some distinction may be made based on how readily different particles are incorporated by biogeochemical cycling
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back into naturally occurring forms. Thus, iron and aluminum are considered less persistent as they would continue to exist in the environment, but in a form indistinguishable from naturally occurring deposits. In contrast, silver, cadmium, selenium, zinc, etc., are likely to persist longer at concentrations and in locations that differ substantially from their typical natural occurrence patterns. In seeking to prioritize efforts to parameterize risk assessment models for nanomaterials, we note that it is easier to first locate a material’s position on the y-axis (as noted earlier, persistence is easier to measure than toxicity). Once some idea of a material’s persistence is assessed, toxicity-testing requirements can be set with (as discussed earlier) the burden of proof for nontoxicity being greater the farther up the y-axis a compound is located.
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Conclusions
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In summary, it is the interaction of toxicity with persistence, which leads to the greatest harm and must be most actively guarded against. While it is clearly important to identify localized environmental concerns, it is absolutely essential to identify potential global environmental impacts associated with materials that are both persistent and toxic. Pre-commercialization risk assessment and regulatory action should focus first on identifying those nanomaterials with the greatest potential for causing wide-spread damage. This may necessitate a change in strategy
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Easier to assess
Toxic
Table 2 Classification of nanomaterials based on persistence and toxicity Toxic
Less toxic
Persistent
Ag nanoparticles, Ni, Co, and Zn oxides, CdSe
Fullerenes carbon nanotubes
Less persistent
Iron oxide nanoparticles
Al nanoparticles, oxidized fullerenes and nanotubes
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from precise exposure assessments in defined settings to simpler, screening models based on equilibriumpartitioning behavior. We propose first screening for persistence and then assessing toxicity, with a higher burden of proof of non-toxicity for nanomaterials that are strongly persistent. More detailed risk assessments based on precise models of environmental exposure routes may be reserved for nanomaterials found to be both persistent and toxic, or for high exposure scenarios.
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J Nanopart Res (2012) 14:786
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Journal : Medium 11051
Dispatch : 27-2-2012
Article No. : 786
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Pages : 7 h TYPESET
MS Code : NANO4301
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