Safe and sustainable by design Ag nanomaterials: A case study to evaluate the bio-reactivity in the environment using a soil model invertebrate.

Safe-and-sustainable-by-design (SSbD) nanomaterials (NMs) or NM-containing products are a priority. Silver (Ag) NMs have a vast array of applications, including biomedical and other products, even as nanopesticides. Thus, their release to the environment is expected to increase. The aim of the present study was to assess the ecotoxicity of the SSbD Ag NM to the soil model species Enchytraeus crypticus (Oligochaeta). The Ag NM tested consists in a SSbD Ag with biomedical applications, a hydroxyethyl cellulose (HEC) coated Ag NMs (AgHEC) and its toxicity was compared to the naked Ag NMs (Ag-Sigma), an Ag-based biomedical product (PLLA-Ag: Poly l-Lactide microfibers doped with Ag), and AgNO3. Effects were assessed both in soil and aqueous media, following the standard OECD guideline in soil (28 days) and the OECD extension (56 days), and short-term pulse (5 days) in aqueous media: reconstituted water (ISO water) and soil:water (S:W) extracts, followed by a 21-days recovery period in soil. Ag materials were thoroughly characterized as synthesized and during the test in media and animals. Results in S:W showed AgHEC was more toxic than Ag-Sigma (ca. 150 times) and PLLA-Ag (ca. 2.5 times), associated with a higher Ag uptake. Higher toxicity was related to a smaller hydrodynamic size and higher suspension stability, which in turn resulted in a higher bioavailability of Ag NMs and released ions, particularly in S:W. Toxicity was correlated with the main physicochemical features, providing useful prediction of AgNMs bioactivity. The ability to test E. crypticus in a range of media with different and/or increasing complexity (water, S:W extracts, soil) provided an excellent source to interpret results and is here recommended.

How to formulate hypotheses and IATAs to support grouping and read-across of nanoforms.

Manufacturing and functionalizing materials at the nanoscale has led to the generation of a whole array of nanoforms (NFs) of substances varying in size, morphology, and surface characteristics. Due to financial, time, and ethical considerations, testing every unique NF for adverse effects is virtually impossible. Use of hypothesis-driven grouping and read-across approaches, as supported by the GRACIOUS Framework, represents a promising alternative to case-by-case testing that will make the risk assessment process more efficient. Through application of appropriate grouping hypotheses, the Framework facilitates the assessment of similarity between NFs, thereby supporting grouping and read-across of information, minimizing the need for new testing, and aligning with the 3R principles of replacement, reduction, and refinement of animals in toxicology studies. For each grouping hypothesis an integrated approach to testing and assessment (IATA) guides the user in data gathering and acquisition to test the hypothesis, following a structured format to facilitate efficient decision-making. Here we present the template used to generate the GRACIOUS grouping hypotheses encompassing information relevant to "Lifecycle, environmental release, and human exposure", "What they are: physicochemical characteristics", "Where they go: environmental fate, uptake, and toxicokinetics", and "What they do: human and environmental toxicity". A summary of the template-derived hypotheses focusing on human health is provided, along with an overview of the IATAs generated by the GRACIOUS project. We discuss the application and flexibility of the template, providing the opportunity to expand the application of grouping and read-across in a logical, evidence-based manner to a wider range of NFs and substances.

How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity.

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping to be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.

Ordered weighted average based grouping of nanomaterials with Arsinh and dose response similarity models.

In the context of the EU GRACIOUS project, we propose a novel procedure for similarity assessment and grouping of nanomaterials. This methodology is based on the (1) Arsinh transformation function for scalar properties, (2) full curve shape comparison by application of a modified Kolmogorov-Smirnov metric for bivariate properties, (3) Ordered Weighted Average (OWA) aggregation-based grouping distance, and (4) hierarchical clustering. The approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. To facilitate the application of the proposed methodology, a software script was developed by using the R programming language which is currently under migration to a web tool. The presented approach was tested against a dataset, derived from literature review, related to immobilization of Daphnia magna and reporting information on several nanomaterials and properties.

Bayesian based similarity assessment of nanomaterials to inform grouping.

Nanoforms can be manufactured in plenty of variants by differing their physicochemical properties and toxicokinetic behaviour which can affect their hazard potential. To avoid testing of each single nanomaterial and nanoform variation and subsequently save resources, grouping and read-across strategies are used to estimate groups of substances, based on carefully selected evidence, that could potentially have similar human health and environmental hazard impact. A novel computational similarity method is presented aiming to compare dose-response curves and identify sets of similar nanoforms. The suggested method estimates the statistical model that best fits the data by leveraging pairwise Bayes Factor analysis to compare pairs of curves and evaluate whether each of the nanoforms is sufficiently similar to all other nanoforms. Pairwise comparisons to benchmark materials are used to define threshold similarity values and set the criteria for identifying groups of nanoforms with comparatively similar toxicity. Applications to use case data are shown to demonstrate that the method can support grouping hypotheses linked to a certain hazard endpoint and route of exposure.

A Decision Support System for preclinical assessment of nanomaterials in medical products: the REFINE DSS.

The application of nanomaterials in medicine has led to novel pharmaceuticals and medical devices that have demonstrated a strong potential for increasing the efficacy/performance and safety of therapeutic and diagnostic procedures to address a wide range of diseases. However, the successful translation of these technologies from their inception (proof-of-concept) to clinical practice has been challenged by substantial gaps in the scientific and technical capacity of R&D companies, especially SMEs, to keep up with the ever-evolving regulatory expectations in the emerging area of nanomedicine. To address these challenges, the EU Horizon 2020 project REFINE has developed a Decision Support System (DSS) to support developers of nanotechnology-enabled health products in bringing their products to the clinic. The REFINE DSS has been developed to support experts, innovators, and regulators in the implementation of intelligent testing strategies (ITS) for efficient preclinical assessment of nanotechnology-enabled health products. The DSS applies logical rules provided by REFINE experts which generate prioritized lists of assays to be performed (i.e. ITSs) for physicochemical characterisation and for immunotoxicological endpoints. The DSS has been tested against several case studies and was validated by internal project experts as well as external ones.

A methodology for the automatic evaluation of data quality and completeness of nanomaterials for risk assessment purposes.

This manuscript proposes a methodology to assess the completeness and quality of physicochemical and hazard datasets for risk assessment purposes. The approach is also specifically applicable to similarity assessment as a basis for grouping of (nanoforms of) chemical substances as well as for classification of the substances according to the Classification, Labeling and Packaging regulation. The unique goal of this approach is to assess data quality in such a way that all the steps are automatized, thus reducing reliance on expert judgment. The analysis starts from available (meta)data as provided in the data entry templates developed by the NanoSafety community and used for import into the eNanoMapper database. The methodology is implemented in the templates as a traffic light system-the providers of the data can see in real time the completeness scores calculated by the system for their datasets in green, yellow, or red. This is an interactive feedback feature that is intended to provide an incentive for anyone inserting data into the database to deliver more complete and higher quality datasets. The users of the data can also see this information both in the data entry templates and on the database interface, which enables them to select better datasets for their assessments. The proposed methodology has been partially implemented in the eNanoMapper database and in a Weight of Evidence approach for the regulatory classification of nanomaterials. It was fully implemented in a publicly available online R tool.

A Weight of Evidence approach to classify nanomaterials according to the EU Classification, Labelling and Packaging Regulation criteria.

In the context of the European Union (EU) Horizon 2020 GRACIOUS project (Grouping, Read-Across, Characterisation and classification framework for regulatory risk assessment of manufactured nanomaterials and Safer design of nano-enabled products), we proposed a quantitative Weight of Evidence (WoE) approach for hazard classification of nanomaterials (NMs). This approach is based on the requirements of the European Regulation on Classification, Labelling and Packaging of Substances and Mixtures (the CLP regulation), which implements the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (UN GHS) in the European Union. The goal of this WoE methodology is to facilitate classification of NMs according to CLP criteria, following the decision trees defined in ECHA's CLP regulatory guidance. In the WoE, results from heterogeneous studies are weighted according to data quality and completeness criteria, integrated, and then evaluated by expert judgment to obtain a hazard classification, resulting in a coherent and justifiable methodology. Moreover, the probabilistic nature of the proposed approach enables highlighting the uncertainty in the analysis. The proposed methodology involves the following stages: (1) collection of data for different NMs related to the endpoint of interest: each study related to each NM is referred as a Line of Evidence (LoE); (2) computation of weighted scores for each LoE: each LoE is weighted by a score calculated based on data quality and completeness criteria defined in the GRACIOUS project; (3) comparison and integration of the weighed LoEs for each NM: A Monte Carlo resampling approach is adopted to quantitatively and probabilistically integrate the weighted evidence; and (4) assignment of each NM to a hazard class: according to the results, each NM is assigned to one of the classes defined by the CLP regulation. Furthermore, to facilitate the integration and the classification of the weighted LoEs, an online R tool was developed. Finally, the approach was tested against an endpoint relevant to CLP (Aquatic Toxicity) using data retrieved from the eNanoMapper database, results obtained were consistent to results in REACH registration dossiers and in recent literature.

Risk Management Framework for Nano-Biomaterials Used in Medical Devices and Advanced Therapy Medicinal Products.

The convergence of nanotechnology and biotechnology has led to substantial advancements in nano-biomaterials (NBMs) used in medical devices (MD) and advanced therapy medicinal products (ATMP). However, there are concerns that applications of NBMs for medical diagnostics, therapeutics and regenerative medicine could also pose health and/or environmental risks since the current understanding of their safety is incomplete. A scientific strategy is therefore needed to assess all risks emerging along the life cycles of these products. To address this need, an overarching risk management framework (RMF) for NBMs used in MD and ATMP is presented in this paper, as a result of a collaborative effort of a team of experts within the EU Project BIORIMA and with relevant inputs from external stakeholders. The framework, in line with current regulatory requirements, is designed according to state-of-the-art approaches to risk assessment and management of both nanomaterials and biomaterials. The collection/generation of data for NBMs safety assessment is based on innovative integrated approaches to testing and assessment (IATA). The framework can support stakeholders (e.g., manufacturers, regulators, consultants) in systematically assessing not only patient safety but also occupational (including healthcare workers) and environmental risks along the life cycle of MD and ATMP. The outputs of the framework enable the user to identify suitable safe(r)-by-design alternatives and/or risk management measures and to compare the risks of NBMs to their (clinical) benefits, based on efficacy, quality and cost criteria, in order to inform robust risk management decision-making.

Comparison of species sensitivity distribution modeling approaches for environmental risk assessment of nanomaterials - A case study for silver and titanium dioxide representative materials.

Species sensitivity distributions (SSDs) are used in chemical safety assessments to derive predicted-no-effect-concentrations (PNECs) for substances with a sufficient amount of relevant and reliable ecotoxicity data available. For engineered nanomaterials (ENMs), ecotoxicity data are often compromised by poor reproducibility and the lack of nano-specific characterization needed describe an ENM under test exposure conditions. This may influence the outcome of SSD modelling and hence the regulatory decision-making. This study investigates how the outcome of SSD modelling is influenced by: 1) Selecting input data based on the nano-specific "nanoCRED" reliability criteria, 2) Direct SSD modelling avoiding extrapolation of data by including long-term/chronic NOECs only, and 3) Weighting data according to their nano-specific quality, the number of data available for each species, and the trophic level abundance in the ecosystem. Endpoints from freshwater ecotoxicity studies were collected for the representative nanomaterials NM-300 K (silver) and NM-105 (titanium dioxide), evaluated for regulatory reliability and scored according to the level of nano-specific characterization conducted. The compiled datasets are unique in exclusively dealing with representative ENMs showing minimal batch-to-batch variation. The majority of studies were evaluated as regulatory reliable, while the degree of nano-specific characterization varied greatly. The datasets for NM-300 K and NM-105 were used as input to the nano-weighted n-SSWD model, the probabilistic PSSD+, and the conventional SSD Generator by the US EPA. The conventional SSD generally yielded the most conservative, but least precise HC5 values, with 95 % confidence intervals up to 100-fold wider than the other models. The inclusion of regulatory reliable data only, had little effect on the HC5 generated by the conventional SSD and the PSSD+, whereas the n-SSWD estimated different HC5 values based on data segregated according to reliability, especially for NM-105. The n-SSWD weighting of data significantly affected the estimated HC5 values, however in different ways for the sub-datasets of NM-300 K and NM-105. For NM-300 K, the inclusion of NOECs only in the weighted n-SSWD yielded the most conservative HC5 of all datasets and models (a HC5 based on NOECs only could not be estimated for NM-105, due to limited number of data). Overall, the estimated HC5 values of all models are within a relatively limited concentration range of 25-100 ng Ag/L for NM-300 K and 1-15 µgTiO2/L for NM-105.

Guiding the development of sustainable nano-enabled products for the conservation of works of art: proposal for a framework implementing the Safe by Design concept.

Nanotechnology provides innovative and promising solutions for the conservation of cultural heritage, but the development and application of new nano-enabled products pose concerns regarding their human health and environmental risks. To address these issues, we propose a sustainability framework implementing the Safe by Design concept to support product developers in the early steps of product development, with the aim to provide safer nano-formulations for conservation, while retaining their functionality. In addition, this framework can support the assessment of sustainability of new products and their comparison to their conventional chemical counterparts if any. The goal is to promote the selection and use of safer and more sustainable nano-based products in different conservation contexts. The application of the proposed framework is illustrated through a hypothetical case which provides a realistic example of the methodological steps to be followed, tailored and iterated along the decision-making process.

Controlling the risks of nano-enabled products through the life cycle: The case of nano copper oxide paint for wood protection and nano-pigments used in the automotive industry.

The widespread use of engineered nanomaterials (ENMs) in consumer products and the overwhelming uncertainties in their ecological and human health risks have raised concerns regarding their safety among industries and regulators. There has been an ongoing debate over the past few decades on ways to overcome the challenges in assessing and mitigating nano-related risks, which has reached a phase of general consensus that nanotechnology innovation should be accompanied by the application of the precautionary principle and best practice risk management, even if the risk assessment uncertainties are large. We propose a quantitative methodology for selecting the optimal risk control strategy based on information about human health and ecological risks, efficacy of risk mitigation measures, cost and other contextual factors. The risk control (RC) methodology was developed in the European FP7 research project SUN and successfully demonstrated in two case studies involving real industrial nano-enabled products (NEPs): nano-scale copper oxide (CuO) and basic copper carbonate (Cu2(OH)2CO3) used as antimicrobial and antifungal coatings and impregnations for the preservation of treated wood, and two nanoscale pigments used for colouring plastic automotive parts (i.e. red organic pigment and carbon black). The application of RC for human health risks showed that although nano-related risks could easily be controlled in automotive plastics case study with modifications in production technology or specific type of engineering controls, nano-related risks due to sanding and sawing copper oxide painted wood were non-acceptable in the use lifecycle stage and would need the identification of a more effective risk control strategy.

Risk Governance of Nanomaterials: Review of Criteria and Tools for Risk Communication, Evaluation, and Mitigation.

Nanotechnologies have been increasingly used in industrial applications and consumer products across several sectors, including construction, transportation, energy, and healthcare. The widespread application of these technologies has raised concerns regarding their environmental, health, societal, and economic impacts. This has led to the investment of enormous resources in Europe and beyond into the development of tools to facilitate the risk assessment and management of nanomaterials, and to inform more robust risk governance process. In this context, several risk governance frameworks have been developed. In our study, we present and review those, and identify a set of criteria and tools for risk evaluation, mitigation, and communication, the implementation of which can inform better risk management decision-making by various stakeholders from e.g., industry, regulators, and the civil society. Based on our analysis, we recommend specific methods from decision science and information technologies that can improve the existing risk governance tools so that they can communicate, evaluate, and mitigate risks more transparently, taking stakeholder perspectives and expert opinion into account, and considering all relevant criteria in establishing the risk-benefit balance of these emerging technologies to enable more robust decisions about the governance of their risks.

Advanced tools for the safety assessment of nanomaterials.

Engineered nanomaterials (ENMs) have tremendous potential to produce beneficial technological impact in numerous sectors in society. Safety assessment is, of course, of paramount importance. However, the myriad variations of ENM properties makes the identification of specific features driving toxicity challenging. At the same time, reducing animal tests by introducing alternative and/or predictive in vitro and in silico methods has become a priority. It is important to embrace these new advances in the safety assessment of ENMs. Indeed, remarkable progress has been made in recent years with respect to mechanism-based hazard assessment of ENMs, including systems biology approaches as well as high-throughput screening platforms, and new tools are also emerging in risk assessment and risk management for humans and the environment across the whole life-cycle of nano-enabled products. Here, we highlight some of the key advances in the hazard and risk assessment of ENMs.

Quantitative human health risk assessment along the lifecycle of nano-scale copper-based wood preservatives.

The use of nano-scale copper oxide (CuO) and basic copper carbonate (Cu2(OH)2CO3) in both ionic and micronized wood preservatives has raised concerns about the potential of these substances to cause adverse humans health effects. To address these concerns, we performed quantitative (probabilistic) human health risk assessment (HHRA) along the lifecycles of these formulations used in antibacterial and antifungal wood coatings and impregnations by means of the EU FP7 SUN project's Decision Support System (SUNDS, www.sunds.gd). The results from the risk analysis revealed inhalation risks from CuO in exposure scenarios involving workers handling dry powders and performing sanding operations as well as potential ingestion risks for children exposed to nano Cu2(OH)2CO3 in a scenario involving hand-to-mouth transfer of the substance released from impregnated wood. There are, however, substantial uncertainties in these results, so some of the identified risks may stem from the safety margin of extrapolation to fill data gaps and might be resolved by additional testing. Our stochastic approach successfully communicated the contribution of different sources of uncertainty in the risk assessment. The main source of uncertainty was the extrapolation from short to long-term exposure, which was necessary due to the lack of (sub)chronic in vivo studies with CuO and Cu2(OH)2CO3. Considerable uncertainties also stemmed from the use of default inter- and intra-species extrapolation factors.

Environmental Impacts by Fragments Released from Nanoenabled Products: A Multiassay, Multimaterial Exploration by the SUN Approach.

Nanoenabled products (NEPs) have numerous outdoor uses in construction, transportation or consumer scenarios, and there is evidence that their fragments are released in the environment at low rates. We hypothesized that the lower surface availability of NEPs fragment reduced their environmental effects with respect to pristine nanomaterials. This hypothesis was explored by testing fragments generated by intentional micronisation ("the SUN approach"; Nowack et al. Meeting the Needs for Released Nanomaterials Required for Further Testing: The SUN Approach. Environmental Science & Technology, 2016 (50), 2747). The NEPs were composed of four matrices (epoxy, polyolefin, polyoxymethylene, and cement) with up to 5% content of three nanomaterials (carbon nanotubes, iron oxide, and organic pigment). Regardless of the type of nanomaterial or matrix used, it was observed that nanomaterials were only partially exposed at the NEP fragment surface, indicating that mostly the intrinsic and extrinsic properties of the matrix drove the NEP fragment toxicity. Ecotoxicity in multiple assays was done covering relevant media from terrestrial to aquatic, including sewage treatment plant (biological activity), soil worms (Enchytraeus crypticus), and fish (zebrafish embryo and larvae and trout cell lines). We designed the studies to explore the possible modulation of ecotoxicity by nanomaterial additives in plastics/polymer/cement, finding none. The results support NEPs grouping by the matrix material regarding ecotoxicological effect during the use phase. Furthermore, control results on nanomaterial-free polymer fragments representing microplastic had no significant adverse effects up to the highest concentration tested.

Comparative, collaborative, and integrative risk governance for emerging technologies.

Various emerging technologies challenge existing governance processes to identify, assess, and manage risk. Though the existing risk-based paradigm has been essential for assessment of many chemical, biological, radiological, and nuclear technologies, a complementary approach may be warranted for the early-stage assessment and management challenges of high uncertainty technologies ranging from nanotechnology to synthetic biology to artificial intelligence, among many others. This paper argues for a risk governance approach that integrates quantitative experimental information alongside qualitative expert insight to characterize and balance the risks, benefits, costs, and societal implications of emerging technologies. Various articles in scholarly literature have highlighted differing points of how to address technological uncertainty, and this article builds upon such knowledge to explain how an emerging technology risk governance process should be driven by a multi-stakeholder effort, incorporate various disparate sources of information, review various endpoints and outcomes, and comparatively assess emerging technology performance against existing conventional products in a given application area. At least in the early stages of development when quantitative data for risk assessment remain incomplete or limited, such an approach can be valuable for policymakers and decision makers to evaluate the impact that such technologies may have upon human and environmental health.

The Essential Elements of a Risk Governance Framework for Current and Future Nanotechnologies.

Societies worldwide are investing considerable resources into the safe development and use of nanomaterials. Although each of these protective efforts is crucial for governing the risks of nanomaterials, they are insufficient in isolation. What is missing is a more integrative governance approach that goes beyond legislation. Development of this approach must be evidence based and involve key stakeholders to ensure acceptance by end users. The challenge is to develop a framework that coordinates the variety of actors involved in nanotechnology and civil society to facilitate consideration of the complex issues that occur in this rapidly evolving research and development area. Here, we propose three sets of essential elements required to generate an effective risk governance framework for nanomaterials. (1) Advanced tools to facilitate risk-based decision making, including an assessment of the needs of users regarding risk assessment, mitigation, and transfer. (2) An integrated model of predicted human behavior and decision making concerning nanomaterial risks. (3) Legal and other (nano-specific and general) regulatory requirements to ensure compliance and to stimulate proactive approaches to safety. The implementation of such an approach should facilitate and motivate good practice for the various stakeholders to allow the safe and sustainable future development of nanotechnology.

Environmental Risk Assessment Strategy for Nanomaterials.

An Environmental Risk Assessment (ERA) for nanomaterials (NMs) is outlined in this paper. Contrary to other recent papers on the subject, the main data requirements, models and advancement within each of the four risk assessment domains are described, i.e., in the: (i) materials, (ii) release, fate and exposure, (iii) hazard and (iv) risk characterisation domains. The material, which is obviously the foundation for any risk assessment, should be described according to the legislatively required characterisation data. Characterisation data will also be used at various levels within the ERA, e.g., exposure modelling. The release, fate and exposure data and models cover the input for environmental distribution models in order to identify the potential (PES) and relevant exposure scenarios (RES) and, subsequently, the possible release routes, both with regard to which compartment(s) NMs are distributed in line with the factors determining the fate within environmental compartment. The initial outcome in the risk characterisation will be a generic Predicted Environmental Concentration (PEC), but a refined PEC can be obtained by applying specific exposure models for relevant media. The hazard information covers a variety of representative, relevant and reliable organisms and/or functions, relevant for the RES and enabling a hazard characterisation. The initial outcome will be hazard characterisation in test systems allowing estimating a Predicted No-Effect concentration (PNEC), either based on uncertainty factors or on a NM adapted version of the Species Sensitivity Distributions approach. The risk characterisation will either be based on a deterministic risk ratio approach (i.e., PEC/PNEC) or an overlay of probability distributions, i.e., exposure and hazard distributions, using the nano relevant models.

Probabilistic risk assessment of emerging materials: case study of titanium dioxide nanoparticles.

The development and use of emerging technologies such as nanomaterials can provide both benefits and risks to society. Emerging materials may promise to bring many technological advantages but may not be well characterized in terms of their production volumes, magnitude of emissions, behaviour in the environment and effects on living organisms. This uncertainty can present challenges to scientists developing these materials and persons responsible for defining and measuring their adverse impacts. Human health risk assessment is a method of identifying the intrinsic hazard of and quantifying the dose-response relationship and exposure to a chemical, to finally determine the estimation of risk. Commonly applied deterministic approaches may not sufficiently estimate and communicate the likelihood of risks from emerging technologies whose uncertainty is large. Probabilistic approaches allow for parameters in the risk assessment process to be defined by distributions instead of single deterministic values whose uncertainty could undermine the value of the assessment. A probabilistic approach was applied to the dose-response and exposure assessment of a case study involving the production of nanoparticles of titanium dioxide in seven different exposure scenarios. Only one exposure scenario showed a statistically significant level of risk. In the latter case, this involved dumping high volumes of nano-TiO2 powders into an open vessel with no personal protection equipment. The probabilistic approach not only provided the likelihood of but also the major contributing factors to the estimated risk (e.g. emission potential).