Developing species sensitivity distributions for metallic nanomaterials considering the characteristics of nanomaterials, experimental conditions, and different types of endpoints

Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint

Assessment and prediction for the ecotoxicity of engineered nanoparticles (ENPs) at the community or ecosystem levels represents a critical step toward a comprehensive understanding of the ecological risks of ENPs. Current studies on predicting the ecotoxicity of ENPs primarily focus on the cellular and individual levels, with limited exploration at the community or ecosystem levels. Herein, we present the first of the reports for the direct prediction of aquatic ecological risk for ENPs at the community level using machine learning (ML) approaches in the field of computational toxicology. Specifically, we extensively collected the threshold concentrations of twelve ENPs including metal- and carbon-based nanoparticles for aquatic species, i.e., hazardous concentrations at which 5% of species are harmed (HC5), established by a species sensitivity distribution. Afterwards, we used eight supervised ML methods including Adaboost, artificial neural network, C4.5 decision tree, K-nearest neighbor, logistic regression, Naive Bayes, random forest, and support vector machine to develop nine classification models and four regression models, respectively, for the qualitative and quantitative prediction of HC5. The evaluation of model performance yielded the internal validation accuracy of all classification models ranging from 71.4 to 100%, and the determination coefficient of regression models ranging from 0.702 to 0.999, indicating that the developed models showed good performance. By using a cross-validation method and an application domain characterization, the selected models were further validated to have powerful predictive ability. Furthermore, the incorporation of three nanostructural descriptors (metal oxide sublimation enthalpy, zeta potential, and specific surface area) linked to toxicity mechanisms (the release of metal ions, the stability of dispersions of particles in aqueous suspensions, and the surface properties of the material) effectively enhanced the prediction power and mechanistic interpretability of the selected models. These findings would not only be beneficial in the screening of ENPs with potential high ecological risks that need to be tested as a priority but also contribute to the development of environmental regulations and standards for ENPs.

Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms

The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.

European nanomaterial legislation in the past 20 years - Closing the final gaps

In 2004, the potential societal implications related to nanotechnology were highlighted in an influential report by the Royal Society and the Royal Academy of Engineering (RS & RAE). It was made clear that legislation is an important tool to tackle the challenges related to nanomaterials and a list of recommendations were put forward. Shortly after, the European Commission also proposed a list of recommendations on how to handle nanomaterial challenges and adopted the so-called "incremental approach", describing that current legislations should be adapted, where relevant, to handle nanomaterials. Now almost 20 years have passed and it seems relevant to take stock and investigate how legislations have been adapted to tackle nano-specific challenges. In this review, we analyze key pieces of European legislations relevant to nanomaterials and assess to what extent these legislations compare with the original recommendations from 2004 by the RS & RAE and the European Commission. We uncover the cross-cutting challenges that remain and provide recommendations on next steps that should be taken to address the risks of nanomaterials. For each recommendation, we assessed whether it was met to a high, medium or low degree by conducting targeted literature searches at Web of Science, screening legislations, guidance documents, databases etc., and applying expert judgement. We found that >90% of the recommendations put forward in 2004 by the RS & RAE and the European Commission have been either met to a high degree (13 out of 29) or met to a medium degree (14 out of 29). This suggests important advancements in the field of nanosafety. At the same time, it is important to address the concerns still left partly or fully unsolved. Such efforts entail e.g. further development of measuring instruments and standardised characterization and risk assessment methods for nanomaterials, application of a uniform nanomaterial definition, maximization of containment of free nanomaterials until hazards assessed/handled and elimination/minimisation of unintentional nanomaterial emission. Furthermore, we recommend prioritising future efforts to ensure enforcement and implementation of existing nano-specific provisions, as well as revision, where needed, of legislations that currently do not account for nanomaterials, such as the Waste Framework Directive.

Nanomaterials in sunscreens: Potential human and ecological health implications

Inorganic nanomaterials such as TiO2 and ZnO provide significant benefits in terms of UV protection, and their use generally has increased in commercial sunscreens. However, more recently there have been concerns about their potential human and ecological health implications, mostly driven by perception rather than by formal assessments. The large and increasing body of literature on these nanomaterials indicates that in most circumstances their risk are minimal. Penetration of the human epidermis is minimal for these nanomaterials, significantly reducing the potential effects that these nanomaterials may pose to internal organs. The excess Zn ion dose is very small compared to normal dietary consumption of Zn, which is a necessary element. The levels of residual nanomaterials or released ions in public swimming pools is also low, with minimal effect in case this water is ingested during swimming or bathing. In natural environments with significant water flow due to wind and water currents, the concentrations of nanomaterials and released ions are generally well below levels that would cause effects in aquatic organisms. However, sensitive habitats with slow currents, such as coral reefs, may accumulate these nanomaterials. The number of studies of the levels and effects of nanomaterials in these sensitive habitats is very small; more research is needed to determine if there is an elevated risk to these ecosystems from the use of sunscreens with these nanomaterials.

Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials

The rapid development of nanomaterials (NMs) and the emergence of new multicomponent NMs will inevitably lead to simultaneous exposure of organisms to multiple engineered nanoparticles (ENPs) at varying exposure levels. Understanding the joint impacts of multiple ENPs and predicting the toxicity of mixtures of ENPs are therefore evidently of importance. We reviewed the toxicity of mixtures of ENPs to a variety of different species, covering algae, bacteria, daphnia, fish, fungi, insects, and plants. Most studies used the independent-action (IA)-based model to assess the type of joint effects. Using co-occurrence networks, it was revealed that 53% of the cases with specific joint response showed antagonistic, 25% synergistic, and 22% additive effects. The combination of nCuO and nZnO exhibited the strongest interactions in each type of joint interaction. Compared with other species, plants exposed to multiple ENPs were more likely to experience antagonistic effects. The main factors influencing the joint response type of the mixtures were (1) the chemical composition of individual components in mixtures, (2) the stability of suspensions of mixed ENPs, (3) the type and trophic level of the individual organisms tested, (4) the biological level of organization (population, communities, ecosystems), (5) the exposure concentrations and time, (6) the endpoint of toxicity, and (7) the abiotic field conditions (e.g., pH, ionic strength, natural organic matter). This knowledge is critical in developing efficient strategies for the assessment of the hazards induced by combined exposure to multiple ENPs in complex environments. In addition, this knowledge of the joint effects of multiple ENPs assists in the effective prediction of hybrid NMs.

Silver nanoparticles adversely affect the swimming behavior of European Whitefish (Coregonus lavaretus) larvae within the low µg/L range

The aim of this study was to determine the effects of silver nanoparticles (AgNPs; speciation: NM-300 K) in the lab on the behavior of larvae in European Whitefish (Coregonus lavaretus), a relevant model species for temperate aquatic environments during alternating light and darkness phases. The behavioral parameters measured included activity, turning rate, and distance moved. C. lavaretus were exposed to AgNP at nominal concentrations of 0, 5, 15, 45, 135, or 405 µg/L (n = 33, each) and behavior was recorded using a custom-built tracking system equipped with light sources that reliably simulate light and darkness. The observed behavior was analyzed using generalized linear mixed models, which enabled reliable detection of AgNP-related movement patterns at 10-fold higher sensitivity compared to recently reported standard toxicological studies. Exposure to 45 µg/L AgNPs significantly resulted in hyperactive response patterns for both activity and turning rates after an illumination change from light to darkness suggesting that exposure to this compound triggered escape mechanisms and disorientation-like behaviors in C. lavaretus fish larvae. Even at 5 µg/L AgNPs some behavioral effects were detected, but further tests are required to assess their ecological relevance. Further, the behavior of fish larvae exposed to 135 µg/L AgNPs was comparable to the control for all test parameters, suggesting a triphasic dose response pattern. Data demonstrated the potential of combining generalized linear mixed models with behavioral investigations to detect adverse effects on aquatic species that might be overlooked using standard toxicological tests.

Impacts of nano-titanium dioxide toward Vallisneria natans and epiphytic microbes

In this study, Vallisneria natans plants were exposed to 5 and 20 nm of titanium dioxide nanoparticles (TiO₂ NPs) anatase and 600-1000 nm of bulk at 5 and 20 mg/L for 30 days. SEM images and EDX spectra revealed that epiphytic biofilms were more prone to TiO₂ NPs adhesion than bare plant leaves. TiO₂ NPs injured plant leaf cells, ruptured epiphytic diatoms membranes and increased the ratio of free-living microbes. The TN, NH₄⁺-N and NO₃--N concentrations significantly decreased, respectively, by 44.9%, 33.6%, and 23.6% compared to bulk treatments after 30 days due to macrophyte damage and a decline in diversity of epiphytic bacterial community and abundance of nitrogen cycle bacteria. TiO₂ NPs size-dependent decrease in bacterial relative abundance was detected, including phylum Cyanobacteria, Planctomycetes, and Verrucomicrobia. Although TiO₂ NPs increased eukaryotic diversity and abundance, abundances of Bacillariophyceae and Vampyrellidae classes and Gastrotricha and Phragmoplastophyta phylum decreased significantly under TiO₂ NPs exposure compared to bulk and control. TiO₂ NPs reduced intensities of interaction relationships among epiphytic microbial genera. This study shed new light on the potential effects of TiO₂ NPs toxicity toward aquatic plants and epiphytic microbial communities and its impacts on nitrogen species removal in wetlands.

Interactive effects of ZnO nanoparticles and temperature on molecular and cellular stress responses of the blue mussel Mytilus edulis

Temperature is an important abiotic factor that modulates all aspects of ectotherm physiology, including sensitivity to pollutants. Nanoparticles are emerging pollutants in coastal environments, and their potential to cause toxicity in marine organisms is a cause for concern. Here we studied the interactive effects of temperature (including seasonal and experimental warming) on sublethal toxicity of ZnO nanoparticles (nano-ZnO) in a model marine bivalve, the blue mussel Mytilus edulis. Molecular markers were used to assess the pollutant-induced cellular stress responses in the gills and the digestive gland of mussels exposed for 21 days to 10 μg l^-1 and 100 μg l^-1 of nano-ZnO or dissolved Zn under different temperature regimes including ambient temperature (10 °C and 15 °C in winter and summer, respectively) or experimental warming (+5 °C). Exposure to high concentration (100 μg l^-1) of nano-ZnO caused oxidative injury to proteins and lipids and induced a marked apoptotic response indicated by increased transcript levels of apoptosis-related genes p53, caspase 3 and the MAPK pathway (JNK and p38) and decreased mRNA expression of anti-apoptotic Bcl-2. No significant induction of inflammatory cytokine-related response (TGF-β and NF-κB) of tissues was observed in nano-ZnO exposed-mussels. Furthermore, the oxidative injury and apoptotic response could differentiate the effects of nano-ZnO from those of dissolved Zn in the mussels. This study revealed that oxidative stress and stress-related transcriptional responses to nano-ZnO were strongly modified by warming and season in the mussels. No single biomarker could be shown to consistently respond to nano-ZnO in all experimental groups, which implies that multiple biomarkers are needed to assess nano-ZnO toxicity to marine organisms under the variable environmental conditions of coastal habitats.

In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, which support filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e. species sensitivity distributions and ultimately for landscape assessment i.e. landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application of such in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impact of single chemicals, multiple chemicals and multiple stressors on living organisms.

Immune responses to ZnO nanoparticles are modulated by season and environmental temperature in the blue mussels Mytilus edulis

Increased production and release of ZnO nanoparticles (nZnO) can cause toxic effects on marine ecosystems and aquatic organisms. However, nZnO toxicity and its modulation by common environmental stressors such as temperature are not yet fully understood. We examined the responses of immune cells (hemocytes) of the blue mussels (Mytilus edulis) exposed to different concentrations (0, 10, 100 μg l^-1) of nZnO or dissolved zinc combined with two temperatures (ambient (10 °C in winter and 15 °C in summer) and warming (+5 °C above ambient temperature)) in winter and summer for 21 days. In winter mussels, exposure to nZnO induced a strong transcriptomic response in multiple immune and inflammation-related genes, stimulated phagocytosis and hemocyte mortality yet suppressed adhesion capacity of hemocytes. In summer mussels, the immune cell responses to nZnO were blunted. The transcriptional responses of hemocytes to dissolved Zn were qualitatively similar but weaker than the responses to nZnO. In the absence of the toxic stress, +5 °C warming lead to dysregulation of the transcription of key immune-related genes in the summer but not the winter mussels. Seasonal warm acclimatization and additional warming in summer suppressed the nZnO-induced transcriptional upregulation of antimicrobial peptides, Toll-like receptors and the complement system. These findings demonstrate that nZnO act as an immunogen in M. edulis and indicate that +5 °C warming might have detrimental effect on innate immunity of the temperate mussel populations in summer when exposure to pathogens is especially high. Capsule: ZnO nanoparticles act as an immunotoxicant inducing a strong immune response in the mussels which is dysregulated by warming in summer but not in winter.

Form-Specific and Probabilistic Environmental Risk Assessment of 3 Engineered Nanomaterials (Nano-Ag, Nano-TiO2 , and Nano-ZnO) in European Freshwaters

The release of engineered nanomaterials (ENMs) to the environment necessitates an assessment of their environmental risks. The currently available environmental risk assessments (ERA) for ENMs are based on an analysis of the total flows of a specific ENM to the environment and on ecotoxicity studies performed with pristine ENMs. It is known that ENMs undergo transformation during product use and release and in technical systems such as wastewater treatment. The aim of the present study was therefore to perform an ERA of 3 ENMs (nano-Ag, nano-TiO2 , and nano-ZnO) based on a form-specific release model and a form-specific analysis of ecotoxicological data. Predicted environmental concentration values were derived using a form-specific material flow model. Species sensitivity distributions were used to derive predicted-no-effect concentrations (PNECs) for the pristine ENMs and for dissolved and transformed Ag and ZnO. For all ENMs, the matrix-embedded form was included in the assessment. A probabilistic assessment was applied, yielding final probability distributions for the risk characterization ratio (RCR). For nano-Ag, the form-specific assessment resulted in a decrease of the mean RCR from 0.061 for the approach neglecting the different release forms to 0.034 because of the much lower PNEC of transformed Ag. Likewise, for nano-ZnO, the form-specific approach reduced the mean RCR from 1.2 to 0.86. For nano-TiO2 , the form-specific assessment did not change the mean RCR of 0.026. This analysis shows that a form-specific approach can have an influence on the assessment of the environmental risks of ENMs and that, given the availability of form-specific release models, an updated ERA for ENMs can be performed. Environ Toxicol Chem 2021;40:2629-2639. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Creating sets of similar nanoforms with the ECETOC NanoApp: real-life case studies

The ECETOC NanoApp was developed to support industry in the registration of sets of nanoforms, as well as regulators in the evaluation of these registration dossiers. The ECETOC NanoApp uses a systematic approach to create and justify sets of similar nanoforms, following the ECHA guidance in a transparent and evidence-based manner. The rational and decision rules behind the ECETOC NanoApp are described in detail in "Janer, G., R. Landsiedel, and W. Wohlleben. 2021. [Rationale and Decision Rules Behind the ECETOC NanoApp to Support Registration of Sets of Similar Nanoforms within REACH. Nanotoxicology 15 (2): 145-122. https://doi.org/10.1080/17435390.2020.1842933]". The decision criteria apply to human health and environmental hazards and risks. Here, we focus mostly on human health hazards; the decision rules are applied to a series of case studies, each consisting of real nanoforms: two barium sulfate nanoforms, four colloidal silica nanoforms, eight ceria nanoforms, and four copper phthalocyanine nanoforms. For each of them, we show step by step how the ECETOC NanoApp rules are applied. The cases include nanoforms that are justified as members of the same set of similar nanoforms based on sufficient similarity of their intrinsic properties (Tier 1). They also include other nanoforms with a relatively high (but insufficient) similarity of intrinsic properties; their similarity could be justified by functional properties (Tier 2). The case studies also include nanoforms that are concluded not to belong to the same set of similar nanoforms. These outcomes of the NanoApp were overall consistent (sometimes conservative) with available in vivo data. We also noted that datasets for various nanoforms were limited and use of the NanoApp may require the generation of data relevant to the decision criteria.

Ecotoxicity to Freshwater Organisms and Cytotoxicity of Nanomaterials: Are We Generating Sufficient Data for Their Risk Assessment?

The aim of the present study was to investigate the eco-cytotoxicity of several forms of nanomaterials (NM), such as nano-CuO, nano-TiO₂, nano-SiO₂ and nano-ZnO, on different aquatic species (Raphidocelis subcapitata, Daphnia magna and Lemna minor) following standard protocols and on human cell lines (Caco-2, SV-80, HepG2 and HaCaT). Predicted no-effect concentrations (PNEC) or hazard concentrations for 5% of the species (HC₅) were also estimated based on the compilation of data available in the literature. Most of the NM agglomerated strongly in the selected culture media. For the ecotoxicity assays, nano-CuO and nano-ZnO even in particle agglomeration state were the most toxic NM to the freshwater organisms compared to nano-TiO₂ and nano-SiO₂. Nano-ZnO was the most toxic NM to R. subcapitata and D. magna, while nano-CuO was found to be very toxic to L. minor. Nano-CuO was very toxic to Caco-2 and HepG2 cells, particularly at the highest tested concentrations, while the other NM showed no toxicity to the different cell lines. The HC₅ and PNEC values are still highly protective, due to data limitations. However, the present study provides consistent evidence of the potential risks of both nano-CuO and nano-ZnO against aquatic organisms and also their effects on public health.

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.

Effects of silver(I) toxicity on microstructure, biochemical activities, and genic material of Lemna minor L. with special reference to application of bioindicator

In this research, several biochemical variations in plant of Lemna minor L. were investigated to reflect Ag⁺ toxicity. Lemna minor L. changed colorless AgNO₃ to colloidal brown at doses equal to and greater than 1 mg L^-1. Optical and fluorescence microscopy revealed the presence of bright spots in roots of tested plant related to Ag/Ag₂O-NPs. Photosynthetic pigment contents of Lemna minor L. declined upon exposure to Ag⁺ with an evidently higher decrease in chlorophyll a than in chlorophyll b. Similarly, Ag⁺ treatment caused an evident reduction in the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). The reduction in antioxidase activity was significantly higher in POD than in SOD and CAT. Ag⁺ treatment resulted in a significant increment in the level of malondialdehyde (MDA) content as the judging criteria of cellular injury which showed sign of dose-related. The alterations occurred in RAPD profiles of treated samples following Ag⁺ toxicity containing loss of normal bands, appearance of new bands, and variation in band intensities compared with the normal plants. In addition, morphological character and biomass of Lemna minor L. subjected to increasing Ag⁺ concentrations were evaluated to reveal Ag⁺ toxicity. Our study demonstrated that Lemna minor L. have a high sensitivity to indicate fluctuation of water quality. It would be beneficial that modulating the genotype of Lemna minor L. to bear high proportion of contaminates.

Systematic Consideration of Parameter Uncertainty and Variability in Probabilistic Species Sensitivity Distributions

The calculation of a species sensitivity distribution (SSD) is a commonly accepted approach to derive the predicted no-effect concentration (PNEC) of a substance in the context of environmental risk assessment. The SSD approach usually is data demanding and incorporates a large number of ecotoxicological values from different experimental studies. The probabilistic SSD (PSSD) approach is able to fully consider the variability between different exposure conditions and material types, which is of great importance when constructing an SSD for any chemical, especially for nanomaterials. The aim of our work was to further develop the PSSD approach by implementing methods to better consider the uncertainty and variability of the input data. We incorporated probabilistic elements to consider the uncertainty associated with uncertainty factors by using probability distributions instead of single values. The new PSSD method (named "PSSD+") computes 10 000 PSSDs based on a Monte Carlo routine. For each PSSD calculated, the hazardous concentration for 5% of species (HC₅ ) was extracted to provide a PNEC distribution based on all data available and their associated uncertainty. The PSSD+ approach also includes the option to consider a species weighting according to a typically constituted biome. We applied this PSSD+ approach to a previously published data set on C nanotubes and Ag nanoparticles. The evaluation of the uncertainty factor distributions and species weighting have shown that the proposed PSSD method is robust with respect to the calculation of the PNEC value. Furthermore, we demonstrated that the PSSD+ can handle both small and more comprehensive data sets because the PNEC distributions are a close representation of the data available. Finally, the sensitivity testing toward data set variations showed that the maximum variation of the mean PNEC was of a factor of about 2, so that the method is relatively insensitive to missing data points as long as the most sensitive species is included. Integr Environ Assess Manag 2020;16:211-222. © 2019 SETAC.

A quantitative structure-activity relationships approach to predict the toxicity of narcotic compounds to aquatic communities

Species may vary markedly in terms of their sensitivity to toxicants, and such variation can be described through the species sensitivity distribution (SSD) approach. Using SSD cumulative functions, it is possible to calculate the hazardous concentration for 5% of the species (HC₅), namely the contaminant concentration at which 5% of species will be affected. HC₅ is often utilised to derive the predicted no-effect concentration, or the concentration at which a chemical will likely have no toxic effects on the different species present in an ecosystem. However, the lack of sufficient ecotoxicological data frequently obstructs the derivation of SSD curves and consequently the HC₅. In the last 30 years, quantitative structure-activity relationship (QSAR) models have been widely used to predict the toxicity of chemicals to single species. The aim of this study was to evaluate the possibility of extending the applicability domain of these models from single species to the community level by predicting the HC₅ values for aquatic communities and bypassing the need to derive SSD curves. This approach's practical advantage is that it would allow information on the toxicity of contaminants to be obtained on a hierarchical scale (aquatic community), which is ecologically more relevant than on the scale of single species, without the need for a robust toxicity data set. In the first part of the study, two simple QSAR models were developed for narcotic and polar narcotic compounds. Then, the QSAR model developed for narcotic compounds was utilised to define the baseline toxicity for aquatic communities and to calculate the toxicity ratios for various specifically acting compounds (insecticides and herbicides).

Material-specific properties applied to an environmental risk assessment of engineered nanomaterials - implications on grouping and read-across concepts

Engineered nanomaterials (ENMs) are intentionally designed in different nano-forms of the same parent material in order to meet application requirements. Different grouping and read-across concepts are proposed to streamline risk assessments by pooling nano-forms in one category. Environmental grouping concepts still are in their infancy and mainly focus on grouping by hazard categories. Complete risk assessments require data on environmental release and exposure not only for ENMs but also for their nano-forms. The key requirement is to identify and to distinguish the production volumes of the ENMs regarding nano-form-specific applications. The aim of our work was to evaluate whether such a grouping is possible with the available data and which influence it has on the environmental risk assessment of ENMs. A functionality-driven approach was applied to match the material-specific property (i.e. crystal form/morphology) with the functions employed in the applications. We demonstrate that for nano-TiO₂, carbon nanotubes (CNTs), and nano-Al₂O₃ the total production volume can be allocated to specific nano-forms based on their functionalities. The differentiated assessments result in a variation of the predicted environmental concentrations for anatase vs. rutile nano-TiO₂, single-wall vs. multi-wall CNTs and α- vs. γ-nano-Al₂O₃ by a factor of 2 to 13. Additionally, the nano-form-specific predicted no-effect concentrations for these ENMs were derived. The risk quotients for all nano-forms indicated no immediate risk in freshwaters. Our results suggest that grouping and read-across concepts should include both a nano-form release potential for estimating the environmental exposure and separately consider the nano-forms in environmental risk assessments.

Physiological and biochemical effect of silver on the aquatic plant Lemna gibba L.: Evaluation of commercially available product containing colloidal silver

This paper aims to evaluate the effects of a product containing colloidal silver in the aquatic environment, using duckweed Lemna gibba as a model plant. Therefore, growth parameters, photosynthetic pigments content and protein content as physiological indices were evaluated. Changes in the content of non-enzymatic antioxidants and activity of several antioxidant enzymes, alongside with the accumulation of hydrogen peroxide and lipid peroxidation end-products were assessed to explore the potential of colloidal silver to induce oxidative stress. The commercially available colloidal silver product contained a primary soluble form of silver. The treatment with colloidal silver resulted in significant physiological and biochemical changes in L. gibba plants and a consequent reduction of growth. Accumulation of silver caused altered nutrient balance in the plants as well as a significant decrease in photosynthetic pigments content and protein concentration. The antioxidative response of L. gibba plants to treatment with colloidal silver was inadequate to protect the plants from oxidative stress caused by metal accumulation. Silver caused concentration-dependent and time-dependent hydrogen peroxide accumulation as well as the elevation of lipid peroxidation levels in L. gibba plants. The use of commercially available products containing colloidal silver, and consequent accumulation of silver, both ionic and nanoparticle form in the environment, represents a potential source of toxicity to primary producers in the aquatic ecosystem.

Nanoecotoxicity assessment of graphene oxide and its relationship with humic acid

The risk assessment of nanomaterials is essential for regulatory purposes and for sustainable nanotechnological development. Although the application of graphene oxide has been widely exploited, its environmental risk is not well understood because several environmental conditions can affect its behavior and toxicity. In the present study, the graphene oxide effect from aquatic ecosystems was assessed considering the interaction with humic acid on 9 organisms: Raphidocelis subcapitata (green algae), Lemna minor (aquatic plant), Lactuca sativa (lettuce), Daphnia magna (planktonic microcrustacean), Artemia salina (brine shrimp), Chironomus sancticaroli (Chironomidae), Hydra attenuata (freshwater polyp), and Caenorhabditis elegans and Panagrolaimus sp. (nematodes). The no-observed-effect concentration (NOEC) was calculated for each organism. The different criteria used to calculate NOEC values were transformed and plotted as a log-logistic function. The hypothetical 5 to 50% hazardous concentration values were, respectively, 0.023 (0.005-0.056) and 0.10 (0.031-0.31) mg L-1 for graphene oxide with and without humic acid, respectively. The safest scenario associated with the predicted no-effect concentration values for graphene oxide in the aquatic compartment were estimated as 20 to 100 μg L-1 (in the absence of humic acid) and 5 to 23 μg L-1 (in the presence of humic acid). Finally, the present approach contributed to the risk assessment of graphene oxide-based nanomaterials and the establishment of nano-regulations. Environ Toxicol Chem 2018;37:1998-2012. © 2018 SETAC.

Environmental risk assessment of engineered nano-SiO2 , nano iron oxides, nano-CeO2 , nano-Al2 O3 , and quantum dots

Many research studies have endeavored to investigate the ecotoxicological hazards of engineered nanomaterials (ENMs). However, little is known regarding the actual environmental risks of ENMs, combining both hazard and exposure data. The aim of the present study was to quantify the environmental risks for nano-Al₂ O₃ , nano-SiO₂ , nano iron oxides, nano-CeO₂ , and quantum dots by comparing the predicted environmental concentrations (PECs) with the predicted-no-effect concentrations (PNECs). The PEC values of these 5 ENMs in freshwaters in 2020 for northern Europe and southeastern Europe were taken from a published dynamic probabilistic material flow analysis model. The PNEC values were calculated using probabilistic species sensitivity distribution (SSD). The order of the PNEC values was quantum dots < nano-CeO₂  < nano iron oxides < nano-Al₂ O₃  < nano-SiO₂ . The risks posed by these 5 ENMs were demonstrated to be in the reverse order: nano-Al₂ O₃  > nano-SiO₂  > nano iron oxides > nano-CeO₂  > quantum dots. However, all risk characterization values are 4 to 8 orders of magnitude lower than 1, and no risk was therefore predicted for any of the investigated ENMs at the estimated release level in 2020. Compared to static models, the dynamic material flow model allowed us to use PEC values based on a more complex parameterization, considering a dynamic input over time and time-dependent release of ENMs. The probabilistic SSD approach makes it possible to include all available data to estimate hazards of ENMs by considering the whole range of variability between studies and material types. The risk-assessment approach is therefore able to handle the uncertainty and variability associated with the collected data. The results of the present study provide a scientific foundation for risk-based regulatory decisions of the investigated ENMs. Environ Toxicol Chem 2018;37:1387-1395. © 2018 SETAC.

A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials

Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs). The extension of conventional (quantitative) structure-activity relationships ((Q)SARs) approach to nanotoxicology, i.e., nano-(Q)SARs, is a possible solution. The preliminary attempts of correlating ENMs' characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (Q)SARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(Q)SARs for metallic ENMs, on the aspects of data sources, reported nano-(Q)SARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.

Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials

As listed by the European Chemicals Agency, the three elements in evaluating the hazards of engineered nanomaterials (ENMs) include the integration and evaluation of toxicity data, categorization and labeling of ENMs, and derivation of hazard threshold levels for human health and the environment. Assessing the hazards of ENMs solely based on laboratory tests is time-consuming, resource intensive, and constrained by ethical considerations. The adoption of computational toxicology into this task has recently become a priority. Alternative approaches such as (quantitative) structure-activity relationships ((Q)SAR) and read-across are of significant help in predicting nanotoxicity and filling data gaps, and in classifying the hazards of ENMs to individual species. Thereupon, the species sensitivity distribution (SSD) approach is able to serve the establishment of ENM hazard thresholds sufficiently protecting the ecosystem. This article critically reviews the current knowledge on the development of in silico models in predicting and classifying the hazard of metallic ENMs, and the development of SSDs for metallic ENMs. Further discussion includes the significance of well-curated experimental datasets and the interpretation of toxicity mechanisms of metallic ENMs based on reported models. An outlook is also given on future directions of research in this frontier.