Comparing the impact of microplastics derived from a biodegradable and a conventional plastic mulch on plant performance.

Agricultural lands have been identified as plastic sinks. One source is plastic mulches, which are a source of micro- and nano-sized plastics in agricultural soils. Because of their persistence, there is now a push towards developing biodegradable plastics, which are designed to undergo (partial) breakdown after entering the environment. Yet, limited research has investigated the impacts of both conventional and biodegradable plastics on distinct plants. Moreover, comparisons among studies are difficult due to differences in experimental design. This study directly compares the effects of artificially weathered conventional polyethylene (PE) and starch-based biodegradable polybutylene adipate terephthalate (PBAT) on four food crops, including two monocots (barley, Hordeum vulgare, and wheat, Triticum aestivum L.) and two dicots (carrot, Daucus carota, and lettuce, Lactuca sativa L.). We investigated the effects of environmentally relevant low, medium, and high (0.01 %, 0.1 %, 1 % w/w) concentrations of PE and starch-PBAT blend on seed germination (acute toxicity), and subsequently on plant growth and chlorophyll through a pot-plant experiment (chronic toxicity). Germination of all species was not affected by both plastics. However, root length was reduced for lettuce and wheat seedlings. No other effects were recorded on monocots. We observed a reduction in shoot length and bud wet weight of carrot seedlings for the highest concentration of PE and starch-PBAT blend. Chronic exposure resulted in a significant decrease in shoot biomass of barley and lettuce. Additionally, a positive increase in the number of leaves of lettuce was observed for both plastics. Chlorophyll content was increased in lettuce when exposed to PE and starch-PBAT blend. Overall, adverse effects in dicots were more abundant than in monocots. Importantly, we found that the biodegradable plastic caused more commonly adverse effects on plants compared to conventional plastic, which was confirmed by a mini-review of studies directly comparing the impact of conventional and biodegradable microplastics.

The biodistribution of polystyrene nanoparticles administered intravenously in the chicken embryo.

Nanoplastics can cause severe malformations in chicken embryos. To improve our understanding of the toxicity of nanoplastics to embryos, we have studied their biodistribution in living chicken embryos. We injected the embryos in the vitelline vein at stages 18-19. We injected polystyrene nanoparticles (PS-NPs) tagged with europium- or fluorescence. Their biodistribution was tracked using inductively-coupled plasma mass spectrometry on tissue lysates, paraffin histology, and vibratome sections analysed by machine learning algorithms. PS-NPs were found at high levels in the heart, liver and kidneys. Furthermore, PS-NPs crossed the endocardium of the heart at sites of epithelial-mesenchymal transformation; they also crossed the liver endothelium. Finally, we detected PS-NPs in the allantoic fluid, consistent with their being excreted by the kidneys. Our study shows the power of the chicken embryo model for analysing the biodistribution of nanoplastics in embryos. Such experiments are difficult or impossible in mammalian embryos. These findings are a major advance in our understanding of the biodistribution and tissue-specific accumulation of PS-NPs in developing animals.

Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size.

A strong need exists for broadly applicable nano-QSARs, capable of predicting toxicological outcomes towards untested species and nanomaterials, under different environmental conditions. Existing nano-QSARs are generally limited to only a few species but the inclusion of species characteristics into models can aid in making them applicable to multiple species, even when toxicity data is not available for biological species. Species traits were used to create classification- and regression machine learning models to predict acute toxicity towards aquatic species for metallic nanomaterials. Afterwards, the individual classification- and regression models were stacked into a meta-model to improve performance. Additionally, the uncertainty and limitations of the models were assessed in detail (beyond the OECD principles) and it was investigated whether models would benefit from the addition of more data. Results showed a significant improvement in model performance following model stacking. Investigation of model uncertainties and limitations highlighted the discrepancy between the applicability domain and accuracy of predictions. Data points outside of the assessed chemical space did not have higher likelihoods of generating inadequate predictions or vice versa. It is therefore concluded that the applicability domain does not give complete insight into the uncertainty of predictions and instead the generation of prediction intervals can help in this regard. Furthermore, results indicated that an increase of the dataset size did not improve model performance. This implies that larger dataset sizes may not necessarily improve model performance while in turn also meaning that large datasets are not necessarily required for prediction of acute toxicity with nano-QSARs.

Chronic toxicity of core-shell SiC/TiO2 (nano)-particles to Daphnia magna under environmentally relevant food rations in the presence of humic acid.

To date, research on the toxicity and potential environmental impacts of nanomaterials has predominantly focused on relatively simple and single-component materials, whilst more complex nanomaterials are currently entering commercial stages. The current study aimed to assess the long-term and size-dependent (60 and 500 nm) toxicity of a novel core-shell nanostructure consisting of a SiC core and TiO2 shell (SiC/TiO2, 5, 25, and 50 mg L-1) to the common model organism Daphnia magna. These novel core-shell nanostructures can be categorized as advanced materials. Experiments were conducted under environmentally realistic feeding rations and in the presence of a range of concentrations of humic acid (0.5, 2, 5, and 10 mg L-1 TOC). The findings show that although effect concentrations of SiC/TiO2 were several orders of magnitude lower than the current reported environmental concentrations of more abundantly used nanomaterials, humic acid can exacerbate the toxicity of SiC/TiO2 by reducing aggregation and sedimentation rates. The EC50 values (mean ± standard error) based on nominal SiC/TiO2 concentrations for the 60 nm particles were 28.0 ± 11.5 mg L-1 (TOC 0.5 mg L-1), 21.1 ± 3.7 mg L-1 (TOC 2 mg L-1), 18.3 ± 5.4 mg L-1 (TOC 5 mg L-1), and 17.8 ± 2.4 mg L-1 (TOC 10 mg L-1). For the 500 nm particles, the EC50 values were 34.9 ± 16.5 mg L-1 (TOC 0.5 mg L-1), 24.8 ± 5.6 mg L-1 (TOC 2 mg L-1), 28.0 ± 10.0 mg L-1 (TOC 5 mg L-1), and 23.2 ± 4.1 mg L-1 (TOC 10 mg L-1). We argue that fate-driven phenomena are often neglected in effect assessments, whilst environmental factors such as the presence of humic acid may significantly influence the toxicity of nanomaterials.

Response of Chlorella vulgaris to exposure to CuO NPs: Contributions of particulate and dissolved metal forms as modulated by tannic acid and pH.

A suspension of copper oxide nanoparticles (CuO NPs) is a mixture of dissolved and particulate Cu, the relative proportions of which highly depend on the water chemistry. However, the relationship between different proportions of particulate and dissolved Cu and the overall toxicity of CuO NPs is still unknown. This study investigated the response of Chlorella vulgaris to CuO NPs at varying solution pH and at different tannic acid (TA) additions, with a focus on exploring whether and how dissolved and particulate Cu contribute to the overall toxicity of CuO NPs. The results of the exposure experiments demonstrated the involvement of both dissolved and particulate Cu in inducing toxicity of CuO NPs, and the inhibition of CuO NPs on cell density of Chlorella vulgaris was found to be significantly (p < 0.05) alleviated with increased levels of TA and pH (< 8). Using the independent action model, the contribution to toxicity of particulate Cu was found to be enhanced with increasing pH values and TA concentrations. The toxic unit indicator better (R2 = 0.86, p < 0.001) explained impacts of CuO NPs on micro-algae cells than commonly used mass concentrations (R2 = 0.27-0.77, p < 0.05) across different levels of pH and TA. Overall, our study provides an additivity-based method to improve the accuracy of toxicity prediction through including contributions to toxicity of both dissolved and particulate Cu and through eliminating the uneven distribution of data due to large variations in total Cu, particulate Cu, dissolved Cu, Cu2+ activities, Cu-TA complexes and other Cu-complexes concentrations with varying water chemistry conditions.

Participatory hackathon to determine ecological relevant endpoints for a neurotoxin to aquatic and benthic invertebrates.

The aim of this study is twofold: i) to determine innovative yet sensitive endpoints for sulfoxaflor and ii) to develop best practices for innovative teaching in ecotoxicology. To this end, a group of 52 MSc students participated in an environmental hackathon, during which they did creative toxicity testing on 5 freshwater invertebrate species: Daphnia magna, Chironomus riparius, Asellus aquaticus, Lymnaea stagnalis, and Anisus vortex. Involving the students in an active learning environment stimulated increased creativity and productivity. In total, 28 endpoints were investigated, including standard endpoints (e.g., mortality) as well as biomechanistic and energy-related endpoints. Despite high variances in the results, likely linked to the limited lab experience of the students and interpersonal differences, a promising set of endpoints was selected for further investigation. A more targeted follow-up experiment focused on the most promising organism and set of endpoints: biomechanistic endpoints of C. riparius larvae. Larvae were exposed to a range of sulfoxaflor concentrations (0.90-67.2 µg/L) for 21 days. Video tracking showed that undulation and swimming were significantly reduced at 11.1 µg sulfoxaflor/L after 9 days of exposure, and an EC50 = 10.6 µg/L for mean velocities of the larvae in the water phase was found. Biomechanistic endpoints proved much more sensitive than mortality, for which an LC50 value of 116 µg/L was found on Day 9. Our results show that performing a hackathon with students has excellent potential to find sensitive endpoints that can subsequently be verified using more targeted and professional follow-up experiments. Furthermore, utilising hackathon events in teaching can increase students' enthusiasm about ecotoxicology, driving better learning experiences.

Trimetal-based nanomaterials induced toxicity to plants: Does it differ from the toxicity of mixed and single-element nanoparticles?

Advanced materials comprising multiple metal alloys have made their way into the market. Trimetal-based nanomaterials (TNMs) are an example of advanced materials which have gained significant traction and are now employed in a wide array of products. It is essential to raise the question if the toxicity of advanced nanomaterials like TNMs differs from the joint effects as manifested by exposure to the single component nanoparticles (NPs). To answer this question, a trimetal-based nanomaterial: bismuth cobalt zinc oxide (BiCoZnO) was tested. This TNM had a mass ratio of 90 % ZnO NPs, 7 % Bi2O3 NPs and 3 % Co3O4 NPs. Nanoparticle-exposed lettuce seedlings (Lactuca sativa L.) showed decreases in relative root elongation (RRE) and biomass production after 21 days of exposure. The 50 % of maximal effective concentration (EC50) value of the TNMs for biomass production was 1.2 mg L-1 when the exposure period was 240 h. This is of the same magnitude as the EC50 values found for ZnO NPs (EC50 = 1.5 mg L-1) and for the mixture of components NPs (MCNPs) which jointly form the TNMs (EC50 = 3.7 mg L-1) after 10 d of exposure. The inhibition of plant root elongation by the TNMs was partially (65 %) attributed to the release of Zn ions, with the actual concentration of released Zn ions being lower in TNMs compared to the actual concentration of Zn ions in case of ZnO NPs. It is therefore to be concluded that the concentration of Zn ions cannot be used as a direct measure to compare the toxicity between traditional and advanced Zn-related nanomaterials. The EC50 values could be assessed within a factor of two; which is helpful when developing advanced alloy nanomaterials and assessing prospective the effects of trimetal-based nanomaterials.

Species-dependent responses of crop plants to polystyrene microplastics.

Only recently there has been a strong focus on the impacts of microplastics on terrestrial crop plants. This study aims to examine and compare the effects of microplastics on two monocotyledonous (barley, Hordeum vulgare and wheat, Triticum aestivum), and two dicotyledonous (carrot, Daucus carota and lettuce, Lactuca sativa) plant species through two complimentary experiments. First, we investigated the effects of low, medium, and high (103, 105, 107 particles per mL) concentrations of 500 nm polystyrene microplastics (PS-MPs) on seed germination and early development. We found species-dependent effects on the early development, with microplastics only significantly affecting lettuce and carrot. When acutely exposed during germination, PS-MPs significantly delayed the germination of lettuce by 24%, as well as promoted the shoot growth of carrot by 71% and decreased its biomass by 26%. No effect was recorded on monocot species. Secondly, we performed a chronic (21 d) hydroponic experiment on lettuce and wheat. We observed that PS-MPs significantly reduced the shoot growth of lettuce by up to 35% and increased its biomass by up to 64%, while no record was reported on wheat. In addition, stress level indicators and defence mechanisms were significantly up-regulated in both lettuce and wheat seedlings. Overall, this study shows that PS-MPs affect plant development: impacts were recorded on both germination and growth for dicots, and responses identified by biochemical markers of stress were increased in both lettuce and wheat. This highlights species-dependent effects as the four crops were grown under identical conditions to allow direct comparison. For future research, our study emphasizes the need to focus on crop specific effects, while also working towards knowledge of plastic-induced impacts at environmentally relevant conditions.

Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles.

Research on theoretical prediction methods for the mixture toxicity of engineered nanoparticles (ENPs) faces significant challenges. The application of in silico methods based on machine learning is emerging as an effective strategy to address the toxicity prediction of chemical mixtures. Herein, we combined toxicity data generated in our lab with experimental data reported in the literature to predict the combined toxicity of seven metallic ENPs for Escherichia coli at different mixing ratios (22 binary combinations). We thereafter applied two machine learning (ML) techniques, support vector machine (SVM) and neural network (NN), and compared the differences in the ability to predict the combined toxicity by means of the ML-based methods and two component-based mixture models: independent action and concentration addition. Among 72 developed quantitative structure-activity relationship (QSAR) models by the ML methods, two SVM-QSAR models and two NN-QSAR models showed good performance. Moreover, an NN-based QSAR model combined with two molecular descriptors, namely enthalpy of formation of a gaseous cation and metal oxide standard molar enthalpy of formation, showed the best predictive power for the internal dataset (R2test = 0.911, adjusted R2test = 0.733, RMSEtest = 0.091, and MAEtest = 0.067) and for the combination of internal and external datasets (R2test = 0.908, adjusted R2test = 0.871, RMSEtest = 0.255, and MAEtest = 0.181). In addition, the developed QSAR models performed better than the component-based models. The estimation of the applicability domain of the selected QSAR models showed that all the binary mixtures in training and test sets were in the applicability domain. This study approach could provide a methodological and theoretical basis for the ecological risk assessment of mixtures of ENPs.

Zooplankton community turnover in response to a novel TiO2-coated nano-formulation of carbendazim and its constituents.

Novel nanomaterial-based pesticide formulations are increasingly perceived as promising aids in the transition to more efficient agricultural production systems. The current understanding of potential unintended (eco)toxicological impacts of nano-formulated pesticides is scarce, in particular with regard to (non-target) aquatic organisms and ecosystems. The present study reports the results of a long-term freshwater mesocosm experiment which assessed responses of individual zooplankton taxa and communities to a novel TiO2-coated nano-formulation of the fungicide carbendazim. Population- and community trends were assessed and compared in response to the nano-formulation and its constituents applied individually (i.e. nano-sized TiO2, carbendazim) and in combination (i.e. nano-sized TiO2 & carbendazim). Minimal differences were observed between effects induced by the nano-formulation and its active ingredient (i.e. carbendazim) when applied at equivalent nominal test concentrations (4 µg L-1). Nano-sized TiO2 was found to affect zooplankton community trends when applied separately at environmentally realistic concentrations (20 µg L-1 nominal test concentration). However, when nano-sized TiO2 was applied in combination with carbendazim, nano-sized TiO2 was found not to alter effects on community trends induced by carbendazim. The findings of the current study provide an extensive and timely addition to the current body of work available on non-target impacts of nano-formulated pesticides.

Nanoplastics causes extensive congenital malformations during embryonic development by passively targeting neural crest cells.

Nanomaterials are widespread in the human environment as pollutants, and are being actively developed for use in human medicine. We have investigated how the size and dose of polystyrene nanoparticles affects malformations in chicken embryos, and have characterized the mechanisms by which they interfere with normal development. We find that nanoplastics can cross the embryonic gut wall. When injected into the vitelline vein, nanoplastics become distributed in the circulation to multiple organs. We find that the exposure of embryos to polystyrene nanoparticles produces malformations that are far more serious and extensive than has been previously reported. These malformations include major congenital heart defects that impair cardiac function. We show that the mechanism of toxicity is the selective binding of polystyrene nanoplastics nanoparticles to neural crest cells, leading to the death and impaired migration of those cells. Consistent with our new model, most of the malformations seen in this study are in organs that depend for their normal development on neural crest cells. These results are a matter of concern given the large and growing burden of nanoplastics in the environment. Our findings suggest that nanoplastics may pose a health risk to the developing embryo.

Toxicokinetics and Particle Number-Based Trophic Transfer of a Metallic Nanoparticle Mixture in a Terrestrial Food Chain.

Herein, we investigated to which extent metallic nanoparticles (MNPs) affect the trophic transfer of other coexisting MNPs from lettuce to terrestrial snails and the associated tissue-specific distribution using toxicokinetic (TK) modeling and single-particle inductively coupled plasma mass spectrometry. During a period of 22 days, snails were fed with lettuce leaves that were root exposed to AgNO3 (0.05 mg/L), AgNPs (0.75 mg/L), TiO2NPs (200 mg/L), and a mixture of AgNPs and TiO2NPs (equivalent doses as for single NPs). The uptake rate constants (ku) were 0.08 and 0.11 kg leaves/kg snail/d for Ag and 1.63 and 1.79 kg leaves/kg snail/d for Ti in snails fed with NPs single- and mixture-exposed lettuce, respectively. The elimination rate constants (ke) of Ag in snails exposed to single AgNPs and mixed AgNPs were comparable to the corresponding ku, while the ke for Ti were lower than the corresponding ku. As a result, single TiO2NP treatments as well as exposure to mixtures containing TiO2NPs induced significant biomagnification from lettuce to snails with kinetic trophic transfer factors (TTFk) of 7.99 and 6.46. The TTFk of Ag in the single AgNPs treatment (1.15 kg leaves/kg snail) was significantly greater than the TTFk in the mixture treatment (0.85 kg leaves/kg snail), while the fraction of Ag remaining in the body of snails after AgNPs exposure (36%) was lower than the Ag fraction remaining after mixture exposure (50%). These results indicated that the presence of TiO2NPs inhibited the trophic transfer of AgNPs from lettuce to snails but enhanced the retention of AgNPs in snails. Biomagnification of AgNPs from lettuce to snails was observed in an AgNPs single treatment using AgNPs number as the dose metric, which was reflected by the particle number-based TTFs of AgNPs in snails (1.67, i.e., higher than 1). The size distribution of AgNPs was shifted across the lettuce-snail food chain. By making use of particle-specific measurements and fitting TK processes, this research provides important implications for potential risks associated with the trophic transfer of MNP mixtures.

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.

Nano- and microplastics commonly cause adverse impacts on plants at environmentally relevant levels: A systematic review.

Over the last years there has been significant research on the presence and effects of plastics in terrestrial systems. Here we summarize current research findings on the effects of nano- and microplastics (NMPs) on terrestrial plants, with the aim to determine patterns of response and sensitive endpoints. We conducted a systematic review (based on 78 studies) on the effects of NMPs on germination, plant growth and biochemical biomarkers. This review highlights that the majority of studies to date have used pristine polystyrene or polyethylene particles, either in a hydroponic or pot-plant setup. Based on these studies we found that effects on plants are widespread. We noted similar responses between and within monocots and dicots to NMPs, except for consistent lower germination seen in dicots exposed to NMPs. During early development, germination and root growth are more strongly affected compared to shoot growth. NMPs induced similar adverse growth effects on plant biomass and length in the most tested plant species (lettuce, wheat, corn, and rice) irrespective of the polymer type and size used. Moreover, biomarker responses were consistent across species; chlorophyll levels were commonly negatively affected, while stress indicators (e.g., ROS or free radicals) and stress respondents (e.g., antioxidant enzymes) were consistently upregulated. In addition, effects were commonly observed at environmentally relevant levels. These findings provide clear evidence that NMPs have wide-ranging impacts on plant performance. However, as most studies have been conducted under highly controlled conditions and with pristine plastics, there is an urgent need to test under more environmentally realistic conditions to ensure the lab-based studies can be extrapolated to the field.

Generating environmental sampling and testing data for micro- and nanoplastics for use in life cycle impact assessment.

Ongoing efforts focus on quantifying plastic pollution and describing and estimating the related magnitude of exposure and impacts on human and environmental health. Data gathered during such work usually follows a receptor perspective. However, Life Cycle Assessment (LCA) represents an emitter perspective. This study examines existing data gathering and reporting approaches for field and laboratory studies on micro- and nanoplastics (MNPs) exposure and effects relevant to LCA data inputs. The outcomes indicate that receptor perspective approaches do not typically provide suitable or sufficiently harmonised data. Improved design is needed in the sampling, testing and recording of results using harmonised, validated and comparable methods, with more comprehensive reporting of relevant data. We propose a three-level set of requirements for data recording and reporting to increase the potential for LCA studies and models to utilise data gathered in receptor-oriented studies. We show for which purpose such data can be used as inputs to LCA, particularly in life cycle impact assessment (LCIA) methods. Implementing these requirements will facilitate proper integration of the potential environmental impacts of plastic losses from human activity (e.g. litter) into LCA. Then, the impacts of plastic emissions can eventually be connected and compared with other environmental issues related to anthropogenic activities.

Ecotoxicity characterization of chemicals: Global recommendations and implementation in USEtox.

Chemicals emitted to the environment affect ecosystem health from local to global scale, and reducing chemical impacts has become an important element of European and global sustainability efforts. The present work advances ecotoxicity characterization of chemicals in life cycle impact assessment by proposing recommendations resulting from international expert workshops and work conducted under the umbrella of the UNEP-SETAC Life Cycle Initiative in the GLAM project (Global guidance on environmental life cycle impact assessment indicators). We include specific recommendations for broadening the assessment scope through proposing to introduce additional environmental compartments beyond freshwater and related ecotoxicity indicators, as well as for adapting the ecotoxicity effect modelling approach to better reflect environmentally relevant exposure levels and including to a larger extent chronic test data. As result, we (1) propose a consistent mathematical framework for calculating freshwater ecotoxicity characterization factors and their underlying fate, exposure and effect parameters; (2) implement the framework into the USEtox scientific consensus model; (3) calculate characterization factors for chemicals reported in an inventory of a life cycle assessment case study on rice production and consumption; and (4) investigate the influence of effect data selection criteria on resulting indicator scores. Our results highlight the need for careful interpretation of life cycle assessment impact scores in light of robustness of underlying species sensitivity distributions. Next steps are to apply the recommended characterization framework in additional case studies, and to adapt it to soil, sediment and the marine environment. Our framework is applicable for evaluating chemicals in life cycle assessment, chemical and environmental footprinting, chemical substitution, risk screening, chemical prioritization, and comparison with environmental sustainability targets.

Approaches to implement safe by design in early product design through combining risk assessment and Life Cycle Assessment.

The Safe by Design (SbD) concept aims to ensure the production, use and disposal of materials and products safely. While there is a growing interest in the potential of SbD to support policy commitments, such as the EU Green Deal and the Circular Economy Action Plan in Europe, methodological approaches and practical guidelines on SbD are, however, largely missing. The combined use of Life Cycle Assessment (LCA) and Risk Assessment (RA) is considered suitable to operationalize SbD over the whole life-cycle of a product. Here, we explore the potential of the combined use of LCA and RA at Technological Readiness Level (TRL) 1-6. We perform a review of the literature presenting and/or developing approaches that combine LCA and RA at early stages of product design. We identify that basic early-on-evaluations of safety (e.g., apply lifecycle thinking to assess risk hotspots, avoid use of hazardous chemicals, minimize other environmental impacts from chemicals) are more common, while more complex assessments (e.g., ex-ante LCA, control banding, predictive (eco)toxicology) require specialized expertise. The application of these simplified approaches and guidelines aims to avoid some obvious sources of risks and impacts at early stages. Critical gaps need to be addressed for wider application of SbD, including more studies in the product design context, developing tools and databases containing collated information on risk, greater collaboration between RA/LCA researchers and companies, and policy discussion on the expansion from SbD to Safe and Sustainable by Design (SSbD).

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.

Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity.

Engineered nanomaterials (ENMs) are ubiquitous nowadays, finding their application in different fields of technology and various consumer products. Virtually any chemical can be manipulated at the nano-scale to display unique characteristics which makes them appealing over larger sized materials. As the production and development of ENMs have increased considerably over time, so too have concerns regarding their adverse effects and environmental impacts. It is unfeasible to assess the risks associated with every single ENM through in vivo or in vitro experiments. As an alternative, in silico methods can be employed to evaluate ENMs. To perform such an evaluation, we collected data from databases and literature to create classification models based on machine learning algorithms in accordance with the principles laid out by the OECD for the creation of QSARs. The aim was to investigate the performance of various machine learning algorithms towards predicting a well-defined in vivo toxicity endpoint (Daphnia magna immobilization) and also to identify which features are important drivers of D. magna in vivo nanotoxicity. Results indicated highly comparable model performance between all algorithms and predictive performance exceeding ∼0.7 for all evaluated metrics (e.g. accuracy, sensitivity, specificity, balanced accuracy, Matthews correlation coefficient, area under the receiver operator characteristic curve). The random forest, artificial neural network, and k-nearest neighbor models displayed the best performance but this was only marginally better compared to the other models. Furthermore, the variable importance analysis indicated that molecular descriptors and physicochemical properties were generally important within most models, while features related to the exposure conditions produced slightly conflicting results. Lastly, results also indicate that reliable and robust machine learning models can be generated for in vivo endpoints with smaller datasets.