Ecotoxicity of nano-metal oxides: A case study on daphnia magna

Ecotoxicological, ecophysiological, and mechanistic studies on zinc oxide (ZnO) toxicity in freshwater environment

The world has faced climate change that affects hydrology and thermal systems in the aquatic environment resulting in temperature changes, which directly affect the aquatic ecosystem. Elevated water temperature influences the physico-chemical properties of chemicals in freshwater ecosystems leading to disturbing living organisms. Owing to the industrial revolution, the mass production of zinc oxide (ZnO) has been led to contaminated environments, and therefore, the toxicological effects of ZnO become more concerning under climate change scenarios. A comprehensive understanding of its toxicity influenced by main factors driven by climate change is indispensable. This review summarized the detrimental effects of ZnO with a single ZnO exposure and combined it with key climate change-associated factors in many aspects (i.e., oxidative stress, energy reserves, behavior and life history traits). Moreover, this review tried to point out ZnO kinetic behavior and corresponding mechanisms which pose a problem of observed detrimental effects correlated with the alteration of elevated temperature.

Influence of ozone microbubble enhanced oxidation on mine effluent mixes and Daphnia magna toxicity

The mining industry often must mix different kinds of water on the mine site during pre-treatment or post-treatment before the final discharge of the treated water to the environment. Microbubble ozonation has proven to be efficient in the removal of contaminants of concern from mine water, such as metals, metalloids, and nitrogen compounds, which can persist in the environment and entail toxicity issues. This study evaluated the efficiency of ozone microbubbles combined with lime precipitation on contaminant removal and its impact on toxicity for Daphnia magna with five different mine effluent mixes from an active mine site located in Abitibi-Témiscamingue, QC, Canada. For the non-acidic mixes, two scenarios were tested: first, pre-treatment of metals using lime precipitation and a flocculant was conducted prior to ozonation; and second, ozonation was conducted prior to metals post-treatment using the same precipitation and flocculation technique. Results showed that the NH₃-N removal efficiency ranged from 90% for the lower initial concentrations (1.1 mg/L) to more than 99% for the higher initial concentrations (58.4 mg/L). Moreover, ozonation without metals pre-treatment improved NH₃-N treatment efficiency in terms of kinetics but entailed abnormal toxicity issues. Results of bioassays conducted on water with metals pre-treatment did not show any toxicity events but showed abnormal toxicity patterns on the mixes treated without metals pre-treatment (diluted effluents were toxic, while undiluted were not). At 50% dilution, the water was toxic, probably due to the potential presence of metal oxide nanoparticles. The confirmation of the source of toxicity requires further investigation.

Quasi-SMILES for predicting toxicity of Nano-mixtures to Daphnia Magna

Quasi-SMILES is an extension of the traditional SMILES. The classic SMILES is a way to represent the molecular structure. The quasi-SMILES is a way to describe all eclectic conditions that are able to affect the activity of a substance or a mixture. Nano-QSAR for prediction of toxicity of Nano-mixtures built up using the database on the corresponding experimental data. Models built up for five random splits of available data in training and validation sets are suggested. The Monte Carlo method of optimization is applied to calculate so-called optimal descriptors. The optimization was carried out with two criteria of predictive potential. These are the so-called index of ideality of correlation (IIC) and correlation intensity index (CII). Applying CII gives the better statistical quality of models for toxicity Nano-mixtures towards Daphnia Magna. The statistical quality of the best model follows the determination coefficients 0.987 (training set) and 0.980 (validation set).

Developing random forest based QSAR models for predicting the mixture toxicity of TiO2 based nano-mixtures to Daphnia magna

During emission, TiO₂ nanoparticles (NPs) might meet various chemicals, including metal ions and organic compounds in aquatic environments (e.g., surface water, sediments). At environmentally safe concentrations, combinations of both TiO₂ NPs and those chemicals might cause cocktail effects (i.e., mixture toxicity) to aquatic organisms. Previous models such as concentration addition and independent action require dose-response curves of single components in the mixtures to predict the mixture toxicity. Structure-activity relationship (QSAR) models might predict the toxicity of nano-mixtures without dose-response curves of single components in the mixtures. However, current quantitative structure-activity relationship (QSAR) models are mainly focused on predicting cytotoxicity (i.e., cell viability) of heterogeneous metallic TiO₂ nanoparticles (NPs) or mixtures of TiO₂ NPs and four metal ions (Cu²⁺, Cd²⁺, Ni^2+, and Zn²⁺). To minimize the experimental cost of nano-mixture risk assessment, in this study, we developed novel nano-mixture QSAR models to predict i) EC₅₀ of 76 nano-mixtures containing TiO₂ NPs and one of eight inorganic/organic compounds (i.e., AgNO₃, Cd(NO₃)₂, Cu(NO₃)₂, CuSO₄, Na₂HAsO₄, NaAsO₂, Benzylparaben and Benzophenone-3), to Daphnia magna(D. magna), and ii) immobilization of D. magna exposed to one of 98 mixtures containing TiO₂ NPs and one of eleven inorganic/organic compounds (i.e., AgNO₃, Cd(NO₃)₂, Cu(NO₃)₂, CuSO₄, Na₂HAsO₄, NaAsO₂, Benzylparaben Benzophenone-3, Pirimicarb, Pentabromodiphenyl Ether and Triton X-100). The nano-mixture QSAR models were developed with mixture descriptors (D_mix) combing quantum descriptors of mixture components (e.g., TiO₂ NPs and its partners) by using different machine learning techniques (i.e., random forest, neural network, support vector machine, and multiple linear regression). Nano-mixture QSAR models built with the random forest algorithm and proposed mixture descriptors exhibited good performance for predicting logEC₅₀ (Adj.R²_test = 0.955 ± 0.003, RMSE_test = 0.016 ± 0.002, and MAE_test = 0.008 ± 0.001) and immobilization (Adj.R²_test = 0.888 ± 0.011, RMSE_test = 11.327 ± 0.730, and MAE_test = 5.933 ± 0.442). The models developed in this study were implemented in a user-friendly application for assessing the aquatic toxicity of TiO₂ based nano-mixtures.

Metallic, metal oxide, and metalloid nanoparticles toxic effects on freshwater microcrustaceans: An update and basis for the use of new test species

In this article, we performed a literature review on the metallic, metal oxide, and metalloid nanoparticles (NP) effects on freshwater microcrustaceans, specifically focusing on (i) the main factors influencing the NP toxicity and (ii) their main ecotoxicological effects. Also, given that most studies are currently developed on the standard test species Daphnia magna Straus, we analyzed (iii) the potential differences in the biological responses between D. magna and other freshwater microcrustacean, and (iv) the ecological implications of considering only D. magna as surrogate of other microcrustaceans. We found that NP effects on microcrustaceans depended on their intrinsic properties as well as the exposure conditions. Among the general responses to different NP, we identified body burial, feeding inhibition, biochemical effects, metabolic changes, and reproductive and behavioral alterations. The differences in the biological responses between D. magna and other freshwater microcrustacean rely on the morphology (size and shape), ecological traits (feeding mechanisms, life cycles), and intrinsic sensitivities. Thus, we strongly recommend the use of microcrustaceans species with different morphological, physiological, and ecological characteristics in future ecotoxicity tests with NP to provide relevant information with regulation purposes regarding the discharge of NP into aquatic environments. PRACTITIONER POINTS: Nanoparticles effects depend on intrinsic and external factors. Nanoparticles affect the morphology, physiology, and behavior. Effects on Daphnia differ from other microcrustaceans. The use of more diverse test species is suggested.

Ecotoxicological effects of new generation pollutants (nanoparticles, amoxicillin and white musk) on freshwater and marine phytoplankton species

Phytoplankton occupies a key trophic level in aquatic ecosystems. Chemical impacts on these primary producers can disrupt the integrity of an entire ecosystem. Two freshwater (Pseudokirchneriella subcapitata-Ps and Scenedesmus obliquus-S) and three marine (Phaeodactylum tricornutum-P, Isochrysis galbana-I, Tetraselmis suecica-T) microalgae species were exposed to dilutions of four chemicals: nanoparticles (n-TiO₂, n-ZnO), amoxicillin (antibiotic), and white musk (personal care fragrance) to determine the half maximal effective concentration (EC₅₀) after 72 h of exposure under standardized and controlled environmental conditions. Cell cultures were exposed to EC₅₀ to determine sublethal effects (72 h) based on biochemical (chlorophylls a, b, c), molecular (changes in outer cell wall structure), and morphological alterations. We report for the first time EC₅₀ values for nanoparticles in not standardized species (S, I and T) and for amoxicillin and white musk in all tested species. Standardized species (Ps and P) were less sensitive than non-standardized in some cases. Fourier-transformed infrared spectroscopy showed a marked spectral alteration (from 10.44% to 90.93%) of treated cultures compared to negative controls; however, principal component analysis disclosed no differences in molecular alteration between the five microalgae species or the two aquatic habitats considered. There was a significant decrease in chlorophylls content in all species exposed to EC₅₀ compared to controls (Kruskal Wallis test; p < 0.05). There was a significant increase in cell-size (Mann-Whitney U test; p < 0.05) in I, P and T exposed to white musk and S exposed to amoxicillin. Findings highlight ecotoxicological risks from new generation pollutants for primary producers in aquatic ecosystems.

Environmental Risk Characterization of an Antiretroviral (ARV) Lamivudine in Ecosystems

Antiretroviral drugs for the treatment of human immunodeficiency virus (HIV) and other viral infections are among the emerging contaminants considered for ecological risk assessment. These compounds have been reported to be widely distributed in water bodies and other aquatic environments, while data concerning the risk they may pose to unintended non-target species in a different ecosystem (environment) is scanty. In South Africa and other developing countries, lamivudine is one of the common antiretrovirals applied. Despite this, little is known about its environmental impacts as an emerging contaminant. The present study employed a battery of ecotoxicity bioassays to assess the environmental threat lamivudine poses to aquatic fauna and flora. Daphnia magna (filter feeders), the Ames bacterial mutagenicity test, Lactuca sativa (lettuce) germination test, and the Allium cepa root tip assay were conducted, testing lamivudine at two concentrations (10 and 100 µg/L), with environmental relevance. The Daphnia magna toxicity test revealed a statistically significant response (p << 0.05) with a mortality rate of 85% on exposure to 100 µg/L lamivudine in freshwater, which increased to 100% at 48-h exposure. At lower concentrations of 10 µg/L lamivudine, 90% and 55% survival rates were observed at 24 h and 48 h, respectively. No potential mutagenic effects were observed from the Ames test at both concentrations of lamivudine. Allium cepa bioassays revealed a noticeable adverse impact on the root lengths on exposure to 100 µg/L lamivudine. This impact was further investigated through microscopic examination, revealing some chromosomal aberration in the exposed Allium cepa root tips. The Lactuca sativa bioassay showed a slight adverse impact on both the germination rate of the seeds and their respective hypocotyl lengths compared to the control. Overall, this indicates that lamivudine poses an ecological health risk at different trophic levels, to both flora and fauna, at concentrations previously found in the environment.

Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity

Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.