The stochastic association of nanoparticles with algae at the cellular level: Effects of NOM, particle size and particle shape

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.

Nanomaterial Characterization in Complex Media-Guidance and Application

A broad range of inorganic nanoparticles (NPs) and their dissolved ions possess a possible toxicological risk for human health and the environment. Reliable and robust measurements of dissolution effects may be influenced by the sample matrix, which challenges the analytical method of choice. In this study, CuO NPs were investigated in several dissolution experiments. Two analytical techniques (dynamic light scattering (DLS) and inductively-coupled plasma mass spectrometry (ICP-MS)) were used to characterize NPs (size distribution curves) time-dependently in different complex matrices (e.g., artificial lung lining fluids and cell culture media). The advantages and challenges of each analytical approach are evaluated and discussed. Additionally, a direct-injection single particle (DI sp)ICP-MS technique for assessing the size distribution curve of the dissolved particles was developed and evaluated. The DI technique provides a sensitive response even at low concentrations without any dilution of the complex sample matrix. These experiments were further enhanced with an automated data evaluation procedure to objectively distinguish between ionic and NP events. With this approach, a fast and reproducible determination of inorganic NPs and ionic backgrounds can be achieved. This study can serve as guidance when choosing the optimal analytical method for NP characterization and for the determination of the origin of an adverse effect in NP toxicity.

Submicron Plastic Adsorption by Peat, Accumulation in Sphagnum Mosses and Influence on Bacterial Communities in Peatland Ecosystems

The smallest fraction of plastic pollution, submicron plastics (SMPs <1 μm) are expected to be ubiquitous in the environment. No information is available about SMPs in peatlands, which have a key role in sequestering carbon in terrestrial ecosystems. It is unknown how these plastic particles might behave and interact with (micro)organisms in these ecosystems. Here, we show that the chemical composition of polystyrene (PS) and poly(vinyl chloride) (PVC)-SMPs influenced their adsorption to peat. Consequently, this influenced the accumualtion of SMPs by Sphagnum moss and the composition and diversity of the microbial communities in peatland. Natural organic matter (NOM), which adsorbs from the surrounding water to the surface of SMPs, decreased the adsorption of the particles to peat and their accumulation by Sphagnum moss. However, the presence of NOM on SMPs significantly altered the bacterial community structure compared to SMPs without NOM. Our findings show that peatland ecosystems can potentially adsorb plastic particles. This can not only impact mosses themselves but also change the local microbial communities.

An analytical workflow for dynamic characterization and quantification of metal-bearing nanomaterials in biological matrices

To assess the safety of engineered nanomaterials (ENMs) and to evaluate and improve ENMs' targeting ability for medical application, it is necessary to analyze the fate of these materials in biological media. This protocol presents a workflow that allows researchers to determine, characterize and quantify metal-bearing ENMs (M-ENMs) in biological tissues and cells and quantify their dynamic behavior at trace-level concentrations. Sample preparation methods to enable analysis of M-ENMs in a single cell, a cell layer, tissue, organ and physiological media (e.g., blood, gut content, hemolymph) of different (micro)organisms, e.g., bacteria, animals and plants are presented. The samples are then evaluated using fit-for-purpose analytical techniques e.g., single-cell inductively coupled plasma mass spectrometry, single-particle inductively coupled plasma mass spectrometry and synchrotron X-ray absorption fine structure, providing a protocol that allows comprehensive characterization and quantification of M-ENMs in biological matrices. Unlike previous methods, the protocol uses no fluorescent dyes or radiolabels to trace M-ENMs in biota and enables analysis of most M-ENMs at cellular, tissue and organism levels. The protocols can be applied by a wide variety of users depending on the intended purpose of the application, e.g., to correlate toxicity with a specific particle form, or to understand the absorption, distribution and excretion of M-ENMs. The results facilitate an understanding of the biological fate of M-ENMs and their dynamic behavior in biota. Performing the protocol may take 7-30 d, depending on which combination of methods is applied.

Co-occurrence of co-contaminants: Cyanotoxins and microplastics, in soil system and their health impacts on plant - A comprehensive review

Cyanotoxins (CTX) and micro/nanoplastics (M/NP) are ubiquitously distributed in every environmental compartment. But the distribution, abundance and associated ecological risks of CTX are still poorly understood in soil system. On the other hand, M/NP could serve as vectors for persistent organic/inorganic pollutants in the natural environment through the sorption of pollutants onto them. Thus, co-occurrence of CTX and M/NP in soils suggests the sorption of CTX onto M/NP. So, major aim of this review is to understand the relevance of CTX and M/NP in soils as co-contaminants, possible interactions between them and ecological risks of CTX in terms of phytotoxicity. In this study, we comprehensively discuss different sources and fate of CTX and the sorption of CTX onto M/NP in soil system, considering the partition coefficient of different phases of soil and mass balance. Phytotoxicity of CTX, CTX mixture and co-contaminants has also been discussed with insights on the mechanism of action. This study indicates the need for the evaluation of sorption between co-contaminants, especially CTX and M/NP, and their phytotoxicity assessment using environmentally relevant concentrations.