A Review on the Environmental Fate Models for Predicting the Distribution of Engineered Nanomaterials in Surface Waters

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.

Statistical analysis, machine learning modeling, and text analytics of aggregation attachment efficiency: Mono and binary particle systems

The aggregation attachment efficiency (α) is the fraction of particle-particle collisions resulting in aggregation. Despite significant research, α predictions have not accounted for the full complexity of systems due to constraints imposed by particle types, dispersed matter, water chemistry, quantification methods, and modeling. Experimental α values are often case-specific, and simplified systems are used to rule out complexity. To address these challenges, statistical analysis was performed on α databases to identify gaps in current knowledge, and machine learning (ML) was used to predict α under various particle types and conditions. Moreover, text analytics was employed to support knowledge from statistics and ML, as well as gain insight into the ideas communicated by current literature. Most studies investigated α in mono-particle systems, but binary or higher systems require more investigation. Furthermore, our work highlights that numerous variables, interactions, and mechanisms influence α behavior, making its investigation complex and difficult for both experiments and modeling. Consequently, future research should incorporate more particle types, shapes, coatings, and surface heterogeneities, and aim to address overlooked variables and conditions. Therefore, building a comprehensive α database can enable the development of more accurate empirical models for prediction.

Environmental behaviors of emerging contaminants in freshwater ecosystem dominated by submerged plants: A review

Persistent exposure of emerging contaminants (ECs) in freshwater ecosystem has initiated intense global concerns. Freshwater ecosystem dominated by submerged plants (SP-FES) has been widely constructed to control eutrophic water. However, the environmental behaviors (e.g. migration, transformation, and degradation) of ECs in SP-FES have rarely been concerned and summarized. This review briefly introduced the sources of ECs, the pathways of ECs entering into SP-FES, and the constituent elements of SP-FES. And then the environmental behaviors of dissolved ECs and refractory solid ECs in SP-FES were comprehensively summarized, and the feasibility of removing ECs from SP-FES was critically evaluated. Finally, the challenges and perspectives on the future development for ECs removal from SP-FES were prospected, giving possible research gaps and key directions. This review will provide theoretical and technical support for the effective removal of ECs in freshwater ecosystem, especially in SP-FES.

Prediction of attachment efficiency using machine learning on a comprehensive database and its validation

Colloidal particles can attach to surfaces during transport, but the attachment depends on particle size, hydrodynamics, solid and water chemistry, and particulate matter. The attachment is quantified in filtration theory by measuring attachment or sticking efficiency (Alpha). A comprehensive Alpha database (2538 records) was built from experiments in the literature and used to develop a machine learning (ML) model to predict Alpha. The training (r-squared: 0.86) was performed using two random forests capable of handling missing data. A holdout dataset was used to validate the training (r-squared: 0.98), and the variable importance was explored for training and validation. Finally, an additional validation dataset was built from quartz crystal microbalance experiments using surface-modified polystyrene, poly (methyl methacrylate), and polyethylene. The experiments were performed in the absence or presence of humic acid. Full database regression (r-squared: 0.90) predicted Alpha for the additional validation with an r-squared of 0.23. Nevertheless, when the original database and the additional validation dataset were combined into a new database, both the training (r-squared: 0.95) and validation (r-squared: 0.70) increased. The developed ML model provides a data-driven prediction of Alpha over a big database and evaluates the significance of 22 input variables.

A two-dimensional nanoparticle characterization method combining differential mobility analyzer and single-particle inductively coupled plasma-mass spectrometry with an atomizer-enabled sample introduction (ATM-DMA-spICP-MS): Toward the analysis of heteroaggregated nanoparticles in wastewater

Characterizing engineered nanoparticles (ENPs) in complex environmental matrices remains a challenging task. This work presents a two-dimensional size analysis method by combining differential mobility analyzer (DMA) and single-particle inductively coupled plasma-mass spectrometry (spICP-MS) with a new atomizer (ATM)-enabled sample introduction that is relatively easy to operate. The tailing of electrical mobility size distributions was solved by heating the aerosol flow, where water-shelled gold nanoparticles (AuNPs) were dehydrated, effectively eliminating the tailing. The improved method has a good sizing performance and can resolve the size fractions of mixed 30 nm and 50 nm AuNPs. It can reliably analyze 7.8 × 10⁵ to 1.9 × 10⁷ # of 50 nm AuNPs (or 4.1 × 10⁵ to 10⁷ # NPs/mL, equivalent to 0.6 to 14.3 μg Au/L) with a linear response and a limit of detection of 7.8 × 10⁵ # AuNPs (equivalent to 4.1 × 10⁵ # AuNPs/mL) that is relevant to NP concentrations in surface water and wastewater samples. The potential of this method to analyze environmental samples was demonstrated by characterizing AuNPs and silver nanoparticles (AgNPs) spiked in wastewater, where both NPs were revealed to form heteroaggregates with colloids existing in wastewater. The method can even directly analyze nanosized Ag particles inherent in the wastewater before adding external AgNPs. The result indicates that ATM-DMA-spICP-MS is a relatively simple two-dimensional size analysis method that has a great potential to characterize heteroaggregated NPs in aqueous environmental samples.

Simulation of the Environmental Fate and Transformation of Nano Copper Oxide in a Freshwater Environment

Production of engineered nanomaterials (ENMs) has rapidly increased, yet uncertainty exists regarding the full extent of their environmental implications. This study investigates the fate, transformation, and speciation of nano copper oxide (nanoCuO) released into Lake Waccamaw, North Carolina, over 101 years. Using the Advanced Toxicant module of the Water Quality Analysis Simulation Program (WASP8), we assessed the accumulation and mass proportions of nanoCuO and Cu²⁺ (the product of nanoCuO's dissolution) in the water column and sediments. Our simulations suggest that when nanoCuO is released into Lake Waccamaw, the highest concentrations of both nanoCuO and Cu²⁺ are found in the surface sediments, followed by the subsurface sediments and the water column. Simulating different heteroaggregation attachment efficiencies of nanoCuO suggested that increases in attachment efficiency increased nanoCuO concentrations and mass proportions in the water column and sediments, while Cu²⁺ exhibited the opposite trends. After 101 years, most nanoCuO in the sediments was attached to particulate organic matter and clay particles at all attachment efficiencies, while low attachment efficiency slowed aggregate formation in the water column. Our results highlight the influence that heteroaggregation has on the behavior of nanoCuO inputs and suggest the potential for legacy contamination of nanoCuO and Cu²⁺ in sediments.

Nanosafety: An Evolving Concept to Bring the Safest Possible Nanomaterials to Society and Environment

The use of nanomaterials has been increasing in recent times, and they are widely used in industries such as cosmetics, drugs, food, water treatment, and agriculture. The rapid development of new nanomaterials demands a set of approaches to evaluate the potential toxicity and risks related to them. In this regard, nanosafety has been using and adapting already existing methods (toxicological approach), but the unique characteristics of nanomaterials demand new approaches (nanotoxicology) to fully understand the potential toxicity, immunotoxicity, and (epi)genotoxicity. In addition, new technologies, such as organs-on-chips and sophisticated sensors, are under development and/or adaptation. All the information generated is used to develop new in silico approaches trying to predict the potential effects of newly developed materials. The overall evaluation of nanomaterials from their production to their final disposal chain is completed using the life cycle assessment (LCA), which is becoming an important element of nanosafety considering sustainability and environmental impact. In this review, we give an overview of all these elements of nanosafety.

Bioaccumulation and depuration of TiO2 nanoparticles by zebrafish through dietary exposure: Size- and number concentration-resolved analysis using single-particle ICP-MS

The bioaccumulation and depuration of TiO₂ nanoparticles (TiO₂NPs) by zebrafish via the dietary exposure following the OECD Test Guideline 305 (OECD TG305) was evaluated using particle size- and number concentration-resolved analysis based on single-particle ICP-MS (spICP-MS). We found that using enzymatic digestion without H₂O₂ or excessive heating can recover 84.0 ± 4.0% and 94.5 ± 3.5% of TiO₂NP mass and number concentrations from fish tissue, respectively, without altering the size distribution of parent TiO₂NPs. OECD TG305 can allow for the evaluation of bioaccumulation and depuration of TiO₂NPs by fish based on the particle mass and number dose metrics. The toxicokinetic modeling can reasonably describe the mass- and number-based measurement data with the derived absorption efficiency α at ~0.2, depuration rate at ~0.5 d^-1, and kinetic biomagnification factor (BMF_k) at ~0.007 comparable with available data. The mass concentration- and number concentration-based bioaccumulation metrics including body burdens are correlated for TiO₂NPs that remained nano-sized in vivo and exhibited marginal physicochemical alterations upon uptake by fish. The result indicates that the traditional mass concentration metric may be used to represent the fish bioaccumulation potential for chemically inert NPs like TiO₂.

Critical review of the characteristics, interactions, and toxicity of micro/nanomaterials pollutants in aquatic environments

A wide range of contaminants of emerging concern such as micro/nanoplastics (MPs/PNPs) and metal-nanoparticles (Me-NPs) from anthropogenic activities have been identified in aquatic environments. The hazardous effects of these micro/nanomaterials as pollutants in organisms and the lack of knowledge about their behavior in aquatic environments have generated growing concern in the scientific community. The nanomaterials have a colloidal-type behavior due to their size range but with differences in their physicochemical properties. This review comprises the behavior of micro/nanomaterials pollutants and the physicochemical interactions between MPs/PNPs and Me-NPs in aquatic environments, and their potential toxicological effects in organisms. Moreover, this article describes the potential use of Me-NPs to remove MPs/PNPs present in the water column due to their photocatalytic and magnetic properties. It also discusses the challenge to determine harmful effects of micro/nanomaterials pollutants in organisms and provides future research directions to improve integrated management strategies to mitigate their environmental impact.

Aquatic Toxicity Effects and Risk Assessment of 'Form Specific' Product-Released Engineered Nanomaterials

The study investigated the toxicity effects of 'form specific' engineered nanomaterials (ENMs) and ions released from nano-enabled products (NEPs), namely sunscreens, sanitisers, body creams and socks on Pseudokirchneriella subcapitata, Spirodela polyrhiza, and Daphnia magna. Additionally, risk estimation emanating from the exposures was undertaken. The ENMs and the ions released from the products both contributed to the effects to varying extents, with neither being a uniform principal toxicity agent across the exposures; however, the effects were either synergistic or antagonistic. D. magna and S. polyrhiza were the most sensitive and least sensitive test organisms, respectively. The most toxic effects were from ENMs and ions released from sanitisers and sunscreens, whereas body creams and sock counterparts caused negligible effects. The internalisation of the ENMs from the sunscreens could not be established; only adsorption on the biota was evident. It was established that ENMs and ions released from products pose no imminent risk to ecosystems; instead, small to significant adverse effects are expected in the worst-case exposure scenario. The study demonstrates that while ENMs from products may not be considered to pose an imminent risk, increasing nanotechnology commercialization may increase their environmental exposure and risk potential; therefore, priority exposure cases need to be examined.

Integrated dynamic probabilistic material flow analysis of engineered materials in all European countries

Uncertainties remain regarding the potential environmental risks of engineered nanomaterials, reflecting missing information on both the exposure and the hazard sides. Probabilistic material flow analysis (PMFA) is a useful exposure assessment tool that maps the flows of a substance through its lifecycle towards the environment, taking into account the uncertainties associated with the input data. In the last years, several refinements have been made to the original PMFA method, increasing its complexity with respect to systems dynamics, fate during recycling and reprocessing and forms of release. In this work, an integrated dynamic probabilistic material flow analysis (IDPMFA) was developed that combines all separate advancements of the method in one overarching software code. The new method was used to assess the forms in which nano-Ag, nano-TiO₂ and nano-ZnO are released into air, soils and surface water. Each European country (EU28, Norway and Switzerland) was studied from the year 2000 to the year 2020. The present model includes new assessments of the forms in which nano-ZnO is released into the environment and of the flows out of reprocessing (last step of recycling) of nano-Ag, nano-TiO₂ and nano-ZnO towards both technical and environmental compartments. The forms of ZnO released to different compartments vary greatly with different proportions between pristine, dissolved, matrix-embedded and transformed forms. The same applies for the forms of the other ENMs released after reprocessing, where different processes result in very different distributions between the various forms. The country-specific assessment showed that it is mainly the different solid waste treatment schemes that influence the distribution to final environmental sinks. Overall, the results of IDPMFA show the great importance of considering the full life cycle of nanoproducts including the different stages of recycling, the differences between countries, and the forms of the released materials. The results from the integrated model will provide useful input information for environmental fate models and for environmental risk assessments.

Environmental effects of stratospheric ozone depletion, UV radiation, and interactions with climate change: UNEP Environmental Effects Assessment Panel, Update 2020

This assessment by the Environmental Effects Assessment Panel (EEAP) of the United Nations Environment Programme (UNEP) provides the latest scientific update since our most recent comprehensive assessment (Photochemical and Photobiological Sciences, 2019, 18, 595-828). The interactive effects between the stratospheric ozone layer, solar ultraviolet (UV) radiation, and climate change are presented within the framework of the Montreal Protocol and the United Nations Sustainable Development Goals. We address how these global environmental changes affect the atmosphere and air quality; human health; terrestrial and aquatic ecosystems; biogeochemical cycles; and materials used in outdoor construction, solar energy technologies, and fabrics. In many cases, there is a growing influence from changes in seasonality and extreme events due to climate change. Additionally, we assess the transmission and environmental effects of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is responsible for the COVID-19 pandemic, in the context of linkages with solar UV radiation and the Montreal Protocol.