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Microplastic particles are ubiquitous in the environment, from the air we breathe to the food we eat. The key question with respect to these particles is to what extent they cause risks for the environment and human health. There is no risk assessment framework that takes into account the multidimensionality of microplastic particles against the background of numerous natural particles, which together encompass an infinite combination of sizes, shapes, densities and chemical signatures. We review the current tenets in defining microplastic characteristics and effects, emphasizing advances in the analysis of the diversity of microplastic particles. We summarize the unique characteristics of microplastic compared with those of other environmental particles, the main mechanisms of microplastic particle effects and the relevant dose metrics for these effects. To characterize risks consistently, we propose how exposure and effect thresholds can be aligned and quantified using probability density functions describing microplastic particle diversity.

Current methods of characterizing plastic debris use arbitrary, predetermined categorizations and assume that the properties of particles are independent. Here we introduce Gaussian mixture models (GMM), a technique suitable for describing non-normal multivariate distributions, as a method to identify mutually exclusive subsets of floating macroplastic and microplastic particles (latent class analysis) based on statistically defensible categories. Length, width, height and polymer type of 6,942 particles and items from the Atlantic Ocean were measured using infrared spectroscopy and image analysis. GMM revealed six underlying normal distributions based on length and width; two within each of the lines, films, and fragments categories. These classes differed significantly in polymer types. The results further showed that smaller films and fragments had a higher correlation between length and width, indicating that they were about the same size in two dimensions. In contrast, larger films and fragments showed low correlations of height with length and width. This demonstrates that larger particles show greater variability in shape and thus plastic fragmentation is associated with particle rounding. These results offer important opportunities for refinement of risk assessment and for modeling the fragmentation and distribution of plastic in the ocean. They further illustrate that GMM is a useful method to map ocean plastics, with advantages over approaches that use arbitrary categorizations and assume size independence or normal distributions.

Land-based plastic waste is the major source for freshwater and marine plastic pollution. Yet, the transport pathways over land remain highly uncertain. Here, we introduce a new conceptual model to forecast plastic transport on land: the Plastic Pathfinder; a numerical model that simulates the spatiotemporal distribution of macroplastic (>0.5 cm) at a river basin scale. The plastic transport driving forces are wind and surface runoff, while plastic transport is resisted by terrain surface friction. The terrain surface friction, a function of the slope and land use, is converted into thresholds that define the critical wind and surface runoff conditions required to mobilize and transport macroplastic waste. When the wind and/or surface runoff conditions exceed their respective thresholds, the model simulates the transport and (re)distribution of plastics, resulting in plastic accumulation hotspots maps and high probability transport route maps. The Plastic Pathfinder contributes to a better mechanistic understanding of plastic transport through terrestrial environments, and upon future calibration and validation, can serve as a practical tool to optimize plastic waste prevention, mitigation, and reduction strategies.

Plastic accumulates in the environment because of insufficient waste handling and the materials' high durability. Better understanding of plastic behaviour in the aquatic environment is needed to estimate transport and accumulation, which can be used for monitoring strategies, prevention measures, and plastic clean-up activities. Plastic transport models benefit from accurate description of particle characteristics, such as rising and settling velocities. For macroplastics, these are however still scarce. In this research, the rising and settling behaviour of three different polymer types (PET, PP, and PE) was investigated, which are the most common in the environment. All of the plastic particles were foils of different surface areas. A new method for releasing rising plastics without interfering the flow and disturbing the column was used. Four models that estimate the velocity based on the characteristics of the plastics are discussed, of which three are from literature, and one is newly derived. These models are validated using the data generated in this research, and data from another study on rising and settling velocities of plastic. From the models that were discussed, the best results are from the newly introduced velocity model for foils (R^2 = 0.96 and 0.58, for both datasets). This model shows potential to estimate the rising and settling velocity of plastics, and should be examined further by using additional data. The results of our paper can be used to further explore the vertical distribution of plastics in rivers, lakes and oceans, which is crucial to optimize future monitoring and cleanup efforts.

The fate of (micro)plastic particles in the open ocean is controlled by physical and biological processes. Here, we model the effects of biofouling on the subsurface vertical distribution of spherical, virtual plastic particles with radii of 0.01–1 mm. For the physics, four vertical velocity terms are included: advection, wind-driven mixing, tidally induced mixing, and the sinking velocity of the biofouled particle. For the biology, we simulate the attachment, growth and loss of algae on particles. We track 10,000 particles for one year in three different regions with distinct biological and physical properties: the low productivity region of the North Pacific Subtropical Gyre, the high productivity region of the Equatorial Pacific and the high mixing region of the Southern Ocean. The growth of biofilm mass in the euphotic zone and loss of mass below the euphotic zone result in the oscillatory behaviour of particles, where the larger (0.1–1.0 mm) particles have much shorter average oscillation lengths ( 5000 m). Our results show that the vertical movement of particles is mainly affected by physical (wind-induced mixing) processes within the mixed layer and biological (biofilm) dynamics below the mixed layer. Furthermore, positively buoyant particles with radii of 0.01–1.0 mm can sink far below the euphotic zone and mixed layer in regions with high near-surface mixing or high biological activity. This work can easily be coupled to other models to simulate open-ocean biofouling dynamics, in order to reach a better understanding of where ocean (micro)plastic ends up.

Earth science reviews 228, 104021 (2022). doi:10.1016/j.earscirev.2022.104021, Published by Elsevier, Amsterdam [u.a.]

The lack of standard approaches in microplastic research limits progress in the abatement of plastic pollution. Here, we propose and test rescaling methods that are able to improve the alignment of methods used in microplastic research. We describe a method to correct for the differences in size ranges as used by studies reporting microplastic concentrations and demonstrate how this reduces the variation in aqueous-phase concentrations caused by method differences. We provide a method to interchange between number, volume, and mass concentrations using probability density functions that represent environmental microplastic. Finally, we use this method to correct for the incompatibility of data as used in current species sensitivity distributions (SSDs), caused by differences in the microplastic types used in effect studies and those in nature. We derived threshold effect concentrations from such a corrected SSD for freshwater species. Comparison of the rescaled exposure concentrations and threshold effect concentrations reveals that the latter would be exceeded for 1.5% of the known surface water exposure concentrations worldwide. Altogether, this toolset allows us to correct for the diversity of microplastic, to address it in a common language, and to assess its risks as one environmental material.

Microplastic debris ending up at the sea surface has become a known major environmental issue. However, how microplastic particles move and when they sink in the ocean remains largely unknown. Here, we model microplastic subject to biofouling (algal growth on a substrate) to estimate sinking timescales and the time to reach the depth where particles stop sinking. We combine NEMO‐MEDUSA 2.0 output, that represents hydrodynamic and biological properties of seawater, with a particle‐tracking framework. Different sizes and densities of particles (for different types of plastic) are simulated, showing that the global distribution of sinking timescales is largely size‐dependent as opposed to density‐dependent. The smallest particles we simulate (0.1 μm) start sinking almost immediately around the globe and their trajectories take the longest time to reach their first sinking depth (relative to larger particles). In oligotrophic subtropical gyres with low algal concentrations, particles between 1 and 0.01 mm do not sink within the simulation time of 90 days. This suggests that in addition to the comparatively well‐known physical processes, biological processes might also contribute to the accumulation of floating plastic (of 1–0.01 mm) in subtropical gyres. Particles of 1 μm in the gyres start sinking largely due to vertical advection, whereas in the equatorial Pacific they are more dependent on biofouling. The qualitative impacts of seasonality on sinking timescales are small, however, localized sooner sinking due to spring algal blooms is seen. This study maps processes that affect the sinking of virtual microplastic globally, which could ultimately impact the ocean plastic budget., Key Points Sinking timescales of virtual particles subject to biofouling are more dependent on the initial size of particles than initial densityLow algal presence in subtropical gyres and minimal biofouling can contribute to particles between 1 and 0.01 mm remaining at the surfaceModeled biofouled particles of 0.1 microns start sinking almost immediately and show a global median sinking timescale of one day

Understanding the multidimensionality of microplastics is essential for a realistic assessment of the risks these particles pose to the environment and human health. Here, we capture size, shape, area, polymer, volume and mass characteristics of >60,000 individual microplastic particles as continuous distributions. Particles originate from samples taken from different aquatic compartments, including surface water and sediments from the marine and freshwater environment, waste water effluents, and freshwater organisms. Data were obtained using state-of-the-art FTIR-imaging, using the same automated imaging post-processing software. We introduce a workflow with two quality criteria that assure minimum data quality loss due to volumetric and filter area subsampling. We find that probability density functions (PDFs) for particle length follow power law distributions, with median slopes ranging from 2.2 for marine surface water to 3.1 for biota samples, and that these slopes were compartment-specific. Polymer-specific PDFs for particle length demonstrated significant differences in slopes among polymers, hinting at polymer specific sources, removal or fragmentation processes. Furthermore, we provide PDFs for particle width, width to length ratio, area, specific surface area, volume and mass distributions and propose how these can represent the full diversity of toxicologically relevant dose metrics required for the assessment of microplastic risks.

Because of their diverse sizes, shapes and densities, environmental microplastics are often perceived as complex. Many studies struggle with this complexity, and either address only a part of this diversity, or present data using discrete classifications for sizes, shapes and densities. We argue that such classifications will never be fully satisfactory, as any definition using classes does not capture the essentially continuous nature of environmental microplastic. Therefore, we propose to simplify microplastics by fully defining them through a 3D probability distribution, with size, shape and density as dimensions. Besides introducing the concept, we parameterize these probability distributions, using empirical data. This parameterization results in an approximate yet realistic representation of ‘true’ environmental microplastic. This approach to simplify microplastic could be applicable to exposure measurements, effect studies and fate modelling. Furthermore, it allows for easy comparison between studies, irrespective of sampling or laboratory setup. We demonstrate how the 3D probability distribution of environmental versus ingested microplastic can be helpful in understanding bioavailability of and exposure to microplastic. We argue that the concept of simplified microplastic will also be helpful in probabilistic risk modelling, which would greatly enhance the understanding of the risk that microplastics pose to the environment.

Recent studies suggest size-selective removal of small plastic particles from the ocean surface, an observation that remains unexplained. We studied one of the hypotheses regarding this size-selective removal: the formation of a biofilm on the microplastics (biofouling). We developed the first theoretical model that is capable of simulating the effect of biofouling on the fate of microplastic. The model is based on settling, biofilm growth, and ocean depth profiles for light, water density, temperature, salinity, and viscosity. Using realistic parameters, the model simulates the vertical transport of small microplastic particles over time, and predicts that the particles either float, sink to the ocean floor, or oscillate vertically, depending on the size and density of the particle. The predicted size-dependent vertical movement of microplastic particles results in a maximum concentration at intermediate depths. Consequently, relatively low abundances of small particles are predicted at the ocean surface, while at the same time these small particles may never reach the ocean floor. Our results hint at the fate of "lost" plastic in the ocean, and provide a start for predicting risks of exposure to microplastics for potentially vulnerable species living at these depths.

Most studies on buoyant microplastics in the marine environment rely on sea surface sampling. Consequently, microplastic amounts can be underestimated, as turbulence leads to vertical mixing. Models that correct for vertical mixing are based on limited data. In this study we report measurements of the depth profile of buoyant microplastics in the North Atlantic subtropical gyre, from 0 to 5 m depth. Microplastics were separated into size classes (0.5–1.5 and 1.5–5.0 mm) and types (‘fragments’ and ‘lines’) and associated with a sea state. Microplastic concentrations decreased exponentially with depth, with both sea state and particle properties affecting the steepness of the decrease. Concentrations approached zero within 5 m depth, indicating that most buoyant microplastics are present on or near the surface. Plastic rise velocities were also measured and were found to differ significantly for different sizes and shapes. Our results suggest that (1) surface samplers such as manta trawls underestimate total buoyant microplastic amounts by a factor of 1.04–30.0 and (2) estimations of depth-integrated buoyant plastic concentrations should be done across different particle sizes and types. Our findings can assist with improving buoyant ocean plastic vertical mixing models, mass balance exercises, impact assessments and mitigation strategies.