Settling behaviors of microplastic disks in water

Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers

Accurately predicting the settling velocity of microplastics in aquatic environments is a prerequisite for reliably modeling their transport processes. An increasing number of settling models have been proposed for microplastics with fragmented, filmed, and fibrous morphologies, respectively. However, none of the existing models demonstrates universal applicability across all three morphologies. Scientists now have to rely on the predominate microplastic morphology extracted from filed samples to determine the appropriate settling model used for transport modeling. Given the spatiotemporal variability in morphologies and the coexistence of diverse morphologies of microplastics in natural aquatic environments, the extracted morphological information poses significant challenges in reliably determining the appropriate model. Evidently, to reliably model the transport of microplastics in aquatic environments, a universal settling model for microplastics with diverse shapes is warranted. To develop such a universal model, a unique shape factor, which can explicitly distinguish between the distinct morphologies of microplastics, was first proposed in this study by using a specifically-modified machine learning method. Using this newly-proposed shape factor, a universal model for predicting the settling velocity of microplastics with distinct morphologies was developed by using a physics-informed machine learning algorithm and then systematically evaluated against independent data sets. The newly-developed model enables reasonable predictions of the settling velocity of microplastic fragments, films, and fibers. In contrast to purely data-driven models, the newly-developed model is characterized by its transparent formulaic structure and physical interpretability, which is conducive to further expansion and improvement. This study can serve as a paradigm for future studies, inspiring the adoption of machine learning techniques in the development of physically-based models to investigate the transport of microplastics in aquatic environments.

Continuous Near-Bed Movements of Microplastics in Open Channel Flows: Statistical Analysis

The ubiquitous distribution of microplastics (MPs) in aquatic environments is linked to their transport in rivers and streams. However, the specific mechanism of bedload microplastic (MP) transport, notably their stochastic behaviors, remains an underexplored area. To investigate this, particle tracking velocimetry was employed to examine the continuous near-bed movements of four types of MPs under nine setups with different experimental conditions in a laboratory flume, with an emphasis on their streamwise transport. It was found that the streamwise velocity of MPs follows a normal distribution, which can be characterized using the proposed equations to estimate the ensemble mean and standard deviation of MP streamwise velocity. The proposed equations show low relative errors of ∼5% when compared to experimental data. This study also revealed similarities in the continuous movement of MPs and sediments in the streamwise diffusion process. A superdiffusive regime was observed, with particle inertia identified as the primary source of this anomalous diffusion. These results indicate that adopting a probabilistic framework may provide a promising avenue for improving numerical models and enhancing the understanding of MP transport behavior.

Real-Time Visualization of Infiltration and Retention of Microplastics with Different Shapes in Porous Media

Infiltration and retention of microplastics in porous media are important for understanding their fate in environments and formulating treatment measures. Given porous media opacity, knowledge is usually obtained indirectly by monitoring microplastic concentration in the effluent and measuring microplastic distribution after removing grains in layers. In this study, real-time visualization of infiltration and retention of microplastics in porous media under vertical water flow is performed using an improved reflective index matching method, considering the different shapes and densities of microplastics and size ratios between microplastics and grains. The spherical microplastics have the largest infiltration depths, with trajectories closest to vertical and accompanied by long acceleration durations and low deceleration frequencies. The cylindrical microplastics deviate from vertical and have stronger transverse oscillations and more frequent decelerations, while the flaky microplastics have the most significant transverse displacements. The infiltration depth can be improved by reducing the size ratio between microplastics and grains and increasing the vertical flow rate, while the density of microplastics has a relatively limited effect. Sliding and rotating of microplastics after collision with grains are observed, responsible for deceleration and transverse displacements. Different retention patterns are found, with the number of types being inversely proportional to the number of principal dimensions of the shape.

Settling velocity of microplastics in turbulent open-channel flow

The settling behavior of microplastics (MPs) plays a pivotal role in their transport and fate in aquatic environments, but the dominant mechanisms and physics governing the settling of MPs in rivers remain poorly understood. To gain mechanistic insights into the velocity lag of MPs in an open-channel flume under different turbulent flow conditions, an experimental study was conducted using three types of MPs: polystyrene, cellulose acetate, and acrylic, of sphere-shaped particles with diameters ranging from 1 mm to 5 mm. A particle tracking technique was employed to record and analyze the MPs velocity within turbulent flows. The results showed a variation in the vertical settling velocity of MPs ωMP ranging from -26 % to +16 %, when compared to their counterparts in still water (ωs). A new formula for the drag coefficient (Cd) of MP particles was developed by introducing the suspension number (u∗/ωs). The developed Cd formula was used to calculate the resultant velocity lag VMP, with a mean relative error of 16 % compared with the measured values. Further, the study highlighted that the MPs with large Stokes numbers are mainly driven by their own inertia and turbulence has less influence on their settling behavior. This study is crucial for understanding the settling behavior of MPs in turbulent flows and developing their transport and fate models for MPs in riverine systems.

Settling behaviors of microplastic disks in acceleration fall

The settling of microplastics (MPs) in the initial acceleration fall stage, i.e., before reaching the terminal settling velocity, has not been investigated, which is however important for understanding MP transport and fate. MP disks sized 3-5 mm, of three shapes and made of three polymers (1.038-1.343 g/cm3) were examined. Five release ways and three release angles (0°, 45°, 90°) were used. MP disks with the release angle of 0° start to zigzag immediately after the release, while the MP disks with the release angles of 45° and 90° first adjust to a horizontal position and then zigzag. The adjustment distances in the vertical and horizontal directions, as well as the maximum vertical settling velocity, are influenced by MP density, size, release angle and release way. The detailed settling trajectory and velocity were also analyzed. Finally, the time-changing drag coefficient of MP disks was examined and discussed.

Machine learning-based prediction for settling velocity of microplastics with various shapes

Microplastics can easily enter the aquatic environment and be transported between water bodies. The terminal settling velocity of microplastics, which affects their transport and distribution in the aquatic environment, is mainly influenced by their size, density, and shape. Due to the difficulty in accurately predicting the terminal settling velocity of microplastics with various shapes, this study focuses on establishing high-performance prediction models and understanding the importance and effect of each feature parameter using machine learning. Based on the number of principal dimensions, the shapes of microplastics are classified into fiber, film, and fragment, and their thresholds are identified. The microplastics of different shape categories have different optimal shape parameters for predicting the terminal settling velocity: Corey shape factor, flatness, elongation, and sphericity for the fragment, film, fiber, and mixed-shape MPs, respectively. By including the dimensionless diameter, relative density and optimal shape parameter in the input parameter combination, the machine learning models can well predict the terminal settling velocity for the microplastics of different shape categories and mixed-shape with R2 > 0.867, achieving significantly higher performance than the existing theoretical and regression models. The interpretable analysis of machine learning reveals the highest importance of the microplastic size and its marginal effect when the dimensionless diameter D* = dn(g/v2)1/3 > 80, where dn is the equivalent diameter, g is the gravitational acceleration, and ν is the fluid kinematic viscosity. The effect of shape is weak for small microplastics and becomes significant when D* exceeds 65.

Settling velocity of submillimeter microplastic fibers in still water

Microplastic fibers (MPFs) are one of the most important MP contaminants of aquatic environments. However, little research has been conducted on the movement of submillimeter MPFs in water. Herein, the settling of 519 submillimeter MPFs in still water was measured and the settling velocity was analyzed. Observations of the settling velocity of MPFs with lengths of 300, 500, and 600 μm showed that most MPFs settled individually or in pairs. The sedimentation of a single fiber could be divided into three patterns, that is, horizontal, inclined, and vertical. The average settling velocity increased with an increase in the MPFs length and orientation angle. As the MPFs length increased, the probability of inclined settlement decreased but that of horizontal settlement increased. The horizontal velocity of single fibers also was investigated, and the horizontal and vertical settling of MPFs exhibited minimal horizontal velocity. Because of the considerable difference between the calculated drag coefficients from existing drag coefficient models and experimental values, a drag coefficient model was developed with a deviation of <3 %. Four settling patterns were identified for two fibers, that is, X shaped, inverted-T shaped, cross shaped, and overlapping. The average velocity of the overlapping settlement of two fibers was considerably higher than that of the other three settling patterns. The average settling velocity of 600-μm two fibers was 1.47 times that of single fibers, indicating that their corresponding drag coefficient was ~46 % that of a single fiber.