The terminal settling velocity of microplastics plays a vital role in the physical behavior of microplastics, and is related to the migration and fate of these microplastics in the ocean. At present, the terminal settling velocity is mostly calculated by formulae, which also leads to a fewer studies on the use of machine-learning models to predict its settling velocity in this field. This study fills this gap by studying the prediction of the settling velocity by machine-learning models and compares it with the traditional formula calculation method. This study evaluates three machine-learning models, namely, random forest, linear regression, and the back propagation neural network. The results of this study show that the prediction results of the three machine-learning models are more accurate than those of traditional formula calculations, with an accuracy increase of 12.79% (random forest), 9.3% (linear regression), and 13.92% (back propagation neural network), respectively. At the same time, according to the results of this study, random forest is better than the other models in the mean absolute error and root mean square error evaluation indicators, which are only 0.0036 and 0.0047. This paper proposes three machine-learning methods to prove that the prediction effect of machine learning is much better than traditional formula calculations, thereby improving the shortcomings in this field. At the same time, it also provides reliable data support for studying the migration behavior of microplastics in water bodies.

Microplastics (MPs), a new type of pollutant, pose a significant threat to the environment at high concentrations. One of the primary sources of MPs in the ocean is river runoff, highlighting the need to investigate the spatial and temporal variations of MPs in rivers that flow into the sea, as well as their contributing factors. In this study, we analyzed MPs distribution and their influence factors in the Lincheng River, China. The Lincheng is the second largest river in Zhoushan island that directly flows into the ocean. MPs in the river water and sediments were detected during the wet season (July 2021), the dry season (November 2021) and the typhoon season (September 2021), and MPs were present in all reaches of the river. The abundance of MPs in the river was moderate compared to other studies, with the river water exhibiting a concentration of 15 ± 2.64 n/L and the sediment containing 318.24 ± 49.53 n/kg of MPs. In surface water, the most commonly found MP was blue man-made cellulose (CE), while the sediment contained mostly fragments of polypropylene (PP) and polypropylene polyethylene blends (PP + PE) in blue and green colors. The abundance of MPs showed significant seasonal differences, with higher abundance during the wet season compared to the dry season and typhoon period. Furthermore, local construction activities may contribute to higher MP abundance. To explore the influence factors of MPs, the Basin Development Index (BDI) was proposed, and a positive correlation between BDI and MP abundance was founded. This correlation indicates that the increase in land use for construction highly contributes to the MP pollution. In conclusion, future long-term monitoring of the abundance of MPs in the Lincheng River is necessary.

With the development of renewable energy and the utilization of marine resources, large-scale offshore floating photovoltaics have gradually attracted widespread attention. In order to develop offshore floating photovoltaics and promote sustainable development, it has become necessary to explore the hydrodynamic characteristics of floating photovoltaic units and floating arrays. In this work, based on the viscous flow theory, the Computational Fluid Dynamics (CFD) and the discrete element method (DEM) methods are used to analyze the hydrodynamics of the floating body unit of offshore floating photovoltaics. The influencing factors include mooring length, mooring radius, and floating unit length. In addition, the hydrodynamic performance of the floating body unit and the floating body array under different wave heights and periods is also discussed to explore the influence of environmental loads on the floating body unit and the floating body array. The results indicate that the mooring tension exhibits an opposite trend with the surge and heave motions when the mooring line length and radius are varied. The motion is found to be more pronounced when the floating body unit length is 0.4 times the wavelength. The heave motion of the floating body unit exhibits a strong linear relationship with wave height, increasing by 0.01 m for every 0.015 m increase in wave height. The motion of the floating body units on both sides connected to the mooring lines decreases as the array length increases.

A frozen soil is a multiphase medium composed of solid particles, ice, and water, and the cementation between the solid particles and ice strengthens with a decrease in temperature. Based on the theory of a composite solid‐state saturated porous medium, a uniform solution for the vertical vibration of an end‐bearing pile in a frozen soil is derived analytically. The axial displacements under impact loading in the time domain are calculated by using the numerical inverse transformation technique. The solution can degenerate into an end‐bearing pile in a saturated soil layer as the temperature approaches the freezing point. If the cementation between the solid particles and ice is ignored, the amplitude of the displacement will be overestimated. The numerical results show that temperature has a significant impact on the dynamic responses of the pile due to variation of the ice content and, consequently, of the cementation between solid particles and ice.

This study investigates the pollution characteristics and environmental risks of crushed microplastics (MPs) generated during plastic recycling, emphasizing their adsorption capacity for heavy metals, particularly lead (Pb). SEM-EDS analysis revealed that crushed MPs exhibit significantly higher adsorption capacity than primary MPs due to their larger surface area and more available adsorption sites, including oxygen-containing functional groups. The adsorption behavior of MPs was influenced by key factors such as MP size, MP quantity, pH, salinity, and biofilm formation. Smaller MPs demonstrated higher adsorption efficiency, while elevated pH enhanced Pb adsorption. Conversely, increased salinity reduced adsorption due to competition for adsorption sites. Increasing MP concentrations improved Pb removal efficiency, but higher MP quantities led to a decrease in maximum adsorption capacity, demonstrating a trade-off between removal efficiency and adsorption capacity. Isothermal adsorption experiments revealed that Pb adsorption on MPs follows a multi-layer mechanism, best characterized by the Freundlich model. The adsorption capacity increased nonlinearly with Pb concentration and stabilized as surface sites became saturated. The formation of biofilms on MPs further enhanced their adsorption capacity by providing additional functional groups and facilitating multi-layer adsorption, increasing ecological risks. Adsorption kinetics were best described by pseudo-second-order and intra-particle diffusion models, indicating chemical adsorption and boundary layer diffusion as dominant mechanisms. Magnetic Fe₃O₄ nanoparticles demonstrated a high recovery efficiency of 99.3% for MPs, highlighting their potential for environmental remediation. However, the presence of adsorbed Pb slightly reduced recovery performance, emphasizing the need to optimize recovery conditions for maximum efficiency. These findings underscore the dual threat posed by crushed MPs: their capacity to adsorb and concentrate harmful substances, increasing ecological toxicity, and the challenges associated with their recovery. This research provides critical insights into mitigating MP pollution and developing effective recovery strategies under realistic environmental conditions.