Particle Image/Tracking Velocimetry (PIV/PTV) for Hydrodynamic Characterization of Microplastics

粒子成像/跟踪测速（PIV/PTV）技术在流体工程、医学和环境工程等基础和应用研究领域有着广泛的应用。结合这两种测速技术允许非侵入性表征的环境载液以及分散相。PIV/PTV技术已被用于参数化多相流，涉及沉积物颗粒，重金属和气泡。它们主要用于研究湍流中的上升或下降行为和夹带。 在过去的十年中，被称为微塑料（MP）的小塑料颗粒，尺寸小于5毫米，由于其对水生动物和人类健康的影响，已成为一种危险的污染物。因此，对它们在水环境中的检测和生态学意义的研究受到了极大的关注。MP在这种环境中的分布与水动力条件密切相关，一些负责动员营养物质，氧气和沉积物的物理过程也能够运输MP。尽管他们的意义，我们的知识的相互作用的MP和流动流体动力学是有限的。 所要求的PIV/PTV系统为申请人提供了一个独特的工具，可以从以前从未探索过的角度对MP进行突破性研究。该设备将支持对多金属化合物的夹带和长距离运输以及沉积多金属化合物的流动性和保留的研究。所提出的系统通过机器学习算法进行增强，使申请人能够在3D中查看MP粒子的运动，跟踪粒子轨迹，方向，形状，面积和速度。此外，该系统的功能，包括分辨率和速度，旨在捕捉非常不规则形状的MP（如微纤维和薄膜）的复杂特征。 然而，所提出的系统的应用不限于MP的表征;该系统将被申请人用于环境流体动力学的其他高级领域，包括充气流表征。此外，约克大学、加拿大环境气候变化大学和温莎大学的四个合作者将在与申请人现有合作的基础上使用该系统并从中受益。

Particle Imaging/Tracking Velocimetry (PIV/PTV) techniques have widespread applications in many fundamental and applied research fields including fluid engineering, medical science, and environmental engineering. Combining these two velocimetry techniques allows non-intrusive characterization of the ambient carrier fluid as well as of the dispersed phase. PIV/PTV techniques have been utilized to parametrize multi-phase flow involving sediment particles, heavy metals, and air bubbles. They have been implemented primarily to investigate the rising or falling behaviours and entrainment in turbulent flow. In the past decade, small plastic particles known as microplastics (MPs), sized smaller than 5mm, have emerged as a hazardous pollutant due to their impact on aquatic animals and human health. Therefore, research on their detection in aquatic environments and ecological implications have gained significant attention. The distribution of MPs in such environments are closely associated with hydrodynamic conditions and some of the physical processes responsible for the mobilization of nutrients, oxygen, and sediments are also capable of transporting MPs. Despite their significance, our knowledge of the interaction of MPs and flow hydrodynamics is limited. The requested PIV/PTV system provides a unique tool for the applicants to carry out breakthrough research on MPs from a perspective that has never been explored before. The equipment will support research on the entrainment and long-distance transport of MPs and mobility and retention of sedimentary MPs. The proposed system is enhanced with machine learning algorithms that enable applicants to view the movement of MP particles in 3D, track particle trajectory, direction, shape, area, and velocity. Furthermore, the system's features, including resolution and speed, are designed to capture intricate features of very irregularly shaped MPs such as microfibres and films. The application of the proposed system, however, is not limited to MPs' characterization; the system will be utilized by applicants in other advanced areas of environmental hydrodynamics including aerated flow characterization. Furthermore, four collaborators at York University, Environment Climate Change Canada, and University of Windsor will be using and benefiting from the system on the basis of exiting collaborations with the applicants.