实验技术与管理

针对常规实验手段无法直接观测多孔介质内微塑料运移现象的问题，开发了一套微塑料运移与滞留的微观可视化实验体系，并通过机器学习进行微塑料的高效识别与计算。实验结果量化了多孔介质结构对微塑料迁移行为的影响：介质粒径增大使微塑料滞留量增加29.8%～56.0%；介质通道宽度的增加使微塑料滞留量增加14.5%～37.6%。实验结果与国内外研究结果吻合。实验还可直观观测不同类型微塑料沉积模式与特征；实验体系具有直观、准确、简便、可拓展的优势，适用于环境等相关学科的创新教学实验研究。

[Objective] Traditional experimental methods cannot facilitate the direct observation of the migration of microplastics withinporous media. To address this issue, this study developed a microscopic visualization experimental system to investigate the migration andretention of microplastics and integrated artificial intelligence for the efficient identification and calculation of microplastics. The aim wasto quantify the impact of the porous media structure on the migration behavior of microplastics and provide an intuitive, accurate, simple,and scalable experimental system suitable for innovative teaching and research in environmental and related disciplines. [Methods] Thisstudy developed a microscopic visualization experimental system to deeply investigate the migration and retention behavior ofmicroplastics in porous media by constructing pore-scale single-channel models. In the experiment, five pore-scale single-channel modelswith different size parameters were developed to simulate the retention of microplastics under different porosity conditions. Theexperimental setup included a microsyringe, a micro-infusion pump, an optical microscope, and a high-resolution camera. Themicrosyringe and micro-infusion pump were used to control the injection of fluids, while the optical microscope and high-resolutioncamera were employed to capture the migration process of microplastics in the channels. Before the experiment, ethanol was used to expel air from the channels, followed by saturation with deionized water. Then, a microplastic suspension treated with an ultrasonic processor was injected into the channels at a rate of 10 μL/h, with images captured at a rate of 2 frames per second. The obtained images were corrected and preprocessed using the ImageJ software to eliminate halos and speckles. Aiming at the small size and large quantity of microplastic particles, this study employed machine learning algorithms for image recognition and counting and developed custom script codes, which were combined with the ImageJ's macro function, to perform the automated batch analysis of data, significantly improving the efficiency and accuracy of microplastic identification, especially with accuracy rates of over 98% for dispersed individual particles and over 95% for particles aggregated in porous media. This method, which combined microscopic visualization technology with artificial intelligence image recognition, provided a novel and efficient experimental method for the study of microplastic environmental behavior. [Results] The experimental results of the microscopic visualization experiment on microplastic transport and retention provided the following conclusions.(1) The results revealed the impact of the porous media structure on the migration behavior of microplastics, with an increase in the media particle size and channel width leading to a significant increase in microplastic retention by 29.8%–56.0% and 14.5%–37.6%, respectively. These findings confirmed the key role of the porous media structure in the retention behavior of microplastics.(2) The experiment visually demonstrated the deposition patterns of microplastics in porous media, which were consistent with existing research findings, further validating the effectiveness of the experimental method.(3) By integrating artificial intelligence image recognition technology, this study developed an efficient method for the identification and counting of microplastics, significantly improving the accuracy and efficiency of data processing. This method not only provided new experimental means for environmental research and microplastic teaching but also showcased the potential application of artificial intelligence technology in the field of environmental science. [Conclusions] This study effectively quantified the impact of the porous media structure on the microplastic retention behavior through a microscopic visualization experimental system and confirmed the consistency of experimental results with existing research. By integrating artificial intelligence image recognition technology, the accuracy of microplasticidentification and efficiency of data processing were significantly improved, providing an innovative experimental method forenvironmental science teaching and research.