Computational polarized holography for automatic monitoring of microplastics in scattering aquatic environments

Automatic monitoring of microplastic (MP) contamination in aquatic ecosystems is crucial for effective management and mitigation strategies. However, this task presents significant challenges due to the dynamic 3D distribution of MPs and the light scattering in the aqueous phase. Traditional MP detection methods are limited in volumetric imaging and anti-scattering capability, often requiring cumbersome manual processing and analysis. In this study, we develop an integrated imaging system based on computational polarized holography, which offers unique advantages in automation, multifunctionality, and affordability. As demonstrated with proof-of-concept experiments, our system enables accurate and efficient 3D tracking of dynamic MPs across an extended detection volume, facilitating high-throughput analysis. In addition, the proposed hybrid de-scattering algorithm substantially improves image quality even when characterizing MPs in scattering milk solutions. Furthermore, an unsupervised clustering method is developed to identify and classify different MPs based on their multimodal features without the need for manual annotation. Although the experiments were implemented in the laboratory, the results demonstrate the robust monitoring efficiency and material-dependent sensitivity of our system. It opens up new opportunities for on-site continuous monitoring of MP pollution in aquatic ecosystems, contributing significantly to sustainable environmental management.