The vertical migration trend of cyanobacterial cells with gas vesicles in water ecosystems can reflect the changes in the natural environment, such as temperature, nutrients, light conditions, etc. The static pressure treatment is one of the most important approaches to study the properties of the cyanobacterial cell and its gas vesicles. In this paper, a polarized light scattering method is used to probe the collapse and regeneration of the cyanobacterial gas vesicles exposed to different static pressures. During the course, both the axenic and wild type strain of cyanobacterial Microcystis were first treated with different static pressures and then recovered on the normal light conditions. Combining the observation of transmission electron microscopy and floating-sinking photos, the results showed that the collapse and regeneration of the cyanobacterial gas vesicles exposed to different static pressures can be characterized by the polarization parameters. The turbidity as a traditional indicator of gas vesicles but subjected to the concentration of the sample was also measured and found to be correlated with the polarization parameters. More analysis indicated that the polarization parameters are more sensitive and characteristic. The polarized light scattering method can be used to probe the cyanobacterial gas vesicles exposed to different static pressures, which has the potential to provide an in situ rapid and damage-free monitoring tool for observing the vertical migration of cyanobacterial cells and forecasting cyanobacterial blooms.

Monitoring the particulate composition changes during the flocculation process is still challenging for the research community. We use an experimental setup based on polarized light scattering to measure the polarization states of the scattered light of the individual particles. We build a classifier based on the support vector machine and feed it with the measured parameters. Results show that the classifier can effectively classify the particulate compositions, such as the sediment particles, flocculants, and flocs, which can be used to monitor the particulate composition changes during the flocculation process. Discussions on the intensity and polarization parameters find that the polarization parameters play a vital role in the classification of the particulate compositions in the flocculation suspensions. Additionally, the further analysis of the experimental data and the related simulations show that the degree of polarization can be an indicator of the flocculation process. We prove that the method based on polarized light scattering may be a potential in situ monitoring tool in the future for the study of the flocculation process.

Microplastics (MPs) have become the widespread contaminants, which raises concerns on their ecological hazards. In-situ detection of MP in water bodies is essential for clear assessment of the ecological risks of MPs. The present study proposes a method based on polarized light scattering which measures the polarization parameters of the scattered light at 120° to detect MP in water. This method takes the advantage of in-situ measurement of the individual particles and the experimental setup in principle is used. By use of the measured polarization parameters equipped by machine learning, the standard polystyrene (PS) spheres, natural water sample, and lab-cultured microalgae are explicitly discriminated, and MP with different physical and chemical properties can be differentiated. It can also characterize the weathering of different MP and identify the specific type from multiple types of MP. This study explores the capability of the proposed method to detect the physical and chemical properties, weathering state and concentration of MP in water which promises the future application in water quality sensing and monitoring.

An effective method to calculate the statistical Mueller matrix (SMM) of suspended particles based on polarized light scattering is presented that takes advantage of the Stokes vectors measurement of individual particles. The calculation method of the SMM is derived based on statistics. Experimental results of Microcystis samples confirm that the SMM can characterize cells of different states. Then, pairwise contrast experiments indicate the great prospect of the SMM applied on the discrimination of suspended particles. It helps to find the optimal incident polarization state to discriminate suspended particles, and it has optimal discrimination ability. The parameter derived from the SMM can simultaneously discriminate particles including microalgae, microplastics, and sand-like particles.