Search

The convergent illuminating beam is the key factor during the individual measurement of suspended particles in aquatic suspensions. When the illuminating beam propagates in the suspension, the particle scattering in the optical path may destroy the convergence of the illuminating beam, especially in suspensions with a high particle concentration. In this paper, using the Monte Carlo simulation, the convergence of the illuminating beam is investigated by changing the physical properties of particles, such as size and concentration, and the optical path length of the illuminating beam. A dimensionless quantity, as the product of the scattering coefficient of suspension and the optical path length, is found to determine the achievement of the convergent beam. Moreover, an individual measurement setup based on the convergence of the illuminating beam is used to measure polystyrene microspheres with different concentrations. The experiment results are consistent with those of the simulations. Furthermore, improvement strategies are proposed and proved to effectively keep the convergence of the illuminating beam in turbid water. The results in this work can provide clues for designing a similar optical apparatus used in aquatic environment monitoring.

Cyanobacterial blooms have become an urgent threat to the aquatic ecosystem, but early warning of the blooms is still challenging for the research community. In this paper, a method based on polarized light scattering and powered by machine learning is proposed to in-situ early warn the cyanobacterial blooms. In this work, the wild types of Microcystis are treated and the cells are individually measured to obtain their polarization parameters. The experimental results show that machine learning algorithms can be used to well identify the states of the Microcystis cells, and the compositions of the mixed samples can be effectively retrieved by this method. Subsequently, one application strategy is suggested to early warn the blooms, which is potential and powerful to achieve the in-situ early warning of cyanobacterial blooms in the future.