Maximizing Realism: Mapping Plastic Particles at the Ocean Surface Using Mixtures of Normal Distributions

Current methods of characterizing plastic debris use arbitrary, predetermined categorizations and assume that the properties of particles are independent. Here we introduce Gaussian mixture models (GMM), a technique suitable for describing non-normal multivariate distributions, as a method to identify mutually exclusive subsets of floating macroplastic and microplastic particles (latent class analysis) based on statistically defensible categories. Length, width, height and polymer type of 6,942 particles and items from the Atlantic Ocean were measured using infrared spectroscopy and image analysis. GMM revealed six underlying normal distributions based on length and width; two within each of the lines, films, and fragments categories. These classes differed significantly in polymer types. The results further showed that smaller films and fragments had a higher correlation between length and width, indicating that they were about the same size in two dimensions. In contrast, larger films and fragments showed low correlations of height with length and width. This demonstrates that larger particles show greater variability in shape and thus plastic fragmentation is associated with particle rounding. These results offer important opportunities for refinement of risk assessment and for modeling the fragmentation and distribution of plastic in the ocean. They further illustrate that GMM is a useful method to map ocean plastics, with advantages over approaches that use arbitrary categorizations and assume size independence or normal distributions.

Automated summary

Current methods of characterizing plastic debris have limitations. By using Gaussian mixture models, we can identify different subsets of plastic particles and improve the accuracy of risk assessment and modeling of plastic distribution in the ocean.