Maximizing Realism: Mapping Plastic Particles at the Ocean Surface Using Mixtures of Normal Distributions.
Environ Sci Technol
; 56(22): 15552-15562, 2022 11 15.
Article
em En
| MEDLINE
| ID: mdl-36305282
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Plásticos
/
Poluentes Químicos da Água
Tipo de estudo:
Risk_factors_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article