Weathering
is a significant process that alters the properties
of microplastics (MPs) and consequently affects their environmental
behaviors. In this study, we introduced a novel approach based on
polarized light scattering technique, which offers advantages in terms
of rapid, high-throughput, and submicron-sized detection. This technique
was successfully applied to characterize the weathered MPs after a
180-day laboratory simulation of coastal environments. By employing
polarization measurements, we obtained a 46-dimensional matrix data
set for the weathered MP fragments and subsequently processed them
using a backpropagation neural network. The successful extraction
of effective polarization pulses confirmed the presence of MP fragments
within the size range of 0.2–60 μm, yielding total accuracies
for size classification ranging from 78.9 to 86.9%. Furthermore, this
technique achieved an overall accuracy of 93.8% in classifying MPs
with different weathering degrees and polymer types, revealing polarization
parameters associated with size and morphological changes play a dominant
role in characterizing the weathering process of MPs. Compared with
conventional approaches, the novel polarized light scattering approach
holds great promise for rapid, high-throughput, and accurate characterization
of MPs with small sizes. The findings of this study provided new insights
into how MPs change after long-term weathering in aquatic environments.