Advancing Microplastic Monitoring: Automatic Correction of the Aggregation and Discontinuity Issues Based on Instrument Imaging

Instrumental imaging accelerates the analysis of microplastics but suffers from reduced detection accuracy during the segmentation of fibers and nonfibers due to particle aggregation and discontinuities. Therefore, this study aimed to develop an automated analytical method to characterize environmental microplastics based on instrumental imaging. By leveraging a manually labeled data set (130,536 particles), our established diffluent amodal instance segmentation former (DAISF) model greatly improved the ability to correct the aggregation and discontinuity issues due to the use of the Gauss–Laplace operator, which has superior segmentation performance. Compared to the instrument detection, this model significantly improved the detection of aggregated fibers and nonfibers by 71.8 ± 19.5% and 89.2 ± 24.1%, respectively, and of discontinuous fibers and nonfibers by 90.2 ± 14.7% and 98.4 ± 4.4%, respectively. The proposed computational method demonstrated superior performance compared to the instrument-based approach, achieving significantly higher recall and F1 scores. Quantitative validation revealed exceptional alignment with ground-truth measurements, exhibiting low relative errors in particle number (≤19.1%), length (≤20.2%), and mass (≤12.4%), representing improvements over the instrumental approach of 31.0-, 3.1-, and 8.8-fold, respectively. Overall, the established approach can accurately obtain microplastic concentrations and multiparameters based on instrumental imaging, indicating its usefulness in the efficient detection and rapid monitoring of environmental microplastics.