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Related Experiment Video

Updated: May 23, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Multimodal artificial intelligence (AI) for estimating the sinking velocity of microplastic-microalgae aggregates

Min-Jeong Cho1, Minhyuk Jeung2, Chung Hyeon Lee3

  • 1Department of Environmental Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan-si, Gyeongbuk 38541, Republic of Korea.

Water Research
|May 21, 2026
PubMed
Summary

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This summary is machine-generated.

A new AI model predicts microplastic-microalgae aggregate sinking velocity. This AI approach, using image, text, and biological data, offers better predictions than traditional methods for understanding marine pollutant fate.

Area of Science:

  • Marine Biology
  • Environmental Science
  • Artificial Intelligence

Background:

  • Microplastics are widespread marine pollutants.
  • Microplastics form aggregates with microalgae (MP-MA), affecting their vertical distribution.
  • Sinking mechanisms of MP-MA aggregates are not fully understood.

Purpose of the Study:

  • To develop a multimodal AI model for estimating MP-MA aggregate sinking velocity.
  • To investigate the influence of microalgal traits and aggregate structure on sinking behavior.
  • To provide an interpretable framework for understanding MP-MA aggregate dynamics.

Main Methods:

  • A multimodal AI model integrating image, text, and biological data was developed.
  • Pretrained BLIP encoders extracted features from microscopy images and species names.
Keywords:
Explainable artificial intelligenceMicroalgaeMicroplasticsMicroplastic–microalgae aggregatesMultimodal deep learningSinking velocity

Related Experiment Videos

Last Updated: May 23, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

  • Explainable AI techniques (attention rollout, SHAP) were used for model interpretation.

    • Main Results:

    • The AI model achieved high accuracy (R²=0.857 train, R²=0.567 validation), outperforming Stokes-based models (R²=0.11).
    • Attention maps highlighted key aggregate regions influencing sinking.
    • SHAP analysis identified cell wall type, plastic particle count, and swimming mode as significant factors.

    Conclusions:

    • Multimodal AI offers a powerful, interpretable tool for predicting MP-MA aggregate sinking.
    • Understanding these factors is crucial for assessing the environmental fate of microplastics and microalgae.
    • This research advances the study of marine aggregate dynamics and pollutant transport.