Cited by 76Microplastics and Plastic PollutionMachine Learning in Materials ScienceAquatic and Environmental Studies

Machine learning-based prediction for settling velocity of microplastics with various shapes

Qian, Shangtuo, Qiao, Xuyang, Zhang, Wenming, Yu, Zijian, Dong, Shunan, Feng, Jiangang·2024·Water Research

Abstract

Microplastics can easily enter the aquatic environment and be transported between water bodies. The terminal settling velocity of microplastics, which affects their transport and distribution in the aquatic environment, is mainly influenced by their size, density, and shape. Due to the difficulty in accurately predicting the terminal settling velocity of microplastics with various shapes, this study focuses on establishing high-performance prediction models and understanding the importance and effect of each feature parameter using machine learning. Based on the number of principal dimensions, the shapes of microplastics are classified into fiber, film, and fragment, and their thresholds are identified. The microplastics of different shape categories have different optimal shape parameters for predicting the terminal settling velocity: Corey shape factor, flatness, elongation, and sphericity for the fragment, film, fiber, and mixed-shape MPs, respectively. By including the dimensionless diameter, relative density and optimal shape parameter in the input parameter combination, the machine learning models can well predict the terminal settling velocity for the microplastics of different shape categories and mixed-shape with R 2 > 0.867, achieving significantly higher performance than the existing theoretical and regression models. The interpretable analysis of machine learning reveals the highest importance of the microplastic size and its marginal effect when the dimensionless diameter D* = d n(g/v 2)1/3 > 80, where d n is the equivalent diameter, g is the gravitational acceleration, and ν is the fluid kinematic viscosity. The effect of shape is weak for small microplastics and becomes significant when D* exceeds 65.

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Crossref · 10.1016/j.watres.2023.121001

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Authors
Qian, S., Qiao, X., Zhang, W., Yu, Z., Dong, S., & Feng, J.
Affiliations
  • Hohai University (CN)
  • University of Alberta (CA)
Corresponding author
Feng, Jiangang
Venue
Water Research
Publisher
Elsevier BV
Published
2024-02
Volume
249
Pages
121001
ISSN
0043-1354 (ISSN-L)
OpenAlex
W4389497996
Cited by
76
References
55
Citations by year
2026: 23 · 2025: 31 · 2024: 22
FWCI
5.07
Type
journal article
Language
en
Open access
closed
Locations
  • Water Research (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Natural Science Foundation of China (42277001, 52279063)
Topics
  • Physical Sciences › Environmental Science › Pollution › Microplastics and Plastic Pollution
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Earth and Planetary Sciences › Earth-Surface Processes › Aquatic and Environmental Studies
Keywords
microplastics, terminal velocity, microplastic shape, shape parameter, sphericity, flatness, elongation, relative density, feature importance
MeSH terms
Microplastics, Environmental Monitoring, Plastics, Water Pollutants, Chemical

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