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Related Experiment Video

Updated: Jul 17, 2025

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Published on: July 28, 2018

Machine learning approaches for predicting microplastic pollution in peatland areas.

Huu-Tuan Tran¹, Mohammed Hadi², Thi Thu Hang Nguyen³

¹Laboratory of Ecology and Environmental Management, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City 700000, Viet Nam; Faculty of Applied Technology, School of Technology, Van Lang University, Ho Chi Minh City 700000, Viet Nam.

Marine Pollution Bulletin

|August 28, 2023

Summary

Scientists can predict microplastic (MP) pollution in peatland sediments using simple measurements like pH and salinity. This research offers a new method for monitoring environmental contamination.

Keywords:

Bayesian analysis LS-SVM LSTM Microplastic prediction Random Forest (RF)Spearman's correlation

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Area of Science:

Environmental Science
Analytical Chemistry
Ecology

Background:

Microplastic (MP) pollution is a growing environmental concern, particularly in sensitive ecosystems like peatlands.
Accurate quantification of MPs in sediments is challenging, hindering effective monitoring and management strategies.

Purpose of the Study:

To explore the potential of predicting microplastic quantities in peatland sediments using easily measurable physicochemical parameters.
To develop and evaluate machine learning models for MP prediction based on sediment characteristics.

Main Methods:

Correlation and Bayesian network analysis were used to identify associations between physicochemical variables (pH, TOC, salinity) and MP quantities.
Three machine learning models—Least-Square Support Vector Machines (LS-SVM), Random Forest (RF), and Long Short-Term Memory (LSTM)—were trained and tested for MP prediction.

Main Results:

Physicochemical properties, specifically pH, Total Organic Carbon (TOC), and salinity, significantly influenced MP quantities and characteristics (color, shape) in peatland sediments.
All three machine learning models achieved considerable accuracy in predicting MP quantities using the selected parameters.

Conclusions:

Basic physicochemical variables can be effectively utilized to predict microplastic pollution levels in peatland sediments.
This approach provides a foundational method for estimating MP contamination in various environmental settings, facilitating broader monitoring efforts.