Laboratory 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.
Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), Aalesund, Norway.
Mar Pollut Bull. 2023 Sep;194(Pt A):115417. doi: 10.1016/j.marpolbul.2023.115417. Epub 2023 Aug 26.
This study explored the potential for predicting the quantities of microplastics (MPs) from easily measurable parameters in peatland sediment samples. We first applied correlation and Bayesian network analysis to examine the associations between physicochemical variables and the number of MPs measured from three districts of the Long An province in Vietnam. Further, we trained and tested three machine learning models, namely Least-Square Support Vector Machines (LS-SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) to predict the composite quantities of MPs using physicochemical parameters and sediment characteristics as predictors. The results indicate that the quantity of MPs and characteristics such as color and shape in the samples were mostly influenced by pH, TOC, and salinity. All three predictive models demonstrated considerable accuracies when applied to the testing dataset. This study lays the groundwork for using basic physicochemical variables to predict MP pollution in peatland sediments and potentially locations and environments.
本研究旨在探讨通过泥炭地沉积物样本中易于测量的参数来预测微塑料（MPs）数量的可能性。我们首先应用相关性和贝叶斯网络分析来检验理化变量与从越南龙川省三个地区测量的 MPs 数量之间的关联。此外，我们还训练和测试了三种机器学习模型，即最小二乘支持向量机（LS-SVM）、随机森林（RF）和长短期记忆网络（LSTM），使用理化参数和沉积物特性作为预测因子来预测 MPs 的综合数量。结果表明，样品中 MPs 的数量以及颜色和形状等特征主要受 pH 值、TOC 和盐度的影响。当应用于测试数据集时，所有三种预测模型都表现出相当高的准确性。本研究为使用基本理化变量来预测泥炭地沉积物中 MP 污染以及潜在的地点和环境奠定了基础。
