Identification of Microplastics in Soils Using 2D Geometric Shape Descriptors

Irada Ismayilova,Sabine Timpf,Tabea Zeyer

doi:10.5194/agile-giss-2-32-2021

Abstract

Abstract. Microplastics (MP), until now mostly studied in aquatic ecosystems, are also largely polluting terrestrial ecosystems, especially soil systems. Overall, there is a lack of robust and fast methods to identify, separate and eliminate MPs from soils. This paper is a first attempt to use 2D shape descriptors and Random Forest Machine Learning method in order to discriminate soil and MP particles. The results of this study demonstrate promising potential of the Machine Learning approach and shape descriptors in this relatively new scientific field of determining MPs in soils.

Highlights

Due to its appealing characteristics, such as cheap price, water resistance and durability, plastics production has greatly increased since having been introduced in the 1950s (Horton and Dixon, 2018; Geyer et al, 2017)
With increased production and wide usage of plastics, an enormous amount of plastics ends up in our environment. This was first realized for oceans (Zarfl et al, 2011) and generally aquatic ecosystems (Prata et al, 2019), where macro and microplastic is wide-spread
In this paper we analysed the potential of a Random Forest (RF) Machine Learning (ML) approach together with 2D geometric shape descriptors, to determine MP and soil particles from optical images taken with a microscope (VK-X1000, Keyence, Japan)

Summary

Introduction

Due to its appealing characteristics, such as cheap price, water resistance and durability, plastics production has greatly increased since having been introduced in the 1950s (Horton and Dixon, 2018; Geyer et al, 2017). With increased production and wide usage of plastics, an enormous amount of plastics ends up in our environment. This was first realized for oceans (Zarfl et al, 2011) and generally aquatic ecosystems (Prata et al, 2019), where macro and microplastic is wide-spread. An increasing number of studies indicate that microplastics (hereafter MP) substantially pollutes terrestrial ecosystems including soils (Bläsing & Amelung 2018). Most methods to determine MP in aquatic environments cannot be straightforwardly transferred to terrestrial systems. In this paper we analysed the potential of a Random Forest (RF) Machine Learning (ML) approach together with 2D geometric shape descriptors, to determine MP and soil particles from optical images taken with a microscope (VK-X1000, Keyence, Japan) In case of soil consisting of a matrix of mineral and organic particles it is challenging to determine MP particles and fibres in low concentrations (Möller et al 2020).

Methods

Results

Discussion

Conclusion