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<p>There is a growing concern that the steady increase in plastic production is leading to a substantial contamination of our environment with microplastic particles. While aquatic ecosystems are more and more studied, there is still a substantial lack in knowledge regrading terrestrial (mainly soil) system. This knowledge gap is partly related to the challenges to detect and analyses microplastic particles in soils. Firstly, it is difficult to extract microplastic from a matrix of organic and inorganic particles of similar size. Secondly, the well-established spectroscopic methods to detect microplastic in water samples are sensitive to organic material and are moreover very time consuming. Eliminating very stable organic particles (e.g. lignin) from soil samples without affecting the microplastic to be measured is hardly possible. Hence, a robust analytical approach is needed to tackle the microplastic detection in soils. In this study, we combine a density separation scheme, a 3D Laser Scanning Confocal Microscope (Keyence VK-X1000, Japan) and a machine learning algorithm to classify and analyses microplastic particles in soil samples. For the analysis a silty loam (16% sand, 59% silt, 25% clay, 1.3% organic carbon) and a loamy sand (72% sand, 18% silt, 10% clay, 0.9% organic carbon) were spiked with different concentrations of high density Polyethylene (HDPE), low density Polyethylene (LDPE), Polystyrene (PS) and Polybutylene adipate terephthalate/Ploy lactic acid (PBAT/PLA) microplastic (HDPE 50 - 100 and 250 - 300 µm, LDPE <50 and 200 - 800 µm, PS <100 µm, PBAT/PLA < 2 mm). The classification with a machine learning algorithm is an essential data processing step to distinguishes between plastic, mineral as well as organic particles left after density separation. In case microplastic adopts the soil color, a combination of optical information and surface characteristics are used for a successful classification. Overall, the 3D Laser Scanning Confocal Microscopy in combination with a machine learning algorithm is a promising tool to detect, quantify and analyses microplastic in soils.</p>

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.

<p>Agricultural soils play a key role as sink of microplastic (MP) coming from different sources, especially via the application of sewage sludge, compost, plastic mulch films, and tire ware. However, the effectiveness of this sink might be substantially reduced in areas subjected to water erosion. The aim of this study is to determine the transport behavior of MP during water erosion events on arable land. More specifically it is analyzed if MP is preferentially transported or behaves more conservative as attached to soil minerals and/or encapsulated in soil aggregates. A series of rainfall simulations were performed over 1.5 years on two plots at two test sites representing different intensively used soils (silty loam and loamy sand) in Southern Germany. The plots (4.5 m x 1.6 m) were spiked with microplastic (high density polyethylene) consisting of two different size fractions, fine MP (MP<sub>f</sub>, 53-100 μm) and coarse MP (MP<sub>c</sub>, 250-300 μm) incorporated into the topsoil (< 10 cm). The results clearly underline the selective nature of MP erosion leading to an enrichment ratio of MP in the eroded sediments of the loamy sand plot of 3.82 to 7.86, compared to an enrichment ratio from the silty loam plots of 1.41 to 5.29. Interestingly, there was no significant difference in enrichment ratios between MP<sub>f</sub> and MP<sub>c</sub>. Over time, an increasing connection between MP and soil particles could be observed. During the first rainfall simulation only 12% (MP<sub>c</sub>) and 34-49% (MP<sub>f</sub>) of the eroded plastic particles were connected to mineral particles or soil aggregates, while during the last simulation 1.5 years later about 31-47% (MP<sub>c</sub>) and 57-67% (MP<sub>f</sub>) of the eroded particles were bond to the soil matrix. Overall, our results indicate a strong dependency of the erosion transport behavior of MP depending on soil characteristics and time since application, while surprisingly we found little effect of MP size. </p>

<p>There is growing concern regarding the pollution of our environment with plastic materials, whereas especially the dimension of microplastic pollution and its ecological effect is widely discussed. Most studies focus on aquatic environments, while studies in terrestrial systems (mainly soils) are rare. This partly results from the challenges arising when microplastic particles need to be separated from organic and mineral particles. Key analytic techniques for microplastic detection in aquatic and terrestrial systems include Fourier transformation-infrared (FT-IR) and micro-Raman spectroscopy, as well as thermal extraction desorption-gas chromatography-mass spectrometry (TED-GC-MS) and pyrolysis-gas chromatography-mass spectrometry (pyr-GC-MS). While the mass spectrometric methods lack to determine particle sizes, the FT-IR and micro-Raman spectroscopy are very costly and time consuming. Moreover, the latter detection methods are very sensitive to organic matter particles, which are difficult to remove fully during soil sample preparation. Hence, a faster and more robust method to determine microplastic in soils is essential for a wider analysis of this environmental problem. In this study, we combine a density separation scheme with a 3D Laser Scanning Confocal Microscope (Keyence VK-X1000, Japan) analysis to determine the number and size of microplastic particles in soil samples. For the analysis a silty loam (16% sand, 59% silt, 25% clay, 1.3% organic carbon) and a loamy sand (72% sand, 18% silt, 10% clay, 0.9% organic carbon) were spiked with different concentrations of high density Polyethylene (HDPE), low density Polyethylene (LDPE) and Polystyrene (PS) microplastic (HDPE 50 - 100 and 250 - 300 µm, LDPE <50 and 200 - 800 µm, PS <100 µm). 3D Laser Scanning Confocal Microscopy show very promising results while using differences in optical characteristic and especially surface roughness, to distinguish between plastic and mineral as well as organic particles left after density separation. Overall, the 3D Laser Scanning Confocal Microscopy is a promising tool for relatively fast detection and quantification of microplastic in soils, which could perfectly complement the also relative fast mass-spectrometric methods to determine plastic types. However, to result in an operational and automated analyzation process further research based on the 3D Laser Scanning Confocal Microscopy analysis is needed.</p>