Microplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm

Plastic films are essential in modern livestock and crop production. According to the Plastics Europe Report 2022, Light Polyethylene (LDPE) and Polypropylene (PP) are the primary plastic material demand in the agricultural sector. Especially for crop production, plastic mulching films cover arable soil to increase temperature, reduce evaporation, and prevent weed growth. However, mechanical and environmental weathering removes microplastics from the mulch film and can stock in the soil. Additionally, sewage sludge and compost use in agriculture lead to further microplastic contamination. Obviously, microplastic input to soils is critically high, but an accurate quantification is still lacking. This is partly caused by challenges in detection and analysis of microplastic in soils. First, it is challenging to extract microplastic from a matrix of organic and inorganic particles of similar size. Second, the well-established spectroscopic methods (e.g., Raman and FTIR) for detecting microplastics in water samples are sensitive to soil organic matter, and they are very time-consuming. Eliminating very stable organic particles (e.g., lignin) from soil samples without affecting the microplastic to be measured is another challenge. Hence, a robust analytical approach to detect microplastic in soils is needed. In this context, we developed a methodological approach that is based on a high-throughput (25 g soil sample) density separation scheme for measurements in a 3D Laser Scanning Confocal Microscope (Keyence VK-X1000, Japan) and subsequently using a Machine-Learning algorithm to classify and analyze microplastic in soil samples. Our aim is to develop a method for a fast screening of microplastic particle numbers in soils while avoiding the use of harmful substances (e.g., ZnCl2) or prolonged organic carbon destruction. For method development, we contaminate three different soil types (sandy soil: 86.6% sand, 9.7% silt, 3.7% clay, 0.58% organic carbon; silty loam: 6% sand, 59% silt, 25% clay, 1.3% organic carbon analysis and loamy sand: 72% sand, 18% silt, 10% clay, 0.9% organic carbon) with transparent LDPE, black LDPE and PP microplastic in three different size ranges (< 50, 50 – 100 and 100 – 250 µm). Moreover, we test our method on microplastic fibers (PP, 1000 µm). The separated microplastic plus organic particles and some small mineral particles were scanned using a 3D Laser Scanning Confocal Microscope. For each sample, the 3D Laser Scanning Confocal Microscope generates three different main outputs: color, laser intensity, and surface characteristics with a pixel size of 2.72 µm. Based on these data outputs, a Machine-Learning algorithm distinguishes between the mineral, organic, and microplastic particles. It was found that color changes of microplastics due to soil contact challenge the classification but can be compensated by surface characteristics that become an essential input parameter for the detection. The presented methodological approach provides an accurate and high-throughput microplastic assessment in soil systems, which is critically needed to understand the boundaries of sustainable plastic application in agriculture.