Pictures of the Great Pacific Garbage Patch have made it clear that our environment has a plastic problem. Plastic in the environment can decompose into fragments of different sizes as a result of natural weathering processes. Already in the 1970s, small plastic fragments were detected in the ocean, which were called microplastics. Today, microplastic is a common term and defines plastic particles in the range of 1 ?m to 5 mm. Because of the small size, microplastics are able to spread into the environment and detected in aquatic and terrestrial systems. Nowadays the effects of microplastic particles in ecosystems are highly discussed. Studies shows microplastics may have a harmful effect on organisms and processes in the environment such as function as pollutant vector for organic pollutants (POPs). However, most studies focus on aquatic systems, while studies in terrestrial systems (mainly soils) are rare. This is partly because it is challenging to separate microplastics from organic and mineral soil 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). Furthermore, the mass spectrometric methods cannot determine particle size, and FT-IR and micro-Raman spectroscopy methods 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 robust method to determine the presence of microplastics in soils, and to measure their particle size is needed. The Project DeMipSo3D combines a density separation scheme with a 3D Laser Scanning Confocal Microscope (3D LSM) to detect microplastic in soil. The 3D LSM method outputs values of color Red-Green-Blue (RGB), laser intensity, differential interference contrast (DIC) and surface roughness. These four different outputs are used to develop a machine learning based microplastic classification. Based on the classification an extensive particle analyses can be processed. The 3D LSM is a promising tool for relatively fast detection and quantification of microplastic in soils, which could perfectly complement existing rapid mass-spectrometric methods which determine plastic types.