Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning

Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning Tabea Scheiterlein, Peter Fiener This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8373800/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Low-density plastics of different origins are a major source of microplastic (MP) contamination in agricultural soil systems. Although several plastic entry pathways are well known, such as the fragmentation of plastic materials used in so-called plasticulture or the contamination of organic fertilisers, including compost and sewage sludge, quantifying the MP contamination of these soil systems remains challenging and time-consuming. This study developed and rigorously tested a hazard-free workflow to overcome these limitations and expand the capabilities for detecting MP. The workflow combines 3D Laser Scanning Confocal Microscopy (Keyence VK-X1000, Japan) with machine-learning-based data analysis and was evaluated using three agricultural topsoils spiked with transparent and black low-density polyethylene and polypropylene particles (<53 µm, 53-100 µm, 100-250 µm) and polypropylene fibres (1000 µm). The method reliably detects both transparent and black MP ≥53 µm in soils with low particulate organic matter content, achieving a mean recovery rate of 80% ± 28%. Transparent MPs were reliably identified, whereas black MPs and fibres were influenced by particulate organic matter. Beyond particle count and size, the approach quantifies surface morphology using high-resolution 3D data. Four 25 g samples (100 g total soil) can be processed within three days, providing a fast, accurate, and environmentally safe tool for MP analysis in agricultural soils. Microplastic Soil Surface analyses 3D Laser Scanning Microscopy Machine Learning Freezing Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction There is a global concern regarding the accumulation of plastic materials in aquatic and terrestrial ecosystems [ 1 ]. In 2022, an estimated 267.7 million tonnes of plastic waste were generated worldwide, with polypropylene (PP, 18.9%) and low-density polyethylene (LDPE, 14.1%) being the most prevalent types [ 2 ]. Once released to the environment, e.g. via waste mismanagement, plastics can appear in a wide range of sizes and shapes [ 3 , 4 ], continue to fragment [ 3 , 5 ], and thereby potentially releasing toxic additives such as plasticizers and brominated flame retardants[ 6 ]. Probably the most important terrestrial microplastic (MP, plastic 1-5000 µm [ 7 ]) sink are soils [ 8 ], where MP enter via a multitude of pathways ranging from MP-laden street runoff contaminated with tyre abrasion [ 9 – 11 ] and paint fragments [ 12 , 13 ], to fragmented litter [ 14 – 16 ] as well as atmospheric deposition [ 17 – 19 ]. In case of arable soil systems, additional input pathways related to agricultural activities come into play, whereby the most important ones are: (i) MP particles resulting from a fragmentation of plastic products, e.g. mulch films, silage bags [ 15 , 20 , 21 ], used in ‘plasticulture’, and (ii) organic fertilisation with MP contaminated materials, e.g. compost and sewage sludge [ 22 – 24 ]. While there are good reasons for the use of the different materials, and it is argued that using plastic products is essential in modern agriculture to achieve the United Nations Sustainable Development Goals (SDGs) [ 25 ], this agricultural management leads to a steady accumulation of MP in agricultural soils [ 15 , 26 ]. MPs might affect soil physical (e.g., aggregation and soil water fluxes) [ 27 , 28 ], chemical and biological soil properties (e.g., bacterial communities, soil fauna, and rhizosphere) [ 29 – 31 ], which stands in contrast to the SDGs. Beyond plastic type and concentration, morphological parameters (size, volume, surface area, and shape) are key factors in ecotoxicological risk to soil invertebrates [ 32 ]. Given this background, considerable efforts have been made in recent years to extract, quantify, and characterize MP particles, fragments, and fibres in soils. To date, there is no method that can fully capture all MP types, sizes, and shapes, along with their diverse characteristics, and is therefore suitable for investigating the various effects of MP on the soil system. It is therefore necessary to use the methods to be employed in accordance with the question being asked. The first and most fundamental distinction is between pyrolysis [ 33 ] and thermogravimetric methods [ 10 , 34 ], and microscopic imaging [ 35 , 36 ] and spectroscopic methods [ 37 – 39 ], which determine either the MP mass or the MP size, shape, and colour, whereby pyrolysis, thermogravimetric, and spectroscopic methods determine the plastic type. More or less all methods require a sample preparation to extract the MP from the soil mineral and organic matrix [ 40 ]. For MP extraction from the mineral soil fraction, most studies used salt solutions (e.g., zinc chloride) with a density of 1.4 ± 0.2 g cm -3 for a density separation, whereas soil organic matter (SOM) is removed via oxidation (e.g., Hydrogen Peroxide or Fenton reaction) [ 40 ]. However, sample purification steps may affect the MP size and surface, or lead to further fragmentation [ 38 , 41 , 42 ]. It is also important to note that most heavier salt solutions used for density separation and SOM oxidation require the use of laboratory gloves and prohibit the use of a laminar flow box, which introduces a high potential for sample contamination or false positive detections [ 43 ]. The need for approaches that minimize the use of hazardous substances to avoid sample manipulation is highlighted by these constraints from purification in the context of designing realistic ecotoxicological studies. After the purification steps, there are still substantial challenges in MP detection, particularly with microscopic methods that focus on particle morphology. For example, fluorescence microscopy, µ-Raman, and µ-Fourier transform infrared (FTIR) spectroscopy have failed to measure black or dark-coloured MP [ 35 , 37 , 44 , 45 ]. µ-FTIR analysis can interfere with MP shape and thickness [ 37 ], and MP additives (fillers, pigments, and dyes) influence µ-Raman analysis [ 45 ]. Also, transparent MP is challenging for µ-Raman [ 45 ], and stereomicroscopy transparent MP can easily be overlooked [ 38 , 46 ]. However, plastic mulching on agricultural soil is mainly black, and greenhouse tunnels are typically covered with transparent plastic films; therefore, the limitation on colour may result in underrepresentation of dark and transparent MP from plasticulture [ 47 ]. Pyr-GC-MS and TED-GC-MS are independent of their limitation of MP colour but limited to concentration and analytical sample mass [ 33 , 34 , 40 ] and miss some of the important parameters in their analysis, like size, shape, or surface morphology, to evaluate the risk assessment of MP contaminated soil. Another obstacle to the reliable determination of MP and its variability in agricultural soil systems is the long processing time required per sample [ 37 ]. Measuring, data processing, and analysis using spectroscopic methods, which are becoming increasingly common, require substantial time and expert knowledge to ensure accuracy [ 48 ]. For example, µ-FTIR data analysis alone takes between 4 and 48 hours [ 49 , 50 ]. A promising approach to overcoming the limitations and challenges in data analysis is the implementation of machine learning techniques (Coleman, 2025), which enable a substantial reduction in the analysis time of spectroscopic data [ 49 , 51 ]. Further, microscopy combined with machine learning used 2D morphological features to detect black MP from tyre wear in soil samples [ 9 ]. However, for reproducible applications in the future, more training data and the availability of the programming code were needed [ 48 ]. To quantify MP surfaces in contaminated complex sample materials, first approaches to measure the surface area and surface roughness in volume-pore-ratio were applied with micro-X-ray computed tomography in combination with Scanning Electron Microscopy (SEM) in the context of analysing plastic fruit sticker degradation in compost [ 23 ], or SEM image-based description was used to analyse the degraded surface of MPs [ 5 , 52 , 53 ]. Analytical approaches to quantify surfaces of MPs in microstructures, as well as 3D high-resolution shape registration in contaminated soils with robustness on SOM and black MP particles, were still missing but were highly needed to evaluate and assess ecotoxicology risk, as well as for transport processes by adhesion and deposition. The general aim of this study is to introduce a new methodology for MP extraction, detection, and analysis in agricultural soils, which will complement and overcome some limitations of the existing set of approaches. Specifically, we aim to develop a method that: (i) enables robust and somewhat faster detection of MPs in lager sample sets, (ii) focus on small transparent and black MP particles (11–250 µm) and fibres (~ 1000 µm length) detection; (iii) quantifies and analyses the MP size, 3D shape, volume and surface in microstructures, and (iv) avoids any treatment with hazardous substances potentially affecting the properties of the analysed MP and to overcome the use of laboratory gloves as well as to ensure the application of a laminar flow-box in sample preparation. 2 Materials and methods 2.1 Cross-contamination prevention The MP extraction was carried out in a laminar flow box (FBS 93-SuSi, Spectec, Germany), and in general, a 100% cotton lab coat was worn. Whenever samples were moved within the laboratory, they were always covered with a stainless-steel lid or kept in a glass Petri dish and only uncovered during the 3D Laser Scanning Confocal Microscopy (3D LSM; VX-K 1000, Keyence, Japan). As this study avoided the use of hazardous substances, neither a fume hood nor laboratory gloves were required. All samples were handled with tweezers or a spatula. The equipment was thoroughly cleaned between uses, following an internal standardised procedure that included rinsing with ultrapure water, washing with 96% ethanol, and heating to 140 °C. 2.2 Test Materials Three different agricultural topsoils, representing different regions in Germany with varying soil management and textures, were used in the study (Table 1). Soil number 1 (S1) was sampled in Dudenhofen (Western Germany) from a depth of 0-20 cm, while the second (S2) and third soil (S3) were sampled in Strass and Freising (both Southern Germany) from a depth of 0-5 cm. S2 and S3 originated from intensive, long-term agriculture, while S1 was under agricultural use for several decades but has been left fallow for at least the last four years. The soils were air dried, sieved to < 2 mm, and divided into 25 g samples with an automated sample divider (PT 100, Retsch, Germany). In the case of S1, a substantially larger amount of particulate organic matter (POM) was found, potentially resulting from the fallow period preceding sampling, while the soil organic carbon (SOC) content and pH value were significantly lower compared to those of S2 and S3 (Table 1). Less POM was visible in S2 and S3, where S3 had the highest SOC and clay content (Table 1). All soil samples were spiked with a set of different MP particles and fibres (Table 2): (i) Commercially available transparent LDPE particles (LDPE-T; LDPE 140 and LDPEXF 1040, Fixatti AG, Germany); (ii) transparent/white PP fibres (PP-fiber; F PP 264, Schwarzwälder Textil-Werke, Germany); (iii) recycled Table 1: Properties of the three test soil textures sampled from topsoil with the soil organic carbon (SOC), calcic carbonate (CaCO 3 ), nitrogen (N), and pH in 0.01 mol calcic chloride CaCl 2 soil elute. Properties Soil 1 (S1) Soil 2 (S2) Soil 3 (S3) Site Dudenhofen (West Germany) Strass (Southern Germany) Freising (Southern Germany) Depth [cm] 0-20 0-5 0-5 Sand [%] 86.6 ± 1 72 ± 0 16 ± 0 Silt [%] 9.7 ± 1 17.5 ± 0.7 60 ± 1.4 Clay [%] 3.7 ± 1 10.5 ± 0.7 24 ± 1.4 SOC [%] 0.58 ± 0.05 1.01 ± 0.06 1.27 ± 0.11 CaCO 3 [%] < 1 < 1 0.14 ± 0.02 N [%] 0.06 ± 0.02 0.12 ± 0.01 0.16 ± 0.01 pH CaCl2 [%] 4.6 ± 0.1 6.9 ± 0.14 7.1 ± 0.28 Table 2: Properties of the microplastic (MP) materials with plastic type light density polyethylene (LDPE) and polypropylene (PP), in the size class, total mass, the mass based concentration (w/w) as C Mass in part per millions (ppm), the MP particle amount per 25 g soil (MP particles /25 g soil) in median with percentiles given in brackets ([percentile 25, percentile 75]). PP-T and LDPE-T are transparent PP and LDPE particles, while LDPE-B particles are black. Size class [µm] mass [mg] CMass [ppm] Plastic type MPparticles/25 g soil 1000 0.1 4 PP-fibre 11 [19, 7] 100-250 0.05 2 PP-T LDPE-T LDPE-B 25 [40, 15] 27 [48, 14] 22 [38, 11] 53-100 0.02 0.8 PP-T LDPE-T LDPE-B 69 [127, 42] 72 [154, 35] 53 [97, 34] <53 0.1 0.4 PP-T LDPE-T LDPE-B 304 [575, 150] 412 [1071, 136] 98 [265, 57] predominant transparent PP (PP-T) from the ocean, manufactured to MP; and (iv) commercially available black LDPE (LDPE-B) agricultural film cryomilled to MP. Dry sieving was used to separate all MP particles into three size classes: < 53 µm, 53-100 µm, and 100-250 µm. The PP-T particles were irregularly shaped fragments, and the LDPE-T and LDPE-B particles were irregularly shaped film fragments, which could also appear in a fibrous shape. The MP spiking concentration was quantified in mass using a microbalance (XP6 Micro Balance, Mettler Toledo, Switzerland). Additionally, the pure MP was measured using 3D LSM to quantify its size distribution, volume, size, and surface morphology. With the volume of a single MP and the corresponding density, the mass of a single MP was calculated to result in the particle amount by the ratio of the total MP spiking mass to a single MP mass. Median, 25 th percentile, and 75 th percentile were calculated for the number of particles (Table 2) in each sample with respect to the unequally distributed MP volume. For spiking, the MP was rinsed with ultrapure water (Elga-Veolia Purelab Flex 2, Germany) and then added to a 25 g soil sample in a 500 ml stainless-steel centrifugation tube (Tube 13507, Sigma Laborzentrifugen GmbH, Germany). Thirty different soil-MP mixtures were analysed (Figure 1A), representing ten MP types and three soils. Each soil-MP mixture consisted of 25 g of sample material and was measured in triplicate, resulting in 90 test samples. Moreover, three soil blanks and 10 MP blanks were added, resulting in a total of 103 test samples. MP blanks contained only MP and ultra-pure water, while the soil blanks contained only soil. The MP blanks were used to monitor the MP background noise and correct the initially detected MP. 2.3 Microplastic extraction A three-step procedure was applied to extract the MP from the spiked soil samples. This procedure has been optimised for the parallel processing of four 25 g subsamples (100 g total sample amount), each representing one soil-MP mixture (Figure 1A): (i) Dispersion: For physical soil aggregate dispersion, 200 ml ultra-pure water was added to each sample and treated by twice with 5 min shaking (shaking plate 3015, GFL Gesellschaft Für Labortechnik mbH, Germany) and 5 min ultrasonic at 480 Watt, resulting in an energy density of 720 J ml -1 (SONOREX RK 102H, 35 kHz, 480 Watt, Bandelin, Germany). (ii) Density separation: For density separation at ρ = 1 g cm -3 , another 200 ml of ultrapure water was added and centrifuged (6-16S, Sigma Laborzentrifugen GmbH, Germany) at 2800 rpm for 30 minutes. (iii) Freezing and Filtration: For freezing and filtration, 50 ml of ultrapure water was carefully added, an aluminium foil cylinder was placed on the top of the centrifugation tube (to guide the ice during freezing), and the sample was then frozen at -18 °C for a minimum of 8 hours. The ice cap containing the low-density fraction (ρ < 1 g cm⁻³) was removed by rinsing the edge between the ice and the centrifuge tube with 96% ethanol (ethanol denatured with 1% methyl ethyl ketone) and finally splitting it with a spatula. To apply the sample to a 2.5 cm diameter, white, phosphate-free, cellulose filter with a pore size of 4 µm (55 cm², 169 G, Macherey-Nagel, Germany), the ice cap was melted in a glass funnel and passed a filtration vessel (30 ml glass filter funnel head with blue PP funnel and two Flour-Caoutchouc seals, EAN: 4032051032088, DURAN, Schott AG, Germany). Once dried, the cellulose filter was carefully removed with a spatula and placed into a glass petri dish for subsequent analysis using the 3D LSM. 2.4 3D Laser Scanning Confocal Microscopy measurement The sample measurement was performed with a 3D LSM, which combines a 404 nm semiconductor laser with a CMOS colour camera embedded in a depth-in-focus optical scanning system (Figure 1B). Scanning the entire sample filter (ø 2.5 cm) while keeping a reasonable measurement time of 6 to 8 hours, a medium magnification of 240x was used in the high-speed scanning mode and a height pitch of 4 µm (height resolution = 4 µm), with a coaxial light brightness and the laser brightness set to 2.5 and 9027, respectively. This setup enabled scanning one sample filter in four parts, achieving a pixel size of 2.72 µm x 2.72 µm. Finally, four sample filters (ø 2.5 cm), representing the extracted material from one 100 g soil-MP mixture, were automatically measured in a row in 24 to 32 hours (Figure 1B). The resulting data from each scan (~281 MB) was automatically saved in the proprietary VK4 file format from Keyence. Using the open-source tool vk4-python-driver (free available via GitHub, (Gunn & Torkian, 2018)) height information, RGB values, and laser refection with resolution of ~5000 x ~5000 pixels or respectively data points, were extracted and transformed into a height map (stored as CSV file, ~200 MB), a RGB image (stored as TIFF file, ~70 MB), and a laser refection image (stored as TIFF file, ~40 MB) (Figure 1B). 2.5 3D Laser Scanning Confocal Microscopy data analysis A three-step Random Forest classifier workflow was implemented (Figure 1C) to automatically distinguish MP particles from soil and background structures. The workflow progressively refines the classification results, from pixel-level segmentation (Pixel Classifier) to object-based segmentation (Object Classifier) and binary segmentation (Binary Object Classifier) between MP and soil particles. All Random Forest Classifiers were trained and tested using identical hyperparameters ( n_estimators = 10, random_state = 42, class_weight = "balanced_subsample"). The Pixel and Object Classifiers were operated within a One-vs-One framework to respect a multiclass classification. This three-step Random Forest classifier workflow enhances classification robustness by combining semantic (pixel-based) information with object-level morphological and topographic (surface morphology) features derived from 3D LSM height data. A subsequent principal component analysis (PCA) was applied to correct for particle-assigned background noise (Figure1D). (i) Pixel-level classification (Pixel Classifier): Raw 3D LSM data were denoised using a median filter applied to the laser reflection and RGB images. In addition, gradient, gradient magnitude, and sine of the gradient were derived from the 2D height map. Five of the 103 test samples were partially annotated and used for classifier training and testing with a random split ratio of 70:30, achieving a prediction accuracy of 98.2%. This step produced a semantic, pixel-based classification. The pixel-level classes “Black MP” and “Transparent MP” were subsequently converted into instance-level objects (Figure 1C). Morphological operations, including line closing, hole filling, and removal of small objects (< 4 connected pixels), were applied. Object-based features were extracted from the laser reflection, RGB images, and the 2D height map, providing surface descriptors such as Mean Peak-to-Valley Height (Rz), Arithmetic Average Surface Roughness (Sa), Root Mean Square Height (Sq), fractal dimension via Box Counting Method, Specific Surface Area (SSA), Surface Area Ratio (SAR), and 3D Shape Index (Table 3). (ii) Object-level classification (Object Classifier): The extracted object-based features were used to train a second Random Forest Classifier. Twelve of the 103 test samples (including the five used in the previous step) were partially annotated and used for classifier training and testing with a random split ratio of 60:40, achieving a prediction accuracy of 94.8%. This step provided an instance-level classification of individual particles. (iii) Binary classification (Binary Object Classifier): In the final step, only selected surface descriptors (Rz, Sa, FD, and SAR; Table 3) were used to train a binary Random Forest classifier (class 1 = “MP”, class 2 = “Soil”). Five samples out of the annotated dataset from the Object Classifier were used for the classifier training and testing, with a random split ratio of 60:40, and a prediction accuracy of 80.7% was achieved. Table 3: Surface quantification with N = measurement points, z = height value, N B (ϵ) = number of boxes with ϵ the box size, k = principal curvatures of the height. (iv) Principal component analysis background noise correction: MP blanks were analysed following the same three-step Random Forest classifier workflow (Figure 1C), with adapted training data and feature selection, to monitor MP background noise and correct (Figure 1D) the initially detected MP. MP blanks made out of transparent MPs were used to analyse black MP background noise signals, and MP blanks made out of black MPs were used to analyse transparent MP background noise signals. For the PCA, the features Area-filled, Rz, Sa, and fractal dimension (Table 3) were used and compared with the principal components of MP background noise from MP blanks with initially detected MP from the three-step Random Forest classifier workflow. A 90 th -percentile threshold was applied to assign MP as background noise or as true MP detection. Finally, only detected MPs with a minimum length > 10.88 µm (4 pixels) were considered to ensure reliable detection. Consequently, the smallest MP size class < 53 µm (Table 2) is reported as 11-53 µm in the results. Based on their spatial coordinates, the final MP objects were reassigned to their corresponding surface and size analyses. 2.6 Method evaluation For method evaluation and quality control, 103 test samples were analysed (2.2 Test materials, Table 1 and 2), consisting of 30 soil-MP combinations (Figure 1A) in triplicate (90 spiked soil samples), along with ten MP blanks and three soil blanks (13 blanks). The blanks were made of one per variation and used to control MP extraction without soil, to control background MP contamination in the agricultural topsoil samples S1, S2, and S3, and to track MP background contamination from the laboratory. The developed method is evaluated based on the recovery rate (RR) between spiking particle amount (MP spiking ) and final detected MP particle amount (MP detected ). An additional evaluation step was performed by quantifying the similarity between the initially spiked MP and the finally detected MP using a PCA-based silhouette coefficient, compensating for the lack of chemical MP (plastic type) identification. Besides, to evaluate the effect of the extraction procedure on the MPs, the fractal dimension of the MP was compared before spiking and after sample processing. (i): Recovery Rate: The RR was determined based on the particle amount ratio of MP detected in the spiked soil samples or blanks to initial MP spiking : With MP spiking_P75 for the spiked MP particle amount of the 75th percentile and with MP spiking_P25 for the spiked MP particle amount of the 25th percentile. For spiked PP-fibres, PP-T and LDPE-T samples, only transparent MP were considered, while for LDPE-B samples, only black MP were considered. Based on the triplicate the arithmetic mean recovery rate with the standard deviation was calculated. (ii) Similarity coefficient: For evaluating the MP detected in comparison to MP spiking on similarity, a PCA was used with the features: Area-filled, Rz, Sa, and fractal dimension (Table 3). The initially MP spiking were separated from the background (cover slip) using a One-vs-One Random Forest Pixel classifier with an accuracy of 98.9%. Based on the Euclidean distance in PCA space, the silhouette coefficient [55] was calculated to evaluate similarity or clustering. The silhouette coefficient ranges from -1 (incorrect clustering) to +1 (well-separated clusters). For similarity, the silhouette coefficient is defined as follows: 0-0.25 strong similarity (no cluster), 0.25-0.55 reasonable similarity, 0.55-0.75 weak similarity, and 0.75-1 no similarity (cluster) (adapted after Magdziak, 2023). (iii) Fractal dimension comparing: For comparing the MP before spiking and after sample processing, the statistical probability density of the dimensionless parameter, fractal dimension, was used. The probability density was estimated by the Gaussian kernel density estimator (Silverman’s rule, bw_adjust = 1.0, 512 evaluation points). To evaluate the effects of the extraction procedure (Figure 1A) in dependence on the sample matrix, a distinction was made between MP detected in ultra-pure water (MP blank), S1, S2, and S3. For a complete comparison detected black soil, considered to be SOM, and transparent soil, considered to be the mineral fraction, were analysed. 2.6 Data processing and statistical analyses Raw 3D LSM data in VK4 format were first converted into 2D height maps using a Bash script executed in Windows PowerShell. Image annotation was performed in Fiji [57] using the open-source plugin LABKIT [58]. All subsequent data processing and analysis were performed using Python v3.12, compiled with the Spyder IDE 5.5.1 (Spyder, 2024) within Anaconda Navigator v2.6.5 [59]. The Python library scikit-learn v1.5.2 [60] was used for machine learning-based data analysis, PCA, and calculating the silhouette score. For image processing, the Python libraries scikit-image [61], SciPy v1.13.1 [62], and the computer vision library OpenCV v4.10.0 were used. The function measure.regionprops from scikit-image was applied for object-based size analysis and feature extraction, while the function Delaunay from SciPy was used for volume calculations. Mathematical operations on object-based height values were performed using NumPy v1.26.4 [63] to quantify surface parameters as defined in Table 3 and to compute statistical measures including mean, standard deviation, median, percentiles and Gaussian kernel density estimator. Data organisation was managed using pandas v2.2.2 [64]. 3 Results and Discussion 3.1 Detection and recovery Within three working days, four 25 g samples (< 2 mm soil; total 100 g) were processed in parallel using the developed workflow, which integrates MP extraction, automated 3D LSM measurement, and machine-learning-based data analysis (Figure 2A-D). This setup enables efficient sample throughput and provides quantitative detection and characterisation of MPs in terms of their amount, size, 3D shapes, and surface morphology (Table 3). Surface morphology represents a central feature of the workflow. It enhances MP detectability by providing additional 3D structural information that distinguishes MPs from SOM and mineral particles. This approach enables the detection of transparent and black MPs in the size range of 11-250 µm as well as fibres (~1000 µm in length), without the need for chemical purification steps or hazardous substances. Transparent MPs appear as dark objects in laser intensity image, which increases their contrast and facilitates differentiation from the background and mineral fraction (Figure 1B). The three-step Random Forest classification applies object-based classification independent of particle size, thereby avoiding artefacts introduced by size-dependent thresholds and ensuring consistent detection across the full MP size spectrum (Figure 1C). In addition to morphological descriptors, the method quantifies ecotoxicologically relevant surface parameters, including surface area, specific surface, and peak-to-valley ratios (Rz), as well as the fractal dimension, which serves as a measure of surface complexity (Table 3). To ensure reliable MP quantification, the following sections compare method performance between soils with low (S2, S3) and high (S1) POM content, followed by an evaluation of RRs and similarity coefficients with respect to MP size, shape (fibres and fragments), colour (transparent and black), and degree of weathering (PP-T). (i) Microplastics particles 100-250 µm and fibres 1000 µm: S2 and S3 achieved reliable quantification with recovery rates of 100% (Equation 2) for PP-T, LDPE-T, and LDPE-B (Figure 2B), as well as for PP-fibres in S3 (Figure 2A). PP-fibres showed a slight overestimation in S2, with recoveries of 106% ± 6.6%. The silhouette coefficient indicated strong to reasonable similarity, ranging from 0.1 for LDPE-T in S2 to 0.3 for LDPE-T in S3, and for PP-fibres and PP-T in S2. In contrast, S1 achieved a 100% recovery only for PP-T, while LDPE-T was slightly overestimated (109% ± 12.8%). Substantial overestimations were observed for PP-fibres (164.9% ± 56.1%) and LDPE-B (396.8% ± 10.3%). The silhouette coefficients in S1 indicated weak similarity, with values of 0.6 for PP-fibres, 0.4 for LDPE-T and PP-T, and 0.3 for LDPE-B. The low silhouette coefficient for LDPE-B likely reflects the false positive assignment of POM resembling MPs, a limitation also observed in the machine learning-based analysis. Transparent MPs were less affected than PP fibres due to the inclusion of colour features in the pixel classifier. The soil blanks (Figure 3) illustrate the occurrence of detected MP particles in the test soils. In particular, S1 showed a high number of black detected MPs, most of which were identified as POM and therefore represent false-positive black MP detection. S2 and S3 were less pronounced due a high POM confusion. Therefore, for S1, an automated and reliable quantification could not be achieved for LDPE-B and PP-fibres. However, the detection results can still be manually corrected in future applications. S1 also showed the highest MP background contamination for transparent MPs compared to S2 and S3 due to its packaging in a white/transparent PP sample bag (Figure 3). The soil background contamination could explain the high silhouette scores for PP-T and LDPE-T, as well as the slight overestimation of LDPE-T in S1. Nevertheless, true positive transparent MPs background contamination, demonstrating that the three-step Random Forest classifier workflow can identify MPs outside the test material set. The MP blanks showed 100% recovery only for LDPE-T, while PP-T, LDPE-B, and PP-fibre were overestimated at 141.5%, 154.3% and 211%, respectively. These results are consistent with the silhouette coefficients of 0.1 for LDPE-T, 0.3 for PP-T and LDPE-B, and 0.4 for PP-fibres. The overestimations and high silhouette coefficients for PP-fibre, PP-T, and LDPE-B likely resulted from detecting multiple fragments as individual MPs instead of a single MP, particularly for the fibres and fibrous parts of MP particles. Excluding S1 enabled reliable quantification of transparent, black, and weathered MPs, achieving a mean RR of 104% ± 14.5%, for the size class 100-250 µm. (ii) Microplastics particles 53-100 µm: S1 achieved RR of 97.6% ± 3.4% for PP-T, 93% ± 7.7% for LDPE-T, and 130.2% ± 27.4% for LDPE-B (Figure 2C). The corresponding silhouette coefficients were 0.2 for LDPE-T and 0.3 for both PP-T and LDPE-B. However, as in the previous case, the recovery for LDPE-B in S1 was affected by false positive detections (Figure 3). For S2 and S3, the recovery rates decreased. LDPE-T showed the highest recoveries within these samples, with 74.6% ± 23.8% in S2 and 61.5% ± 10.8% in S3. LDPE-B achieved recoveries of 64.2% ± 7.8% in S2 and 41.2% ± 6.4% in S3. PP-T presented the lowest recovery rates, with 37.7% ± 5.1% in S2 and 34.3% ± 16.1% in S3. Silhouette coefficients in S2 and S3 were 0.3 for PP-T, LDPE-T, and for LDPE-B in S3, with the highest value observed for LDPE-B in S2. The MP blanks showed 100% recovery for all materials (PP-T, LDPE-T, and LDPE-B). The lower recovery rates observed in S2 and S3 are likely related to the higher loam and clay content of these topsoils (Table 1). An overlaying effect can be excluded, as the higher recoveries in S1, which contained more POM, did not lead to a similar decrease. PP-T with a weathered surface morphology may have adhered more strongly to soil particles, making surface morphology-based detection more challenging. Therefore, more training data is needed for MPs below 100 µm to ensure reliable detection of weathered surface morphologies in combination with soil particle attachment. By excluding S1, this method enabled the detection of transparent and black MPs, achieving a mean RR of 59.1% ± 25.7% for the size class 53-100 µm. (iii) Microplastics 11-53 µm: All MPs, except LDPE-B in S1, showed strongly decreasing RR (Figure 2D). However, the LDPE-B results in S1 were again influenced by false positive detections. The highest recovery was achieved for the LDPE-B blank, with a RR of 26.2% and a silhouette coefficient of 0.3. By excluding S1, this method enabled a detection of transparent and black MPs, achieving a mean RR of 14% ± 12.4% for the size class 11-53 µm. Particularly, the smallest size class (11-53 µm) is affected by a false background noise assignment, which was well observed in the LDPE-B blank. In addition, the low recoveries could also be due to the procedure in the MP extracting workflow (e.g. ultrasonication), soil particle attachment, and soil particle covering. Detection of MPs 11-53 µm was possible; however, for reliable quantification, a more robust background noise detection is required. By excluding S1, the overall mean RR across LDPE-T, LDPE-B, PP-T (11-250 µm), and PP fibres in the MP blanks, S2, and S3 was 65.25% ± 45% using the method developed here. The MP blanks alone showed a mean RR of 96.6% ± 63.5%, while the combined mean RR for S2 and S3 was 60% ± 39.5%. Specifically, S2 achieved a mean RR of 63.2% ± 39.5%, and S3 a mean RR of 56.9% ± 40%. When S1 and, in addition, the MP size fraction 11-53 µm were excluded, the overall mean RR for transparent and black MP in the size range 53-250 µm across MP blanks, S2, and S3 increased to 87.2% ± 34.7%. In this case, the MP blanks reached a mean RR of 129.6% ± 42.6%, and the combined mean RR for S2 and S3 was 80% ± 28%. Here, S2 achieved a mean RR of 83.6% ± 26.5%, while S3 reached 76.7% ± 29.7%. For all transparent MP, a mean RR of 59.8% ± 42.7% was obtained for the size range 11-250 µm, increasing to 81% ± 28.9% for the size range 53-250 µm. Similarly, black MP showed a mean RR of 60.5% ± 31.9% in the size range 11-250 µm and 76.3% ± 26.6% in the size range 53-250 µm. A review found that only 35% of the studies performed recovery tests based on spiking experiments [40]. With the various MP testing materials, it is challenging to compare RRs across different studies, particularly for MP < 250 µm and light-density black plastic. To compare this study with the current state, we selected studies that focused on comparable MP size, MP colour, MP material, and soil type. Fluorescence microscopy was tested on RR and achieved for white LDPE 20-150 µm in a sandy soil which was comparable to S2 a mean RR of 82% ± 15% and a mean RR of 88% ± 7% in a loamy soil which was comparable to S3, whereas for black MP PBAT/PLA 100-250 µm the authors achieved only a mean RR of 17% ± 7% in the sandy soil and 45 ± 20% in the loamy soil [35]. For focal plane array (FPA)-µ-FTIR a mean RR of 57% ± 13% for transparent LDPE 10-150 µm in a sandy soil and 29% ± 0% in a loamy soil was achieved and for black PBAT/PLA 10-250 µm a RR of 50% ± 2% was achieved in loamy soil and 49% ± 16% in sandy soil [38]. A study for detecting black tyre wear particles > 35 µm with stereomicroscopy in combination with machine learning achieved a mean RR of 85.4% ± 9.5% in plastic-free spiked soil samples [9]. A RR > 80% was achieved by using stereomicroscopy in combination with heat and was tested on white LDPE < 150 µm and PP < 400 µm [36]. For black MPs, the 3D LSM method, in combination with machine learning, achieved higher mean RRs compared to Fluorescence microscopy and FPA-µ-FTIR. Whereby the stereomicroscopy in combination with machine learning achieved higher RRs for black tyre wear MP than the developed method in this study. However, the applied extraction procedure in this study was suitable for extracting light-density MP and would not be able to extract PBAT/PLA or tyre wear MP. For LDPE-T, the FPA-µ-FTIR had comparable RRs to the tested transparent MP in this study. The Fluorescence microscopy had comparable RRs to our transparent MP in the size range of 53-250 µm [35]. Further, the spiking concentration of 3 mg MP per 10 g soil (30 mg kg −1 ) [35, 38] and 0.017 mg per 5 g soil (17 mg kg -1 ) [9] was higher than the spiking concentration in this study (Table 2). We focused on choosing spiking concentration as realistic as possible based on the amount of the common global MP concentration amounts are up to 13000 items kg −1 (325 items per 25 g) of dry soil and 4.5 mg kg −1 (0.11 mg per 25 g) of dry soil [65], whereas a review study from 2024 estimated a global mean of 2900 ± 7600 MP items kg −1 (73 ± 190 items per 25 g), with maximum concentration in arable soils with sludge amendments 3700 ± 8800 MP items kg −1 (93 ± 220 items per 25 g) [66]. In this study, the RR was calculated with a statistical range (Equation 2). Due to the MP overlaying under the 3D LSM, it was impossible to count particles by particle, especially for the size classes 53-100 and < 53 µm. The final detected volume could only be used for MP spiking mass-particle number conversion, where the MP was laid on a flat background (cover slip). Whereby the detected MP on the blanks and in the test soils, the background was wavy (due to the cellulose filter (Figure 1B, 3D profile) and resulted in a non-comparable volume. Therefore, mass and volume-based particle number estimation was applied to the MP spiking with quantified volumes. Other studies also had problems to count the MPs and applied for example an empirical model based on the assumption of spherical particles to get the relationship between the weight added MP and detected MP in particle amount [36], or estimated the volume for particle number-mass conversion in dependency on different shapes [67, 68] or used in addition to count particles under the microscope a particle counter to analyse particle size distribution to apply finally a mass-particle number conversion [35]. In addition to RR values, the analysis of a reliable size limit detection is an important factor for method application. In this study, we developed a method that enables the reliable detection and, consequently, quantification of MPs down to a size of 53 µm. MPs down to a size of 11 µm were detectable without reliability, due to their low recoveries (Figure 2D). The reliable quantification limit of 53 µm results from the PCA background noise correction, which partly misclassified the spiking material MP 11-53 µm as background noise. This behaviour could be a result of a substantial similarity between background noise signals in the MP blanks and the spiking MP of 11-53 µm in the PCA features of Area-filled, Rz, Sa, and fractal dimension (Table 3) or the background noise signal resulted from detached MP fragments from the MP spiking material during the MP extraction procedure (Figure 1A). However, the main advantage of the PCA background noise correction was to produce a particle-assigned correction that results in a final particle size distribution, shape, and surface morphology of the true detected MPs. Other studies have reported the reliable detection of MP down to a size limit of 100 µm using µ-FTIR [68], while possible detection limits down to 10 µm have been described [69]. Using microscopy-based approaches, reliable detection has been achieved down to 63 µm with video microscopy [22], 49 µm [70], 30 µm [71], and 35 µm [9] with Stereomicroscopy, and 20 µm with Fluorescence microscopy [35]. Corresponding possible detection limits were reported as low as 20 µm [36] and 15 µm for stereomicroscopy [9], and 3.5 µm for Fluorescence microscopy [35]. With smaller MP particle sizes, automated MP detection and particle counting are becoming important and are already available for spectroscopy [49, 72]. For digital microscopy, automated image processing, especially for detecting and counting MPs < 250 µm, was already used with machine learning based Weka segmentation [9] or threshold-based detection in combination with fluorescing particles [35]. Automated and manual counting become complicated when particles adhere to each other and form agglomerates. In digital image processing, a watershed segmentation is applied to split agglomerated particles [9, 35, 72], which can lead to a size-dependent particle splitting and may become an issue for irregular particle shapes [35]. With the three-step Random Forest classifier developed and applied in this study, a watershed application was not necessary. We focused on size- and shape-independent MP detection, where the use of a supervised machine learning algorithm depended on the labelled data used for the training process. The method evaluation showed that especially soil with high POM amount requires more labelled data to detect reliable black MP and MP-fibres. However, a high POM amount could also lead to overlaying MP [73]; therefore, a POM reduction via oxidation or enzymatic digestion could be an option to overcome this limitation. Size-dependent detection was only used for the assignment of the PCA background noise correction, where the size dependency was derived from the background noise signals, not from our spiking MP test material. A size component was also implemented to calculate the silhouette coefficient, providing an additional value for method evaluation beyond the RR. 3.2 Microplastic extraction procedure Beyond its role in detection, the quantified surface morphology enables a detailed evaluation of potential alterations caused by the extraction procedure. Fractal dimension serves as a dimensionless parameter to assess possible effects of ultrasonic treatment or freezing treatment on the MP surface. As the fractal dimension increases, the surface becomes more complex. Here, the MP particles in the size of 100-250 µm were analysed, before spiking and after the MP extraction and detection procedure in the MP blanks, S1, S2, and S3 (Figure 4A-C), as well as the detected soil from S1, S2, and S3 (Figure 4D). For all scenarios, an increasing fractal dimension was observed after the MP extraction and detection procedure, regardless of whether it was performed on MP blanks, S1, S2, or S3. The lowest deviation in fractal dimension distribution in comparison to MP spiking was detected for LDPE-T in the MP blank (Figure 4A), and the largest increase was detected for PP-T in the MP blank (Figure 4C). LDPE-T detected in S1, S2, and S3 also showed partial overlap with MP spiking and MP blank, whereby the curve profile differed (Figure 4A). The distribution of LDPE-B in MP blanks, S1, S2, and S3, exhibited a clear shift from MP spiking, characterised by an increasing fractal dimension (Figure 4B). LDPE-B in S1 had two peaks in the fractal dimension distribution, whereby, by comparing the fractal dimension distribution with the three soils (Figure 4D), the second peak could be a result of the false positive MP detection. Comparing PP-T in MP blanks (Figure 4C), the distribution of the fractal dimension is more comparable to the soil (Figure 4D). PP-T and LDPE-B detected in the MP blank also showed an overestimation in the RR (Figure 2B) and a reasonable silhouette coefficient, which could result from detached MP fragments from the MP spiking material. The lower overestimation from PP-T and the higher fractal dimension led to the assumption that bigger MP fragments were detached, due to the initial material origin, which could have weathered surface structures. LDPE-B presented more fibrous shapes with thin extensions, which could detach a higher amount of small MP fragments and led to a higher overestimation in the RR. The increasing complexity in surface morphology could result from the dispersion step by ultrasonication with an energy density of 720 J ml⁻¹. Ultrasonic energy above 60 J ml⁻¹ is known to disturb POM [74]; however, its effects on MPs remain insufficiently quantified [44]. Other studies have also used ultrasonication for soil aggregate dispersion, with an energy density of 720 J ml⁻¹ [75], the same as in our study, and even higher, such as 21600 J ml⁻¹ [70] and 105600 J ml⁻¹ [76]. Other studies used ultrasonication in the spiking procedure, to generate a homogeneous suspension and prevent agglomeration of the MP [9, 35] or to clean extracted MP after SOM removal from detached soil particles [35]. Not in every study is it comprehensible which energy density was applied to the samples due to missing information [44]. Instead of ultrasonication to disperse soil aggregates, other studies have used freeze-drying for gentle but more time-intensive (24 hours) dispersion [69] or chemical oxidation to remove SOM [21, 35, 38]. However, a study detected lower RRs with chemical oxidation by Fenton’s reagent, especially for light and transparent MP [22]. Further, Fenton’s reagent is an exothermic reaction and could also lead to MP alteration [41]. For MP extraction by density separation, zinc chloride is widely used [22, 35, 68, 69], due to its reasonable extraction rate [40]; however, with the corrosive behaviour, zinc chloride could also lead to MP alteration [38, 40]. This study focused on light-density MP materials such as PP and LDPE, which are among the two most widely produced plastics worldwide, with LDPE films being particularly common in crop and livestock agriculture [2, 3]. Therefore, ultra-pure water for density separation was used, as in other studies focused on light-density MP detection [36, 70, 73]. Additionally, pretests revealed an issue with density separation using saturated sodium chloride and calcium chloride in the freezing step, which was crucial for the clean removal of the light fraction after centrifugation [77–79]. At a freezing temperature of -24.25 °C, the sodium chloride solution crystallises and consists of a mixture of hydrohalites and ice crystals [80], whereby the saturated calcic chloride solution did not freeze. 4 Conclusion With a combination of a hazardous-free MP extraction procedure, 3D LSM measurement, and a three-step Random Forest classifier workflow for detection and analysis of 100 g (soil < 2mm) samples in three days, a fully automated approach for MP quantification in soil was developed and evaluated in this study. A general RR of 80% ± 28% across S2 and S3 presents a reliable detection for transparent and black MP (53–250 µm) and MP fibres (~ 1000 µm). The method did show some potential for a detection down to 11 µm but as it is the RRs are not satisfying. The strength of the developed method is the reliable detection either for transparent or black MP, resulting a MP size, surface morphology as well as the 3D shape analyses on a microscale with a resolution of 2.72 µm x 2.72 µm x 4 µm (x, y, z), ensures an environmentally safe procedure by avoiding hazardous substances and a time intensive SOM removal is not necessary. Furthermore, the three-step Random Forest Classifier enables MP particle detection and counting without the need for watershed segmentation, resulting in size- and shape-independent detection, which can be further improved by adding more labelled training data. With the use of ultra-pure water for density separation, the method is limited to light-density MP materials. The use of ultrasonication at 720 J ml⁻¹ for soil aggregate dispersion led to a change in surface complexity of the MP testing materials; therefore, adaptations should be made in the soil dispersion step. The advantage of quantifying surface morphology in terms of dimensionless value, fractal dimension, to analyse surface complexity could be applied directly to evaluate the MP extraction procedure. Furthermore, the method evaluation across the three agricultural topsoils (S1, S2, and S3) presented a limitation for soils with a high POM amount, due to the false positive detection of black MP and MP fibres. To achieve the aim of quantifying the MP volume, an adaptation should be made to the filter material to result in a flat background instead of a wavy background during the detection procedure. Despite the limitations of the developed method, the automated and reliable detection of transparent and black MP in the size range of 53–250 µm, as well as MP fibres of ~ 1000 µm, without SOM removal from 100 g of soil < 2 mm within three days, including particle size distribution, shape, and surface morphology, represents a major advancement for assessing the ecotoxicological risk of MP in agricultural soils. Furthermore, the combination of 3D LSM and machine learning constitutes a powerful tool for MP analysis in agricultural soils to complement the existing set of approaches. Declarations Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding This work was supported by the Federal Ministry of Education and Research as part of the initiative Plastics in the Environment (grant no. 02WPL1447A-G). Authors’ contributions TS: Conceptualisation, methodology, machine learning, data processing, investigation, quality control, writing – original draft, visualisation; PF: Resources, writing review and editing, funding acquisition and supervision. Acknowledgements We would like to acknowledge the members of the Water and Soil Resource Research group at the University of Augsburg, especially Luisa Wanger and Simon Scherer. Additionally, gratitude is extended to Bernhard Leitner and Jakob Faustmann from the Fraunhofer IGCV for providing polypropylene microplastic particles. References UNEP UNEP. Turning off the tap: how the world can end plastic pollution and create a circular economy. 2023; https://wedocs.unep.org/20.500.11822/42277 . 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1","display":"","copyAsset":false,"role":"figure","size":562580,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow (A–D, left) and 3D Laser Scanning Confocal Microscope (3D LSM) output with data analysis (right) for transparent low-density polyethylene (100–250 µm) in soil 3. Transparent microplastics (MPs) appear black in the laser intensity image; height ranges from low (blue) to high (red). Pixel Classifier results are colour-coded (arrows: false positives); final MP detections are marked by white rectangles.\u003c/p\u003e","description":"","filename":"Figure1MethodFlowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-8373800/v1/b07abb23c570904d6a47a670.png"},{"id":100218365,"identity":"d24cae5a-406c-416c-92db-4806cb089476","added_by":"auto","created_at":"2026-01-14 09:04:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26531,"visible":true,"origin":"","legend":"\u003cp\u003eMean recovery rates inclusive standard deviation (error bars) for the three microplastic (MP) size classes (Figure 2 A-D), the three different soils (S1 to S3) and the MP blank. The recovery rate of 100% is indicated by a horizontal line. For the abbreviations of the different plastic particles and fibres, see Table 2.\u003c/p\u003e","description":"","filename":"Figure2Recovery.png","url":"https://assets-eu.researchsquare.com/files/rs-8373800/v1/f51ce43a7b0af9aa121d98e1.png"},{"id":100370414,"identity":"f9d68418-d67d-426a-957e-9937aca4eae8","added_by":"auto","created_at":"2026-01-16 08:05:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5930,"visible":true,"origin":"","legend":"\u003cp\u003eTransparent and black microplastic (MP-T, MP-B) detected in the soil blanks of S1, S2 and S3.\u003c/p\u003e","description":"","filename":"Figure3SoilBlanks.png","url":"https://assets-eu.researchsquare.com/files/rs-8373800/v1/2c72fd583eee8b6b429fa45f.png"},{"id":100218364,"identity":"e689b150-745f-4e28-9b79-84ef495e4b33","added_by":"auto","created_at":"2026-01-14 09:04:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33199,"visible":true,"origin":"","legend":"\u003cp\u003eProbability density distribution of the fractal dimension for the microplastic (MP) size 100-250 µm before spiking, detected in the MP blank and in S1-S2 (A-C), and the detected soil S1-S2 (D). The detected soil differs between transparent (T) and black (B) soil. For the abbreviations of the different plastic particles, see Table 2.\u003c/p\u003e","description":"","filename":"Figure4ProparbilityDensity.png","url":"https://assets-eu.researchsquare.com/files/rs-8373800/v1/2e4c38370c72b951059fb1dc.png"},{"id":100383573,"identity":"3397b22f-a48a-4013-b49d-5c08228179af","added_by":"auto","created_at":"2026-01-16 10:47:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1824586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8373800/v1/7a6d2bb2-e449-48ca-a562-2405befcdfd1.pdf"},{"id":100370393,"identity":"05d5f5d7-d62c-4194-84b5-6482d28710f6","added_by":"auto","created_at":"2026-01-16 08:05:39","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1927628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GraphicalAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-8373800/v1/7b7369b9df7c2e8d30dd8517.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThere is a global concern regarding the accumulation of plastic materials in aquatic and terrestrial ecosystems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2022, an estimated 267.7\u0026nbsp;million tonnes of plastic waste were generated worldwide, with polypropylene (PP, 18.9%) and low-density polyethylene (LDPE, 14.1%) being the most prevalent types [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Once released to the environment, e.g. via waste mismanagement, plastics can appear in a wide range of sizes and shapes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], continue to fragment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and thereby potentially releasing toxic additives such as plasticizers and brominated flame retardants[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProbably the most important terrestrial microplastic (MP, plastic 1-5000 \u0026micro;m [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]) sink are soils [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], where MP enter via a multitude of pathways ranging from MP-laden street runoff contaminated with tyre abrasion [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and paint fragments [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], to fragmented litter [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] as well as atmospheric deposition [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In case of arable soil systems, additional input pathways related to agricultural activities come into play, whereby the most important ones are: (i) MP particles resulting from a fragmentation of plastic products, e.g. mulch films, silage bags [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], used in \u0026lsquo;plasticulture\u0026rsquo;, and (ii) organic fertilisation with MP contaminated materials, e.g. compost and sewage sludge [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. While there are good reasons for the use of the different materials, and it is argued that using plastic products is essential in modern agriculture to achieve the United Nations Sustainable Development Goals (SDGs) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], this agricultural management leads to a steady accumulation of MP in agricultural soils [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMPs might affect soil physical (e.g., aggregation and soil water fluxes) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], chemical and biological soil properties (e.g., bacterial communities, soil fauna, and rhizosphere) [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which stands in contrast to the SDGs. Beyond plastic type and concentration, morphological parameters (size, volume, surface area, and shape) are key factors in ecotoxicological risk to soil invertebrates [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven this background, considerable efforts have been made in recent years to extract, quantify, and characterize MP particles, fragments, and fibres in soils. To date, there is no method that can fully capture all MP types, sizes, and shapes, along with their diverse characteristics, and is therefore suitable for investigating the various effects of MP on the soil system. It is therefore necessary to use the methods to be employed in accordance with the question being asked. The first and most fundamental distinction is between pyrolysis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and thermogravimetric methods [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and microscopic imaging [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and spectroscopic methods [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], which determine either the MP mass or the MP size, shape, and colour, whereby pyrolysis, thermogravimetric, and spectroscopic methods determine the plastic type. More or less all methods require a sample preparation to extract the MP from the soil mineral and organic matrix [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor MP extraction from the mineral soil fraction, most studies used salt solutions (e.g., zinc chloride) with a density of 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 g cm\u003csup\u003e-3\u003c/sup\u003e for a density separation, whereas soil organic matter (SOM) is removed via oxidation (e.g., Hydrogen Peroxide or Fenton reaction) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, sample purification steps may affect the MP size and surface, or lead to further fragmentation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. It is also important to note that most heavier salt solutions used for density separation and SOM oxidation require the use of laboratory gloves and prohibit the use of a laminar flow box, which introduces a high potential for sample contamination or false positive detections [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The need for approaches that minimize the use of hazardous substances to avoid sample manipulation is highlighted by these constraints from purification in the context of designing realistic ecotoxicological studies.\u003c/p\u003e \u003cp\u003eAfter the purification steps, there are still substantial challenges in MP detection, particularly with microscopic methods that focus on particle morphology. For example, fluorescence microscopy, \u0026micro;-Raman, and \u0026micro;-Fourier transform infrared (FTIR) spectroscopy have failed to measure black or dark-coloured MP [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. \u0026micro;-FTIR analysis can interfere with MP shape and thickness [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and MP additives (fillers, pigments, and dyes) influence \u0026micro;-Raman analysis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Also, transparent MP is challenging for \u0026micro;-Raman [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and stereomicroscopy transparent MP can easily be overlooked [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, plastic mulching on agricultural soil is mainly black, and greenhouse tunnels are typically covered with transparent plastic films; therefore, the limitation on colour may result in underrepresentation of dark and transparent MP from plasticulture [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Pyr-GC-MS and TED-GC-MS are independent of their limitation of MP colour but limited to concentration and analytical sample mass [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and miss some of the important parameters in their analysis, like size, shape, or surface morphology, to evaluate the risk assessment of MP contaminated soil.\u003c/p\u003e \u003cp\u003eAnother obstacle to the reliable determination of MP and its variability in agricultural soil systems is the long processing time required per sample [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Measuring, data processing, and analysis using spectroscopic methods, which are becoming increasingly common, require substantial time and expert knowledge to ensure accuracy [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For example, \u0026micro;-FTIR data analysis alone takes between 4 and 48 hours [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. A promising approach to overcoming the limitations and challenges in data analysis is the implementation of machine learning techniques (Coleman, 2025), which enable a substantial reduction in the analysis time of spectroscopic data [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Further, microscopy combined with machine learning used 2D morphological features to detect black MP from tyre wear in soil samples [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, for reproducible applications in the future, more training data and the availability of the programming code were needed [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo quantify MP surfaces in contaminated complex sample materials, first approaches to measure the surface area and surface roughness in volume-pore-ratio were applied with micro-X-ray computed tomography in combination with Scanning Electron Microscopy (SEM) in the context of analysing plastic fruit sticker degradation in compost [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], or SEM image-based description was used to analyse the degraded surface of MPs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Analytical approaches to quantify surfaces of MPs in microstructures, as well as 3D high-resolution shape registration in contaminated soils with robustness on SOM and black MP particles, were still missing but were highly needed to evaluate and assess ecotoxicology risk, as well as for transport processes by adhesion and deposition.\u003c/p\u003e \u003cp\u003eThe general aim of this study is to introduce a new methodology for MP extraction, detection, and analysis in agricultural soils, which will complement and overcome some limitations of the existing set of approaches. Specifically, we aim to develop a method that: (i) enables robust and somewhat faster detection of MPs in lager sample sets, (ii) focus on small transparent and black MP particles (11\u0026ndash;250 \u0026micro;m) and fibres (~\u0026thinsp;1000 \u0026micro;m length) detection; (iii) quantifies and analyses the MP size, 3D shape, volume and surface in microstructures, and (iv) avoids any treatment with hazardous substances potentially affecting the properties of the analysed MP and to overcome the use of laboratory gloves as well as to ensure the application of a laminar flow-box in sample preparation.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Cross-contamination prevention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MP extraction was carried out in a laminar flow box (FBS 93-SuSi, Spectec, Germany), and in general, a 100% cotton lab coat was worn. Whenever samples were moved within the laboratory, they were always covered with a stainless-steel lid or kept in a glass Petri dish and only uncovered during the 3D Laser Scanning Confocal Microscopy (3D LSM; VX-K 1000, Keyence, Japan). As this study avoided the use of hazardous substances, neither a fume hood nor laboratory gloves were required. All samples were handled with tweezers or a spatula. The equipment was thoroughly cleaned between uses, following an internal standardised procedure that included rinsing with ultrapure water, washing with 96% ethanol, and heating to 140 \u0026deg;C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Test Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree different agricultural topsoils, representing different regions in Germany with varying soil management and textures, were used in the study (Table 1). Soil number 1 (S1) was sampled in Dudenhofen (Western Germany) from a depth of 0-20 cm, while the second (S2) and third soil (S3) were sampled in Strass and Freising (both Southern Germany) from a depth of 0-5 cm. S2 and S3 originated from intensive, long-term agriculture, while S1 was under agricultural use for several decades but has been left fallow for at least the last four years. The soils were air dried, sieved to \u0026lt; 2 mm, and divided into 25 g samples with an automated sample divider (PT 100, Retsch, Germany). In the case of S1, a substantially larger amount of particulate organic matter (POM) was found, potentially resulting from the fallow period preceding sampling, while the soil organic carbon (SOC) content and pH value were significantly lower compared to those of S2 and S3 (Table 1). Less POM was visible in S2 and S3, where S3 had the highest SOC and clay content (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll soil samples were spiked with a set of different MP particles and fibres (Table 2): (i) Commercially available transparent LDPE particles (LDPE-T; LDPE 140 and LDPEXF 1040, Fixatti AG, Germany); (ii) transparent/white PP fibres (PP-fiber; F PP 264, Schwarzw\u0026auml;lder Textil-Werke, Germany); (iii) recycled\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: Properties of the three test soil textures sampled from topsoil with the soil organic carbon (SOC), calcic carbonate (CaCO\u003csub\u003e3\u003c/sub\u003e), nitrogen (N), and pH in 0.01 mol calcic chloride CaCl\u003csub\u003e2\u003c/sub\u003e soil elute.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProperties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil 1 (S1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil 2 (S2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil 3 (S3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eDudenhofen\u003c/p\u003e\n \u003cp\u003e(West Germany)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eStrass\u003c/p\u003e\n \u003cp\u003e(Southern Germany)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eFreising\u003c/p\u003e\n \u003cp\u003e(Southern Germany)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepth [cm]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSand [%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e86.6 \u0026plusmn; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e72 \u0026plusmn; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e16 \u0026plusmn; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSilt [%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e9.7 \u0026plusmn; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e17.5 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e60 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClay [%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.7 \u0026plusmn; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e10.5 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e24 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOC [%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.58 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1.01 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1.27 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaCO\u003csub\u003e3\u003c/sub\u003e [%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026lt; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026lt; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.14 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN [%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.06 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.12 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.16 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epH\u003csub\u003eCaCl2\u0026nbsp;\u003c/sub\u003e[%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4.6 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e6.9 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e7.1 \u0026plusmn; 0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2: Properties of the microplastic (MP) materials with plastic type light density polyethylene (LDPE) and polypropylene (PP), in the size class, total mass, the mass based concentration (w/w) as C\u003cem\u003e\u003csub\u003eMass\u003c/sub\u003e\u003c/em\u003e in part per millions (ppm), the MP particle amount per 25 g soil (MP\u003cem\u003e\u003csub\u003eparticles\u003c/sub\u003e\u003c/em\u003e/25 g soil) in median with percentiles given in brackets ([percentile 25, percentile 75]). PP-T and LDPE-T are transparent PP and LDPE particles, while LDPE-B particles are black.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize class [\u0026micro;m]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emass [mg]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMass [ppm]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlastic type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMPparticles/25 g soil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePP-fibre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e11 [19, 7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100-250\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePP-T\u003c/p\u003e\n \u003cp\u003eLDPE-T\u003c/p\u003e\n \u003cp\u003eLDPE-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e25 [40, 15]\u003c/p\u003e\n \u003cp\u003e27 [48, 14]\u003c/p\u003e\n \u003cp\u003e22 [38, 11]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e53-100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePP-T\u003c/p\u003e\n \u003cp\u003eLDPE-T\u003c/p\u003e\n \u003cp\u003eLDPE-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e69 [127, 42]\u003c/p\u003e\n \u003cp\u003e72 [154, 35]\u003c/p\u003e\n \u003cp\u003e53 [97, 34]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;53\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePP-T\u003c/p\u003e\n \u003cp\u003eLDPE-T\u003c/p\u003e\n \u003cp\u003eLDPE-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e304 [575, 150]\u003c/p\u003e\n \u003cp\u003e412 [1071, 136]\u003c/p\u003e\n \u003cp\u003e98 [265, 57]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003epredominant transparent PP (PP-T) from the ocean, manufactured to MP; and (iv) commercially available black LDPE (LDPE-B) agricultural film cryomilled to MP. Dry sieving was used to separate all MP particles into three size classes: \u0026lt; 53 \u0026micro;m, 53-100 \u0026micro;m, and 100-250 \u0026micro;m. The PP-T particles were irregularly shaped fragments, and the LDPE-T and LDPE-B particles were irregularly shaped film fragments, which could also appear in a fibrous shape.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe MP spiking concentration was quantified in mass using a microbalance (XP6 Micro Balance, Mettler Toledo, Switzerland). Additionally, the pure MP was measured using 3D LSM to quantify its size distribution, volume, size, and surface morphology. With the volume of a single MP and the corresponding density, the mass of a single MP was calculated to result in the particle amount by the ratio of the total MP spiking mass to a single MP mass. Median, 25\u003csup\u003eth\u003c/sup\u003e percentile, and 75\u003csup\u003eth\u003c/sup\u003e percentile were calculated for the number of particles (Table 2) in each sample with respect to the unequally distributed MP volume. For spiking, the MP was rinsed with ultrapure water (Elga-Veolia Purelab Flex 2, Germany) and then added to a 25 g soil sample in a 500 ml stainless-steel centrifugation tube (Tube 13507, Sigma Laborzentrifugen GmbH, Germany).\u003c/p\u003e\n\u003cp\u003eThirty different soil-MP mixtures were analysed (Figure 1A), representing ten MP types and three soils. Each soil-MP mixture consisted of 25 g of sample material and was measured in triplicate, resulting in 90 test samples. Moreover, three soil blanks and 10 MP blanks were added, resulting in a total of 103 test samples. MP blanks contained only MP and ultra-pure water, while the soil blanks contained only soil. The MP blanks were used to monitor the MP background noise and correct the initially detected MP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Microplastic extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA three-step procedure was applied to extract the MP from the spiked soil samples. This procedure has been optimised for the parallel processing of four 25 g subsamples (100 g total sample amount), each representing one soil-MP mixture (Figure 1A):\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(i) Dispersion:\u0026nbsp;\u003c/strong\u003eFor physical soil aggregate dispersion, 200 ml ultra-pure water was added to each sample and treated by twice with 5 min shaking (shaking plate 3015, GFL Gesellschaft F\u0026uuml;r Labortechnik mbH, Germany) and 5 min ultrasonic at 480 Watt, resulting in an energy density of 720 J ml\u003csup\u003e-1\u003c/sup\u003e (SONOREX RK 102H, 35 kHz, 480 Watt, Bandelin, Germany).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(ii) Density separation:\u003c/strong\u003e For density separation at \u003cem\u003e\u0026rho;\u003c/em\u003e = 1 g cm\u003csup\u003e-3\u003c/sup\u003e, another 200 ml of ultrapure water was added and centrifuged (6-16S, Sigma Laborzentrifugen GmbH, Germany) at 2800 rpm for 30 minutes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(iii) Freezing and Filtration:\u0026nbsp;\u003c/strong\u003eFor freezing and filtration, 50 ml of ultrapure water was carefully added, an aluminium foil cylinder was placed on the top of the centrifugation tube (to guide the ice during freezing), and the sample was then frozen at -18 \u0026deg;C for a minimum of 8 hours. The ice cap containing the low-density fraction (\u0026rho; \u0026lt; 1 g cm⁻\u0026sup3;) was removed by rinsing the edge between the ice and the centrifuge tube with 96% ethanol (ethanol denatured with 1% methyl ethyl ketone) and finally splitting it with a spatula. To apply the sample to a 2.5 cm diameter, white, phosphate-free, cellulose filter with a pore size of 4 \u0026micro;m (55 cm\u0026sup2;, 169 G, Macherey-Nagel, Germany), the ice cap was melted in a glass funnel and passed a filtration vessel (30 ml glass filter funnel head with blue PP funnel and two Flour-Caoutchouc seals, EAN: 4032051032088, DURAN, Schott AG, Germany). Once dried, the cellulose filter was carefully removed with a spatula and placed into a glass petri dish for subsequent analysis using the 3D LSM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 3D Laser Scanning Confocal Microscopy measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample measurement was performed with a 3D LSM, which combines a 404 nm semiconductor laser with a CMOS colour camera embedded in a depth-in-focus optical scanning system (Figure 1B). Scanning the entire sample filter (\u0026oslash; 2.5 cm) while keeping a reasonable measurement time of 6 to 8 hours, a medium magnification of 240x was used in the high-speed scanning mode and a height pitch of 4 \u0026micro;m (height resolution = 4 \u0026micro;m), with a coaxial light brightness and the laser brightness set to 2.5 and 9027, respectively. This setup enabled scanning one sample filter in four parts, achieving a pixel size of 2.72 \u0026micro;m x 2.72 \u0026micro;m. Finally, four sample filters (\u0026oslash; 2.5 cm), representing the extracted material from one 100 g soil-MP mixture, were automatically measured in a row in 24 to 32 hours (Figure 1B). The resulting data from each scan (~281 MB) was automatically saved in the proprietary VK4 file format from Keyence. Using the open-source tool vk4-python-driver (free available via GitHub, (Gunn \u0026amp; Torkian, 2018)) height information, RGB values, and laser refection with resolution of ~5000 x ~5000 pixels or respectively data points, were extracted and transformed into a height map (stored as CSV file, ~200 MB), a RGB image (stored as TIFF file, ~70 MB), and a laser refection image (stored as TIFF file, ~40 MB) (Figure 1B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3D Laser Scanning Confocal Microscopy\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edata analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA\u0026nbsp;three-step Random Forest classifier workflow was implemented (Figure 1C) to automatically distinguish MP particles from soil and background structures. The workflow progressively refines the classification results, from pixel-level segmentation (Pixel Classifier) to object-based segmentation (Object Classifier) and binary segmentation (Binary Object Classifier) between MP and soil particles. All Random Forest Classifiers were trained and tested using identical hyperparameters (\u003cem\u003en_estimators\u003c/em\u003e = 10, \u003cem\u003erandom_state\u003c/em\u003e = 42, \u003cem\u003eclass_weight\u003c/em\u003e = \u0026quot;balanced_subsample\u0026quot;). The Pixel and Object Classifiers were operated within a One-vs-One framework to respect a multiclass classification. This three-step Random Forest classifier workflow enhances classification robustness by combining semantic (pixel-based) information with object-level morphological and topographic (surface morphology) features derived from 3D LSM height data. A subsequent\u0026nbsp;principal component analysis (PCA) was applied to correct for particle-assigned background noise (Figure1D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(i) Pixel-level classification (Pixel Classifier):\u0026nbsp;\u003c/strong\u003eRaw 3D LSM data were denoised using a median filter applied to the laser reflection and RGB images. In addition, gradient, gradient magnitude, and sine of the gradient were derived from the 2D height map. Five of the 103 test samples were partially annotated and used for classifier training and testing with a random split ratio of 70:30, achieving a prediction accuracy of 98.2%. This step produced a semantic, pixel-based classification.\u003c/p\u003e\n\u003cp\u003eThe pixel-level classes \u0026ldquo;Black MP\u0026rdquo; and \u0026ldquo;Transparent MP\u0026rdquo; were subsequently converted into instance-level objects (Figure 1C). Morphological operations, including line closing, hole filling, and removal of small objects (\u0026lt; 4 connected pixels), were applied. Object-based features were extracted from the laser reflection, RGB images, and the 2D height map, providing surface descriptors such as Mean Peak-to-Valley Height (Rz), Arithmetic Average Surface Roughness (Sa), Root Mean Square Height (Sq), fractal dimension via Box Counting Method, Specific Surface Area (SSA), Surface Area Ratio (SAR), and 3D Shape Index (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(ii)\u003c/strong\u003e \u003cstrong\u003eObject-level classification (Object Classifier):\u0026nbsp;\u003c/strong\u003eThe extracted object-based features were used to train a second Random Forest Classifier. Twelve of the 103 test samples (including the five used in the previous step) were partially annotated and used for classifier training and testing with a random split ratio of 60:40, achieving a prediction accuracy of 94.8%. This step provided an instance-level classification of individual particles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(iii)\u003c/strong\u003e \u003cstrong\u003eBinary classification (Binary Object Classifier):\u0026nbsp;\u003c/strong\u003eIn the final step, only selected surface descriptors (Rz, Sa, FD, and SAR; Table 3) were used to train a binary Random Forest classifier (class 1 = \u0026ldquo;MP\u0026rdquo;, class 2 = \u0026ldquo;Soil\u0026rdquo;). Five samples out of the annotated dataset from the Object Classifier were used for the classifier training and testing, with a random split ratio of 60:40, and a prediction accuracy of 80.7% was achieved.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Surface quantification with N = measurement points, z = height value, N\u003csub\u003eB\u003c/sub\u003e(ϵ) = number of boxes with ϵ the box size, k = principal curvatures of the height.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(iv)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePrincipal component analysis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ebackground noise correction:\u0026nbsp;\u003c/strong\u003eMP blanks were analysed following the same three-step Random Forest classifier workflow (Figure 1C), with adapted training data and feature selection, to monitor MP background noise and correct (Figure 1D) the initially detected MP. MP blanks made out of transparent MPs were used to analyse black MP background noise signals, and MP blanks made out of black MPs were used to analyse transparent MP background noise signals. For the PCA, the features Area-filled, Rz, Sa, and fractal dimension (Table 3) were used and compared with the principal components of MP background noise from MP blanks with initially detected MP from the three-step Random Forest classifier workflow. A 90\u003csup\u003eth\u003c/sup\u003e-percentile threshold was applied to assign MP as background noise or as true MP detection. Finally, only detected MPs with a minimum length \u0026gt; 10.88 \u0026micro;m (4 pixels) were considered to ensure reliable detection. Consequently, the smallest MP size class \u0026lt; 53 \u0026micro;m (Table 2) is reported as 11-53 \u0026micro;m in the results. Based on their spatial coordinates, the final MP objects were reassigned to their corresponding surface and size analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Method evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor method evaluation and quality control, 103 test samples were analysed (2.2 Test materials, Table 1 and 2), consisting of 30 soil-MP combinations (Figure 1A) in triplicate (90 spiked soil samples), along with ten MP blanks and three soil blanks (13 blanks). The blanks were made of one per variation and used to control MP extraction without soil, to control background MP contamination in the agricultural topsoil samples S1, S2, and S3, and to track MP background contamination from the laboratory. The developed method is evaluated based on the recovery rate (RR) between spiking particle amount (MP\u003cem\u003e\u003csub\u003espiking\u003c/sub\u003e\u003c/em\u003e) and final detected MP particle amount (MP\u003cem\u003e\u003csub\u003edetected\u003c/sub\u003e\u003c/em\u003e). An additional evaluation step was performed by quantifying the similarity between the initially spiked MP and the finally detected MP using a PCA-based silhouette coefficient, compensating for the lack of chemical MP (plastic type) identification. Besides, to evaluate the effect of the extraction procedure on the MPs, the fractal dimension of the MP was compared before spiking and after sample processing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(i): Recovery Rate:\u003c/strong\u003e The RR was determined based on the particle amount ratio of MP\u003cem\u003e\u003csub\u003edetected\u003c/sub\u003e\u003c/em\u003e in the spiked soil samples or blanks to initial MP\u003cem\u003e\u003csub\u003espiking\u003c/sub\u003e\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eWith MP\u003cem\u003e\u003csub\u003espiking_P75\u003c/sub\u003e\u003c/em\u003e for the spiked MP particle amount of the 75th percentile and with MP\u003cem\u003e\u003csub\u003espiking_P25\u003c/sub\u003e\u003c/em\u003e for the spiked MP particle amount of the 25th percentile. For spiked PP-fibres, PP-T and LDPE-T samples, only transparent MP were considered, while for LDPE-B samples, only black MP were considered. Based on the triplicate the arithmetic mean recovery rate with the standard deviation was calculated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(ii) Similarity coefficient:\u003c/strong\u003e For evaluating the MP\u003cem\u003e\u003csub\u003edetected\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003ein comparison to MP\u003cem\u003e\u003csub\u003espiking\u003c/sub\u003e\u003c/em\u003e on similarity, a PCA was used with the features: Area-filled, Rz, Sa, and fractal dimension (Table 3). The initially MP\u003cem\u003e\u003csub\u003espiking\u003c/sub\u003e\u003c/em\u003e were separated from the background (cover slip) using a One-vs-One Random Forest Pixel classifier with an accuracy of 98.9%. Based on the Euclidean distance in PCA space, the silhouette coefficient [55] was calculated to evaluate similarity or clustering. The silhouette coefficient ranges from -1 (incorrect clustering) to +1 (well-separated clusters).\u0026nbsp;For similarity, the silhouette coefficient is defined as follows: 0-0.25 strong similarity (no cluster), 0.25-0.55 reasonable similarity, 0.55-0.75 weak similarity, and 0.75-1 no similarity (cluster) (adapted after Magdziak, 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(iii) Fractal dimension comparing:\u003c/strong\u003e For comparing the MP before spiking and after sample processing, the statistical probability density of the dimensionless parameter, fractal dimension, was used. The probability density was estimated by the Gaussian kernel density estimator (Silverman\u0026rsquo;s rule, bw_adjust = 1.0, 512 evaluation points). To evaluate the effects of the extraction procedure (Figure 1A) in dependence on the sample matrix, a distinction was made between MP detected in ultra-pure water (MP blank), S1, S2, and S3. For a complete comparison detected black soil, considered to be SOM, and transparent soil, considered to be the mineral fraction, were analysed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Data processing and statistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw 3D LSM data in VK4 format were first converted into 2D height maps using a Bash script executed in Windows PowerShell. Image annotation was performed in Fiji [57] using the open-source plugin LABKIT [58].\u003c/p\u003e\n\u003cp\u003eAll subsequent data processing and analysis were performed using Python v3.12, compiled with the Spyder IDE 5.5.1 (Spyder, 2024) within Anaconda Navigator v2.6.5 [59]. The Python library scikit-learn v1.5.2 [60] was used for machine learning-based data analysis, PCA, and calculating the silhouette score.\u003c/p\u003e\n\u003cp\u003eFor image processing, the Python libraries scikit-image [61], SciPy v1.13.1 [62], and the computer vision library OpenCV v4.10.0 were used. The function measure.regionprops from scikit-image was applied for object-based size analysis and feature extraction, while the function Delaunay from SciPy was used for volume calculations.\u003c/p\u003e\n\u003cp\u003eMathematical operations on object-based height values were performed using NumPy v1.26.4 [63] to quantify surface parameters as defined in Table 3 and to compute statistical measures including mean, standard deviation, median, percentiles and Gaussian kernel density estimator. Data organisation was managed using pandas v2.2.2 [64].\u0026nbsp;\u003c/p\u003e"},{"header":"3 Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Detection and recovery\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin three working days, four 25 g samples (\u0026lt; 2 mm soil; total 100 g) were processed in parallel using the developed workflow, which integrates MP extraction, automated 3D LSM measurement, and machine-learning-based data analysis (Figure 2A-D). This setup enables efficient sample throughput and provides quantitative detection and characterisation of MPs in terms of their amount, size, 3D shapes, and surface morphology (Table 3).\u0026nbsp;Surface morphology represents a central feature of the workflow. It enhances MP detectability by providing additional 3D structural information that distinguishes MPs from SOM and mineral particles. This approach enables the detection of transparent and black MPs in the size range of 11-250 \u0026micro;m as well as fibres (~1000 \u0026micro;m in length), without the need for chemical purification steps or hazardous substances. Transparent MPs appear as dark objects in laser intensity image, which increases their contrast and facilitates differentiation from the background and mineral fraction (Figure 1B). The three-step Random Forest classification applies object-based classification independent of particle size, thereby avoiding artefacts introduced by size-dependent thresholds and ensuring consistent detection across the full MP size spectrum (Figure 1C).\u0026nbsp;In addition to morphological descriptors, the method quantifies ecotoxicologically relevant surface parameters, including surface area, specific surface, and peak-to-valley ratios (Rz), as well as the fractal dimension, which serves as a measure of surface complexity (Table 3). To ensure reliable MP quantification, the following sections compare method performance between soils with low (S2, S3) and high (S1) POM content, followed by an evaluation of RRs and similarity coefficients with respect to MP size, shape (fibres and fragments), colour (transparent and black), and degree of weathering (PP-T).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(i) Microplastics particles 100-250 \u0026micro;m and fibres 1000 \u0026micro;m:\u003c/strong\u003e S2 and S3 achieved reliable quantification with recovery rates of 100% (Equation 2) for PP-T, LDPE-T, and LDPE-B (Figure 2B), as well as for PP-fibres in S3 (Figure 2A). PP-fibres showed a slight overestimation in S2, with recoveries of 106% \u0026plusmn; 6.6%. The silhouette coefficient indicated strong to reasonable similarity, ranging from 0.1 for LDPE-T in S2 to 0.3 for LDPE-T in S3, and for PP-fibres and PP-T in S2. In contrast, S1 achieved a 100% recovery only for PP-T, while LDPE-T was slightly overestimated (109% \u0026plusmn; 12.8%). Substantial overestimations were observed for PP-fibres (164.9% \u0026plusmn; 56.1%) and LDPE-B (396.8% \u0026plusmn; 10.3%). The silhouette coefficients in S1 indicated weak similarity, with values of 0.6 for PP-fibres, 0.4 for LDPE-T and PP-T, and 0.3 for LDPE-B. The low silhouette coefficient for LDPE-B likely reflects the false positive assignment of POM resembling MPs, a limitation also observed in the machine learning-based analysis. Transparent MPs were less affected than PP fibres due to the inclusion of colour features in the pixel classifier. The soil blanks (Figure 3) illustrate the occurrence of detected MP particles in the test soils. In particular, S1 showed a high number of black detected MPs, most of which were identified as POM and therefore represent false-positive black MP detection. S2 and S3 were less pronounced due a high POM confusion. Therefore, for S1, an automated and reliable quantification could not be achieved for LDPE-B and PP-fibres. However, the detection results can still be manually corrected in future applications. S1 also showed the highest MP background contamination for transparent MPs compared to S2 and S3 due to its packaging in a white/transparent PP sample bag (Figure 3). The soil background contamination could explain the high silhouette scores for PP-T and LDPE-T, as well as the slight overestimation of LDPE-T in S1. Nevertheless, true positive transparent MPs background contamination, demonstrating that the three-step Random Forest classifier workflow can identify MPs outside the test material set. The MP blanks showed 100% recovery only for LDPE-T, while PP-T, LDPE-B, and PP-fibre were overestimated at 141.5%, 154.3% and 211%, respectively. These results are consistent with the silhouette coefficients of 0.1 for LDPE-T, 0.3 for PP-T and LDPE-B, and 0.4 for PP-fibres. The overestimations and high silhouette coefficients for PP-fibre, PP-T, and LDPE-B likely resulted from detecting multiple fragments as individual MPs instead of a single MP, particularly for the fibres and fibrous parts of MP particles. Excluding S1 enabled reliable quantification of transparent, black, and weathered MPs, achieving a mean RR of 104% \u0026plusmn; 14.5%, for the size class 100-250 \u0026micro;m.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(ii) Microplastics particles 53-100 \u0026micro;m:\u0026nbsp;\u003c/strong\u003eS1 achieved RR of 97.6% \u0026plusmn; 3.4% for PP-T, 93% \u0026plusmn; 7.7% for LDPE-T, and 130.2% \u0026plusmn; 27.4% for LDPE-B (Figure 2C). The corresponding silhouette coefficients were 0.2 for LDPE-T and 0.3 for both PP-T and LDPE-B. However, as in the previous case, the recovery for LDPE-B in S1 was affected by false positive detections (Figure 3). For S2 and S3, the recovery rates decreased. LDPE-T showed the highest recoveries within these samples, with 74.6% \u0026plusmn; 23.8% in S2 and 61.5% \u0026plusmn; 10.8% in S3. LDPE-B achieved recoveries of 64.2% \u0026plusmn; 7.8% in S2 and 41.2% \u0026plusmn; 6.4% in S3. PP-T presented the lowest recovery rates, with 37.7% \u0026plusmn; 5.1% in S2 and 34.3% \u0026plusmn; 16.1% in S3. Silhouette coefficients in S2 and S3 were 0.3 for PP-T, LDPE-T, and for LDPE-B in S3, with the highest value observed for LDPE-B in S2. The MP blanks showed 100% recovery for all materials (PP-T, LDPE-T, and LDPE-B). The lower recovery rates observed in S2 and S3 are likely related to the higher loam and clay content of these topsoils (Table 1). An overlaying effect can be excluded, as the higher recoveries in S1, which contained more POM, did not lead to a similar decrease. PP-T with a weathered surface morphology may have adhered more strongly to soil particles, making surface morphology-based detection more challenging. Therefore, more training data is needed for MPs below 100 \u0026micro;m to ensure reliable detection of weathered surface morphologies in combination with soil particle attachment. By excluding S1, this method enabled the detection of transparent and black MPs, achieving a mean RR of 59.1% \u0026plusmn; 25.7% for the size class 53-100 \u0026micro;m.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(iii) Microplastics 11-53 \u0026micro;m:\u0026nbsp;\u003c/strong\u003eAll MPs, except LDPE-B in S1, showed strongly decreasing RR (Figure 2D). However, the LDPE-B results in S1 were again influenced by false positive detections. The highest recovery was achieved for the LDPE-B blank, with a RR of 26.2% and a silhouette coefficient of 0.3. By excluding S1, this method enabled a detection of transparent and black MPs, achieving a mean RR of 14% \u0026plusmn; 12.4% for the size class 11-53 \u0026micro;m. Particularly, the smallest size class (11-53 \u0026micro;m) is affected by a false background noise assignment, which was well observed in the LDPE-B blank. In addition, the low recoveries could also be due to the procedure in the MP extracting workflow (e.g. ultrasonication), soil particle attachment, and soil particle covering. Detection of MPs 11-53 \u0026micro;m was possible; however, for reliable quantification, a more robust background noise detection is required.\u003c/p\u003e\n\u003cp\u003eBy excluding S1, the overall mean RR across LDPE-T, LDPE-B, PP-T (11-250 \u0026micro;m), and PP fibres in the MP blanks, S2, and S3 was 65.25% \u0026plusmn; 45% using the method developed here. The MP blanks alone showed a mean RR of 96.6% \u0026plusmn; 63.5%, while the combined mean RR for S2 and S3 was 60% \u0026plusmn; 39.5%. Specifically, S2 achieved a mean RR of 63.2% \u0026plusmn; 39.5%, and S3 a mean RR of 56.9% \u0026plusmn; 40%. When S1 and, in addition, the MP size fraction 11-53 \u0026micro;m were excluded, the overall mean RR for transparent and black MP in the size range 53-250 \u0026micro;m across MP blanks, S2, and S3 increased to 87.2% \u0026plusmn; 34.7%. In this case, the MP blanks reached a mean RR of 129.6% \u0026plusmn; 42.6%, and the combined mean RR for S2 and S3 was 80% \u0026plusmn; 28%. Here, S2 achieved a mean RR of 83.6% \u0026plusmn; 26.5%, while S3 reached 76.7% \u0026plusmn; 29.7%. For all transparent MP, a mean RR of 59.8% \u0026plusmn; 42.7% was obtained for the size range 11-250 \u0026micro;m, increasing to 81% \u0026plusmn; 28.9% for the size range 53-250 \u0026micro;m. Similarly, black MP showed a mean RR of 60.5% \u0026plusmn; 31.9% in the size range 11-250 \u0026micro;m and 76.3% \u0026plusmn; 26.6% in the size range 53-250 \u0026micro;m.\u003c/p\u003e\n\u003cp\u003eA review found that only 35% of the studies performed recovery tests based on spiking experiments\u0026nbsp;[40]. With the various MP testing materials, it is challenging to compare RRs across different studies, particularly for MP \u0026lt; 250 \u0026micro;m and light-density black plastic. To compare this study with the current state, we selected studies that focused on comparable MP size, MP colour, MP material, and soil type. Fluorescence microscopy was tested on RR and achieved for white LDPE 20-150 \u0026micro;m in a sandy soil which was comparable to S2 a mean RR of 82% \u0026plusmn; 15% and a mean RR of 88% \u0026plusmn; 7% in a loamy soil which was comparable to S3, whereas for black MP PBAT/PLA 100-250 \u0026micro;m the authors achieved only a mean RR of 17% \u0026plusmn; 7% in the sandy soil and 45 \u0026plusmn; 20% in the loamy soil\u0026nbsp;[35]. For focal plane array (FPA)-\u0026micro;-FTIR a mean RR of 57% \u0026plusmn; 13% for transparent LDPE 10-150 \u0026micro;m in a sandy soil and 29% \u0026plusmn; 0% in a loamy soil was achieved and for black PBAT/PLA 10-250 \u0026micro;m a RR of 50% \u0026plusmn; 2% was achieved in loamy soil and 49% \u0026plusmn; 16% in sandy soil\u0026nbsp;[38]. A study for detecting black tyre wear particles \u0026gt; 35 \u0026micro;m with stereomicroscopy in combination with machine learning achieved a mean RR of 85.4% \u0026plusmn; 9.5% in plastic-free spiked soil samples\u0026nbsp;[9]. A RR \u0026gt; 80% was achieved by using stereomicroscopy in combination with heat and was tested on white LDPE \u0026lt; 150 \u0026micro;m and PP \u0026lt; 400 \u0026micro;m\u0026nbsp;[36]. For black MPs, the 3D LSM method, in combination with machine learning, achieved higher mean RRs compared to Fluorescence microscopy and FPA-\u0026micro;-FTIR. Whereby the stereomicroscopy in combination with machine learning achieved higher RRs for black tyre wear MP than the developed method in this study. However, the applied extraction procedure in this study was suitable for extracting light-density MP and would not be able to extract PBAT/PLA or tyre wear MP. For LDPE-T, the FPA-\u0026micro;-FTIR had comparable RRs to the tested transparent MP in this study. The Fluorescence microscopy had comparable RRs to our transparent MP in the size range of 53-250 \u0026micro;m\u0026nbsp;[35]. Further, the spiking concentration of 3 mg MP per 10 g soil (30 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e)\u0026nbsp;[35, 38]\u0026nbsp;and 0.017 mg per 5 g soil (17 mg kg\u003csup\u003e-1\u003c/sup\u003e)\u0026nbsp;[9]\u0026nbsp;was higher than the spiking concentration in this study (Table 2). We focused on choosing spiking concentration as realistic as possible based on the amount of the common global MP concentration amounts are up to 13000 items kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e (325 items per 25 g) of dry soil and 4.5 mg kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e(0.11 mg per 25 g) of dry soil\u0026nbsp;[65], whereas a review study from 2024 estimated a global mean of 2900 \u0026plusmn; 7600 MP items kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e (73 \u0026plusmn; 190 items per 25 g), with maximum concentration in arable soils with sludge amendments 3700 \u0026plusmn; 8800 MP items kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e (93 \u0026plusmn; 220 items per 25 g)\u0026nbsp;[66].\u003c/p\u003e\n\u003cp\u003eIn this study, the RR was calculated with a statistical range (Equation 2). Due to the MP overlaying under the 3D LSM, it was impossible to count particles by particle, especially for the size classes 53-100 and \u0026lt; 53 \u0026micro;m. The final detected volume could only be used for MP spiking mass-particle number conversion, where the MP was laid on a flat background (cover slip). Whereby the detected MP on the blanks and in the test soils, the background was wavy (due to the cellulose filter (Figure 1B, 3D profile) and resulted in a non-comparable volume. Therefore, mass and volume-based particle number estimation was applied to the MP spiking with quantified volumes. Other studies also had problems to count the MPs and applied for example an empirical model based on the assumption of spherical particles to get the relationship between the weight added MP and detected MP in particle amount\u0026nbsp;[36], or estimated the volume for particle number-mass conversion in dependency on different shapes\u0026nbsp;[67, 68]\u0026nbsp;or used in addition to count particles under the microscope a particle counter to analyse particle size distribution to apply finally a mass-particle number conversion\u0026nbsp;[35].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to RR values, the analysis of a reliable size limit detection is an important factor for method application. In this study, we developed a method that enables the reliable detection and, consequently, quantification of MPs down to a size of 53 \u0026micro;m. MPs down to a size of 11 \u0026micro;m were detectable without reliability, due to their low recoveries (Figure 2D). The reliable quantification limit of 53 \u0026micro;m results from the PCA background noise correction, which partly misclassified the spiking material MP 11-53 \u0026micro;m as background noise. This behaviour could be a result of a substantial similarity between background noise signals in the MP blanks and the spiking MP of 11-53 \u0026micro;m in the PCA features of Area-filled, Rz, Sa, and fractal dimension (Table 3) or the background noise signal resulted from detached MP fragments from the MP spiking material during the MP extraction procedure (Figure 1A). However, the main advantage of the PCA background noise correction was to produce a particle-assigned correction that results in a final particle size distribution, shape, and surface morphology of the true detected MPs. Other studies have reported the reliable detection of MP down to a size limit of 100 \u0026micro;m using \u0026micro;-FTIR [68], while possible detection limits down to 10 \u0026micro;m have been described [69]. Using microscopy-based approaches, reliable detection has been achieved down to 63 \u0026micro;m with video microscopy [22], 49 \u0026micro;m [70], 30 \u0026micro;m [71], and 35 \u0026micro;m [9] with Stereomicroscopy, and 20 \u0026micro;m with Fluorescence microscopy [35]. Corresponding possible detection limits were reported as low as 20 \u0026micro;m [36] and 15 \u0026micro;m for stereomicroscopy [9], and 3.5 \u0026micro;m for Fluorescence microscopy [35].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith smaller MP particle sizes, automated MP detection and particle counting are becoming important and are already available for spectroscopy [49, 72]. For digital microscopy, automated image processing, especially for detecting and counting MPs \u0026lt; 250 \u0026micro;m, was already used with machine learning based Weka segmentation [9] or threshold-based detection in combination with fluorescing particles [35]. Automated and manual counting become complicated when particles adhere to each other and form agglomerates. In digital image processing, a watershed segmentation is applied to split agglomerated particles [9, 35, 72], which can lead to a size-dependent particle splitting and may become an issue for irregular particle shapes [35]. With the three-step Random Forest classifier developed and applied in this study, a watershed application was not necessary. We focused on size- and shape-independent MP detection, where the use of a supervised machine learning algorithm depended on the labelled data used for the training process. The method evaluation showed that especially soil with high POM amount requires more labelled data to detect reliable black MP and MP-fibres. However, a high POM amount could also lead to overlaying MP [73]; therefore, a POM reduction via oxidation or enzymatic digestion could be an option to overcome this limitation. Size-dependent detection was only used for the assignment of the PCA background noise correction, where the size dependency was derived from the background noise signals, not from our spiking MP test material. A size component was also implemented to calculate the silhouette coefficient, providing an additional value for method evaluation beyond the RR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Microplastic extraction procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeyond its role in detection, the quantified surface morphology enables a detailed evaluation of potential alterations caused by the extraction procedure. Fractal dimension serves as a dimensionless parameter to assess possible effects of ultrasonic treatment or freezing treatment on the MP surface. As the fractal dimension increases, the surface becomes more complex. Here, the MP particles in the size of 100-250 \u0026micro;m were analysed, before spiking and after the MP extraction and detection procedure in the MP blanks, S1, S2, and S3 (Figure 4A-C), as well as the detected soil from S1, S2, and S3 (Figure 4D). For all scenarios, an increasing fractal dimension was observed after the MP extraction and detection procedure, regardless of whether it was performed on MP blanks, S1, S2, or S3. The lowest deviation in fractal dimension distribution in comparison to MP spiking was detected for LDPE-T in the MP blank (Figure 4A), and the largest increase was detected for PP-T in the MP blank (Figure 4C). LDPE-T detected in S1, S2, and S3 also showed partial overlap with MP spiking and MP blank, whereby the curve profile differed (Figure 4A). The distribution of LDPE-B in MP blanks, S1, S2, and S3, exhibited a clear shift from MP spiking, characterised by an increasing fractal dimension (Figure 4B). LDPE-B in S1 had two peaks in the fractal dimension distribution, whereby, by comparing the fractal dimension distribution with the three soils (Figure 4D), the second peak could be a result of the false positive MP detection. Comparing PP-T in MP blanks (Figure 4C), the distribution of the fractal dimension is more comparable to the soil (Figure 4D). PP-T and LDPE-B detected in the MP blank also showed an overestimation in the RR (Figure 2B) and a reasonable silhouette coefficient, which could result from detached MP fragments from the MP spiking material. The lower overestimation from PP-T and the higher fractal dimension led to the assumption that bigger MP fragments were detached, due to the initial material origin, which could have weathered surface structures. LDPE-B presented more fibrous shapes with thin extensions, which could detach a higher amount of small MP fragments and led to a higher overestimation in the RR. The increasing complexity in surface morphology could result from the dispersion step by ultrasonication with an energy density of 720 J ml⁻\u0026sup1;. Ultrasonic energy above 60 J ml⁻\u0026sup1; is known to disturb POM [74]; however, its effects on MPs remain insufficiently quantified [44]. Other studies have also used ultrasonication for soil aggregate dispersion, with an energy density of 720 J ml⁻\u0026sup1; [75], the same as in our study, and even higher, such as 21600 J ml⁻\u0026sup1; [70] and 105600 J ml⁻\u0026sup1; [76]. Other studies used ultrasonication in the spiking procedure, to generate a homogeneous suspension and prevent agglomeration of the MP [9, 35] or to clean extracted MP after SOM removal from detached soil particles [35]. Not in every study is it comprehensible which energy density was applied to the samples due to missing information [44]. Instead of ultrasonication to disperse soil aggregates, other studies have used freeze-drying for gentle but more time-intensive (24 hours) dispersion [69] or chemical oxidation to remove SOM [21, 35, 38]. However, a study detected lower RRs with chemical oxidation by Fenton\u0026rsquo;s reagent, especially for light and transparent MP [22]. Further, Fenton\u0026rsquo;s reagent is an exothermic reaction and could also lead to MP alteration [41]. For MP extraction by density separation, zinc chloride is widely used [22, 35, 68, 69], due to its reasonable extraction rate [40]; however, with the corrosive behaviour, zinc chloride could also lead to MP alteration [38, 40]. This study focused on light-density MP materials such as PP and LDPE, which are among the two most widely produced plastics worldwide, with LDPE films being particularly common in crop and livestock agriculture [2, 3]. Therefore, ultra-pure water for density separation was used, as in other studies focused on light-density MP detection [36, 70, 73]. Additionally, pretests revealed an issue with density separation using saturated sodium chloride and calcium chloride in the freezing step, which was crucial for the clean removal of the light fraction after centrifugation [77\u0026ndash;79]. At a freezing temperature of -24.25 \u0026deg;C, the sodium chloride solution crystallises and consists of a mixture of hydrohalites and ice crystals [80], whereby the saturated calcic chloride solution did not freeze.\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eWith a combination of a hazardous-free MP extraction procedure, 3D LSM measurement, and a three-step Random Forest classifier workflow for detection and analysis of 100 g (soil\u0026thinsp;\u0026lt;\u0026thinsp;2mm) samples in three days, a fully automated approach for MP quantification in soil was developed and evaluated in this study. A general RR of 80% \u0026plusmn; 28% across S2 and S3 presents a reliable detection for transparent and black MP (53\u0026ndash;250 \u0026micro;m) and MP fibres (~\u0026thinsp;1000 \u0026micro;m). The method did show some potential for a detection down to 11 \u0026micro;m but as it is the RRs are not satisfying. The strength of the developed method is the reliable detection either for transparent or black MP, resulting a MP size, surface morphology as well as the 3D shape analyses on a microscale with a resolution of 2.72 \u0026micro;m x 2.72 \u0026micro;m x 4 \u0026micro;m (x, y, z), ensures an environmentally safe procedure by avoiding hazardous substances and a time intensive SOM removal is not necessary. Furthermore, the three-step Random Forest Classifier enables MP particle detection and counting without the need for watershed segmentation, resulting in size- and shape-independent detection, which can be further improved by adding more labelled training data. With the use of ultra-pure water for density separation, the method is limited to light-density MP materials. The use of ultrasonication at 720 J ml⁻\u0026sup1; for soil aggregate dispersion led to a change in surface complexity of the MP testing materials; therefore, adaptations should be made in the soil dispersion step. The advantage of quantifying surface morphology in terms of dimensionless value, fractal dimension, to analyse surface complexity could be applied directly to evaluate the MP extraction procedure. Furthermore, the method evaluation across the three agricultural topsoils (S1, S2, and S3) presented a limitation for soils with a high POM amount, due to the false positive detection of black MP and MP fibres. To achieve the aim of quantifying the MP volume, an adaptation should be made to the filter material to result in a flat background instead of a wavy background during the detection procedure. Despite the limitations of the developed method, the automated and reliable detection of transparent and black MP in the size range of 53\u0026ndash;250 \u0026micro;m, as well as MP fibres of ~\u0026thinsp;1000 \u0026micro;m, without SOM removal from 100 g of soil\u0026thinsp;\u0026lt;\u0026thinsp;2 mm within three days, including particle size distribution, shape, and surface morphology, represents a major advancement for assessing the ecotoxicological risk of MP in agricultural soils. Furthermore, the combination of 3D LSM and machine learning constitutes a powerful tool for MP analysis in agricultural soils to complement the existing set of approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Federal Ministry of Education and Research as part of the initiative Plastics in the Environment (grant no. 02WPL1447A-G).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTS: Conceptualisation, methodology, machine learning, data processing, investigation, quality control, writing – original draft, visualisation; PF: Resources, writing review and editing, funding acquisition and supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the members of the Water and Soil Resource Research group at the University of Augsburg, especially Luisa Wanger and Simon Scherer. 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Image Vis Comput. 1992;10:557\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0262-8856(92)90076-F\u003c/span\u003e\u003cspan address=\"10.1016/0262-8856(92)90076-F\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microplastics-and-nanoplastics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mina","sideBox":"Learn more about [Microplastics and Nanoplastics](http://microplastics.springeropen.com)","snPcode":"43591","submissionUrl":"https://submission.nature.com/new-submission/43591/3","title":"Microplastics and Nanoplastics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microplastic, Soil, Surface analyses, 3D Laser Scanning Microscopy, Machine Learning, Freezing","lastPublishedDoi":"10.21203/rs.3.rs-8373800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8373800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLow-density plastics of different origins are a major source of microplastic (MP) contamination in agricultural soil systems. Although several plastic entry pathways are well known, such as the fragmentation of plastic materials used in so-called plasticulture or the contamination of organic fertilisers, including compost and sewage sludge, quantifying the MP contamination of these soil systems remains challenging and time-consuming. This study developed and rigorously tested a hazard-free workflow to overcome these limitations and expand the capabilities for detecting MP. The workflow combines 3D Laser Scanning Confocal Microscopy (Keyence VK-X1000, Japan) with machine-learning-based data analysis and was evaluated using three agricultural topsoils spiked with transparent and black low-density polyethylene and polypropylene particles (\u0026lt;53 µm, 53-100 µm, 100-250 µm) and polypropylene fibres (1000 µm). The method reliably detects both transparent and black MP ≥53 µm in soils with low particulate organic matter content, achieving a mean recovery rate of 80% ± 28%. Transparent MPs were reliably identified, whereas black MPs and fibres were influenced by particulate organic matter. Beyond particle count and size, the approach quantifies surface morphology using high-resolution 3D data. Four 25 g samples (100 g total soil) can be processed within three days, providing a fast, accurate, and environmentally safe tool for MP analysis in agricultural soils.\u003c/p\u003e","manuscriptTitle":"Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 09:04:53","doi":"10.21203/rs.3.rs-8373800/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"294390937725938619119443097683492228092","date":"2026-01-13T05:02:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292918682339092517954153121620030878916","date":"2026-01-12T16:40:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39703909489213499793414107767064016894","date":"2026-01-12T16:29:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-12T14:32:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-18T13:03:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-18T13:01:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microplastics and Nanoplastics","date":"2025-12-16T08:48:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microplastics-and-nanoplastics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mina","sideBox":"Learn more about [Microplastics and Nanoplastics](http://microplastics.springeropen.com)","snPcode":"43591","submissionUrl":"https://submission.nature.com/new-submission/43591/3","title":"Microplastics and Nanoplastics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cf0b2c4-855f-416e-8d81-285c1f84cdd4","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T07:10:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 09:04:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8373800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8373800","identity":"rs-8373800","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}