Soil Physical Structure Controls Microplastic Accumulation and Partitioning in Pistachio Orchards

Soil Physical Structure Controls Microplastic Accumulation and Partitioning in Pistachio Orchards | 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 Soil Physical Structure Controls Microplastic Accumulation and Partitioning in Pistachio Orchards Kübra Polat, Hikmet Günal, Murat Birol, Miraç Kılıç, Mesut Budak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9418234/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Microplastics (MPs) are increasingly recognized as pervasive contaminants in terrestrial environments. However, the influence of soil physical structure on their accumulation and internal partitioning remains insufficiently understood. This study investigated the relationship between MP abundance, size distribution, and morphology, and key hydro-structural soil properties in 42 pistachio orchard soils from a semi-arid region of Türkiye. Soil samples were analyzed for MP content (0.1-5 mm), organic matter, texture, porosity, water retention characteristics, and bulk density. Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) models, combined with SHAP (SHapley Additive exPlanations) analysis, were used to identify primary predictors of MP accumulation. MP abundance ranged from 100 to 8,533 particles kg − 1 (median = 1,433 particles kg − 1 ) with the highest levels recorded in former landfill sites (median = 4,633 particles kg − 1 ). Fine MPs (0-200 µm) dominated the size distribution and showed a significant negative correlation with macroporosity (r= -0.49, p < 0.01), indicating enhanced mobility in well-connected pore systems. In contrast, larger particles (500 µm-1 mm) were positively correlated with microporosity (r = 0.49, p < 0.01) and clay content (r = 0.39, p < 0.05), suggesting size-dependent retention mechanisms. Morphology-specific analysis revealed that fragments were positively associated with aggregate stability, whereas granules showed negative relationship with available water content. SHAP analysis identified organic matter, silt content, and bulk density as the most influential predictors of MP accumulation. The RF model demonstrated superior generalization performance on the test set (R²= 0.47) compared with the GBDT model, which showed clear overfitting. These findings indicate pore-size compatibility as a key mechanism governing MP distribution and emphasize the critical role of soil structure in regulating MP dynamics in agroecosystems. microplastics soil physical properties sewage sludge machine learning SHAP analysis Figures Figure 1 Figure 2 Figure 3 Introduction Microplastics (MPs), defined as plastic particles smaller than 5 mm, have become ubiquitous contaminants in both aquatic and terrestrial environments. While early research primarily focused on aquatic systems, recent studies demonstrate that soils may act as major reservoirs for MPs due to continuous inputs and the inherent retention capacity of porous media (Rillig and Lehmann, 2020 ; En-Nejmy et al., 2024 ). The accumulation of MPs in soils is of increasing concern, as these particles may alter soil physical properties, disrupt biogeochemical processes, and ultimately threaten ecosystem functioning and agricultural sustainability (Baho et al., 2021 ; Rillig et al., 2019; Wang et al., 2024 ). Moreover, MPs are increasingly recognized as active agents that influence soil structure, aggregation, and hydraulic behavior (de Souza Machado et al., 2018 ). Agricultural soils receive MPs from multiple sources. Organic amendments such as compost and manure represent important entry pathways (Weithmann et al., 2018 ; Iswahyudi et al., 2025 ), while the land application of sewage sludge is widely recognized as one of the dominant contributor to long-term accumulation (Corradini et al., 2019 ; Zhou et al., 2024 ). Plastic mulching is another major source of secondary MPs through weathering and fragmentation (Huang et al., 2020 ). Atmospheric deposition has also emerged as a significant input route, even in remote rural areas (Kernchen et al., 2024 ). Despite these well-documented sources, the processes controlling the transport, retention, and spatial distribution of MPs within the soil matrix are still insufficiently understood. Once introduced into soil, MP behavior is governed by both particle properties (size, shape, density) and the physical architecture of the soil matrix, particularly pore-size distribution, pore connectivity, and aggregate stability. Laboratory experiments have demonstrated that particle size and morphology strongly influence filtration, straining, and retention in porous media (Zhang and Liu, 2018; Waldschläger and Schüttrumpf, 2019 ; Elrahmani et al., 2025 ). However, most existing studies rely on controlled column or batch experiments that fail to capture the structural heterogeneity and dynamic processes of real field soils (Maqbool et al., 2025 ). Consequently, field-based evidence linking MP accumulation to intrinsic soil hydro-structural properties remains limited. Conventional statistical methods often cannot adequately capture the complex, nonlinear, and interactive effects that regulate MP behavior in soils. Interpretable machine-learning approaches, such as Random Forest and Gradient Boosting Decision Trees, combined with SHAP (SHapley Additive exPlanations) analysis, offer a powerful solution by enabling both accurate prediction and transparent interpretation of variable importance The objective of this study was therefore to investigate the relationships between microplastic accumulation and soil hydro-structural properties in pistachio orchard soils under contrasting management histories in a semi-arid region of Türkiye. Specifically, we aimed to (i) quantify MP abundance, size distribution, and morphology, (ii) evaluate their associations with key soil physical and hydraulic properties, and (iii) identify the dominant predictors of MP accumulation using interpretable machine-learning models supported by SHAP analysis. We hypothesized that soil structure ,particularly pore-size distribution and organic matter content, plays a fundamental role in controlling MP retention and partitioning. This field-based study provides new insights into the mechanisms governing MP behavior in agroecosystems and contributes to the development of predictive frameworks for microplastic contamination risk assessment. Materials and Methods Study Area The study was conducted in pistachio orchards in Siirt Province, Türkiye, where Siirt pistachio is the dominant crop. The total pistachio cultivation area in the region is approximately 1.703 million hectares (Fig. 1 ). Sewage sludge from the Siirt Municipal Wastewater Treatment Plant is commonly applied by local farmers as a fertilizer and soil conditioner. The region has a continental climate typical of south-eastern Anatolia, with hot, dry summers and cold, wet winters. The mean annual temperature is 16.3°C, and the mean annual precipitation is 716 mm. Historical temperature extremes range from − 19.3°C to 46.0°C (Anonymous 2023 ). Soil Sampling and Laboratory Analyses Soil samples were collected from 42 sites at 0–15 cm depth from both sewage-sludge-amended and unamended pistachio orchards. Strict contamination-control measures were followed: samples were transported in aluminum foil bags, and all tools and laboratory equipment were thoroughly cleaned before and after use. At each site, disturbed samples for microplastic analysis were sieved to < 5 mm and stored in glass containers. Additional subsamples were sieved to < 2 mm for physical and chemical analyses. Undisturbed soil cores (100 cm 3 steel cylinders, two per site) were collected to determine bulk density and field capacity. All samples were air-dried at room temperature, and visible plant residues were removed manually. Soil texture was determined by the Bouyoucos hydrometer method (Bouyoucos 1962 ). Bulk density was measured on the undisturbed cores (Blake and Hartge 1986 ). Field capacity and permanent wilting point were determined using pressure plates at -33 kPa and − 1500 kPa, respectively. Available water content was calculated as the difference between field capacity and permanent wilting point (Klute 1986 ). Water-filled pore space (WFPS) was calculated as the ratio of volumetric water content to total porosity (Linn and Doran 1984 ). Aggregate stability was measured by wet sieving (Kemper and Rosenau 1986 ), and organic matter content was determined by the modified Walkley-Black method (Nelson and Sommers 1982 ). Microplastic extraction and classification Soil samples previously sieved to < 5 mm were oven-dried at 60°C for 24 h. A 30 g subsample was subjected to three consecutive density separations using a NaCl solution (1.2 g cm − 3 ), followed by filtration through a 33 µm mesh (Liu et al., 2020 ). Remaining OM was digested with 30% H 2 O 2 at 60°C for 72 h. The final suspension was vacuum-filtered onto Whatman GF/C filters. Microplastic particles were identified visually using a Zeiss stereomicroscope at 1× to 50× magnification. Images were processed with ImageJ software, and particles were classified into five size classes (0–50 µm to > 5 mm) and into morphological categories (fibers, fragments, films, granules). Machine learning modelling Microplastic abundance (particles kg − 1 soil) was modeled using RF and GBDT algorithms. Input features were selected to represent mechanistic drivers while minimizing multicollinearity. Spearman rank correlation and Variance Inflation Factor (VIF) analyses were performed to detect redundancy. Clay content was excluded due to strong collinearity with microporosity (r = 0.86, p < 0.01) and wilting point (r = 0.81, p < 0.01); it was replaced by microporosity (a direct structural indicator) and wilting point (a measure of water retention at high suction). The final predictor set included organic matter content, silt content, bulk density, available water content, wilting point, sand content, aggregate stability, and macroporosity. All VIF values were below 10, confirming acceptable multicollinearity. Table 1 Spearman correlation coefficients among soil physical and hydraulic properties Clay AS OM WP AW TP MicP MacP BD WFPS 0.39* -0.13 0.81** 0.42** 0.51** 0.86** -0.20 -0.41** 0.42** Silt -0.43** 0.39* 0.02 -0.11 0.14 -0.04 0.12 -0.25 -0.11 Sand -0.27 0.08 -0.80** -0.38* -0.58** -0.84** 0.09 0.50** -0.31* AS 1 -0.27 0.23 0.39* 0.24 0.37* -0.05 0.00 0.13 OM 1 0.16 -0.24 -0.08 -0.03 -0.19 -0.20 0.12 WP 1 0.14 0.40** 0.85** -0.35* -0.49** 0.54** AW 1 0.32* 0.59** -0.19 0.10 0.39* TP 1 0.53** 0.57** -0.70** -0.25 MicP 1 -0.31* -0.39* 0.57** MacP 1 -0.43** -0.90** BD 1 0.26 WFPS 1 AS: Aggregate stability; OM: Organic matter; WP: Wilting point; TP: Total porosity; MicP: Microporosity; MacP: Macroporosity; BD: Bulk density; WFPS: Water filled pore space Tree-based models were run without data standardization. The RF model used 400 estimators, while the GBDT model used 300 estimators, a learning rate of 0.03, and a maximum depth of 3. Model performance was evaluated using 3-fold cross-validation (KFold, shuffle = True, random state = 42). Variable importance was assessed via RF permutation importance and GBDT gain-based metrics. SHAP (SHapley Additive exPlanations) values were calculated to quantify and interpret feature contributions (Lundberg and Lee 2017 ). Mean absolute SHAP values were used to rank global feature importance, and SHAP summary plots illustrated the direction and magnitude of each variable’s influence on MP accumulation (Lundberg et al. 2020 ). Statistical analysis Descriptive statistics and correlation analyses were performed using XLSTAT software (version 2023.3.1, Addinsoft, Paris, France). Because of the non-normal distribution and high variance of MP abundance data, median values and interquartile ranges were used as measures of central tendency and dispersion. Spearman rank correlation analysis was applied to examine relationships between soil hydro-structural properties and microplastic characteristics. All machine-learning analyses, including model fitting, cross-validation, and SHAP interpretation, were conducted in Python using the scikit-learn library (Pedregosa et al. 2011 ). Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). Results The soils in the study area showed considerable variability in physical and hydraulic properties, with several parameters exhibiting non-normal distributions (Table 2 ). Clay content ranged from 10.0% to 60.9% (mean = 38.5%, CV = 37%), while silt and sand averaged 21.9% and 39.6%, respectively. Aggregate stability was relatively high (mean = 60.7%, CV = 23%). Organic matter content varied widely from 0.74% to 11.36% (mean = 2.92%, CV = 63%). Water retention characteristics were also diverse: field capacity ranged from 14.1% to 42.9%, wilting point from 6.8% to 28.0%, and available water content averaged 14.0% (CV = 26%). Total porosity ranged from 0.35 to 0.76 cm³ cm⁻³ (mean = 0.50 cm 3 cm − 3 ). Microporosity and macroporosity averaged 0.32 and 0.18 cm 3 cm − 3 , respectively, with macroporosity showing the greatest variability (CV = 48%) and a positively skewed distribution. Bulk density was relatively uniform (mean = 1.42 g cm − 3 , CV = 11%), and water-filled pore space ranged from 34.2% to 87.8% (mean = 65.6%). Table 2 Descriptive statistics of soil physical, hydraulic, and microplastic-related properties across the study sites. Variables Unit Min Max Mean SD CV % Skewness Kurtosis Clay % 10.0 60.9 38.5 14.1 37 -0.27 -0.95 Silt 10.0 36.6 21.9 6.4 29 0.09 -0.83 Sand 18.6 77.7 39.6 14.6 37 0.86 0.05 Aggregate Stability 32.2 79.1 60.7 13.8 23 -0.45 -1.05 Organic Matter 0.74 11.36 2.92 1.84 63 2.39 8.21 Field Capacity 14.1 42.9 32.4 6.9 21 -0.73 -0.21 Wilting Point 6.8 28.0 18.4 5.4 29 -0.13 -0.67 Available Water 6.0 21.4 14.0 3.6 26 -0.26 -0.49 Total Porosity cm 3 /cm 3 0.4 0.8 0.5 0.1 19 0.91 0.42 Micro Porosity 0.1 0.4 0.3 0.1 21 -0.74 -0.21 Macro Porosity 0.1 0.4 0.2 0.1 48 1.01 0.42 Bulk Density g/cm 3 1.04 1.72 1.42 0.15 11 -0.32 -0.16 WFPS % 34.2 87.8 65.6 13.3 20 -0.58 -0.47 WFPS: Water filled pore space, SD: Standard deviation, CV: Coefficient of variation Microplastic abundance in the pistachio orchard soils varied markedly depending on prior management practices and site history (Table 3 ). Due to strong positive skewness (1.23) and high variability (CV = 114%), median values were used as the primary measure of central tendency. Across all 42 sites, MP abundance ranged from 100 to 8,533 particles kg − 1 , with an overall median of 1,433 particles kg − 1 . The highest accumulation occurred in soils located on or adjacent to former landfill or construction sites (median = 4,633 particles kg − 1 , IQR: 867-6,867 particles kg − 1 ). Soils amended with manure showed a median of 667 particles kg − 1 (CV = 103%), while conventional farming sites had the lowest median (633 particles kg − 1 ). Sewage-sludge-amended soils had a median abundance of 1,433 particles kg − 1 . These results indicate that past land use and specific management practices were major factors influencing MP distribution. Table 3 Descriptive statistics of soil microplastic abundance (particles kg − 1 soil) by management history and legacy site conditions in pistachio orchards Microplastic Source Min Max Q1 Median Q3 Mean CV % Skewness Kurtosis Total number of plastics 100 8533 392 1433 2425 2424 114 1.23 -0.07 Former Landfill or Construction site 133 8533 867 4633 6867 4060 75 -0.13 -1.70 Manure Applied 200 7733 400 667 2867 1605 103 1.36 1.01 Conventional Farming 233 3233 317 633 1142 780 74 0.52 -1.08 Sewage Sludge Applied 100 1833 392 1433 2425 2424 114 1.23 -0.07 CV: Coefficient of variation; Q1: First Quartile; Q3: Third Quartile The relative abundance of MPs differed substantially among morphological and size categories (Table 4 ). Granules were the dominant morphology (median = 733 particles kg − 1 , IQR: 167-2,217 particles kg − 1 ), followed by fibers (median = 200 particles kg − 1 , IQR: 133–300 particles kg⁻¹). Fragments and films were far less abundant, both showing a median of 0 particles kg⁻¹ and highly skewed distributions. With respect to particle size, the 0-200 µm fraction constituted the largest proportion (median = 750 particles kg − 1 , IQR: 167–2,217 particles kg − 1 ). Abundance decreased progressively with increasing particle size, with medians of 33 particles kg − 1 for both the 200–500 µm and 500 µm–1 mm fractions. The 1–2 mm and 2–5 mm classes had medians of 67 and 50 particles kg − 1 , respectively. High coefficients of variation, particularly for films (348%) and fragments (157%), indicated considerable spatial heterogeneity in MP shape and size within the orchards. Table 4 Descriptive statistics of microplastics (particles kg − 1 soil) by shape and size class in soil samples. Fiber Min Max Q1 Median Q3 Mean CV % Skewness Kurtosis 33 533 133 200 300 228 58 0.84 -0.26 Fragment 0 233 0 0 67 33 157 1.97 4.18 Film 0 100 0 0 0 6 348 3.68 12.44 Granule 0 8200 167 733 2217 1707 133 1.56 1.14 0µm-200µm 0 8300 167 750 2217 1732 133 1.57 1.20 200µm-500µm 0 233 0 33 92 52 117 1.15 0.49 500µm-1mm 0 167 33 33 67 51 80 1.20 1.17 1mm-2mm 0 300 33 67 67 66 93 1.79 3.97 2mm-5mm 0 267 33 50 100 73 92 1.08 0.55 CV: Coefficient of variation; Q1: First Quartile; Q3: Third Quartile Spearman rank correlation analysis revealed distinct relationships between MP size fractions and soil properties (Table 5 ). The finest fraction (0-200 µm) showed a significant positive correlation with aggregate stability (r = 0.31, p < 0.05) but strong negative correlations with macroporosity (r = -0.49, p < 0.01), total porosity (r = -0.41, p < 0.01), and field capacity (r = − 0.41, p < 0.01). The 200–500 µm fraction was negatively correlated only with macroporosity (r = -0.48, p < 0.01). Intermediate-sized MPs (500 µm–1 mm) were positively correlated with microporosity (r = 0.49, p < 0.01), clay content (r = 0.39, p < 0.05), and water-filled pore space (r = 0.41, p < 0.01), while showing a negative association with wilting point (r = -0.33, p < 0.05). The largest particles (2–5 mm) were positively associated with organic matter content (r = 0.36, p < 0.05) and bulk density (r = 0.36, p < 0.05). Table 5 Spearman correlation coefficients between selected soil properties and the abundance of microplastic size categories. Soil Property 0µm-200µm 200µm-500µm 500µm-1mm 1mm-2mm 2mm-5mm Clay -0.30 -0.08 0.39* 0.03 0.17 Silt 0.20 0.04 -0.30 0.10 -0.11 Sand 0.22 0.03 -0.23 -0.06 -0.17 Organic Matter -0.11 -0.16 0.09 0.20 0.36* Aggregate Stability 0.31* 0.15 0.17 -0.10 -0.13 Total Porosity -0.41** -0.11 0.36* 0.06 0.06 Micro Porosity -0.14 0.20 0.49** 0.03 -0.04 Macro Porosity -0.49** -0.48** 0.02 0.08 0.18 Bulk Density -0.28 -0.28 0.08 0.21 0.36* Field Capacity -0.41** -0.12 0.36* 0.06 0.06 Wilting Point -0.08 -0.29 -0.33* 0.09 0.27 Available Water -0.03 -0.13 -0.17 -0.11 -0.05 Water Filled Pore Space -0.12 0.17 0.41** -0.01 -0.23 Asterisks indicate significance levels: *p < 0.05, **p < 0.01 Shape-specific correlations also showed clear patterns (Table 6 ). Total MP abundance was significantly negatively correlated with available water content (r = -0.46, p < 0.01) and microporosity (r = -0.33, p < 0.05). Granules exhibited strong negative correlations with available water content (r = -0.49, p < 0.01), microporosity (r = -0.41, p < 0.01), and clay content (r = -0.31, p < 0.05), but a positive correlation with organic matter (r = 0.31, p < 0.05). Films were positively correlated with both organic matter (r = 0.41, p < 0.01) and silt content (r = 0.32, p < 0.05). Fragments showed a significant positive association only with aggregate stability (r = 0.32, p < 0.05). Fibers, present in all samples, displayed no statistically significant correlations with any measured soil parameter. Table 6 Spearman correlation coefficients between selected soil properties and the relative abundance of microplastic shape categories. Soil property Total MP Fiber Fragment Film Granule Clay -0.22 0.15 0.21 -0.06 -0.31* Silt 0.18 -0.03 -0.07 0.32* 0.20 Sand 0.15 -0.15 -0.16 -0.06 0.22 Organic Matter 0.27 -0.02 0.25 0.41** 0.31* Aggregate Stability -0.06 0.10 0.32* 0.03 -0.10 Total Porosity -0.21 0.08 0.17 0.04 -0.28 Micro-porosity -0.33* 0.03 0.18 0.07 -0.41** Macro-porosity -0.09 -0.06 -0.05 -0.08 -0.08 Bulk Density -0.06 -0.11 -0.30 -0.14 -0.02 Field Capacity -0.33* 0.04 0.17 0.06 -0.41** Wilting Point -0.06 0.23 0.25 0.15 -0.14 Available Water -0.46** -0.22 -0.02 -0.12 -0.49** Water Filled Pore Space -0.08 0.03 0.07 0.07 -0.12 Asterisks indicate significance levels: *p < 0.05, **p < 0.01 Predictor characteristics and feature importance VIF analysis confirmed that multicollinearity among the selected predictors was within acceptable limits (all VIF values < 10). SHAP analysis identified OM content and silt content as the most influential predictors of MP abundance (Fig. 3 a). Bulk density and available water content had negative contributions, while macroporosity, aggregate stability, and sand content showed comparatively lower predictive importance. The SHAP summary plot (Fig. 3 b) revealed that higher values of OM and silt contents were associated with increased MP abundance predictions, whereas higher bulk density and available water were linked to lower predicted MP abundance. Model performance, evaluated by three-fold cross-validation, is summarized in Table 7 . The GBDT model achieved an excellent fit on the training set (R 2 = 0.992) but showed clear signs of overfitting on the test set (R 2 = 0.435). In contrast, the RF model demonstrated more stable and generalizable performance, with a training R 2 of 0.721 and a test R 2 of 0.474, respectively. The RF model also yielded lower RMSE and MAE values on the test set. Table 7 Model performance summary Model Dataset RMSE MAE R 2 Bias Random Forest Training 334.122 252.521 0.721 0.000 Test 490.060 340.270 0.474 9.361 Gradient Boosting Training 56.505 34.352 0.992 0.000 Test 563.011 414.223 0.435 61.167 RMSE: Root Mean Square Error; MAE: Mean Absolute Error Discussion Effects of management history The present study provides field-based evidence that management history and legacy land-use practices are primary drivers of MP accumulation in pistachio orchard soils in a semi-arid region of southeastern Türkiye. Soils located on or adjacent to former landfill or construction sites exhibited the highest MP abundances (median = 4,633 particles kg − 1 ), substantially exceeding those recorded in conventional farming sites (median = 633 particles kg − 1 ). Sewage-sludge-amended orchards showed intermediate levels (median = 1,433 particles kg − 1 ), more than double the conventional sites, yet notably lower than the dramatic increases (723-1,445%) reported in long-term sewage-sludge experiments (Ramage et al., 2025 ). These moderate concentrations likely reflect a combination of site-specific factors, including advanced wastewater treatment at the Siirt Municipal Wastewater Treatment Plant, lower application frequencies, and potential vertical migration of fine particles through macropores under semi-arid wetting-drying cycles (Maqbool et al., 2025 ; Casella et al., 2023 ). Manure-amended soils displayed only marginally elevated MP levels (median = 667 particles kg − 1 ), supporting the view that manure constitutes a relatively minor source compared with sewage sludge and legacy contamination in this system (Iswahyudi et al., 2025 ; Zhou et al., 2024 ). The pronounced accumulation at legacy sites aligns with recent Turkish studies documenting elevated MP pollution around open dumping and scrapyard areas, highlighting uncontrolled waste disposal as a persistent regional hotspot (Akca et al., 2024 ; Asadi et al., 2025 ). Soil physical controls on microplastic size partitioning Soil hydro-structural properties emerged as critical regulators of size-dependent MP partitioning, exerting stronger direct control than OM content alone. The finest MP fraction (0-200 µm) was significantly negatively correlated with macroporosity (r = -0.49, p < 0.01), total porosity, and field capacity, indicating preferential retention in soils with limited macropore connectivity. This observation is mechanistically consistent with physical straining and size-exclusion processes, whereby retention efficiency is governed by the ratio of particle diameter to pore-throat dimensions (Rillig and Lehmann, 2020 ; Santamarina et al., 2002 ). In well-connected macropore networks, transient preferential flow and wetting–drying cycles can remobilize fine particles, reducing their surface-layer residence time (Elrahmani et al., 2025 ; Maqbool et al., 2025 ). In contrast, larger particles (2–5 mm) showed positive associations with OM content and bulk density, suggesting limited mobility and surface entrapment. Intermediate-sized MPs (500 µm-1 mm) were positively correlated with microporosity and clay content, consistent with selective filtration within fine-pore domains (Waldschläger and Schüttrumpf, 2019 ). Recent research further demonstrates that soil texture modulates MP-induced changes in hydraulic properties; for instance, polyester microfibers can enhance porosity and plant-available water in silt-loam soils while simultaneously altering tortuosity and saturated conductivity (Neubert et al., 2025 ). No significant linear relationships were detected between OM content and any MP size fraction in the present dataset, despite OM’s widely reported role in MP retention. This discrepancy likely arises from the complex, nonlinear, and context-dependent interactions that bulk correlation analyses fail to capture under heterogeneous field conditions in semi-arid orchards (Yao et al., 2023 ; Rillig et al., 2017 ). The results therefore emphasize that pore-size distribution and textural properties dominate MP size partitioning in these pistachio systems. Shape-dependent interactions with soil physical structure Morphology-specific analyses revealed additional layers of complexity in MP–soil interactions. Fiber-shaped particles, present in every sample, displayed no significant correlations with any measured hydro-structural parameter, implying that their distribution is governed predominantly by external inputs such as atmospheric deposition or amendment quality rather than by intrinsic soil filtration (Waldschläger and Schüttrumpf, 2019 ; Rillig and Lehmann, 2020 ). Granule-shaped MPs exhibited strong negative correlations with available water content, microporosity, and clay content, yet a positive association with OM, suggesting reduced straining of rounded particles within fine-pored, clay-rich matrices. Fragment-shaped particles were uniquely and positively correlated with aggregate stability (r = 0.32, p < 0.05), indicating physical encapsulation and protection within stable micro-aggregates (Rillig and Lehmann, 2020 ; Baho et al., 2021 ). Film-shaped particles showed significant positive associations with both OM and silt content, possibly reflecting enhanced surface adhesion and organo-mineral complexation in fine-textured, carbon-rich domains (Yao et al., 2023 ; Waldschläger and Schüttrumpf, 2019 ). These distinct shape-dependent patterns demonstrate that MP morphology actively modulates retention, transport, and ecological fate, reinforcing the necessity of morphology- and size-resolved assessments rather than relying solely on total abundance. Dynamic interactions between MPs and soil structure The observed negative relationship between fine MPs and macroporosity further highlights the importance of preferential flow pathways in MP transport under semi-arid conditions. Soils with higher macroporosity appear to facilitate downward advective movement of small particles, thereby limiting surface accumulation (Jarvis, 2007; Schefer et al., 2025 ). Similarly, the inverse correlations of total MP abundance with available water content and microporosity suggest lower retention in soils possessing greater water-holding capacity. Although laboratory and mesocosm studies have documented reciprocal effects, whereby MPs, particularly fibers, can disrupt aggregate formation, reduce bulk density, and alter pore networks (Lehmann et al., 2019 ; Liang et al., 2021 ; Wang et al., 2024 ; Saljnikov et al., 2025 ), no such feedback was evident for fiber abundance in the present orchard dataset. In these perennial pistachio systems, intrinsic soil hydro-structural properties appear to exert a dominant influence on MP distribution. Nevertheless, the potential for long-term bidirectional interactions cannot be dismissed, especially under repeated sludge applications or changing climate regimes that intensify wetting-drying cycles (Neubert et al., 2025 ; de Souza Machado et al., 2018 ). Machine learning models The machine-learning component of the study provided additional mechanistic insight. The Random Forest model delivered more stable and generalizable performance (test R² = 0.474) than the GBDT model, which exhibited clear overfitting. SHAP analysis identified OM content and silt content as the two most influential positive predictors of total MP abundance, while bulk density and available water content exerted negative effects. These findings align with the emerging use of interpretable machine-learning frameworks in MP research; recent physics-informed models have successfully integrated experimental data with partial differential equations to predict accumulation dynamics with high mechanistic fidelity (Godasiaei et al., 2026). Although the hydro-structural variables explained a moderate proportion of variance, predictive power remained lower than that achieved by hyperspectral or imaging-based approaches (Ai et al., 2023 ). This limitation suggests that intrinsic soil properties primarily govern retention mechanisms, whereas absolute MP loadings are largely determined by external loading rates and management legacies. Future predictive frameworks should therefore integrate site-specific amendment histories, legacy contamination data, and high-resolution pore-network characteristics to enhance risk-assessment accuracy in agroecosystems (Tran et al., 2023 ; Ihezukwu et al., 2025 ). Conclusion Hydro-structural soil properties play a fundamental role in regulating MP accumulation, size partitioning, and shape-specific retention in pistachio orchard soils under contrasting management regimes in a semi-arid environment. This field study demonstrates that pore-size distribution, aggregate stability, and textural characteristics exert stronger direct control on MP dynamics than organic matter content alone. The finest MP fraction (0-200 µm) was preferentially retained in soils with low macroporosity, consistent with physical straining and size-exclusion mechanisms, while larger particles showed greater dependence on microporosity and bulk density. Morphology-specific patterns further revealed that granule-shaped MPs were associated with low clay content and reduced water retention, film-shaped particles with silty and organic-rich domains, and fragment-shaped particles with high aggregate stability, indicating physical encapsulation within stable micro-aggregates. Fiber-shaped particles, present in all samples, showed no clear structural correlations, suggesting their distribution is governed primarily by external inputs and atmospheric transport. Management history emerged as a critical driver of overall MP abundance. Legacy landfill and construction sites acted as pronounced contamination hotspots (median = 4,633 particles kg − 1 ), while sewage-sludge-amended orchards exhibited moderate but still elevated levels (median = 1,433 particles kg − 1 ) compared with conventional farming sites. These moderate concentrations, relative to the dramatic accumulations reported in long-term sludge experiments, indicate the mitigating potential of advanced wastewater treatment and regional edaphoclimatic conditions under semi-arid conditions. The Random Forest model, supported by SHAP analysis, provided stable and generalizable predictions (test R 2 = 0.474) and identified organic matter and silt content as the two most influential predictors of total MP abundance. These results underscore the necessity of integrating intrinsic soil hydro-structural indicators with site-specific management history and legacy contamination data to explain MP distributions in perennial agroecosystems. Overall, this study advances current understanding by providing empirical evidence from a commercially important orchard system and emphasizes that morphology- and size-resolved MP assessments are essential, as total abundance alone may overlook critical compositional and mechanistic patterns. Future monitoring programs and risk-assessment frameworks should combine high-resolution soil physical data with management legacies to develop targeted mitigation strategies. Such integrated approaches will be vital for safeguarding soil health, pistachio productivity, and food security in regions facing increasing plastic pressure. Declarations Author contributions H.G. contributed to the conceptualization, formal analysis, investigation, methodology, supervision, and validation of the study, and participated in writing and editing the manuscript. K.P. conceived and designed the study, performed data curation, formal analysis, and visualization. M.K. conducted the modelling, evaluated the data, and participated in the review and editing of the manuscript. M.B. (Black Sea Agricultural Research Institute) contributed to data curation and manuscript review and editing. M.B. (Siirt University) contributed to validation, writing of the original draft, and manuscript review and editing. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Competing Interest Authors declare no conflict of interest. All authors have read and agreed to the published version of the manuscript. Ethical standards Not applicable. Declaration of generative AI in scientific writing AI tools were used only to improve the English language and grammar of the manuscript; no part of the data analysis, interpretation, or scientific content was generated using AI. Data availability statements Data will be made available on request. References Ai, W., Chen, G., Yue, X., & Wang, J. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 21 Apr, 2026 Submission checks completed at journal 21 Apr, 2026 First submitted to journal 14 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9418234","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634255007,"identity":"cf82be0d-45b9-41e8-ba30-e895b0994e40","order_by":0,"name":"Kübra Polat","email":"","orcid":"","institution":"Harran University","correspondingAuthor":false,"prefix":"","firstName":"Kübra","middleName":"","lastName":"Polat","suffix":""},{"id":634255008,"identity":"f6779663-9531-40ed-bdf7-9dea266190b1","order_by":1,"name":"Hikmet Günal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYBACA2Y2GJO58TFcmIeglgQQk7HZGFmLBE4tDAgtbdJEaTFnZ0vd8PFHbeJ29oNt1QU19/L5ZzcwPnjbxlBn3oBdi2Uz27GbMxKOJ+7sSWy7PeNYseWMOweYDee2MUjIHMDhsMPsbbd5Eo4lbjgA1MLDlmDAcCOBTZoXqAWXy8Ba/oC0nH/YVszzL8FA/kYC+2/8WtiO3WZIqEnccCOxjZm3LcHAAGgLMwEtaTd70g4Yb7jxsFmaty/BwPBGYrPknHMSkjNwaTl/zOzGD5s62Q3nkw9+5vmWYCB3I/nghzdlNvw4QxkCDiNzGBsY8EQLDNQRUjAKRsEoGAUjGQAAVvNdgOAtgXEAAAAASUVORK5CYII=","orcid":"","institution":"Harran University","correspondingAuthor":true,"prefix":"","firstName":"Hikmet","middleName":"","lastName":"Günal","suffix":""},{"id":634255011,"identity":"2e057ce8-c15c-4a02-ac84-a6a3575f7511","order_by":2,"name":"Murat Birol","email":"","orcid":"","institution":"Black Sea Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Murat","middleName":"","lastName":"Birol","suffix":""},{"id":634255012,"identity":"51c849da-9e7f-44ee-a4a9-c5e1d186cf07","order_by":3,"name":"Miraç Kılıç","email":"","orcid":"","institution":"Turgut Özal University","correspondingAuthor":false,"prefix":"","firstName":"Miraç","middleName":"","lastName":"Kılıç","suffix":""},{"id":634255013,"identity":"c2da6297-2edf-4128-a1b6-dffeb49a95ab","order_by":4,"name":"Mesut Budak","email":"","orcid":"","institution":"Siirt University","correspondingAuthor":false,"prefix":"","firstName":"Mesut","middleName":"","lastName":"Budak","suffix":""}],"badges":[],"createdAt":"2026-04-14 17:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9418234/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9418234/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108522427,"identity":"82180708-2d04-41fe-8ccc-c2ea99575605","added_by":"auto","created_at":"2026-05-05 14:31:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":616391,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area soil sampling points\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9418234/v1/07d716641a5f0bba6c21c1b2.png"},{"id":108804550,"identity":"d12cd297-9705-4f84-9df7-97c4179e38b7","added_by":"auto","created_at":"2026-05-08 15:21:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1322210,"visible":true,"origin":"","legend":"\u003cp\u003eMicroplastic particles of diverse morphology and size in sewage sludge-amended pistachio orchard soils\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9418234/v1/ba19a9d8cb7dc25bab511b89.png"},{"id":108522429,"identity":"699d7966-44db-435c-add1-8117ec1c1d9c","added_by":"auto","created_at":"2026-05-05 14:31:39","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":367877,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Mean absolute SHAP values showing overall feature importance for predicting MP abundance. \u003cstrong\u003e(b)\u003c/strong\u003e SHAP summary plot illustrating the direction and magnitude of each variable’s impact on model predictions; color represents feature value (red = high, blue = low), and position on the x-axis indicates the effect on the model output (positive values to the right, negative values to the left).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9418234/v1/9fe4ddb09f4ba7e214d58a44.jpeg"},{"id":108811798,"identity":"b7adfc85-f26b-4bc4-92b8-47bcfd87cdf7","added_by":"auto","created_at":"2026-05-08 16:07:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3653363,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9418234/v1/807f3f92-71d5-4de7-8571-2bbfc67d9cbd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Soil Physical Structure Controls Microplastic Accumulation and Partitioning in Pistachio Orchards","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicroplastics (MPs), defined as plastic particles smaller than 5 mm, have become ubiquitous contaminants in both aquatic and terrestrial environments. While early research primarily focused on aquatic systems, recent studies demonstrate that soils may act as major reservoirs for MPs due to continuous inputs and the inherent retention capacity of porous media (Rillig and Lehmann, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; En-Nejmy et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The accumulation of MPs in soils is of increasing concern, as these particles may alter soil physical properties, disrupt biogeochemical processes, and ultimately threaten ecosystem functioning and agricultural sustainability (Baho et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rillig et al., 2019; Wang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, MPs are increasingly recognized as active agents that influence soil structure, aggregation, and hydraulic behavior (de Souza Machado et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgricultural soils receive MPs from multiple sources. Organic amendments such as compost and manure represent important entry pathways (Weithmann et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Iswahyudi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while the land application of sewage sludge is widely recognized as one of the dominant contributor to long-term accumulation (Corradini et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Plastic mulching is another major source of secondary MPs through weathering and fragmentation (Huang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Atmospheric deposition has also emerged as a significant input route, even in remote rural areas (Kernchen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite these well-documented sources, the processes controlling the transport, retention, and spatial distribution of MPs within the soil matrix are still insufficiently understood.\u003c/p\u003e \u003cp\u003eOnce introduced into soil, MP behavior is governed by both particle properties (size, shape, density) and the physical architecture of the soil matrix, particularly pore-size distribution, pore connectivity, and aggregate stability. Laboratory experiments have demonstrated that particle size and morphology strongly influence filtration, straining, and retention in porous media (Zhang and Liu, 2018; Waldschl\u0026auml;ger and Sch\u0026uuml;ttrumpf, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Elrahmani et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, most existing studies rely on controlled column or batch experiments that fail to capture the structural heterogeneity and dynamic processes of real field soils (Maqbool et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Consequently, field-based evidence linking MP accumulation to intrinsic soil hydro-structural properties remains limited. Conventional statistical methods often cannot adequately capture the complex, nonlinear, and interactive effects that regulate MP behavior in soils. Interpretable machine-learning approaches, such as Random Forest and Gradient Boosting Decision Trees, combined with SHAP (SHapley Additive exPlanations) analysis, offer a powerful solution by enabling both accurate prediction and transparent interpretation of variable importance\u003c/p\u003e \u003cp\u003eThe objective of this study was therefore to investigate the relationships between microplastic accumulation and soil hydro-structural properties in pistachio orchard soils under contrasting management histories in a semi-arid region of T\u0026uuml;rkiye. Specifically, we aimed to (i) quantify MP abundance, size distribution, and morphology, (ii) evaluate their associations with key soil physical and hydraulic properties, and (iii) identify the dominant predictors of MP accumulation using interpretable machine-learning models supported by SHAP analysis. We hypothesized that soil structure ,particularly pore-size distribution and organic matter content, plays a fundamental role in controlling MP retention and partitioning. This field-based study provides new insights into the mechanisms governing MP behavior in agroecosystems and contributes to the development of predictive frameworks for microplastic contamination risk assessment.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eStudy Area\u003c/p\u003e \u003cp\u003eThe study was conducted in pistachio orchards in Siirt Province, T\u0026uuml;rkiye, where Siirt pistachio is the dominant crop. The total pistachio cultivation area in the region is approximately 1.703\u0026nbsp;million hectares (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sewage sludge from the Siirt Municipal Wastewater Treatment Plant is commonly applied by local farmers as a fertilizer and soil conditioner. The region has a continental climate typical of south-eastern Anatolia, with hot, dry summers and cold, wet winters. The mean annual temperature is 16.3\u0026deg;C, and the mean annual precipitation is 716 mm. Historical temperature extremes range from \u0026minus;\u0026thinsp;19.3\u0026deg;C to 46.0\u0026deg;C (Anonymous \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSoil Sampling and Laboratory Analyses\u003c/p\u003e \u003cp\u003eSoil samples were collected from 42 sites at 0\u0026ndash;15 cm depth from both sewage-sludge-amended and unamended pistachio orchards. Strict contamination-control measures were followed: samples were transported in aluminum foil bags, and all tools and laboratory equipment were thoroughly cleaned before and after use. At each site, disturbed samples for microplastic analysis were sieved to \u0026lt;\u0026thinsp;5 mm and stored in glass containers. Additional subsamples were sieved to \u0026lt;\u0026thinsp;2 mm for physical and chemical analyses. Undisturbed soil cores (100 cm\u003csup\u003e3\u003c/sup\u003e steel cylinders, two per site) were collected to determine bulk density and field capacity.\u003c/p\u003e \u003cp\u003eAll samples were air-dried at room temperature, and visible plant residues were removed manually. Soil texture was determined by the Bouyoucos hydrometer method (Bouyoucos \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1962\u003c/span\u003e). Bulk density was measured on the undisturbed cores (Blake and Hartge \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Field capacity and permanent wilting point were determined using pressure plates at -33 kPa and \u0026minus;\u0026thinsp;1500 kPa, respectively. Available water content was calculated as the difference between field capacity and permanent wilting point (Klute \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Water-filled pore space (WFPS) was calculated as the ratio of volumetric water content to total porosity (Linn and Doran \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Aggregate stability was measured by wet sieving (Kemper and Rosenau \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), and organic matter content was determined by the modified Walkley-Black method (Nelson and Sommers \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMicroplastic extraction and classification\u003c/p\u003e \u003cp\u003eSoil samples previously sieved to \u0026lt;\u0026thinsp;5 mm were oven-dried at 60\u0026deg;C for 24 h. A 30 g subsample was subjected to three consecutive density separations using a NaCl solution (1.2 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), followed by filtration through a 33 \u0026micro;m mesh (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Remaining OM was digested with 30% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e at 60\u0026deg;C for 72 h. The final suspension was vacuum-filtered onto Whatman GF/C filters. Microplastic particles were identified visually using a Zeiss stereomicroscope at 1\u0026times; to 50\u0026times; magnification. Images were processed with ImageJ software, and particles were classified into five size classes (0\u0026ndash;50 \u0026micro;m to \u0026gt;\u0026thinsp;5 mm) and into morphological categories (fibers, fragments, films, granules).\u003c/p\u003e \u003cp\u003eMachine learning modelling\u003c/p\u003e \u003cp\u003eMicroplastic abundance (particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil) was modeled using RF and GBDT algorithms. Input features were selected to represent mechanistic drivers while minimizing multicollinearity. Spearman rank correlation and Variance Inflation Factor (VIF) analyses were performed to detect redundancy. Clay content was excluded due to strong collinearity with microporosity (r\u0026thinsp;=\u0026thinsp;0.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and wilting point (r\u0026thinsp;=\u0026thinsp;0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01); it was replaced by microporosity (a direct structural indicator) and wilting point (a measure of water retention at high suction). The final predictor set included organic matter content, silt content, bulk density, available water content, wilting point, sand content, aggregate stability, and macroporosity. All VIF values were below 10, confirming acceptable multicollinearity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman correlation coefficients among soil physical and hydraulic properties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.31*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.39*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.57**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.90**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWFPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAS: Aggregate stability; OM: Organic matter; WP: Wilting point; TP: Total porosity; MicP: Microporosity; MacP: Macroporosity; BD: Bulk density; WFPS: Water filled pore space\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTree-based models were run without data standardization. The RF model used 400 estimators, while the GBDT model used 300 estimators, a learning rate of 0.03, and a maximum depth of 3. Model performance was evaluated using 3-fold cross-validation (KFold, shuffle\u0026thinsp;=\u0026thinsp;True, random state\u0026thinsp;=\u0026thinsp;42). Variable importance was assessed via RF permutation importance and GBDT gain-based metrics. SHAP (SHapley Additive exPlanations) values were calculated to quantify and interpret feature contributions (Lundberg and Lee \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Mean absolute SHAP values were used to rank global feature importance, and SHAP summary plots illustrated the direction and magnitude of each variable\u0026rsquo;s influence on MP accumulation (Lundberg et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics and correlation analyses were performed using XLSTAT software (version 2023.3.1, Addinsoft, Paris, France). Because of the non-normal distribution and high variance of MP abundance data, median values and interquartile ranges were used as measures of central tendency and dispersion. Spearman rank correlation analysis was applied to examine relationships between soil hydro-structural properties and microplastic characteristics. All machine-learning analyses, including model fitting, cross-validation, and SHAP interpretation, were conducted in Python using the scikit-learn library (Pedregosa et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R\u0026sup2;).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe soils in the study area showed considerable variability in physical and hydraulic properties, with several parameters exhibiting non-normal distributions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Clay content ranged from 10.0% to 60.9% (mean\u0026thinsp;=\u0026thinsp;38.5%, CV\u0026thinsp;=\u0026thinsp;37%), while silt and sand averaged 21.9% and 39.6%, respectively. Aggregate stability was relatively high (mean\u0026thinsp;=\u0026thinsp;60.7%, CV\u0026thinsp;=\u0026thinsp;23%). Organic matter content varied widely from 0.74% to 11.36% (mean\u0026thinsp;=\u0026thinsp;2.92%, CV\u0026thinsp;=\u0026thinsp;63%). Water retention characteristics were also diverse: field capacity ranged from 14.1% to 42.9%, wilting point from 6.8% to 28.0%, and available water content averaged 14.0% (CV\u0026thinsp;=\u0026thinsp;26%). Total porosity ranged from 0.35 to 0.76 cm\u0026sup3; cm⁻\u0026sup3; (mean\u0026thinsp;=\u0026thinsp;0.50 cm\u003csup\u003e3\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Microporosity and macroporosity averaged 0.32 and 0.18 cm\u003csup\u003e3\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively, with macroporosity showing the greatest variability (CV\u0026thinsp;=\u0026thinsp;48%) and a positively skewed distribution. Bulk density was relatively uniform (mean\u0026thinsp;=\u0026thinsp;1.42 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, CV\u0026thinsp;=\u0026thinsp;11%), and water-filled pore space ranged from 34.2% to 87.8% (mean\u0026thinsp;=\u0026thinsp;65.6%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of soil physical, hydraulic, and microplastic-related properties across the study sites.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCV %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAggregate Stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic Matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField Capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWilting Point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Porosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ecm\u003csup\u003e3\u003c/sup\u003e/cm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicro Porosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacro Porosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulk Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/cm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWFPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eWFPS: Water filled pore space, SD: Standard deviation, CV: Coefficient of variation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMicroplastic abundance in the pistachio orchard soils varied markedly depending on prior management practices and site history (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Due to strong positive skewness (1.23) and high variability (CV\u0026thinsp;=\u0026thinsp;114%), median values were used as the primary measure of central tendency. Across all 42 sites, MP abundance ranged from 100 to 8,533 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an overall median of 1,433 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The highest accumulation occurred in soils located on or adjacent to former landfill or construction sites (median\u0026thinsp;=\u0026thinsp;4,633 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, IQR: 867-6,867 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Soils amended with manure showed a median of 667 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (CV\u0026thinsp;=\u0026thinsp;103%), while conventional farming sites had the lowest median (633 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Sewage-sludge-amended soils had a median abundance of 1,433 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. These results indicate that past land use and specific management practices were major factors influencing MP distribution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of soil microplastic abundance (particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil) by management history and legacy site conditions in pistachio orchards\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroplastic Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCV %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of plastics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer Landfill or Construction site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManure Applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConventional Farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSewage Sludge Applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eCV: Coefficient of variation; Q1: First Quartile; Q3: Third Quartile\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe relative abundance of MPs differed substantially among morphological and size categories (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Granules were the dominant morphology (median\u0026thinsp;=\u0026thinsp;733 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, IQR: 167-2,217 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), followed by fibers (median\u0026thinsp;=\u0026thinsp;200 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, IQR: 133\u0026ndash;300 particles kg⁻\u0026sup1;). Fragments and films were far less abundant, both showing a median of 0 particles kg⁻\u0026sup1; and highly skewed distributions. With respect to particle size, the 0-200 \u0026micro;m fraction constituted the largest proportion (median\u0026thinsp;=\u0026thinsp;750 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, IQR: 167\u0026ndash;2,217 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Abundance decreased progressively with increasing particle size, with medians of 33 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for both the 200\u0026ndash;500 \u0026micro;m and 500 \u0026micro;m\u0026ndash;1 mm fractions. The 1\u0026ndash;2 mm and 2\u0026ndash;5 mm classes had medians of 67 and 50 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. High coefficients of variation, particularly for films (348%) and fragments (157%), indicated considerable spatial heterogeneity in MP shape and size within the orchards.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of microplastics (particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil) by shape and size class in soil samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFiber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCV %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFragment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFilm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGranule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026micro;m-200\u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u0026micro;m-500\u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u0026micro;m-1mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1mm-2mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2mm-5mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eCV: Coefficient of variation; Q1: First Quartile; Q3: Third Quartile\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpearman rank correlation analysis revealed distinct relationships between MP size fractions and soil properties (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The finest fraction (0-200 \u0026micro;m) showed a significant positive correlation with aggregate stability (r\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but strong negative correlations with macroporosity (r = -0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), total porosity (r = -0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and field capacity (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The 200\u0026ndash;500 \u0026micro;m fraction was negatively correlated only with macroporosity (r = -0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Intermediate-sized MPs (500 \u0026micro;m\u0026ndash;1 mm) were positively correlated with microporosity (r\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), clay content (r\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and water-filled pore space (r\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while showing a negative association with wilting point (r = -0.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The largest particles (2\u0026ndash;5 mm) were positively associated with organic matter content (r\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and bulk density (r\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman correlation coefficients between selected soil properties and the abundance of microplastic size categories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Property\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026micro;m-200\u0026micro;m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u0026micro;m-500\u0026micro;m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500\u0026micro;m-1mm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1mm-2mm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2mm-5mm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic Matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAggregate Stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Porosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicro Porosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacro Porosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.49**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.48**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulk Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField Capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWilting Point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.33*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater Filled Pore Space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAsterisks indicate significance levels: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eShape-specific correlations also showed clear patterns (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Total MP abundance was significantly negatively correlated with available water content (r = -0.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and microporosity (r = -0.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Granules exhibited strong negative correlations with available water content (r = -0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), microporosity (r = -0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and clay content (r = -0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but a positive correlation with organic matter (r\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Films were positively correlated with both organic matter (r\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and silt content (r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Fragments showed a significant positive association only with aggregate stability (r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Fibers, present in all samples, displayed no statistically significant correlations with any measured soil parameter.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman correlation coefficients between selected soil properties and the relative abundance of microplastic shape categories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil property\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal MP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFiber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFragment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFilm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGranule\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.31*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic Matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAggregate Stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Porosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicro-porosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.33*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.41**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacro-porosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulk Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField Capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.33*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.41**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWilting Point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.46**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.49**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater Filled Pore Space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAsterisks indicate significance levels: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePredictor characteristics and feature importance\u003c/p\u003e \u003cp\u003eVIF analysis confirmed that multicollinearity among the selected predictors was within acceptable limits (all VIF values\u0026thinsp;\u0026lt;\u0026thinsp;10). SHAP analysis identified OM content and silt content as the most influential predictors of MP abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Bulk density and available water content had negative contributions, while macroporosity, aggregate stability, and sand content showed comparatively lower predictive importance. The SHAP summary plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) revealed that higher values of OM and silt contents were associated with increased MP abundance predictions, whereas higher bulk density and available water were linked to lower predicted MP abundance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModel performance, evaluated by three-fold cross-validation, is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The GBDT model achieved an excellent fit on the training set (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.992) but showed clear signs of overfitting on the test set (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.435). In contrast, the RF model demonstrated more stable and generalizable performance, with a training R\u003csup\u003e2\u003c/sup\u003e of 0.721 and a test R\u003csup\u003e2\u003c/sup\u003e of 0.474, respectively. The RF model also yielded lower RMSE and MAE values on the test set.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel performance summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBias\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e334.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e252.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e490.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e340.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGradient Boosting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e563.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e414.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eRMSE: Root Mean Square Error; MAE: Mean Absolute Error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEffects of management history\u003c/p\u003e \u003cp\u003eThe present study provides field-based evidence that management history and legacy land-use practices are primary drivers of MP accumulation in pistachio orchard soils in a semi-arid region of southeastern T\u0026uuml;rkiye. Soils located on or adjacent to former landfill or construction sites exhibited the highest MP abundances (median\u0026thinsp;=\u0026thinsp;4,633 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), substantially exceeding those recorded in conventional farming sites (median\u0026thinsp;=\u0026thinsp;633 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Sewage-sludge-amended orchards showed intermediate levels (median\u0026thinsp;=\u0026thinsp;1,433 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), more than double the conventional sites, yet notably lower than the dramatic increases (723-1,445%) reported in long-term sewage-sludge experiments (Ramage et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These moderate concentrations likely reflect a combination of site-specific factors, including advanced wastewater treatment at the Siirt Municipal Wastewater Treatment Plant, lower application frequencies, and potential vertical migration of fine particles through macropores under semi-arid wetting-drying cycles (Maqbool et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Casella et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Manure-amended soils displayed only marginally elevated MP levels (median\u0026thinsp;=\u0026thinsp;667 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), supporting the view that manure constitutes a relatively minor source compared with sewage sludge and legacy contamination in this system (Iswahyudi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The pronounced accumulation at legacy sites aligns with recent Turkish studies documenting elevated MP pollution around open dumping and scrapyard areas, highlighting uncontrolled waste disposal as a persistent regional hotspot (Akca et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Asadi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil physical controls on microplastic size partitioning\u003c/p\u003e \u003cp\u003eSoil hydro-structural properties emerged as critical regulators of size-dependent MP partitioning, exerting stronger direct control than OM content alone. The finest MP fraction (0-200 \u0026micro;m) was significantly negatively correlated with macroporosity (r = -0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), total porosity, and field capacity, indicating preferential retention in soils with limited macropore connectivity. This observation is mechanistically consistent with physical straining and size-exclusion processes, whereby retention efficiency is governed by the ratio of particle diameter to pore-throat dimensions (Rillig and Lehmann, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Santamarina et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In well-connected macropore networks, transient preferential flow and wetting\u0026ndash;drying cycles can remobilize fine particles, reducing their surface-layer residence time (Elrahmani et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Maqbool et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast, larger particles (2\u0026ndash;5 mm) showed positive associations with OM content and bulk density, suggesting limited mobility and surface entrapment. Intermediate-sized MPs (500 \u0026micro;m-1 mm) were positively correlated with microporosity and clay content, consistent with selective filtration within fine-pore domains (Waldschl\u0026auml;ger and Sch\u0026uuml;ttrumpf, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recent research further demonstrates that soil texture modulates MP-induced changes in hydraulic properties; for instance, polyester microfibers can enhance porosity and plant-available water in silt-loam soils while simultaneously altering tortuosity and saturated conductivity (Neubert et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). No significant linear relationships were detected between OM content and any MP size fraction in the present dataset, despite OM\u0026rsquo;s widely reported role in MP retention. This discrepancy likely arises from the complex, nonlinear, and context-dependent interactions that bulk correlation analyses fail to capture under heterogeneous field conditions in semi-arid orchards (Yao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rillig et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The results therefore emphasize that pore-size distribution and textural properties dominate MP size partitioning in these pistachio systems.\u003c/p\u003e \u003cp\u003eShape-dependent interactions with soil physical structure\u003c/p\u003e \u003cp\u003eMorphology-specific analyses revealed additional layers of complexity in MP\u0026ndash;soil interactions. Fiber-shaped particles, present in every sample, displayed no significant correlations with any measured hydro-structural parameter, implying that their distribution is governed predominantly by external inputs such as atmospheric deposition or amendment quality rather than by intrinsic soil filtration (Waldschl\u0026auml;ger and Sch\u0026uuml;ttrumpf, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rillig and Lehmann, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Granule-shaped MPs exhibited strong negative correlations with available water content, microporosity, and clay content, yet a positive association with OM, suggesting reduced straining of rounded particles within fine-pored, clay-rich matrices. Fragment-shaped particles were uniquely and positively correlated with aggregate stability (r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating physical encapsulation and protection within stable micro-aggregates (Rillig and Lehmann, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Baho et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Film-shaped particles showed significant positive associations with both OM and silt content, possibly reflecting enhanced surface adhesion and organo-mineral complexation in fine-textured, carbon-rich domains (Yao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Waldschl\u0026auml;ger and Sch\u0026uuml;ttrumpf, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These distinct shape-dependent patterns demonstrate that MP morphology actively modulates retention, transport, and ecological fate, reinforcing the necessity of morphology- and size-resolved assessments rather than relying solely on total abundance.\u003c/p\u003e \u003cp\u003eDynamic interactions between MPs and soil structure\u003c/p\u003e \u003cp\u003eThe observed negative relationship between fine MPs and macroporosity further highlights the importance of preferential flow pathways in MP transport under semi-arid conditions. Soils with higher macroporosity appear to facilitate downward advective movement of small particles, thereby limiting surface accumulation (Jarvis, 2007; Schefer et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, the inverse correlations of total MP abundance with available water content and microporosity suggest lower retention in soils possessing greater water-holding capacity. Although laboratory and mesocosm studies have documented reciprocal effects, whereby MPs, particularly fibers, can disrupt aggregate formation, reduce bulk density, and alter pore networks (Lehmann et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Saljnikov et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), no such feedback was evident for fiber abundance in the present orchard dataset. In these perennial pistachio systems, intrinsic soil hydro-structural properties appear to exert a dominant influence on MP distribution. Nevertheless, the potential for long-term bidirectional interactions cannot be dismissed, especially under repeated sludge applications or changing climate regimes that intensify wetting-drying cycles (Neubert et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; de Souza Machado et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMachine learning models\u003c/p\u003e \u003cp\u003eThe machine-learning component of the study provided additional mechanistic insight. The Random Forest model delivered more stable and generalizable performance (test R\u0026sup2; = 0.474) than the GBDT model, which exhibited clear overfitting. SHAP analysis identified OM content and silt content as the two most influential positive predictors of total MP abundance, while bulk density and available water content exerted negative effects. These findings align with the emerging use of interpretable machine-learning frameworks in MP research; recent physics-informed models have successfully integrated experimental data with partial differential equations to predict accumulation dynamics with high mechanistic fidelity (Godasiaei et al., 2026). Although the hydro-structural variables explained a moderate proportion of variance, predictive power remained lower than that achieved by hyperspectral or imaging-based approaches (Ai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This limitation suggests that intrinsic soil properties primarily govern retention mechanisms, whereas absolute MP loadings are largely determined by external loading rates and management legacies. Future predictive frameworks should therefore integrate site-specific amendment histories, legacy contamination data, and high-resolution pore-network characteristics to enhance risk-assessment accuracy in agroecosystems (Tran et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ihezukwu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHydro-structural soil properties play a fundamental role in regulating MP accumulation, size partitioning, and shape-specific retention in pistachio orchard soils under contrasting management regimes in a semi-arid environment. This field study demonstrates that pore-size distribution, aggregate stability, and textural characteristics exert stronger direct control on MP dynamics than organic matter content alone. The finest MP fraction (0-200 \u0026micro;m) was preferentially retained in soils with low macroporosity, consistent with physical straining and size-exclusion mechanisms, while larger particles showed greater dependence on microporosity and bulk density. Morphology-specific patterns further revealed that granule-shaped MPs were associated with low clay content and reduced water retention, film-shaped particles with silty and organic-rich domains, and fragment-shaped particles with high aggregate stability, indicating physical encapsulation within stable micro-aggregates. Fiber-shaped particles, present in all samples, showed no clear structural correlations, suggesting their distribution is governed primarily by external inputs and atmospheric transport.\u003c/p\u003e \u003cp\u003eManagement history emerged as a critical driver of overall MP abundance. Legacy landfill and construction sites acted as pronounced contamination hotspots (median\u0026thinsp;=\u0026thinsp;4,633 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), while sewage-sludge-amended orchards exhibited moderate but still elevated levels (median\u0026thinsp;=\u0026thinsp;1,433 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared with conventional farming sites. These moderate concentrations, relative to the dramatic accumulations reported in long-term sludge experiments, indicate the mitigating potential of advanced wastewater treatment and regional edaphoclimatic conditions under semi-arid conditions.\u003c/p\u003e \u003cp\u003eThe Random Forest model, supported by SHAP analysis, provided stable and generalizable predictions (test R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.474) and identified organic matter and silt content as the two most influential predictors of total MP abundance. These results underscore the necessity of integrating intrinsic soil hydro-structural indicators with site-specific management history and legacy contamination data to explain MP distributions in perennial agroecosystems.\u003c/p\u003e \u003cp\u003eOverall, this study advances current understanding by providing empirical evidence from a commercially important orchard system and emphasizes that morphology- and size-resolved MP assessments are essential, as total abundance alone may overlook critical compositional and mechanistic patterns. Future monitoring programs and risk-assessment frameworks should combine high-resolution soil physical data with management legacies to develop targeted mitigation strategies. Such integrated approaches will be vital for safeguarding soil health, pistachio productivity, and food security in regions facing increasing plastic pressure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eH.G. contributed to the conceptualization, formal analysis, investigation, methodology, supervision, and validation of the study, and participated in writing and editing the manuscript. K.P. conceived and designed the study, performed data curation, formal analysis, and visualization. M.K. conducted the modelling, evaluated the data, and participated in the review and editing of the manuscript. M.B. (Black Sea Agricultural Research Institute) contributed to data curation and manuscript review and editing. M.B. (Siirt University) contributed to validation, writing of the original draft, and manuscript review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u0026nbsp;\u003c/strong\u003eAuthors declare no conflict of interest. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical standards\u0026nbsp;\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI in scientific writing\u0026nbsp;\u003c/strong\u003eAI tools were used only to improve the English language and grammar of the manuscript; no part of the data analysis, interpretation, or scientific content was generated using AI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u0026nbsp;\u003c/strong\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAi, W., Chen, G., Yue, X., \u0026amp; Wang, J. (2023). 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Microplastic accumulation and transport in agricultural soils with long-term sewage sludge amendments. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e, \u003cem\u003e480\u003c/em\u003e, Article, 136263. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhazmat.2024.136263\u003c/span\u003e\u003cspan address=\"10.1016/j.jhazmat.2024.136263\" 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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"microplastics, soil physical properties, sewage sludge, machine learning, SHAP analysis","lastPublishedDoi":"10.21203/rs.3.rs-9418234/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9418234/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroplastics (MPs) are increasingly recognized as pervasive contaminants in terrestrial environments. However, the influence of soil physical structure on their accumulation and internal partitioning remains insufficiently understood. This study investigated the relationship between MP abundance, size distribution, and morphology, and key hydro-structural soil properties in 42 pistachio orchard soils from a semi-arid region of T\u0026uuml;rkiye. Soil samples were analyzed for MP content (0.1-5 mm), organic matter, texture, porosity, water retention characteristics, and bulk density. Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) models, combined with SHAP (SHapley Additive exPlanations) analysis, were used to identify primary predictors of MP accumulation. MP abundance ranged from 100 to 8,533 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (median\u0026thinsp;=\u0026thinsp;1,433 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) with the highest levels recorded in former landfill sites (median\u0026thinsp;=\u0026thinsp;4,633 particles kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Fine MPs (0-200 \u0026micro;m) dominated the size distribution and showed a significant negative correlation with macroporosity (r= -0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating enhanced mobility in well-connected pore systems. In contrast, larger particles (500 \u0026micro;m-1 mm) were positively correlated with microporosity (r\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and clay content (r\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting size-dependent retention mechanisms. Morphology-specific analysis revealed that fragments were positively associated with aggregate stability, whereas granules showed negative relationship with available water content. SHAP analysis identified organic matter, silt content, and bulk density as the most influential predictors of MP accumulation. The RF model demonstrated superior generalization performance on the test set (R\u0026sup2;= 0.47) compared with the GBDT model, which showed clear overfitting. These findings indicate pore-size compatibility as a key mechanism governing MP distribution and emphasize the critical role of soil structure in regulating MP dynamics in agroecosystems.\u003c/p\u003e","manuscriptTitle":"Soil Physical Structure Controls Microplastic Accumulation and Partitioning in Pistachio Orchards","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 14:31:35","doi":"10.21203/rs.3.rs-9418234/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"201023911657119300504637763898818085607","date":"2026-05-18T07:18:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265523896694310025108814040621368576045","date":"2026-05-13T17:31:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T15:12:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T10:13:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-21T10:13:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2026-04-14T16:55:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f3d6f662-5b9a-4f8c-a262-399a2223ec59","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"201023911657119300504637763898818085607","date":"2026-05-18T07:18:27+00:00","index":46,"fulltext":""},{"type":"reviewerAgreed","content":"265523896694310025108814040621368576045","date":"2026-05-13T17:31:43+00:00","index":41,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T14:31:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 14:31:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9418234","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9418234","identity":"rs-9418234","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}