Search Results (1,141)

Research on microplastics (MPs) in agricultural soils has received increasing attention due to their potential ecological risks and adverse effects on the food chain. Recently, geostatistical approaches have been increasingly used to assess the spatial distribution of MPs in soils. Therefore, this study aims to predict the abundance of MPs in the soil of an agricultural micro-watershed using geostatistical methods and to produce a continuous map of the interpolated MPs. Soil samples were collected, and MP abundance was determined using the density separation method. Subsequently, exploratory data analysis was conducted, followed by the construction of the experimental semivariogram, theoretical variogram model fitting, ordinary kriging interpolation, cross-validation and, inverse distance weighting (IDW) interpolation. MPs were detected in all samples, with average abundances of 3826, 2553, and 3407 pieces kg⁻¹ in forest, pasture, and agricultural land use systems, respectively. The experimental semivariogram showed that the spatial distribution of MPs has a weak spatial dependence structure. The Kriging and IDW maps showed a distribution of MPs in the range of 600 to 7400 pieces kg⁻¹, with higher concentrations of MPs for forest and agricultural areas. Additionally, the map reveals a high abundance of MPs, with greater concentrations in depressions and areas near roads and urban centers, allowing for identifying critical points within the micro-watershed. This study offers important insights into the presence of MPs across various land uses, emphasizing the need for proactive measures to prevent and mitigate their accumulation in soil. Full article

Intense chemical weathering in tropical environments poses challenges for conventional geochemical exploration, as primary lithological signatures become heavily altered. Stream sediment geochemistry provides a robust alternative for detecting anomalous geochemical patterns under these conditions. In this study, 636 stream sediment samples and 15 rock samples were evaluated using Principal Component Analysis (PCA), Median + 2 Median Absolute Deviation (MAD), and Concentration–Area (C–A) fractal modeling to identify potential anomaly zones. These results were compared with the traditional Mean plus 2 Standard Deviation (SD) approach. The findings indicated that Mean + 2SD offers a conservative threshold but overlooks anomalies in heterogeneous datasets, while Median + 2MAD provides robustness against outliers. The C-A fractal model effectively characterizes low- and high-order anomalies by capturing multiscale variability. Elements such as Au–Ag–Hg–Se–Sb–As form a system indicating low- to intermediate-sulphated epithermal mineralization. Au–Pb points to polymetallic hydrothermal mineralization along intrusive contacts. The southern region is a primary mineralization center controlled by an intrusive–volcanic boundary, whereas the east and west areas exhibit secondary mineralization, characterized by altered lava breccia. The correlation between shallow epithermal and deeper intrusive-related porphyry systems, especially regarding Au–Ag, offers new insights into the metallogenic landscape of the Sunda–Banda arc. Beyond regional significance, this research presents a geostatistical workflow designed to mitigate exploration uncertainty in geochemically complex zones, providing a structured approach applicable to volcanic-arc mineralized provinces worldwide. Full article

Affordable optical data from Unmanned Aerial Vehicles (UAVs) coupled with process-based models could constitute an integrative platform to map complex spatio-temporal patterns of nitrate leaching and reduce uncertainties in tightening the nitrogen (N) cycle of silvopastoral systems. This study uses field data from a commercial farm in Denmark with lactating sows housed in paddocks with pastures flanking a central zone of poplars, either pruned (P) or unpruned (tall, T), each with resources (feed and hut) on the same (S) or opposite side (O) of the tree zone. The poplar leaf area index derived from canopy cover using a computer vision approach on true-colour UAV imagery was fed to a process-based model alongside soil data and geostatistical analyses to derive the soil water balance across the paddocks and explicitly map the variation in soil nitrate leaching. The results showed clear patterns not seen before of nitrate leaching hotspots shifting from high values in the pre-study year without animals to diluted lower values in the main study year involving the pigs. The results also showed a seasonal and spatial variation of 7 to 860 kg N ha⁻¹ year⁻¹, a wide leaching range otherwise difficult to capture, by employing only a process-based model using mean effective parameters. Nitrate leaching was in the order PO > PS > TO > TS. The N cycle was tightened with T regardless of S/O. The approach could be improved with more machine learning-aided process-based modelling to operationally monitor complex silvopastoral systems to alleviate nitrate leaching in outdoor pig systems. Full article

Real-time, in situ soil diagnostics are increasingly relevant for precision agriculture, but their efficacy under varying field and climatic conditions remains underexplored. This study assesses the 2022/23 version of the Stenon FarmLab, a multi-sensor soil analysis tool, over a 10-month period and across 1187 measurements on six fields (five cropped, one grassland) in northeast Germany. Despite the common approach of comparing a field sensor against lab results, in this paper, the FarmLab’s outputs are benchmarked using various approaches, such as time series, correlation, and geostatistical analysis, to fully evaluate the temporal and spatial stability and alignment with known soil heterogeneity. While physical soil parameters such as temperature and soil texture showed robust detection accuracy, key agronomic metrics—including mineral nitrogen (Nmin), soil organic carbon (SOC), and phosphorus—exhibited poor temporal consistency and low correlation with expected spatial patterns. Measurement errors and high sensitivity to weather conditions restrict data quality, particularly under frost and drought. Spatial clustering of more temporally stable parameters (e.g., pH, soil texture) allowed for limited zone delineation. We conclude that while the FarmLab shows partial potential for on-site soil sensing, significant limitations in nutrient measurement reliability currently prevent its use in operational precision agriculture. Enhancements in sensor calibration, environmental compensation, and software are needed for broader applicability. Full article

Particle size distribution (PSD), also referred to as grain-size distribution (GSD), is a fundamental characteristic of granular materials, influencing packing density, porosity, permeability, and mechanical behavior across soils, sediments, and industrial powders. Accurate and reproducible representation of PSD is essential for computational modeling, digital twin development (i.e., virtual replicas of physical systems), and machine learning applications in geosciences and engineering. Despite the widespread use of classical distributions (log-normal, Weibull, Gamma), there remains a lack of systematic frameworks for generating synthetic datasets with controlled statistical properties and reproducibility. This paper introduces a unified computational framework for generating virtual PSDs/GSDs with predefined statistical characteristics and a specified number of grain-size fractions. The approach integrates parametric modeling with two histogram-based allocation strategies: the equal-width method, maintaining uniform bin spacing, and the equal-probability method, distributing grains according to quantiles of the target distribution. Both methods ensure statistical representativeness, reproducibility, and scalability across material classes. The framework is demonstrated on representative cases of soils (Weibull), sedimentary and industrial materials (Gamma), and food powders (log-normal), showing its generality and adaptability. The generated datasets can support sensitivity analyses, experimental validation, and integration with discrete element modeling, computational fluid dynamics, or geostatistical simulations. Full article

Soil organic carbon (SOC) is a key indicator for assessing pasture degradation. This study presents an integrated, field-based approach to analyzing SOC dynamics in pastures of Rio de Janeiro state (Brazil). Unlike methods based exclusively on remote sensing or modeling, our analysis is based on 350 georeferenced soil samples collected by Embrapa Solos and complemented by historical land use data, providing robust and reliable empirical evidence. Statistical methods (ANOVA, Tukey test), geostatistical interpolation (kriging), and unsupervised clustering (k-means) were used to characterize the spatiotemporal distribution of SOC. The results revealed patterns linked to both topographic and anthropogenic drivers, enabling the objective delineation of degraded versus non-degraded pastures. SOC levels below 40 g/kg in areas under 300 m elevation were strongly associated with degradation due to intensive use. In contrast, degradation at higher altitudes was primarily linked to sloping terrain more prone to water erosion. This methodological approach demonstrates the potential of combining field data with data mining tools to detect degradation patterns and inform targeted land management. The findings reaffirm SOC as a vital indicator of soil quality and highlight the importance of sustainable pasture practices in conserving carbon stocks and mitigating climate change. The proposed threshold-based method offers a practical foundation for diagnosing degraded pastures and identifying priority areas for restoration. Full article

Geochemical data from mine tailings are layered, compositional, and noisy, complicating automated domaining. This study introduces a continuity-aware workflow the Geostatistical k-means Recurrent Neural Network (GkRNN) that links compositional preprocessing and geostatistical continuity to sequence learning, allowing depth order and lateral context to influence final domain labels. The workflow begins with a centered log-ratio (CLR) transform, followed by construction of a spectral embedding derived from kernelized direct and cross variograms. Clustering is carried out in this embedded space, and depth sequences are regularized with a hidden Markov model (HMM) model and a long short-term memory (LSTM) network. When applied to a multivariate set of tailing drillholes, stratigraphically coherent zones were obtained, depthwise proportions were stabilized, and vertical as well as lateral semivariograms remained consistent with laminated material. Compared with k-means and Gaussian Mixture baselines, over-segmentation was reduced and the intended layered architecture was recovered in most drillholes. The result is a reproducible domaining workflow that enables clearer grade estimation and more transparent risk evaluation. Full article

Although access to quality public spaces encourages physical activity (PA), their unequal distribution can exacerbate social inequalities. This study examined the relationship between the availability of spaces for PA and social marginalization in urban Basic Geostatistical Areas (Spanish acronym AGEB) in Mexico, using national databases on urban facilities and demographics. AGEB calculated space densities for PA, and the bivariate Moran’s I and LISA methodology were followed to identify global and local patterns. A weak negative spatial correlation was detected (I = −0.006) at the national level, with clusters of AGEBs with low marginalization and low density of spaces for PA. Contrasts were observed among the three most populous metropolitan areas: Mexico City and Guadalajara showed significant positive correlations, while Monterrey exhibited a different pattern. The urban furniture earmarked for PA is insufficient and its distribution reproduces socio-spatial inequalities. The dynamics differ across metropolises, underscoring the need for localized policies that will prioritize the provision of public spaces in marginalized communities. Full article

Understanding how different population groups interact with urban environments is essential for analyzing spatial dynamics and informing urban planning, especially in cities experiencing high visitor pressure. This study presents a methodological framework for the spatial and temporal delineation of urban areas based on user-generated location-based data. By collecting nearly 1 million Google Maps reviews in the municipality of Donostia-San Sebastián, we identify and classify user profiles based on their spatiotemporal behavior. First, we collect points of interest (POIs) and associated reviews, including profile identifiers and timestamps. Then, we perform user-level webscraping to reconstruct review histories, enabling us to infer the predominant geographical origin of each user. Users are classified as residents or tourists using both spatial prevalence and temporal activity patterns. The resulting data is aggregated onto a hexagonal grid for geostatistical analysis. Using the Getis-Ord Gi* statistic and Mann-Kendall trend tests, we identify hotspots and long-term trends of activity for different population segments. Additionally, we propose novel indicators such as predominant periods of activity and diversity of geographical origin per cell to characterize heterogeneous patterns of urban use. Our results reveal distinct behavioral patterns, highlighting a more evenly distributed use of urban space by residents, with spatially overlapping yet temporally offset activities across central areas where tourists tend to concentrate their interactions. This spatiotemporal concentration is intensified as the tourists’ origin becomes more distant, suggesting that proximity shapes urban engagement. The proposed methodology offers a replicable strategy for urban analysis using publicly accessible user-generated data and contributes to the understanding of sociospatial dynamics in tourism-intensive cities. Full article

Coal-free zones, particularly scouring zones, reduce recoverable reserves and increase water inrush risk in coal mining. Existing sedimentological, geophysical, and geostatistical methods are often constrained by coring conditions, lithological interpretation accuracy, and geological complexity. Given that well log signals provide the most continuous carriers of geological information, this study integrates Singular Spectrum Analysis (SSA), Maximum Entropy Spectral Analysis (MESA), and Integrated Prediction Error Filter Analysis (INPEFA) to establish a multi-curve framework for analyzing the genesis and logging responses of coal-free zones. A two-stage SSA workflow was applied for noise reduction, and a Trend–Fluctuation Composite (TFC) curve was constructed to enhance depositional rhythm detection. The minimum singular value order (N), naturally derived from SSA-decomposed INPEFA curves, emerged as a quantitative indicator of mine water inrush risk. The results indicate that coal-free zones resulted from inhibited peat-swamp development followed by fluvial scouring and are characterized by dense inflection points and frequent cyclic fluctuations in TFC curves, together with the absence of low anomalies in natural gamma-ray logs. By integrating multi-curve logs, core data, and in-mine three-dimensional direct-current resistivity surveys, the genetic mechanisms and boundaries of coal-free zones were effectively delineated. The proposed framework enhances logging-based stratigraphic interpretation and provides practical support for working face layout and mine water hazard prevention. Full article

This study develops a three-dimensional (3D) geostatistical model of the Ciénaga de Oro Formation in the southern Sinú–San Jacinto Basin (Colombia), integrating structural, sedimentological, and petrophysical data to identify new hydrocarbon storage-prone zones. The structural model was constructed from seismic interpretation, well log correlation, and velocity models derived from VSP and check shots. Sedimentological models were generated by means of facies definition through field—outcrops description, well-log analysis, integrating computed tomography and digital rock analysis (Digital SCAL), complemented by automatic facies classification through a multi-layer perceptron (MLP) neural network. In this framework, Petrophysical properties, including porosity, permeability, density and clay volume, were interpolated using geostatistical sequential Gaussian simulation (SGS) and kriging, accounting for directional anisotropy (N45W), using the previously defined structural model as a basis. Analysis of the ANH-SSJ-La Estrella-1X and ANH-SSJ-Nueva Esperanza-1X wells revealed reservoir variability: clean sandstones associated with distributary channels exhibited the highest quality (Φ > 20%, K > 1000 mD), while heterolithic sandstones linked to delta-front mouth bars were identified as new secondary reservoir-prone zones (Φ > 10%, K > 10 mD). The proposed methodology provides a robust, integrated and replicable workflow for reservoir characterization in complex sedimentary environments and reduces exploration uncertainty, supporting both prospect evaluation and development planning. Full article

Global climate warming and the evolution of land-use patterns are jointly reshaping the spatial configuration of fruit tree cultivation. Focusing on apricot (Prunus armeniaca L.) in China, this study constructs a comprehensive suitability assessment framework driven by the dual forces of climate change and farmland transformation. By integrating multi-source climate datasets, projected land-use data, and geostatistical analysis, the study evaluates the impacts of climate and farmland changes on the potential cultivation suitability of apricot under four SSP scenarios (SSP126, SSP245, SSP370, and SSP585) during 2021–2100. The results indicate that: (1) climate warming generally expands potential suitable areas, showing a latitudinal shift from low to high regions; (2) under moderate- to high-emission scenarios, moderately suitable areas increase significantly, whereas highly suitable areas degrade in the long term due to excess heat and water stress; (3) farmland transformation exerts a crucial constraint between climatic potential and actual plantability, as resource reduction and spatial mismatch limit development potential; and (4) climate factors contribute approximately 72% to suitability variation, while farmland factors contribute about 28%, with a significant spatial interaction between the two. This study reveals the dynamic evolution of apricot suitability patterns under the dual drivers of climate and land changes, providing a scientific basis for fruit industry optimization and spatial land-use planning. Full article

Variograms are a cornerstone of spatial analysis in geostatistics, traditionally applied to real-valued variables under the intrinsic hypothesis. Many soil properties, particularly when integrating magnetic and geochemical measurements, can be expressed as complex-valued variables that capture both magnitude and phase information. In the case of magnetic susceptibility, the imaginary component reflects energy losses associated with viscous magnetization, which in soils can indicate the presence of pedogenic ferrimagnetic minerals, while its relative increase may also reveal anthropogenic magnetite contamination. This study examines the formulation and application of variograms for such complex-valued variables in the context of soil research. Two complementary definitions are considered: an intrinsic-based approach, which directly estimates the variogram from increments and is applicable under the intrinsic hypothesis, and a covariance-based approach, which requires stronger second-order stationarity. Simulated complex-valued soil property data with controlled spatial structures were used to compare the behaviour of these formulations with their real-valued counterparts. The findings indicate that complex-valued variograms preserve additional spatial information, particularly related to local phase shifts, while maintaining compatibility with conventional variographic modelling. Full article

Spatial distribution of trace elements (TEs) in soils of the city of Dschang (Cameroon) was studied to identify their origin (geogenic vs. anthropogenic). The topsoil (at different depths) of 71 sites was analyzed using the rapid portable X-ray fluorescence analysis method. Soils from locations associated with metal-related activities exhibited the highest levels of contamination (average concentrations in mg kg⁻¹: As, 8.2; Cr, 213.7; Cu, 201.8; Pb, 97.4; Zn, 838.0), followed by household waste dumps and agricultural plots (levels close to those of cultivated low-lying areas). The observed decrease in TE concentrations with depth (notably for Zn) supports the hypothesis of a human origin (compared with soil-geochemical background of control sites). Geostatistical approach indicated an underestimation of health risks associated with the consumption of crops from several sites. Specifically, 87.32%, 49.30%, and 47.89% of the sites exceeded the Food Crops Reference Value (FCRV) for Cr, Zn, and Cu, respectively. Additionally, the number of contaminated sites for each TE varies depending on the method: Cu > Zn > Pb > Cr > As = Ni > Cd and Cr > Zn > Cu > Ni > Pb > As > Cd with the geostatistical and FCRV approach respectively. From the first step of the soil chemical quality investigation, our study highlights the need to use methods based on health risks, especially for sensitive uses of soils such as food production. Full article

The Tafrata Irrigated Perimeter (TIP) in Taourirt province, located in a semi-arid environment, faces pressures from intensive agriculture and unsustainable resource use, leading to soil degradation, low organic matter, salinity risks, and nutrient imbalances. Despite the need for effective management, limited studies have used spatial and geostatistical tools to assess soil quality in the region. This study aims to evaluate the physico-chemical quality of TIP soils and to identify management priorities for sustainable agricultural development. To achieve this, 84 soil samples analyzed for particle size, density, electrical conductivity, pH, organic matter, total carbonate content, potassium, and phosphorus. GIS was used to generate thematic maps. Findings show that 55% of the area consists of balanced sandy loam soils, with 76% of samples having slightly alkaline pH. Phosphorus and potassium concentrations average 35.23 (mg∙kg⁻¹) and 166.06 (mg∙kg⁻¹), respectively. While 76% of soils are non-saline, 87% have moderate carbonate content. Organic matter is critically low at 1.46%, raising concerns about soil fertility and water retention. The study emphasizes the need for sustainable agricultural practices to manage soil variability and improve fertility, offering actionable insights to support long-term soil health and resource sustainability in the TIP. Full article