Geostatistical Methods Research Articles

Spatial and ecological health impacts of potentially toxic elements in road dust from long-term mining activities: A case study of the Bayan Obo deposit.

Spatial and ecological health impacts of potentially toxic elements in road dust from long-term mining activities: A case study of the Bayan Obo deposit.

Spatiotemporal variations and influencing factors of heavy metals of topsoil in Pearl River Basin, China.

Spatiotemporal variations and influencing factors of heavy metals of topsoil in Pearl River Basin, China.

A Geostatistical Study of Heavy Metal Contamination and Soil Quality in Industrial Areas of Erode and Namakkal

ABSTRACT This study assesses soil quality in the industrial areas of Erode and Namakkal districts, Tamil Nadu, focusing on heavy metal contamination from industrial effluents. Soil Quality Index (SQI) analysis supported by geostatistical methods revealed moderate soil quality (58.79%) with poor quality in 6.36% of samples. Heavy metals include Chromium (Cr) at 80.15 mg/kg near the Mohanur sugar factory, Cadmium (Cd) at 23.83 mg/kg near the Kodumudi iron and steel industry, and Arsenic (As) at 99.8 mg/kg near the Pallipalayam sugar industry exceeding the safe permissible limits. The SQI was highest near SIDCO industrial areas indicating better soil management and lowest near textile and sugar factories due to heavy contamination. The study highlights severe soil degradation near industrial hotspots, driven by effluents containing heavy metals and other pollutants. Effective remediation strategies, including organic amendments, waste treatment, and phytoremediation, are recommended to restore soil health. The findings underscore the need for sustainable practices and ongoing soil quality monitoring to mitigate industrial pollution and support environmental sustainability.

Geostatistically consistent history matching of reservoir flow model using a commercial simulator

Background. The paper examines the possibility and features of implementing the geostatistically consistent history matching of a reservoir model using reservoir flow simulation software. This approach assures maintaining the principles of the geological model creation during the solution of the inverse problem. For comparison purposes, a previously known test model is used implementing similar approach using author-developed algorithms. Objective. To analyze features and limitations of the geostatistically consistent automated history matching of a 3D model using a commercial simulator. Materials and methods. Synthetic 3D model of a heterogeneous reservoir, tNavigator reservoir flow simulator with built-in automation and history matching tools, geostatistical methods. Results. The geostatistically consistent automated history matching procedure is implemented in tNavigator on a synthetic model of a five-spot waterflooding element. The errors in the recovered values of the synthetic model parameters are analyzed depending on the final value of the objective function (measurement accuracy) and initial guess. Conclusions. It is possible to implement the geostatistically consistent history matching procedure using the automation tools of modern reservoir flow simulators. However, the built-in automated history matching algorithms do not provide enough efficiency for application to real reservoirs without using proxy models.

Factors Influencing the Spatial Distribution of Soil Total Phosphorus Based on Structural Equation Modeling

Soil total phosphorus plays an important role in soil fertility, plant growth, and bioge-ochemical cycles. This study aims to determine the spatial distribution characteristics of soil total phosphorus and identify its main influencing factors in the study area, thereby providing a basis for the scientific management of soil total phosphorus. Here, we conducted a comprehensive analysis by combining classical statistical analysis, ge-ostatistics methods, Pearson correlation analysis, one-way analysis of variance (ANOVA), and structural equation modeling (SEM) to explore the spatial distribution patterns of soil total phosphorus and its influencing factors. The results showed that soil total phosphorus in the study area ranged from 161.00 to 991.00 mg/kg, with an average of 495.71 mg/kg. Spatially, soil total phosphorus exhibited a patchy distribu-tion pattern, with high values primarily concentrated in cultivated areas along rivers and low values mainly located in forested areas in the southeastern and central re-gions. Additionally, the nugget effect of soil total phosphorus was 71.5%, indicating a moderate level of spatial variability. The Pearson correlation analysis revealed that soil total phosphorus content was significantly correlated with multiple factors, including land use types, soil parent material, distance from settlements, slope, and soil pH. Based on these findings, we employed ANOVA to analyze the impacts of various fac-tors. The results indicated that soil total phosphorus content showed significant differences under the influence of different factors. Subsequently, we further explored in depth the action paths through which these factors affect soil total phosphorus us-ing SEM. The SEM results showed that the absolute values of the total effects of the influencing factors on soil total phosphorus, ranked from highest to lowest, were as follows: land use types (0.499) > soil parent material (0.240) > distance from settle-ments (0.178) > slope (0.161) > elevation (0.127) > soil pH (0.114) > normalized differ-ence vegetation index (0.103). These findings provide a scientific foundation for the effective management of soil total phosphorus in similar study areas.

Multivariate geostatistical methods for analysing the contribution of urban lakes and neighbouring greenery to mitigating PM2.5 under stressor indicators

Multivariate geostatistical methods for analysing the contribution of urban lakes and neighbouring greenery to mitigating PM2.5 under stressor indicators

Assessment of the vulnerability and sensitivity of groundwater for drinking water supply and irrigation: The case of the Mitidja alluvial aquifer (Northern Algeria)

The aim of this study is to combine the hydrochemical data, geostatistical methods, and numerical approaches with the water pollution vulnerability index of the Mitidja alluvium. This index is obtained by applying the DRASTIC model and a numerical rating system to develop a methodology based on the water sensitivity index. The socio-economic development has led to the overexploitation of groundwater and surface water resources, coupled with insufficient rainfall, which has exacerbated the sensitivity and vulnerability of this precious resource. Compared to previous studies, the most recent sensitivity map serves as an important decision support tool for relevant authorities. According to the survey, this index was very low, accounting for 45.43% of the total drinking water area in 2010. It decreased to 8.25% and later increased to 28.06% in 2018. The high and very high sensitivity index to water pollution (SI) accounted for 5.34% and 9.87% in 2010, and 19.77% and 15.78% in 2018. The variation in irrigation water sensitivity was similar that of drinking water sources (DWS). The medium and high sensitivity indices (SI) increased from 27.21% and 18.20% to 37.19% and 42.01%, reflecting a significant and alarming increase in groundwater sensitivity, vulnerability, and pollution within the study area. The results of the geostatistical approach yielded some interesting results, considering the water intended for drinking water supply and the water intended for irrigation separately in the Mitidja alluvial aquifer.

Modeling and analysis of filtration processes in oil reservoirs of small fields by reserves

This study focuses on modeling and analyzing filtration processes in oil reservoirs of small fields, using the Northern Karamandybas field as a case study. It examines the geological and physico-chemical characteristics of the oil-bearing reservoirs and presents a hydrodynamic model developed for the J-VII, J-VIII, and J-IX horizons. The model is built upon PVT analysis of oil, gas chromatography of dissolved gas, and incorporates detailed reservoir properties, oil characteristics, and the geological structure of productive formations. A key novelty of this research is the integration of geostatistical methods, history-matching techniques, and permeability distribution analysis to evaluate the efficiency of water injection in a highly heterogeneous reservoir. Unlike previous studies that rely solely on deterministic models, this study employs a data-driven approach that accounts for geological uncertainties, ensuring more reliable reservoir performance predictions. The adaptive water injection strategy optimizes injection rates based on real-time permeability variations, filling a critical gap in understanding the impact of heterogeneity on waterflooding efficiency. The modeling results demonstrate that water injection enhances production efficiency by maintaining reservoir pressure, improving oil displacement, and minimizing water-cut, thereby reducing development costs for reservoirs. The integration of stochastic modeling and historical data calibration ensures a balanced approach to reservoir management, improving forecasting accuracy. This research provides a foundation for further studies and practical recommendations for the optimal development of small, geologically complex oil fields.

Applications of machine learning in potentially toxic elemental contamination in soils: A review.

Applications of machine learning in potentially toxic elemental contamination in soils: A review.

Statistical Learning and Topkriging Improve Spatio‐Temporal Low‐Flow Estimation

AbstractThis study evaluates the potential of a novel hierarchical space‐time model for predicting monthly low‐flow in ungauged basins. The model decomposes the monthly low‐flows into a mean field and a residual field, where the mean field represents the seasonal low‐flow regime plus a long‐term trend component. We compare four statistical learning approaches for the mean field, and three geostatistical methods for the residual field. All model combinations are evaluated using a hydrologically diverse dataset of 260 stations in Austria and the predictive performance is validated using nested 10‐fold cross‐validation. The best model for monthly low‐flow prediction is a combination of a model‐based boosting approach for the mean field and topkriging for the residual field. This model reaches a median of 0.73 across all stations, outperforming an XGBoost model on the same data set. Model performance is generally higher for stations with a winter regime (median = 0.84) than for summer regimes ( = 0.70), and lowest for the mixed regime type ( = 0.68). The proposed model appears to be most useful in headwater catchments and provides robust estimates not only for moderate events, but also for extreme low‐flow events. The favorable performance is due to the hierarchical model structure, which effectively combines different types of information: the low‐flow regime estimated from average climate and catchment characteristics, and the actual flow conditions estimated from flow records of neighboring catchments. This information is readily available for most regions of the world, making the model easily transferable to other studies.

Methods Used in the Detection of Heavy Metal Pollution in Soils

Heavy metal contamination in soils poses a major environmental risk to ecosystem health and human life. In this study, the methods used for the detection of heavy metals were analyzed. Although traditional laboratory techniques (AAS, ICP-MS, XRF) offer high sensitivity, they are costly and time-consuming. Geostatistical methods model the distribution of pollution by spatial analyses, and remote sensing techniques (satellite and UAV imaging) provide rapid detection over large areas. Machine learning approaches (RF, SVM, ANN) improve prediction accuracy by processing large data sets. Hybrid methods combine these techniques to provide more reliable and comprehensive analyses. In the future, heavy metal monitoring processes are projected to become more effective with real-time sensors, AI-based predictions, and cloud computing systems.

Integrating Environmental Variables into Geostatistical Interpolation: Enhancing Soil Mapping for the MEDALUS Model in Montenegro

Geostatistical methods are important in analyzing natural resources providing input data for complex mathematical models that address environmental processes and their spatial distribution. Ten interpolation methods and one empirical-based classification grounded in empirical knowledge, with a total of 929 soil samples, were used to create the most accurate spatial prediction maps for clay, sand, humus, and soil depth in Montenegro. These analyses serve as a preparatory phase and prioritize the practical application of the obtained results for the implementation and improvement of the MEDALUS model. This model, used to assess sensitivity to land degradation, effectively integrates into broader current and future research. The study emphasizes the importance of incorporating auxiliary variables, such as topography, climate, and vegetation data, enhancing explanatory power and accuracy in delineating the environmental characteristics, ensuring better adaptability to the studied area. The results were validated by the coefficient of determination (R2) and root mean square error (RMSE). For the clay, EBKRP (empirical Bayesian kriging regression prediction) achieved R2 = 0.35 and RMSE = 6.95%, for the sand, it achieved R2 = 0.34 and RMSE = 17.38%, for the humus, it achieved R2 = 0.50 and RMSE = 3.80%, and for the soil depth, it achieved R2 = 0.76 and RMSE = 5.36 cm. These results indicate that EBKRP is the optimal method for accurately mapping soil characteristics in future research in Montenegro.

Geochemical and Mineralogical Differentiation of Tepekent Basalts: A Multivariate Analysis Approach (NW of Konya-Central Anatolia)

This study investigates the mineralogical, petrographic, and geochemical characteristics of Miocene-aged basaltic rocks from the Tepekent region to distinguish and correlate them with other members of the Sulutus Volcanic Complex (SVC), particularly the Ulumuhsine and Yükselen basalts. Advanced geostatistical methods such as Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and k-medoids clustering analysis were applied to correlate the basaltic lava flows. While some overlaps were identified in whole-rock compositions, significant differences were observed in the mineral chemistry. The investigated basalts are primarily composed of plagioclase, with lesser amounts of olivine, pyroxene, and Fe-Ti oxides. Clinopyroxenes from the Tepekent basalts exhibit oscillatory zoning in MgO, CaO, Cr2O3, and TiO2 contents, indicating magma recharge from a more mafic mantle source. Olivine phenocrysts show disequilibrium with their host magma but are in equilibrium with the most mafic Ulumuhsine basalt, suggesting they were derived from earlier solidified phases and subsequently incorporated into the system during magma ascent or convective processes within the magma chamber. Irregular An fluctuations and sieve textures in plagioclase crystals further support the presence of magma replenishment processes. Although isotopic data are indispensable in provenance studies to definitively identify magma sources and establish genetic relationships in greater detail, this study demonstrates how mineral chemistry and geostatistical analyses can effectively differentiate basaltic lava flows and elucidate complex magma chamber processes. The findings highlight the interplay between crustal contamination, mantle-derived magma replenishment, and multi-stage magmatic evolution, providing valuable insights into the volcanic history of Central Anatolia.

A Geospatial Analysis of the Lung Cancer Burden in Philadelphia, Using Pennsylvania Cancer Registry Data from 2008-2017.

(1) Background: Lung cancer is the deadliest and second most prevalent cancer in Pennsylvania (PA), and African American patients are disproportionately affected. Lung cancer morbidity and mortality in Philadelphia County are among the highest in PA. Geographic information systems (GIS) are useful to explore geospatial variations in the cancer burden and risk factors. Therefore, we used GIS to analyze the lung cancer burden in Philadelphia to assess which areas of the city have the highest morbidity and mortality, identify potential clusters, and determine which census tract-level characteristics were associated with higher tract-level cancer burden. (2) Methods: Using secondary data from the Pennsylvania Cancer Registry, age-adjusted standardized incidence and mortality ratios (SIR and SMR) were calculated by census tract, and choropleth maps were created to visualize geographic variations in the disease burden. Two geostatistical methods were used to determine the presence of lung cancer clusters. Multivariable regression analyses were performed to identify which census-tract level characteristics correlated with a higher lung cancer burden. (3) Results: Three distinct geographical lung cancer clusters were identified. After controlling for demographics and other covariates, adult smoking prevalence, prevalence of chronic obstructive pulmonary disease, and percentage of residential addresses vacant were positively associated with higher lung cancer SIR and SMR. (4) Conclusions: Our findings may inform cancer control efforts within the region and guide future municipal-level GIS analyses of the lung cancer burden.

Geostatistics‐Based Inverse Distance Weighted Interpolation Method for Geological Boreholes

ABSTRACTThe adaptive inverse distance weighted interpolation technique significantly enhances the calculation accuracy of geological boreholes by employing membership functions to adaptively compute interpolation weights. However, these functions typically utilize conventional mathematical forms, and identifying more suitable membership functions based on the sample distribution pattern remains a critical research area for improving computational accuracy. This study introduces a geostatistical method to examine the spatial distribution characteristics of geological information and establishes corresponding membership functions for position weight mapping calculations. A high‐precision geological borehole geostatistics‐based inverse distance weighted (GIDW) interpolation method is subsequently developed. The results showed that using the correlation distance as the interpolation search neighborhood radius to define the interpolation range for the points to be estimated can optimize interpolation accuracy. The minimum absolute interpolation error for 10 sample boreholes is recorded at 2.12%, with an average minimum error of 21.07%. In addition, the membership function, designed based on the actual drilling conditions in the statistical area, proved more accurate and suitable for calculations. The GIDW method demonstrates robust stability and minimizes the error introduced by abrupt feature points within the area. These results offer valuable insights for developing high‐precision interpolation methods suitable for geological data.

Spatial analysis of environmental pollutants distribution for case study Al-Taji region in Baghdad city using remote sensing techniques

In this project, GIS and remote sensing techniques were used to detect soil of Baghdad. The field work is in Al-Taji (2022-2023). An XRF device is used to measure the concentrations of heavy metals in the soil. This study also confirmed that inverse distance weight (IDW) geostatistical methods can quickly estimate the map element distributions used in environmental health risk assessment. Spatial analysis will be done to illustrate the main regions of concentrations distribution for heavy pollutants .The XRF measurements of the minerals showed (Zn, Pb, Sr, Fe, Mn, Ni, Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Cr, V, Cu) in Al Taji soil showed that the concentrations of some of these elements exceeded the standard value such as (Na, Ca, Ni, Sr, Cl, S, Zn, Cu,) Using the ASD device, the spectral reflectivity of soil samples is measured in the areas of Taji. It is found that the reflectivity increased in wet areas and decreased in the desert areas with respect to soil. The study is characterized by the presence of two strange elements that studies could not find ten years ago, clarified the presence of the element (Sr), which is close to the properties of nuclear in the soil and the presence of element (V) showed that the conditions experienced by this region, whether military or environmental, are what helped the presence of these elements. A spatial distribution map is drawn for the spread of Minerals in the soil of Al-Taji in Baghdad using the IDW method.

Estimation of the Total Carbon Stock of Dudles Forest Based on Satellite Imagery, Airborne Laser Scanning, and Field Surveys

We present our carbon stock estimation method developed for mixed coniferous and deciduous forests in the Hungarian hilly region, covering diverse site conditions. The method consists of four complex steps, integrating traditional field surveys with modern remote sensing and GIS. The first step involves comprehensive field data collection at systematically distributed sampling points. The second step is tree species mapping based on satellite image time series. The third step uses Airborne Laser Scanning to estimate aboveground biomass and derive the carbon stock of roots. The final step involves evaluating and spatially extending field and laboratory data on litter and humus from sampling points using geostatistical methods, followed by aggregating the results for the forest block and individual forest sub-compartments. New elements were developed and implemented into the complex methodology, such as aboveground biomass calculation with voxel aggregation and underground carbon stock spatial extension with EBK regression prediction. Additionally, we examined how the accuracy of our method, designed for a 200 m sampling grid, decreases as the distance between sampling points increases.

An Integrated Geodata Science Workflow for Resource Estimation: A Case Study from the Merensky Reef, Bushveld Complex

Integrated workflows for mineral resource estimation from exploration to mining must be able to process typical geodata (e.g., borehole data), perform data engineering (e.g., geodomaining), and spatial modeling (e.g., block modeling). Several methods exist, however they can only handle individual subtasks, and are either semi or fully automatable. Thus, an integrated workflow has not been established, which is needed to handle bigger geodata sets, perform remote monitoring, or provide short-term operational feedback. Bigger (more voluminous, higher velocity and higher dimensional) geodata sets are both emerging and anticipated in future exploration and mining operations, necessitating a geodata science counterpart to traditional, segregated, and routinely manual geostatistical workflows for resource estimation. In this paper, we demonstrate a prototype that integrates various data processing, pointwise geodomaining, domain boundary delineation, combinatorics-based visualization, and geostatistical modeling methods to create a modern resource estimation workflow. For the purpose of geodomaining, we employed a fully semi-automated, machine learning-based workflow to perform spatially aware geodomaining. We demonstrate the effectiveness of the method using actual mining data. This workflow makes use of methods that are properly geodata science-based as opposed to merely data science-based (explicitly leverages the spatial aspects of data). The workflow achieves these benefits through the use of objective metrics and semi-automated modeling practices as part of geodata science (e.g., cross-validation), enabling high automation potential, practitioner-agnosticism, replicability, and objectivity. We also evaluate the integrated resource estimation workflow using a real dataset from the platiniferous Merensky Reef of the Bushveld Complex (South Africa) known for its high nugget effect.

Mineral resource estimation for a placer gold deposit in Cordón Baquedano mining district, Tierra del Fuego, Chile (54°S)

ABSTRACT The genesis of gold placer deposits is related to different superficial processes, having varied characteristics and complex mechanisms of mineral concentrations. They are often exploited by medium to small-scale miners, with high geological uncertainty and/or without proper geological characterisation. This represents a difficulty for mineral resource estimation, and usually simpler geometric methods are employed. The Cordón Baquedano Gold Mining District (CBGMD) in southernmost South America has been a source of detrital gold for over a century. Some attempts of industrialised mining occurred but without success. Furthermore, the existing metallogenic models are over 30 years old and do not represent the complexity and variety of the deposits found in the district. This research follows the common practices for mineral inventory estimation to create a resource model for the Mina Nueva Deposit (MND) in the CBGMD. Mineralisation and resource models are proposed by using traditional and geostatistical methods. The results of this study indicate that the gold resources in the MND varies from 8,070 to 10,730 ounces using newly acquired dataset, and from 16,720 to 18,280 ounces for a database that incorporates new and historical information. This illustrates the great complexity and variability of results when estimating mineral resources within placer gold deposits in this area.

Geochemical Behavior of Uranium and Arsenic in Watercourse Sediments of the Los Planes Watershed, Baja California Sur, Mexico: Assessment of Anthropogenic and Natural Factors.

In Baja California Sur (BCS), Mexico, the municipality of La Paz has reported higher cancer rates compared to nearby areas, linked to arsenic contamination from abandoned gold mines and naturally high uranium (U) and arsenic (As) levels in sediments. This study evaluates the impact of human activities on natural U and As anomalies in watercourse sediments of the Los Planes watershed and adjacent areas. The geochemical database included 229 analyses from the Mexican Geological Service (SGM 2017) and nine samples analyzed via Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Multivariate statistical and geostatistical methods were used to interpret the data. Using the kriging method for U and the nearest neighbor algorithm for As, spatial models were developed to define the anomalies' positions and extents. Hierarchical cluster analysis on 85 analyses and 28 parameters identified six clusters representing different influence areas. The study found As concentrations exceeding the Mexican limit of 22mg/kg for soils in 13 cases, with a maximum of 1520mg/kg, primarily due to historic gold mine contamination. U concentrations ranged from 0.53mg/kg to 7.35mg/kg, within international protection limits, originating from Sierra la Gata's granites and granodiorites, with potential secondary enrichment in topsoil. The possibility of anthropogenic U impact from phosphatic fertilizers is noted, warranting further investigation.