Search Results (84)

A 3D model integrating mineralogical, petrological, and geostatistical resource estimation was developed for Zone C of the Kofi Birimian gold deposit in Western Mali. Petrographic analysis identified two forms of gold mineralization: (i) native gold or electrum inclusions within pyrite, and (ii) disseminated native gold along pyrite fractures. Four types of hydrothermal alteration–epidotization, chloritization, carbonatization, and albitization were observed microscopically. Statistical analysis of geochemical data classified five lithologies: mafic dyke, felsic dyke, diabase, faulted breccia, and intermediate quartz diorite. Minerals identified petrographically were corroborated by multivariate correlations among elements (Cr, Fe, Ni, Al, Ti, Na, and Ca), as revealed by Principal Component Analysis (PCA). A 3D borehole-based model revealed spatial correlations between hydrothermal alteration zones and associated geochemical anomalies, notably tourmalinization (B) and albitization (Na), with the latter serving as a key indicator for new exploration targets. The spatial associations of anomalous Ag, B, Hg, As, and Na commonly linked to tourmalinization suggest favorable zones for gold and silver mineralization. Geostatistical analysis identified isotropic continuous mineralized structures for most elements, including gold. Spherical isotropic variograms with ranges from 35 to 75 m were fitted for in situ resource estimation (e.g., silver ≈ 40 m; gold ≈ 60 m). The resulting estimated resources (indicated + inferred), based on a 1.0 g/t Au cut-off, are 2.476 Mt at 3.5 g/t Au indicated (0.278 Moz or 8.67 t), and 1.254 Mt at 2.78 g/t Au inferred (0.112 Moz or 3.49 t). This study provides a framework for identifying new mineralized zones, and the multidisciplinary approach demonstrates the connections between mineralogy and the information embedded in geochemical datasets, which are revealed through appropriate tools and an understanding of the underlying processes. Full article

Multivariate space–time datasets are often collected at discrete, regularly monitored time intervals and are typically treated as components of time series in environmental science and other applied fields. To effectively characterize such data in geostatistical frameworks, valid and practical covariance models are essential. In this work, we propose several classes of multivariate spatio-temporal covariance matrix functions to model underlying stochastic processes whose discrete temporal margins correspond to well-known autoregressive and moving average (ARMA) models. We derive sufficient and/or necessary conditions under which these functions yield valid covariance matrices. By leveraging established methodologies from time series analysis and spatial statistics, the proposed models are straightforward to identify and fit in practice. Finally, we demonstrate the utility of these multivariate covariance functions through an application to Kansas weather data, using co-kriging for prediction and comparing the results to those obtained from traditional spatio-temporal models. Full article

In this study, we evaluated the spatial variability in droplet deposition in herbicide applications using a remotely piloted aircraft (RPA) in pasture areas. The investigation was conducted in a square grid (50.0 m × 50.0 m), with 121 sampling points, at two operational flight heights (3.0 and 4.0 m). Droplet deposition was quantified using the fluorescent dye rhodamine B, and the droplet spectrum was characterised using water-sensitive paper tags. Geostatistical analysis was implemented to characterise spatial dependence, complemented by multivariate statistical analysis. Droplet deposition ranged from 1.01 to 9.02 and 1.10–6.10 μL cm⁻² at 3.0 and 4.0 m flight heights, respectively, with the coefficients of variation between 19.72 and 23.06% for droplet spectrum parameters. All droplet spectrum parameters exhibited a moderate to strong spatial dependence (relative nugget effect ≤75%) and a predominance of adjustment to the exponential model, with spatial dependence indices ranging from 12.55 to 47.49% between the two flight heights. Significant positive correlations were observed between droplet deposition and droplet spectrum parameters (r = 0.60–0.79 at 3.0 m; r = 0.37–0.66 at 4.0 m), with the correlation magnitude decreasing as the operational flight height increased. Cross-validation indices demonstrated acceptable accuracy in spatial prediction, with a mean estimation error ranging from −0.030 to 0.044 and a root mean square error ranging from 0.81 to 2.25 across parameters and flight heights. Principal component analysis explained 99.14 and 85.72% of the total variation at 3.0 and 4.0 m flight heights, respectively. The methodological integration of geostatistics and multivariate statistics provides a comprehensive understanding of the spatial variability in droplet deposition, with relevant implications for the optimisation of phytosanitary applications performed using RPAs. Full article

Soil acidity is one of the main limiting factors for coffee production in Peruvian rainforests. The objective of this study is to predict the spatial acidity variability for recommending site-specific liming and phosphorus fertilization treatments. We analyzed thirty-six edaphoclimatic variables, eight methods for estimating liming doses, and three geospatial variables from 552 soil samples in the Pichanaqui district of Peru. Multivariate statistics, nonparametric comparison, and geostatistical analysis with Ordinary Kriging interpolation were used for data analysis. The results showed low coffee yields (0.70 ± 0.16 t ha⁻¹) due to soil acidification. The interquartile ranges (IQR) were found to be 3.80–5.10 for pH, 0.21–0.87 cmol Kg⁻¹ for Al⁺³, and 2.55–6.53 mg Kg⁻¹ for available P, which are limiting soil conditions for coffee plantations. Moreover, pH, Al⁺³, Ca⁺², and organic matter (OM) were the variables with the highest accuracy and quality in the spatial prediction of soil acidity (R² between 0.77 and 0.85). The estimation method of liming requirements, MPM (integration of pH and organic material method), obtained the highest correlation with soil acidity-modulating variables and had a high spatial predictability (R² = 0.79), estimating doses between 1.50 and 3.01 t ha⁻¹ in soils with organic matter (OM) > 4.00%. The MAC (potential acidity method) method (R² = 0.59) estimated liming doses between 0.51 and 0.88 t ha⁻¹ in soils with OM < 4.00% and potential acidity greater than 0.71 cmol Kg⁻¹. Regarding phosphorus fertilization (DAP), the results showed high requirements (median = 137.21 kg ha⁻¹, IQR = 8.28 kg ha⁻¹), with high spatial predictability (R² = 0.74). However, coffee plantations on Ferralsols, with Paleogene parental material, mainly in dry forests, had the lowest predicted fertilization requirements (between 6.92 and 77.55 kg ha⁻¹ of DAP). This research shows a moderate spatial variation of acidity, the need to optimize phosphorus fertilization, and an optimal prediction of liming requirements using the MPM and MAC methods, which indicate high requirements in the southwest of the Pichanaqui district. Full article

The hydrosystem composed of Lake Togo, Lagoon of Togoville, and Lagoon of Aného is located in the coastal zone of Togo and receives important and different kinds of mining waste that cause its degradation. This study aims to evaluate the physicochemical and metallic quality of these waters and determine the possible sources of these contaminants using geostatistical, multivariate, and special analysis methods. These waters were very mineralized according to the average conductivity (15.51 mS/cm). Average contents (μg/L) in trace elements varied from 2.46 μg/L for As to 141.63 μg/L for Pb. Average levels of Cd, Pb, Cr, and Ni were significantly higher than the WHO standards. Trace elements and physicochemical parameters showed strong spatial variations with the highest values recorded downstream of the hydrosystem. The main possible source of trace element pollution was the intrusion of seawater loaded with phosphate effluent, followed by atmospheric deposition and soil leaching. This hydrosystem, therefore, deserves special attention for better planning its management. Full article

This paper introduces a groundbreaking decentralized approach for real-time bus monitoring and geo-location, leveraging advanced geo-statistical and multivariate statistical methods. The proposed long short-term memory (LSTM) model predicts bus arrival times with confidence intervals and reconstructs missing positioning data, offering cities an accurate, resource-efficient tracking solution within typical infrastructure limits. By employing decentralized data processing, our system significantly reduces network traffic and computational load, enabling data sharing and sophisticated analysis. Utilizing the Haversine formula, the system estimates pessimistic and optimistic arrival times, providing real-time updates and enhancing the accuracy of bus tracking. Our innovative approach optimizes real-time bus tracking and arrival time estimation, ensuring robust performance under varying traffic conditions. This research demonstrates the potential of integrating advanced statistical techniques with decentralized computing to revolutionize public transit systems. Full article

Mediterranean saline wetlands are significant ecological habitats defined by seasonal water availability and various biological communities, forming a unique ecotone that combines traits of both freshwater and marine environments. Moreover, they are regarded as notable natural and economic resources. Since the sustainable management of protected wetlands necessitates a multidisciplinary approach, the purpose of this study is to provide a comprehensive picture of the hydrological, hydrochemical, and ecological dynamics of a man-made groundwater dependent ecosystem (GDE) by combining remote sensing, hydrochemical data, geostatistical tools, and ecological indicators. The study area, called “Le Soglitelle”, is located in the Campania plain (Italy), which is close to the Domitian shoreline, covering a surface of 100 ha. The Normalized Difference Water Index (NDWI), a remote sensing-derived index sensitive to surface water presence, from Sentinel-2 was used to detect changes in the percentage of the wetland inundated area over time. Water samples were collected in four campaigns, and hydrochemical indexes were used to investigate the major hydrochemical seasonal processes occurring in the area. Geostatistical tools, such as principal component analysis (PCA) and independent component analysis (ICA), were used to identify the main hydrochemical processes. Moreover, faunal monitoring using waders was employed as an ecological indicator. Seasonal variation in the inundation area ranged from nearly 0% in summer to over 50% in winter, consistent with the severe climatic oscillations indicated by SPEI values. PCA and ICA explained over 78% of the total hydrochemical variability, confirming that the area’s geochemistry is mainly characterized by the saltwater sourced from the artesian wells that feed the wetland. The concentration of the major ions is regulated by two contrasting processes: evapoconcentration in summer and dilution and water mixing (between canals and ponds water) in winter. Cl⁻/Br⁻ molar ratio results corroborated this double seasonal trend. The base exchange index highlighted a salinization pathway for the wetland. Bird monitoring exhibited consistency with hydrochemical monitoring, as the seasonal distribution clearly reflects the dual behaviour of this area, which in turn augmented the biodiversity in this GDE. The integration of remote sensing data, multivariate geostatistical analysis, geochemical tools, and faunal indicators represents a novel interdisciplinary framework for assessing GDE seasonal dynamics, offering practical insights for wetland monitoring and management. Full article

The issue of sulfate (SO₄²⁻) pollution in groundwater has already attracted widespread attention from scientists. However, at the large-scale regional level, especially in areas with groundwater overexploitation, the pollution mechanisms and sources of sulfate remain unclear. This study innovatively investigates the spatial distribution characteristics and sources of SO₄²⁻ in the groundwater of the Hutuo River alluvial fan area, an understudied region facing significant environmental challenges due to overexploitation. Utilizing a combination of hydrochemical analysis, multivariate statistical methods, and geostatistical techniques, we reveal that the mean concentration of SO₄²⁻ is significantly higher (127 mg/L) in overexploited areas, with an exceedance rate of 5.1%. Our findings uncover substantial spatial heterogeneity in SO₄²⁻ concentrations, with particularly high levels in the river valley plain (RVP) (175 mg/L) and the upper area of the alluvial fan (UAF) (169 mg/L), which we attribute to distinct human activities. A novel contribution of our study is the identification of groundwater depth as a critical factor influencing SO₄²⁻ distribution (p < 0.001). We also demonstrate that the higher proportion of sulfate-type waters in overexploited areas is primarily due to the accelerated oxidation of sulfide minerals caused by overexploitation. Principal component analysis (PCA) and correlation analysis further identify the main sources of SO₄²⁻ as industrial wastewater, domestic sewage, the dissolution of evaporites, and the oxidation of sulfide minerals. By integrating geostatistical techniques, we present the spatial distribution of sulfate pollution sources at a fine scale, providing a comprehensive and spatially explicit understanding of the pollution dynamics. These results offer a novel scientific basis for developing targeted strategies to control sulfate pollution and protect the sustainable use of regional groundwater resources. Our study thus fills a critical knowledge gap and provides actionable insights for groundwater management in similar regions facing overexploitation challenges. Full article

Groundwater is a critical drinking water source in arid regions globally, where reliance on groundwater is highest. However, disparities in groundwater availability, access, and quality pose challenges to water security. This case study employs geostatistical tools, multivariate regression, and clustering analysis to examine the intersection of groundwater level changes (availability), socioeconomic and regulatory factors (access), and nitrate and arsenic contamination (quality) across 1881 groundwater-supplied drinking water service areas in Arizona. Groundwater availability declined over 20-year and 10-year periods, particularly outside designated management areas, with mean annual decline rates ranging from −15.97 to −0.003 m/year. In contrast, increases (0.003 to 13.41 m/year) were concentrated in urban and managed areas. Karst aquifers show long-term resilience but short-term vulnerability. Non-designated areas exhibit mixed effects, reflecting variable management effectiveness. Disparities in groundwater access emerge along various socioeconomic and regulatory lines. Communities with higher Black populations are twice as likely (OR = 2.01, p < 0.001) to experience groundwater declines, while Hispanic/Latino communities have lower depletion risks (OR = 0.92, p < 0.001). Tribal oversight significantly reduces groundwater decline risk (OR = 0.62, p < 0.001), whereas state–primacy areas show mixed effects. Higher female populations correlate with increased groundwater declines, while older populations (65+) experience greater stability. Married-family households and institutional housing are associated with greater declines. Migrant worker housing shows protective effects in long-term models. Rising groundwater levels are associated with higher nitrate and arsenic detection, reinforcing recharge-driven contaminant mobilization. Nitrate exceedance (OR = 1.05) responds more to short-term groundwater changes, while arsenic exceedance persists over longer timescales (OR = 1.01–1.05), reflecting their distinct hydrogeochemical behaviors. Community water systems show higher pollutant detection rates than domestic well areas, suggesting monitoring and infrastructure differences influence contamination patterns. Tribal primacy areas experience lower groundwater declines but show mixed effects on water quality, with reduced nitrate exceedance probabilities; yet they show variable arsenic contamination patterns, suggesting that governance influences availability and contamination dynamics. These findings advance groundwater sustainability research by quantifying disparities across multiple timescales and socio-hydrogeological drivers of groundwater vulnerability. The results underscore the need for expanded managed aquifer recharge, targeted regulatory interventions, and strengthened Tribal water governance to reduce inequities in availability, access, and contamination risk to support equitable and sustainable groundwater management. Full article

(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. Full article

Laterite-type bauxite deposits typically exhibit a highly irregular boundary between the bauxite and underlying ferricrete units. This irregularity cannot be accurately modeled using data collected from sparsely spaced drillholes (e.g., $76.2\times 76.2$ m or $250\times 250$ ft). Geological models that assume a sharp and nearly horizontal bauxite/ferricrete contact can result in significant errors when calculating the in situ bauxite resource (in volume) and in misclassifying ore and waste during mining operations. Two primary sources of uncertainty must be addressed when modeling lateritic bauxite deposits: (1) grade uncertainty associated with variations in Al₂O₃ and SiO₂% concentrations, and (2) geometric uncertainty related to lateral variations in the bauxite/ferricrete contact. Among these, geometric uncertainty is more critical, as accurately estimating bauxite ore tonnage depends on the precise modeling of the lateral variation in the boundary between the bauxite and underlying ferricrete units. This study evaluates the uncertainty of the bauxite resource within a selected mine area in northern Queensland, Australia, particularly in cases where experimental data are sparse and limited. To address this, the position variable (bauxite floor elevations) and the thickness of the bauxite unit are jointly simulated under two scenarios. In the first scenario, the histograms, variogram model parameters, and the estimated trend of the variables of interest are assumed to be known with certainty; that is, parameter uncertainty is not considered in the modeling process. In the second scenario, the histograms, variogram model parameters, and the estimated trend are considered uncertain, and parameter uncertainty is explicitly incorporated into the modeling process using the multivariate spatial bootstrap procedure. The methodology is applied to both scenarios, showing that incorporating parameter uncertainty in geostatistical modeling results in greater dispersion of the uncertainty associated with the in situ bauxite resource. The results show that the $95\%$ confidence intervals for the in situ bauxite ore volume, derived from bauxite thickness realizations, vary depending on whether parameter uncertainty is considered. When parameter uncertainty is incorporated, the interval is (390,123 ${\mathrm{m}}^3$ and 393,223 ${\mathrm{m}}^3$), whereas without parameter uncertainty, it is (382,332 ${\mathrm{m}}^3$ and 384,373 m³). This comparison highlights that incorporating parameter uncertainty provides a more realistic assessment of resource risk in the modeling process. Full article

The phenomenon of seawater intrusion is becoming increasingly problematic, particularly in low-lying coastal regions and areas that rely heavily on aquifers for their freshwater supply. It is, therefore, vital to address the causes and consequences of this phenomenon in order to ensure the security of water resources and the sustainable use of water. The objective of this paper was twofold: firstly, to delineate zones with different salinization levels over time; secondly, to investigate the factors controlling seawater intrusion of the Nile Delta aquifer. Aquifer data were collected in Sharkia governorate, Egypt, over three historical periods of years: 1996, 2007, and 2018. The dataset used to create the linear model of coregionalization consisted of hydrogeological (water level), hydrodynamic (pH, EC, Na, Mg, K, Ca, HCO₃, SO₄), and auxiliary (distances from salt and freshwater sources) variables. Cokriging was applied to produce spatial thematic maps of the studied variables for the three years of the survey. In addition, factorial cokriging was applied to understand the processes beyond the change in the aquifer water quality and map the zones with similar characteristics. Results of mapping the first factor at long range over the three years indicated that there was an increase in seawater intrusion, especially in the northeastern part of the study area. The main cause of aquifer salinization over time was the depletion of the groundwater resource due to overexploitation. Full article

In the realm of hydrological engineering, integrating extensive geospatial raster data from remote sensing (Big Data) with sparse field measurements offers a promising approach to improve prediction accuracy in groundwater studies. In this study, we integrated multisource data by applying the LMC to model the spatial relationships of variables and then utilized block support regularization with collocated block cokriging (CBCK) to enhance our predictions. A critical engineering challenge addressed in this study is support homogenization, where we adjusted punctual variances to block variances and ensure consistency in spatial predictions. Our case study focused on mapping groundwater table depth to improve water management and planning in a mixed land use area in Southeast Brazil that is occupied by sugarcane crops, silviculture (Eucalyptus), regenerating fields, and natural vegetation. We utilized the 90 m resolution TanDEM-X digital surface model and STEEP (Seasonal Tropical Ecosystem Energy Partitioning) data with a 500 m resolution to support the spatial interpolation of groundwater table depth measurements collected from 56 locations during the hydrological year 2015–16. Ordinary block kriging (OBK) and CBCK methods were employed. The CBCK method provided more reliable and accurate spatial predictions of groundwater depth levels (RMSE = 0.49 m), outperforming the OBK method (RMSE = 2.89 m). An OBK-based map concentrated deeper measurements near their wells and gave shallow depths for most of the points during estimation. The CBCK-based map shows more deeper predicted points due to its relationship with the covariates. Using covariates improved the groundwater table depth mapping by detecting the interconnection of varied land uses, supporting the water management for agronomic planning connected with ecosystem sustainability. Full article

Due to their complex morphology, karst terrains are particularly more fragile and vulnerable to environmental damage compared to most natural systems. Their hydraulic properties, such as their transmissivity (T) and spatial variability, can be relevant for understanding groundwater flow and, consequently, for the sustainable management of water resources. The application of geostatistical methods allows for spatial interpolation and mapping based on observations combined with uncertainty quantification. Direct measurements of T are typically scarce, while those of the specific capacity (S_c) are more frequent. We established a linear and spatial relationship between the logarithms of T and S_c measured in 174 wells in a semi-arid karst region in northeastern Brazil. These relationships were used to construct a cross-variogram, whose Linear Model of Coregionalization proved valid. The values and the cross-variogram of logT and logS_c were used to generate interpolations over 2554 values of logS_c, which did not spatially coincide with logT. We used ordinary co-kriging (CO-OK) and conditional sequential Gaussian co-simulation (CO-SGS) to generate the interpolations. The cross-variogram of logT and logS_c, when considering 174 wells, was isotropic with an exponential structure, a nugget effect of approximately 20% of the sill, and a range of 5 km. Cross-validation indicated an optimal number of 10 neighboring wells used in CO-OK, and we used 500 stochastic realizations in CO-SGS, which were then used to generate maps of logT estimates, deviations derived from the interpolations, and probabilistic scenarios. The resulting transmissivity maps are relevant for the design of groundwater management strategies, including stochastic approaches where the transmissivity realizations can be used to parameterize multiple executions of numerical flow models. Full article

The increasing variability of precipitation, higher temperatures, and recurring droughts in the semi-arid regions due to climate change are leading to increased aridity, resulting in scarcer water resources for crops. The present study aimed to analyse the spatial distribution of climate variables related to water resources in the olive-growing areas throughout Extremadura, southwestern Spain. To perform this task, three climate variables were used: the potential evapotranspiration of the crop, the FAO aridity index, and the annual water requirement. Considering data from 58 weather stations located throughout Extremadura and 17 along boundaries with at least a 30-year length (within the 1991–2021 period), each variable was computed at each station. After calculating some descriptive statistics, a multivariate geostatistical (regression-kriging) algorithm, incorporating secondary information on elevation and latitude, was used to accurately map each climate variable. Later, temporal trends and their magnitude were analysed using the Mann–Kendall test and the Sen’s estimator, respectively. The highest evapotranspiration and water requirements are located in the southern part of the region, which has large areas dedicated to olive cultivation. In the northern part of the region, there is greater spatial variability in evapotranspiration and, consequently, in water requirements for olive groves due to the more rugged topography. Similarly, the olive-growing areas with the highest aridity are also in the south of Extremadura. In most areas of Extremadura, olive cultivation requires appropriate irrigation for optimal productivity. According to evapotranspiration trends, the water requirements will become greater in the future. However, it is not guaranteed that the water supply will be sufficient in olive-growing areas where aridity is higher and water resources are scarce. The results of this study are very important for evaluating water deficit and water resources in vulnerable olive-growing areas throughout Extremadura. Full article