Geostatistical Analysis Research Articles

Source Analysis and Bioavailability of Soil Cadmium in Poyang Lake Plain of China Based on Principal Component Analysis and Positive Definite Matrix Factor

In order to identify the source and bioavailability of soil Cd in the alluvial geological high background area, 3799 topsoil samples and 140 root soil samples were collected from the Jiulong area on the south bank of Poyang Lake, Jiangxi Province. The results confidently indicate that the range of topsoil Cd content is 0.02~8.12 ppm, with an exceedance rate of 14.6%. It is noteworthy that exceedance points were mostly distributed in quaternary sediments. The geostatistical analysis clearly shows that the spatial distribution characteristic of topsoil Cd is higher in the north and lower in the south. The area with extremely high values (>90% quantile) is predominantly located in the tidal flat and low-lying areas of the mainstream of the Xinjiang River. The PCA analysis confirms that the main source of topsoil Cd is man-made activities related to industry and mining. Soil Cd is primarily derived from upstream industrial and mining discharge (58.5%), as revealed by the PMF model analysis. The active Cd content in root soil is 77 ppb, with higher levels observed in the north and lower levels in the south. These findings suggest a significant risk of Cd diffusion along the upstream stream and deposition in the low-lying parts of the north. It is worth noting that Cd’s bioavailability and active forms in paddy fields are the highest, which can have a detrimental effect on food security. The correlation analysis suggests that genetic sources primarily control the bioavailability of soil Cd, followed by soil physicochemical properties such as SOC, Fe-Al oxides, and soil texture. Preventing source pollution and cutting off water system transmission are effective methods for preventing and controlling soil Cd pollution. These methods ensure the safety of cultivated land in the lakeside area of Poyang Lake Plain.

Toxic element characterization against a typical high geology background: Pollution enrichment, source tracking, spatial distribution, and ecological risk assessment

The geological environment determines the initial content of various elements in soil, while the late input of toxic elements produced through weathering and leaching is a persistent threat to food security and human health. In this study, we selected the Lou Shao Basin, a black rock system background, and combined geostatistical analysis and multivariate statistics to quantify the specific contribution of weathering of the black rock system, and to analyze the source traces, spatial distributions, and ecological risks of the soil toxicity of elements. The results show that the soils in the study area are acidic, which is related to the weathering of sulfides in the black rock system. The concentrations of most elements in the soil were determined to exceed the soil background values, and the Cd, Se and N contents, exceeded more than five times, especially Se, Mo nearly as high as 13 times. Strong positive correlation between Se, Cu, V and P, low correlation between N and Se, Cu, V, P, Ni and Cd.72.52%, 43%, 77.79%, 82%, 77%, and 44.1% of Cd, Se, Ni, Cu, B, and Mo came from the black rock system, respectively, which were greatly affected by geogenic weathering; V, Zn, Pb, and As are mainly from biomass burning sources; N and P are mainly from agricultural surface sources. Comparison found that the Cd and Se elements in the rocks in the study area were 16.78 times and 1.36 times higher than the world shale average, respectively, and need to pay attention to the weathering process of the two, and the spatial distribution of the 12 elements in soils showed a striped and centralized block distribution pattern, specifically around the distribution of carbonate and metamorphic rocks and other high-geology blocks. The ecological risk results showed that Cd was the main element causing high ecological risk, followed by Se and N, which were at moderate to high ecological risk levels, and Se and N showed similar ecological risk patterns, which may be related to the fact that selenium can promote the uptake and transformation of nitrogen. The present results add to the endogenous sources of toxic elements, quantify the source contributions of toxic elements in soils with high geologic backgrounds, fill this knowledge gap, and provide new insights for pollution control and ecological protection in areas with high geochemical backgrounds.

Geostatistical Analysis of Lineament Domains: The Study Case of the Apennine Seismic Province of Italy

Regional-scale swarms of subparallel linear topographic features, known as lineament domains, are a common feature of planetary surfaces. Lineament domains are superficial manifestations of the crustal stress field trajectory. Notably, one of the effects of active tectonics is seismicity. Italy is one of the most seismically active regions in the Mediterranean, with many destructive earthquakes that have occurred in past centuries. Here, we assess the seismic meaning of the main lineament domain in the tectonically active region of Central Italy. We describe the use of an automated analysis of satellite imagery coupled with spatial grid analysis to identify three lineament domains of the Central Apennines. Spatial and azimuthal comparisons of the main lineament domain (i.e., the Apennine Domain), with the known locations of earthquakes (moment magnitude of Mw > 5.5) that occurred during the past century, revealed the most seismically active tectonic areas and their spatial distributions. Further, we present a conceptual seismo-geodynamic model for the Central Apennines, which is characterized by regional arching and explains the presence of an extensional tectonic regime in the upper crustal layer of the active Apennines fold-and-thrust belt.

Quantitative assessment of ecological risk from pollution source based on geostatistical analysis and APCS-MLR model.

The safety of human health and agricultural production depends on the quality of farmland soil. Risk assessment of heavy metal pollution sources could effectively reduce the hazard of soil pollution from various sources. This study has identified and quantitatively analyzed pollution sources with geostatistical analysis and the APCS-MLR model. The potential ecological risk index was combined with the APCS-MLR model which has quantitatively calculated the source contribution. The results revealed that As, Cr, Cd, Pb, Zn, and Cu were enriched in soil. Geostatistical analysis and the APCS-MLR model have apportioned four pollution sources. The Mn and Ni were attributed to natural sources; As and Cr were from agricultural activities; Cu and Zn were originated from natural sources; Cd and Pb were derived from atmospheric deposition. Atmospheric deposition and agricultural activities were the largest contributors to ecological risk of heavy metals in soil, which accounted for 56.21% and 36.01% respectively. Atmospheric deposition and agricultural activities are classified as priority sources of pollution. The combination of source analysis receptor model and risk assessment is an effective method to quantify source contribution. This study has quantified the ecological risks of soil heavy metals from different sources, which will provide a reliable method for the identification of primary harmfulness sources of pollution for future studies.

Examination of the role of mutualism in immune evasion.

Though the earliest stages of oncogenesis, post initiation, are not well understood, it is generally appreciated that a successful transition from a collection of dysregulated cells to an aggressive tumour requires complex ecological interactions between cancer cells and their environment. One key component of tumorigenesis is immune evasion. To investigate the interplay amongst the ecological behaviour of mutualism and immune evasion, we used a computational simulation framework. Sensitivity analyses of the growth of a virtual tumour implemented as a 2D-hexagonal lattice model suggests tumour survival depends on the interplay between growth rates, mutualism and immune evasion. In 60% of simulations, cancer clones with low growth rates, but exhibiting mutualism were able to evade the immune system and continue progressing suggesting that tumours with equivalent growth rates and no mutualism are more likely to be eliminated than tumours with mutualism. Tumours with faster growth rates showed a lower dependence upon mutualism for progression. Geostatistical analysis showed decreased spatial heterogeneity over time for polyclonal tumours with a high division rate. Overall, these results suggest that in slow growing tumours, mutualism is critical for early tumorigenesis.

Mapping soil trace metal distribution using remote sensing and multivariate analysis.

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

Geostatistical assessment of groundwater arsenic contamination in the Padana Plain

Arsenic (As) in groundwater from natural and anthropogenic sources is one of the most common pollutants worldwide affecting people and ecosystems. A large dataset from >3600 wells is employed to spatially simulate the depth-averaged As concentration in phreatic and confined aquifers of the Padana Plain (Northern Italy). Results of in-depth geostatistical analysis via PCA and simulations within a Monte Carlo framework allow the understanding of the variability of As concentrations within the aquifers. The most probable As contaminated zones are located along the piedmont areas in the confined aquifers and in the lowland territories in the phreatic aquifers. The distribution of the As contaminated zones has been coupled with hydrogeological, geological, and geochemical information to unravel the sources and mechanisms of As release in groundwater. The reductive dissolution of Fe oxyhydroxides and organic matter mineralization under anoxic conditions resulted to be the major drivers of As release in groundwater. This phenomenon is less evident in phreatic aquifers, due to mixed oxic and reducing conditions. This large-scale study provides a probabilistic perspective on As contamination, e.g. quantifying the spatial probability of exceeding national regulatory limits, and to outline As major sources and drivers.

Evaluation of Acid Rain Impacts Using Geostatistical Analysis and Remotely Sensed Data in Kirkuk City, Iraq

Acid rain is linked to the process of urbanization and industrialization and harms buildings. This study aims to evaluateacid rain in Kirkuk city using GIS-based spatial analysis, mathematical statistical modeling, and laboratory tests andexamine the harmful effects on the city buildings and human health. Precipitation ranges were determined based onTheisen’s polygon techniques. The methodology is based on two types of laboratory measurements of specific rainfallsamples within the city for the period of January 2021 to March 2022. Geographical Information Systems GISs wereused to verify the spatial distribution and quantity of precipitation data in the study area. Remote sensing RS was alsoused for mapping rainfall rates during the study period. According to spatial distribution maps, the acidity level of rainwas classified as (least acidic) ranging between (5.5 and 5.6). Specifically in the southern and south-eastern parts of thestudy area. Besides, spatial distributions have been presented with high accuracy with 95% and 83% accuracy ratings in2021 and 2022, respectively. Thus, we attempt to offer some useful guidelines for conserving buildings so that they arepreserved for a long time. RS and GIS techniques are best suited for estimating and modeling rainfall datacharacteristics.

Innovative mapping of groundwater redox status and cation exchange conditions in a GIS environment

Understanding the complexities of regional groundwater quality is crucial for managing groundwater resources. Groundwater quality assessment involves investigating specific dissolved groundwater components, for example, comparing them to established standards. To fully understand all aspects of groundwater quality, one should assess composite properties, one being the redox status and another being the cation exchange condition. The first may, for example, impose a control on the degradation of organic micropollutants. While groundwater numerical indices can be easily interpolated and visualised using various GIS applications, consistently mapping the redox status and cation exchange conditions as non-numerical indices remains challenging. Furthermore, no study has yet conducted a regional-scale mapping of cation exchange classes in a GIS environment using extensive groundwater samples. To deepen our understanding of these groundwater components, we employed ArcGIS in this study to map the redox and cation exchange conditions in two stages. First, we mapped the groundwater components of interest, including Cl, SO4, SO4/Cl, Fe, NO3 and base exchanges of Na and Mg, by the most appropriate interpolation method identified by a geostatistical analysis. Then, variables were combined, and the conditional functions were used in ArcMap's Math toolbox to determine redox status or cation exchange classes. Our innovative GIS method for mapping regional redox status and cation exchange conditions was developed for 3350 groundwater sampling locations in the coastal lowlands of the Western Netherlands. The method was successful, with generally 75%–95% similarity between predicted and observed situations for most classes. The introduced method is more straightforward than others and can map other non-numerical linguistic indices like Wilcox groundwater and irrigation water classifications, as well.

Salinity distribution in agricultural land by geophysical, hydrochemical and geostatistical approaches: a pilot area located in Qelabshowah–Belqas, East Nile Delta region, Egypt

The study area is situated in the Qelabshowah–Belqas region, known for its Quaternary deposits. This research aims to demonstrate the two-dimensional (2D) variation of subsurface layers and salinity distribution using geoelectrical data, hydrochemical analysis, and geostatistical analysis. DC resistivity measurements were taken at fifteen vertical electrical sounding (VES) survey points using a Schlumberger array (AB/2 = 100 m) along three profiles. In addition, an electrical resistivity tomography (ERT) survey was conducted with a dipole–dipole array across one profile. Seven surface water samples were collected in the area. From the 1D and 2D inversion of VES and ERT data sets, three-to-four geoelectric layers were identified, including unconsolidated surface deposits, saturated clayey sand, saturated sand, and a salt-rich layer. The 2D inversion of VES data revealed an ancient salt-rich layer deposited in swampy conditions over a conductive wet sand layer along profile one due to salt mineral infiltration and dissolution. The 2D inversion of ERT data showed accurate lateral geometric accuracy compared to the 2D inversion of VES data, highlighting geological features, such as caves in the second layer and a buried water canal on the ground surface. Surface water samples showed high salinity levels with sodium hazards, indicating an Na–Cl composition. Geoelectric and hydrochemical data sets were geostatistically analyzed using spherical variogram supported ordinary Kriging interpolation. The analysis indicated weak to moderate spatial dependency for true resistivity parameters, while sodium content (SC) and permeability index (PI) showed strong spatial correlation. The 2D spatial distribution resistivity maps based on the 1D inversion of VES data displayed a general decrease in resistivity with depth, likely due to clay minerals or moist soil in the second layer and saline irrigation water infiltration in the third layer. The 2D spatial distribution of SC and PI showed a high concentration zone, posing a potential risk to agricultural crops regardless of soil permeability. It is recommended to use these maps when cultivating plants that can tolerate high sodium levels during the reclamation process.

Statistical appraisal of geothermal heat flow observations in the Arctic

Abstract. Geothermal heat flow is an important boundary condition for ice sheets, affecting, for example, basal melt rates, but for ice-covered regions, we only have sparse heat flow observations with partly high uncertainty of up to 30 m W m−2. In this study, we first investigate the agreement between such pointwise heat flow observations and solid Earth models, applying a 1D steady-state approach to perform a statistical analysis for the entire Arctic region. We find that most of the continental heat flow observations have a high reliability and agreement to solid Earth models, except a few data points, such as, for example, the NGRIP (North Greenland Ice Core Project) point in central Greenland. For further testing, we perform a conditional simulation with focus on Greenland in which the local characteristics of heat flow structures can be considered. Simple kriging shows that including or excluding the less reliable NGRIP point has a large influence on the surrounding heat flow. The geostatistical analysis with the conditional simulation supports the assumption that NGRIP might not only be problematic for representing a regional feature but likely is an outlier. Basal melt estimates show that such a local spot of high heat flow results in local high basal melt rates but leads to less variation than existing geophysical models.

Mapping pollen allergenicity from urban trees in Valencia: A tool for green infrastructure planning

Urban trees provide many benefits to citizens but also have associated disservices such as pollen allergenicity. Pollen allergies affect 40% of the European population, a problem that will be exacerbated with climate change by lengthening the pollen season. The allergenic characteristics of the urban trees and urban parks of the city of Valencia (Spain) have been studied. The Value of Potential Allergenicity (VPA) was calculated for all species. The most abundant allergenic trees with a very high VPA were the cypresses, followed by Platanus x hispanica and species of genera Morus, Acer and Fraxinus, with a high VPA. On the contrary, Citrus x aurantium, Melia azedarach, Washingtonia spp., Brachychiton spp. and Jacaranda mimosifolia were among the most abundant low allergenic trees. VPA was mapped for the city and a hot spot analysis was applied to identify areas of clustering of high and low VPA values. This geostatistical analysis provides a comprehensive representation of the VPA patterns which is very useful for urban green infrastructure planning. The Index of Urban Green Zone Allergenicity (IUGZA) was calculated for the main parks of the city. The subtropical and tropical flora component included many entomophilous species and the lowest share of high and very high allergenic trees in comparison with the Mediterranean and Temperate components. Overall, a diversification of tree species avoiding clusters of high VPA trees, and the prioritization of species with low VPA are good strategies to minimize allergy-related impacts of urban trees on human health.

Spatial distribution, sources, and human health risk assessment of elevated nitrate levels in groundwater of an agriculture-dominant coastal area in Hainan Island, China

In order to comprehend the groundwater nitrate contamination in Hainan Island, which is recognized as China's sole “tropical agricultural production base,” the distribution characteristics of nitrate in different aquifers and land-use types were elucidated through hydrochemical and geostatistical analysis methods. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and absolute principal component score-multiple linear regression (APCS-MLR) were employed for qualitative and quantitative analysis of the nitrate source. The results showed that NO3−-high (>88.57 mg/L) groundwater accounted for about 10 % of the total distribution area of the western coastal area in Hainan Island. Moreover, the proportions of NO3−-high groundwater in pore water and fissure water were 10 % and 7 %, respectively. The order of groundwater nitrate concentration in different land-use types was as follows: bare land, cultivated land, construction land, grassland, and woodland. The main sources of elevated nitrate concentrations in pore water were animal feces and nitrogen/potassium fertilizers, accounting for 45.4 % of the overall contribution. These substances infiltrated through the vadose zone, leaching out and mobilizing selenium within this zone, ultimately leading to the characteristic enrichment patterns observed in pore water, including nitrate, arsenic, potassium, and selenium. Furthermore, the occurrence of high nitrate in fissure water was mainly attributed to the leaching of cultured manure, septic tank leakage, and infiltration of domestic sewage, accounting for a contribution rate of 32.9 %. Simultaneously, under oxidizing conditions, selenium became activated and entered groundwater through water flow, thereby exhibiting common enrichment characteristics with nitrate and potassium in fissure water. In addition, the assessment of human health risks revealed that 16 % and 40 % of groundwater samples posed unacceptable non-carcinogenic risks to adults and children respectively, with a higher risk for local children. The study recommends the reasonable application of chemical fertilizers, as well as the strengthening of sewage treatment capacity and standardized disposal of manure and feces, to reduce nitrate contamination of groundwater at source and ensure the safety of drinking water.

Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

AbstractA precise soil organic carbon (SOC) content estimate is crucial soil quality parameter for agricultural produce and ecological safety. Moreover, geospatial modeling of SOC is critical when there are limited laboratory equipment and chemical reagents for soil analysis. This study used geostatistics—ordinary kriging (OK) and inverse distance weighting (IDW)—to map SOC in Libokemkem area, Northwest Ethiopia, for improved SOC management. About 107 soil samples were obtained from the plow layer at a 20‐cm depth and SOC was determined. Statistical Package for Social Sciences version 24.0 was used to generate descriptive statistics, and geostatistical analysis was also performed on the data using ArcGIS platform. The coefficient of determination (R2) and root mean square error (RMSE) derived from the validation of the predicted maps were used to assess the models. The results revealed homogeneity (coefficient of variation < 10%), low (0.12%–1.74%), and optimal (1.74%–4.06%) mean levels of SOC in study area. The OK showed an R2 of 0.74 and an RMSE of 13%, and the IDW revealed an R2 of 0.69 and an RMSE of 14%. The semivariogram results indicate a moderate dependence for SOC with stable, circular, spherical, exponential, and Gaussian models. We conclude that the sustainable monitoring of SOC is significant in enhancing soil quality. However, further study considering all drivers of spatial variability for SOC in the study and other soil sampling approaches improving performance of the prediction models is needed.

Greenery in times of crisis: Accessibility, residents' travel preferences and the impact of travel time

Urban green spaces (UGSs) played a pivotal role in easing the effects of COVID-19 pandemic restrictions. It is, therefore, relevant to identify factors influencing why people choose specific UGSs. This paper aims to investigate the preferences of residents of Kraków (Poland) regarding the selection of UGSs in times of crisis. Distance or travel time from home are among the key factors affecting the choice of UGSs. The paper verifies whether such other factor considerations as age, type of housing, and physical activity influence travel preferences to UGSs. We analyse residents' preferences regarding travelling to UGSs by integrating survey research with statistical and geostatistical methods, which is the innovative value of the research. The Computer Assisted Web Interview (CAWI) survey yielded 1353 returned questionnaires, which provided input data when combined with geostatistical analyses of network distance (routing). With nonparametric statistical methods, we could identify preferences of Kraków residents regarding travelling to UGSs during times of crisis. The primary results show that travel time was not always a factor when selecting UGSs. In times of crisis, the respondents were willing to cover larger distances to UGSs, namely up to 51 minutes on foot and 16 minutes by bicycle. We also demonstrated that there is a relationship between age and UGS travel time and a link between type of housing and UGS travel time. The results are inconclusive regarding the importance of distance to UGSs for people who are physically active. Note that accessibility recommendations for travel time often presented in the literature did not apply to Kraków in times of crisis.

Lineament Domain Analysis to Unravel Tectonic Settings on Planetary Surfaces: Insights from the Claritas Fossae (Mars)

Linear geo-textures are widely recognized on synthetic scaled images of planetary surfaces and consist of elongated alignments of tonal contrasts. When these linear patterns are clustered in azimuthal sets and organized in domains occurring on specific terranes, they reflect the structural grain of the crust and provide clues on the stress trajectories. In this way, the geostatistical analysis of lineament domains represents a useful tool to highlight the geotectonic settings of planetary surfaces. In this work, we applied a lineament domain analysis to better frame the tectonic evolution of the Claritas Fossae (CF) area on Mars, the origin of which is still debated, and both dip–slip and strike–slip tectonics have been described in the literature. A twofold approach was followed that included the identification of a linear pattern with manual and automatic approaches. The automatic method confirmed and validated the results of the manual detection. The statistical analysis of the identified lineaments showed their clustering in two domains that persisted on different terranes separated by the regionally sized scarp associated with the CF. This scarp is the surface manifestation of the CF crustal fault. The spatial distribution of the two domains and their constant angular relationship of about 30° allowed relating one domain to the main CF fault and the other domain to the extensional deformation associated with the fault kinematics. Our results suggest that the CF frames well within a regional setting characterized by right–lateral kinematics with about 20% transtension. Temporal constraints derive from the ages of the terrains where the two domains develop. On this basis, we propose that a first tectonic event occurred in the Noachian age followed by a reactivation occurring after the emplacement of the Late Hesperian lavas.

A novel bioprospecting strategy via 13C-based high-throughput probing of active methylotrophs inhabiting oil reservoir surface soil

Methane-oxidizing bacteria (MOB) have long been considered as a microbial indicator for oil and gas prospecting. However, due to the phylogenetically narrow breath of ecophysiologically distinct MOB, classic culture-dependent approaches could not discriminate MOB population at fine resolution, and accurately reflect the abundance of active MOB in the soil above oil and gas reservoirs. Here, we presented a novel microbial anomaly detection (MAD) strategy to quantitatively identify specific indicator methylotrophs in the surface soils for bioprospecting oil and gas reservoirs by using a combination of 13C-DNA stable isotope probing (SIP), high-throughput sequencing (HTS), quantitative PCR (qPCR) and geostatistical analysis. The Chunguang oilfield of the Junggar Basin was selected as a model system in western China, and type I methanotrophic Methylobacter was most active in the topsoil above the productive oil wells, while type II methanotrophic Methylosinus predominated in the dry well soils, exhibiting clear differences between non- and oil reservoir soils. Similar results were observed by quantification of Methylobacter pmoA genes as a specific bioindicator for the prediction of unknown reservoirs by grid sampling. A microbial anomaly distribution map based on geostatistical analysis further showed that the anomalous zones were highly consistent with petroleum, geological and seismic data, and validated by subsequent drilling. Over seven years, a total of 24 wells have been designed and drilled into the targeted anomaly, and the success rate via the MAD prospecting strategy was 83 %. Our results suggested that molecular techniques are powerful tools for oil and gas prospecting. This study indicates that the exploration efficiency could be significantly improved by integrating multi-disciplinary information in geophysics and geomicrobiology while reducing the drilling risk to a greater extent.

Análise e previsão espacial de curvas de retenção de água em dois tipos de solo

ABSTRACT Soil is a medium that stores and transfers air, water, nutrients and heat to microorganisms and plants. Its water storage capacity is studied by analysing water retention curves (WRCs), which establish the relationship between soil water content and the force with which water is retained. The objective of this work was to evaluate the capacity of functional geostatistics in predicting the spatial distribution of water retention curves in two types of soils. Experimental areas with two types of soil, Andisol and Oxisol, were selected, and a regular grid of 75 sites was established, from which water retention curves were obtained at two depths. The curves were subjected to geostatistical functional analysis (GF), and the applicability of this method was evaluated by obtaining the usable water table (LAA) and comparing the results with experimental data obtained using traditional methods. Based on cross-validation, it was verified that GF produced a better fit for the Andisol since the coefficient of determination between the LAA values for the measured data and predictions was high, with an R2 of 99%; however, the proposed methodology was also reliable for the Oxisol, since an R2 of 94% was obtained at the two depths studied.

Disparities in School Performance in Romania. A Geostatistical Analysis of Baccalaureate Results between 2015-2022

This study analyses the evolution of the main indicators of students’ high school performance at the high school graduation exams (Romanian Baccalaureate) from a geographical perspective. Both participation and pass rates fluctuate depending on the type of high school, gender and place of residence. Behind these quantifiable characteristics there are significant disparities in (in)equality of educational opportunities influenced by the questionable efficiency of the education system in providing quality education to every student, regardless of their parents’ financial background. Although the upper cycle of secondary school (11-12th grades) is not legally compulsory, successful graduation is essential for the economic, social and cultural insertion of the future adults in a functioning society. The lack of performance of some high schools and vulnerable groups of students, who face problems of accessibility, motivation, opportunity, and financial support, as well as the worrying numbers of exam absentees, must be brought baccalaureate to the permanent agenda of the policy makers, not exclusively during the exam period. The complexity of the official data from 2015-2022 has allowed the geostatistical analysis of school performance, highlighting the deep discrepancies within the system, specifically between urban and rural students, the administrative and economic hierarchy of the locality and the prestige of the high school attended. The insights of this analysis can be used to identify optimal solutions to enhance the research about educational disparities.

Temporal Stability of Management Zone Patterns: Case Study with Contact and Non-Contact Soil Electrical Conductivity Sensors in Dryland Pastures.

Precision agriculture (PA) intends to validate technological tools that capture soil and crop spatial variability, which constitute the basis for the establishment of differentiated management zones (MZs). Soil apparent electrical conductivity (ECa) sensors are commonly used to survey soil spatial variability. It is essential for surveys to have temporal stability to ensure correct medium- and long-term decisions. The aim of this study was to assess the temporal stability of MZ patterns using different types of ECa sensors, namely an ECa contact-type sensor (Veris 2000 XA, Veris Technologies, Salina, KS, USA) and an electromagnetic induction sensor (EM-38, Geonics Ltd., Mississauga, ON, Canada). These sensors were used in four fields of dryland pastures in the Alentejo region of Portugal. The first survey was carried out in October 2018, and the second was carried out in September 2020. Data processing involved synchronizing the geographic coordinates obtained using the two types of sensors in each location and establishing MZs based on a geostatistical analysis of elevation and ECa data. Although the basic technologies have different principles (contact versus non-contact sensors), the surveys were carried out at different soil moisture conditions and were temporarily separated (about 2 years); the ECa measurements showed statistically significant correlations in all experimental fields (correlation coefficients between 0.449 and 0.618), which were reflected in the spatially stable patterns of the MZ maps (averaging 52% of the total area across the four experimental fields). These results provide perspectives for future developments, which will need to occur in the creation of algorithms that allow the spatial variability and temporal stability of ECa to be validated through smart soil sampling and analysis to generate recommendations for sustained soil amendment or fertilization.