Ordinary Kriging Interpolation Research Articles

Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco

Accurate assessment of soil quality is crucial for sustainable agriculture and soil conservation. Thus, this study aimed to assess soil quality in the agricultural ecosystem of the Mnasra region within the Gharb Plain of Morocco, employing a comprehensive approach integrating multivariate analysis and geostatistical techniques. Thirty soil samples were collected from the surface layers across thirty selected sites. The results showed significant variations in soil properties across the study area, influenced by factors such as soil texture, parent material, and agricultural practices. Pearson correlation and principal component analysis (PCA) were employed to analyze the relationships among soil properties and compute the Soil Quality Index (SQI). The SQI revealed values ranging from 0.48 to 0.74, with 46.66% of sampled soils classified as “Good” and 53.33% as “Fair”. Geostatistical analysis, particularly ordinary kriging (OK) interpolation and semivariogram modeling, highlighted the spatial variability of soil properties, aiding in mapping soil quality across the landscape. The integrated approach demonstrates the importance of combining field assessments, statistical analyses, and geospatial techniques for comprehensive soil quality evaluation and informed land management decisions. These findings offer valuable insights for decision-makers in monitoring and managing agricultural land to promote sustainable development in the Gharb region of Morocco.

An efficient method for modeling and evaluating the bench terrain of open-pit mines

In order to quantitatively analyze the roughness of the bench floor during open-pit mine blasting, this study proposes a real-time measuring method for the three-dimensional terrain of the bench floor during the excavation process. Real-time monitoring is conducted at the boundary and discrete internal points of the workbench floor during electric shovel operation, utilizing real-time kinematic global navigation satellite system (RTK-GNSS) positioning technology. An improved convex hull algorithm is introduced to automatically extract the optimal boundary of discrete point clouds based on their spatial distribution characteristics. This study establishes a digital elevation model (DEM) using five interpolation algorithms for 3D terrain visualization simulation. Through cross-validation, a comparative analysis of the DEM accuracy, the simulation results of the ordinary kriging interpolation algorithm were found to be optimized. The optimized interpolation algorithm is applied to simulate the 3D terrain in the Dexing open-pit copper mine, and the relevant terrain parameters were calculated. This dataset can serve as a precise foundation for the real-time path planning of elevation blasting design and ground leveling operations.

The spatial variation and drivers of soil δ13C in an alpine-gorge region on the eastern Qinghai-Tibetan Plateau

Soil stable carbon isotope (soil δ13C) can reflect soil carbon metabolic processes and record environmental and vegetation change information, so mapping the spatial distribution of soil δ13C (isoscapes of soil δ13C) can help us better understand the spatial variability of ecosystem carbon cycles. However, efficient approaches for obtaining the isoscapes of soil δ13C remain challenging, especially in mountainous areas with complex terrain. A total of 150 soil samples were collected and their soil δ13C was measured to investigate the spatial variation of soil δ13C in an alpine-gorge region on the eastern Qinghai-Tibetan Plateau which is characterized by complex terrain. Four prediction methods included ordinary kriging interpolation, multiple linear regression, random forest regression, and geographically weighted regression (GWR) were selected and compared to find the best prediction model for mapping the isoscapes of soil δ13C. The pathways of vegetation, topography, soil, and spatial factors influencing the spatial variability of soil δ13C were explored using variance partitioning analysis and structural equation model. The soil δ13C was significantly different among vegetation types, and ranged from −27.155‰ to −9.647‰. The spatial heterogeneity of soil δ13C showed moderate variation, and was dominated by spatial structural factors. The GWR model had higher prediction accuracy in the modeling soil δ13C in comparison to other models. Soil carbon to nitrogen ratio and normalized difference vegetation index were the main factors determining the spatial variability of soil δ13C, and other topographic and soil factors indirectly regulated the spatial distribution of soil δ13C by influencing these two factors. Our results provided a useful tool (GWR model) for mapping the isoscapes of soil δ13C and explored the factors controlling the spatial variability of soil δ13C in the alpine-gorge region on the eastern Qinghai-Tibetan Plateau, which are important for an in-depth understanding of soil carbon cycling in mountain ecosystems.

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.

Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest–Ordinary Kriging Model

The accurate prediction of soil contamination in abandoned mining areas is necessary to address their environmental risks. This study employed a combined model of machine learning and geostatistics to predict the spatial distribution of soil contamination using heavy metal data collected in an abandoned metal mine. An exploratory data analysis was used to identify patterns in the collected data, the root mean squared error (RMSE) and coefficient of determination (R2) were used to verify the predicted values, and the model was validated using K-fold cross-validation. The prediction results were produced as a map by applying hyperparameter tuning to Random Forest (RF) and Ordinary Kriging (OK) through GridSearchCV using optimal parameter selections. Furthermore, the prediction residuals of the RF model were calculated, and the RF prediction map and OK interpolation results of the RF prediction residuals were summarized to construct an RF–OK prediction map. The RMSE and R2 values for the RF, OK, and RF–OK interpolation models were 66.214, 65.101, and 52.884 mg/kg and 0.867, 0.871, and 0.915, respectively. In addition, the optimization results with the minimum RMSE and maximum R2 were obtained through hyperparameter tuning. The proposed RF–OK hybrid model demonstrated superior prediction performance compared to the individual models.

Downscaling and merging multiple satellite precipitation products and gauge observations using random forest with the incorporation of spatial autocorrelation

Publicly available satellite precipitation products (SPPs) are essential inputs for hydrological models. However, the existing SPPs suffer from coarse resolutions and large uncertainties, which greatly hinder their widespread applications. Thus, to fully utilize the unique characteristics of each individual SPP, a spatial random forest (SRF)-based downscaling and merging (SRF-DM) method is proposed to fuse multiple SPPs and gauge observations in this paper. The proposed method incorporates spatial autocorrelation information between precipitation observations through a covariate. This covariate is estimated using ordinary kriging interpolation on spatial neighbors that have a high correlation with the target location. The proposed method was used to generate 1-km daily precipitation data using the datasets from five satellite precipitation products (IMERG, GSMaP, CHIRPS, MORPH, and PERSIANN) and gauge observations in the period from January 1, 2015 to December 31, 2019 over Sichuan province, China. The performance of SRF-DM was compared with the original SPPs, XGBoost-based downscaling and merging (XGB-DM), two double-layer machine learning (ML) methods, namely RF-RF and RF-artificial neural network (RF-ANN), and three single ML methods, namely RF, ANN, and RF-based Merging Procedure (RF-MEP). The experimental results demonstrate the following findings: (i) SRF-DM is more accurate than the original SPPs across different temporal scales (i.e., daily, monthly, and seasonal) in terms of statistical and categorical accuracy measures, (ii) SRF-DM outperforms the six ML-based methods in estimating high precipitation and capturing spatial precipitation patterns, (iii) the spatial autocorrelation between neighboring precipitation data is the most significant factor in both downscaling and merging, and (iv) the performance of SRF-DM is influenced by gauge density, but it still yields good results even with a limited number of gauges. On the whole, SRF-DM is considered an effective tool for generating daily precipitation products with high accuracy and resolution, which is primarily attributed to its satisfactory performance in regions with diverse precipitation patterns and topography.

Spatial Variability of Selected Soil Chemical Parameters in Low-yielding Paddy Production Block in Mahaweli System H, Sri Lanka

Although water availability for paddy production in Mahaweli System H is abundant, paddy yield is lower in Nochchiyagama (278 km2), chosen for soil assessment. Paddy yield data were used to identify the low-yielding division using the area-weighted average. Twenty-five random locations were generated, and soil samples were collected for pH, soil conductivity (EC), salinity, and total dissolved solids (TDS) analysis. ArcGIS software was used to create spatial distribution maps and related geostatistical analyses for each parameter. Vector files were created with their associated properties, and thematic maps were generated using spatial interpolation techniques, such as universal kriging (UK), ordinary kriging (OK), and inverse distance weighted (IDW) methods of interpolation techniques to identify the best interpolation method for soil chemical parameters mapping. The entire Nochchiyagama land was observed to have a slightly acidic pH (5.6-5.9) range that may have affected rice crop growth due to nutrient mobility and uptake issues. The spatial interpolation evaluation suggests that at least two-thirds of the area observed for lower TDS levels (591-654 mg/L) is potentially unsafe paddy production. Elevated levels of EC (3.1-7.24 dS/m) along with TDS may lead to physiological drought due to interferences in ion uptake. In overall, spatial interpolation evaluation indicators suggest that the UK method was observed with a lower mean relative error (MRE) than the other two interpolations. However, EC distribution showed low MRE in both IDW and OK interpolation techniques. The IDW method was observed to have a lower RMSE (Root mean square error). The UK spatial interpolation performed better for TDS and salinity predictions than other methods. This study found consistent regional differences in low paddy yields in Mahaweli system H using the UK method for analyzing soil chemical parameters. 
 Keywords: Electrical conductivity, Interpolation errors, Salinity, Spatial interpolation

Source-oriented risk assessment of heavy metal(loid)s in agricultural soils around a multimetal smelting area near the Yellow River, China.

Heavy metal(loid) (HM) contamination in agricultural soils, particularly in areas severely impacted by smelting industries, has attracted worldwide attention. In this study, agricultural soils were collected in a flourishing multimetal smelting area near the Yellow River in central China. By an integrated approach encompassing the positive matrix factorization model, ordinary kriging interpolation and hierarchical clustering analysis (PMF-OK-HC), a total of four major sources and their mass contributions were identified, namely, soil parent material (56.6%), industrial waste and Mo smelting (24.0%), metal smelting and traffic emissions (12.8%), and coal combustion (6.7%). On this basis, the health risk of HMs was evaluated by Monte Carlo simulations and showed that a higher risk, with a higher proportion of exceeding-thresholds risk, was observed for children than for adults in terms of both noncarcinogenic and carcinogenic risks. Exposure pathways of oral ingestion in children could result in a higher attributed risk than other pathways. Furthermore, source-oriented risk assessment (SORA) revealed that the sources of coal combustion, industrial waste and Mo smelting had the highest contributions to noncarcinogenic and carcinogenic risks. Overall, for effective environmental management in agricultural soil, the framework of SORA was verified as an effective tool in the identification of the priority control of HMs and their sources.

A Comparison of Spatial Interpolation Methods for Regionalizing Maximum Daily Rainfall Data in South Sulawesi, Indonesia

The aim of this research is to compare between the Inverse Distance Weighted (IDW) and Ordinary Kriging (OK) interpolation methods for regionalization of areas within the South Sulawesi province based on maximum daily rainfall. The data utilized consists of maximum daily rainfall data from 56 rain stations within the South Sulawesi from 1986 to 2021. The spatial interpolation methods applied include the power 2 IDW, and OK. Various semivariogram models, namely Spherical, Gaussian, and Exponential, are employed within the OK method. The selection of the best method is based on the smallest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. The findings of this research reveal that the optimal method for regionalization of maximum daily rainfall is the OK method with a Gaussian semivariogram model. The RMSE values for this method are 57.45, and the MAE values are 46.49. The results of the spatial interpolation demonstrate that the South Sulawesi is divided into four zones characterized by maximum daily rainfall (in mm) as follows: Zone I: less than 230 mm (Eastern and Southeastern regions), Zone II: 230-260 mm (Northern region), Zone III: 260-280 mm (Western region), and Zone IV: more than 280 mm (Southwestern region).

The Least Limiting Water Range to Estimate Soil Water Content Using Random Forest Integrated with GIS and Geostatistical Approaches

In this study, as a result of the determination of the Least limiting water range (LLWR) contents of alluvial lands with different soils, which are distributed in the Bafra Plain where intensive agricultural activities are carried out, the compression and aeration problems in the area are revealed with distribution maps. In addition, the predictability of LLWR was evaluated with the random forest (RF) algorithm, which is one of the machine learning algorithms, and the usability of the prediction values distribution maps was revealed. The LLWR contents of the soils varied in the range of 0.049- 0.273 cm3 cm-3 for surface soils. While there were aeration problems in 6.72%, compaction problems in 20.16%, both aeration and compaction problems in 0.8% of the surface soils examined in the study area, 72.32% were determined under optimal conditions. For the 20-40 cm depth, an aeration problem in the 5.88%, a compaction problem in the 28.57%, and both an aeration and a compaction problem in the 2.52% of the points were detected. In the estimation of LLWR with the random forest algorithm, the RMSE value obtained for 0-20 cm depth was determined as 0.0218 cm3 cm-3, and for 20-40 cm it was determined as 0.0247 cm3 cm-3 . In the distribution maps of the observed and predicted values obtained, the lowest RMSE value was determined by Simple Kriging interpolation methods for 0-20 cm depth and Ordinary Kriging interpolation methods for 20-40 cm. While the distribution of obtained and predicted values in surface soils was similar, variations were found in the distribution of areas with low LLWR below the surface. As a result of the study, it has been revealed that LLWR can be obtained with a low error rate with the RF algorithm, and distribution maps can be created with lower error in surface soils.

Study on the regional ASF prediction method based on the ordinary kriging interpolation

The eLoran system functions as a robust backup to the Global Navigation Satellite System (GNSS), providing substantial signal power, robust anti-jamming capabilities, and an easily maintainable ground system. Nonetheless, the propagation time delay of the system, primarily driven by the Additional Secondary Phase Factor (ASF), significantly influences the positioning accuracy. Compensating for ASF can effectively enhance the positioning performance. Traditional propagation delay theories frequently yield substantial discrepancies between the predicted and measured values in regions characterized by extended propagation distances and varying topographic features. To address this issue, we conducted ASF measurements within a selected test area using a limited number of measurement points. We employed the ordinary Kriging interpolation method to predict ASF values across the entire test area, and used cross-validation to validate our predictions. The results confirmed the accuracy and effectiveness of the Kriging interpolation algorithm in predicting ASF values within specific regions. The cross-validation demonstrated that the errors remained within acceptable ranges. Furthermore, we applied Ordinary Kriging, Inverse Distance Weighting, and Radial Basis Function Interpolation methods to evaluate the positioning accuracy of the test area before and after ASF correction. Compared to other methods, using the Ordinary Kriging interpolation algorithm for predicting ASF values resulted in a corrected positioning accuracy of up to 68.8 m at various test locations. This approach effectively resolves the challenge of low accuracy in theoretical calculations in complex environments. By utilizing ordinary Kriging interpolation, we required measurements of only a few ASF values within a specific region to create an ASF correction map, addressing the challenges related to inaccurate theoretical calculations in complex pathways and avoiding time-consuming and labor-intensive large-scale measurements. The results of this study offer valuable theoretical support for improving the accuracy of land-based navigation systems.

Prediction of Spatial Distribution of Soil Organic Carbon in Helan Farmland Based on Different Prediction Models

Soil organic carbon (SOC) is widely recognized as an essential indicator of the quality of arable soils and the health of ecosystems. In addition, an accurate understanding of the spatial distribution of soil organic carbon content for precision digital agriculture is important. In this study, the spatial distribution of organic carbon in topsoil was determined using four common machine learning methods, namely the back-propagation neural network model (BPNN), random forest algorithm model (RF), geographically weighted regression model (GWR), and ordinary Kriging interpolation method (OK), with Helan County as the study area. The prediction accuracies of the four different models were compared in conjunction with multiple sources of auxiliary variables. The prediction accuracies for the four models were BPNN (MRE = 0.066, RMSE = 0.257) > RF (MRE = 0.186, RMSE = 3.320) > GWR (MRE = 0.193, RMSE = 3.595) > OK (MRE = 0.198, RMSE = 4.248). Moreover, the spatial distribution trends for the SOC content predicted with the four different models were similar: high in the western area and low in the eastern area of the study region. The BPNN model better handled the nonlinear relationship between the SOC content and multisource auxiliary variables and presented finer information for spatial differentiation. These results provide an important theoretical basis and data support to explore the spatial distribution trend for SOC content.

Exploring spatial patterns of cardiovascular disease among women in India

Abstract Cardiovascular disease (CVD) has become a leading cause of mortality and morbidity in India, surpassing the global rate. While CVD is influenced by gender due to sex-specific risk factors in women, it remains underdiagnosed, and undertreated in women. WHO provides a list of ‘best buys’ which does not consider sex-specific risk factors. Hence, we assessed prevalence, unawareness and spatial variations of CVDs in women (15-49 years). We used NFHS (2019-20) WHO causal links between underlying drivers, behavioral risk factors, metabolic/physiological risk factors, and CVDs. We used ordinary kriging interpolation techniques to measure the spatial heterogeneity of CVD prevalence and spatial autocorrelation techniques using global Moran I statistic. Furthermore, ordinary least square were used to examine spatial predictors and geographically weighted regression and multiscale geographically weighted regression model were used to map the local spatial variations. CVDs prevalence in women is 11.7% [95%, CI: 0.117-0.118], its unawareness is 12% [95%, CI: 0.116-0.117] among women. Spatial regression analysis suggests observed geographical variation of CVDs significantly correlated with risk factors- metabolic (BMI), behavioral (tobacco consumption), and underlying (sex-specific - women's education as a proxy for autonomy and socioeconomic depriviation- household wealth). India has a high burden of CVDs in women, with state-level disparities and a lack of awareness about the disease. Women are not a target population for CVDs in SDG 3.4 and India's National Health policy, 2017; however, its rising epidemic burden on women demands a need for policy choices targeted at women. The study provides evidence to inform the policy choices in reducing CVDs burden in women. It suggests a need to develop gender-specific guidelines for CVD prevention and management in India. Key messages • The study provides evidence to inform the policy choices in reducing CVDs burden in women and suggests a need to develop gender-specific guidelines for CVD prevention and management in India. • India has high burden & unawareness of CVDs in women with risk factors- BMI, tobacco consumption, sex-specific - women's education as a proxy for autonomy; socioeconomic deprivation- household wealth.

Soil pH mapping as a function of land use, elevation, and rainfall in the lake tana basin, northwestern of ethiopia

AbstractSoil acidity has become a serious problem in the northern highlands of Ethiopia, limiting land productivity mainly that of crop yield. Research had been done to try to come up with results showing the area was affected by severe acidity, but based on few watersheds and districts in a haphazard way. The objective of this research was therefore to develop a comprehensive soil pH map that covers a broader area of 27 districts covering 2,810,055 ha of land to assess the degree of soil acidity in the Tana Sub‐basin, northwestern highlands of Ethiopia. Using GPS (3‐m precision), 2652 soil point data were collected. Precipitation, elevation, and land use cover were taken into account when creating an interpolated soil pH map. Using adjacent probed pH values, ordinary kriging interpolation (R2 = 0.65; root‐mean‐square error = 0.37125) technique was used to estimate the unmeasured locations, while the spatial pH variability was mapped using the geostatistical (GS+10) software. All Food and Agriculture Organization of the United Nations soil pH categories, with 11.22%, 19.97%, 18.77%, 14.59%, 9.64%, and 6.29% of the soil samples being extremely acidic, strongly acidic, moderately acidic, slightly acidic, neutral, moderately alkaline and strongly alkaline, respectively, were observed. The interpolation result showed a moderate coefficient of variation (10.52%) indicating that the research area had medium variability. In addition, the nugget‐to‐total semivariance ratio ranged from 25% to 75%, indicating moderate geographic dependence. The pH semivariograms best fitted the exponential model, which means the one with maximum (R2 = 0.834) and with minimum residual sum of squares (1.819E‐06) value for mapping of pH. Our result showed that soil pH was inversely dependent on precipitation and altitude, while it varies with land use type. The research result/map could give useful tools for effective integrated land management to minimize soil acidification by policymakers, agriculturalists, and other stakeholder groups. We also recommended future routine updates on the size and distribution of surface soil acidity in the study area.

Analysis of Spatial and Temporal Variability and Coupling Relationship of Soil Water and Salt in Cultivated and Wasteland at Branch Canal Scale in the Hetao Irrigation District

The Hetao Irrigation District is a typical salinized irrigation district in China, and soil salinization restricts agricultural development. To explore the spatial and temporal variability of soil water and salt and the coupling relationship in the Hetao Irrigation District, a field experiment was carried out at the scale of the Yichang Irrigation District branch canal in the downstream of the Hetao Irrigation District. Fifty-three soil sampling points were established to analyze the spatial and temporal variability of soil water content and total salt content and the coupling relationship using geostatistics and the coupling degree model. The results showed that soil water content in the study area belonged to medium variability and weak variability, and soil total salt content belonged to strong variability and medium variability. The theoretical models of soil water content and total salt content semi-variance function in the study area following the Gaussian model, with the block-base ratio less than 25%, with strong spatial autocorrelation, and the spatial correlation gradually increased with the increase of soil depth. The total salt content of the soil in the study area was interpolated with higher accuracy using radial basis functions as compared to ordinary kriging interpolation. In terms of temporal changes in salinity, the average salt accumulation rate of the 0–100 cm soil layer in the study area was 20.17% when salinity increased from May to June; the average desalination rate was 16.37% when salinity decreased from June to August. The main factors affecting soil salinity in cultivated land during the growing period were irrigation, precipitation, and planting crops, and the main factors affecting soil salinity in wasteland were precipitation and topography. The average coupling degree of soil water and salt in wasteland in the study area was lower than that of cultivated land, ranging from 65.15% to 86.59% of that of cultivated land. The level of coordination is marginal coordination for cultivated land and marginal disorder for wasteland. The study provides a theoretical basis for the prevention and control of soil salinization in arid areas.

Characterising a rock fracture rough surface using spatial continuity and kriging: a new approach to meshing coupled thermo–hydraulic–mechanical–chemical (THMC) models

Abstract. Fluid flow through low permeability rocks is mainly accomplished through fractures. In order to model fluid flow, coupled thermo–hydraulic–mechanical–chemical (THMC) numerical models are used, which rely on fracture surface representations to construct a distribution model of the empty space (aperture) between the two fracture faces. The traditionally used statistical representations of fracture surfaces often overlook spatial continuity (SC), i.e. how well correlated points are in direction and distance. Examples are the fractures' aperture distribution random sampling to the joint roughness coefficient. This may result in a poor representation of the aperture distribution and thus a poor model. The first aim of this study is to investigate the possibility of characterising a fracture surface roughness using its SC parameters, an upscaled fracture surface and ordinary kriging (OK) interpolation algorithm. This method provides better control over the aperture model creation, which will have implications for its complexity and computation times. The second aim is to utilise the SC information and the distribution of a fracture in order to extrapolate (i.e. blind predict) the distribution of the fracture where no observations exist. A statistical analysis was performed in a greywacke in order to acquire the parameters necessary to describe the SC of the fracture surface topography. The surface was then interpolated using the OK algorithm. These parameters and the surface distribution will be used to inform the OK algorithm to extrapolate the fracture to where no data has yet been acquired. A reasonable match between the kriged and original surfaces has been achieved and the fit quantified by analysing the error between the two and by R2, which offer positive measurements of methodological quality. The aperture can easily be calculated from the difference between both complementary surfaces. The aperture between the two original fracture surfaces versus the two kriged fracture surfaces was also quantified and compared, yielding good results. This method may provide a new alternative to current storing and computing solutions for fracture representation, especially in aperture distribution calculation for coupled THMC numerical models and simulations. To verify advances in accuracy and computing times for this method, results between models derived from the original versus kriging aperture data will have to be compared. Another potential applicability of the SC information is to know a priori from any modelling which directions are those of Darcy's flow and which are the directions of the highest and lowest dilation rates with shearing, which will have to be confirmed with future planned modelling work.

Reconstructing and Nowcasting the Rainfall Field by a CML Network

AbstractCurrently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation.

Magnetic signature and X-ray fluorescence for mapping trace elements in soils originating from basalt and sandstone

The knowledge of the lithological context is necessary to interpret trace elements concentrations in the soil. Soil magnetic signature (χ) and soil X-ray fluorescence (XRF) are promising approaches in the study of the spatial variability of trace elements and the environmental monitoring of soil quality. This research aimed to assess the efficiency of measurements of χ and XRF sensors for spatial characterization of zinc (Zn), manganese (Mn), and copper (Cu) contents in soils of a sandstone-basalt transitional environment, using machine learning modeling. The studied area consisted of the Western Plateau of São Paulo (WPSP), with soils originating from sandstone and basalt. A total of 253 soil samples were collected at a depth of 0.0–0.2 m. The soils were characterized by particle size and chemical analysis: organic matter (OM), cation exchange capacity (CEC), ammonium oxalate-extracted iron (Feo), sodium dithionite-citrate-bicarbonate-extracted iron (Fed), and sulfuric acid-extracted iron (Fet). Hematite (Hm), goethite (Gt), kaolinite (Kt), and gibbsite (Gb) contents were obtained by X-ray diffraction (XRD). Magnetite (Mt) and maghemite (Mh) contents were obtained by soil χ, while trace elements contents were obtained by XRF and predicted by χ. Descriptive analysis, the test of means, and correlation were performed between attributes. Zn, Mn, and Cu contents were predicted using the machine learning algorithm random forest, and the spatial variability was obtained using the ordinary kriging interpolation technique. Landscape dissections influenced iron oxides, which had the highest contents in slightly dissected environments. Trace elements contents were not influenced by landscape dissections, demonstrating that lithological knowledge is necessary to characterize trace elements in soils. The prediction models developed through the machine learning algorithm random forest showed that χ can be used to characterize trace elements. The similar spatial pattern of trace elements obtained by XRF and χ measurements confirm the applicability of these sensors for mapping it under lithological and landscape transition, aiming for sustainable strategic planning of land use and occupation.

The impact of urban green space landscape on PM2.5 in the central urban area of Nanchang city, China

With China's rapid urbanization and industrialization, PM2.5 as a significant air pollutant, has drawn considerable public attention and strengthen the public's awareness of air quality. As a semi-natural landscape, many studies indicated that urban green space could influence the concentration and distribution of PM2.5 to a certain extent. However, most current researches were conducted at population or community scale, and how the urban green space affects PM2.5 at landscape scale was seldom studied. Taking the central urban area of Nanchang city, China, as a case, this study firstly encrypted the monitoring points by the land use regression model (LUR), and simulated the spatial distribution of PM2.5 concentration with high-precision based on the ordinary kriging interpolation method. Then, based on the urban green space data interpreted by remote sensing, the influence of urban green space landscape characteristics on PM2.5 concentration was quantitatively analyzed. The results showed that: (1) The PM2.5 concentration in the study area decreases continuously from the city center to the periphery; (2) Landscape pattern of green space has a significant effect on PM2.5 concentration. Patch green space has a stronger effects on PM2.5 concentrations than corridor green space did; (3) The attributes of area and NDVI of green space have a significant negative correlation with PM2.5 concentration. (4) The effective distance that green space can affect PM2.5 in the study area was less than 100 m. This study is expected to provide some references for lightning PM2.5 pollution by means of optimization of urban green space.

Identification of the Groundwater Quality and Potential Noncarcinogenic Health Risk Assessment of Nitrate in the Groundwater of El Milia Plain, Kebir Rhumel Basin, Algeria

In this study, we analyzed the quality and the potential noncarcinogenic health risk of nitrate in groundwater in the El Milia plain, Kebir Rhumel Basin, Algeria. Moran’s I and the ordinary kriging (OK) interpolation technique were used to examine the spatial distribution pattern of the hydrochemical parameters in the groundwater. It was found that the hydrochemical parameters Ca, Cl, and HCO3 showed strong spatial autocorrelation in the El Milia plain, indicating a spatial dependence and clustering of these parameters in the groundwater. The groundwater quality was evaluated using the entropy water quality index (EWQI). The results showed that approximately 86% of the total groundwater samples in the study area fall within the moderate groundwater quality category. The spatial map of the EWQI values indicated an increasing trend from the south-west to the northeast, following the direction of groundwater flow. The highest EWQI values were observed near El Milia city in the center of the plain. This spatial pattern suggests variations in groundwater quality across the study area, with potentially higher risks near the city center. The potential noncarcinogenic health risks associated with nitrate contamination in groundwater for adults and children through the drinking water pathway were assessed using the hazard quotient (HQ). The results revealed that approximately 5.7% of the total groundwater samples exceeded the HQ limit for adults, indicating potential health risks. Moreover, a higher percentage, 14.28%, of the total groundwater samples exceeded the HQ limit for children, highlighting their increased vulnerability to noncarcinogenic health hazards associated with nitrate contamination in the study area. Taking timely action and ensuring strict compliance with regulations in groundwater management are crucial for protecting public health, preserving the environment, addressing water scarcity, and achieving sustainable development goals.