Indicator Kriging Research Articles

Heavy rainfalls in Poland and their hyetographs.

In the light of observed variability in precipitation patterns, there is a growing need for comprehensive data mining of regularly updated rainfall recording databases. Therefore, an analysis of heavy rainfall and hyetographs was conducted using a 30-year high-resolution dataset from 100 rain gauges across Poland, covering 31 646 rainfall events. Distributions of rainfall depths, durations, and intensities were explored, and maxima were compared to global records. Spatial analysis revealed significant variations in the frequency, depths, and durations of extreme rainfall across different regions. Cluster analysis determined model hyetographs for each station. The likelihood of regions belonging to clusters with three to five model hyetographs was assessed using Indicator Kriging. Findings underscore the importance of using local, characteristics rainfalls in hydrodynamic modelling of drainage systems and future rainfall scenarios. These results provide a foundational step towards understanding and monitoring the impacts of climate change on rainfall characteristics, especially extremes, in future decades.

Assessing the groundwater quality of El Fahs aquifer (NE Tunisia) using multivariate statistical techniques and geostatistical modeling

This study is the first attempt to characterize the quality status of El Fahs aquifer by combining graphical tools, multivariate statistical techniques and traditional geostatistical methods. Water samples are collected from thirty-six observation wells during April 2016 to characterize the physicochemical properties of the aquifer. Subsequently, these samples are partitioned into three hydrochemically distinct water classes (i.e., C1, C2, and C3) using the K-means clustering method. Principal Component Analysis is used to reduce the dimensionality of the dataset prior performing the clustering computations, resulting in clusters of higher quality than the non-reduced case in terms of Silhouette coefficient. Piper diagram is used to display the chemical composition of the samples, revealing the dominant role of Mg–Ca–Cl water type for all three classes, whereas Sodium and Sulfate were found to be the second most important cations and anions respectively. Indicator kriging (IK) is used to identify the probability of occurrence of the hydrochemical classes beyond the sampling locations. It is found that Class 1, associated with fresh groundwater component, is most probable to occur at the central part of the plain, mainly due to the presence of a dense hydrological network, whereas Classes 2 (agricultural activities) and 3 (dissolution of evaporate geological formations) are expected to occur at the southern and northern regions respectively. IK also identified the regions associated with high levels of uncertainty, mostly occurring in a large portion of the northern area due to the absence of available hydrochemical information. The results showed that integration of graphical methods, multivariate statistical techniques and geostatistical modeling, is an efficient approach for characterizing the hydrochemical status of the aquifer system, to spatially optimize the groundwater monitoring well networks and quantify the uncertainty levels of the water classes in a systematic way.

SALINE MAPS OF SOME SOILS OF MUTHANNA GOVERNORATE

performed based on data collected from 15 soil samples from 0 to 20 cm deep. We used salinity of the irrigated soils of Al-Muthana governorate in southern Iraq. These maps were 20% of the world's land. This study was made in order to map the spatial distribution of soil or anthropogenic processes, which is certainly an environmental problem that affects more than environment and agricultural production. The main causes of this salinization come from natural It is important to study the problem of salinization, soil salinity has adverse effects both on the ordinary kriging (OK) to analyze the spatial variability of soil salinity, The results confirmed that the use of saline maps was supportive in giving a clear vision of the of 12.11 dS m-1, for developing probability maps to determine the distribution of risk areas. showed the different classes of salinity with the highest value of 23.17 dS m-1 and lowest value map predicted by the electrical conductivity (EC) values using the ordinary kriging (OK) method while indicator kriging (IK) was used to analyze salinity versus threshold values . The salinity salinity of the soils for the study area

Fine-tuning the use of moss transplants to map pollution by Potentially Toxic Elements (PTEs) in urban areas

Mosspheres are a kind of moss transplants which offer a novel approach for detecting atmospheric pollution using devitalized mosses, as they reflect the atmospheric deposition of certain elements and polycyclic hydrocarbons. However, due to the unique features of the mosspheres such as the low elemental concentrations in the cultured material, the data treatment needs to be different from that of conventional biomonitoring studies. In this article, our objectives are to identify the best parameter for expressing the levels of chemical elements accumulated by mosspheres, and to apply a recently developed method to assess the probability of pollution of each sample and of the study area. To do this, we used data from a study in which 81 mosspheres were exposed in a medium-sized city in southwestern Europe.Comparing different pollution indices, we selected the enrichment rate (ER) as the most useful, as it is resilient to fluctuations in the initial concentrations and takes into account the time factor, allowing for greater comparability among studies. Then, we determined that the statistical distribution of the ERs of most elements fitted a normal distribution, showing that most samples did not differ significantly from the background concentrations for these elements. On the other hand, for Ni, Pb and Zn there was a subpopulation of samples above background values. In these cases, we determined the probability of pollution of each sample. Finally, we used indicator kriging to calculate the probability of pollution across the study area, identifying the polluted areas, which for some elements match the distribution of the main industries and highways, indicating that this is a suitable protocol to map elemental pollution in urban areas.

Summarizing soil chemical variables into homogeneous management zones – case study in a specialty coffee crop

Homogeneous management zones (HMZs) delineation is important for the application of precision agriculture because farm management decisions are based on it. Diverse soil chemical characteristics are important for the HMZs delineation. However, summarizing several variables into homogeneous zoning while taking into account the spatial distribution pattern of soil chemical characteristics is a challenge. Addressing this challenge is important to produce HMZs oriented for practical use for the farmers. In this work, 17 soil chemical variables were jointly analyzed for HMZ delineation by using indicator kriging (IK) to interpolate a soil fertility index (SFI). Soil samples were taken from a 4.5 ha area in a quasi-regular grid at 0 - 0.20 m depths in November 2019 (66 samples) and May 2021 (40 samples). Soil P, K, Ca, Mg, S, Na, H, Mn, Fe, Zn, B, cation exchange capacity, aluminum saturation, total organic carbon, base saturation, and organic matter were analyzed. In May 2021 the coffee yield was sampled together with the soil. Applying the SFI and then interpolating it using IK were effective for summarizing soil chemical variables into binary HMZs, showing a zone with higher priority for fertilization (therefore, lower general soil fertility) and another zone with low priority for fertilization. The summarizing process of several variables into binary HMZ was validated by evaluating the boxplots of each variable in each HMZ. Also, higher soil fertility areas presented a higher average coffee yield. Results indicated that joint use of SFI and IK was adequate to delineate HMZs in terms of summarizing soil fertility and separating coffee yield average variability. Delineating management zones by using the SFI approach is flexible for relatively limited sampled studies (less than one hundred samples) where machine learning and geostatistical methods may fail for lack of data.

Spatial classification in the presence of measurement error

In recent decades, spatial classification has received considerable attention in a wide array of disciplines. In practice, binary response variable is often subject to measurement error, misclassification. To account for the misclassified response in spatial classification, we proposed validation data-based adjustment methods that use interval validation data to rectify misclassified responses. Regression calibration and multiple imputation methods are utilized to correct the misclassified outcomes at the locations where the gold-standard device is not available. Generalized linear mixed model and indicator Kriging are applied for spatial classification at unsampled locations. Simulation studies are performed to compare the proposed methods with naive methods that ignore the misclassification. It was found that the proposed models significantly improve prediction accuracy. Additionally, the proposed models are applied for precipitation detection in South Korea.

Predicting areal extent of groundwater contamination through geostatistical methods exploration in a data-limited rural basin

The areal extent of regulatory standard exceedance for groundwater arsenic, fluoride, and nitrate was estimated and compared for multiple methods to evaluate groundwater contamination in a rural basin of New Mexico, USA. First, three geostatistical kriging techniques – Ordinary Kriging (OK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK) – were explored. Next, the exploration was extended to multivariate analysis (Co-OK, Co-UK, and EBK-regression) using groundwater pH as the other variable, compared to interpolation only considering the contaminant as a primary variable. Finally, non-parametric Indicator Kriging (IK) was utilized for probability mapping and assessing the likelihood of surpassing critical thresholds for contaminant concentrations. The contamination area changes with different kriging methods deployed, and the performances of kriging variants vary for the three chemical components. The models that best fit the empirical data were determined. The study examined the influence of the explanatory variable on the multivariate kriging prediction accuracy. The multivariate kriging approaches enhanced surface prediction accuracy and tended to cover the data limitation of the primary variable interpolations. The multivariate kriging improved spatial prediction of arsenic and fluoride by increasing the Empirical Coverage Probability (ECP) up to 0.05–0.25 from case to case; however, no improvement was observed in ECP for nitrate contamination distribution, as the explanatory variable pH was only weakly correlated with nitrate, suggesting that a stronger correlation between the primary and secondary variables can increase the probability of obtaining prediction improvements through multivariate kriging. This study developed a methodology for characterizing and identifying locations where groundwater had elevated concentrations of contaminants, threatening human health through drinking water exposure. By improving the spatial characterization of groundwater contaminants within the data-limited rural basin, policymakers planning improvements will support mitigation strategies to manage contamination sources of groundwater resources.

A Multi-Objective Approach for Optimizing the Layout of Additional Boreholes in Mineral Exploration

Accurate subsurface exploration requires an optimal network of boreholes. This paper proposes a multi-objective approach to optimize the layout of additional exploratory boreholes. In order to illustrate this approach, geochemical analyses of core samples at the eastern part of the Kahang copper deposit, Central Iran, were used. A measure of the grade uncertainty (kriging standard deviation) and a confidence measure on the ore/waste classification were first calculated by implementing ordinary and indicator kriging. An ore value function was then determined to measure the total value of each block by considering the grades of all the effective variables and their ore membership degree derived from a fuzzy treatment of the grades. Finally, a misclassification cost is defined for each block based on the expected economic effects of ore loss and waste dilution. As a result, an index for the selection of additional boreholes was introduced in order to maximize the kriging standard deviation, the ore misclassification cost, and the ore value and to minimize the confidence measure. Applied to the Kahang copper deposit, this index allowed the prioritization of areas for infill sampling, leading to the recommendation for eight vertical and two directional additional boreholes.

Comparison of Soft Indicator and Poisson Kriging for the Noise-Filtering and Downscaling of Areal Data: Application to Daily COVID-19 Incidence Rates

This paper addresses two common challenges in analyzing spatial epidemiological data, specifically disease incidence rates recorded over small areas: filtering noise caused by small local population sizes and deriving estimates at different spatial scales. Geostatistical techniques, including Poisson kriging (PK), have been used to address these issues by accounting for spatial correlation patterns and neighboring observations in smoothing and changing spatial support. However, PK has a limitation in that it can generate unrealistic rates that are either negative or greater than 100%. To overcome this limitation, an alternative method that relies on soft indicator kriging (IK) is presented. The performance of this method is compared to PK using daily COVID-19 incidence rates recorded in 2020–2021 for each of the 581 municipalities in Belgium. Both approaches are used to derive noise-filtered incidence rates for four different dates of the pandemic at the municipality level and at the nodes of a 1 km spacing grid covering the country. The IK approach has several attractive features: (1) the lack of negative kriging estimates, (2) the smaller smoothing effect, and (3) the better agreement with observed municipality-level rates after aggregation, in particular when the original rate was zero.

The distribution characteristics and geological control factors of shallow high-arsenic groundwater in the Hetao Plain, Inner Mongolia, from the perspective of Late Pleistocene–Holocene depositional environments

The alluvial-lacustrine strata that were formed by the evolution of rivers and lakes in the Hetao Plain during the Late Quaternary have an important influence on the formation and distribution of shallow high-arsenic groundwater. This study analyzed the distribution characteristics and depositional environments of shallow high-arsenic groundwater in study area using 1179 groundwater samples and more than 1100 pieces of drilling data. The indicator kriging statistics and the study results of the Quaternary lithofacies paleogeography show that the study area can be divided into three high-arsenic probabilistic distribution areas, namely, the Houtao Plain (HTP), the Yellow River Channel Belt (YRCB), and the Eastern Hubao Plain (EHBP). The depositional environment of the HTP was shaped by the alluviation of the Yellow River during the Late Pleistocene-Holocene. The YRCB is still affected by the alluviation of the Yellow River presently, and the EHBP was almost unaffected by the Yellow River. The high-arsenic groundwater in the EHBP is mostly distributed in the relatively continuous alluvial-lacustrine strata and has a typical hydrochemical type of HCO3, with the highest Meq(HCO3-/SO42-) and the highest reduction degree of SO42-. By contrast, the high-arsenic groundwater in the alluvial-lacustrine environments of the HTP and the YRCB accounts for only 14.77% and 20.13%, respectively, and has only less than 40% of HCO3 dominant type water. The high-arsenic groundwater in these two areas is generally located in the alluvial or alternating fluvial-lacustrine strata. However, the two areas exist more than three alluvial-lacustrine layers with a thickness of over 2m each, which play a critical role in the formation of high-arsenic groundwater. Moreover, affected by alluvial aquifers in the same system, the high-arsenic groundwater in both the HTP and the YRCB is not intensively distributed and does not represent a typical HCO3 dominant type. The S2- produced by the massive reduction of SO42- might co-precipitate with Fe and As, which may explain why the EHBP has lower arsenic concentration than the HTP and the YRCB, both of which have a lower reduction degree of SO42-.

Geospatial model of composition of water service lines in Flint, Michigan: Validation using excavation data and a new compositional geostatistical approach.

Following the Flint drinking water crisis, a service line (SL) replacement program was implemented to replace lead SLs and galvanized SLs connecting residences to Flint's water system, leading to the excavation and inspection over a 5-year period (2016-2020) of a total of 26,750 lines, representing close to 50% of all tax parcels in the City of Flint. These recent data were used to validate an earlier geospatial model created by residual indicator kriging (IK) to predict the probability that a home has a lead, galvanized, or copper private-side SL based on neighboring house inspections (i.e., 3254 homes visited in 2017) and secondary information (i.e., built year and city records on SL composition). Receiver operating characteristic curves indicated an average frequency of detection (i.e., area under the curve [AUC]) of 0.9 for copper and galvanized material and 0.6 for lead service lines. Predicting the composition of SL at unmonitored residences by IK, however, can result in negative probabilities of occurrence and probabilities that do not sum to 1. These limitations were overcome by adopting simplicial IK, whereby data undergo a logratio transform before the geospatial analysis. This first application of a compositional approach to SL data improved the detection of lead SLs (AUC = 0.8 vs. 0.6) while providing coherent predictions. Incorporating secondary information, in particular using standardized cokriging and a new rescaled cross-semivariogram estimator introduced to correct for geographical clustering of house inspections, increased the accuracy of the prediction.

Increase in the extent of mass coral bleaching over the past half-century, based on an updated global database.

The recurrence of mass coral bleaching and associated coral mortality in the past few decades have raised questions about the future of coral reef ecosystems. Although coral bleaching is well studied, our understanding of the spatial extent of bleaching events continues to be limited by geographical biases in data collection. To address this gap, we updated a previous observational database and spatially modelled the probability of past bleaching occurrence. First, an existing raw observational database was updated to cover the 1963-2017 period using searches of the academic and grey literature and outreach to coral reef monitoring organizations. Then, in order to provide spatially-explicit global coverage, we employed indicator kriging to spatially model the probability of bleaching occurrence each year from 1985 through 2017 at 0.05° x 0.05° lat-long resolution. The updated raw database has 37,774 observations, including 22,650 positive bleaching reports, three times that in the previous version. The spatial interpolation suggests that 71% of the world's coral reefs likely (>66% probability) experienced bleaching at least once during the 1985 and 2017 period. The mean probability of bleaching across all reefs globally was 29-45% in the most severe bleaching years of 1998, 2005, 2010 and 2016. Modelled bleaching probabilities were positively related with annual maximum Degree Heating Weeks (DHW), a measure of thermal stress, across all years (p<0.001), and in each global bleaching event (p<0.01). In addition, the annual maximum DHW of reef cells that very likely (>90% probability) experienced bleaching increased over time at three times the rate of all reef cells, suggesting a possible increase in reef thermal tolerance. The raw and spatially interpolated databases can be used by other researchers to enhance real-time predictions, calibrate models for future projections, and assess the change in coral reef response to thermal stress over time.

Assessment of groundwater nitrate pollution using the Indicator Kriging approach

This paper presents the results of investigations on groundwater nitrate pollution to assess its spatial and temporal evolution through the indicator kriging method. With this approach, the direct estimation of the local conditional probabilities of nitrate concentrations permits the identification of areas more vulnerable to pollution without establishing a precise estimate of the physical quantities under investigation. The aspect of interest is the possibility of defining areas of increased vulnerability to nitrate pollution, even in the case of sample points non-homogeneously distributed. In this case, using classical geostatistical tools such as ordinary kriging would lead to incorrect assessments. On the other hand, using the Indicator Kriging indicates areas most prone to deterioration over time and, therefore, need attention. The selected case study can be considered a benchmark due to its high geomorphologic and structural complexity and the scarce availability of homogeneous monitoring data. Results indicated an increasing trend in nitrate pollution over time, often exceeding the recommended threshold for drinking water use stated by the EU Nitrate Directive. Moreover, there is no correspondence to either land use variation or the use of fertilisers over the same period. This behaviour could depend on a long delay between the leaching of nitrates from the soil to the groundwater. This finding suggests the importance of systematic monitoring of groundwater quality, especially for those vulnerable systems characterized by slow response time to external pressures and of great significance in water resource supplies.

Integrating facies analysis and geostatistical methods in an onshore oil field using petrophysical groups

Reservoir facies studies are of great importance in different stages of exploration and development of hydrocarbon fields. We have aimed to generate a reservoir facies model for the Asmari Formation in an onshore oil field located in southwest Iran. Input data for electrofacies (EF) clustering algorithms are used, which include gamma-ray (GR), density (RHOB), porosity, and sonic logs from four wells. We obtain the petrophysical group (PG) and EF class using core drill data (mercury injection capillary pressure) and well-logs analysis. The integration of PGs and log EF significantly decreases the uncertainty in reservoir modeling, which alternatively enhances field development decisions. We compare the multiresolution graph-based clustering (MRGC) and k-means clustering methods. EF clustering results find nine EF classes. We delineate high-quality reservoirs based on lower GR, RHOB, and high-porosity logs. Next, we use the clustering results in the static reservoir modeling process, using the sequential index simulation and indicator kriging methods. The comparison between the facies obtained models and existing drilling core data finds that the absolute percentage error of the MRGC algorithm is less than that of the k-means algorithm. The results obtained by this study can provide useful information for the development of hydrocarbon exploration plans in the studied oil field.

BLOCK MODEL OPTIMIZATION AND RESOURCE ESTIMATION OF THE ANGOURAN MINE BY TRANSFERRING THE EXPLORATORY DATA FROM THE LOCAL COORDINATE SYSTEM TO THE UTM

Resource estimation is one of the most important steps in the mining process. Precise resource estimation has a significant influence on the optimization of subsequent mining steps, i.e. mine planning and scheduling. The previous resource estimation in the Angouran Mine was conducted based on the provided information in the local coordinate system which causes considerable errors in estimations. Therefore, an attempt is made in this research to optimize the block model of the Angouran Mine and resource estimation based on the information in the UTM global coordinate system. For this purpose, exploratory data is firstly transferred from the local coordinate system to the UTM environment. Then, block model optimization is conducted using indicator kriging (IK) in which the waste blocks are removed and the block model was optimized. Finally, resource estimation is performed using the inverse distance weighting (IDW) and simple kriging (SK) methods. After variogram analyses in different directions, it was found that the mine deposit is anisotropic. Also, validation results showed that the acquired correlation coefficient in the carbonate and sulfide sections for IDW, SK and IK is 0.86, 0.87 and 0.92, and 0.88, 0.87 and 0.92, respectively. Finally, the obtained grades and tonnages are compared with the actual data of the exploratory boreholes, mined blocks and previous resource estimation in the mine. Comparative results showed that the obtained grades and tonnages from both previous and new models are over-estimated and higher than the actual values. The minimum errors of grade estimation equal 46% and 23.1% for previous and new resource estimations (before and after the waste removal), respectively. Also, the mining errors of tonnage estimation are 50.29% and 28.37% for previous and new models, respectively. This field comparison proved that transferring the exploratory data to the UTM system, utilization of the IK to remove the waste blocks and applying the SK for resource estimation lead to the optimization of the block model and a reduction in the estimation error compared to the previous estimations for the mine.

COMPUTATION OF DREDGED MATERIAL AND SELECTION OF THE BEST KRIGING METHOD

In this study, it is demanded that a preparatory survey for an oil well location be carried out and the estimated number of dredged materials be calculated at various sections inside the perimeter of an oil mining lease (OML 38) located at Ovhor field, Niger Delta Region, Nigeria. Seven sub-sectional areas initially earmarked for dredging at predetermined depths were delineated for different purposes to accomplish the dredging task. The total sum of the seven sectional areas calculated was 89,321.31m2. Of the seven sub-sectional areas, the volume of dredged material was calculated for only five by multiplying the obtained two-dimensional areas by the designed depth. The two sub-sections exempted from dredging were because depths are appropriate at these locations. The total volume of dredged material, therefore, calculated was 56,630.73m3. Recording and monitoring of tidal observations and analysis complement bathymetry and dredging activities. Due to the rugged terrain, interpolation remains a viable option to predict spatial variability at unsampled dredged lines. To achieve this, different kriging methods were investigated before finally choosing the best. The model that made the most accurate predictions was “Indicator Kriging” with a mean error of 0.000364 and a root-mean-square-standardized error of 0.806. This result is geostatistically acceptable when predicting spatial variability at an un-sampled location. The indicator kriging approach was subsequently used to predict the spatial variability of the un-sampled locations encountered in the study area.

Spatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India

False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I &gt; 0), except the coastal and interior districts of Karnataka (at I &lt; 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India.

A Novel Approach for Resource Estimation of Highly Skewed Gold Using Machine Learning Algorithms

With the complicated geology of vein deposits, their irregular and extremely skewed grade distribution, and the confined nature of gold, there is a propensity to overestimate or underestimate the ore grade. As a result, numerous estimation approaches for mineral resources have been developed. It was investigated in this study by using five machine learning algorithms to estimate highly skewed gold data in the vein-type at the Quartz Ridge region, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), Decision Tree Ensemble (DTE), Fully Connected Neural Network (FCNN), and K-Nearest Neighbors (K-NN). The accuracy of MLA is compared to that of geostatistical approaches, such as ordinary and indicator kriging. Significant improvements were made during data preprocessing and splitting, ensuring that MLA was estimated accurately. The data were preprocessed with two normalization methods (z-score and logarithmic) to enhance network training performance and minimize substantial differences in the dataset’s variable ranges on predictions. The samples were divided into two equal subsets using an integrated data segmentation approach based on the Marine Predators Algorithm (MPA). The ranking shows that the GPR with logarithmic normalization is the most efficient method for estimating gold grade, far outperforming kriging techniques. In this study, the key to producing a successful mineral estimate is more than just the technique. It also has to do with how the data are processed and split.

Health risk assessment from exposure to dissolved trace element concentration in drinking groundwater resources of Central Ganga Alluvial Plain: a case study of Lucknow region

ABSTRACT This study explores ‘Indicator Kriging’ approach for assessment of health risk from exposure to trace elements concentration in drinking water resources of the Central Ganga Alluvial Plain (CGAP), northern India. The estimates for were generated using analysis of groundwater samples (n = 100) collected from the Lucknow monitoring area to map the predicted area of health risk. The predicted probability maps have reclassified into a unified scale to generate Trace Element Risk Index (TERI), which has further integrated with human population count data to generate Health Risk Index of Lucknow. The results indicate that the risk is potentially alarming in urban areas as relatively high there are referring to the local (point) sources of contamination. Approximately 23.15% human population residing in about 69.77% of the total area is at moderate-to-high health risk probability. The findings of this study could help planning substantial remediation measures on long-term basis.

Spatial distribution and identification of potential risk regions to rice blast disease in different rice ecosystems of Karnataka.

Rice is a globally important crop and highly vulnerable to rice blast disease (RBD). We studied the spatial distribution of RBD by considering the 2-year exploratory data from 120 sampling sites over varied rice ecosystems of Karnataka, India. Point pattern and surface interpolation analyses were performed to identify the spatial distribution of RBD. The spatial clusters of RBD were generated by spatial autocorrelation and Ripley’s K function. Further, inverse distance weighting (IDW), ordinary kriging (OK), and indicator kriging (IK) approaches were utilized to generate spatial maps by predicting the values at unvisited locations using neighboring observations. Hierarchical cluster analysis using the average linkage method identified two main clusters of RBD severity. From the Local Moran’s I, most of the districts were clustered together (at I > 0), except the coastal and interior districts (at I < 0). Positive spatial dependency was observed in the Coastal, Hilly, Bhadra, and Upper Krishna Project ecosystems (p > 0.05), while Tungabhadra and Kaveri ecosystem districts were clustered together at p < 0.05. From the kriging, Hilly ecosystem, middle and southern parts of Karnataka were found vulnerable to RBD. This is the first intensive study in India on understanding the spatial distribution of RBD using geostatistical approaches, and the findings from this study help in setting up ecosystem-specific management strategies against RBD.