Ecotoxicology and geostatistical techniques employed in subtropical reservoirs sediments after decades of copper sulfate application.

Spatial distribution linked to geostatistical techniques contributes to sum up information into an easier-to-comprehend knowledge. This study compares copper spatial distribution in surface sediments and subsequent categorization according to its toxicological potential in two reservoirs, Rio Grande (RG) and Itupararanga (ITU) (São Paulo-Brazil), where copper sulfate is applied and not applied, respectively. Sediments from 47 sites in RG and 52 sites in ITU were collected, and then, copper concentrations were interpolated using geostatistical techniques (kriging). The resulting sediment distributions were classified in categories based on sediment quality guides: threshold effect level and probable effect level; regional reference values (RRVs) and enrichment factor (EF). Copper presented a heterogenic distribution and higher concentrations in RG (2283.00 ± 1308.75 mg/kg) especially on the upstream downstream, associated with algicide application as well as the sediment grain size, contrary to ITU (21.81 ± 8.28 mg/kg) where a no-clear pattern of distribution was observed. Sediments in RG are predominantly categorized as "Very Bad", whereas sediments in ITU are mainly categorized as "Good", showing values higher than RRV. The classification is supported by the EF categorization, which in RG is primarily categorized as "Very High" contrasting to ITU classified as "Absent/Very Low". Copper total stock in superficial sediment estimated for RG is 4515.35 Ton of Cu and for ITU is 27.45 Ton of Cu.

Mineral micronutrient status and spatial distribution among the Ethiopian population.

Multiple micronutrient deficiencies are widespread in Ethiopia. However, the distribution of Se and Zn deficiency risks has previously shown evidence of spatially dependent variability, warranting the need to explore this aspect for wider micronutrients. Here, blood serum concentrations for Ca, Mg, Co, Cu and Mo were measured (n 3102) on samples from the Ethiopian National Micronutrient Survey. Geostatistical modelling was used to test spatial variation of these micronutrients for women of reproductive age, who represent the largest demographic group surveyed (n 1290). Median serum concentrations were 8·6 mg dl-1 for Ca, 1·9 mg dl-1 for Mg, 0·4 µg l-1 for Co, 98·8 µg dl-1 for Cu and 0·2 µg dl-1 for Mo. The prevalence of Ca, Mg and Co deficiency was 41·6 %, 29·2 % and 15·9 %, respectively; Cu and Mo deficiency prevalence was 7·6 % and 0·3 %, respectively. A higher prevalence of Ca, Cu and Mo deficiency was observed in north western, Co deficiency in central and Mg deficiency in north eastern parts of Ethiopia. Serum Ca, Mg and Mo concentrations show spatial dependencies up to 140-500 km; however, there was no evidence of spatial correlations for serum Co and Cu concentrations. These new data indicate the scale of multiple mineral micronutrient deficiency in Ethiopia and the geographical differences in the prevalence of deficiencies suggesting the need to consider targeted responses during the planning of nutrition intervention programmes.

Spatial distribution of heavy metals in soils of the flood plain of the Seversky Donets River (Russia) based on geostatistical methods.

Soil contamination by heavy metals (HM) is a worldwide problem for human health. To reduce risk to human health from exposure to toxic chemicals associated with soil contamination, it is necessary to monitor and assess HM concentrations in the soil for places where the concentration exceeds the acceptable levels. Spatial patterning is a necessary tool for assessment of the exposure risk of HM contamination. Soil sampling (n = 65) was carried out in technogenically polluted soils located at Rostov oblast to study the content and spatial distribution of four HM (Cu, Zn, Pb, and Cr) in the surface layer (0-20 cm) of the impact zone of former Lake Atamanskoe (floodplain of the Seversky Donets River valley, Rostov region) with an area of 3.91 km2. Extremely high values of HM concentrations were found with the maximum values of 702 mg/kg, 72,886 mg/kg, 2300 mg/kg, 259 mg/kg for Cu, Zn, Pb, and Cr, respectively. Inverse distance-weighted (IDW) interpolation was used to prepare 3D monoelement images of HM. Lognormal kriging and indicator kriging techniques were applied to create elemental spatial distribution maps and HM probability maps. The results showed that the total content of Cu, Zn, Pb, and Cr was moderately spatially dependent (nugget-to-sill ratio ranged from 31 to 38%), whereas the contamination index Zc formed strong spatial dependence patterns (nugget-to-sill ratio ranged from 0 to 21.4%). The obtained results of this study could serve as a guide to the authorities in identifying those areas which need remediation. Moreover, this study provides a tool for assessing the hygienic situation in the vicinity of Kamensk-Shakhtinsky (Rostov region) for decision making that can help to minimize the environmental risk of technogenic soil contamination of HM.

Geostatistical surfaces of climatological normals of mean air temperature in Minas Gerais.

Air temperature, a vital component for the terrestrial environment sustainability, can be used as an indicator and an important factor used in short- and long-term meteorological modeling at different scales. Temperature must be monitored on spatial and temporal scale with high precision. Terrain elevation can be used as the main influence factor depending on the measurement scale. In small and medium scales, factors related to local relief were modeled with geostatistics including external variables in temperature modeling. We aimed to evaluate the use of universal kriging in the modeling of air temperature in order to create temperature surfaces at each km[Formula: see text] in Minas Gerais State, Brazil using altitude, longitude and latitude covariates. The organized mean air temperature data of climatological normals of the National Institute of Meteorology were submitted to summary statistics, statistical regression and geostatistical analysis. Monthly and annual normals of the mean air temperature compensated for the period 1981 to 2010 were modeled using temperature as dependent variable and altitude, longitude and latitude as co-variables. Multiple regression modeling performed on temperature using altitude, longitude and latitude covariates determined significant parameters for monthly and annual mean air temperature global prediction. Relief and coordinates were used as external drift on variography and universal kriging with block for local temperature interpolation and prediction in order to generate 1-km moderate resolution surfaces of monthly and annual mean air temperature. Universal kriging determined smoothing effect of standard deviation of geospatial variation with prediction errors varying between 0.6 and [Formula: see text]C. Higher prediction error values were observed between June and August. Mean air temperature local prediction presented greater errors mainly in the lower altitude regions and in the colder months. In both monthly and annual temperature predictions, universal kriging with external drift enabled to circumvent the problem of performing spatial prediction from sparse punctual attribute data, conferring a temperature downscaling effect in Minas Gerais.

An ego-centred approach for the evaluation of spatial effects on health in urban areas based on parametric semi-variogram models: concept and validation.

BACKGROUND: Neighbourhood is a complex structure but of high relevance for health. Its operationalisation remains however a challenge.The aim of this work is to present a new application of the use of semi-variograms as an approach for the evaluation of spatial effects on health. For this, we propose to estimate two parameters providing a measure of an average neighbourhood or spatial effect at city level without having to predefine any notion of physical neighbourhood. METHODS: We present the statistical method to estimate the parameters of this correlation neighbourhood by fitting an exponential model to the empirical semi-variogram at short distances. With a simulation study, we show for which sample size and sampling density the method performs well and illustrate how to use the method with data from a birth cohort using the outcome birthweight. RESULTS: For small sample sizes (500) the method provides reliable estimates if the density of observations is high. For larger sample sizes other parameters influencing the quality of estimates are the maximal distance at which the semi-variograms are estimated. CONCLUSIONS: Given the complexity of spatial scales relative to neighbourhood spatial processes, our approach offers the possibility to incorporate existing approaches to the operationalisation of neighbourhood in quantitative analyses while providing a measure of the part of health inequalities which could be possibly due to unmeasured spatial exposure as well as a measure of their spatial scale.

Statistical analysis of active fire remote sensing data: examples from South Asia.

Active fires emit aerosols and greenhouse gases in the atmosphere. In this paper, the behavior of active fires over a period of 1 year in Nepal, Bhutan, and Sri Lanka is studied using spatial statistics. In these countries, these fires are mainly forest and vegetation fires; they wreak havoc to the environment by damaging flora and fauna and emitting toxic gases. This study is based on data acquired through remote sensing of data acquisition platform, NASA's MODIS. Spatial statistics is used here to study the incidence of such fires with respect to geographical location. The behaviors of parameters of various autoregressive models like Spatial Durban Model, Spatial Lag Model, Spatial Error Model, Manski Model, and Kelegian Prucha Model are minutely analyzed. The best model with the highest pseudo R2 is selected. The spatial behavior of the fire radiative power (FRP) for the three countries is also predicted using spatial interpolation and kriging. The burning potential of vegetations in unsampled areas is envisaged by thus predicting FRP. This study gives a country-wise perspective to the behavior of fire; this is with reference to South Asia. It holds a great significance for countries of the developing world which lack a strong backbone of good-quality official records. Through the statistical analyses of data collected by such platforms, important information on impact of forest fires can be indirectly assessed.

Geostatistical analysis of precipitation in the island of Crete (Greece) based on a sparse monitoring network.

Based on the predictions of General Circulation Models, significant reduction of precipitation in Mediterranean areas is a possible scenario. Hence, better understanding of the spatial and temporal precipitation patterns is necessary in order to quantify desertification risks and design suitable mitigation measures. This study uses monthly precipitation measurements from a sparse network of 54 monitoring stations on the Mediterranean island of Crete (Greece). The study period extends from 1948 to 2012. The data reveal strong correlations between the western and eastern parts of the island. However, the average annual precipitation in the West is about 450 mm higher than that in the East. We construct a spatial model of average annual precipitation in Crete. The model involves a topographic trend and residuals with anisotropic spatial correlations which are fitted with a recently developed variogram function. We use regression kriging to generate annual precipitation maps and to identify locations of high estimation uncertainty. To our knowledge, this is the most detailed spatial analysis of precipitation in Crete to date. We present the analysis in detail for the year 1971. The trend accounts for ≈ 74% of the total variance. The highest precipitation estimate is 2331 mm in the West and 1781 mm in the East. The highest relative estimation uncertainty (≈ 20%) is observed along the southeastern coastline of the island, where the lowest values of annual precipitation are observed. This region includes one of the major agricultural areas of the island. The same overall patterns are found for other years in the study. Finally, we find no statistical evidence for a decrease in the global (over the entire island) annual precipitation during the study period.

On the problem of the spatial distribution delineation of the groundwater quality indicators via multivariate statistical and geostatistical approaches.

This paper highlights the advantages of multivariate statistical and geostatistical methods to compile the hydro-geochemical properties of groundwater. A total of 123 samples were collected from wells located in Saveh aquifer, in 2015. Seven parameters including total dissolved solids (TDS), sodium adsorption ratio( SAR), electrical conductivity (EC), sodium (Na+), total hardness (TH), chloride (Cl-), and sulfate (SO42-) were analyzed, compiled, and interpreted statistically and geostatistically. At first, factor analysis gave rise to produce a factor representing 94% of the variability. Also, variography was calculated and compiled to define spatial regression and experimental variograms were plotted by GS+ software, then, the best theoretical models were fitted on the variograms and an estimation map was prepared based on geostatistical relationship presented in the paper. Smoothing effect is one of the main drawbacks of forward geostatistical methods, on the contrary, inversed methods are subjected to no smoothing effect. Results showed that geostatistical inversed methods could reveal more reliable results than forward methods. Eventually, the map of the estimated factor, as well as error maps, was compiled. According to the evaluation of fractal dimensions, the estimated factor explained the variability of all hydrogeochemical parameters and groundwater quality was categorized as the safe, normal, and anomalous class, ranged from - 1.10 to 1.10, 1.11 to 3.1, and more than 3.1, respectively.

Spatiotemporal interpolation of air pollutants in the Greater Cairo and the Delta, Egypt.

This paper analyses the spatiotemporal variability of air pollutants in Egypt using monthly averages from the air quality monitoring network from 2011 to 2015. Particulate Matters (PM10) Nitrogen Dioxide (NO2) and Sulfur Dioxide (SO2), measured by the monitoring stations network are studied. A log transformation is applied for the three pollutants to achieve normality. The sum-metric function is utilized for modelling the spatiotemporal variogram as it gave the smallest Mean Squared Error (MSE) compared to other forms namely separable, metric, and product sum models. Therefore, employing the gstat package in R together with the trans-Gaussian spatiotemporal kriging, the maps are generated for the interpolated surfaces for the monthly averages of 2015 and the corresponding standard error values. These maps will help the decision maker to understand and visualize the spatial and temporal variability of the measured pollutants and hence undertake the necessary policies and decisions. The results show that the down town area has the highest pollutants levels. As concerns the temporal dimension, the highest values are depicted during the month of February as compared to the rest of the year. Furthermore, Egypt is suffering from a serious PM10 problem for the area and period under study that extremely exceed the WHO and Egyptian guidelines.

Spatial modeling of rat bites and prediction of rat infestation in Peshawar valley using binomial kriging with logistic regression.

In this study, we propose to develop a geostatistical computational framework to model the distribution of rat bite infestation of epidemic proportion in Peshawar valley, Pakistan. Two species Rattus norvegicus and Rattus rattus are suspected to spread the infestation. The framework combines strengths of maximum entropy algorithm and binomial kriging with logistic regression to spatially model the distribution of infestation and to determine the individual role of environmental predictors in modeling the distribution trends. Our results demonstrate the significance of a number of social and environmental factors in rat infestations such as (I) high human population density; (II) greater dispersal ability of rodents due to the availability of better connectivity routes such as roads, and (III) temperature and precipitation influencing rodent fecundity and life cycle.

An optimal sampling approach to modelling whole-body vibration exposure in all-terrain vehicle driving.

Exposure to whole-body vibration (WBV) presents an occupational health risk and several safety standards obligate to measure WBV. The high cost of direct measurements in large epidemiological studies raises the question of the optimal sampling for estimating WBV exposures given by a large variation in exposure levels in real worksites. This paper presents a new approach to addressing this problem. A daily exposure to WBV was recorded for 9-24 days among 48 all-terrain vehicle drivers. Four data-sets based on root mean squared recordings were obtained from the measurement. The data were modelled using semi-variogram with spectrum analysis and the optimal sampling scheme was derived. The optimum sampling period was 140 min apart. The result was verified and validated in terms of its accuracy and statistical power. Recordings of two to three hours are probably needed to get a sufficiently unbiased daily WBV exposure estimate in real worksites. The developed model is general enough that is applicable to other cumulative exposures or biosignals. Practitioner Summary: Exposure to whole-body vibration (WBV) presents an occupational health risk and safety standards obligate to measure WBV. However, direct measurements can be expensive. This paper presents a new approach to addressing this problem. The developed model is general enough that is applicable to other cumulative exposures or biosignals.

Spatial assessment of water quality parameters in Jhelum city (Pakistan).

In this study, we assess the drinking water quality of Jhelum city. Two hundred and ninety-two drinking water samples were randomly collected in the study area. These samples were chemically analyzed for three key toxic (in excess) elements such as pH, total dissolved solids (TDS), and calcium. Geostatistical techniques such as variogram and kriging were used to investigate the spatial variations of these minerals across the city. The spatial structure for each element was found to be anisotropic, and thus, anisotropic variograms were used. The kriging predictions revealed significant concentrations of the above-stated elements at some locations in the study area. While comparing with the World Health Organization, United States Environmental Protection Agency, and Pakistan Environmental Protection Agency standards, the water samples were found to be unsatisfactory for drinking. We conclude that the drinking water in this region is of poor quality and needs proper treatment to make it palatable.

Using codispersion analysis to quantify and understand spatial patterns in species-environment relationships.

The analysis of spatial patterns in species-environment relationships can provide new insights into the niche requirements and potential co-occurrence of species, but species abundance and environmental data are routinely collected at different spatial scales. Here, we investigate the use of codispersion analysis to measure and assess the scale, directionality and significance of complex relationships between plants and their environment in large forest plots. We applied codispersion analysis to both simulated and field data on spatially located tree species basal area and environmental variables. The significance of the observed bivariate spatial associations between the basal area of key species and underlying environmental variables was tested using three null models. Codispersion analysis reliably detected directionality (anisotropy) in bivariate species-environment relationships and identified relevant scales of effects. Null model-based significance tests applied to codispersion analyses of forest plot data enabled us to infer the extent to which environmental conditions, tree sizes and/or tree spatial positions underpinned the observed basal area-environment relationships, or whether relationships were a result of other unmeasured factors. Codispersion analysis, combined with appropriate null models, can be used to infer hypothesized ecological processes from spatial patterns, allowing us to start disentangling the possible drivers of plant species-environment relationships.

Characterization of spatial distribution of Tetranychus urticae in peppermint in California and implication for improving sampling plan.

Twospotted spider mite, Tetranychus urticae Koch, is an important pest of peppermint in California, USA. Spider mite feeding on peppermint leaves causes physiological changes in the plant, which coupling with the favorable environmental condition can lead to increased mite infestations. Significant yield loss can occur in absence of pest monitoring and timely management. Understating the within-field spatial distribution of T. urticae is critical for the development of reliable sampling plan. The study reported here aims to characterize the spatial distribution of mite infestation in four commercial peppermint fields in northern California using spatial techniques, variogram and Spatial Analysis by Distance IndicEs (SADIE). Variogram analysis revealed that there was a strong evidence for spatially dependent (aggregated) mite population in 13 of 17 sampling dates and the physical distance of the aggregation reached maximum to 7 m in peppermint fields. Using SADIE, 11 of 17 sampling dates showed aggregated distribution pattern of mite infestation. Combining results from variogram and SADIE analysis, the spatial aggregation of T. urticae was evident in all four fields for all 17 sampling dates evaluated. Comparing spatial association using SADIE, ca. 62% of the total sampling pairs showed a positive association of mite spatial distribution patterns between two consecutive sampling dates, which indicates a strong spatial and temporal stability of mite infestation in peppermint fields. These results are discussed in relation to behavior of spider mite distribution within field, and its implications for improving sampling guidelines that are essential for effective pest monitoring and management.

Reflectance-based identification of parasitized host eggs and adult Trichogramma specimens.

A wide range of imaging and spectroscopy technologies is used in medical diagnostics, quality control in production systems, military applications, stress detection in agriculture, and ecological studies of both terrestrial and aquatic organisms. In this study, we hypothesized that reflectance profiling can be used to successfully classify animals that are otherwise very challenging to classify. We acquired hyperspectral images from adult specimens of the egg parasitoid genus Trichogramma (T. galloi, T. pretiosum and T. atopovirilia), which are ~1.0 mm in length. We also acquired hyperspectral images from host eggs containing developing Trichogramma instar and pupae. These obligate egg endoparasitoid species are commercially available as natural enemies of lepidopteran pests in food production systems. Because of their minute size and physical resemblance, classification is time consuming and requires a high level of technical experience. The classification of reflectance profiles was based on a combination of average reflectance and variogram parameters (describing the spatial structure of reflectance data) of reflectance values in individual spectral bands. Although variogram parameters (variogram analysis) are commonly used in large-scale spatial research (i.e. geoscience and landscape ecology), they have only recently been used in classification of high-resolution hyperspectral imaging data. The classification model of parasitized host eggs was equally successful for each of the three species and was successfully validated with independent data sets (>90% classification accuracy). The classification model of adult specimens accurately separated T. atopovirilia from the other two species, but specimens of T. galloi and T. pretiosum could not be accurately separated. Interestingly, molecular-based classification (using the DNA sequence of the internally transcribed spacer ITS2) of Trichogramma species published elsewhere corroborates the classification, as T. galloi and T. pretiosum are closely related and comparatively distant from T. atopovirilia. Our results emphasize the importance of using high-spectral and high-spatial resolution data in the classification of organism relatedness, and hyperspectral imaging may be of relevance to a wide range of commercial (i.e. producers of biocontrol agents), taxonomic and evolutionary research applications.

Assessing the significance of global and local correlations under spatial autocorrelation: a nonparametric approach.

We propose a method to test the correlation of two random fields when they are both spatially autocorrelated. In this scenario, the assumption of independence for the pair of observations in the standard test does not hold, and as a result we reject in many cases where there is no effect (the precision of the null distribution is overestimated). Our method recovers the null distribution taking into account the autocorrelation. It uses Monte-Carlo methods, and focuses on permuting, and then smoothing and scaling one of the variables to destroy the correlation with the other, while maintaining at the same time the initial autocorrelation. With this simulation model, any test based on the independence of two (or more) random fields can be constructed. This research was motivated by a project in biodiversity and conservation in the Biology Department at Stanford University.

Study on the influence of pore water pressure on shear mechanical properties and fracture surface morphology of sandstone.

To further investigate the weakening effect of pore water pressure on intact rock mechanics properties and characteristics of fracture surface after failure, direct shear tests of sandstone were conducted under different pore pressure. A 3D scanner was employed to digitize the morphology of the post-shear fracture surface. The variogram function was applied to quantify the anisotropic characteristics of post-shear fracture surface. The relationship between deformation during shear failure of intact rock and quantitative parameters of fracture surface after shear failure was initially established. It can be found that amplitudes of the sinusoidal surface determine the maximum value of variogram, and period affect lag distance that reach the maximum value of variogram. Test results revealed that the increase of pore pressure has obvious weakening effect on shear strength and deformation of rock. Moreover, the increase of pore pressure makes the shear fracture surface flatter. It can be obtained that both Sillmax and Rangemax are positively related to shear strain, but negatively related to normal strain.

Upscaling ground-based backpack gamma-ray spectrometry to spatial resolution of UAV-based gamma-ray spectrometry for system validation.

Advances in the development of gamma-ray spectrometers have resulted in devices that are ideal for use in conjunction with the increasingly reliable systems of autonomously flying uncrewed aerial vehicles (UAVs) that have recently become available on the market. Airborne gamma-ray spectrometry (GRS) measurements have many different applications. Here, the technique is applied to a former uranium mining and processing site, which is characterized by relatively low specific activities and, hence, low count rates, requiring relatively large detectors and correspondingly big size UAVs. The future acceptance of the use of such UAV-based GRS systems for radionuclide mapping depends on their ability to measure absolute specific activities of natural radionuclides such as U-238 in near-surface soil that are consistent with the results of established and proven ground-based systems. To determine absolute specific activities on the ground, the gamma radiation data from airborne detectors must be corrected for attenuation caused by the flight altitude above ground. In recent years, mathematical procedures for altitude correction have been developed, that are specifically tailored to the working range of several tens of meters typical for UAVs. However, very limited experimental validation of these theoretical approaches is available. A very large dataset consisting of about 3000 UAV-based and 19,000 backpack-based measurements was collected at a low-grade uranium ore dump in Yangiabad, Uzbekistan. We applied different geostatistical interpolation methods to compare the data from both survey techniques by upscaling backpack data to airborne data. Compared to backpack systems, UAV-based systems have lower spatial resolution, so measurements average over larger areal units (or in geostatistical terminology: "spatial support"). Taking into account the change in spatial support, we illustrate that (1) the UAV-based measurements show good agreement with the upscaled backpack measurements and that (2) UAV surveys provide good delineation of contrasts of the relatively smooth U-238 specific activity distribution typical for former uranium mining and processing sites. We are able to show that the resolution of UAV-based systems is sufficient to map extended uranium waste facilities.

Nanoscale Prognosis of Colorectal Cancer Metastasis from AFM Image Processing of Histological Sections.

Early ascertainment of metastatic tumour phases is crucial to improve cancer survival, formulate an accurate prognostic report of disease advancement, and, most importantly, quantify the metastatic progression and malignancy state of primary cancer cells with a universal numerical indexing system. This work proposes an early improvement to metastatic cancer detection with 97.7 nm spatial resolution by indexing the metastatic cancer phases from the analysis of atomic force microscopy images of human colorectal cancer histological sections. The procedure applies variograms of residuals of Gaussian filtering and theta statistics of colorectal cancer tissue image settings. This methodology elucidates the early metastatic progression at the nanoscale level by setting metastatic indexes and critical thresholds based on relatively large histological sections and categorising the malignancy state of a few suspicious cells not identified with optical image analysis. In addition, we sought to detect early tiny morphological differentiations indicating potential cell transition from epithelial cell phenotypes of low metastatic potential to those of high metastatic potential. This metastatic differentiation, which is also identified in higher moments of variograms, sets different hierarchical levels for metastatic progression dynamics.

Sustainable futures in agricultural heritage: Geospatial exploration and predicting groundwater-level variations in Barind tract of Bangladesh.

Groundwater resources are one of the essential aspects of achieving self-sufficiency in a country's agricultural production, poverty alleviation, and socioeconomic development, particularly in agricultural heritage management and practices. In the Barind Tract in Bangladesh, groundwater levels have steadily declined due to growing irrigation demand. Surface water sources become scarce during the dry season, and groundwater levels fall to levels that make minimum cultivation challenging. In these circumstances, determining the current status of groundwater is key to any action in the future. This study investigated the existing geospatial pattern and critical zone of groundwater level in Chapainawabganj District, a significant area of the Barind Tract of Bangladesh, and predicted future groundwater levels considering multiple factors. Kriging, a sophisticated geostatistical method, was performed to examine the geographical pattern and groundwater variations, and time series analysis was employed to determine data trends and make future projections. The current study used groundwater level data from 23 monitoring stations over 10 years (2009-2018). Exponential, Gaussian, and Spherical models were cross-matched here for the best predictor model in four fitness measures to determine groundwater concentrations (RMSE, ME, RMSS, ASE), and Box-Jenkins ARIMA (3,1,0) was found best-fit for predictions, and variance estimation. Likewise, cross-validation has been assessed for the accuracy of anticipated results across spatial scales. Although more research is needed to identify the underlying mechanisms, critical zones, and their pattern of modification, possible recharge zones and their locations have been identified. Future groundwater levels, critical zones, and recharge locations have been indicated for the research area and potential recommendations.