**1. Introduction**

Groundwater reaches the aperture of the landmass and its interior through natural, artificial, and indirect recharge. It was used by at least 2 billion people [1]. This infringement of this precious resource can be attributed to its nearest obtainability with partial efforts and spatial obstinacy. Aquifers at specific demand locations are being drained, and this state may lead to deprivation of this indispensable resource unless allayed [2]. The hydraulic conductivity and permeability of the rocks depend on the rocks' porosity [3]. Heavy metals are among the most significant pollutants of groundwater sources [4,5]. Nonetheless, the toxicity of heavy metals depends on their concentration levels in the environment. With increasing concentrations in the environment and decreasing soils' capacity toward retaining heavy metals, they leach into groundwater and soil solution [6,7]. Then, these toxic heavy metals can be accumulated and concentrated via the food chain in living tissues [8]. Some of these heavy metals are Arsenic (As), Lead (Pb), Nickle (Ni), Chromium (Cr), and Zinc (Zn). They can be categorized as critical heavy metals that pollute groundwater and affect human health [9]. One of the advanced techniques used in groundwater quality data interpolation is geostatistics [10]. The results obtained from the geostatistics can help a decision-maker to adopt suitable remedial measures to protect the quality of groundwater sources [11].

**Citation:** Salem, I.B.; Nazzal, Y.; Howari, F.M.; Sharma, M.; Mogaraju, J.K.; Xavier, C.M. Geospatial Assessment of Groundwater Quality with the Distinctive Portrayal of Heavy Metals in the United Arab Emirates. *Water* **2022**, *14*, 879. https://doi.org/10.3390/w14060879

Academic Editor: Dimitrios E. Alexakis

Received: 12 January 2022 Accepted: 9 March 2022 Published: 11 March 2022

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Focus has been devoted to studying groundwater quality and quantity to prevent groundwater contamination [12]. The native hydrogeological conditions and pollutant loads are dependent on lateral physicochemical interactions on the surface and sub-surface. Geostatistical analysis is used in subjecting data to interpolate by understanding the resemblances [13]. Radial functions and inverse distance weighted values are significant in data smoothing [14]. We can investigate uncertainties and produce surface predictions to get related information without subjecting data to erroneous functions and unnecessary manipulations. The geostatistical tools help us understand data in graphical forms while maintaining the native peaks and troughs, as witnessed by field reports [15]. Threedimensional visualization evolved as an in-silico currency to reveal native information about the local geology and visualization thereof [16]. The sample points measured spatially can be used in the autocorrelation process in the ordinary kriging method [17]. The association between the transformation between the measured and predicted values can be described with a semi variogram [18]. Semi variance is the product of slope that appears within a fitted model and distance between the location pairs. Geostatistical techniques improved the spatial data distribution with delimited accuracy while maintaining portability [19]. Primarily, spatial analysis mandates the use of GIS and statistics. GIS apparatus is expected to deliver predictions and improved interpretation accuracy per se. Though technical constraints limit us, we can present a near-accurate portrayal of processes and features with GIS tools equipped with geostatistics [20].

Inverse distance weighting (IDW) can be stated as a simple interpolation technique. The weighted average is considered within a neighborhood in IDW [21]. The analyst can regulate a specific mathematical form of the weight function and the neighborhood size [22]. It is necessary to consider IDW with natural neighbor in analyzing substantial data sets. It uses cluster scatter points to identify datasets under investigation. This method is apt for discrete sample data [23].

The geostatistical analysis such as ordinary kriging provides insights into the groundwater situation. Kriging assumes that random processes with spatial autocorrelation can mimic at least some of the spatial variation observed in natural events and that the spatial autocorrelation must be explicitly modeled. However, it has much versatility as a simple prediction tool. An integrated approach to the assessment combining aquifer-based preselection criteria and multivariate non-parametric geostatistics was proposed to overcome the traditional approach's limitations and include the intrinsic hydrogeological and geochemical heterogeneity into the definition of groundwater water quality [24]. Arsenic (As) is one of the most harmful inorganic contaminants in water streams for the environment and human health [25]. The correlations between different groundwater quality indices and the causes and impacting variables of groundwater pollution can be tracked using statistical and multivariate techniques [26]. The meta-evaluation of the groundwater quality index was attempted [27]. The quality of groundwater that is being used for irrigation can be affected by several factors [28].

Groundwater is a vital resource for human life. Land use changes and urbanization harmed groundwater. This study examines the geographical variability of selected heavy metals in the UAE's Liwa region. There were limited studies that used geostatistics to explain the groundwater quality variability across this study area. This work is focused on the application of the geostatistical tools to explain the groundwater contamination. The objectives of this study are: (1) To describe the geospatial relationship between the observed groundwater variables using geostatistics, and multivariate analysis; (2) To determine the parameters' variability at various sample points spread over the study area. PCA was used to define the parameters controlling the groundwater chemistry and the information using significant variables was revealed; (3) To investigate the suitability of water for drinking (Using Weighted Arithmetic Water Quality Index (WA-WQI); (4) To employ ordinary kriging and select an appropriate model representing spatial distribution and spatial dependence variables.
