Geochemical Modeling Source Provenance, Public Health Exposure, and Evaluating Potentially Harmful Elements in Groundwater: Statistical and Human Health Risk Assessment (HHRA)

Groundwater contamination by potentially harmful elements (PHEs) originating from the weathering of granitic and gneissic rock dissolution poses a public health concern worldwide. This study investigated physicochemical variables and PHEs in the groundwater system and mine water of the Adenzai flood plain region, in Pakistan, emphasizing the fate distribution, source provenance, chemical speciation, and health hazard using the human health risk assessment HHRA-model. The average concentrations of the PHEs, viz., Ni, Mn, Cr, Cu, Cd, Pb, Co, Fe, and Zn 0.23, were 0.27, 0.07, 0.30, 0.07, 0.06, 0.08, 0.68, and 0.23 mg/L, respectively. The average values of chemical species in the groundwater system, viz., H+, OH−, Ni2+, Mn2+, Mn3+, Cr3+, Cr6+, Cu+, Cu2+, Cd2+, Pb2+, Pb4+, Co2+, Co3+, Fe2+, Fe3+, and Zn2+, were 1.0 × 10−4 ± 1.0 × 10−6, 1.0 × 10−4 ± 9.0 × 10−7, 2.0 × 10−1 ± 1.0 × 10−3, 3.0 × 10−1 ± 1.0 × 10−3, 1.0 × 10−22 ± 1.0 × 10−23, 4.0 × 10−6 ± 2.0 × 10−6, 4.0 × 10−11 ± 2.0 × 10−11, 9.0 × 10−3 ± 1.0 × 10−2, 2.0 × 10−1 ± 2.0 × 10−3, 7.0 × 10−2 ± 6.0 × 10−2, 5.0 × 10−2 ± 5.0 × 10−2, 2.0 × 10−2 ± 1.5 × 10−2, 6.0 × 10−2 ± 4.0 × 10−2, 8.0 × 10−31 ± 6.0 × 10−31, 3.0 × 10−1 ± 2.0 × 10−4, 4.0 × 10−10 ± 3.0 × 10−10, and 2.0 × 10−1 ± 1.0 × 10−1. The mineral compositions of PHEs, viz. Ni, were bunsenite, Ni(OH)2, and trevorite; Mn viz., birnessite, bixbyite, hausmannite, manganite, manganosite, pyrolusite, and todorokite; Cr viz., chromite and eskolaite; Cu viz., CuCr2O4, cuprite, delafossite, ferrite-Cu, and tenorite; Cd viz., monteponite; Pb viz, crocoite, litharge, massicot, minium, plattnerite, Co viz., spinel-Co; Fe viz., goethite, hematite, magnetite, wustite, and ferrite-Zn; and Zn viz., zincite, and ZnCr2O4 demarcated undersaturation and supersaturation. However, EC, Ca2+, K+, Na+, HCO3−, Cr, Cd, Pb, Co, and Fe had exceeded the WHO guideline. The Nemerow’s pollution index (NPI) showed that EC, Ca2+, K+, Na+, HCO3−, Mn, Cd, Pb, Co, and Fe had worse water quality. Principal component analysis multilinear regression (PCAMLR) and cluster analysis (CA) revealed that 75% of the groundwater contamination originated from geogenic inputs and 18% mixed geogenic-anthropogenic and 7% anthropogenic sources. The HHRA-model suggested potential non-carcinogenic risks, except for Fe, and substantial carcinogenic risks for evaluated PHEs. The women and infants are extremely exposed to PHEs hazards. The non-carcinogenic and carcinogenic risks in children, males, and females had exceeded their desired level. The HHRA values of PHEs exhibited the following increasing pattern: Co > Cu > Mn > Zn > Fe, and Cd > Pb > Ni > Cr. The higher THI values of PHEs in children and adults suggested that the groundwater consumption in the entire region is unfit for drinking, domestic, and agricultural purposes. Thus, all groundwater sources need immediate remedial measures to secure health safety and public health concerns.


Introduction
Groundwater is a vital freshwater resource for humans worldwide [1][2][3]. Most groundwater is used in domestic, industrial, and agricultural sectors [4,5]. Groundwater is composed of inorganic substances that contain potentially harmful elements (PHEs) that contaminate groundwater worldwide [6]. The quality of groundwater has deteriorated with the growth of industrialization, urbanization, and mining activities [7][8][9]. The presence of abundant mines and improper management mainly causes severe environmental impacts on the groundwater system of Adenzai mining and the flood plain region of Pakistan. Moreover, the evaluation of groundwater resources that measures quality and availability for domestic and commercial suitability is important [10,11]. Apart from abundant mines, waste disposal, and industrial effluents, the concentrations of the groundwater PHEs in the surrounding water aquifer increases with seasonal variability due to precipitation, and it gradually reduces as a result of evaporation processes [12][13][14][15]. The seasonal distribution of PHEs during precipitation and evaporation is evident due to the alteration of the groundwater tables from wet to dry showing seasonal fluctuations [16][17][18][19]. However, the level of the groundwater table varies from place to place, even within the same location. Changes in precipitation across seasons and years produce fluctuations in the water table level. The water table rises in late winter and early spring when snow melts and there is abundant precipitation [20]. While groundwater levels tend to be low in the summer and autumn seasons due to evaporation and dryness, they are abundant during the rainy season or in the winter and spring [10,21].
Most of the recent literature reports that the term heavy elements has gradually interchanged and shifted to potentially harmful elements (PHEs) [6]. Groundwater sources contain potentially toxic elements, which includes poisonous elements [6,22,23]. The current study includes the following potential PHEs: nickel (Ni), manganese (Mn), chromium (Cr), copper (Cu), cadmium (Cd), lead (Pb), cobalt (Co), iron (Fe), and zinc (Zn). Though the higher levels of PHEs in the groundwater are toxic in nature, a few of them are used as food supplements [22,23]. Therefore, groundwater contamination is of prime importance, particularly potentially harmful elements (PHEs) [6,24]. Moreover, the higher content of PHEs in groundwater aquifers is extremely hazardous and causes health implications.
The human health risk assessment model (HHRA-model) is a helpful technique for understanding the severity of PHEs posed through groundwater ingestion [25]. According to the world health organization, about 80% of waterborne diseases are caused by water pollution [26][27][28]. Preferably, water pollution is a significant health concern in different societies and agricultural zones worldwide [29]. The excessive ingestion of groundwater containing PHEs such as Cd, Cr, Cu, Mn, Co, Ni, Pbs, and Zn causes potential ecological and biological health risks [7,24,[30][31][32]. The contamination of groundwater resources has important repercussions for human health. PHEs are highly harmful due to their bioavailability, harmfulness, and persistency, which ultimately causes water-borne diseases [33][34][35].
The study of PHEs within the groundwater aquifer is challenging and requires evaluation of a vast data set and modern methodologies. However, the identification of PHEs in the groundwater system needs several evaluation methods. These evaluation methods include factor analysis (FA), water quality grading (WQG), the Nemerow pollution index (NPI), the pollution load index (PLI), principal component analysis multilinear regression (PCAMLR), cluster analysis (CA), agriculture indexing, the Fuzzy comprehensive method (FCM), GIS interpolation, quantile-quantile Q-Q plot, saturation indexing, and groundwater

Climate, Hydrology, and Hydrogeology
The area's hydrology defines how much groundwater is utilized by the local residents for drinking and domestic purposes. The residents consume from different groundwater resources, including hand pumps, springs, tube wells, and dug wells. These groundwater resources receive surface recharge from River Swat and Panjkora and snowfall on the surrounding mountains. The water that has lesser depth usually has a lower temperature and is predominantly used for domestic, industrial, and agricultural purposes. The area's hydrogeology includes rock minerals such as epidote, plagioclase,

Climate, Hydrology, and Hydrogeology
The area's hydrology defines how much groundwater is utilized by the local residents for drinking and domestic purposes. The residents consume from different groundwater resources, including hand pumps, springs, tube wells, and dug wells. These groundwater resources receive surface recharge from River Swat and Panjkora and snowfall on the surrounding mountains. The water that has lesser depth usually has a lower temperature and is predominantly used for domestic, industrial, and agricultural purposes. The area's hydrogeology includes rock minerals such as epidote, plagioclase, quartz, galena, hornblende, apatite, and sphene; these rocks interact with the groundwater [50]. Chackdara (Adenzai), granite gneisses rock, covers 60 km 2 in the north of the Malakand granitic zone. The geological setting (outlined geological compositions containing Chackdara orthogenesis) is a type of intrusive igneous rock usually called Mekhband, and formations extensively prevail in the area (see Figure 2). Most rock formations exist as coarse-granite, intermediate, and uniform; all these rocks include essential minerals of quartz, muscovite, biotite plagioclase, magnetite, and feldspar granite gneissic gem. quartz, galena, hornblende, apatite, and sphene; these rocks interact with the groundwater [50]. Chackdara (Adenzai), granite gneisses rock, covers 60 km 2 in the north of the Malakand granitic zone. The geological setting (outlined geological compositions containing Chackdara orthogenesis) is a type of intrusive igneous rock usually called Mekhband, and formations extensively prevail in the area (see Figure 2). Most rock formations exist as coarse-granite, intermediate, and uniform; all these rocks include essential minerals of quartz, muscovite, biotite plagioclase, magnetite, and feldspar granite gneissic gem.

Preparation and Analysis of Groundwater Samples
The groundwater samples were collected from the Adenzai flood plain region, northern Pakistan, for physiochemical and PHE analysis. In the study, the groundwater samples were filtered using 0.42-µm Whatman filter paper to safeguard sophisticated instruments [51]. The pH of groundwater samples was adjusted with the addition of 1-2 mL of ultrapure HNO3; afterward, the samples were kept in a refrigerator at 4 °C until further analysis [29,52].
The groundwater samples (n = 50) were collected from shallow groundwater (n = 24), mid-depth groundwater (n = 14), deeper depth groundwater (n = 12), and mine waters (n = 7). The samples were collected from different sources, including bore wells, springs, open dug wells, hand pumps, and community wells. Before collecting the samples, a handheld GPS (HC Garmin) was used to identify the geographic coordinates of each sampling point (see Figure 1). The samples were stored in 100-mL polyethylene bottles, rinsed and soaked three times with double deionized water. All the water samples were gathered for the cations, and PHEs analysis was acidified by putting in 2-3 drops of HNO3. The addition of acid in water samples avoided the precipitation of PHEs, and most importantly, it protected the activities of microbes in the groundwater. All groundwater samples were analyzed three times, and the mean was included in the results. The instrument used to determine PHEs was Inductively Coupled Plasma Mass Spectrometry "ICPMS" (Agilent 7500 ICPMS model manufactured by the USA), under standardized

Preparation and Analysis of Groundwater Samples
The groundwater samples were collected from the Adenzai flood plain region, northern Pakistan, for physiochemical and PHE analysis. In the study, the groundwater samples were filtered using 0.42-µm Whatman filter paper to safeguard sophisticated instruments [51]. The pH of groundwater samples was adjusted with the addition of 1-2 mL of ultrapure HNO 3 ; afterward, the samples were kept in a refrigerator at 4 • C until further analysis [29,52].
The groundwater samples (n = 50) were collected from shallow groundwater (n = 24), mid-depth groundwater (n = 14), deeper depth groundwater (n = 12), and mine waters (n = 7). The samples were collected from different sources, including bore wells, springs, open dug wells, hand pumps, and community wells. Before collecting the samples, a handheld GPS (HC Garmin) was used to identify the geographic coordinates of each sampling point (see Figure 1). The samples were stored in 100-mL polyethylene bottles, rinsed and soaked three times with double deionized water. All the water samples were gathered for the cations, and PHEs analysis was acidified by putting in 2-3 drops of HNO 3 . The addition of acid in water samples avoided the precipitation of PHEs, and most importantly, it protected the activities of microbes in the groundwater. All groundwater samples were analyzed three times, and the mean was included in the results. The instrument used to determine PHEs was Inductively Coupled Plasma Mass Spectrometry "ICPMS" (Agilent 7500 ICPMS model manufactured by the USA), under standardized operating conditions. The quantization limits for Ni, Cr, Cu, Cd, Pb, and Zn were recorded to be 0.5, 0.1, 0.1, 0.02, 0.5, and 0.5 mg/L, respectively. The percentile recovery of PHEs, viz., Ni, Mn, Cr, Cu, Cd, Pb, Co, Fe, and Zn, were recorded to be 96%, 97%, 95%, 97%, 95%, 93%, 91%, 98%, and 95%, respectively.

Questioner Survey
To find the overall adverse impacts of contaminated groundwater, a question-based interview was conducted in the Adenzai region, northern Pakistan. The medical and environmental experts randomly surveyed 1500 households out of 130 thousand individuals, approximately, and this was the public help that was involved in this research. The local respondents included males and females with a secondary level of education. The age of children ranged from 10-14 and adults 15-65 years.
The questionnaire survey included age, body weight, monthly income, education, waterborne diseases, bathing habits, migration habits, smoking or non-smoking habits, occupational exposure, and other health-associated problems. The questionnaires were filled out by respondents of the proposed area with great care regarding information. The expert of the medical team helped the respondents in understanding the questionnaire, and sometimes the questions were explained verbally in their native language. The comments section was included in the designed questionnaire to encourage the respondents to give feedback and add some new comments/information regarding the need for the survey. The survey team explained the queries for the local consumers, who consume from different groundwater resources such as dug wells, tube well, springs, hand pumps, community wells, and mine water. Moreover, meetings and interviews were arranged with the expert doctors of the local hospitals and health units who work to collect waterborne diseases in the proposed study area.

Health Risk Assessment
The purpose of the risk assessment included chronic daily ingestion (CDI), hazard quotient (HQ), and health indices (HI) to calculate the health risk via exposure to PHE concentrations through groundwater ingestion. The impacts of this risk assessment are expressed in terms of carcinogen and non-carcinogen using the HHRA model [53]. The exposure pathways included oral ingestion, dermal, and inhalation. However, oral ingestion is the most important route for PHE ingestion in the ground and surface water, soil, and food [6,24]. Therefore, oral ingestion was considered for carcinogenic and noncarcinogenic exposure in this study.
However, the carcinogenic risk level of PHEs was determined by Equation (3): The CR is the cancer risk, and 'CSF' is the cancer slope factor measured in mg/kg-day −1 [54].

Statistical Analysis
All the groundwater data, including descriptive statistics such as range, mean, standard deviation, and Pearson correlation with a significance level of 0.05 level, were employed and calculated using the XLSTAT 2021 computer package. The multivariate statistical analysis included the pollution index (PI), cluster analysis (CA), and principal component analysis multilinear regression (PCA-MLR), which were performed by mixing  the technique via SPSS, IBM statistic software version 20, and XLSTAT 2021 [31,38,43].

Pollution Index
Pollution indices (PI) are considered a useful statistical technique used to define the contribution and impacts of different groundwater contaminants. However, PI for groundwater is calculated by using Equation (4):

Nemerow's Pollution Indexing (NPI)
Nemerow's pollution indexing (NPI) is an important method used to test the quality of the groundwater. This testing approach is also known as Row's pollution index (RPI). The steps for calculating NPI index values are very simple when compared to other water quality assessment methods. This method was used to check the water quality indexing of groundwater sources compared to mine water for nineteen parameters. Mathematically, the following formula was used to calculate the NPI [57].
Nemerow s pollution Index = C n S n (5) where C n is the concentration of the nth parameter of groundwater, and S n is the standard limit of the nth parameter.

Cluster Analysis
Clustering is a multivariate method used to assemble the groundwater data and to form different groups. Cluster analysis classifies groundwater variables and formulates groups based on homogeneity and heterogeneity; the variables of most similar samples fall within the same groups. Those dissimilar in characteristics developed different clusters. The most useful cluster method exhibited as hierarchical agglomerative clustering provides a homogenous relationship for the overall data set and is represented by a dendrogram plot [58]. The dendrogram typically provides three groups: cluster 1, cluster 2, and cluster 3. Similarly, the Euclidean distance was measured and represented invariance, which resembles two groundwater samples [59]. Clustering analysis was calculated via Ward's method, which evaluated the distance among clusters to minimize the square sum of two sets formed during analysis [60,61].

Principal Component Analysis Multilinear Regression
Principal component analysis multilinear regression (PCA-MLR) is a valuable technique used to understand the pollution sources of PHEs in groundwater in terms of % age contribution. The pollution index (PI) was calculated for overall groundwater parameters [9]. Then, PCA was measured through the Varimax rotation reduction dimension method [13,29]. The three components, F1, F2, and F3, and pollution load (PI) were loaded in the spreadsheet of SPSS (version 20). The PI was loaded as a dependent value, and the three factors were loaded as independent values after regression. The R 2 values were taken from the model summary, then the R 2 of individual factors F1, F2, and F3 were calculated by removing one component and leaving all the other components as independent values. After the R 2 calculation, the R 2 difference was estimated by subtracting the R 2 value of each element from the overall R 2 values. The percentage contribution was calculated by summing the R 2 differences.

Mapping
A global positioning system (GPS name HC Garmin) handheld instrument was used to collect the geographic coordinates of each sample point. The coordinates were designed in an excel sheet and plotted in ArcGIS software version 10.6 to generate a study area map ( Figure 1) and geological map ( Figure 2). The distribution map was designed and described the pattern of groundwater contaminants in the form of low and high concentrations to understand the contamination level of PHEs in the groundwater aquifer.

Quality Assurance and Quality Control
The precision and accuracy of the PHEs in the groundwater analysis were ensured by using certified reference materials (CRMs). The reference materials were obtained from the Ministry of Environmental Protection Agency (Beijing, China) and were used for groundwater PHEs analysis. The certified reference materials used for groundwater were GSB 07-1183-2000, GSBZ 50004-88, GSB 07-1185-2000, and GSBZ 50016-90. Different standards and reagent blanks were also used to ensure accurate and precise results. Moreover, the chemical ion balance error (CIBE) was calculated in meq/L [29,62]; to check the accuracy of the water found to be ±5% errors, Equation (6) was used. Table 1 represents the range and mean values of PHEs and the associated physicochemical groundwater and mining water variables in the Adenzai flood plain region of Pakistan. Groundwater and mine water findings revealed that most PHEs had exceeded the WHO guidelines [63]. The groundwater samples (n = 50) were grouped into three classes based on their depth, viz., shallow (depth ≤ 40 m), mid-depth (41-80 m), and deeper depth (>80 m), respectively. The groundwater results based on their depth and mine profile altered their geochemical composition. The ranges of concentrations of pH, EC, temp, depth, and TDS in the shallow depth groundwater were 7.   [52,[64][65][66]. However, the PHE results of the current studies are consistent with those outlined by [6]. It was observed that Cr has excellent mobility under oxidizing conditions. The highest Ni, Mn, and Cr concentrations were observed in the mid-depth groundwater samples attributed to the weathering and dissolution of ultramafic rock, containing minerals such as dunite and peridotite in the bed host chromite deposits.

Geochemical Profile of Physicochemical Variables and Potential Harmful Elements
The increasing geographic trend indicated the following pattern, according to the depth profile of groundwater: shallow groundwater > mid-depth groundwater > deep groundwater, respectively (see Table 1). Overall, 48%, 30%, 64%, 62%, and 62% of the groundwater samples contribute to Cr, Cd, Pb, Co, and Fe contamination in the entire study area. The Cd contamination in groundwater was mainly released from agriculture practices, sewage sludge, and corrosion of plumbing, water pipe, and phosphate fertilizers [44]. At the same time, the highest Pb concentrations up to 0.20 mg/L were reported in the shallow groundwater in the Badwan area. This study showed that the groundwater system is heavily contaminated with Pb due to leaching and transportation from the plumbing system [63]. However, the Co and Fe content in the groundwater of the Adenzai area exceeded the WHO guideline values of 0.04 and 0.3 mg/L. Therefore, the groundwater was unfit for drinking and domestic purposes regarding Cr, Cd, Pb, Co, and Fe. However, the Ni, Mn, Cu, and Zn concentrations showed no contamination in the study area. Overall, the groundwater samples exceeded the WHO guidelines for EC, Ca, K, Na, HCO 3 , and PHEs, viz., Cr, Cd, Pb, Co, and Fe, respectively. However, their percentage exceedance was 90%, 4%, 2%, 12%, 10%, 46%, 32%, 62%, 56%, and 62%, respectively, whereas the pH, TDS, Mg, Cl, SO 4 , Ni, Mn, Cu, and Zn concentrations in the groundwater system were within the Pak, EPA, and WHO guideline values [63,67].

Geochemistry of Underground Mines Water
The existence of mines and mineral deposits in the flood plain contributes to PHEs in the groundwater system. Mainly, PHEs occur in solid phases with a moderate tendency of mobilization in the groundwater system; though, it can be released into surrounding aquifers under some conditions. Table 1  Groundwater pumped from the underground mine situated in the mafic and ultramafic rock showed elevated concentrations of PHEs compared to the background water. It was observed that the local inhabitants of this area used mined water for drinking and domestic purposes. Mainly, neutral to alkaline pH levels and elevated concentrations of PHEs were observed in the underground mining water. The author observed that the local residents consume mine water for their domestic use. The PHE analysis of mine water showed that Mn, Cr, Pb, Cd, Co, and Fe had exceeded the WHO guideline values, while the rest of the PHEs showed satisfactory results. The increasing trend of PHEs recorded in the mine water was Fe > Mn > Zn > Pb > Ni > Co > Cd > Cr > Cu, respectively. The PHEs, viz., Pb, Cd, Cr, Co, and Mn, suggest that these metals are highly harmful from a health perspective. The elevated levels of PHEs in the underground mine water of the study area should not be used for drinking, domestic, or agriculture purposes.

Geochemical Facies and Control Mechanism
Gibb's plot and the Chadha diagram were used to understand the hydrogeochemical regime and geochemical composition of groundwater samples in the Adenzai flood plain area. The Chadha diagram is the expanded version of the piper trilinear diagram demonstrating that most of the water samples were mixed cations, with HCO 3 − and Cl − as the dominating ions, forming NaHCO 3 and NaCl type water. According to Gibb's plot, the geochemistry of the groundwater samples was mainly controlled by weathering and recharge mechanisms (see Figure 3). At the same time, precipitation and evaporation were the least significant in the entire area.  However, Chadha and Tamta designed a new hydrogeochemical sequence for groundwater classification using a difference of cations (Ca 2+ + Mg 2+ ) and (Na + + K + ) and anions such as HCO3 -and (Cl -+ SO4 2− ) as concentrations calculated in meq/L as a percentile [68]. The groundwater samples of the entire study area showed four water types. The primary groundwater composition was based on NaHCO3 and accounted for 50.8% contribution, 35% CaHCO3 type, NaCl type 17.5%, and Ca-Mg-Cl contributed 1.75%, respectively (see Figure 4). The geochemical facies of shallow groundwater were mainly controlled by NaHCO3 type, while mid-depth and deep groundwater were controlled by Ca-HCO3 type, and regardless of the type of mine water, it was occupied by NaCl type. The main factors that influenced the shallow groundwater were ion exchange processes and the alteration of CaHCO3 type into NaHCO3 type, whereas weathering and recharge mechanisms mainly controlled mid-depth and deep groundwater, which is composed of CaHCO3 type. At the same time, mine water is controlled by evaporation processes that However, Chadha and Tamta designed a new hydrogeochemical sequence for groundwater classification using a difference of cations (Ca 2+ + Mg 2+ ) and (Na + + K + ) and anions such as HCO 3 and (Cl -+ SO 4 2− ) as concentrations calculated in meq/L as a percentile [68]. The groundwater samples of the entire study area showed four water types. The primary groundwater composition was based on NaHCO 3 and accounted for 50.8% contribution, 35% CaHCO 3 type, NaCl type 17.5%, and Ca-Mg-Cl contributed 1.75%, respectively (see Figure 4). The geochemical facies of shallow groundwater were mainly controlled by NaHCO 3 type, while mid-depth and deep groundwater were controlled by CaHCO 3 type, and regardless of the type of mine water, it was occupied by NaCl type. The main factors that influenced the shallow groundwater were ion exchange processes and the alteration of CaHCO 3 type into NaHCO 3 type, whereas weathering and recharge mechanisms mainly controlled mid-depth and deep groundwater, which is composed of CaHCO 3 type. At the same time, mine water is controlled by evaporation processes that play significant inputs in the formation of saline NaCl-type water, whereas, Ca-Mg-Cl-type water is formed due to the reverse ion-exchange method. The control mechanism using Gibb's plot and the geochemical results of the current study were compared with Jehan et al., 2020, and the findings were consistent [52].
anions such as HCO3 -and (Cl -+ SO4 2− ) as concentrations calculated in meq/L as a percentile [68]. The groundwater samples of the entire study area showed four water types. The primary groundwater composition was based on NaHCO3 and accounted for 50.8% contribution, 35% CaHCO3 type, NaCl type 17.5%, and Ca-Mg-Cl contributed 1.75%, respectively (see Figure 4). The geochemical facies of shallow groundwater were mainly controlled by NaHCO3 type, while mid-depth and deep groundwater were controlled by Ca-HCO3 type, and regardless of the type of mine water, it was occupied by NaCl type. The main factors that influenced the shallow groundwater were ion exchange processes and the alteration of CaHCO3 type into NaHCO3 type, whereas weathering and recharge mechanisms mainly controlled mid-depth and deep groundwater, which is composed of CaHCO3 type. At the same time, mine water is controlled by evaporation processes that play significant inputs in the formation of saline NaCl-type water, whereas, Ca-Mg-Cltype water is formed due to the reverse ion-exchange method. The control mechanism using Gibb's plot and the geochemical results of the current study were compared with Jehan et al., 2020, and the findings were consistent [52]. Field-1 indicates Ca-HCO 3 -type water compiling weathering and a recharge mechanism, and Field-2 shows that Ca-MgCl groundwater determines the ionic reverse exchange. Field-3 reveals evaporation forming the NaCl type, and Field-4 defines NaHCO 3 -type elaborate ion-exchange processes. Table 2 reveals the geochemical speciation of groundwater and mine water in the Adenzai flood plain region. The ranges of concentrations of H + , OH − , Ni 2+ , Mn 2+ , Mn 3+ , Cr 3+ , Cr 6+ , Cu + , Cu 2+ , Cd 2+ , Pb 2+ , Pb 4+ , Co 2+ , Co 3+ , Fe 2+ , Fe 3+ , and Zn 2+ were recorded in the groundwater of the Adenzai flood plain region up to 1.0 × 10 −4 -1.15 × 10 −4 ,

Geochemical Speciation of PHEs
The ranges of concentrations of H + , OH − , Ni 2+ , Mn 2+ , Mn 3+ , Cr 3+ , Cr 6+ , Cu + , Cu 2+ , Cd 2+ , Pb 2+ , Pb 4+ , Co 2+ , Co 3+ , Fe 2+ , Fe 3+ , and Zn 2+ observed in the mine water in the Adenzai flood plain region were 1.0 × 10 .0 × 10 −10 , and 8.0 × 10 −4 ± 2.0 × 10 −4 , respectively. The highest concentrations of dissolved ions were reported for Pb 4+ , Pb 2+ , Ni 2+ , Mn 2+ , Cd 2+ , Cu + , Cu 2+ , Fe 2+ , and Zn 2+ , respectively. The dissolved chemical ions in the groundwater of the present study were compared with the study reported by Rashid et al., 2021, which recorded a little lower concentration, attributed to the sufficient dilution from the Riverine water of River Swat [6]. However, comparing the groundwater and mine water profiles suggested that both waters are saturated from the same parent rock that was situated in the underlying bedrock settings. However, mine water showed higher concentrations than shallow water, mid-depth, and deep groundwater due to mining actions and human inputs. Table 2. Chemical speciation of PHEs in groundwater and mine water in the Adenzai flood plain area, Pakistan.

Non-Carcinogenic and Carcinogenic Risk of PHEs
The non-cancer risk posed by PHEs, viz., Mn, Cu, Co, Fe, and Zn, in children, males, and females, is described in Figure 5 and Table 4 suggested that children are more severely influenced by PHE ingestion than males and females in the study area. The results of the cancer risk posed by PHEs, viz., Ni, Cr, Cd, and Pb, in children and adults are outlined in Figure 5 and Table 4.

Pearson Correlations for the Interrelationship of Measured Ions and Trace Metals
The Pearson correlation matrix of PHEs and physicochemical variables in the groundwater samples were listed (see Table S1). The interrelationship of various water variables had significant positive and negative impacts, measuring the degree of closeness and establishing a linear relationship between the dependent and independent groundwater variables. The correlation coefficient (r) mainly indicated the merging of the sampling points close in a straight line, such as the coefficient of determination (R 2 ) values of Cr, Cd, and Pb, respectively. The inter-elemental analysis favored the results of the principal component analysis, multilinear regression (PCA-MLR).
It is worth noting that higher concentrations of Cr, Pb, Cd, Fe, and Co were observed in most areas of the study area. This is not surprising because all of these contaminants are directly influenced by mining activities, coal combustion, and agriculture practices. Meanwhile, the acidic-to-neutral environment mostly favors the dissolution of PHEs. Outside of the mining areas, the groundwater is influenced by natural and anthropogenic activities. .09 in the study area. The groundwater contamination resulted from the weathering and dissolution of mafic and ultramafic rock, mining action, steel industrial effluents, agriculture practices, and transportation sectors. The transportation sector extensively uses leaded gasoline and petrol for vehicular transportation. However, the increasing order contamination factors was described as Pb > Cu > Co > Fe > Cr > Cd > Mn > Ni > Zn, respectively. The overall groundwater quality revealed moderate contamination in the entire region.

Spatial Distribution of Groundwater Variables and PHEs Using Q-Q Plotting
Groundwater physicochemical variables and PHEs were used to construct Q-Q plotting to determine normal distribution. This is a graphical way of plotting data from the first data set's quantiles against the second data set. The first data set is labeled "anticipated expected normal value", plotted against the second data set denoted by "observed values". These numbers align up and down at the 45 • reference line plotting data in SPSS software. The quantile values were arranged in a straight diagonal line. If both data sets came from the same distribution source, this reference line would be linear (normal distribution).
The findings of groundwater Q-Q normal standard plots are described in (see Figure 5). The groundwater data set shows the normal distribution for some observations, but few data deviated significantly by adopting a curved pattern away from a straight reference line. The deviated observations are evident by occupying the extreme points known as outliers. Q-Q plotting determined a normal distribution and the outliers in the groundwater samples. The groundwater was assembled within three quartiles, viz., Q1, Q2, and Q3. First quartile Q1 was the lower quartile, Q2 was the median, and Q3 was the upper quartile.
The numerical values of groundwater data occurred below the median occupied in the first quartile, and the higher-concentration values were reported in the Q3 upper quartile. However, the normal values of groundwater samples were assembled in the second quartile Q2. The lower quartile arranged groundwater samples in increasing sequence within 25%. The upper quartile Q3 accumulated groundwater data points and accounted for 75%. Thus, normal distribution in groundwater data was reported in the Q2-median quartile that occurred between the Q1 and Q3 quartiles in the study area.

Cluster Analysis
Cluster analysis is a valuable technique to represent the groundwater data in clusters/groups. It helped in understanding the groundwater data and in displaying patterns of the most similar objects. According to this technique, the most likely observations fell within the same class/category [69]. The most similar observations were arranged in three different classes, which were classes C1, C2, and C3 (see Figure 6). Overall, the results of the clustering analysis (CA) and PCA-MLR showed three major processes. Through these processes, different classes/groups were constructed to form a dendrogram [70]. The targeted groundwater observations were tested through Ward's method, and the squared Euclidean distance was measured for the similarity index. Class C1 represents 17 groundwater samples from Badwan, Chatpat, Ramial, and Osaky (5, 4, 3, and 5). The second class was composed of twenty-nine (n = 29) groundwater samples from Badwan, Chatpat, Ramial, Osaky, Warsak, and Rambora areas (3, 2, 5, 3, 8, and 8, respectively). The third class, C3, represented two groundwater samples from Chatpat. Cluster C1 was considered less polluted, and the %age contribution was 60.4%. Class C2 showed moderate pollution, and the %age contribution was 36%. Class C3 revealed severe pollution, and the %age contribution was 4.2%. However, variability was 41.6% within the class, and between the class it was 58.4% (see Figure 6). The range and mean centroid distances between the three classes, C1, C2 and C3, were (0.17-0.69 and 0.39), (0.28-0.81 and 0.43), and (0.25-0.25 and 0.25), respectively. However, the groundwater samples were grouped into clusters, and C1, C2, and C3 were classified as low, medium, and high polluted areas corresponding to the PHE concentrations. The overall groundwater observation was expressed via the clustering plot (see Figures 7 and S5).

Pollution Source Identification
The principal component analysis multi-linear regression was performed on the normalized groundwater data. This approach determined the pollution sources in terms of %age contribution. Once the pollution source was identified, it was easy to minimize its impact on groundwater. These studies mainly focused on loading factors to define hydrological processes for particular areas [71]. It calculated three significant loading factors, F1, F2, and F3, respectively. The PCA-MLR results of fifty groundwater samples were described for nineteen groundwater variables. The overall loading factors and the

Pollution Source Identification
The principal component analysis multi-linear regression was performed on the normalized groundwater data. This approach determined the pollution sources in terms of %age contribution. Once the pollution source was identified, it was easy to minimize its impact on groundwater. These studies mainly focused on loading factors to define hydrological processes for particular areas [71]. It calculated three significant loading factors, F1, F2, and F3, respectively. The PCA-MLR results of fifty groundwater samples were described for nineteen groundwater variables. The overall loading factors and the

Pollution Source Identification
The principal component analysis multi-linear regression was performed on the normalized groundwater data. This approach determined the pollution sources in terms of %age contribution. Once the pollution source was identified, it was easy to minimize its impact on groundwater. These studies mainly focused on loading factors to define hydrological processes for particular areas [71]. It calculated three significant loading factors, F1, F2, and F3, respectively. The PCA-MLR results of fifty groundwater samples were described for nineteen groundwater variables. The overall loading factors and the correlation between the first and second loading factors are described in Figure 8. However, the calculated loading factors, F1, F2, and F3, cumulative %age (80.223), and individual factor variance based on percentage were 40.28, 23.53, and 16.41 and are listed in Table 6 and Figure 8a. Mine water representing F1, F2, and F3 loading factors, their cumulative %age (85.452), and individual factor variance were 43.482, 25.936, and 16.034 (see Table 6, and Figure 8b). correlation between the first and second loading factors are described in Figure 8.  Table 6, and Figure 8b).
(a) (b)    The first loading factor, F1, showed a 40.285 % variability with the eigenvalues of 4.95 (see Figure 8). The significant variables of F1 were pH, EC, depth, TDS, Ca, Mg, Na, HCO 3 , SO 4 , Mn, Cd, and Pb, and their correlating coefficient values were 0.658, 0.561, −0.652, 0.585, −0.648, −0.512, 0.883, 0.623, 0.762, 0.523, 0.541, and 0.520, respectively (see Table 6). The component F1 showed ionic strength, major ion constituents, bedrock composition, and groundwater rock interaction, suggesting that groundwater contamination takes its inception from the weathering of granite rocks, ion exchange, and the dissolution of minerals in the entire region. Mostly, the groundwater variables of factor F1 revealed the genesis of the geogenic source. However, pH, EC, and TDS resulted from mineral dissolution, the leakage of aquifer compositions, and saline bases [72][73][74], whereas Ca 2+ , Na 2+ , HCO 3 -, and SO 4 2− originated from geogenic sources, i.e., ion exchange processes, rock weathering, water-rock interaction, and dissolution of albite muscovite, and dolomite minerals. The potential sources contributing to Ca 2+ , Na 2+ , and SO 4 2− were attributed to be calcite (CaCO 3 ), plagioclase (NaAlSi 3 O 8 -CaAl 2 Si 2 O 8 ), and sulfate bearing minerals such as gypsum polyhalite (K 2 Ca 2 Mg (SO 4 ) 6 , (CaSO 4 ·2H 2 O). However, the sources of HCO 3 in groundwater take their genesis from water transportation, and from interactions with calcium or magnesium carbonate rocks containing dolomite and limestone-forming bicarbonates. The potential sources of Mn were soil weathering, organic matter, gasoline, compost, silage pile, and bedrock composition collectively released into the water aquifer, while Cd in the groundwater system were triggered from shale, fossil fuels, and coal during volcanic action. However, Pb takes its genesis from the lead pipe, plumbing materials, corrosion, and pipe fixture. The loading factor F1 determined that geogenic inputs accounted for 75% of contribution in groundwater (see Figure 8).
The second loading factor showed a 23.526 % variance with the eigenvalues of 2.985 (see Figure 8). This factor contributed favorable loading for Ni, Cr, Cu, and Fe with correlation coefficient (r) values of 0.547, 0.734, 0.515, and 0.671. However, the negative loading observed for EC, depth, and TDS showed correlation coefficient (r) values that were −0.623, −0.521, and −0.613, respectively (Table 6 and Figure 8). The mineral phases suggested that Ni contamination in the water aquifer takes its origin from underlying bedrock materials, viz., bunsenite NiO, Ni (OH) 2 , and trevorite minerals. Cr contamination in the groundwater system takes its genesis from chromite FeCr 2 O 4 and eskolaite Cr 2 O 3 minerals, while Cu, Cr, and Fe get into the water through water-rock interaction with CuCr 2 O 4 , delafossite CuFeO 2 , and Cu-Ferrite CuFe 2 O 4 . However, Fe get into groundwater through interaction and the dissolution of goethite FeO(OH), hematite Fe 2 O 3 , magnetite Fe 3 O 4 , and wustite FeO minerals. Thus, EC, depth, and TDS negated values reflected that these water variables have an inverse correlation with water variables and loading factors. However, a negative loading in PCA essentially signified that those specific properties of water are missing a latent variable associated with the main PCA component. Additionally, groundwater variables were attributed to industrial discharge, electroplating, agrochemical fertilizer, and coal combustion [75]. The second loading factor determined mixed sources contributing natural and anthropogenic inputs in groundwater by accounting for 18% contribution (see Figure 9).
The third loading factor, F3 contributed 16.412% variability with the eigenvalues of 1.826 (see Table 6). The significant positive variables of F3 were Cl and Co, with correlating coefficient (r) values of 0.714 and 0.586, respectively. At the same time, Ni showed a negative correlation, and its correlating coefficient (r) value was −0.527 (see Table 6). The Cl got into the groundwater system from mineral deposits, seawater spray, industrial waste, and agriculture runoff. However, Co took its origin from mafic, ultramafic, and metamorphic rocks containing cobaltite and sulfosalt minerals. The third factor showed that anthropogenic pollution can play a vital role in the contamination of groundwater systems. The significant loading represents anthropogenic inclusion from industrial and agricultural activities, domestic wastes, poultry effluents, and Zn fertilizer [30]. The Ni gets into the water system through the weathering and erosion of mafic, ultramafic rock, water-rock interaction, and mineral dissolution [75,76]. The loading factor F3 recorded anthropogenic sources accounting for 7% of contribution (see Figure 8).  The third loading factor, F3 contributed 16.412% variability with the eigenvalues of 1.826 (see Table 6). The significant positive variables of F3 were Cl and Co, with correlating coefficient (r) values of 0.714 and 0.586, respectively. At the same time, Ni showed a negative correlation, and its correlating coefficient (r) value was −0.527 (see Table 6). The Cl got into the groundwater system from mineral deposits, seawater spray, industrial waste, and agriculture runoff. However, Co took its origin from mafic, ultramafic, and metamorphic rocks containing cobaltite and sulfosalt minerals. The third factor showed that anthropogenic pollution can play a vital role in the contamination of groundwater systems. The significant loading represents anthropogenic inclusion from industrial and agricultural activities, domestic wastes, poultry effluents, and Zn fertilizer [30]. The Ni gets into the water system through the weathering and erosion of mafic, ultramafic rock, water-rock interaction, and mineral dissolution [75,76]. The loading factor F3 recorded anthropogenic sources accounting for 7% of contribution (see Figure 8).
The PCA results of mine water were also described in three factors: F1, F2, and F3, with a total variability of 85.452 with the eigenvalues of 11.434 (see Table 6). The first loading factor, F1, revealed a 43.482% variability with the eigenvalues of 6.137 (see Figure  8b). The positive significant loading noticed for pH, EC, TDS, K, Na, Cl, SO4, Ni, Mn, Cr, Pb, Co, and Fe, had corresponding correlating coefficient values of 0.579, 0.643, 0.594, 0.932, 0.733, 0.921, 0.842, 0.923, 0.892, 0.633, 0.642, 0.714, and 0.873, and the negative loading for Ca 2+ and Mg 2+ had correlating coefficient values of −0.540 and −0.588, respectively (see Table 6). The groundwater contamination was evident from the saline water intrusion, leaking of aquifer composition, ion exchange, weathering and erosion of mafic and ultramafic rock, geochemical modification of sulfide minerals, water-rock interaction, and mineral dissolution [75,76]. Moreover, mineral phases of the current study proposed that Ni contamination could take its genesis from underground geological settings containing bunsenite NiO, Ni (OH)2, trevorite minerals, and Cr resulting from chromite FeCr2O4 and eskolaite Cr2O3. However, Fe may get into groundwater through the interaction and dissolution of goethite FeO(OH), hematite Fe2O3, magnetite Fe3O4, wustite FeO, Delafossite CuFeO2, and Cu-Ferrite CuFe2O4 minerals, whereas Ca 2+ and Mg 2+ could result from calcite and dolomite minerals.
The second loading factor, F2, of mine water showed 25.936% variability with the eigenvalues of 3.285 (see Figure 8b). This factor revealed positive loading for temp, Na, HCO3, Cu, Cd, and Zn with correlation coefficient (r) values of 0.764, 0.579, 0.765, 0.498, 0.464, and 0.871, and negative loading for depth, Ca, Mg, and Co with values of −0.694, Natural sources includes weathering of granitic rock, mafic and ultramafic contain schistose rocks , water rock interaction, ion-exchange, and minerals dissolution.
Mixed sources includes weathering, and dissolution of minerals and ultra mafic agrochemical, fertilizer, industrial discharge, electroplating and coal combustion. Anthropogenic sources includes agriculture, mining and industrial activities. The PCA results of mine water were also described in three factors: F1, F2, and F3, with a total variability of 85.452 with the eigenvalues of 11.434 (see Table 6). The first loading factor, F1, revealed a 43.482% variability with the eigenvalues of 6.137 (see Figure 8b). The positive significant loading noticed for pH, EC, TDS, K, Na, Cl, SO 4 Table 6). The groundwater contamination was evident from the saline water intrusion, leaking of aquifer composition, ion exchange, weathering and erosion of mafic and ultramafic rock, geochemical modification of sulfide minerals, water-rock interaction, and mineral dissolution [75,76]. Moreover, mineral phases of the current study proposed that Ni contamination could take its genesis from underground geological settings containing bunsenite NiO, Ni (OH) 2  The second loading factor, F2, of mine water showed 25.936% variability with the eigenvalues of 3.285 (see Figure 8b). This factor revealed positive loading for temp, Na, HCO 3 , Cu, Cd, and Zn with correlation coefficient (r) values of 0.764, 0.579, 0.765, 0.498, 0.464, and 0.871, and negative loading for depth, Ca, Mg, and Co with values of −0.694, −0.696, −0.735, and −0.609, respectively. The possible contamination source of temperature could be solar radiation, sunlight, and industrial pollution releasing greenhouse gases, CO 2 , and other gases, whereas Na + originates from granitic rock, and HCO 3 takes genesis from sedimentary rock contains minerals of calcite and dolomite. However, Cu in mine water is attributed to a sedimentary rock containing azurite, bornite, chalcocite, cuprite, and malachite minerals. In comparison, Cd gets into the water through water-rock interaction with cadmoselite, greenockite, and otavite minerals. In contrast, Zn is attributed to smithsonite, sphalerite, hemimorphite, zincite, hydrozincite, and willemite. However, the mineral phases suggested the following minerals for Cu to be cuprite, delafossite, Cu-ferrite, tenorite, Cd (monteponite), Zn from ferrite-Zn, and zincite (present study). Moreover, depth, Ca, Mg, and Co showed negative values, reflecting that these water variables have an inverse correlation with water variables and loading factors. However, a negative loading in PCA essentially signifies that those specific properties of water are missing a latent variable associated with the main PCA component. However, Ca and Mg could take their genesis from calcite and dolomite minerals, and Co could be attributed from serpentinite, dunite, and basalt mineral deposits. Cobalt is also influenced by cobaltite, a sulfosalt mineral containing cobalt, sulfur, and arsenic found in metamorphic rocks [77].
The third loading factor, F3 of mine water, determined a 16.034% variability with the eigenvalues of 2.012 (see Table 6). The favorable loading attributed for pH, temp, Cr, and Cd had correlation coefficient (r) values recorded as 700, 0.602, 0.718, and 0.751, and the negative loading for HCO 3 was −0.562, respectively. The possible contamination source of F3 in mine water could be a geochemical modification of sulfide minerals. Additionally, groundwater variables are attributed to industrial discharge, electroplating, agrochemical fertilizer, and coal combustion [75], whereas Cd could be influenced by the weathering of schistose rocks, but it mainly occurs in sulphide minerals.

Implication for the Sustainable Management of Groundwater Resources
Groundwater contains several essential elements such as K + , Ca ++ , Mg ++ , S 2− , Cl − , Ni, Cu, Mn, Fe, and Zn required for human health and body growth [66,78]. Most of these essential elements are required in a specific amount included in our daily food nutrition [79]. However, the excessive intake of the heavy elements interchangeably used with PHEs can cause health implications due to their persistent, genotoxic, and bioavailable nature. Moreover, PHEs can easily enter the food chain, interacting with the marine and terrestrial life that probably migrate and transport water contaminants to the ecosystem [80]. Thus, pollutants can easily transfer from one biological system into another, causing potential health risks to humans and the ecosystem [81]. However, the excessive ingestion of PHE implication for the sustainable management of groundwater resources is considered harmful to human health. The lower to moderate PHE ingestion poses the following diseases in human beings: hearing loss, lower intelligence, flu, growth retardation, and gastrointestinal problems [82], while excessive exposure to exceeded PHEs recorded irritability, sleep disturbances, vomiting, constipation, cramps, loss of appetite, exhaustion, convulsions, neurological impairment, coma, organ failure, and death [30,66]. Moreover, the carcinogenic and noncarcinogenic health implications recorded in the current study showed that children are more susceptible to PHEs than adults. Thus, PI and NPI were calculated to test the contamination level of PHEs, which indicated moderate to severe pollution in the entire region. This problem is extremely aggravated in the countries where alternate water sources are negligible and could increase the importance of groundwater for domestic, viz., cooking and drinking, purposes. Therefore, it is extremely important to monitor and safeguard the existing groundwater resources by preventing the further deterioration of water resources.
Our research strongly recommends that groundwater management and government authorities should take action to establish a monitoring network for groundwater systems. This can aid in the real-time monitoring of groundwater quality and availability and draw up plans to avoid groundwater deterioration. Local governments should push for more stringent steps to establish safe drinking water wells. Furthermore, appropriate initiatives should be implemented to raise public knowledge about the importance of using groundwater resources in a sustainable and safe manner.

Conclusions
The groundwater of Adenzai, the floodplain region is Pakistan, exceeded the WHO guideline values of EC, Ca 2+ , K + , Na + , HCO 3 − , Cr, Cd, Pb, Co, and Fe up to 90%, 18%, 16%, 28%, 16%, 60%, 44%, 78%, 76%, and 76%, respectively. Nemerow's pollution index showed that EC, Ca, K, Na, HCO 3 , Mn, Cd, Pb, Co, and Fe had worse water quality, and their percentage exceedances were 90%, 12%, 10%, 24%, 56%, 10%, 38%, 100%, 80%, and 72%. Groundwater PHE contamination was more evident in the shallow aquifer, as compared to the mid-depth and deep aquifers, due to the weathering of granitic and gneissic rock and known existing mines. The mineral prospects of groundwater become precipitated due to supersaturation and dissolution, which is a result of undersaturation. PCAMLR and CA recorded that 75% of groundwater contamination takes it's origin from geogenic sources, while 18% was from mixed and 7% was from anthropogenic sources. However, CA classified groundwater data into less, moderate, and severe polluted clusters. The HHRA-model suggested potential non-carcinogenic and carcinogenic risks in children and adults. The THI for non-carcinogenic and carcinogenic risks indicated that women and their infants are highly exposed to PHEs hazards. The higher THI values of the HHRA-model suggested that the groundwater sources were unfit for drinking and domestic needs. However, it is highly recommended that the government and non-government organizations play their role in minimizing the PHE concentration in the groundwater sources in the entire region and that they provide alternate sources of water for drinking and domestic purposes. Moreover, the use of agrochemical fertilizers and the consumption of mine water should also be reduced by raising awareness programs regarding the sustainable use and management of groundwater resources. Thus, all groundwater sources need immediate remedial measures to avoid public health concerns.
Supplementary Materials: The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/ijerph19116472/s1, Figure S1: Reveals concentration profile of Cr, vs. Ni, Mn, Cu, Cd, Pb, Co, Fe, and Zn of groundwater in the Adenzai, Pakistan. Figure S2: The concentrations plot of Cd vs. pH, Ni, Mn, Cr, Cu, Pb, Co, Fe, and Zn in the groundwater Adenzai, Pakistan. Figure S3: The concentration profile of Pb vs pH, Ni, Mn, Cr, Cu, Cd, Co, Fe, and Zn of groundwater in the Adenzai, Pakistan. Figure S4: Shows northing plots against pH, Ni, Mn, Cr, Cu, Cd, Pb, Co, and Zn of groundwater in the Adenzai region, Pakistan. Figure S5: Profile plot of overall parameters of groundwater in the Adenzai, flood plain area Pakistan. Table S1: Pearson correlation of potentially harmful elements in groundwater of Adenzai flood plain region of Pakistan. Table S2