Principal Component Analysis (PCA)–Geographic Information System (GIS) Modeling for Groundwater and Associated Health Risks in Abbottabad, Pakistan

Abstract

Drinking water quality is a major problem in Pakistan, especially in the Abbottabad region of Pakistan. The main objective of this study was to use a Principal Component Analysis (PCA) and integrated Geographic Information System (GIS)-based statistical model to estimate the spatial distribution of exceedance levels of groundwater quality parameters and related health risks for two union councils (Mirpur and Jhangi) located in Abbottabad, Pakistan. A field survey was conducted, and samples were collected from 41 sites to analyze the groundwater quality parameters. The data collection includes the data for 15 water quality parameters. The Global Positioning System (GPS) Essentials application was used to obtain the geographical coordinates of sampling locations in the study area. The GPS Essentials is an android-based GPS application commonly used for collection of geographic coordinates. After sampling, the laboratory analyses were performed to evaluate groundwater quality parameters. PCA was applied to the results, and the exceedance values were calculated by subtracting them from the World Health Organization (WHO) standard parameter values. The nine groundwater quality parameters such as Arsenic (As), Lead (Pb), Mercury (Hg), Cadmium (Cd), Iron (Fe), Dissolved Oxygen (DO), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Colony Forming Unit (CFU) exceeded the WHO threshold. The highly exceeded parameters, i.e., As, Pb, Hg, Cd, and CFU, were selected for GIS-based modeling. The Inverse Distance Weighting (IDW) technique was used to model the exceedance values. The PCA produced five Principal Components (PCs) with a cumulative variance of 76%. PC-1 might be the indicator of health risks related to CFU, Hg, and Cd. PC-2 could be the sign of natural pollution. PC-3 might be the indicator of health risks due to As. PC-4 and PC-5 might be indicators of natural processes. GIS modeling revealed that As, Pb, Cd, CFU, and Hg exceeded levels 3, 4, and 5 in both union councils. Therefore, there could be greater risk for exposure to diseases such as cholera, typhoid, dysentery, hepatitis, giardiasis, cryptosporidiosis, and guinea worm infection. The combination of laboratory analysis with GIS and statistical techniques provided new dimensions of modeling research for analyzing groundwater and health risks.

1. Introduction

Drinking contaminated water is recognized as a major problem in undeveloped countries, although rarely highlighted in developing countries [1,2]. There are two types of water contamination sources in terms of their origins: point and diffuse. Industrial sites, municipalities, agricultural installations, manure storage, and dumping sites are all examples of significant point sources. They are easier to identify and regulate than diffuse (non-point) sources, such as nitrates and pesticides leaching into surface and groundwater due to rainfall, soil infiltration, and surface runoff from agricultural land. The diffuse sources generate significant changes in the pollutant load of water over time [3]. Pakistan is a water-stressed country, where access to fresh drinking water is around 1200 m³ per capita, and around 70% of Pakistan’s population relies on groundwater for domestic purposes [4]. Groundwater in Pakistan is contaminated by toxic chemicals and disease-causing microbiological organisms found in household and industrial wastes and effluent. This increases the local population’s diseases such as cholera, typhoid, dysentery, hepatitis, giardiasis, cryptosporidiosis, and guinea worm infection. For example, poor water quality is responsible for 30% of all diseases and 40% of all fatalities in Pakistan [5]. Groundwater is contaminated by both anthropogenic (industrial and household waste) and geological sources (underlying surface composition and topography) [6,7].

The most common source of groundwater pollution is open dumping sites. Most dumping sites are used as final disposal locations for various types of garbage, including solid and liquid municipal trash and industrial wastes. These operate without proper technical controls around the world. These sites frequently operate illegally and without environmental permits, and authorities fail to take preventative measures [8]. The dumping sites that are uncontrolled and poorly designed are regarded as serious potential polluters of groundwater contamination [8,9]. However, the pollution level could be affected by several factors, including the quantity and composition of the leachate, the length of time for which the site has been operational, the soil type, groundwater level, and distance from agricultural land or water sources [10]. These factors contribute to groundwater contamination, which could have many health and environmental consequences, especially in developing countries such as Pakistan. Toxic chemicals and disease-causing microbiological organisms could be present in open dumping sites. The poor water quality, for example, is said to be responsible for many diseases and fatalities in Pakistan [11]. It directly influences the ecosystem, community health, and the economy [12]. In Pakistan, the open disposal of municipal solid waste is a common practice [13]. An open dumping site is a land disposal site that does not protect or shield the territory or domain, and it is subject to open burning and visible to community vectors and scavengers [14]. Open dumping deteriorates the natural environment. Due to open dumping water, land, air, and health are the most affected areas [15]. Pakistan is the world’s sixth most populous country, and as a result, it generates a large amount of wastes [16]. According to the [17] Pakistan Environmental Protection Agency, Pakistan produces about 48.5 million tons of solid waste annually, and this figure has been increasing by more than 2% each year. Pakistan estimates that 87,000 tons of solid waste are produced per day, mostly from urban areas [18]. The increase in the civil population is causing an increase in the amount of garbage produced, which must be handled.

Many studies focused on groundwater contamination from geological and anthropogenic sources and the ramifications for public health [19,20]. They concluded that anthropogenic sources constituted a greater risk to groundwater quality compared to geological sources. A study on groundwater quality indicated that unconsolidated deposits could reduce pathogen numbers to acceptable levels as the contaminated water flows through them [21]. Therefore, bacterial contamination occurs when the water table is shallow, or the contaminants directly contact the groundwater through open wells [22]. This means that groundwater’s bacteriological contamination in deep wells could be due to direct and localized factors such as poor sanitation around the well and discharge of industrial and domestic wastes. It was reported by [23] that the chemical composition of groundwater could be controlled by many factors, which include the timing of precipitation, groundwater recharging, depth to the groundwater, soil type, presence of organic matter, and moisture content. They concluded that the effect of the combination of these factors could create diverse water types that could change in composition spatially and temporally. Water samples collected from major cities of Pakistan, such as Karachi, Faisalabad, Kasur, Gujrat, and Rawalpindi, revealed that the analyzed samples from these cities were unfit to drink. The major source of water contamination was the anthropogenic source from household and industrial wastes [24]. Similar findings were reported by [25], who found that around four million acre feet (MAF) of industrial and household waste and effluent every year in Pakistan is discharged directly into water bodies, aside from a small proportion of 3% that is brought under treatment.

Several methods, including statistical and Geographic Information System (GIS)-based techniques, were used for groundwater studies [26,27]. Statistical analysis is a powerful and commonly used technique for analyzing water quality data [28]. Various researchers employed statistical techniques such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Multivariate Analysis of Variance (MANOVA) for water studies [29,30]. PCA is a commonly used technique in water quality studies for reducing the dimensionality of datasets and increasing interpretability, while minimizing information loss. For example, [31] studied the hydrochemistry of groundwater in Syria’s Upper Jezireh Basin using PCA and found that PC analysis reduced 20 variables into four PCs (F1, F2, F3, and F4) that explained 81.9% of the total variance. The F1 (47.1%) explained the groundwater mineralization, whereas F2 (17%) showed isotopic enrichment and nitrate pollution. A similar study was conducted in Semarang, Central Java, (Indonesia), using 19 water quality parameters and a PC-based cluster analysis. It was found that anthropogenic factors mainly affected water quality, and PC analysis was able to explain all the significant factors [32]. A PC-based water quality parameters study was also carried out in Abu Dhabi, United Arab Emirates (UAE) [33]. The study concluded that employing multivariate statistical approaches in conjunction with GIS would produce better results compared to a single method for assessing water quality parameters. The main disadvantage of statistical methods is their inability to include the spatial dimension of water quality parameters and nonlinear relationships [34]. GIS-based techniques are effective tools for spatial groundwater modeling. Application of GIS with Global Positioning System (GPS) is helpful for the identification of sample sites and covering spatial dimensions of the water quality parameters [35]. The data obtained from the GPS survey might be transferred into GIS software for additional modeling and analysis. For example, groundwater modeling in Central Antalya, Turkey, was done using GIS and the Analytic Hierarchy Process (AHP) [36]. Some researchers used hybrid models for groundwater mapping, such as Ground Water Potential (GWP) with Random Subspace (RS), Multilayer Perception (MLP), Naïve Bayes Tree (NBTree), and Classification and Regression tree (CART) algorithms [37,38,39,40].

Groundwater quality maps were created using novel ensemble Weights-of-Evidence (WoE) with Logistic Regression (LR) and Functional Tree (FT) models in the Ningtiaota region of Shaanxi Province, China [41]. Overall, all three models performed well for groundwater spring potential evaluation. However, the FT model’s prediction capability was better compared to those of other models [41]. A similar study was conducted by [42], who developed a ground potential map of river effluent across rocky terrain using a statistical–GIS-based technique. The river effluent was divided into three potential zones. The ground potential map was found to be closer to the field observations. The current study employed a novel integrated statistical–GIS-based technique to estimate groundwater quality parameter exceedance levels and related health risks in Abbottabad.

Abbottabad is a major city located along the China–Pakistan Economic Corridor (CPEC) route. The city experienced significant anthropogenic and developmental activities in the past three decades. Furthermore, there was a large influx of people from surrounding districts and cross-border migration from Afghanistan, resulting in the degradation of local water infrastructure and water quality. Abbottabad’s groundwater is regarded as the third worst for drinking purposes in Khyber Pakhtunkhwa (KPK) province, Pakistan. Therefore, it is critical to identify groundwater contamination and its effects on public health to improve the local community’s health and well-being. For this purpose, two union councils (Mirpur and Jhangi) were selected based on population and level of discharge from homes and industrial sources. Based on population, the research aimed to use PCA and GIS to model spatial exceedance levels of groundwater quality parameters and related health risks. The specific objectives of this study were three-fold: (i) assess the quality of groundwater parameters using laboratory analysis, (ii) model the groundwater data using PCA to find major PCs, and, finally, (iii) the geospatial modeling of the exceedance parameters and related health risks.

2. Materials and Methods

2.1. Study Area

The study area includes Jhangi and Mirpur union councils of Abbottabad district located in the KP province of Pakistan, as presented in Figure 1a,b. The study sites are located at the base of the Himalayan range, in the active monsoon zone. It is surrounded by the Sarban hills and has a cool temperate climate for most of the year. The average annual maximum and minimum temperatures in the Abbottabad district are 22.76 °C and 11.41 °C, respectively, with an annual precipitation of 1366 mm. The temperature drops below 0 °C during the winter, with significant snowfall on the surrounding forest-covered hills. The district of Abbottabad’s average relative humidity was recorded at 56% [43]. The elevation range of the whole area is from 1191 m to 2626 m and its total area is 1967 km² [44].

The groundwater data were collected from 41 sampling sites. Boring wells in Mirpur and Jhangi union councils were used as sampling locations (Figure 1). The groundwater samples were collected from the entire study area, near and far from the dumping sites, shown as groundwater sampling sites (gw) in Figure 1. The depth of the groundwater wells varied from 80 ft to 100 ft on average. Groundwater in the study area is contaminated by anthropogenic (industrial and household waste) and geological sources (underlying surface composition and topography). The common sources of contamination are industrial sites, domestic effluent, and open dumping sites. The collection of solid waste was accomplished in a few selected locations under the jurisdiction of Tehsil Municipal Administration (TMA) of Abbottabad. The solid waste is mostly dumped in open dumping sites by most communities living in Abbottabad city. These scattered open dumping sites affect the beauty of the city and they create: (i) bad smell; (ii) air pollution; (iii) soil contamination; (iv) water pollution; (v) poor aesthetic, and (vi) health risks. Open dumping sites could cause great damage to the ecosystem by releasing toxic compounds such as dioxins and furans into the air [45].

Until now, no study was done to model the impact of solid waste and natural factors on groundwater wells in Pakistan and in Abbottabad, specifically. Therefore, considering the importance of the research and study area, the Jhangi and Mirpur union councils of Abbottabad were selected. According to the 2017 census, the population of Abbottabad is 1.33 million. The population of Jhangi is 18,037, and for Mirpur, the total population is 46,206 [44]. Both union councils have residential neighborhoods with narrow streets, open drains, and small shops. The residents normally select open plots, open spaces, grounds, and the vicinity of shopping areas for dumping solid waste. These solid wastes include plastic bags, papers, clothes, cans, plastic bottles, kitchen garbage, etc.

2.2. Data

The groundwater data were collected from 41 sampling sites. Boring wells in Mirpur and Jhangi union councils were used as sampling locations (Figure 1). The data include 15 water quality parameters, which are pH, Total Dissolved Solids (TDSs), Electrical Conductivity (EC), Dissolved Oxygen (DO), Colony Forming Unit (CFU), Turbidity (Tur), Arsenic (As), Mercury (Hg), Lead (Pb), Cadmium (Cd), Calcium (Ca), Zinc (Zn), Potassium (K), Sodium (Na), and Iron (Fe). The samples were also collected near the three dumping sites in both union councils. Two dump sites were in Mirpur and one in the Jhangi union council area. The data for all these parameters were tested in the laboratory by dividing them into three groups, which are: (i) physicochemical analysis; (ii) microbiological analysis; and (iii) heavy metal analysis. The data obtained based on three types of analyses for groundwater, were used for PCA and exceedance. For PCA, this dataset was normalized by using their maximum values. The normalized dataset was used to obtain PCA [46,47]. The Landsat 8 Operational Land Imager (OLI) satellite image (30 m spatial resolutions) extracted the built-up area, prepared by [48], on 25 May 2017, and overlaid on classified parameter exceedance maps. The OLI is a sensor mounted on the Landsat 8 satellite that collects non-thermal data for LULC classification. The entire scene’s (34.1688° N, 73.2215° E) cloud cover for the Landsat 8 data was 13% but it was near zero over our studied region. The data were downloaded from the United State Geological Survey website (https://earthexplorer.usgs.gov). This provided support for analyzing the potential population of built-up areas that could have exposure to health risks due to parameter exceedance.

2.3. Methods

The schematic diagram of the methods adopted in this study is presented in Figure 2. The main components of the methodology are: (i) field survey and sampling; (ii) laboratory analysis; (iii) PCA; and (iv) GIS-based modeling. From Figure 2, it is obvious that the field survey was accomplished using GPS Essentials application. This application was used for collecting geographic coordinates (GCS_WGS_1984) of open dumping and groundwater sampling sites. The groundwater samples, collected in the field, were used for laboratory analysis. The laboratory analysis was accomplished for seven physicochemical parameters, one microbiological parameter, and seven parameters related to heavy metals. Figure 2 provides further information on the data obtained from laboratory analysis, which was used for: (i) preparation of graphs against WHO standards; (ii) normalization of data and its application for PCA to obtain five PCs; (iii) exceedance and GIS modeling; (iv) production of maps with exceedance levels; and (v) interpretation of maps for health risks in the population of the study area. The details of all methods are provided in subsequent sections.

2.3.1. Field Survey and Sampling

The groundwater samples were collected from 41 sampling sites, as discussed in Section 2.1. All these sampling sites, i.e., “gw”, are shown in Figure 1c. The gw represents groundwater well samples collected during the field survey. The groundwater was collected from bore wells in disinfected bottles after siphoning for 5–15 min [49]. The samples were collected once in a month from January to March 2021. The winter season was chosen for sampling because temperature plays an important role in sample preservation, and microbial growth is reduced at low temperatures. Three samples (gw1, gw2, and gw3) from a depth of 90 feet were used as control samples. The average distance between the two sample points was approximately 0.5 km. The distance between dumping sites was approximately 1.6 km. The dump sites were close to residential areas. These samples were brought to the laboratory, stored in plastic polythene containers, and cleaned with nitric acid. These samples were used to analyze physico-chemical, microbiological, and heavy metals for 15 parameters. In physicochemical analysis, seven parameters were tested: pH, TDS, EC, DO, Tur, Ca, and Na. CFU was considered for microbiological analysis. In heavy metals, seven parameters were analyzed: Cd, As, Hg, Zn, Pb, K, and Fe.

2.3.2. Laboratory Analysis

For physicochemical analysis, pH, TDS, and EC of the groundwater samples were tested using a multi-parameter probe called HANNA HI9828, while DO and Tur were obtained using the DO meter and turbidity meter, respectively. The physicochemical parameters such as Na and Ca were analyzed through Atomic Absorption Spectroscopy. The serial dilution technique was used for microbiological analysis. Heavy metals were detected and measured by Atomic Absorption Spectroscopy (AAS) [50]. The details about the instruments and Quality Assurance and Quality Control (QA/QC) used in the current study’s laboratory analysis are given in

Table 1. The instruments and QA/QC used in the current study’s laboratory analysis.

Parameters	Instrument Detection Limit
As	1 (mg/L)
Hg	4.2 (mg/L)
Pb	0.45 (mg/L)
Cd	0.028 (mg/L)
Fe	0.1 (mg/L)
K	0.043 (mg/L)
Zn	0.018 (mg/L)
Ca	0.092 (mg/L)
Na	0.012 (mg/L)
pH	0–14
EC	99.9 µs/cm
TDS	50 ppm
Turbidity	0.05 NTU/FNU

.

2.3.3. Principal Component Analysis (PCA)

PCA was accomplished for groundwater by using SPSS 23 (Statistical Package for the Social Sciences) is a software program used by researchers in various disciplines for quantitative analysis of complex data. It was developed by International Business Machines Corporation (IBM) located in Armonk, New York, USA [47]. PCA was used to the extract main PCs using an eigenvalue of 1 as a cut-off [47]. The varimax normalized rotation derived the loading values for all the parameters under the major PCs [15,16]. Each PC comprises a set of associated parameters with positive and negative loading values, which are used to interpret the primary processes involved in analyzing and characterizing water quality. The variance was obtained for all PCs, and the cumulative variance (%) was also achieved. In statistics, variance is used to measure dispersion, which indicates the distances of numbers from their average values [51]. The loading values of all PCs were categorized into three classes, i.e., strong > 0.75, 0.75 > moderate > 0.5, and 0.5 > weak > 0.4, with parameter loading values less than 0.40 not being considered because of their lesser significance. These PCs were used to interpret the natural and anthropogenic processes involved in water. The results for all the parameters, obtained through laboratory analysis, were normalized by dividing their values by their respective maximum values [47,52]. These sets of normalized values were used in PCA.

2.3.4. GIS-Based Modeling

An exceedance equation was developed based on original values obtained from the laboratory analysis and WHO standard parameter values for groundwater wells, as given in

Table 2. WHO standards for drinking water [58].

Parameters	WHO Drinking Water Standards
Physicochemical Parameters
pH	6.5–8.5
TDS (ppm)	1000
Tur (NTU)	<5
EC (µS cm−1)	400
DO (ppm)	6.5–8
Na (mg/L)	200
Ca (mg/L)	100
Microbiological Parameter
Total coliforms	0 (CFU/mL)
Heavy Metals
As (mg/L)	0.05
Hg (mg/L)	0.001
Pb (mg/L)	0.01
Cd (mg/L)	0.003
Zn (mg/L)	3
K (mg/L)	12
Fe (mg/L)	0.3

. The parameter exceedance values obtained from the exceedance equation were imported into GIS software (ArcMap 10.5) and these were used for modeling exceedance in GIS. For modeling, the Inverse Distance Weighting (IDW) interpolation technique was used to develop parameter exceedance maps. IDW is an interpolation technique in which interpolation is estimated based on the values at neighboring locations primarily weighted by distance from the interpolation location. The IDW technique was used in various studies [53,54,55]. With this modeling, the exceedance values for all the study area locations could be obtained in case of no data. The IDW is better than kriging because it is easy to understand, and it does not suffer outliers like Kriging. Ref. [56] found the accuracy of the measured and estimated arsenic level using IDW and Kriging techniques. They found that the correlation coefficient between estimated and measured arsenic was greater with IDW compared to Kriging. Ref. [57] showed that IDW is superior to Kriging in estimating whole landfill methane flux. Ref. [55] believed that ordinary Kriging is not accurate, since it requires uniform distribution, which can rarely be met. Five classes were generated in the maps of all parameters. These classes were interpreted as: (i) Level 1 exceedance; (ii) Level 2 exceedance; (iii) Level 3 exceedance; (iv) Level 4 exceedance; and (v) Level 5 exceedance. The exceedance pattern for all parameters was displayed in the maps based on these classes. The built-up area was extracted from the LULC classified image mentioned in Section 2.2. The Land Use/Land Cover (LULC) map was prepared using the artificial intelligence technique of Support Vector Machine (SVM) in Envi 5.3 software by [48]. Finally, these GIS maps were used to study the health risks and impacts of exceeded parameters on the population residing in the study area.

3. Results and Discussion

3.1. Results of Laboratory Analysis for Parameters

The laboratory analysis indicated that six parameters (i.e., pH, Tur, Na, Ca, Zn, and K) were within the permissible limit of WHO guidelines. It was found that nine parameters (i.e., DO, TDS, CFU, As, Hg, Pb, Cd, Fe, and EC) exceeded WHO guidelines. DO and TDS were according to WHO guidelines for 37 wells, and these were exceeded only for four wells, gw12, gw22, gw31, and gw34. The major exceeded parameters were CFU, As, Hg, Pb, Cd, Fe, and EC. The CFU range was from 1000 CFU/mL to 126,000 CFU/mL for 35 wells. It was ≥30,000 CFU/mL for gw10, gw14, gw22, gw38, and gw40.

Table 3. Physiochemical parameters for groundwater wells.

Locations	pH (0–14)	Turbidity NTU	TDS (ppm)	EC (µS cm−1)	Sodium (mg/L)	Calcium (mg/L)
gw1	7.03	0.48	120	226	11.12	145.8
gw2	7.26	0.71	112	213	9.62	134.5
gw3	6.73	0.51	119	226	11.73	136.2
gw4	8.14	1.03	103	192	9.63	100.3
gw5	7.61	0.69	52	98	8.99	23.82
gw6	7.94	1.69	48.3	97	5.71	76.28
gw7	7.51	1.84	227	455	12.44	71.39
gw8	7.01	1.51	46.7	93	3.03	60.31
gw9	7.78	1.79	272	546	17.73	54.49
gw10	7.34	2.8	284	568	9.83	78.97
gw11	7.32	2.88	316	633	0.99	73.49
gw12	6.93	2.65	572	1144	15.34	114
gw13	7.56	2.15	349	697	11.74	97.94
gw14	7.42	1.67	312	624	10.77	85.13
gw15	7.11	2.04	450	901	11.96	95.09
gw16	7.02	2.39	364	730	7.88	80.47
gw17	7.84	1.87	385	770	9.57	82.72
gw18	7.14	1.64	272	544	6.35	70.23
gw19	7.4	1.91	365	730	9.17	80.22
gw20	7.17	2.58	325	651	8.45	74.22
gw21	7.47	1.34	265	531	7.28	49.79
gw22	7.07	4.4	633	1266	14.28	76.85
gw23	7.97	3.01	393	786	24.95	45.03
gw24	7.17	1.64	278	556	5.82	71.22
gw25	6.99	0.92	334	662	13.77	111.21
gw26	7.05	1.24	377	744	5.15	134.32
gw27	6.6	0.8	256	514	22.35	98.17
gw28	6.96	3.45	424	849	10.84	76.92
gw29	6.95	3.26	320	641	18.78	82.73
gw30	6.91	2.76	72	146	7.84	45.29
gw31	6.88	1.73	511	1019	17.52	23.56
gw32	6.39	0.8	476	938	11.23	39.48
gw33	6.76	3.78	391	770	8	44.76
gw34	6.87	2.74	512	1009	4.89	62.87
gw35	6.84	0.65	475	936	4.52	77.21
gw36	8.49	2.34	85	160	6.159	39.81
gw37	8.58	3.02	91	161	5.829	48.58
gw38	8.45	2.15	114	216	12.21	76.47
gw39	8.5	2.18	90	173	4.006	46.32
gw40	8.16	2.4	98	188	9.776	69.41
gw41	8.36	2.08	101	198	11.43	74.21

and

Table 4. Heavy metals and microbial concentrations for groundwater wells.

Locations	K (mg/L)	Pb (mg/L)	Fe (mg/L)	Zn (mg/L)	Cd (mg/L)	Hg (mg/L)	As (mg/L)	CFU
gw1	1.73	0.223	2.7	0.421	0.066	1.851	17.7	0
gw2	0.89	0.25	1.2	0.194	0.017	1.821	13	0
gw3	1.41	0.418	3.04	0.058	0.021	1.058	13.5	5000
gw4	1.2	0.535	1.59	0.061	0.121	1.653	15.83	7000
gw5	0.64	0.274	2.68	0.226	0.085	2.558	13.59	3000
gw6	1.74	0.545	1.96	0.24	0.063	1.488	9.97	2000
gw7	3.55	0.506	2.52	0.247	0.104	2.16	12.63	1000
gw8	0.62	0.326	2.76	0.202	0.082	2.358	12.04	6000
gw9	2.3	0.481	3.93	0.246	0.007	4.012	8.63	3000
gw10	3.34	0.603	3.67	0.188	0.088	2.178	10.93	30,000
gw11	1.88	0.564	4.76	0.19	0.031	1.78	9.62	7000
gw12	3.65	0.624	3.76	0.185	0.023	2.045	9.06	3000
gw13	2.33	0.828	4.86	0.129	0.068	2.469	7.28	1000
gw14	1.68	0.643	4.12	0.154	0.019	2.753	9.38	30,000
gw15	3.7	0.706	2.24	0.215	0.036	2.113	13.43	21,000
gw16	2.38	0.656	1.95	0.229	0.093	2.442	11.74	3000
gw17	3.29	0.754	2.67	0.099	0.135	2.482	10.71	10,000
gw18	2.21	0.775	4.37	0.057	0.041	2.339	11.89	6000
gw19	2.25	0.678	0.79	0.214	0.101	3.556	6.72	7000
gw20	2.18	0.814	1.59	0.084	0.081	2.999	12.7	1000
gw21	2.34	0.738	3.47	0.197	0.001	3.363	14.38	4000
gw22	3.02	0.84	3.32	0.09	0.027	2.209	16.02	32,000
gw23	2.34	0.958	2.6	0.149	0.115	3.089	13.46	21,000
gw24	1.74	0.85	4.98	0.136	0.121	3.113	10.99	0
gw25	1.06	0.739	1.03	0.821	0.133	1.789	3.28	1000
gw26	1.88	0.278	3.67	1.021	0.05	1.014	15.14	6000
gw27	1.92	0.442	0.6	0.527	0.071	1.212	3.87	0
gw28	3.7	0.466	3.66	1.188	0.001	0.778	16.85	14,000
gw29	1.85	0.241	2.54	0.565	0.138	1.719	12.39	5000
gw30	1.35	0.332	3	1.133	0.067	0.791	6.58	2000
gw31	0.79	0.675	0.98	1.068	0.04	1.938	14.12	5000
gw32	1.81	0.71	3.39	0.555	0.038	1.561	17.12	1000
gw33	1.86	0.507	4.46	0.646	0.016	0.96	0	5000
gw34	3.44	0.343	4.26	0.252	0.084	1.995	5.59	3000
gw35	2.22	0.517	2.31	0.963	0.044	0.564	5.58	0
gw36	2.305	0.571	0.016	0.222	0.129	1.242	2.746	3000
gw37	2.179	0.568	0.084	1.642	0.143	1.756	3.585	0
gw38	3.169	0.245	0.602	0.366	0.366	13.85	2.981	126,000
gw39	1.907	0.504	0.112	0.838	0.145	3.219	3.337	4000
gw40	2.751	0.323	0.217	0.293	0.142	4.977	0.387	50,000
gw41	1.585	0.378	0.122	1.365	0.129	3.455	0.361	8000

indicate the values of parameters for 41 groundwater wells.

The value of Hg ranges from 0.558 mg/L to 13.849 mg/L for 41 wells. The highest value of Hg (i.e., 13.849 mg/L) was for gw38. gw40 also showed a higher value of 4.976 mg/L. The minimum limit of Pb was 0.213 mg/L, its maximum limit was 0.948 mg/L for 40 wells, and its value was gw23. The range of Cd was from 0.004 mg/L to 0.363 mg/L for 39 wells, and its highest value was for gw38. The range was from 0.3 mg/L to 4.68 mg/L for Fe, and its highest value was for gw24. EC was from 155 µS cm⁻¹ to 966 µS cm⁻¹, and its highest value was for gw22. The exceeded values of exceeded parameters were utilized for GIS modeling. The study findings reveal that there are two types of groundwater well pollution sources: natural and anthropogenic. Open dumping sites are the most obvious anthropogenic source, as evidenced by high levels of groundwater parameter values found in the samples collected from groundwater wells near the open dumping sites. In comparison to non-dumping areas, samples collected from groundwater wells near open dumping sites contained higher levels of As, Hg, Pb, CFU, and Cd. Similar results were reported by [56], who assessed groundwater parameters such as As, Hg, and CFU, and found that groundwater sites near dumping sites had higher levels of pollution compared to non-dumping sites. Cd contamination might come from plastics and electronic waste, whereas Pb contamination could be from batteries and old lead-based paints [59]. Groundwater contamination was also discovered in non-dumping sites in both union councils, which might be caused by natural sources such as geological formation and rock weathering.

3.2. PCA

Five PCs were obtained based on PCA. These PCs were PC-1, PC-2, PC-3, PC-4, and PC-5, which showed variance of 27.663%, 18.592%, 12.992%, 9.439%, and 7.292%, respectively, as given in

Table 5. PCs with loading values for 15 groundwater quality parameters.

Var.	PC-1	PC-2	PC-3	PC-4	PC-5
CFU	0.898
Hg	0.913
Cd	0.825
pH	0.590			0.488
EC		0.880
TDS		0.879
K		0.711
Tur		0.599		0.463
Fe		0.537	0.415
Zn			−0.809
DO			0.791
As			0.554
Ca				−0.808
Pb		0.420		0.528
Na					0.859
Variance (%)	27.663	18.592	12.992	9.439	7.292
Cumul. (%)	27.663	46.255	59.246	68.686	75.978

. The cumulative variance for all these PCs was 75.978%. PC-1 revealed that four parameters (i.e., CFU, Hg, Cd, and pH) were correlated with each other. All parameters of PC-1 were strongly positively loaded, except pH, which was moderate with a positive value. PC-1 could be related to health risks due to toxic elements such as CFU, Hg, and Cd. Anthropogenic activities might include pollution related to heavy metals and microbial contamination in groundwater. From PC-1, it is obvious that heavy metals such as CFU, Hg, and Cd could contribute to pollution. Their values were found well above WHO guidelines according to laboratory analysis. This could be related to residential and commercial wastes from all dumping sites. Other factors that could contribute to the presence of toxic elements in groundwater wells include agricultural runoff and household effluents, which might have contributed to high CFU levels in groundwater wells. Fecal pollution in open dumping sites might also be linked to human and bird feces [60]. The high levels of Hg and Cd in the study area could be attributed to geological and soil formation. The exceeded Hg might cause diseases such as kidney damage, insomnia, and memory loss [61]. The higher exposure to Hg could also lead to death [62]. Cd is carcinogenic, and it might cause death due to multiorgan failure [60]. pH might be affected by chemicals in the water, determining the solubility and biological availability of chemical components such as nutrients (i.e., phosphorus, nitrogen, and carbon) and heavy metals [63]. PC-1 also exhibited a strong positive value of CFU, which might be an indicator of fecal pollution related to fecal coliforms [6]. Fecal coliform is a sub-group of coliform bacteria, which live and reproduce in the gut of humans and other warm-blooded animals. The CFU showed very high values according to laboratory analysis, except for a few sampling sites. The study area residents are using this water for drinking and consumptive purposes. The consumption of such water could be extremely harmful, and it could lead to diseases such as diarrhea, cholera, typhoid, paratyphoid, hepatitis A, dermatitis, and enteric fever [61]. Overall, PC-1 could be an indicator of health risks due to CFU, Hg, and Cd.

According to Table 5, PC-2 revealed that three parameters (i.e., EC, TDS, K) positively correlated with each other. High TDS levels might be related to calcium and sodium found in natural sources, sewage, and urban runoff [64]. EC could be related to the dissolution of minerals in groundwater [65]. Fe could be associated to weathering of rocks and disposal of toxic effluents from open dumping sites [43]. The correlated Tur in PC-2 could be related to urban runoff from the road [66]. PC-2 could indicate natural processes due to EC, TDS, K, and Tur. PC-3 in Table 5 indicates that DO, As, and Fe are positively correlated, while Zn is found to be strong with a negative value. Long-term exposure to As through drinking water could cause arsenic poisoning, leading to skin cancer [67]. According to WHO, the biggest threat to public health is groundwater contamination due to As which is available in organic and inorganic forms. Organic arsenic is found in less toxic seafood, whereas inorganic As is found in groundwater and is highly toxic and carcinogenic. Acute arsenic poisoning could cause diarrhea, vomiting, abdominal pain, and muscle cramping. In some cases, it might cause death [68]. Based on all these facts, PC-3 could be an indicator of health risks due to As pollution. The other strongly positively correlated parameter is DO. DO is a crucial metric in determining water quality as it represents the physical and biological processes in the water [69]. Mine wastes and leachate water from overburdened dumps of coal mines in the vicinity might cause the elevated Fe levels found in the groundwater. From PC-3, it could be interpreted that DO could determine the solubility and toxicity of As, as it was very high for all gws, which could be a major health threat for the population living in the study area. PC-3 might be the indicator of natural processes due to the presence of Zn and could be found in trace amounts in almost all igneous rocks. The most prevalent zinc ores are sulfides such as sphalerite and wurtzite [70].

According to Table 5, PC-4 revealed that pH, Tur, Ca, and Pb are correlated. pH and Tur are found to be loaded as weak (i.e., less than 0.5) with positive values. While Ca is loaded as strong (i.e., >0.75) with a negative value. Clay minerals, including montmorillonite, illite, and chlorite, could be responsible for the high Ca, Na, and Mg concentrations in groundwater. Most calcium ions in groundwater come from limestone, dolomite, gypsum, and anhydrite leaching, although they could also come through the cation exchange process [71]. Therefore, PC-4 indicates natural processes due to pH, Tur, and Ca.

PC-5 shows that Na is loaded as strong (i.e., >0.75) with a positive value. Clay minerals, including montmorillonite, illite, and chlorite, are responsible for the high Ca, Na, and Mg concentrations in groundwater [72]. Therefore, PC-5 could be an indicator of natural processes due to Na.

3.3. GIS Modeling for Exceeded Parameters

The exceedance equation (Equation (1)) for groundwater (GW) was developed based on measured values and WHO standards for groundwater parameters.

Exceedance (GW) = Measured values (GW) − WHO Standards (GW)

(1)

where exceedance (GW) represents the groundwater quality parameters that were exceeded, measured values (GW) are the values obtained in the laboratory, and WHO Standards (GW) are the groundwater parameter values recommended by WHO, as provided in Table 1.

Furthermore, the parameters of groundwater wells that contributed to exceedance levels (Level 1 to Level 5) are presented in the form of spatial distribution maps (Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7). Figure 3 depicts the results for As in the union councils of Jhangi and Mirpur. The wells gw1, gw2, gw3, gw4, gw5, gw20, gw21, gw22, gw23, and gw32 contributed to Level 5 As in the Jhangi union council area (Figure 3). The wells that contributed to Level 4 in Jhnagi are gw10, gw11, gw14, and gw17, while gw12 contributed to exceedance Level 3. The results for Jhangi indicate that Level 2 exceedances are attributed to groundwater wells gw9, gw13, and gw19, while Level 1 exceedances are attributed to gw33, gw34, gw35, gw36, gw40, and gw41, as shown in Figure 3. The highest As (Level 5) was found in both residential and non-residential areas, which showed the contribution of both anthropogenic and natural factors. The anthropogenic factors could be As-containing wastewater from residential and agriculture sources. In contrast, natural factors such as geological formations (e.g., sedimentary deposits/rocks, volcanic rocks, and soils) might have contributed to the high As level in Jhangi.

In Mirpur, gw7, gw8, gw15, gw16, gw22, gw25, gw26, gw28, and gw29 contributed to Level 5 exceedance of As. As shown in Figure 3, the groundwater wells in Mirpur that contributed to Level 4 are gw10, gw11, and gw14, while gw17 contributed to Level 3 (Mirpur). Level 2 is attributed to gw9, gw13, and gw19, while Level 1 is attributed to gw33, gw34, gw35, gw36, gw37, gw40, and gw41 in the Mirpur area.

The analysis revealed that some groundwater wells in Jhangi union council, such as gw5, gw40, and gw41, were located near the open dumping sites, whereas gw18, gw29, gw30, gw38, and gw39 were located near the open dumping sites in Mirpur union council areas. The level of exceedance caused by open dumping sites in Mirpur was higher than in Jhangi union council, as shown in Figure 3. For example, exceedance levels in Mirpur near dumping sites were at Levels 4 and 5, whereas Level 1 was observed in Jhangi union council areas. The high level of As in Mirpur in the groundwater wells such as gw7, gw8, gw15, gw18, gw29, and gw30 could be due to the potential leachate from the nearby dumping sites. The study findings reveal that, as we moved away from the dumping sites, the level of groundwater exceedance decreased. Similar findings were reported by [73], who found that those concentrations of pollutants decreased significantly within 324 m of the dumping sites. The total distance travelled by pollutants is proportional to the material of the aquifer. This groundwater contamination could endanger local groundwater resource users, as well as the natural environment and human health [9].

Figure 4 depicts the Pb results in the Jhangi union council. The findings for Jhangi revealed that gw1, gw2, gw3, gw5, gw35, gw42, and gw41 contributed to Pb Level 1. As shown in Figure 4, the wells in Jhangi that contributed to Level 4 were gw12, gw14, gw20, and gw33, while Level is contributed by gw10, gw11, gw27, and gw38, and the wells in Jhangi that contributed to Level 2 were gw9, gw34, and gw36. The results indicate that wells that contributed to Level 5 are gw13, gw17, gw21, gw22, and gw24 in the areas of Jhangi. The Pb services line pipes for water distribution near the residential area could be the reason for a high Pb concentration in groundwater. In Mirpur, gw15, gw25, and gw26 contributed to Level 5 exceedance of Pb. The wells in Jhangi that contributed to Level 4 were gw15, gw16, and gw31, while Level 2 was attributed to gw7, gw29, and gw39, as shown in Figure 4. Level 1 exceedance of Pb was attributed to gw8, gw27, gw30, and gw38 in the Mirpur area.

The study results indicate that some groundwater wells in Jhangi union council, such as gw5, gw40, and gw41, were located near open dumping sites, whereas gw31 and gw18 were located near dumping sites in Mirpur union council. The level of exceedance caused by open dumping sites in Mirpur was higher compared to Jhangi union council, as shown in Figure 4. Pb exceedance in groundwater near a dumping site in Mirpur, for example, was at Level 4, while Level 1 exceedance was discovered in Jhangi union council. The high levels of Pb in Mirpur groundwater wells such as gw31 and gw18 could be caused by potential leachate from a nearby dumping site. The Pb present in hospital and household waste dumping sites might contaminate groundwater through precipitation and surface runoff in the study area. Several studies reported similar findings concerning groundwater contamination from open waste dumping sources [74,75,76], leading to the general conclusion that heavy metals such as Pb pose a significant risk to local groundwater resource users, as well as the natural environment and human health [9]. The health consequences include constipation, abdominal pain, tiredness, headache, loss of appetite, memory loss, and pain or tingling in the hands and/or feet, which are all symptoms.

Figure 5 shows the exceedance level for Hg in the Jhangi union council. According to the findings for Jhangi, no well contributed to Hg Level 5. The groundwater wells in Jhangi that contributed to Level 4 are gw9 and gw40, as shown in Figure 5. Level 3 is caused by gw5, gw14, gw19, gw20, gw21, gw23, and gw41 in the Jhangi area, while Level 2 is attributed to gw10, gw12, gw13, gw17, gw31, and gw34, while Level 1 is due to gw1, gw2, gw3, gw4, gw11, gw32, gw33, gw35, gw36, and gw37. A similar pattern is seen in low-income areas, which could be attributed to natural Hg deposits in the local rock formation. Excess Hg in groundwater could cause various problems, including kidney damage, insomnia, and memory loss.

In Mirpur, gw38 contributed to Level 5 exceedance of Hg. The wells in Mirpur that contributed towards Level 3 were gw24 and gw39, as shown in Figure 5. Level 2 is attributed to gw7, gw8, gw15, gw16, gw18, gw27, and gw31, while Level 5 is caused by gw25, gw26, gw28, gw29, and gw30 in the Mirpur area. According to the study findings, there were no wells that contributed to Level 4 in the Mirpur union council areas.

The analysis reveals that some groundwater wells in Jhangi union council, such as gw5, gw40, and gw41, were located near an open waste dumping site, whereas in Mirpur, gw18, gw29, gw38, gw39, and gw27 were located near the dumping sites. As shown in Figure 5, the level of exceedance caused by dumping sites in Mirpur was higher than in Jhangi union council. The exceedance level in Mirpur near the dumping site was at Level 5 and Level 4 near gw27, gw38, and gw39, while in Jhangi, gw5, gw40, and gw41 contributed to Level 4. The remaining groundwater wells in both union councils contributed to Level 3 exceedance. The high level of Hg in Mirpur in groundwater wells such as gw38, gw39, and gw27 is due to the potential leachate from the nearby dumping site. The Hg present in the wastes is from sources such as electrical switches, fluorescent light bulbs, batteries, thermometers, and some medical waste. After use, mercury-containing products form part of the municipal solid waste (MSW) that is collected and disposed of into open dumping sites, where mercury and other pollutants could pollute groundwater sources through leaching and percolation. Consequently, mercury might become an important constituent of the resulting leachate which is often characterized by high loads of dissolved organic matter, inorganic macro-components, metals, and other xenobiotic organic compounds [77,78]. Usually, leachate accumulates percolate through the soil to contaminate the groundwater [76]. Exposure to mercury might cause brain, liver, kidney, and developmental disorders, particularly in young children and developing fetuses [79]. It poses a significant risk to local groundwater resource users, as well as the natural environment and health [9].

Figure 6 shows the results for Cd in the Jhangi union council. In Jhangi, gw37 and gw40 contributed to Cd Level 4. As shown in Figure 6, wells in Jhangi that contributed to Level 3 were gw4, gw5, gw10, gw17, gw19, gw23, gw36, and gw41, while gw1, gw13, and gw20 contributed to Level 2. According to the findings, Level 1 is caused by the wells gw2, gw3, gw9, gw11, gw12, gw14, gw21, gw22, gw32, gw33, and gw35 in the Jhangi area. The high values are recorded near agricultural fields and vegetation in the Jhangi union council; therefore, they could be related to Cd-containing fertilizer which might contaminate groundwater through percolation processes. The sources of groundwater contamination in Jhangi might be industrial waste or fertilizer contamination.

In Mirpur, gw38 contributed to Level 5 exceedance of Cd. As shown in Figure 6, the wells in Mirpur that contributed to Level 4 were gw29 and gw39, while gw7, gw8, gw16, gw24, and gw25 contributed to Level 3 (Mirpur). Level 2 is attributed to gw30, while Level 1 is attributed to gw15, gw18, gw26, gw28, and gw31 in Mirpur areas.

The study findings indicate that high levels of Cd (Level 4 and Level 5) were found near gw27, gw38, gw39, and gw29, which were located near open dumping sites in Mirpur union council areas. Level 4 is only found near gw5, gw40, and gw41 in Jhangi union council areas. Level 3 exceedance values were found in the remaining wells in both union councils (Figure 6). The high Cd levels in the waste could be attributed to sources such as batteries, plastics, and cards. Exposure to Cd might result in several negative health effects, including cancer, acute inhalation exposure to cadmium (high levels in a short period of time), and flu-like symptoms (chills, fever, and muscle pain), as well as lung damage. It also endangers local groundwater resource users, as well as the natural environment [74]. After use, Cd-containing products such as plastics and pigments form part of the MSW that is collected and disposed of into open dumping sites where Cd could be leached into landfill leachates [80].

Figure 7 shows the results for CFU in the Jhangi union council. According to the findings for Jhangi, gw40 contributed to CFU Level 5. As shown in Figure 7, groundwater wells in Jhangi that contributed to Level 4 were gw10, gw14, gw22, gw23, and gw41, while Level 3 is attributed to gw4, gw11, and gw17 in the Jhangi area. Level 1 is attributed to the remaining wells in the Jhangi areas. There were no wells near Level 2 in the Jhangi union council. Coliform bacteria washed into the ground by rain are usually filtered out as the water goes through the soil and into groundwater systems. However, poorly constructed, cracked, or unsealed wells could allow coliform bacteria to enter groundwater and contaminate drinking water.

In Mirpur, gw38 contributed to Level 5 exceedance of CFU. The wells in Mirpur that contributed to Level 4 were gw15 and gw28, while gw8, gw18, and gw26 contributed to Level 2 (Mirpur), as shown in Figure 7. Level 1 is attributed to gw7, gw16, gw18, gw24, and gw25 in the Mirpur areas; no wells were contributed to Level 3 in the Mirpur union council areas.

The study findings showed that the level of pollution caused by dumping sites in Mirpur was higher than in Jhangi union council, as shown in Figure 7. The CFU levels were found to be at Level 5 in Mirpur, close to the dumping site, and Level 4 in Jhangi union council areas. Both union councils’ remaining groundwater wells contributed to Level 3 exceedance. The high level of CFU in Mirpur groundwater wells such as gw38, gw39, and gw27 is attributed to nearby dumping sites. Household, hospital, and food waste are the sources of high CFU in dumping sites, which has several health implications. This water is used for drinking and consumption by the population of the study area. Consuming such water is exceedingly dangerous and could result in illnesses such as diarrhoea, cholera, typhoid, paratyphoid, hepatitis A, dermatitis, and enteric fever [81].

The human health risk maps were produced by stimulating groundwater vulnerability assessment using a GIS-based system called the ArcPRZM-3 tool [81]. The health risk maps with three categories of health risk, i.e., low, moderate, and high, were generated for Independence and Randolph counties of Arkansas. It was found that the percentage of areas under high health risk was 5high compared to other health risk classes. The health risk area under Levels 4 and 5 was very high in Mirpur for As, CFU, and Hg. The health risk at Level 3 and Level 4 was high for both Jhangi and Mirpur. The human health risk assessment was accomplished using fuzzy WQI related to groundwater contamination. According to this study, the carcinogenic health risk was high due to Cd, As, and Hg.

4. Conclusions

The combination of field sampling, laboratory analysis, GIS, and advanced statistical modeling in water quality is a new contribution to research and science. PCA modeling provided five PCs that represented the natural processes and anthropogenic pollution. Groundwater was contaminated due to microbial and heavy metal pollution related to natural and anthropogenic sources, especially open dumping sites. In both union councils, As, CFU and Pb were found to be Level 5.

The field-collected groundwater contamination data were utilized for studying the impact on a larger area with the help of remote sensing satellite data. Overall, the communities living in the study area are exposed to hazardous metals such as As and Pb, which are carcinogenic. GIS-based modeling provided effective maps for exceedance levels linked to health risks in the study area. The study area population is at high health risk due to the consumption of unfit water. Both open dumping sites and natural sources could affect the groundwater. It is recommended to analyze water wells near and away from dumping sites for the whole of Abbottabad and obtain information on groundwater contamination and health risks. The number of open dumping sites in Abbottabad city provides information on the negligence of relevant agencies for cleaning the city.