Introduction

The water scarcity is increasing all over the world due to high population rate, urbanization, and non-management of resources (Anabaraonye et al. 2023; Wang et al. 2023). Pakistan being the 6th most populous country of the world with per capita availability of water at brink of scarcity (≤ 1000 m3) (Xu et al. 2023). The demand of fresh water has increased stress on aquifers, especially in developing industrial estates of the country. Peshawar, Mardan, Charsadda, Swabi, and Nowshera have more than 5 million of the 11.4 million population (Ali and Nawaz 2020). These districts are spine of new trade and tunnel development also deprived of fresh water resources. The major fresh water source is Peshawar Valley Aquifer (PVA) compensating the needs. The unique hydrological settings further signify the importance of sustainability of PVA as all main rivers (Kabul, Swat, and Indus) passing through it, that are carrying a major part of industrial effluents (Rana et al. 2021). Due to improvement in social and strategic conditions specifically the construction of China–Pakistan Economic Corridor (CPEC), the country’s economy has contemplated positive growth during 2014–2018 (Hussain and Rao 2020). Industrial estate played a vital role in this growth. The Gadoon Amazai Industrial Estate (GAIE) is a significant industrial estate of Khyber Pakhtunkhwa (KP) province of Pakistan, and due to its vicinity to CPEC, it is continuously evolving and observing drastic infrastructure development (Ali et al. 2020). The extensive infrastructure development requires excessive water which in turn poses threat to local surface and groundwater resources (Noor et al. 2023). The excessive pumping coupled with industrial effluents from GAIE and waste of construction sites at CPEC are contaminating to pollution of the surface and groundwater resources (Sultan et al. 2021). Use of wastewater for irrigation in industrial sector is causing damage to the food resources and human health (Mishra 2023). The toxic metals find their path to human chain via irrigated crops which are baleful to human health (Akhtar et al. 2021). In major districts of Khyber Pakhtunkhwa province (Mardan, Swabi, Peshawar, Nowshera, and Charsada), more than 5 million of the 11.4 million population are deprived of fresh surface water resources (Khan et al. 2015; Ashraf et al. 2019). The surplus population uses water from groundwater resources, which are more vulnerable to health as the untreated wastes are dumped into the streams. The situation gets worse due to the absence of sewerage and drainage systems (Ahmad et al. 2022). It is the utmost need of local government and public health departments to protect the aquatic systems by managing groundwater resources (Daud and Nisar 2022).

Many researchers have analyzed the water and soil samples in industrial area for the assessment of heavy metals (Nawab et al. 2023; Shah et al. 2022). The Maira area (western part of Peshawar Basin) was explored using electrical resistivity methods and results were used for groundwater flow modelling and management of the water resources (Farid et al. 2013). The prospect area is being proposed for the industrial park and construction sector in near future, so the groundwater characterization and shallow geophysical surveys are the unconditional need of investors contributing to the industrial park.

Geophysical techniques, particularly electrical resistivity measurements, are very useful and widely applied for identifying the subsurface aquifer parameters, such as lithology, thickness, lateral extent, and other geoelectrical properties relating to the aquifer (Qureshi and Sayed 2014). The resistivity method is fast, cost-effective, and can and public health departments to protect the environment by sustainably managing surface and groundwater resources. Resistivity methods are accurately used for shallow unconfined aquifers (Daud and Nisar 2022). Research was carried out auspiciously for evaluation of hydraulic parameters based on vertical electrical resistivity measurements for the types of aquifers due to the provision of acceptable results for spatial resolution of hydrogeological parameters. Electrical resistivity and hydraulic conductivity of the subsurface aquifer are related to the porosity and saturation of water in the aquifer. There are many studies with empirical and semi-empirical equations integrating geoelectrical and hydraulic parameters of aquifers, under different geological conditions. Both direct and inverse relations between resistivity and hydraulic conductivity are reported by researchers (Farid et al. 2013; González et al. 2021).

To measure trends in groundwater quality, several researchers have defined graphical and statistical tools ranging from simple regression analysis to more elaborate parametric and non-parametric methodologies. Complex data interpretation using multivariate geostatistical GIS tools have become a necessity for a better understanding of water quality and management of water resources (Binley et al. 2015). Among the geostatistical tools Inverse Distance Weightage (IDW) has opted for spatial variability of electric, hydraulic and chemical parameters. IDW is an optimal and powerful tool as it considers each value. The application of interpolation methods for spatial variations of soil and water properties has been analyzed in several studies (Vörösmarty and Sahagian 2000).

The present study explores the shallow subsurface for the very first time in the area using integrated geophysical, geological, geospatial, and physicochemical studies of water to demarcate the subsurface aquifer system and its vulnerability. The study further investigates the role of the industrial estate (lying in the vicinity) and ongoing development projects associated with one belt and road (joint initiative by Pakistan and China) in degradation of the environment and health of local residents. The geophysical parameters will be used to calculate the aquifer thickness and Dar–Zarrouk equations will be put to test in calculation of aquifer vulnerability to contamination by surficial sources (Mohammed et al. 2023). The pumping test data are used to demarcate the aquifer potential (Masoud et al. 2022; Falowo 2023). Furthermore, these parameters were correlated with geophysical parameters to further develop a relationship regarding aquifer variability. The quality of water was analyzed using measurements such as alkalinity (pH), electrical conductivity (EC), total dissolved solids (TDS), and turbidity (Rahmanian et al. 2015; Ullah 2022). This present study is proficient for the local government in different frameworks for the commercialization and decision-making for urban land planning in Khyber Pakhtunkhwa.

Study area

Swabi District is located in the Peshawar basin, at the southern foothills of the Himalayas encompassed between longitudes 72° 30′ 30′′ E and 72° 43′ 30′′ E, and latitudes 33° 56′ 30′′ N and 34° 18′ 00′′ N (Fig. 1d). It covers an area of ~ 48 km2. It is bounded by Khairabad ranges in the south, in the north by Main Mantle Thrust (MMT), in the east by Gandaf Mountains, in west by Kabul River, and in the east by River Indus (Fig. 1b) (Kruseman and Naqavi 1988; Hussain and Yeats 2009).

Fig. 1
figure 1

a Provincial boundaries of Pakistan. b Tectonic map of Peshawar with Main Mantle Thrust in north, Khairabad fault KF in south (Hussain and Yeats 2009). c Boundary of Swabi district encompassing the study area in square, d base map of study area includes the geophysical data (Vertical electrical sounding VES), hydrogeological data (pumping wells PW) and pumping wells, and physiochemical data (water samples from outlets of industries, streams, and the aquifer of the area

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The study area is part of the Peshawar basin. The stratigraphic sequence of Peshawar basin is composed of sedimentary to metasedimentary rocks of Precambrian to Jurassic. The formation of Peshawar basin started in the Pleistocene when more than 300 m of sediment was deposited by uplifting of the Attock-Cherat (Hussain and Yeats 2009). The un-lithified sediments of Peshawar basin are largely lacustrine silt with fluvial sand and gravel (Ahmad et al. 2022; Qasim et al. 2014). The alluvial fill of Quaternary age with a thickness of several hundred meters is overlying the basement rock (Farid et al. 2013; Shahzad et al. 2022). Two dominant streams of the area include Polah Khwar and Wuch Khwar that drain into river Indus. Majority of the plain represents the industrial zone, and industrial wastes are dumped into the Polah Khwar and Wuch Khwar (Kruseman and Naqavi 1988). The significance of the area increased further after inception of CPEC route that passes nearby. Climate change has been a strong influencer in KP province causing prolonged droughts and decrease in rain patterns (Rahman et al. 2021). The area experiences an average precipitation of ~ 868 mm/year, with the most precipitation in the monsoon. The precipitation amount shows a significant decreasing trend of -358.1 to − 309.9 km2 year−1, whereas average temperature ranges from 53.96 °F in January to 94.64 °F in June (Ullah et al. 2022). The Kabul River and the Indus River are recharge sources of the aquifer systems of Peshawar basin. Literature review revealed that the Peshawar basin has plenty of water that is the result of glacier melts in the north and northwest regions and is used for drinking and crops (Tariq et al. 2016). The shallow water table in the area is due to sustained and substantial recharge from the rivers and canals, making the aquifer vulnerable to contamination. Groundwater sources are mainly used in domestic water supply. The water is drawn through the dug wells, bore wells, and hand pumps (Ali et al. 2018) (Fig. 2).

Fig. 2
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Hydro-stratigraphic sections of the boreholes near the study area (Farid et al. 2013)

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Materials and methods

Geophysical investigations

VES data for 13 points were acquired across the study area using a DDC-8 resistivity meter with detection depth of 500 m and resolution of < 1 mm. The instrument has current measuring range of 0.1–3000 mA, while working in environment with temperature range of 10–50 °C. The survey was conducted using Schlumberger configuration with maximum current electrode spacing of 400 m. In case of high ground resistance, saline water was used to make the ground conductive. The apparent resistivity was calculated using current electrode spacing (AB/2), voltage (V), current (I), and geometrical factor (K) (Parasnis 2012)

(1)

The processing of the data comprises curve matching in IPI2WIN software, which determines true resistivity of geoelectric units having variable thickness. The software provides user friendly environment that allows field data analysis. In case of difficulty encountered while matching the field data with master curve, the iterations were performed to adjust true resistivity and thickness. Iterations are used till the best matching of the measured curve with master curve. During the iteration process, RMS error is set as default value which is less than 5% in majority of points. It is important to note that inverse problems present the non-unique solutions that can make geoelectrical models are subject to ambiguous interpretations. To address these non-uniqueness problems, borehole data can be used to optimize the results (Attwa et al. 2014).

The true resistivity values were marked on the basis of standard resistivity values for the study area (Niaz et al. 2018). The aquifer vulnerability mapping was carried out by calculating Dar–Zarrouk parameters of longitudinal conductance (LC) and Transverse resistance (R) (Nisar et al. 2021)

(2)
(3)

The hydraulic parameters, transmissivity (T), and hydraulic conductivity (k) were calculated by help of Dar–Zarrouk geoelectric parameters following equation (Kazakis et al. 2016):

$${\text{Calculated transmissivity }}T = 0.{3}0{79}*{\text{TR}} + {299}.{81}\left( {{\text{m}}^{{2}} /{\text{day}}} \right).$$
(4)

The flow of water in aquifer is not only governed by transmissivity, but hydraulic conductivity (k) also plays role and it was estimated using aquifer thickness (h), using the following equation (Kuang et al. 2020):

$${\text{Hydraulic conductivity}}\left( k \right) = T/h.$$
(5)

Hydrogeological investigations

Pumping test analysis was carried out in 16 boreholes to determine the hydraulic properties of the aquifer (transmissivity T and specific capacity S). The field data contain time and drawdown (∆s), and the time was plotted on x-axis and drawdown on y-axis of log–log graph. The time-drawdown curve was processed using a single pumping test tool, i.e., Neumann method in Aquifer Test Pro software. Other than time and drawdown, the static water level and saturated thickness of aquifer are important to complete the processing of the data (Haripriya et al. 2014).

Physiochemical tests of water samples

Water samples are collected at two separate times of the year. One group of samples was collected in pre-monsoon (February 2021) and other group was collected in the monsoon period (September 2021) following proper protocols of United States environmental protection agency (US EPA 2022). The samples were collected in two seasons to capture the spatio-temporal variations of physicochemical parameters. This will enhance the understanding of contribution of anthropogenic and geogenic sources to contaminate the groundwater. Water samples were preserved in Polystyrene bottles (washed with distilled water). The physical parameters like pH, EC, TDS, and turbidity were measured using pH meter, TDS meter, and digital turbidity meter, respectively.

Microbial analysis

The bacterial count was determined for the groundwater samples using membrane–filtering (MF) procedure. A 100 ml amount of each sample is filtered through a filter paper with diameter of 47 mm and a pore size of 0.45 mm (Forster and Pinedo 2015). The eosin methylene blue (EMB) agar nutrient was used for bacterial count analysis. There is a differential growth of faecal coliform (E. coli) bacteria on EMB agar, which makes it a selective medium for the development of Gram-negative bacteria. Using a selective medium, simultaneous determinations of total coliforms and E.coli concentrations were made (Manafi 2000). The Petri dishes were placed in an incubator set to 37 °C for 24 h before being examined for the presence of bacterial colonies. Bacterial (and other) cells trapped on the membrane grew into colonies were counted. The collected data were compared with criteria given by the World Health Organization for drinking water (Nguyen et al. 2022).

Results and discussion

The quaternary alluvial aquifer in the study area was identified at shallow depth with overlying clays and mixed lithology of clay and gravel, evidenced in literature (Daud and Nisar 2022). The aquifer thickness ranges from 6.32 m (VES-21) to 341 m (VES-40) (Fig. 3b) (Table 1). The apparent resistivity ₰a of the geoelectric unit range between 35.7 and 144 Ω-m. The VES-05 has a minimum value of apparent resistivity and VES-09 has the maximum (Table 1). Inversion of VES data classifies the subsurface unconsolidated lithology into 4–5 geoelectric units composed of lacustrine fine mixed silty clay, and fluvial gravels and dry boulders (Fig. 3a and b). The fourth geoelectric unit is bedrock. Spatial variability of aquifer thickness shows that it is decreasing in the northeastern part of the study area (Fig. 1). Pleistocene lakes within the Peshawar Basin are responsible for the presence of parallel bedded, fining upward sand layers, silts, and clays (Bibi et al. 2020). The progradation of alluvial fans due to Attock–Cherat ranges along the southern boundary cause subsidence and fragmented by erosion to form the piedmont plains on the gentler slopes of the alluvial fans. The drainage system of the basin (Kabul, Kalpani, and Indus rivers) ponded low-energy food plain and flood-pond sediments in southwest and center of the basin, perhaps resulting in interbedded lacustrine and alluvial sediments near Main Boundary Thrust MBT (Qasim et al. 2014).

Fig. 3
figure 3

Generation of lithologs of the study area using apparent resistivity data (₰a) and thickness (h). a VES -08 shows 05 electrical units. Clay with gravel is the shallowest geoelectric unit and the aquifer starts with 4 m depth. b VES-40 has 4 geoelectric units with dry boulders as the shallowest layer. It has dry boulders as the shallowest geoelectric unit, and the aquifer starts from 10 m

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Table 1 Geo-electric parameters and hydraulic parameters of aquifer using vertical electrical sounding data
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Dar–Zarrouk parameters

The Dar–Zarrouk parameters (D–Z) are defined by the longitudinal conductance LC and transverse unit resistance R, which are two of the most important parameters in electrical prospecting. Since Dar–Zarrouk parameters are the bulk parameters, taking the relationship between hydraulic conductivity and resistivity, and further, it results in calculated transmissivity values (Straface et al. 2007; Adabanija and Ajibade 2020).

Longitudinal conductance (LC)

According to Ajiboye (2022), the protective capacity of the aquifer correlates with longitudinal conductance. The protective capacity of a hydrologic system is actually the overlying strata with varying permeabilities, that can restrain the groundwater contamination (Sathe and Mahanta 2019). Aquifer is vulnerable to contamination in the presence of permeable layer lying over the aquifer (Hussain et al. 2017; Oke 2017). The sands and gravels have good hydraulic conductivity and permeability to percolate the industrial effluents into the subsurface hydrologic systems. While clays can perform best to enhance the protective capacity of an aquifer due to less hydraulic conductivity (Oseji et al. 2018; Ekanem et al. 2021). The LC clay content of the aquifer affects the values of transmissivity. Results from Dar–Zarrouk (D–Z) equations show that the highest LC value (6.11 mS) is for VES-40 and the lowest value (0.12 mS) is for VES-21 (Table 1). The interpolation models show localized high values of LC near Kotha village (Fig. 4a). There is moderate-to-good protective capacity with hydraulic conductivity (ranged 0.12–2.15 m/day) (Table 1) of the aquifer which is due to the presence of intercalations which may occur in flood piedmonts of Swabi district (Bibi et al. 2020). The shallow depth of aquifer, the unconfined nature, and good LC contribute to increase in vulnerability to contamination or pollution arising from industrial effluents. The contaminated groundwater samples are adding to the evidence for contamination of aquifer which have been discussed earlier by Khattak et al. (2021) and Hussain et al. (2021).

Fig. 4
figure 4

Interpolation of Geoelectr5ic parameters and hydraulic parameters (from VES by applying Dar Zarrouk equations) using Inverse distance weightage (IDW) spatial tool analysis: a longitudinal conductance of aquifer; b transverse resistance of aquifer; c transmissivity of aquifer

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Transverse resistance

The increased value of transverse resistance (R) is an indication of increasing thickness of a highly resistive rock. The value of R has a direct relationship with the transmissivity of an aquifer. The results of D–Z equations (Table 1) show that the transverse resistance for the aquifer ranges between 331.8 and 22,372.5 Ω-m2 for VES-21 and VES-26, respectively. The interpolation map of R marks localized high values in Kotha village and northern part of study area that is evidence of increased thickness of aquifer (VES -09, VES-29 having 144 m and 157 m thickness of aquifer, respectively) (Fig. 4b). The map also shows regionally low values in the northern part of the study area as well as Maini village which reveals decreased aquifer thickness due to intercalations of clays or clay mounds at the base of Gandaf Mountains (Fig. 4b).

Calculated transmissivity (T)

The value of transmissivity calculated using the transverse resistance has values ranging between 401.97 and 7188.30 m2/day for VES-21 and VES 26 respectively (Table 1). The values of calculated transmissivity from VES data are compared with the transmissivity of pumping test analysis, the values are comparable for the area but the values by VES have deciphered the real transmissivity (Table 2) The interpolation maps using IDW shows that the transmissivity has regionally high values in Kotha village and localized high values near Maini village (Fig. 4c). High transmissivity values mean that there will be less drawdown in the wells of Kotha and Maini village.

Table 2 Hydraulic parameters using pumping test analysis and their comparison with neighboring vertical electrical soundings
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Hydraulic conductivity (k)

Hydraulic conductivity ranges from 14 to 63.605 m/day for VES-05 and VES-21, respectively (Table 1). The results of hydraulic conductivity confirm the lithology of the aquifer as gravel (Table 8) (Theel et al. 2020). A similar interpretation was done by VES and in previous studies; it is evidenced by the electrical resistivity tomography tool. The hydraulic conductivity is high in western part of study area near Kotha, and Maini villages that represents the highly permeable aquifer in these villages (Fig. 1) (Table 1), and hence, there is less travel time of contamination to travel in the aquifer causing pollution of the drinking water.

Hydraulic parameters of an aquifer by pumping tests

Pumping test analyses provide data for the calculation of transmissivity (T) and specific capacity (S).

Transmissivity (T)

The pumping test evaluations show transmissivity ranging from 13 to 14,900 m2/day (Table 2). The transmissivity also varies throughout the area. The value of T for the pumping well PW-8 located at the eastern most part of study area is extremely high which may conclude the recharge boundary near the aquifer. Interpolation maps of T show only localized high values near the GAIE, in eastern part of area. The values of transmissivity are moderately high in Kotha and Gandaf. This is also confirmed by the high value of LC in theses villages (Figs. 4a and 5a). As unconfined aquifers play a major role in aquifer recharge, the villages that are along the streams mostly comprise of coarser lithologies and have good transmissivity.

Fig. 5
figure 5

Interpolation of hydraulic parameters (from pumping test analysis) using inverse distance weightage (IDW) spatial analysis tool: a transmissivity T using pumping test analysis and b specific capacity S of an aquifer

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Specific capacity (S)

It is a measure of possible discharge from the well divided by observed drawdown. It measures the effectiveness of well yield (production). Estimation of the specific capacity for the thick aquifer is very valuable for water resources evaluation (Bohling et al. 2007). The specific capacities range from 23 to 7934m3/day (Table 2). The results show that specific capacity varies from well to well throughout the study area. If the specific yield can vary largely in its magnitude for an unconfined aquifer, the hydraulic conductivity will be heterogeneous as well. However, the analysis of spatial variations of hydraulic conductivity are rare. Therefore, spatial variations of specific capacity can give a better idea of aquifer over a large area using spatial tools. The interpolation map shows that the specific capacity has locally high values in GAIE (Fig. 5b) representing the deposition of coarse-grained sediments. This difference may be the result of the sediment transportation, where alluvium on the eastern margin (near Gandaf mountains) has not been transported as far from the sediment source and is therefore poorly sorted, leading to lesser permeability and hence lesser yield of the well. The present results are also supported by Newman (2021).

Physiochemical properties of water samples

One hundred and ten samples were examined for the physiochemical properties. Out of all, 60 samples were collected from streams and outlets of different industries.

pH

The pH value in dry season for the surface water (SW) samples ranges 4.7–9.5 (Table 3) and for groundwater GW samples ranges 6.1–8.2 (Table 4). The maximum number of samples has values less than 7.7, so due to pollution of industry, the water is acidic. Most of samples have anomalous values as compared to the standard (WHO 2022). The pH value in post-monsoon season for the SW samples 6.6–8.8 (Table 5) and for groundwater (GW) samples ranges 7.2–8.3 (Table 6). The samples taken from the surface water near the industrial zone (GAIE) were found to be acidic. The results of the study of the drinking water demonstrate that the post-monsoon water samples are alkaline, and the findings are comparable to those of earlier research (Shahab et al. 2019). Interpolation map using IDW technique for pH of the water samples collected in pre-monsoon shows the high value of alkalinity (> 7.7) regionally extended throughout the study area (including Kotha, Topi, Maini, and GAIE), providing strong evidence for pollution caused by GAIE (Fig. 6a). High alkalinity in surface water samples collected near Gandaf Mountains is an indication of Peshawar plain alkaline igneous complex PPIAC specially dissolution of carbonatites (Ren et al. 2022). The IDW maps of the groundwater samples of pre-monsoon period show the localized high values of pH near Gandaf, Maini, and GAIE. Increased alkalinity (> 7.7) is found in the aforementioned areas (Fig. 7a). The spatial variations show that the pH values for post-monsoon surface samples are greater than 7.7, and regionally, the surface water samples are alkaline (Fig. 8a). Figure 9a shows the localized increased values of pH for groundwater samples near GAIE. The spatio-temporal analysis concludes that the pre-monsoon period has acidity in surface samples while post-monsoon samples are alkaline in nature.

Table 3 Physiochemical parameters of surface water samples from surface resources of pre-monsoon period, Swabi District, Pakistan
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Table 4 Physiochemical parameters of samples from groundwater resources of pre-monsoon period, Swabi District, Pakistan
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Table 5 Physiochemical properties of water samples form surface resources and outlets of industries of post-monsoon period, Swabi, KPK
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Fig. 6
figure 6

Geospatial analysis of physiochemical parameters of surface water resources of pre-monsoon time period using inverse distance weightage (IDW) tool

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Fig. 7
figure 7

Geospatial analysis of physiochemical parameters of groundwater resources of pre-monsoon period using IDW tool

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Fig. 8
figure 8

Geospatial analysis of physiochemical parameters of surface water resources of post-monsoon time using IDW tool

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Fig. 9
figure 9

Geospatial analysis of physiochemical parameters of groundwater resources of post-monsoon period using IDW tool

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Turbidity

The value of turbidity for pre-monsoon period ranged 0.03–27.6 NTU for SW samples (Table 3) and 0–354.30 NTU (Table 4) for GW samples. The turbidity values of SW samples ranged 0.09–68.5 NTU (Table 5) and for GW samples ranged 0.09–7.31 NTU (Table 6) for the post-monsoon period. The turbidity for surface water as well as groundwater exceeds the allowed range (5 NTU). 100% samples from outlets and streams carrying industrial wastewater in pre-monsoon period have exceeding values than the permissible level (Table 3) of turbidity (WHO 2022).The interpolation map of turbidity of surface samples of pre-monsoon period shows the highly localized values near the GAIE and Maini village, which is one more evidence of contamination of surface water resources and caused negative impact on recreation in Mardan, Charsadda, and Swabi districts (Fig. 6b) (Xie et al. 2017). Interpolation mapping of turbidity for groundwater samples of pre-monsoon period shows the localized high values near Maini and GAIE (Fig. 7b), and the reason of turbidity is evidenced from heavy metal dissolution in surface water and the seepage or percolation through the unconfined aquifer. The interpolation map shows the turbidity values are locally high only in GAIE (Fig. 8b). The IDW map of groundwater samples of the post-monsoon period shows the localized high turbidity values in GAIE (Fig. 9b). Due to the fact that bacteria cling to the floating debris in muddy water, the level of microbiological contamination gets increased in groundwater samples. These germs are shielded from disinfectants by these floating particles (Rahmanian et al. 2015). According to the findings of the study of the subsurface water resources, the samples of the post-monsoon are cloudier than those taken before it, and the results of the study revealed that the samples taken from bore wells and hand pumps did not meet the standards recommended for drinking.

Electrical conductivity EC

The electrical conductivity EC value for the (SW) and (GW) ranges between 4106 and 4000 us/cm (Table 3) and 925–313.13 us/cm (Table 4), respectively. Surface water (SW) had EC readings that surpass the allowable limits (1000 µS/cm), while the electrical conductivity value for groundwater GW is within the permitted limit (WHO 2022). The EC values of wet season of SW samples are in the range of 232–1052 μS/cm (Table 5) and EC for GW ranges 192–597 μS/cm (Table 6). The results are in best correlation, as EC for SW samples in both dry and wet seasons are higher than groundwater. The result is very conclusive as in case of surface samples of dry season value of EC is high, which is directly related to increased major and minor ions in industry effluents. Interpolation maps of EC for surface water samples of pre-monsoon period represent the localized high values near GAIE and Maini village (Fig. 6c). EC has localized high values in groundwater samples of pre-monsoon in northeastern part of study area along with Gandaf and GAIE (Fig. 7c). As in the previous research, the maximum electrical conductivity was observed in samples taken from GAIE, where effluents and soluble salts from various businesses enhanced the concentration (Amin et al. 2014). IDW maps of surface samples of post-monsoon period show the regionally high values towards north in the study area; it reveals the chemical dissolution of rocks and minerals of Peshawar Plain Alkaline Igneous Province (PPIAC) and carbonatites consisting of sodium and potassium (with calcium) (Hussain et al. 2019), revealed by high content of calcium in this area (Fig. 8c). The IDW maps (Fig. 7d) show the high values of EC throughout in surface samples of northern part of study area and is evidenced by (Efremenko et al. 2023). The IDW map of groundwater samples of post-monsoon period shows regionally high values of EC in GAIE, and Gandaf mountains (Fig. 9c). TDS is also over the threshold, indicating that industrial effluents are introducing inorganic contaminants to surface water resources. Because surface water resources include elevated concentrations of electrical conductivity (EC) and heavy metal ions. It is not advisable from these sources for drinking and irrigation purposes.

Total dissolved solids (TDS)

TDS level defines the dissolution of minerals, and metallic ions from waste materials and salts. The TDS may produce a change in the taste of the water and may lead to irritation of the gastrointestinal tract as well as a physiological response. If the effluents are introduced into irrigation water, this might lead to salinity issues. TDS stands for total dissolved solids (Rakib et al. 2020).

The values of TDS for the SW samples during the dry season varied anywhere from 31 to 2000 mg/l (Table 3), whereas GW samples had concentrations ranging from 35 to 1020 mg/l (Table 4). The values of total dissolved solids in surface water SW acquired during the pre-monsoon period are elevated than the permitted limit (1000 mg/l) (WHO 2022). The levels of TDS measured during the rainy season ranged from 106 to 466 mg/l (Table 5) for samples taken from SW that contain 149–295 m/l (Table 6) for GW samples. The TDS readings during the period of post-monsoon fall within the acceptable range of the limit. The previous findings on the TDS levels of the industrial discharge from various locations of Pakistan match with results of this study (Liu et al. 2011). Interpolation maps of TDS for surface water samples of pre-monsoon period represent the regionally high values in northwestern and southeastern part of study area, covering a part of region near Tarbela dam and villages near GAIE (Fig. 6d). The results of TDS are comparable to the report of the neighboring Mardan district. TDS for groundwater samples of pre-monsoon period has localized high values for Maini village (Fig. 7d). The IDW maps (Fig. 9d) shows localized high values of TDS in groundwater samples of the Topi village which may add bitter and medicinal taste to drinking water and is residents are unable to use groundwater for drinking purpose (Masood et al. 2011). When groundwater becomes contaminated, it is difficult to restore to an acceptable quality.

Microbial analysis

Drinking water microbiological safety study has total coliform TC, faecal coliform FC, Escherichia coli heterotrophic plate counts (E. coli) as key elements. During the pre-monsoon period, the TPC fluctuated from 0 to 681 in groundwater (Table 4). The study of the findings for the pre-monsoon period shows that the microbiological content is higher than the limits of 0 (WHO 2022); furthermore, it decreases in descending order as the distance from GAIE increases. The E. coli value in GW during the pre-monsoon season might vary anywhere from 0 to 580 (Table 4). In GW (Table 6) of the after-monsoon period, the TPC varied from 0 to 400, and the range of E. coli bacteria in GW is from 0 to 350 (Table 6). The majority of the water samples taken from the subsurface indicated E. coli at levels that are much higher than the allowable limits (0 CFU) for drinking water, as determined by the findings of a microbiological examination (WHO 2022). As compared to the rainy season, dry season has an elevated bacterial concentration, which is associated with industrial pollution (Ali and Mohammad 2022). Coliform bacteria, both total and faecal, should not be present in drinking water (WHO 2022). Within the scope of this investigation, the highest possible count of coliforms is 580 CFU. Coliform levels are high in the groundwater taken close to industrial zones (Koul et al. 2022). The presence of a sizeable number of E. coli is due to sewage and domestic wastewater of residential areas associated with the GAIE. E. coli lives in intestine of warm-blooded animals, so their presence in water is the index of the faecal pollution. The faecal pollution is associated with excreta of animals in water bodies (Naidoo and Olaniran 2014). These coliforms designate the study area with inadequate sewage system.

Table 6 Physiochemical properties of groundwater samples of post-monsoon period, Swabi District, KPK
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Conclusions

The present study decisively encompasses the aquifer vulnerability using both geophysical, geospatial, and hydrogeological tools across the Swabi district, Peshawar basin. The inversion of geophysical data set (VES) accurately revealed multi-episodic alluvial lithology. The identified aquifer lithologies comprise gravel and sand. The shallow aquifer system in the area is at increased threat to the surficial contamination as coarser sediments are episodically distributed on the surface throughout the area, these surficial sediments provide pathways for carrying contamination to the underlying aquifer. The Dar–Zarrouk parameters indicated that aquifer is less vulnerable towards the eastern part of the area due to the presence of finer lithologies (silt and clay) that will prevent infiltration of surficial contaminants into underlying aquifer. The calculations further revealed that the aquifer has increased thickness in Kotha village as compared to Maini, suggesting Kotha as a large water supplying area for the development then Maini. The study further signifies that the geoelectrical tools can be used as quantifying tools for aquifer transmissivity in areas where no previous boreholes are drilled which makes it cost-effective as compared to pumping tests.

Physiochemical parameters of pre-monsoon period indicate the acidification of surface and groundwater resources due to high ionic solubility, caused by the release of heavy metals from chemical, paint and pigment, and textile industries. While the surface and groundwater of the post-monsoon period are relatively alkaline in nature as a result of heavy rains and ultimate quick recharge of unconfined alluvial aquifers. This also confirms the geophysical interpretation about surficial infiltration zones. The Microbial analysis, as well as physiochemical analysis, revealed that water resources near GAIE are not suitable for drinking and irrigation purposes. The interaction of surface water and groundwater through spatio-temporal variations of physicochemical properties revealed the anthropogenic activities in industrial zone are responsible for water quality issues. Keeping in view the growing population and being neighbor to one of the greatest development zones in Pakistan, current research will play an important part in planning safe water management practices carried out significantly.