Groundwater quality is significantly impacted by urbanization and land use land cover (LULC) changes. The current study investigated the impact of LULC on groundwater quality in Quetta city, Baluchistan province, Pakistan. During the years 2015 and 2021, a total of 58 groundwater samples were collected from drinking wells for chemical analysis. The hydrogeochemistry of groundwater was investigated using Gibbs diagrams, Piper diagrams, and spatial distribution maps. The water quality trend was depicted using the Wilcox, USSL diagram, and Water Quality Index (WQI) from 2015 to 2021. The LULC analysis of Quetta was carried out on Google Earth Engine's cloud-computing platform using imagery from the Sentinel-2 satellite with low cloud cover (<10%). The LULC data was later used to calculate the rate of landcover conversion between both years 2015 and 2021 which help to identify the spatial distribution of groundwater and assess its vulnerability to pollution. The findings indicate an increase in the urban and agricultural classes while a decrease in the barren class. Moreover, according to the Piper diagram, groundwater in Quetta was primarily classified as CaMgCl type, CaCl type, and NaCl type. The Gibbs diagrams show water-rock interactions and rock weathering as the dominant evolution of hydrogeochemistry. The majority of the groundwater samples in both years were suitable for irrigation, according to the Wilcox diagram, USSL diagram, and other agricultural indices. The WQI demonstrated that the groundwater sources in the area are safe for human consumption; however, in the northern parts, WQI values are declining due to urbanization over six years.
Research Article
Assessment of landcover impacts on the groundwater quality using hydrogeochemical and geospatial techniques
https://doi.org/10.21203/rs.3.rs-2771650/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 13 Sep, 2023
Read the published version in Environmental Science and Pollution Research →
You are reading this latest preprint version
WQI
GIS
Wilcox
Principal Component Analysis
Land Use and Landcover
Water is an essential and indispensable natural resource for the sustenance of life, ecological balance, and economic progress (Baloch and Mangi, 2019; Baloch et al., 2020; Iqbal et al., 2021; Talpur, 2022; Zhang et al., 2022). Groundwater constitutes about 30% of the world's freshwater reserves which is most accessible (Ghani et al., 2022; Iqbal et al., 2023) and caters to the diverse needs of several sectors, such as residential, commercial, industrial, and agricultural sectors (Jat Baloch et al., 2021a). However, unbridled global population growth, along with rapid urbanization and industrialization, has given rise to a severe decline in water tables and degradation of aquifer systems (Odhiambo, 2017),(Rashid et al., 2020; Talpur et al., 2020a). In Pakistan, groundwater is the primary source of drinking water (Abbas et al., 2015; Jamil et al., 2019). The quality of groundwater and drinkable water sources are rapidly deteriorating in many urban areas of Pakistan. According to multiple studies on groundwater quality, water contamination is a major problem in Pakistan, which has been exacerbated by water scarcity and rising demand for a variety of reasons (Ghani et al., 2022; Jamil et al., 2019; Memon et al., 2011; Ullah et al., 2022b). Moreover, despite Pakistan's annual average groundwater potential of 67,841 m3, only 51,189 m3 of groundwater is harvested each year. Most people obtain their water through dug wells and tube wells, which are more subject to contamination via seepage, sewers, and percolation of contaminated water (Ahmad et al., 2021; Rashid et al., 2023; Rehman et al., 2019).
Human activities, such as urbanization, industrialization, and mining, have altered land cover and water quality (Ullah et al., 2022a). LULC models, which combine natural and social aspects of human activity, can analyze these changes over time and space. Remote sensing (RS) and geographic information system (GIS) can be used to analyze LULC, and RS information has helped in spatial studies of urban patterns and planning on different scales, linking local environmental study to international, national, and regional management and protection of LULC (Baloch et al., 2022). Furthermore, groundwater quality is evaluated by measuring chemical, physical, and microbiological characteristics, similar to assessing drinking water quality. This determines groundwater suitability for its intended use and ensures the conservation and sustainable management of local groundwater resources (Hagage et al., 2022). The compliance of these values with the guidelines set by the World Health Organization (WHO) and country-specific regulations determines the suitability of water for domestic and agricultural use. To assess the suitability of groundwater for human consumption, experts have developed the Water Quality Index (WQI) (Ullah et al., 2022b) (Talib et al., 2019) (Brown et al., 1972).
Several scientists have employed Geographic Information System (GIS) equipped with the WQI, agricultural indices, and Inverse Distance Weighting (IDW) interpolation method to study the quality of groundwater for human consumption and agricultural use (Derdour et al., 2020; Deshmukh and Aher, 2016; Mirzaei and Sakizadeh, 2016). For environmental managers, the capacity to conduct a geographical analysis of groundwater quality is becoming easy due to much advancement in the development of tools. Furthermore, these tools can be used to investigate the relationship between LULC changes and groundwater quality (Henri et al., 2021; Rajaei et al., 2021) (Chacha et al., 2018). With advanced tools, spatial analysis of groundwater quality is now more efficient, enabling a better understanding of environmental factors and groundwater quality relationships. This is crucial for effective management strategies to protect and preserve groundwater resources. Integration of different data sources provides a comprehensive understanding of groundwater quality dynamics, identifying potential risks and allowing proactive measures to mitigate them.
Numerous studies on groundwater quality have been undertaken in Bangladesh, China, India, Iran, and Pakistan, using a variety of methodologies and approaches (Adnan and Iqbal, 2014; Adnan et al., 2019; Khan and Jhariya, 2018; Sajjad et al., 2022). These techniques include modeling based on GIS and remote sensing, regression modeling of water quality parameters, and others (Melly et al., 2018; Yimer et al., 2008). No previous studies, especially in the Quetta valley, have thoroughly examined how LULC affects groundwater quality. The water table in Quetta valley is falling at an alarming rate due to agricultural activities and rapid population growth, putting the aquifer under severe stress (Khan et al., 2013). The Water And Sanitation Authority has observed a water level decline of up to 24 meters in the past 30 years (AHMED et al., 2019). Agriculture can affect water quality positively and negatively. Fertilizers, pesticides, and herbicides can contaminate groundwater, while conservation tillage, cover cropping, and crop rotation can reduce soil erosion and nutrient loss. Urbanization, agriculture, and deforestation contribute to land use changes that degrade water quality by increasing the runoff, flooding, and pollution from sewage and industrial waste. Studies worldwide confirm the significant impact of land use and cover changes on water quality (Iqbal et al., 2023). Although many researchers have analyzed the hydrogeochemistry of groundwater, no one has investigated the connection between LULC and water quality in Quetta city. In recent years, urbanization has accelerated, necessitating a study of urbanization effects on groundwater quality. Therefore, the objectives of the study were to: (1) evaluate hydrogeochemistry and groundwater quality for drinking agriculture use; (2) comprehend the urbanization depicted by LULC changes, and (3) Establish the link between groundwater quality and LULC patterns in Quetta between 2015 and 2021. The goal of this study was to create a scientific foundation for groundwater management and long-term urban development.
2.1. Study area
Quetta valley is situated between 30°16" North and 67°6" East in northwest Pakistan, and is the capital of Baluchistan Province (Fig. 1). The 300 km2 metropolis of Quetta has a population of 2.3 million people (Census 2017). The sampling was performed in Quetta city, the provincial capital that is connected to Afghanistan to the west. The climate is primarily sandy, with long, cold winters and brief, warm summers. Weathering from the surrounding geological formations resulted in the formation of unconsolidated (sand, silt, and clay) to semi-consolidated (claystone, sandstone, and subordinate conglomerate overlying calcareous and carbonaceous strata) and quaternary deposits that cover much of the basin (Rahman et al., 2022). Chilton limestone (light to dark grey, black, and brownish to bluish grey) and massive white limestone (fine-grained, oolitic, and reefoid) were discovered in the Quetta area during the Middle Jurassic Period and are found in the form of rocks in the east and west parts, with the thickness reaching 1800 m (Sagintayev et al., 2012). Lower Jurassic rocks with thicknesses of up to 1800 m can be found in the Quetta area. At the base of the Lower Jurassic, grey to dark grey, thin- to medium-bedded coarse-grained shelly, oolitic, pesolitic, and pellitic limestone is interbedded with shale and sandstone. Chocolate brown or dark grey Tertiary to Cretaceous limestone with thicknesses ranging from 25 to 60 m is exposed in the southeast and southwest. The lower tertiary Eocene coastal shelf sequence in the northeast is 915 m thick and composed of shale with interbedded sandstone. The Upper Jurassic and Cretaceous (Kjm) shale with interbedded limestone is about 130 m thick, but it narrows near Quetta (Kazmi et al., 2005). In the early twentieth century, the primary irrigation sources in Baluchistan's upland districts were karezes and springs. At present, groundwater is the only available source in the Quetta Valley, which residents use for drinking, agriculture, industry purposes. Because of overexploitation, the water table in the Quetta Valley's aquifer system is rapidly dropping, putting groundwater under severe stress. Between 1987 and 2013, groundwater levels in the Quetta Valley fell by 2.8 to 30.66 meters (Durrani et al., 2018). Rainwater infiltrates the piedmont and gravels of the basin, passing through a variety of lithological layers before replenishing aquifers in the central plain.
Insert fig.1 here
2.2. Laboratory Analysis
To assess the quality of groundwater in Quetta, Pakistan, 58 groundwater samples were collected from drinking wells in both 2015 and 2021. Groundwater samples were collected in 1.5 L polyethylene bottles. During the filtration process, groundwater samples were extracted using a 0.45µm membrane. Physical parameters such as pH, electrical conductivity (EC), and total dissolved solids (TDS), were analyzed in situ by a multi-parameter analyzer (Hanna HI9829). Sulfate (SO4-2) and nitrate (NO3- N) were investigated using an ultraviolet spectrophotometer. Chloride (Cl-) and bicarbonate (HCO3-) were examined through the titration method. The measurement of major cations such as Mg2+ and Ca2+ volumetric titration with ethylene diamine tetraacetic acid (EDTA, 0.05N) with ≤ 2% analytical error was used. Analysis of Na+ and K+, the flame photometer was used to get the values. The charge balance error was calculated for each sample, as given in equation 1.
All ionic ranges are measured in (meq/L). The samples within ±5 ranges were chosen for further evaluation.
2.3. Detection of changes in water quality
The chemical analysis of 58 groundwater samples from 28 wells were used from the study area to measure groundwater quality changes between 2015 and 2021. The measured physiochemical parameters include pH, EC, TDS, anions such as HCO3-, CL-, SO42-, and NO3- N as well as cations such as K+, Mg2+, Ca2+, and Na+. To achieve this objective, the study employs a variety of methods for analyzing groundwater quality changes, interpolation methods for mapping, sodium adsorption ratio (SAR), Wilcox and USSL for assessing agricultural quality, and WQI for assessing potable water quality.
2.4. United States Salinity Laboratory (USSL) and Wilcox diagram diagrams
The USSL diagram for irrigation water describes 16 classes using SAR as a sodium hazard index and EC as a salinity hazard index. Wilcox (1955) classified groundwater for irrigation purposes by relating the percentage of sodium and the EC. The USSL and Wilcox diagrams are both used to assess the quality of groundwater for agricultural purposes (Rafique et al., 2009).
2.5. Water Quality Index (WQI)
WQI was used to evaluate the groundwater quality for drinking purpose in the study area. The WQI values were determined to assess the suitability of the groundwater (Iqbal et al., 2021). The World Health Organization's (2011) drinking water standards were used to calculate WQI (Table 3). The WQI is computed using the equation. 2.
2.6. Statistical and Hydrochemical analysis
Statistical software XL STAT 2021 was used to calculate the mean values for each physiochemical parameter, including the minimum, maximum, average, and standard deviation. The Piper diagram is commonly used to represent the hydrogeochemical type and relative content of major anions and cations in different samples, and it can also depict some potential geochemical processes that can aid in assessing groundwater quality (Purushotham et al., 2011). Grapher was utilized for the creation of the Pipper diagram. Gibbs diagram was used to find the evolution of groundwater.
2.7. LULC classification
Under the Copernicus program, the European Space Agency (ESA) and European Commission (EC) launched the Sentinel-2 (S-2) satellite in the year 2015. The revisit interval for the Sentinel-2A and 2B is 10 days individually and 5 days when combined. The enhanced spatial and spectral resolution of S-2 images provided new opportunities for environmental monitoring and research. To detect land use changes between the year 2015 to 2021, Sentinel-2 satellite high-resolution imagery covering the study area was obtained, and a 10-m-resolution image composite was created. S-2 Multispectral Instrument (MSI) data are available in the Google Earth Engine (GEE) catalog as Level-2A Surface Reflectance (SR) data, computed by running the sen2cor processor (Gascon et al., 2017). For the land use classification, image composites for both years were processed separately and were subjected to a supervised Random Forest (RF) machine learning algorithm within GEE cloud computing platform. Random forest (RF) is a nonparametric supervised ML algorithm (Lee et al., 2016). The RF classifier is nonlinear, comparatively fast, and robust to noisy training data classifiers. As an extension of CART, the RF classifier generates numerous classification trees to improve the prediction performance of the model. Using Google Earth's high-resolution satellite imagery, the training datasets were created through the visual interpretation of composite images for both years and simple random sampling. Based on the literature review, a total of three major land use classes were present, including (1) agriculture (2) barren land, and (3) built-up areas. During the preparation of training sample points, “consistency” and “reliability” principles (John et al., 2017) were carefully maintained which suggests minimizing the mixed pixels problem by avoiding sample collection from the edge of two specific land cover classes and fragmented landscapes (Phan et al., 2020), please see Table 1.
Insert Table 1 here
3.1. Spatial distribution of Physicochemical parameters of the groundwater
Groundwater hydrogeochemical properties for 2015 and 2021 are listed in Table 1 and compared to the WHO 2022 recommended drinking water quality standard (Organization, 2022). The minimum (min) and maximum (max) pH values for 2015 were pH = 7.4 and pH = 7.8, while the corresponding values for 2021 were pH = 6.8 and pH = 8.2. Many natural and anthropogenic factors influence the pH level in the groundwater (Fig. 2a and Fig. 3a). The middle and northern parts of the study area show the rising trend of pH when interpolated spatially and temporally. Pesticides and fertilizers have contributed to the highest pH concentration in the agricultural region. The ion concentrations in groundwater are directly proportional to the EC, resulting in increased salinity. In 2015, the minimum and maximum EC values were 424 μS/cm and 1040 μS/cm, respectively, while in 2021, the minimum and maximum EC values were 260 μS/cm /cm and 2935 μS/cm, respectively. Spatiotemporal interpolation of EC shows an increasing trend, particularly in the central and eastern regions of the study area, which could be attributed to anthropogenic activities and mineral weathering (Fig. 2b and Fig. 3b). The term TDS refers to the numerous dissolved minerals present in water (Narsimha and Sudarshan, 2017). TDS recorded in 2015 varied from 271-666 mg/L (Fig. 2c) and in 2021 the TDS content ranged from 158-1901 mg/L (Fig. 3c). TDS interpolation map shows an increasing trend in the majority of the study area. The high TDS value in groundwater indicated ion dissolution, which could be attributed to salt and mineral deposition over time (Li et al., 2021). Total hardness (TH) in groundwater ranged from 110 to 322 mg/L in 2015 and from 105 to 640 mg/L in 2021. The southern and northern parts of our study area showed an increasing trend of TH (Fig. 2d and Fig. 3d). This may be attributed due to the geogenic and anthropogenic sources.
Ca2+ is a common cation found in groundwater. As shown in Fig. 2e and Table 2, the Ca2+ content in 2015 ranged from 30 to 82 mg/L, with the highest Ca2+ content found in the study area's central areas. Ca2+ is a common cation found in groundwater. The minimum and maximum values of Ca2+ for the year 2021 were found to be Ca2+=37 mg/L and Ca2+=120 mg/L (Fig. 3a). Ca2+ concentration is increasing, particularly in the central, northern, and southern regions (Fig. 2e and Fig. 3e). The dissolution of calcareous material along the groundwater flow channel raises the Ca2+ concentration in aquifers. Mg2+ is commonly found in sedimentary deposits such as magnesite, dolomite, and Mg-bearing minerals (Kovalevsky, 2004). In 2015, groundwater Mg2+ concentrations ranged from 0.08 to 36.50 mg/L, and in 2021, it was between 1.0 and 85 mg/L (Fig. 2i and Fig. 3i). Mg2+ spatial-temporal interpolation is increasing, particularly in the northern, southern, and western regions. Due to the abundance of limestone, dolomitic limestone, and evaporitic rocks in the study area, they are the main source of magnesium in the groundwater (Abdelaziz et al., 2020).In 2015, the min and max values of Na+ were found to be Na+ = 3 and Na+ = 94, respectively, and in 2021, the min and max values in the study area were Na+ = 16 and Na+ = 411, respectively. Due to the increased dissolution of Na-containing materials, spatial and temporal interpolation of Na+ shows a gradual increase, especially in the northern, southern, and western regions (Fig. 2j and Fig. 3j). The concentration of K+ in 2015 ranged from 0 to 2mg/L and in 2021 it varied from 1 to 6 mg/L. In both the eastern and western portions of our study area, the spatiotemporal interpolation of K+ shows a gradual increase from 2015 to 2021(Fig. 2h and Fig. 3h). The increased concentration of K+ in groundwater is caused by anthropogenic sources.
The level of chlorine in fresh, uncontaminated water should be less than 250 mg/L. The samples from the study area revealed that the lowest and highest Cl- values in 2015 were 42 mg/L and 165 mg/L, respectively (Fig. 2f), while the lowest and highest values in 2021 were 20 mg/L and 390 mg/L, respectively (Fig. 3f). The Quetta sub-central, catchment's northern, and southern regions show a steady increase in the Spatiotemporal interpolation of Cl-. In the study area, the Cl- level appears to be higher in the central and northern, and southern regions. This is most likely caused by changes in the water chemical composition along the flow direction as well as groundwater contamination from rural sewage leaks. The HCO3− varied from 40 to 200 mg/L in 2015. The spatial distribution map of HCO3− is shown in Fig. 2g, demonstrating that elevated concentration was found in the northern part, while low content was observed in the southern part. In 2021, the HCO3− content varied from 65 to 350 mg/L in the central and southeastern regions, and the spatiotemporal interpolation of HCO3− shows an increasing trend in these areas. (Fig. 3g). The greater concentration of HCO3− in the groundwater suggests that mineral dissolution is more prevalent (Ramesh and Elango, 2012). In this study, the min and max values of NO3- N for the year 2015 were found to be NO3-N = 1.00 and NO3-N = 6.4 (Fig. 2k), and for 2021, the min and max values were NO3-N = 0.70 and NO3-N = 8.4, respectively (Fig. 3k). Based on the interpolation map, it can be concluded that agricultural activities are the primary contributor to the high nitrate levels in the study area.SO42- concentrations in the study region ranged from 7 to 154 mg/L in 2015, and 32 to 668 mg/L in 2021. The Spatiotemporal interpolation of SO42- displays an increasing trend from 2015 to 2021 (Fig. 2l and Fig. 3l). In the study area, a large part of the aquifer has a relatively high amount of sulfate because of natural and human activities.
Insert Table 2 here
Insert Fig. 2 here
Insert Fig. 3 here
3.2. Hydrochemical Facies
Piper diagrams are frequently used to determine the major cations and anions that influence the hydrogeochemical types of groundwater (Piper, 1944). The groundwater type of Quetta City in 2015 and 2021 is depicted in Figure 4. The lower left triangle's groundwater samples were mostly plotted in zones A and B, with a few in zone G. This shows that the majority of the groundwater in the study area was of the Calcium type, No dominant type (Mixed water quality type), and Chloride type. The groundwater samples plotted in zones 4, 5, and 2 in the middle upper rhombus show that the main hydrogeochemical types of groundwater in Quetta city during the two years were Mixed CaMgCl type, CaCl type, and NaCl type.
Insert Fig. 4here
3.3. Groundwater Chemistry Formation Mechanism
The Gibbs diagram is frequently used to establish a relationship between lithology and groundwater (Jat Baloch et al., 2021b). The dissolved hydrochemical compositions are divided into three sections in this diagram to represent the three main ways groundwater changes over time: evaporation, rock, and precipitation (Gibbs, 1970). As shown in Fig 5, nearly all of the groundwater samples were plotted in the middle section of the two sub-diagrams. This suggests that rock weathering and water-rock interactions were the most important factors influencing groundwater chemical evolution in Quetta City in both years (Aghazadeh and Mogaddam, 2010).
Insert Fig. 5 here
3.4. Groundwater Quality (GWQ) Assessment
3.4.1. Assessment of GWQ for drinking Purpose
In the study area, the WQI method was used to assess the quality and suitability of groundwater for drinking. Furthermore, WQI values are classified into five categories: excellent (<25), good (26-50), poor (51-75), very poor (76-100), and unsuitable (100>). Table 3 shows the suitability of groundwater samples for this study, according to WQI.
Insert Table 3 here
The WQI index was calculated using chemical analysis of 58 groundwater samples collected between 2015 and 2021 to assess changes in drinking-water quality. (Fig. 6). For the year 2015, most of the water samples (n = 14) were considered "excellent”. The groundwater samples (n = 15) fell in the "good" category. There was not a single sample that fell into the category of "unsafe for human consumption." On average, the water quality of the samples was quite good, suggesting that the groundwater sources in the analyzed areas are safe for human consumption. For the year 2021, 13 samples were categorized as “excellent” for drinking, 15 were classified as “good”, and only one sample was considered as not suitable for drinking. In most areas, groundwater quality has declined in 2021 (from “excellent” to “good”), as seen by the WQI values across all sample sites. The results also showed a significant decrease in WQI values in the northern part of the region in the year 2021 (Fig. 6b), which is downstream of residential areas and in the vicinity of agricultural lands.
Insert Fig. 6 here
3.4.2. Assessment of Groundwater Quality for Irrigation Purposes
The appropriateness of the groundwater in the study area for irrigation was determined using the Sodium Adsorption Ratio (SAR), Wilcox, and USSL diagrams. The sodium hazard in groundwater can be classified as low (SAR 10), medium (10-18), moderate (18-26), or very high (SAR > 26) using the SAR classification (Talpur et al., 2020b). For the year 2015, the SAR ranged from 0.51 to 11.42, with an average of 7.60, indicating a low, medium, or high risk of sodium (Fig. 7A). In 2021, the SAR ranged from 2.51 to 32.64, with a mean of 10.12 demonstrating a low to very high sodium risk (Fig. 7B).
Insert Fig. 7 here
The USSL diagram for 2015 shows that the majority of the samples in the C2S1 and C1S1 groups could be used for irrigation with minimal Na+ exchange. The USSL diagram for 2015 shows that the majority of the samples in the C2S1 and C1S1 groups could be used for irrigation with minimal Na+ exchange (Fig. 8). These types of water are suitable for agricultural use. In 2021, the USSL diagram shows 10 samples belonging to the C1S1 group, and 16 belonging to C1S2 group representing that they could be used for irrigation with minimal, while 3 samples belong to C1S3. These types of water are not suitable for agricultural use.
Insert Fig. 8 here
Insert Fig. 9 here
The Wilcox diagram explains the use of groundwater for irrigation use by graphing EC against the percentage of Na+ used (Talib et al., 2019). As shown in Fig. 9 and Fig. 10, approximately 60% of the samples in 2015 were very good to good, with 13.7% belonging to good to permissible and 24.13% falling into permissible to doubtful categories for irrigation use. In 2021, 41.37 % of groundwater samples are very good to good, 10.3 % are good to permissible, 37.9 % are permissible to doubtful, and 10.3 % are doubtful to unsuitable for irrigation use.
Insert Fig. 10 here
3.5. LULC classification
The results of LULC classification and the areas for different land cover types are presented in Fig. 11. The dominant landcover class (for the year 2021) in the study area is barren land, which covers an area of 83 km2, and urban buildup has a share of 31% covering an area of 49 km2. While the agriculture class contributes only 15.6% which is about 25 km2 area. The agricultural land is scattered across the study area in the north, northeast, and east, also mainly distributed in the central parts of the sub-catchment. Barren land is mainly located in the southwest and east of the study area.
Insert Fig. 11 here
It is evident from the above analysis that the buildup class has expanded the most during 2015-2021 with an area change of 7.50%. To further understand the landcover changes that occurred in the target region, a pixel-by-pixel analysis was performed for change detection analysis, provided in Fig. 12. The figure suggests that approximately 10.5% of barren land is converted to agricultural land, ~4.49% agricultural land to barren, ~5.85% agricultural to buildup, and ~7.12% buildup to agricultural land cover class. Overall, the change detection analysis revealed that barren land is the most common landcover class that is altered by local communities for various uses such as construction and agriculture. The effect of land use and land cover on water quality differs across different geographical categories. Extensive farming, for instance, involves the cultivation of huge farms using less pesticides and fertilizers, which has less of an effect on underground water supplies. In contrast, intensive farming practices typically involve the cultivation of smaller farms that make extensive use of fertilizers and pesticides, both of which have a significant negative influence on groundwater quality. Also, LULC changes are one of the key factors that affect the groundwater system (Sajjad et al., 2022). Groundwater is getting worse in developing countries, especially in cities, because there aren't enough sensible rules about how to use land. In South Asia, population growth, drought, and heat wave risks are putting a lot of stress on groundwater resources in the big cities of Pakistan, India, and Bangladesh (Mekonnen and Hoekstra, 2016). Also, contaminants made by humans on the surface of the land reach groundwater resources and lower the quality of the water (Seo and Lee, 2016). These changes were also observed in this study.
Insert Fig. 12 here
3.6. Impact of Land-Use Land Cover on Groundwater quality
Different patterns emerge from a spatial analysis of groundwater quality indicators in Quetta between 2015 and 2021. Some factors affecting groundwater quality have been changing at an irregular rate, while others have a more direct trend, making it more difficult to ascertain the underlying reasons for the observed changes. Groundwater quality has changed the most in regions with both a lot of farmlands and lots of people living there. Increases in sulfate and SAR concentrations have been seen, for instance, in the study region since the mixed forest was cleared for agricultural or construction uses. Increased usage of fertilizers like nitrate and phosphate, respectively, is a direct result of agricultural land development and its subsequent increase in the quantity of these contaminants in water supplies. The research's findings are very consistent with those of earlier studies carried out in other parts of the world (Geist and Lambin, 2001; Hansen et al., 2013; Hosonuma et al., 2012), among others. 32% of the world's tropical forests were destroyed between 2000 and 2012. The most significant direct source of deforestation is agricultural land development, but other factors include tree removal and the construction of infrastructures such as highways and cities. Much of Africa's, Asia's, and South America's forest cover has been destroyed to make way for agricultural expansion. Three distinct studies (d’Annunzio et al., 2015; Gutiérrez-Vélez et al., 2011; Nepstad et al., 2008) indicated that more than 70% of tree felling in these regions was done to make way for agricultural usage. Similar deforestation and conversion to agricultural use have occurred in Iran in recent decades (Mohammadi et al., 2010; Rajaei et al., 2021). Approximately 1.6% of the woods in the northern Iranian Tajan watershed were destroyed between 1986 and 2010 (Sujatha and Reddy, 2003).
The findings of this study are consistent with those of a WQI water quality assessment of Quetta (Ullah et al., 2022b). It revealed that the nearby bodies of surface water were mildly polluted and might be classified as belonging to the average quality class found in urban areas. However, most Quetta valley studies have only looked at quality indicators, and the results reveal that agricultural development is a major reason why the concentration of parameters has increased. NO3-N derived from agricultural fertilizers, and the majority of recommended solutions to improve water quality focus on farming practices such as crop selection, fertilizer application, and farm management. The table 4 provides a comparison of the Water Quality Index (WQI) classes with different land use types (Agriculture, Barren, and Buildup) in two different years (2015 and 2021). In 2015, the majority of the land use "Agriculture" had a WQI class of "Good," with 22.37 km2 of the land, while 17.08 km2 has "Excellent" class falling into this category. Meanwhile, for year 2023 the "Barren" land use type had the majority of its area classified as "Good" (68.29 km2), followed by "Excellent" (38.43 km2). The "Buildup" land use type had a relatively smaller area, with 10.34 km2 classified as "Good" and 8.13 km2 classified as "Excellent."In 2021, most of the "Agriculture" land use type had a WQI class of "Excellent," with 21.09 km2 falling into this category. The second largest WQI class was "Good," with 80.56 km2. The "Barren" land use type also had the majority of its area classified as "Good," with 42.43 km2, followed by "Excellent," with 6.44 km2. The "Buildup" land use type had a relatively small area, with the majority (0.93 km2) classified as "Poor" and 0.42 km2 classified as "Very Poor."
Insert Table 4 here
This study explored the hydrogeochemistry, groundwater quality, and urbanization in Quetta City, in 2015, and 2021. The hydrogeochemical types of groundwater in Quetta City in 2015, and 2021 were Mixed CaMgCl type and CaCl type. The dominant evolution of groundwater chemistry was through rock weathering and water-rock interactions. An increase in TDS and SO42- Concentration was observed in most of the parts of Quetta city during the 6 years between 2015 and 2021. The Wilcox diagram, USSL diagram, and other agricultural indices revealed that the majority of the groundwater samples were suitable for irrigation in both years. The World Quality Index demonstrated that the groundwater sources in the analyzed areas are safe for human consumption; however, in the northern parts of the study area, WQI values are declining due to urbanization over six years. The study identified and classified three major LULC types (agricultural land, bare land, and build-up or urban) in which urbanization was observed at 7%, agriculture by 3%, and barren land was reduced by 10% between 2015 and 2021. In conclusion, the integration of physicochemical parameters, water quality index (WQI), USSL and Wilcox diagram, statistical analysis, and GIS tools are an effective and flexible methodology that offers a thorough picture of groundwater quality and adaptable processes. For further investigation in the future, the integration of datasets such as groundwater flow pattern, depth to water level, aquifer characteristics, lithology, lineament, and soil characteristics could provide a better understanding of groundwater resources of the study area. It is advised that, as a developing nation, Pakistan must adopt a comprehensive plan for water-related concerns such as water supply, quality, and consumption, as well as prevention against diseases caused by water pollution.
Author Contributions
“Conceptualization, J.I. and G.A..; methodology, J.I; software, G.A.; validation, M.Y.J.B.; formal analysis, J.I. and MYJB; investigation, G.A.; resources, G.A.; data curation, J.I.; writing—original draft preparation, J.I.; writing—review and editing, J.I and EH; visualization, S.M.; supervision, C.S.; All authors have read and agreed to the published version of the manuscript.”
Funding
The research work was supported by National Natural Science Foundation of China (Nos.4217072366).
Acknowledgements
The authors gratefully acknowledge the help of editors and reviewers in the future.
Competent interest
The authors have no relevant financial or non-financial interests to disclose.
Data Availability
The data will be provided on request to corresponding author.
Ethics approval
Not applicable
Consent to participate
All authors reviewed and approved the final manuscript.
Consent to publish
All authors approved this for publication.
- Abbas Z, Su C, Tahira F, Mapoma HWT, Aziz SZ (2015) Quality and hydrochemistry of groundwater used for drinking in Lahore, Pakistan: analysis of source and distributed groundwater. Environ Earth Sci 74(5):4281–4294
- Abdelaziz KK et al (2020) Influence of land use land cover change on groundwater recharge in the continental terminal area of Abidjan, ivory coast. J Water Resour Prot 12(5):431–453
- Adnan S, Iqbal J (2014) Spatial analysis of the groundwater quality in the Peshawar District. Pakistan Procedia Engineering 70:14–22
- Adnan S et al (2019) A simple approach of groundwater quality analysis, classification, and mapping in Peshawar. Pakistan Environ 6(12):123
- Aghazadeh N, Mogaddam AA (2010) Assessment of groundwater quality and its suitability for drinking and agricultural uses in the Oshnavieh Area, Northwest of Iran. J Environ Prot 1(01):30
- Ahmad W et al (2021) Impact of land use/land cover changes on water quality and human health in district Peshawar Pakistan. Sci Rep 11(1):1–14
- AHMED Z, EHSAN MI, AKHTAR R, REHMAN, J.U (2019) EVALUATION OF GROUNDWATER VULNERABILITY TO CONTAMINATION BY DRASTIC RISK MAPPING IN QUETTA VALLEY,BALOCHISTAN. EVALUATION, 4(1)
- Baloch MYJ, Mangi SH (2019) Treatment of synthetic greywater by using banana, orange and sapodilla peels as a low cost activated carbon. J Mater Environmental Sci 10(10):966–986
- Baloch MYJ et al (2020) Effects of Arsenic Toxicity on the Environment and Its Remediation Techniques: A Review. J Water Environ Technol 18(5):275–289
- Baloch MYJ et al (2022) Hydrogeochemical mechanism associated with land use land cover indices using geospatial, remote sensing techniques, and health risks model. Sustainability 14(24):16768
- Brown RM, McClelland NI, Deininger RA, O’Connor MF (1972) A water quality index—crashing the psychological barrier, Indicators of environmental quality. Springer, pp 173–182
- Chacha N, Njau KN, Lugomela GV, Muzuka AN (2018) Hydrogeochemical characteristics and spatial distribution of groundwater quality in Arusha well fields, Northern Tanzania. Appl Water Sci 8(4):1–23
- d’Annunzio R, Sandker M, Finegold Y, Min Z (2015) Projecting global forest area towards 2030. For Ecol Manag 352:124–133
- Derdour A, Mahamat Ali MM, Chabane Sari SM (2020) Evaluation of the quality of groundwater for its appropriateness for drinking purposes in the watershed of Naâma, SW of Algeria, by using water quality index (WQI). SN Appl Sci 2(12):1–14
- Deshmukh KK, Aher SP (2016) Assessment of the impact of municipal solid waste on groundwater quality near the Sangamner City using GIS approach. Water Resour Manage 30(7):2425–2443
- Durrani IH, Adnan S, Ahmad M, Khair S, Kakar E (2018) Observed long-term climatic variability and its impacts on the ground water level of Quetta alluvial. Iran J Sci Technol Trans A: Sci 42(2):589–600
- Gascon F et al (2017) Copernicus Sentinel-2A calibration and products validation status. Remote Sens 9(6):584
- Geist HJ, Lambin EF (2001) What drives tropical deforestation. LUCC Rep Ser 4:116
- Ghani J et al (2022) Hydrogeochemical Characterization, and Suitability Assessment of Drinking Groundwater: Application of Geostatistical Approach and Geographic Information System. Front Environ Sci 10:874464
- Gibbs RJ (1970) Mechanisms controlling world water chemistry. Science 170(3962):1088–1090
- Gutiérrez-Vélez VH et al (2011) High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon. Environ Res Lett 6(4):044029
- Hagage M, Madani AA, Elbeih SF (2022) Quaternary groundwater aquifer suitability for drinking in Akhmim, Upper Egypt: an assessment using water quality index and GIS techniques. Arab J Geosci 15(2):1–14
- Hansen MC et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853
- Henri CV, Harter T, Diamantopoulos E (2021) Stochastic assessment of the effect of land-use change on nonpoint source-driven groundwater quality using an efficient scaling approach. Stoch Env Res Risk Assess 35(5):959–970
- Hosonuma N et al (2012) An assessment of deforestation and forest degradation drivers in developing countries. Environ Res Lett 7(4):044009
- Iqbal J et al (2021) Hydrogeochemical Assessment of Groundwater and Suitability Analysis for Domestic and Agricultural Utility in Southern Punjab. Pakistan Water 13(24):3589
- Iqbal J et al (2023) Groundwater fluoride and nitrate contamination and associated human health risk assessment in South Punjab, Pakistan. Environmental Science and Pollution Research, pp 1–20
- Jamil A et al (2019) Spatio-Temporal Glacier Change Detection Using Deep Learning: A Case Study Of Shishper Glacier In Hunza, International Symposium on Applied Geoinformatics
- Jat Baloch MY et al (2021a) Shallow Groundwater Quality Assessment and Its Suitability Analysis for Drinking and Irrigation Purposes. Water 13(23):3361
- Jat Baloch MY et al (2021) Shallow Groundwater Quality Assessment and Its Suitability Analysis for Drinking and Irrigation Purposes. 13 23:3361
- John R et al (2017) Magnetisation switching of FePt nanoparticle recording medium by femtosecond laser pulses. Sci Rep 7(1):1–8
- Kazmi A, Abbas G, Younas S (2005) Water resources and hydrogeology of Quetta Basin, Balochistan, Pakistan. Geological Survey of Pakistan, Quetta
- Khan AS, Khan SD, Kakar DM (2013) Land subsidence and declining water resources in Quetta Valley. Pakistan Environ earth Sci 70(6):2719–2727
- Khan R, Jhariya D (2018) Assessment of land-use and land-cover change and its impact on groundwater quality using remote sensing and GIS techniques in Raipur City, Chhattisgarh, India. J Geol Soc India 92(1):59–66
- Kovalevsky VS (2004) Groundwater studies: an international guide for hydrogeological investigations. Unesco
- Li J, Chen Q, Wang T, Wang H, Ni J (2021) Hydrochemistry and nutrients determined the distribution of greenhouse gases in saline groundwater. Environ Pollut 286:117383
- Mekonnen MM, Hoekstra AY (2016) Four billion people facing severe water scarcity. Sci Adv 2(2):e1500323
- Melly BL, Gama PT, Schael DM (2018) Spatial patterns in small wetland systems: identifying and prioritising wetlands most at risk from environmental and anthropogenic impacts. Wetlands Ecol Manage 26(6):1001–1013
- Memon M, Soomro MS, Akhtar MS, Memon KS (2011) Drinking water quality assessment in Southern Sindh (Pakistan). Environ Monit Assess 177(1):39–50
- Mirzaei R, Sakizadeh M (2016) Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran. Environ Sci Pollut Res 23(3):2758–2769
- Mohammadi J, Shataee Joibary S, Yaghmaee F, Mahiny A (2010) Modelling forest stand volume and tree density using Landsat ETM + data. Int J Remote Sens 31(11):2959–2975
- Narsimha A, Sudarshan V (2017) Contamination of fluoride in groundwater and its effect on human health: a case study in hard rock aquifers of Siddipet, Telangana State, India. Appl Water Sci 7(5):2501–2512
- Nepstad DC, Stickler CM, Filho BS-, Merry F (2008) Interactions among Amazon land use, forests and climate: prospects for a near-term forest tipping point. Philosophical Trans royal Soc B: Biol Sci 363(1498):1737–1746
- Odhiambo GO (2017) Water scarcity in the Arabian Peninsula and socio-economic implications. Appl Water Sci 7(5):2479–2492
- Organization WH (2022) Guidelines for drinking–water quality: incorporating the first and second addenda
- Phan TN, Kuch V, Lehnert LW (2020) Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition. Remote Sens 12(15):2411
- Piper AM (1944) A graphic procedure in the geochemical interpretation of water-analyses. Eos Trans Am Geophys Union 25(6):914–928
- Purushotham D, Prakash M, Narsing Rao A (2011) Groundwater depletion and quality deterioration due to environmental impacts in Maheshwaram watershed of RR district, AP (India). Environ Earth Sci 62(8):1707–1721
- Rafique T et al (2009) Geochemical factors controlling the occurrence of high fluoride groundwater in the Nagar Parkar area, Sindh, Pakistan. J Hazard Mater 171(1–3):424–430
- Rahman NU et al (2022) Middle-Late Permian and Early Triassic foraminiferal assemblages in the Western Salt Range, Pakistan. Rudarsko-geološko-naftni zbornik, 37(3)
- Rajaei F et al (2021) Application of integrated watershed management measures to minimize the land use change impacts. Water 13(15):2039
- Ramesh K, Elango L (2012) Groundwater quality and its suitability for domestic and agricultural use in Tondiar river basin, Tamil Nadu, India. Environ Monit Assess 184(6):3887–3899
- Rashid A et al (2023) Groundwater Quality, Health Risk Assessment, and Source Distribution of Heavy Metals Contamination around Chromite Mines: Application of GIS, Sustainable Groundwater Management, Geostatistics, PCAMLR, and PMF Receptor Model. Int J Environ Res Public Health 20(3):2113
- Rashid A et al (2020) Geochemical modeling, source apportionment, health risk exposure and control of higher fluoride in groundwater of sub-district Dargai. Pakistan Chemosphere 243:125409
- Rehman G, Ain QT, Zaheer M, Bao L, Iqbal J (2019) An analysis of two-dimensional flow through a water reservoir using mathematical approach. Environ Contam Rev : ECR 2(1):11–13
- Sagintayev Z et al (2012) A remote sensing contribution to hydrologic modelling in arid and inaccessible watersheds, Pishin Lora basin, Pakistan. Hydrol Process 26(1):85–99
- Sajjad MM et al (2022) Impact of Climate and Land-Use Change on Groundwater Resources, Study of Faisalabad District. Pakistan Atmos 13(7):1097
- Seo JY, Lee S-I (2016) Integration of GRACE, ground observation, and land-surface models for groundwater storage variations in South Korea. Int J Remote Sens 37(24):5786–5801
- Sujatha D, Reddy BR (2003) Quality characterization of groundwater in the south-eastern part of the Ranga Reddy district, Andhra Pradesh, India. Environ Geol 44(5):579–586
- Talib MA et al (2019) Hydrogeochemical characterization and suitability assessment of groundwater: a case study in Central Sindh. Pakistan Int J Environ Res public health 16(5):886
- Talpur SA (2022) Arsenic Removal from Groundwater using Iron Pyrite. Influences Factors and Removal Mechanism
- Talpur SA et al (2020a) Hydrogeochemical signatures and suitability assessment of groundwater with elevated fluoride in unconfined aquifers Badin district, Sindh, Pakistan. SN Appl Sci 2(6):1–15
- Talpur SA et al (2020) Hydrogeochemical signatures and suitability assessment of groundwater with elevated fluoride in unconfined aquifers Badin district, Sindh, Pakistan. SN Appl Sci 2:1–15
- Ullah Z et al (2022a) Groundwater contamination through potentially harmful metals and its implications in groundwater management. Frontiers in Environmental Science, 10: 2077
- Ullah Z et al (2022b) Non-carcinogenic health risk evaluation of elevated fluoride in groundwater and its suitability assessment for drinking purposes based on water quality index. Int J Environ Res Public Health 19(15):9071
- Yimer F, Messing I, Ledin S, ABdelkadir A (2008) Effects of different land use types on infiltration capacity in a catchment in the highlands of Ethiopia. Soil Use Manag 24(4):344–349
- Zhang W et al (2022) Physiological characteristics, geochemical properties and hydrological variables influencing pathogen migration in subsurface system: What we know or not? Geoscience Frontiers: 101346
Table 1. General information about various land cover classes with labels identified in the study area.
|
Land cover classes |
Description |
|
Agriculture |
Coniferous, broadleaf, and Mix Forest; Grassland, herbs, and shrubs |
|
Barren land |
Barren land and exposed rock surfaces |
|
Build-up |
Urban and rural settlements; Asphalt and Concrete |
Table 2. Descriptive statistics of groundwater parameters in the study area (2015/2021)
|
Parameter |
Mean |
Standard deviation |
Minimum |
Maximum |
WHO standards |
|
pH |
7.7/ 7.6 |
0.11/0.36 |
7.40/ 6.80 |
7.8/ 8.4 |
6.5–8.5 |
|
EC (μS/cm) |
695/ 795 |
126.9/484.6 |
424/ 260 |
1040/ 2935 |
1000 |
|
TDS (mg/L) |
442.6/492.7 |
82.2/ 310.0 |
271//158 |
666/ 1901 |
1000 |
|
Hardness (mg/L) |
209/ 234 |
53.7/ 109 |
110/ 105 |
322/ 640 |
300 |
|
Ca2+ (mg/L) |
57.2/ 54.4 |
12.48/ 20.70 |
30/ 36 |
82/ 120 |
200 |
|
Cl- (mg/L) |
109.6/ 94 |
32.07/ 68.4 |
41/ 20 |
165/ 390 |
250 |
|
HCO3- (mg/L) |
99.1/ 144 |
32.32/ 57.1 |
40/65 |
200/350 |
250 |
|
K+ (mg/L) |
1.0/ 1.7 |
0.267/ 1.23 |
0.0/ 1.0 |
2.0/ 6.0 |
12 |
|
Mg2+ (mg/L) |
16.24/ 23.97 |
9.95/ 19.00 |
0.08/ 1.0 |
36.50/ 85 |
50 |
|
Na+ (mg/L) |
61.17/ 85.9 |
18.45/ 70.5 |
3/ 16 |
94/ 411 |
200 |
|
NO3-N(mg/L) |
1.83/ 0.94 |
1.2/ 0.70 |
1.00/ 0.00 |
6.4/ 2.10 |
10 |
|
SO42- (mg/L) |
94/ 141 |
28.9/112 |
7.0/ 32 |
154/ 668 |
250 |
Table 3. Water Quality Index (WQI) classification for 2015-2021 of the study area.
|
WQI |
Water Type |
No. of Samples |
|
|
2015 |
2021 |
||
|
0-25 |
Excellent |
14 |
13 |
|
26-50 |
Good |
15 |
15 |
|
51-75 |
Poor |
0 |
0 |
|
76-100 |
Very poor |
0 |
0 |
|
100> |
Unsuitable for drinking |
0 |
1 |
Table 4. Cross-area* tabulation of WQI and Landcover of target area for year 2015 and 2021.
|
2015 |
2021 |
|||||
|
WQI Class |
Agriculture |
Barren |
Buildup |
Agriculture |
Barren |
Buildup |
|
Excellent |
17.08 |
38.43 |
8.13 |
2.03 |
6.44 |
9.06 |
|
Good |
22.37 |
68.29 |
10.34 |
21.09 |
80.56 |
42.43 |
|
Poor |
- |
- |
- |
0.47 |
1.03 |
0.93 |
|
Very Poor |
- |
- |
- |
0.11 |
0.67 |
0.42 |
|
Unsuitable |
- |
- |
- |
0.04 |
0.11 |
0.16 |
*Area in Km2