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An assessment of groundwater quality for irrigation and drinking purposes around brick kilns in three districts of Balochistan province, Pakistan, through water quality index and multivariate statistical approaches
Accepted Manuscript An assessment of groundwater quality for irrigation and drinking purposes around brick kilns in three districts of Balochistan province, Pakistan, through water quality index and multivariate statistical approaches
Khanoranga, Sofia Khalid PII: DOI: Reference:
S0375-6742(18)30190-0 https://doi.org/10.1016/j.gexplo.2018.11.007 GEXPLO 6222
To appear in:
Journal of Geochemical Exploration
Received date: Revised date: Accepted date:
22 March 2018 24 October 2018 17 November 2018
Please cite this article as: Khanoranga, Sofia Khalid , An assessment of groundwater quality for irrigation and drinking purposes around brick kilns in three districts of Balochistan province, Pakistan, through water quality index and multivariate statistical approaches. Gexplo (2018), https://doi.org/10.1016/j.gexplo.2018.11.007
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ACCEPTED MANUSCRIPT An assessment of groundwater quality for irrigation and drinking purposes around brick kilns in three districts of Balochistan province, Pakistan, through water quality index and multivariate statistical approaches Khanoranga*, Sofia Khalid
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Fatima Jinnah Women University, The Mall, Rawalpindi, Pakistan
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Sofia Khalid ([email protected])
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*Corresponding Author: Khanoranga ([email protected])
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ACCEPTED MANUSCRIPT Abstract Groundwater quality monitoring is important in an arid region like Balochistan province, Pakistan. This province is characterized by lack of recharge, high evaporation rate coupled with increasing withdrawal from aquifers leading to water scarcity situation. The aim of the current study was to assess the impacts of brick kilns pollution on the groundwater quality of the three districts of Balochistan. The groundwater quality was assessed through twenty-two
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(22) physiochemical parameters using standard protocols. The results of the study showed that analyzed physicochemical parameters were found above the permissible limits of WHO with few exceptions. According to
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Piper Hill diagram water of the study area was classified as CaCl and NaCl type. The calculated groundwater quality index (WQI) showed that water of the study area exhibited poor quality for drinking purpose. Sodium absorption
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ratio, residual sodium carbonate, sodium percentage, and permeability index were assessed for the suitability of the groundwater for irrigation purposes. Furthermore, the groundwater of the study area was found suitable for irrigation
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purpose only in terms of sodium absorption ratio and permeability index. Multivariate statistical approaches such as principal component analysis, cluster analysis, and correlation matrix were successfully used for spatial variability,
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source apportionment and interdependence of various variables in the current study. The results of these multivariate
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statistical approaches revealed the contribution of both natural and anthropogenic activities in altering the hydrochemistry of the groundwater of the study area. It is concluded, that remedial measures are urgently needed to
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safeguard the health of local people depending on the consumption of contaminated groundwater of the study area.
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Groundwater
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Keywords: Magnesium hazard, Sodium absorption ratio, Water quality index, Apportionment, Cluster analysis,
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Introduction
Groundwater is one of the most precious sources of the drinking water widely used in many parts of the world. It is utilized for human consumption such as domestic, agriculture and industries activities and is usually, considered pure water compare to other sources due to several filtration mechanisms in underground soil (Jamshidzadeh and Barzi 2018; Thompson et al.2018). Hence, it is very dynamic in nature and is easily affected by the increased
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agricultural, urban and industrial activities (Daud et al. 2017; Ghazanfar et al.2017). The groundwater quality is mainly depending upon its mineral composition derived from the geological origin of the certain area. Usually, the
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mineral composition of groundwater reflects the type of recharge, water-rock interaction, soil gas interaction, the composition of aquifers the water interacts in the saturated zone, the reactions that take place in aquifers and the
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residence time of water in aquifers (Saxena and Ahmed 2001). Thus, the quality of groundwater is assessed from natural processes occurring in aquifers (precipitation and dissolution of minerals, ion exchange) and anthropogenic
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activities (Nawab et al. 2016; Shakir et al. 2016; Iqbal et al. 2017). Groundwater naturally contains impurities of various trace metals while passing downward through the hydrological cycle. However, these metals are also
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introduced in water bodies through anthropogenic activities such as, through use of agrochemical (pesticides and
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fertilizers), combustion of coal and other low quality fuel in brick kilns and other industries, untreated industrial effluents discharge, improper disposal of municipal solid wastes (Nawab et al. 2018;Vesali Naseh et al. 2018). Some
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of the heavy metals are essential for normal growth of human beings and plants (Memon et al. 2016; Samreen et al. 2017). The concentration of these metals higher than normal level can cause lethal impacts on the environment in
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the form of water and soil pollution indirectly affecting human beings (Mortuza and Al-Misned 2017). Numerous agents are considered as major water pollutants such as inorganic salts, toxic metals, cations and anions i.e.NO 3-
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,PO4, SO42-, Ca2+, Mg2+, F- (Zafar et al. 2107; Anwar et al. 2017; Imran et al. 2017). For instance, lead (Pb), arsenic (As) and mercury (Hg) are among the most important contaminants widely reported from water quality perspectives. In Pakistan, groundwater contamination due to heavy metals is widely reported in different parts of the country, more importantly, the situation is worse in arid and semiarid regions where access to good quality water is already a critical problem for both irrigation and drinking purposes (Brahman et al. 2016). Various important cations such as Na+, Ca2+, Mg2+, K+ are required for the normal function of living organisms and adequate amount of these cations are naturally present in drinking water. Nevertheless, their higher concentration in water makes it unfit for drinking and irrigation purposes. For instance, the higher concentration of sodium in the form of different salts makes water
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ACCEPTED MANUSCRIPT salty and exerts osmotic pressure on aquatic biota, and also cause hypertension and high blood pressure in humans (Khattan 2018). In many developing countries like Bangladesh, India and Africa water quality are mostly deteriorated due to natural and anthropogenic sources (Chabukdhara et al. 2017; Li et al. 2017). Pakistan is also among those developing countries, facing a similar situation of freshwater scarcity due to exhausting available water resources (Hussain et al. 2017; Khan et al. 2107). The most obvious, reasons for the declining water resources in
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Pakistan are lack of management practices and contamination of the existing water resources due to various
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contaminants (Hashmi et al. 2009). The situation is aggravated by factors like lack of reservoirs and along period of
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drought in certain part of the country. In arid regions of Pakistan low rainfall and high evaporation rates result in less groundwater recharge. Groundwater chemistry of the area is significantly altered through anthropogenic activities
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such as through, land use /land cover change and brick kilns pollution (Mohamed et al. 2009; Purandara et al. 2018).The decline in the water quantity and quality and the increasing demand because of overpopulation has
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created stress on the existing freshwater bodies leading to critical water shortage in a major part of the country (Khair et al. 2012; Ishaque and Shaikh 2017). In a certain region of the Sindh and Balochistan province of Pakistan,
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people already have no access to safe drinking water and are compelled to use brackish water for consumption
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(Ullah et al. 2009). In Balochistan, thewater table is annually dropping by 3.5m and in the near future, this source will be completely exhausted (van Steenbergen et al. 2015; Khair et al. 2015). Tube wells are the major source of
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groundwater in several parts of the Balochistan and water from these is used both for irrigation and human consumption (Qadir et al. 2007). Nevertheless, due to over-exploitation of groundwater for irrigation and other
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activities tube wells are drying up causing water scarcity in Balochistan. Groundwater table has now reached its threshold level and in coming years its consequences will be more drastic (Khair et al. 2012; Sadaf et al. 2017). The
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limited quantity coupled with the over increasing demand has deteriorated the water quality. Evaluation of the important physicochemical parameters governing the hydrochemistry of the certain area is extensively conducted throughout the world. These physicochemical parameters play significant role in the groundwater management practices (Kattan 2018). Various water quality indices developed for assessing the groundwater either for drinking or irrigation purposes are significant approaches widely used across the globe (Singh et al. 2018; Rana et al. 2018; Shooshtarian et al. 2018). The benefit of such indices is very effective in the provision of groundwater quality to public and concerned authorities for water management purposes. The current study was carried out to determine the
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ACCEPTED MANUSCRIPT groundwater quality of three districts of Balochistan for drinking and irrigation purposes in the vicinity of brick kilns through integrated approach of water quality indices and multivariate statistical techniques. 2.
Materials and methods
2.1. Study area
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Balochistan is one of the largest and the driest province of Pakistan. It lies between 24 o, 53' and 32o, 05' north
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latitudes and 60o,52' and 72o ,18o east longitudes (Sarangzai et al. 2012). The climate of Balochistan varies
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dramatically from harsh winters to hot summers (Sarangzai et al. 2015). Balochistan exhibits diverse climatic and physical features due to the extensions of Afghanistan and Iranian deserts. The temperature of the area varies with
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elevations from the sea level. Seasonal variations in the range of temperature and precipitation have been considerably noticed in the last few years, due to its mountainous nature and its strong dependency on the regional
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air as the main factor of rain (Malik et al. 2012; Gadiwala et al. 2017). The mean annual, maximum and minimum temperature reported in Balochistan is 27Co, 31Co and 16Co respectively. The highest rainfall occurs from November
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to December. Similarly, June is the hottest and January is the coldest month of the year. The average precipitation is very low in this area (Naseem et al. 2002).Owing, to its fragile climate it often suffers from prolonged and harsh
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spells of drought, floods, and earthquakes.
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Balochistan is located in Triassic strata, characterized by various sedimentary and tectonometallic basins such as Balochistan basin, Indus Suture, Sulaiman and Kirthar basins. Rocks of the study area are composed of ultra mafic,
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igneous and sedimentary types. Balochistan is confirmed as the reservoir of various indigenous minerals (Malkani 2015). The deposits of mines are located in different zones such as, minerogenic zone and metallogenic zones. There
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are about nine zones of minerals in Pakistan, only five of them are located in Baluchistan. Moreover, large deposits of metal such as lead, copper and zinc have been found in different areas of Balochistan. Similarly, gold and silver deposits in association with Saindak copper ore are also of great interest for investors of the globe .Balochistan is also the custodian of barite and natural gas largest reservoir in the Pakistan.Furthermore, it hosts large deposits of different types of coal mainly sub-bituminous type in Quetta, Duki and Harnai region. Beside, these natural resources the occurrence of large deposits of sulphur, magnesite and silica are also reported in the region. (Malkani et al. 2017).
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The brick sector of Balochistan is mainly supported by the high production of bricks from three districts i.e. Quetta, Pishin and Mastung (Fig.1).The current study was conducted in aforementioned districts of Balochistan. Sampling was carried out in Kuchlak located on the outskirt of Quetta, where approximately 30 brick kilns were operating in the vicinity of agricultural fields. In Pishin about 50-60 brick kilns were located in two different areas of Yaroo and
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Saranan. Similarly, in Mastung sampling was carried out from Tehsil Dasht, where about 130 brick kilns were
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operating. The design of brick kilns was an old bull’s trench type commonly used throughout the country. Coal,
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tubewell and was also used for irrigation and human consumption.
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wood and rubber tires were normally used as fuel at all sites. Water for operational activities was supplied from a
Fig .1 Map of the study area
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ACCEPTED MANUSCRIPT 2.2. Water sampling Thirty (30) water samples were collected from different sites in the study area. Samples were collected in precleaned newly purchased polythene bottles, treated with 5% Con. HNO3 and then rinsed with deionized water prior to field sampling with the aim to reduce any chance of addition of external impurities. Water samples were collected 2− − in two separate polythene bottles, one was used for the analysis of anions (NO− 3 , SO4 , Cl , HCO3 , F ) and other
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physiochemical parameters (pH, EC,TDS) whereas, the samples in second bottles were slightly acidified with few drops of Con.HNO3, to reduce chemical variation, adsorption, and precipitation, and to remain the metal ions
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dissolved in water solution. Acidified water samples were used for the analysis of various trace heavy metals (Cu, Cd, Zn, Ni, Pb, Cr, Fe, Mn, As, Hg) and light metals (Na+, K+,Ca2+,Mg2+) in water samples of the study area.
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Standard protocols of APHA (1992, 1998) were followed for the analysis of water quality parameters. pH, EC,TDS were determined through pH meter (Model 6230N, JENCO), TDS meter (EuTech, CON 11, Singapore) and E.C
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meter (EuTech, CON11, Singapore) respectively. K+ and Na+ were measured through flame photometer (DN7101, 2− Italy) whereas, Cl- and HCO− 3 were measured through titration method (APHA, 1992), respectively. SO4 was
UV-Spectrophotometer (Hach-8051) and NO− 3 was measured by UV
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determined through SulfaVer4 by
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Spectrophotometer,Hach-8051 (APHA, 1998). Fluoride was assessed using Ion selective electrode(CRISON, GLP 22+). Arsenic (As) and Mercury (Hg) were assessed using flame atomic absorption spectrophotometer (220 Spectra
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AA, Varian) while the rest of metals were assessed through atomic absorption spectrophotometer (AA-7000
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Shimadzu).
2.3. Quality control (QC) and quality assurance (QA)
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All the chemicals used in the analyses were of analytical grades. Each sample was analyzed in triplicate and mean of the triplicate was used for further data interpretation. Standard solutions were prepared through the dilution (1000 mg/L) of the certified reference solution (Merck) of corresponding metal with deionized water and working standards were analyzed after every 10 samples. All the used containers in the fields and laboratory were washed with washing detergent, rinsed several times with de-ionized water and soaked into 10% Con.HNO3 kept overnight and then rinsed again with de-ionized water. Procedural blanks were also run with samples. Percentage recoveries for studied heavy metals ranged from 85 to 95% (±0.145).
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ACCEPTED MANUSCRIPT 2.4. Multivariate statistical analysis
The multivariate statistical analysis was performed to understand variations in a large set of data. It is useful tool widely used for modeling and interpreting large data set to reduce the dimensionality and extract information for evaluation of water quality and management (Rakotondrabe et al. 2018). In the currents study, principal component
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analysis (PCA) and cluster analysis (CA) were used to identify the pattern of variation and source apportionment
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using XL STAT (2017). A basic statistic such as mean, range and the standard deviation was computed in Microsoft
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Excel 2015.
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2.5. Water quality index
Water quality index (WQI) is an important tool in testing water quality in terms of its potability and management
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perspectives. It is water rating scale based on the assessment of different quality parameters, showing the influence of a variety of parameters on the overall quality of drinking water. Its main purpose is to reduce a large set of water
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quality data into the comprehensible and informative way. It is very effective in the provision of water quality data in a very simpler way to public and policymaker and management authorities. These indices are significantly used to
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assess the quality of water in various countries of the world (Alobaidy et al. 2010;Aghazadeh and Mogaddam 2010;
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Keesari,et al. 2016).
Water quality index was computed in the following four steps in the current study;
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In the very first step, weight was assigned to the most important parameter having a vital role in deteriorating the overall quality of water for human consumption. In the current study water quality index has been calculated
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2+ 2+ considering the key physicochemical parameter that included pH, EC,TDS,HCO3-, F − ,Cl− , SO42-, NO− 3 , Ca , Mg ,
Na+,K+, Cd, Cr, Cu, Mn, Fe, Zn, Ni,As and Hg respectively. Depending upon their relative importance in effecting the water quality, weight (wi) was assigned to each studied parameter (Table 2). 1.
The relative weight (Wi) was determined by the following equation: 𝑾𝒊 =
𝒘𝒊 ∑𝒏 𝒊=𝟏 𝒘𝒊
(1)
Whereas “Wi” is the relative weight, “wi” is the weight of each individual parameter and “n” is the number of parameters.
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In the 2ndstep a quality rating scale (qi) was developed by dividing the observed concentration of each parameter by its respective WHO (2011) standard and then multiplying the result by 100. 𝐂
𝐪𝐢 = ( 𝐢 ) × 100 (2) 𝐒𝐢
Whereas “Ci” is the observed concentration of each parameter and “Si” are WHO standards 3.
In the final stage of WQI, SI was determined for each water quality parameter by multiplying relative
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weight (𝑊𝑖 )with quality rating scale (qi). The sum of SI is equivalent to the water quality index.
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SI= Wiqi(3) 𝐖𝐐𝐈 = ∑ 𝑆𝐼𝑖 (4)
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2.6. Irrigation water quality evaluation
The quality of irrigation water denotes its mineral composition and also illustrates its effects on plants and soil. It is
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correlated with the type of soil and plant ecosystem and their management. High-quality crops can only be yielded through high-quality irrigation water. The chemical composition of irrigation water directly affects plants yield in
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terms of toxicity /deficiency or indirectly by affecting nutrient availability. The irrigation water chemistry varies
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with the source, regional differences in geology and climate. For example, groundwater with high salt concentration can be extremely detrimental to plants by altering metabolic processes, retarding plant growth through decreasing
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water absorption. Irrigation water quality was assessed through sodium adsorption ratio (SAR), residual sodium carbonate (RSC), sodium percentage (Na%), permeability index (PI) and magnesium hazard (MH). These all
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parameters are crucial in assessing groundwater quality for irrigation purpose (Verma et al. 2017).
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2.6.1. Sodium adsorption ratio (SAR) Sodium adsorption ratio is an important measure of the sodium hazard. It is a measure of sodium concentration with respect to calcium and magnesium. It is calculated according to the following standard formula (Patterson 1994).
𝐒𝐀𝐑 =
𝐍𝐚+
(𝟓)
𝐂𝐚𝟐++𝐌𝐠 𝟐+
√
𝟐
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𝟐+ 𝐑𝐒𝐂 = [(𝐇𝐂𝐎𝟑− + 𝐂𝐎− + 𝐌𝐠 𝟐+ )] (6) 𝟑 ) − (𝐂𝐚
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2.6.3. Sodium percentage (%)
The assessment of sodium percentage (%) is crucial for the management of water for an irrigation purpose as high
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concentration of sodium in water and soil retard the growth of plants by decreasing soil permeability it is assessed
(7)
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𝐍𝐚% = (𝐍𝐚+ + 𝐊 + ) × 𝟏𝟎𝟎/(𝐂𝐚𝟐+ + 𝐌𝐠 𝟐+ + 𝐍𝐚+ + 𝐊 + )
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according to the following formula (Ghalib 2017).
2.6.4. Permeability index (PI)
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Long-term use of mineral-rich water decreases the permeability of soil indirectly affecting crop production.
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According to the standard criteria proposed by Doneen permeability index is computed through this formula (Falowo et al. 2017).
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𝟐+ 𝐏𝐈 = (𝐍𝐚+ + √𝐇𝐂𝐎− + 𝐌𝐠 𝟐+ + 𝐍𝐚+ ) (8) 𝟑 ) × 𝟏𝟎𝟎/(𝐂𝐚
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2.6.5. Magnesium hazard (MH)
Usually Ca2+ and Mg 2+ are present in the state of equilibrium. Sometimes due to thehigh concentration of Mg 2+ the
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equilibrium is disturbed and excess of Mg affects the growth of plants by converting water into more alkaline. Concentration is expressed in meq/L. Magnesium hazard (MH) is calculated according to the following formula (Abdulhussein 2018). 𝐌𝐇 = 𝐌𝐠 𝟐+ × 𝟏𝟎𝟎/𝐂𝐚𝟐+ + 𝐌𝐠 𝟐+ 3.
(9)
Results and discussion
Physicochemical parameters of the groundwater are considered as prime principles in identifying the type, quality and nature of the water (Selvakumar et al. 2017). The physicochemical characteristics of the groundwater of the study sites are presented in the Table (1).
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ACCEPTED MANUSCRIPT The pH of the drinking water of the study area was within the permissible limits (6.5-8.5) of WHO (2011). However, the highest pH range was reported in the water of Quetta. The alkaline pH might be due to the calcareous nature of the aquifers of the study area. The results of our study are in line with several other studies conducted in other parts of the country (Rasool et al. 2017; Ali et al. 2017; Arshad and Imran 2017). The EC values of the groundwater of the study sites were within the permissible limit of WHO (2011) except
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Mastung. The higher concentration of the EC might be the dissolution of minerals in groundwater through water-
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rock interaction (Baig et al. 2009).The primary lethal effect of high EC of the water is the failure of plants to
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compete with the ions in the soil solution creating physiological drought condition (Naseem et al. 2010). The TDS values for the groundwater of the study sites were within the permissible limits of WHO (2011) except
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Mastung. The greater TDS values for the drinking water might be due to the geogenic sources of various minerals in the Mastung, reported being rich in deposits of coal, chromites, marble, and limestone (Mustafa et al. 2017).
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The concentration of HCO3-and Cl− in the drinking water of three sites was within the permissible limits of WHO (2011). The concentration of HCO3- was comparatively greater in the water of Quetta whereas the concentration of
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Cl− was larger in the water of Mastung than other two districts. The high concentration of HCO3- and Cl− can be
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attributed to the dissolution of minerals in water such as silicate and sedimentary rocks, carbonate precipitates and atmospheric CO2 dissolution in groundwater (Kumar and Puri 2012). Similarly, the concentration of NO− 3 in the
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drinking water of the, all three sites were within the maximum acceptable level. The concentration of NO− 3 in the
(Soomro et al. 2017).
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current study was lower than the concentration of nitrate reported in the drinking water of Thar, desert of Pakistan
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The concentration of sulphate (SO42-) in the drinking water of the studied water samples was within the permissible limits of WHO (2011) except Quetta. The higher concentration of sulphate (SO42-) in the groundwater of Quetta may be because of the mineralization of sulphide rocks and discharge of industrial and household waste (Majidano et al. 2017). The concentration of fluoride (F − ) was greater than the permissible limits of WHO (2011) in the drinking water of all three sites. The higher concentration in the water may be due to fluoride rocks (fluorite) and other possible reasons might be agricultural inputs, combustion of coal in brick kilns, industrial emission and atmospheric deposition (Gao et al. 2016; Khalid and Mansab 2015). Elevated fluoride (F − ) levels in the groundwater have also
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ACCEPTED MANUSCRIPT been reported in the other parts of the country as potential health hazard after arsenic contamination (Farooqi and Zafar 2016; Arshad and Imran 2017; Qurat-ul-Ain et al. 2017). The order of anions in the present study was HCO 3>SO42- >Cl− >F-. The concentration of sodium (Na+ ) was within the permissible limits of WHO (2011) except in the groundwater of Mastung. The higher concentration of sodium in Mastung district might be associated with its geological
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characteristics and dissolution of lithogenic sodium, ion exchange of sodium for calcium by clay in the aquifers through cation exchange process (Guo and wang, 2005). Results of the current study are in accordance with Rafqiue
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et al. (2009) who reported the drinking water quality of Nagar Parkar, Sindh province, Pakistan. The results of their
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study confirmed the evidence of the high concentration of sodium and Fluoride in their study sites. The concentration of calcium (Ca2+ ) was within the recommended limits of WHO (2011) in the drinking water of
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all the three districts. It is naturally present in the drinking water in the form of calcium carbonate or calcium chloride. Conversely, some other studies also reported similar results around the world (Rapant et al. 2017; Alam et
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al. 2017). The concentration of magnesium (Mg2+) was within the permissible limits of WHO(2011) except in the groundwater of Quetta. Magnesium is the most abundant element in the earth crust. It is widely present in most
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common mineral rocks such as dolomite and magnetite. Magnesium (Mg 2+) gets its way into drinking water from
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different sources both natural and anthropogenic. More precisely, it enters into drinking water from various mafic and ultramafic rocks and from fertilizers application, cattle feed and industrial processes using magnesium in
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different procedural steps are the prominent anthropogenic sources (Razowska-Jaworek 2014). Ample literature exists regarding the concentration of magnesium in the drinking water and its higher level is
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associated with different health disorders (Daud et al. 2017; Rasool et al. 2017). The concentration of potassium (K +) was within the permissible limits of WHO (2011) except in the groundwater of the Mastung. The higher concentration of potassium in the drinking water of Mastung might be attributed to weathering of rocks such as feldspar and the fertilizer application (Mumtaz et al. 2017). The dominant cations in the current study exhibited Na+>Mg2+>Ca2+>K+ pattern of variance. The concentration of the studied heavy metals ( As, Hg, Ni, Cd, Cr, Fe and Pb ) in the groundwater of the study sites was found above the permissible limits of WHO (2011) except Cu, Zn, and Mn. The higher concentration of these
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ACCEPTED MANUSCRIPT metals might be attributed to the geological composition of rocks (mafic and ultramafic) of the study sites and anthropogenic activities such as agricultural and combustion of coal in brick kilns (Kumari et al. 2017).The nonpoint sources can also be responsible for the elevated level of these hazardous metals in the drinking water such as atmospheric deposition which later leads to leaching of these metals in the underground water (Ali et al. 2017). The results of our study are in agreement with other studies (Fatmi et al. 2009; Podgorski et al. 2016). These studies
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reported the higher concentration of various metals in the groundwater and confirmed that both natural (weathering
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of rocks) and anthropogenic activities such as the application of agrochemicals frequently used in the agricultural
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lands and the combustion of coal are equally responsible for degradation of the groundwater. The concentration of light metals was greater compared to the heavy metals in all studied water samples of the study sites.
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3.1. Water classification
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Water is generally classified according to Piper Hill diagram (1944). It is important in inferring the hydrogeochemical properties of water by describing the dominant cations and anions, influencing the
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hydrochemistry of the area (Walton, 1970). The water of the studied sites from three district of Balochistan was
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plotted on piper diagram. The result of the study showed that water of the Quetta was CaCl type and whereas the water of Pishin and Mastung was of NaCl type (Fig.2, 3, 4). Spatial variation in hydrochemistrymaybe due to
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differences in lithological heterogeneity and diverse geological mechanism operating in underground aquifers(Jiang et al. 2017;Majeed et al. 2018).NaCl and CaCl, hydrofacies may be due to cations exchange process and the water-
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rock interaction such as the dissolution of halite type of rocks and calcium carbonate types of rock. The results of the current study also depicted that the origins of these ions are mainly natural. The study area (Balochistan) is reported
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to be rich in mineral deposits (Malkani 2015). The probable sources of the light metals (Na, Ca, Mg. K) in the groundwater are the weathering of primary parent rock materials. As reported by other researchers that light metals are mainly originated from the lithogenic sources reflecting the mineralogical composition of parent rocks (Kumar et al. 2009; Bhatti et al. 2018).
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Fig.2 Piper diagram showing the classification of water type of Quetta, Balochistan
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Fig.3 Piper diagram showing the classification of water type of Pishin, Balochistan
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Fig.4 Piper diagram showing the classification of water type of Mastung, Balochistan
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3.2. Drinking water quality assessment The water quality index computed for the water samples of the three districts are presented in Table (3). Drinking
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water was classified according to the grading system followed by Ketata-Rokbani et al. (2011) Table (4). Water quality parameters were compared with the maximum permissible limits of WHO (2011) and relative weight (Wi) is presented in Table (2). Results of the current study illustrated that water quality index (WQI) ranged between139.99 to188.33, 78.27to 223.27 and 177.33 to 311.41 for the groundwater of Quetta, Pishin, and Mastung respectively. According to water quality index, groundwater quality of all three sites was poor for drinking purpose. This can be attributed to the presence of high concentration of various heavy metals and variation in some of the other physicochemical parameters due to anthropogenic and geogenic sources. Water was collected from brick kilns sites being operational for many years with continuous combustion activities using coal and other low-quality fuel for
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ACCEPTED MANUSCRIPT baking of bricks at high temperature. These combustion activities might be the major source of various emissions which later deposited over soil surfaces. Correspondingly, various agricultural activities were also practiced in the vicinity of brick kilns using agrochemicals to enhance their production might be another anthropogenic source of different metals in drinking water. Conversely, the study area is also reported to be rich in various minerals such as having huge deposits of limestone, fluorite dolomite, calcite and coal (Malkani et al. 2015). Moreover, the chemical
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composition of groundwater of certain area depends upon the mineral composition of aquifers through which it
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flows by reacting with the mineral in the aquifer depending upon certain factors such as the hydrological cycle and
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flow direction (Yidana et al. 2010). The results of our study are in accordance with the study conducted by Shabir and Ahmed (2015). They assessed water quality of Islamabad and Rawalpindi through water quality index (WQI)
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and concluded that major proportion of their study area possessed poor water quality. 3.3. Irrigation water quality assessment
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3.3.1. Sodium absorption ratio (SAR)
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EC and SAR are usually used reciprocally to demonstrate the quality of water for irrigation purpose. SAR ranged from 1.50 to 1.89, 4.40-6.03 and4.30-6.59 meq/L for the drinking water of Quetta, Pishin, and Mastung, respectively
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(Table 5). According to sodium hazards,SAR values greater than 9 were regarded as unsuitable for irrigation
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purpose (Awais et al. 2017). In the view of the current results, the SAR values fall within the safe range. Furthermore, Mastung has shown greater SAR values compared to other two study sites. High SAR values create
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sodium salinity hazard by reducing soil water availability affecting the growth of crops by reducing the ratio of calcium and magnesium major nutrients. The higher concentration of sodium in the study area can be linked to clay
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minerals and weathering of various other rocks (Vasanthavigar et al. 2010). Rasool et al. (2016) assessed the quality of tube well for irrigation and drinking purpose in one of the areas of Punjab. The results of their study depicted that water quality of tube well was marginally suitable for irrigation purpose.
3.3.2. Residual sodium carbonate (RSC) Residual sodium carbonate (RSC) is considered as avaluable tool in examining the suitability of ground water for irrigation through carbonate and bicarbonate ratio (Selvakumar et al. 2017). The negative value of RSC indicates the excess of sodium ion by offsetting calcium and magnesium, through precipitating calcium as CO 2 and leaving
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ACCEPTED MANUSCRIPT sodium as dominant cations. Furthermore, the positive value of RSC indicates the increased concentration of calcium and magnesium due to the reaction of HCO3- to form calcium bicarbonate and magnesium bicarbonate (Chitsazan et al. 2017). The groundwater of the study area was classified according to the RSC classification system Table (6) and RSC value less than 1.25 is regarded safe for irrigation purpose. The result of the current study showed that RSC ranged between -12.50 to-10.95,-0.67 to -0.12 and 0.99-1.43 for the drinking water of Quetta,
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Pishin and Mastung substantially (Table 5). The result of the study also confirmed that water of the study area was
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safe for irrigation purposes except in case of Mastung area. Usually, the water of arid and semi-arid region is
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characterized by high RSC values resulting intosodification and salinization of the agriculture soil (Prasad and Bose 2001). Hence, the results of our study are in accordance to Bashir et al. (2017) who evaluated the quality of water of
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Sanghar Sindh, Pakistan for drinking and irrigation purpose using different statistical tools and indices. The results of their study ascertained that water of their study area was suitable for irrigation purpose with few exceptions.
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3.3.3. Sodium percentage (%)
Sodium percentage (%) in the study areas ranged from 27.37 to 29.52, 84.24 to 88.80 and 68.28 to75.31% in the
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water of the Quetta, Pishin, and Mastung, respectively (Table 5). The water of the study area was classified
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according to the standard classification of Na + (%) (Table 7) followed by many researchers (Srinivas et al. 2017; ElAziz 2017; Islam et al. 2017).
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The groundwater of the Quetta falls into the category of “Good”, whereas groundwaterof Pishin and Mastung represented “permissible” to “doubtful” category. It is evident from the result that water of the Pishin and Mastung
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possess a high percentage of sodium as compared to Quetta district. The most probable reasons for such high level of sodium in the water might be lithological sources causing the dissolution of such minerals in water and the other
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reason might be the use of agrochemicals used in agriculture activities. High level of sodium percentage (%) in soil cause devastating impacts on soil structure, aeration, and infiltration (Singaraja et al. 2014). For instance, agricultural activities carried out with more alkaline water for longer periods have been reported to decline yield of crops due to osmotic pressure caused in soil plants system by the accumulation of sodium in the soil. The high osmotic pressure in soil-plant system retards the ability of plants to take water or nutrients from soil medium ( Naseem et al. 2012).
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ACCEPTED MANUSCRIPT 3.3.4. Permeability index (PI) The permeability of the soil is greatly affected by long-term use of mineral-rich (Ca2+, Mg2+, Na+, HCO3-) water for irrigation purpose (Singh and Singh 2008). Thus, permeability index manipulates the water quality by degrading agricultural soil (Obiefuna and Sheriff2011).The long-term use of irrigation water rich in minerals reduces aeration in soil and makes it hard to plow and also retard the emergence of seedlings. Doneen developed a criterion for the
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classification of water according to the level of Permeability index. This criterion included three classes i.e. I, II and
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III. The class I and II are regarded as good for irrigation purposes with permeability index of 75 or above. The class
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III water is characterized as unsuitable for irrigation with permeability index of 25 or below it (Doneen 1975; Raju 2007). The results of the current study showed that Permeability index (PI) ranged between 27.72 to 30.01, 84.24 to
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88.80 and 85.11-91.33 meq/L for the groundwater of Quetta, Pishin and Mastung, respectively (Table 5). According to this classification groundwater of the, all the three districts was suitable for irrigation. Results of our study are in
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line with other studies (Kurdi and Eslamkish 2017; Aher and Gaikwad 2017).
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3.3.5. Magnesium hazard (MH)
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The result of the current study illustrated that magnesium hazard (MH) ranged from 83.79 to 89.40, 61.29 to 70.65 and 51.94 to69.23 meq/L for the water of Quetta, Pishin and Mastung, respectively (Table 5). According to the
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criteria developed for the classification of water (Table 6) in terms of magnesium hazard the water of the study area was found unsuitable for agriculture purposes. The high concentration of magnesium in water increases the
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alkalinity of water strongly distressing the yield of crops. Similarly, the high concentration of magnesium retards the infiltration of soil as magnesium is 50% larger than the calcium and therefore not attracted to clay particles. As a
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result of this mechanism, a lot of water is adsorbed between magnesium and clay particles reducing soil infiltration capability (Hussain et al. 2017). It is also supported by the fact that the water of the study area (Pishin and Mastung) was NaCl type reflecting the low concentration of calcium and high concentration of magnesium. The results of the current study are in accordance with several other studies reported in several other developing countries (Patel et al. 2017; Golekar et al. 2017; Padhi et al. 2017).
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ACCEPTED MANUSCRIPT 3.4. Multivariate statistical analysis
3.4.1. Cluster analysis(CA) Cluster analysis is multivariate statistical approach, used for the classification of a set of objects into groups based on their similar properties (Hu etal 2017). In the current study cluster analysis was applied on the groundwater data
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to determine similarity in the ground water compositions among the study sites. The resulting dendrogram of the
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cluster analysis (CA) grouped together the studied samples into three main clusters (Fig.5). Cluster 1 includes
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sample Q1 -Q10 (Quetta), cluster 2 includes sample P1-P20 (Pishin) whereas cluster 3 includes M1- M3 (Mastung) respectively. Water samples representing similar composition are grouped into one cluster whereas; water samples
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of different compositions were grouped into other clusters. The results of the study indicated that water of the study area varied in the composition due to differences in the level of heavy metals and other physicochemical properties
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and are clustered into three different clusters each one comprises of the water samples of one area. It is evident from the result that samples in one cluster comprise of similar hydrogeochemical properties such as major ionic
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composition and other heavy metals. The cluster analysis (CA) has commonly been used throughout the world and regarded as good tool to represent spatial variations in the groundwater hydrochemistry of aquifers (Luo et al. 2017;
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Yamazaki et al. 2017).
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ACCEPTED MANUSCRIPT Dendrogram
Dendrogram(Wards method) 2000000 1843772 1800000 1643772
1600000
1443772
1200000
1043772
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800000
1243772
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1000000
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Dissimilarity
Dissimilarity
1400000
600000
843772
400000
C1
C2
443772 C3
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M25 M30 M24 M21 M22 M26 M29 M23 M27 M28 P18 P11 P15 P12 P13 P19 P17 P14 P16 P20 Q4 Q8 Q9 Q6 Q7 Q1 P10 Q2 Q3 Q5
0
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643772
200000
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Fig.5 Dendrogram showing spatial variability among the water quality in study sites of Balochistan ( Q, P and
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M symbols are representing groundwater samples from Quetta, Pishin and Mastung, respectively).
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3.4.2. Principal component analysis
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Principal component analysis (PCA) was applied on twenty-two (22) variables on the whole water quality data set of study sites. PCA generated many principal components (PC), only those components were retained having
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eigenvalue greater than 1. The results of the principal component analysis contributed maximum four (4) components responsible for 84.93%of the total variance in the data (Table 7). The first component of the PCA was responsible for 39.67%of variances with an eigenvalue of 8.73. It includes the most significant parameters controlling the groundwater chemistry of the study area i.e. pH(0.79), EC(0.84), TDS(0.95), HCO3-(0.95), Cl− (0.91), Na+(0.92), Mg2+(0.85), K+(0.94), SO42-(0.95), Cd(-0.64), As(0.64) and F- (0.81). The second component accounted for about20.31% variance with eigenvalue 4.47. Factor 2 had a strong positive loading for Mn (0.71) and Fe(0.88)
and
negative
loading
of
NO− 3 (0.83) .Similarly, the third component accounted for 15.48% variance with eigenvalue3.41 dominated by
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ACCEPTED MANUSCRIPT positive loading of Cr (0.63), Pb (0.65) and Cu (0.64). The fourth component of the PCA was accounted for about 9.48% of total variance with an eigenvalue of 2.09 and dominated by Ca2+ (0.64), Ni (0.63) and Hg (-0.79). One of the most important significance of the PCA in the water quality analysis is the interpretation of the whole data set in a way that each variable is interpreted based on multiple or specific hydrochemicalprocess. For instance, in the current study, the first component of the PCA included the most important water quality parameters involving
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those mechanisms and relationships, governing the whole hydrochemistry of the groundwater. In the first component, the positive correlation between EC and TDS and the other major anions reveals the dominance of
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anions which might be due to mineralization of geological components of the aquifers. Meanwhile, the relationship of pH with other parameters indicates that pH is the major driver controlling the behavior of many ions such as F-,
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As, SO42-, Fe, and Cd. Similarly, the correlation among the light metals was also clearly depicted showing that these light metals might be originated from weathering of rocks employing various mechanisms such as ion exchange,
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redox potential and water-rock interaction (Hussain et al. 2017).The negative loading of the Cd in the first component indicating that it might be originated from anthropogenic sources such as brick kilns combustion and
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agricultural activities.
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The second component of the PCA included those parameters generated from geological processes such as Fe, Mn and also that generated from anthropogenic sources such as NO− 3 (Khound and Bhattacharyya, 2017).The third
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component was dominated by those parameters (Cr, Pb, Cu) originated only from anthropogenic sources such as
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coal and other low-quality fuel used in the combustion activities. The other component included mixed loading of parameters(Ca2+, Ni, Hg) released both from natural and anthropogenic sources like weathering of calcite rocks,
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combustion of coal and other low-quality fuel used in the burning activities of brick kilns.
3.4.3. Correlation matrix analysis The results from correlation matrix are presented in the Table (9) for the whole data set of the three sites of study. The results have depicted that majority of the variables have shown an erratic pattern of correlation, the significant positive correlations, showing close connection of these parameters with one another, controlling the whole hydrochemistry of the region. For instance, pH is one of the most influential parameters controlling the mobility of most of the metals in the water and soil mediums (Farooqi et al. 2009). The positive correlation between pH,
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ACCEPTED MANUSCRIPT fluoride(F-)and arsenic(As) indicates that high pH favors the mobility of these ions, for instance, at higher pH, Arsenic( As) starts to desorb from the iron oxide in the groundwater (Shakoor et al. 2015).The significant positive correlation of pH with As and F also describe that, the high concentration of these ions in the groundwater of the study area might be due to the alkalization phenomenon (Rafique et al. 2008).The positive correlation between pH andSO42-indicates that the mobility of the SO42-is also pH dependent as at low pH, SO42- is found to be immobile for
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the plant uptake (Firdous et al. 2016).Nevertheless, the negative correlation of pH with Fe, Cd, Cu, Cr and Mn
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shows that these micronutrients are only available at low pH so high pH might restrict their availability in the
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solution (Lomaglio et al.2017).
EC and TDS had shown very strong correlation with one another and also with other variables such as Na+,
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Ca2+,Mg2+, HCO3-, SO42-,Cl-and F- indicating that EC and TDS are managed by the concentration of these ions, on the other hand, it is also showing the dominance of these ions in the study area. Usually, Ca 2+, Mg2+,and HCO3- are
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the dominant ions in natural water (Singh et al. 2008). The positive correlation among HCO 3-,SO42-,As and pH suggesting oxidative dissolution of As in the groundwater which might be the main factor governing the enrichment
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of As in the groundwater of the study area (Farooqi et al. 2007).Similarly, the positive correlation among F-,HCO3-
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,SO42-, pH and Na+ favors the dissolution of F- in the groundwater. For instance, OH − and HCO3- ions are simultaneously released during the leaching and dissolution of F - in the groundwater, OH − replaces exchangeable F
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under alkaline condition (Rafique et al. 2009; Gupta and Ayoob 2016).
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The positive correlation of Na+ and Cl- shows that these trigger the water chemistry indicating much possible process involved in the hydrological process of the area (Sundaray 2010). Since, the study area is characterized as
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arid to semiarid region of the country so main possible source for the high concentration of sodium and chloride and their close relationship might be due to the excessive evaporation and the dissolution of halite rocks and other anthropogenic activities such as agricultural and brick kilns operational activities (Kumar et al. 2006; Elumalai et al. 2017). The positive correlation among various heavy metals and light metals are indicative of the same sources. The main sources of these heavy metals in the groundwater may be the agriculture (application of inorganic nitrogenous and phosphate fertilizers and animal manures) and brick kilns activities in the study area. Brick kilns use different types of low quality fuel for baking of bricks in the study area. The combustion of coal and other fuels such as wood, rubber tires and other plastic wastes are responsible for the emissions of different types of heavy metals in the
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ACCEPTED MANUSCRIPT surrounding ambient environment. These emissions are assumed to be in the gaseous form or in the form of particulate matter which gets deposited on the soil due to dry and wet deposition. The accumulation of heavy metals in the soil then leads to the leaching of these metals into the ground water reservoirs. Thus, brick kilns in this way might be deteriorating the ground water of the study area.
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It is evident from the results of the current study that natural, as well as anthropogenic sources both, are responsible
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for the high concentration of various light and heavy metals in the groundwater (Ćujić et al. 2017). It is briefly, concluded that groundwater chemistry of the studied area is greatly influenced both by geological as well as
Conclusion
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4.
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anthropogenic contamination
Groundwater is a significant resource for human consumption and its preservation in terms of its quality and
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availability for future generations is of utmost importance. Physicochemical properties are assessed to evaluate the
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quality of groundwater for drinking and agricultural purposes. In the current study, the groundwater quality around functional brick kilns in three districts of Balochistan was successfully evaluated through water quality index and
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multivariate statistical approaches. The results of the current study showed that majority of water quality parameters were beyond the permissible limits of WHO with few exceptions. Geochemistry of the study area display
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Na+>Mg2+>Ca2+>K+ andHCO3->SO42->Cl->F- a pattern of the dominance of the major cations and anions. The three districts have CaCl and NaCl type of water, reflecting the geology of the study area. Water quality index (WQI) also
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showed that groundwater of three districts was found to be unsuitable for drinking purpose through exhibiting poor quality. Similarly, the quality of water for irrigation was assessed in terms of irrigation quality indicators such as
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SAR, RSC, Na (%), PI and MH hazard and the results showed that groundwater of the study sites was suitable for irrigation only in terms of SAR and PI. Additionally, principal component analysis (PCA), cluster analysis (CA) and correlation matrix were found significant tools in identifying the source of pollution, spatial variability, and interrelationship among different variables, governing the hydrochemistry of groundwater through various mechanisms. The results of multivariate statistical approaches showed that groundwater of the study area is greatly influenced both from the geological composition of the area and anthropogenic activities such as agricultural and brick kilns activities carried out in the vicinity of the study area. It is briefly, concluded that water quality index (WQI) and irrigation quality indicators are useful tools in giving a comprehensive picture of the ground quality to
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ACCEPTED MANUSCRIPT the public and other concerned authorities for planning management strategies in preserving this vital source for future. Acknowledgment The first author graciously acknowledges Sardar Bahadur Khan Women’s University Quetta, Balochistan and
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Higher Education Commission (HEC) Pakistan by providing a scholarship for her Ph.D. studies.
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Funding
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This research did not receive any specific grant from funding agencies in the public, commercial or not for profit
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sectors. Conflict of interest
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The authors declare that they have no conflict of interest.
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ACCEPTED MANUSCRIPT Table 1 Physicochemical properties of the groundwater of the study area (n=30) Quetta Parameter
Range
Pishin
Mean±SD
Range
Mastung
Mean±SD
Range
Mean±SD
WHO Standard
7.99-7
7.59±0.38
7.9-6.8
7.57-0.35
7.9-7.1
7.51±0.24
6.5 -8.5
EC µS/cm
943-765
895.3±61.73
1106-999
1075.5±35.85
1567-1234
1470.5±112.46
1500
TDS (mg/L)
897-740
847.5±53.66
708-543
660.4±52.93
1076-564
815.3±180.11
1000
HCO3-(mg/L)
386-170
323.7±70.78
160-115
142.86±13.25
240-199
228.7±15.11
500
Na+(mg/L)
117-67
102.2±15.41
176-116
156.8±16.35
245-140
188.6±35.70
200
Ca2+(mg/L)
24.5-19
22.51±1.8
20-16
18.85±1.41
28-20
25.5±2.46
75
Mg2+(mg/L)
136-76
118.3±21.41
27-19
23.8±2.66
30-18
24.1±3.84
50
K+(mg/L)
7.8-2.8
5.87±1.68
2-1.3
1.72±0.187
12.6-1.4
8.009±3.86
12
Cl− (mg/L)
70-50
62.3±6.25
125-90
108.2±9.003
230-175
207.5±19.19
250
SO42-(mg/L)
274-160
45.399±1.28
188-134
168.2±15.03
189-126
161.2±22.58
250
Cd(mg/L)
0.35-0.003
0.06±0.10
0.10-0.003
0.05±0.03
0.10-0.02
0.05±0.03
0.003
Cr(mg/L)
0.09-0.01
0.04±0.02
0.10-0.02
0.052±0.03
0.20-0.13
0.16±0.03
0.05
Pb(mg/L)
0.10-0.02
0.07±0.03
0.44-0.03
0.11±0.12
0.19-0.03
0.11±0.05
0.01
Cu(mg/L)
0.09-0.031
0.05±0.02
0.06-0.03
0.04±0.01
0.97-0.01
0.14±0.3
2
Mn(mg/L)
0.91-0.02
0.12±0.27
0.76-0.05
0.21±0.24
0.20-0.12
0.16±0.03
0.4
Fe(mg/L)
1.10-0.30
0.64±0.29
0.98-0.03
0.27±0.30
0.96-0.40
0.69±0.20
0.3
Zn(mg/L)
0.41-0.01
0.11±0.12
0.26-0.01
0.09±0.08
0.26-0.01
0.09±0.08
3
NO− 3 (mg/L)
1.76-0.87
1.15±0.278
0.98-0.36
0.67±0.21
2.76-0.53
1.74±0.712
45
Ni(mg/L)
0.14-0.10
0.09±0.031
1.20-0.37
0.81±0.37
0.94-0.15
0.49±0.27
0.02
As (mg/L)
0.02-0.01
0.01±1.67
0.06-0.03
0.05±10.44
0.01-0.05
0.01±3.64
0.01
Hg(mg/L)
0.01-0.003
0.02±2.15
0.06-0.03
0.05±10.443
0.09-0.02
0.05±21.37
0.001
F- (mg/L)
1.53-0.26
0.82±0.34
1.58-0.28
0.92±0.44
1.96-0.96
1.59±0.31
1.5
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AN
US
PT
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M
37
T
pH
ACCEPTED MANUSCRIPT Table 2 Assignment of relative weight to the studied groundwater quality parameters Chemical Parameter
WHO standards
Weight (wi)
Relative Weight (Wi)
6.5 -8.5
4
0.05
EC
1500
4
0.05
TDS
1000
4
0.05
HCO3-
500
3
0.04
Na+
200
3
Ca2+
75
2
Mg2+
50
K+
12
Cl− SO42-
Cu
Fe
Hg
IP
CR
250
4
0.05
250
5
0.06
0.003
5
0.06
0.05
5
0.06
0.01
5
0.06
2
2
0.02
0.4
4
0.05
0.3
4
0.05
3
3
0.04
45
5
0.06
0.02
4
0.05
10
5
0.06
6
5
0.06
1.5
5
0.06
85
1
AC
As
CE
Zn
Ni
0.02
PT
Mn
NO− 3
2
ED
Pb
0.02 0.02
US
M
Cr
0.04
2
AN
Cd
T
pH
F− ∑
38
ACCEPTED MANUSCRIPT Table 3 Drinking quality of groundwater of the study area according to WQI classification Range
Mean±Sdt
Water type
Quetta
137.39-188.33
160.07±17.53
Poor Water
Pishin
78.27-223.26
153.63±44.32
Poor Water
Mastung
177.33-311.41
238.59±38.60
Very poor Water
Type of water
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Range
IP
Table 4 Water classification grading based on water quality Index (WQI)
T
Site
<50
Excellent Good
US
50-100 100-200
Poor Very Poor
AN
200-300
AC
CE
PT
ED
M
>300
39
Water unsuitable for drinking purpose
ACCEPTED MANUSCRIPT Table 5 Calculated irrigation quality indices for groundwater of the study area Quetta
Mastung
(meq/L)
Range
Mean±Std
Range
Mean±Std
Range
Mean±Std
SAR
1.50-1.89
1.89±0.18
4.40-6.03
5.63±0.49
4.30-6.59
536±0.86
RSC
(-12.50)-(-10.95)
-10±10.95
(-0.67)-(-0.12)
-0.40±0.19
0.99-1.43
1.13±0.14
Na%
27.37-29.52
29.52±1.78
65.89-72.04
70.02±1.90
68.29-75.31
71.31±2.41
PI
27.72-30.01
30.01±1.85
84.24-88.80
86.29±1.53
MH
83.79-89.40
89.40±2.61
61.29-70.65
67.66±3.07
CR US AN M ED PT CE AC
40
85.11-91.33
88.03±1.80
51.94-69.23
60.14±5.24
IP
Pishin
T
Parameter
ACCEPTED MANUSCRIPT
Table 6 Water quality classification based on SAR. RSC, PI and MH SAR(meq/L)
Wate quality
RSC(meq/L)
Water
PI(meq/L)
quality
Water
MH(meq/L)
quality
Water quality
Good
<1.25
Good
>75%
Good
<50
Suitable
6-9
Doubtful
1.25-2.5
Doubtful
25-75%
Suitable
>50
Unsuitable
>9
Unsuitable
>2.5
Unsuitable
<25%
Unsuitable
AC
CE
PT
ED
M
AN
US
CR
IP
T
0-6
41
ACCEPTED MANUSCRIPT Table 7 Water classification based on Na+ percentage (%) Water quality
< 20
Excellent
20-40
Good
40-60
Permissible
60-80
Doubtful
>80
Unsuitable
AC
CE
PT
ED
M
AN
US
CR
IP
T
Na%
42
ACCEPTED MANUSCRIPT Table 8 Factor loading of each physicochemical parameters of groundwater with their variance and eigen values Variables
F1
F2
F3
F4
0.79
-0.37
0.42
0.25
EC
0.84
0.41
0.01
0.09
TDS
0.95
0.24
0.09
0.02
HCO3-
0.95
-0.29
-0.53
-0.42
Na+
0.92
0.29
-0.03
0.10
Ca2+
0.02
0.19
-0.51
0.64
Mg2+
0.85
0.42
0.18
-0.12
K+
0.94
0.26
0.05
0.03
Cl−
0.91
0.31
-0.13
-0.05
SO42-
0.95
0.25
0.17
-0.03
0.27
0.61
0.06
-0.64
Cr
-0.55
0.50
0.63
0.06
Pb
-0.59
0.42
0.65
-0.08
0.09
0.13
0.64
0.22
-0.17
0.71
-0.50
0.17
-0.18
0.75
-0.37
0.03
-0.07
0.88
-0.24
-0.33
-0.03
-0.83
0.07
-0.41
0.05
-0.08
-0.47
0.63
0.65
-0.49
0.42
0.001
0.14
0.03
0.18
-0.79
F-
0.81
-0.48
0.33
-0.05
Eigenvalue
8.73
4.47
3.41
2.09
Variability (%)
39.67
20.31
15.48
9.48
Cumulative %
39.67
59.97
75.45
84.93
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M
Cd
AN
US
CR
IP
T
pH
Cu
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Mn Fe
CE
Zn
Ni As Hg
AC
NO− 3
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ACCEPTED MANUSCRIPT Table 9 Results of the correlation analysis of the physiochemical parameters of the groundwater of study area Variables
pH
EC
TDS
HCO3-
Na+
Ca2+
Mg2+
K+
Cl−
SO42-
Cd
Cr
Pb
Cu
Mn
Fe
pH
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EC
0.24
1
TDS
0.22
0.94
1
HCO3
0.57
0.96
0.93
1
Na+
0.32
0.92
0.93
0.96
1
Ca2+
0.40
0.74
0.61
0.67
-0.59
1
Mg2+
0.36
0.58
0.53
0.62
0.49
0.04
1
K+
0.06
-0.10
0.04
0.03
0.10
0.50
-0.26
1
Cl−
0.34
0.88
0.88
0.95
0.94
0.48
0.52
0.06
1
SO42-
0.79
0.95
0.93
0.97
0.98
0.47
0.55
-0.03
0.96
1
Cd
-0.54
0.15
0.33
0.05
0.14
0.12
-0.19
0.08
0.06
0.06
1
Cr
-0.67
0.11
0.03
0.01
0.10
0.11
-0.27
-0.14
0.12
0.05
0.38
1
Pb
0.04
-0.28
-0.38
-0.29
-0.19
0.23
-0.51
-0.15
-0.20
-0.20
-0.38
0.43
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Cu
-0.58
0.11
-0.07
-0.11
-0.16
-0.24
-0.12
-0.44
-0.23
-0.15
0.29
0.65
0.28
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Mn
-0.74
-0.07
0.04
-0.17
-0.01
-0.19
-0.57
-0.03
-0.06
0.45
0.70
0.23
0.31
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Fe
-0.53
-0.42
-0.53
-0.50
-0.39
-0.05
-0.57
-0.11
-0.39
-0.43
-0.06
0.76
0.78
0.53
0.56
1
Zn
-0.34
0.45
0.55
0.32
E C
-0.08
NO− 3
-0.01
-0.28
-0.40
-0.20
Ni
-0.02
-0.32
-0.25
-0.09
As
0.89
-0.20
-0.07
Hg
0.87
0.30
F-
0.89
0.67
-
NO− 3
Zn
Ni
As
Hg
F-
T P
C A
I R
C S
T P
D E
U N
A
M
0.39
-0.19
-0.19
-0.02
0.35
0.41
0.46
0.25
-0.29
0.12
0.65
-0.09
1
-0.30
-0.13
0.20
0.44
-0.13
-0.27
-0.30
-0.07
-0.19
-0.20
-0.35
0.06
-0.36
1
-0.10
0.13
0.13
0.27
0.05
-0.18
-0.24
-0.03
-0.001
-0.47
-0.12
0.02
-0.53
0.43
1
0.58
0.56
0.09
-0.19
0.63
-0.06
-0.25
0.19
-0.01
-0.23
-0.12
-0.05
-0.10
-0.16
0.44
0.54
1
0.24
0.34
0.26
0.18
0.29
-0.02
0.33
0.40
-0.39
-0.53
-0.04
-0.39
-0.60
-0.46
-0.04
0.08
-0.34
-0.34
1
0.77
0.53
0.71
0.19
0.36
-0.14
0.45
0.53
-0.37
-0.58
-0.08
-0.54
-0.48
-0.56
0.05
-0.16
-0.25
-0.48
0.92
44
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ACCEPTED MANUSCRIPT Highlights The groundwater quality was assessed in the vicinity of brick kilns in Balochistan
Elevated concentrations of heavy metals were recorded in the groundwater
Water quality index (WQI) suggested low quality for drinking purposes
Groundwater quality was found unfit for irrigation due to high MH and Na%
Natural and anthropogenic factors were responsible for low quality of groundwater
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Khanoranga, Sofia Khalid PII: DOI: Reference:
S0375-6742(18)30190-0 https://doi.org/10.1016/j.gexplo.2018.11.007 GEXPLO 6222
To appear in:
Journal of Geochemical Exploration
Received date: Revised date: Accepted date:
22 March 2018 24 October 2018 17 November 2018
Please cite this article as: Khanoranga, Sofia Khalid , An assessment of groundwater quality for irrigation and drinking purposes around brick kilns in three districts of Balochistan province, Pakistan, through water quality index and multivariate statistical approaches. Gexplo (2018), https://doi.org/10.1016/j.gexplo.2018.11.007
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ACCEPTED MANUSCRIPT An assessment of groundwater quality for irrigation and drinking purposes around brick kilns in three districts of Balochistan province, Pakistan, through water quality index and multivariate statistical approaches Khanoranga*, Sofia Khalid
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Fatima Jinnah Women University, The Mall, Rawalpindi, Pakistan
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Sofia Khalid ([email protected])
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*Corresponding Author: Khanoranga ([email protected])
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ACCEPTED MANUSCRIPT Abstract Groundwater quality monitoring is important in an arid region like Balochistan province, Pakistan. This province is characterized by lack of recharge, high evaporation rate coupled with increasing withdrawal from aquifers leading to water scarcity situation. The aim of the current study was to assess the impacts of brick kilns pollution on the groundwater quality of the three districts of Balochistan. The groundwater quality was assessed through twenty-two
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(22) physiochemical parameters using standard protocols. The results of the study showed that analyzed physicochemical parameters were found above the permissible limits of WHO with few exceptions. According to
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Piper Hill diagram water of the study area was classified as CaCl and NaCl type. The calculated groundwater quality index (WQI) showed that water of the study area exhibited poor quality for drinking purpose. Sodium absorption
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ratio, residual sodium carbonate, sodium percentage, and permeability index were assessed for the suitability of the groundwater for irrigation purposes. Furthermore, the groundwater of the study area was found suitable for irrigation
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purpose only in terms of sodium absorption ratio and permeability index. Multivariate statistical approaches such as principal component analysis, cluster analysis, and correlation matrix were successfully used for spatial variability,
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source apportionment and interdependence of various variables in the current study. The results of these multivariate
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statistical approaches revealed the contribution of both natural and anthropogenic activities in altering the hydrochemistry of the groundwater of the study area. It is concluded, that remedial measures are urgently needed to
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safeguard the health of local people depending on the consumption of contaminated groundwater of the study area.
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Groundwater
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Keywords: Magnesium hazard, Sodium absorption ratio, Water quality index, Apportionment, Cluster analysis,
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ACCEPTED MANUSCRIPT 1.
Introduction
Groundwater is one of the most precious sources of the drinking water widely used in many parts of the world. It is utilized for human consumption such as domestic, agriculture and industries activities and is usually, considered pure water compare to other sources due to several filtration mechanisms in underground soil (Jamshidzadeh and Barzi 2018; Thompson et al.2018). Hence, it is very dynamic in nature and is easily affected by the increased
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agricultural, urban and industrial activities (Daud et al. 2017; Ghazanfar et al.2017). The groundwater quality is mainly depending upon its mineral composition derived from the geological origin of the certain area. Usually, the
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mineral composition of groundwater reflects the type of recharge, water-rock interaction, soil gas interaction, the composition of aquifers the water interacts in the saturated zone, the reactions that take place in aquifers and the
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residence time of water in aquifers (Saxena and Ahmed 2001). Thus, the quality of groundwater is assessed from natural processes occurring in aquifers (precipitation and dissolution of minerals, ion exchange) and anthropogenic
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activities (Nawab et al. 2016; Shakir et al. 2016; Iqbal et al. 2017). Groundwater naturally contains impurities of various trace metals while passing downward through the hydrological cycle. However, these metals are also
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introduced in water bodies through anthropogenic activities such as, through use of agrochemical (pesticides and
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fertilizers), combustion of coal and other low quality fuel in brick kilns and other industries, untreated industrial effluents discharge, improper disposal of municipal solid wastes (Nawab et al. 2018;Vesali Naseh et al. 2018). Some
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of the heavy metals are essential for normal growth of human beings and plants (Memon et al. 2016; Samreen et al. 2017). The concentration of these metals higher than normal level can cause lethal impacts on the environment in
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the form of water and soil pollution indirectly affecting human beings (Mortuza and Al-Misned 2017). Numerous agents are considered as major water pollutants such as inorganic salts, toxic metals, cations and anions i.e.NO 3-
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,PO4, SO42-, Ca2+, Mg2+, F- (Zafar et al. 2107; Anwar et al. 2017; Imran et al. 2017). For instance, lead (Pb), arsenic (As) and mercury (Hg) are among the most important contaminants widely reported from water quality perspectives. In Pakistan, groundwater contamination due to heavy metals is widely reported in different parts of the country, more importantly, the situation is worse in arid and semiarid regions where access to good quality water is already a critical problem for both irrigation and drinking purposes (Brahman et al. 2016). Various important cations such as Na+, Ca2+, Mg2+, K+ are required for the normal function of living organisms and adequate amount of these cations are naturally present in drinking water. Nevertheless, their higher concentration in water makes it unfit for drinking and irrigation purposes. For instance, the higher concentration of sodium in the form of different salts makes water
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ACCEPTED MANUSCRIPT salty and exerts osmotic pressure on aquatic biota, and also cause hypertension and high blood pressure in humans (Khattan 2018). In many developing countries like Bangladesh, India and Africa water quality are mostly deteriorated due to natural and anthropogenic sources (Chabukdhara et al. 2017; Li et al. 2017). Pakistan is also among those developing countries, facing a similar situation of freshwater scarcity due to exhausting available water resources (Hussain et al. 2017; Khan et al. 2107). The most obvious, reasons for the declining water resources in
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Pakistan are lack of management practices and contamination of the existing water resources due to various
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contaminants (Hashmi et al. 2009). The situation is aggravated by factors like lack of reservoirs and along period of
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drought in certain part of the country. In arid regions of Pakistan low rainfall and high evaporation rates result in less groundwater recharge. Groundwater chemistry of the area is significantly altered through anthropogenic activities
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such as through, land use /land cover change and brick kilns pollution (Mohamed et al. 2009; Purandara et al. 2018).The decline in the water quantity and quality and the increasing demand because of overpopulation has
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created stress on the existing freshwater bodies leading to critical water shortage in a major part of the country (Khair et al. 2012; Ishaque and Shaikh 2017). In a certain region of the Sindh and Balochistan province of Pakistan,
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people already have no access to safe drinking water and are compelled to use brackish water for consumption
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(Ullah et al. 2009). In Balochistan, thewater table is annually dropping by 3.5m and in the near future, this source will be completely exhausted (van Steenbergen et al. 2015; Khair et al. 2015). Tube wells are the major source of
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groundwater in several parts of the Balochistan and water from these is used both for irrigation and human consumption (Qadir et al. 2007). Nevertheless, due to over-exploitation of groundwater for irrigation and other
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activities tube wells are drying up causing water scarcity in Balochistan. Groundwater table has now reached its threshold level and in coming years its consequences will be more drastic (Khair et al. 2012; Sadaf et al. 2017). The
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limited quantity coupled with the over increasing demand has deteriorated the water quality. Evaluation of the important physicochemical parameters governing the hydrochemistry of the certain area is extensively conducted throughout the world. These physicochemical parameters play significant role in the groundwater management practices (Kattan 2018). Various water quality indices developed for assessing the groundwater either for drinking or irrigation purposes are significant approaches widely used across the globe (Singh et al. 2018; Rana et al. 2018; Shooshtarian et al. 2018). The benefit of such indices is very effective in the provision of groundwater quality to public and concerned authorities for water management purposes. The current study was carried out to determine the
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ACCEPTED MANUSCRIPT groundwater quality of three districts of Balochistan for drinking and irrigation purposes in the vicinity of brick kilns through integrated approach of water quality indices and multivariate statistical techniques. 2.
Materials and methods
2.1. Study area
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Balochistan is one of the largest and the driest province of Pakistan. It lies between 24 o, 53' and 32o, 05' north
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latitudes and 60o,52' and 72o ,18o east longitudes (Sarangzai et al. 2012). The climate of Balochistan varies
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dramatically from harsh winters to hot summers (Sarangzai et al. 2015). Balochistan exhibits diverse climatic and physical features due to the extensions of Afghanistan and Iranian deserts. The temperature of the area varies with
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elevations from the sea level. Seasonal variations in the range of temperature and precipitation have been considerably noticed in the last few years, due to its mountainous nature and its strong dependency on the regional
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air as the main factor of rain (Malik et al. 2012; Gadiwala et al. 2017). The mean annual, maximum and minimum temperature reported in Balochistan is 27Co, 31Co and 16Co respectively. The highest rainfall occurs from November
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to December. Similarly, June is the hottest and January is the coldest month of the year. The average precipitation is very low in this area (Naseem et al. 2002).Owing, to its fragile climate it often suffers from prolonged and harsh
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spells of drought, floods, and earthquakes.
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Balochistan is located in Triassic strata, characterized by various sedimentary and tectonometallic basins such as Balochistan basin, Indus Suture, Sulaiman and Kirthar basins. Rocks of the study area are composed of ultra mafic,
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igneous and sedimentary types. Balochistan is confirmed as the reservoir of various indigenous minerals (Malkani 2015). The deposits of mines are located in different zones such as, minerogenic zone and metallogenic zones. There
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are about nine zones of minerals in Pakistan, only five of them are located in Baluchistan. Moreover, large deposits of metal such as lead, copper and zinc have been found in different areas of Balochistan. Similarly, gold and silver deposits in association with Saindak copper ore are also of great interest for investors of the globe .Balochistan is also the custodian of barite and natural gas largest reservoir in the Pakistan.Furthermore, it hosts large deposits of different types of coal mainly sub-bituminous type in Quetta, Duki and Harnai region. Beside, these natural resources the occurrence of large deposits of sulphur, magnesite and silica are also reported in the region. (Malkani et al. 2017).
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The brick sector of Balochistan is mainly supported by the high production of bricks from three districts i.e. Quetta, Pishin and Mastung (Fig.1).The current study was conducted in aforementioned districts of Balochistan. Sampling was carried out in Kuchlak located on the outskirt of Quetta, where approximately 30 brick kilns were operating in the vicinity of agricultural fields. In Pishin about 50-60 brick kilns were located in two different areas of Yaroo and
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Saranan. Similarly, in Mastung sampling was carried out from Tehsil Dasht, where about 130 brick kilns were
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operating. The design of brick kilns was an old bull’s trench type commonly used throughout the country. Coal,
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tubewell and was also used for irrigation and human consumption.
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wood and rubber tires were normally used as fuel at all sites. Water for operational activities was supplied from a
Fig .1 Map of the study area
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ACCEPTED MANUSCRIPT 2.2. Water sampling Thirty (30) water samples were collected from different sites in the study area. Samples were collected in precleaned newly purchased polythene bottles, treated with 5% Con. HNO3 and then rinsed with deionized water prior to field sampling with the aim to reduce any chance of addition of external impurities. Water samples were collected 2− − in two separate polythene bottles, one was used for the analysis of anions (NO− 3 , SO4 , Cl , HCO3 , F ) and other
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physiochemical parameters (pH, EC,TDS) whereas, the samples in second bottles were slightly acidified with few drops of Con.HNO3, to reduce chemical variation, adsorption, and precipitation, and to remain the metal ions
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dissolved in water solution. Acidified water samples were used for the analysis of various trace heavy metals (Cu, Cd, Zn, Ni, Pb, Cr, Fe, Mn, As, Hg) and light metals (Na+, K+,Ca2+,Mg2+) in water samples of the study area.
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Standard protocols of APHA (1992, 1998) were followed for the analysis of water quality parameters. pH, EC,TDS were determined through pH meter (Model 6230N, JENCO), TDS meter (EuTech, CON 11, Singapore) and E.C
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meter (EuTech, CON11, Singapore) respectively. K+ and Na+ were measured through flame photometer (DN7101, 2− Italy) whereas, Cl- and HCO− 3 were measured through titration method (APHA, 1992), respectively. SO4 was
UV-Spectrophotometer (Hach-8051) and NO− 3 was measured by UV
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determined through SulfaVer4 by
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Spectrophotometer,Hach-8051 (APHA, 1998). Fluoride was assessed using Ion selective electrode(CRISON, GLP 22+). Arsenic (As) and Mercury (Hg) were assessed using flame atomic absorption spectrophotometer (220 Spectra
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AA, Varian) while the rest of metals were assessed through atomic absorption spectrophotometer (AA-7000
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Shimadzu).
2.3. Quality control (QC) and quality assurance (QA)
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All the chemicals used in the analyses were of analytical grades. Each sample was analyzed in triplicate and mean of the triplicate was used for further data interpretation. Standard solutions were prepared through the dilution (1000 mg/L) of the certified reference solution (Merck) of corresponding metal with deionized water and working standards were analyzed after every 10 samples. All the used containers in the fields and laboratory were washed with washing detergent, rinsed several times with de-ionized water and soaked into 10% Con.HNO3 kept overnight and then rinsed again with de-ionized water. Procedural blanks were also run with samples. Percentage recoveries for studied heavy metals ranged from 85 to 95% (±0.145).
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ACCEPTED MANUSCRIPT 2.4. Multivariate statistical analysis
The multivariate statistical analysis was performed to understand variations in a large set of data. It is useful tool widely used for modeling and interpreting large data set to reduce the dimensionality and extract information for evaluation of water quality and management (Rakotondrabe et al. 2018). In the currents study, principal component
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analysis (PCA) and cluster analysis (CA) were used to identify the pattern of variation and source apportionment
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using XL STAT (2017). A basic statistic such as mean, range and the standard deviation was computed in Microsoft
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Excel 2015.
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2.5. Water quality index
Water quality index (WQI) is an important tool in testing water quality in terms of its potability and management
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perspectives. It is water rating scale based on the assessment of different quality parameters, showing the influence of a variety of parameters on the overall quality of drinking water. Its main purpose is to reduce a large set of water
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quality data into the comprehensible and informative way. It is very effective in the provision of water quality data in a very simpler way to public and policymaker and management authorities. These indices are significantly used to
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assess the quality of water in various countries of the world (Alobaidy et al. 2010;Aghazadeh and Mogaddam 2010;
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Keesari,et al. 2016).
Water quality index was computed in the following four steps in the current study;
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In the very first step, weight was assigned to the most important parameter having a vital role in deteriorating the overall quality of water for human consumption. In the current study water quality index has been calculated
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2+ 2+ considering the key physicochemical parameter that included pH, EC,TDS,HCO3-, F − ,Cl− , SO42-, NO− 3 , Ca , Mg ,
Na+,K+, Cd, Cr, Cu, Mn, Fe, Zn, Ni,As and Hg respectively. Depending upon their relative importance in effecting the water quality, weight (wi) was assigned to each studied parameter (Table 2). 1.
The relative weight (Wi) was determined by the following equation: 𝑾𝒊 =
𝒘𝒊 ∑𝒏 𝒊=𝟏 𝒘𝒊
(1)
Whereas “Wi” is the relative weight, “wi” is the weight of each individual parameter and “n” is the number of parameters.
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In the 2ndstep a quality rating scale (qi) was developed by dividing the observed concentration of each parameter by its respective WHO (2011) standard and then multiplying the result by 100. 𝐂
𝐪𝐢 = ( 𝐢 ) × 100 (2) 𝐒𝐢
Whereas “Ci” is the observed concentration of each parameter and “Si” are WHO standards 3.
In the final stage of WQI, SI was determined for each water quality parameter by multiplying relative
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weight (𝑊𝑖 )with quality rating scale (qi). The sum of SI is equivalent to the water quality index.
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SI= Wiqi(3) 𝐖𝐐𝐈 = ∑ 𝑆𝐼𝑖 (4)
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2.6. Irrigation water quality evaluation
The quality of irrigation water denotes its mineral composition and also illustrates its effects on plants and soil. It is
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correlated with the type of soil and plant ecosystem and their management. High-quality crops can only be yielded through high-quality irrigation water. The chemical composition of irrigation water directly affects plants yield in
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terms of toxicity /deficiency or indirectly by affecting nutrient availability. The irrigation water chemistry varies
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with the source, regional differences in geology and climate. For example, groundwater with high salt concentration can be extremely detrimental to plants by altering metabolic processes, retarding plant growth through decreasing
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water absorption. Irrigation water quality was assessed through sodium adsorption ratio (SAR), residual sodium carbonate (RSC), sodium percentage (Na%), permeability index (PI) and magnesium hazard (MH). These all
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parameters are crucial in assessing groundwater quality for irrigation purpose (Verma et al. 2017).
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2.6.1. Sodium adsorption ratio (SAR) Sodium adsorption ratio is an important measure of the sodium hazard. It is a measure of sodium concentration with respect to calcium and magnesium. It is calculated according to the following standard formula (Patterson 1994).
𝐒𝐀𝐑 =
𝐍𝐚+
(𝟓)
𝐂𝐚𝟐++𝐌𝐠 𝟐+
√
𝟐
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ACCEPTED MANUSCRIPT 2.6.2. Residual sodium carbonate (RSC) Residual sodium carbonate ratio is another measure of the suitability of water for irrigation purpose. It is computed according to the following formula and all the concentrations were expressed in meq/L (Gupta and Gupta 1987).
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𝟐+ 𝐑𝐒𝐂 = [(𝐇𝐂𝐎𝟑− + 𝐂𝐎− + 𝐌𝐠 𝟐+ )] (6) 𝟑 ) − (𝐂𝐚
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2.6.3. Sodium percentage (%)
The assessment of sodium percentage (%) is crucial for the management of water for an irrigation purpose as high
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concentration of sodium in water and soil retard the growth of plants by decreasing soil permeability it is assessed
(7)
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𝐍𝐚% = (𝐍𝐚+ + 𝐊 + ) × 𝟏𝟎𝟎/(𝐂𝐚𝟐+ + 𝐌𝐠 𝟐+ + 𝐍𝐚+ + 𝐊 + )
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according to the following formula (Ghalib 2017).
2.6.4. Permeability index (PI)
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Long-term use of mineral-rich water decreases the permeability of soil indirectly affecting crop production.
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According to the standard criteria proposed by Doneen permeability index is computed through this formula (Falowo et al. 2017).
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𝟐+ 𝐏𝐈 = (𝐍𝐚+ + √𝐇𝐂𝐎− + 𝐌𝐠 𝟐+ + 𝐍𝐚+ ) (8) 𝟑 ) × 𝟏𝟎𝟎/(𝐂𝐚
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2.6.5. Magnesium hazard (MH)
Usually Ca2+ and Mg 2+ are present in the state of equilibrium. Sometimes due to thehigh concentration of Mg 2+ the
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equilibrium is disturbed and excess of Mg affects the growth of plants by converting water into more alkaline. Concentration is expressed in meq/L. Magnesium hazard (MH) is calculated according to the following formula (Abdulhussein 2018). 𝐌𝐇 = 𝐌𝐠 𝟐+ × 𝟏𝟎𝟎/𝐂𝐚𝟐+ + 𝐌𝐠 𝟐+ 3.
(9)
Results and discussion
Physicochemical parameters of the groundwater are considered as prime principles in identifying the type, quality and nature of the water (Selvakumar et al. 2017). The physicochemical characteristics of the groundwater of the study sites are presented in the Table (1).
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ACCEPTED MANUSCRIPT The pH of the drinking water of the study area was within the permissible limits (6.5-8.5) of WHO (2011). However, the highest pH range was reported in the water of Quetta. The alkaline pH might be due to the calcareous nature of the aquifers of the study area. The results of our study are in line with several other studies conducted in other parts of the country (Rasool et al. 2017; Ali et al. 2017; Arshad and Imran 2017). The EC values of the groundwater of the study sites were within the permissible limit of WHO (2011) except
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Mastung. The higher concentration of the EC might be the dissolution of minerals in groundwater through water-
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rock interaction (Baig et al. 2009).The primary lethal effect of high EC of the water is the failure of plants to
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compete with the ions in the soil solution creating physiological drought condition (Naseem et al. 2010). The TDS values for the groundwater of the study sites were within the permissible limits of WHO (2011) except
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Mastung. The greater TDS values for the drinking water might be due to the geogenic sources of various minerals in the Mastung, reported being rich in deposits of coal, chromites, marble, and limestone (Mustafa et al. 2017).
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The concentration of HCO3-and Cl− in the drinking water of three sites was within the permissible limits of WHO (2011). The concentration of HCO3- was comparatively greater in the water of Quetta whereas the concentration of
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Cl− was larger in the water of Mastung than other two districts. The high concentration of HCO3- and Cl− can be
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attributed to the dissolution of minerals in water such as silicate and sedimentary rocks, carbonate precipitates and atmospheric CO2 dissolution in groundwater (Kumar and Puri 2012). Similarly, the concentration of NO− 3 in the
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drinking water of the, all three sites were within the maximum acceptable level. The concentration of NO− 3 in the
(Soomro et al. 2017).
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current study was lower than the concentration of nitrate reported in the drinking water of Thar, desert of Pakistan
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The concentration of sulphate (SO42-) in the drinking water of the studied water samples was within the permissible limits of WHO (2011) except Quetta. The higher concentration of sulphate (SO42-) in the groundwater of Quetta may be because of the mineralization of sulphide rocks and discharge of industrial and household waste (Majidano et al. 2017). The concentration of fluoride (F − ) was greater than the permissible limits of WHO (2011) in the drinking water of all three sites. The higher concentration in the water may be due to fluoride rocks (fluorite) and other possible reasons might be agricultural inputs, combustion of coal in brick kilns, industrial emission and atmospheric deposition (Gao et al. 2016; Khalid and Mansab 2015). Elevated fluoride (F − ) levels in the groundwater have also
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ACCEPTED MANUSCRIPT been reported in the other parts of the country as potential health hazard after arsenic contamination (Farooqi and Zafar 2016; Arshad and Imran 2017; Qurat-ul-Ain et al. 2017). The order of anions in the present study was HCO 3>SO42- >Cl− >F-. The concentration of sodium (Na+ ) was within the permissible limits of WHO (2011) except in the groundwater of Mastung. The higher concentration of sodium in Mastung district might be associated with its geological
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characteristics and dissolution of lithogenic sodium, ion exchange of sodium for calcium by clay in the aquifers through cation exchange process (Guo and wang, 2005). Results of the current study are in accordance with Rafqiue
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et al. (2009) who reported the drinking water quality of Nagar Parkar, Sindh province, Pakistan. The results of their
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study confirmed the evidence of the high concentration of sodium and Fluoride in their study sites. The concentration of calcium (Ca2+ ) was within the recommended limits of WHO (2011) in the drinking water of
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all the three districts. It is naturally present in the drinking water in the form of calcium carbonate or calcium chloride. Conversely, some other studies also reported similar results around the world (Rapant et al. 2017; Alam et
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al. 2017). The concentration of magnesium (Mg2+) was within the permissible limits of WHO(2011) except in the groundwater of Quetta. Magnesium is the most abundant element in the earth crust. It is widely present in most
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common mineral rocks such as dolomite and magnetite. Magnesium (Mg 2+) gets its way into drinking water from
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different sources both natural and anthropogenic. More precisely, it enters into drinking water from various mafic and ultramafic rocks and from fertilizers application, cattle feed and industrial processes using magnesium in
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different procedural steps are the prominent anthropogenic sources (Razowska-Jaworek 2014). Ample literature exists regarding the concentration of magnesium in the drinking water and its higher level is
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associated with different health disorders (Daud et al. 2017; Rasool et al. 2017). The concentration of potassium (K +) was within the permissible limits of WHO (2011) except in the groundwater of the Mastung. The higher concentration of potassium in the drinking water of Mastung might be attributed to weathering of rocks such as feldspar and the fertilizer application (Mumtaz et al. 2017). The dominant cations in the current study exhibited Na+>Mg2+>Ca2+>K+ pattern of variance. The concentration of the studied heavy metals ( As, Hg, Ni, Cd, Cr, Fe and Pb ) in the groundwater of the study sites was found above the permissible limits of WHO (2011) except Cu, Zn, and Mn. The higher concentration of these
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reported the higher concentration of various metals in the groundwater and confirmed that both natural (weathering
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of rocks) and anthropogenic activities such as the application of agrochemicals frequently used in the agricultural
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lands and the combustion of coal are equally responsible for degradation of the groundwater. The concentration of light metals was greater compared to the heavy metals in all studied water samples of the study sites.
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3.1. Water classification
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Water is generally classified according to Piper Hill diagram (1944). It is important in inferring the hydrogeochemical properties of water by describing the dominant cations and anions, influencing the
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hydrochemistry of the area (Walton, 1970). The water of the studied sites from three district of Balochistan was
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plotted on piper diagram. The result of the study showed that water of the Quetta was CaCl type and whereas the water of Pishin and Mastung was of NaCl type (Fig.2, 3, 4). Spatial variation in hydrochemistrymaybe due to
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differences in lithological heterogeneity and diverse geological mechanism operating in underground aquifers(Jiang et al. 2017;Majeed et al. 2018).NaCl and CaCl, hydrofacies may be due to cations exchange process and the water-
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rock interaction such as the dissolution of halite type of rocks and calcium carbonate types of rock. The results of the current study also depicted that the origins of these ions are mainly natural. The study area (Balochistan) is reported
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to be rich in mineral deposits (Malkani 2015). The probable sources of the light metals (Na, Ca, Mg. K) in the groundwater are the weathering of primary parent rock materials. As reported by other researchers that light metals are mainly originated from the lithogenic sources reflecting the mineralogical composition of parent rocks (Kumar et al. 2009; Bhatti et al. 2018).
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Fig.2 Piper diagram showing the classification of water type of Quetta, Balochistan
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Fig.3 Piper diagram showing the classification of water type of Pishin, Balochistan
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Fig.4 Piper diagram showing the classification of water type of Mastung, Balochistan
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3.2. Drinking water quality assessment The water quality index computed for the water samples of the three districts are presented in Table (3). Drinking
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water was classified according to the grading system followed by Ketata-Rokbani et al. (2011) Table (4). Water quality parameters were compared with the maximum permissible limits of WHO (2011) and relative weight (Wi) is presented in Table (2). Results of the current study illustrated that water quality index (WQI) ranged between139.99 to188.33, 78.27to 223.27 and 177.33 to 311.41 for the groundwater of Quetta, Pishin, and Mastung respectively. According to water quality index, groundwater quality of all three sites was poor for drinking purpose. This can be attributed to the presence of high concentration of various heavy metals and variation in some of the other physicochemical parameters due to anthropogenic and geogenic sources. Water was collected from brick kilns sites being operational for many years with continuous combustion activities using coal and other low-quality fuel for
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ACCEPTED MANUSCRIPT baking of bricks at high temperature. These combustion activities might be the major source of various emissions which later deposited over soil surfaces. Correspondingly, various agricultural activities were also practiced in the vicinity of brick kilns using agrochemicals to enhance their production might be another anthropogenic source of different metals in drinking water. Conversely, the study area is also reported to be rich in various minerals such as having huge deposits of limestone, fluorite dolomite, calcite and coal (Malkani et al. 2015). Moreover, the chemical
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composition of groundwater of certain area depends upon the mineral composition of aquifers through which it
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flows by reacting with the mineral in the aquifer depending upon certain factors such as the hydrological cycle and
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flow direction (Yidana et al. 2010). The results of our study are in accordance with the study conducted by Shabir and Ahmed (2015). They assessed water quality of Islamabad and Rawalpindi through water quality index (WQI)
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and concluded that major proportion of their study area possessed poor water quality. 3.3. Irrigation water quality assessment
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3.3.1. Sodium absorption ratio (SAR)
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EC and SAR are usually used reciprocally to demonstrate the quality of water for irrigation purpose. SAR ranged from 1.50 to 1.89, 4.40-6.03 and4.30-6.59 meq/L for the drinking water of Quetta, Pishin, and Mastung, respectively
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(Table 5). According to sodium hazards,SAR values greater than 9 were regarded as unsuitable for irrigation
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purpose (Awais et al. 2017). In the view of the current results, the SAR values fall within the safe range. Furthermore, Mastung has shown greater SAR values compared to other two study sites. High SAR values create
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sodium salinity hazard by reducing soil water availability affecting the growth of crops by reducing the ratio of calcium and magnesium major nutrients. The higher concentration of sodium in the study area can be linked to clay
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minerals and weathering of various other rocks (Vasanthavigar et al. 2010). Rasool et al. (2016) assessed the quality of tube well for irrigation and drinking purpose in one of the areas of Punjab. The results of their study depicted that water quality of tube well was marginally suitable for irrigation purpose.
3.3.2. Residual sodium carbonate (RSC) Residual sodium carbonate (RSC) is considered as avaluable tool in examining the suitability of ground water for irrigation through carbonate and bicarbonate ratio (Selvakumar et al. 2017). The negative value of RSC indicates the excess of sodium ion by offsetting calcium and magnesium, through precipitating calcium as CO 2 and leaving
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ACCEPTED MANUSCRIPT sodium as dominant cations. Furthermore, the positive value of RSC indicates the increased concentration of calcium and magnesium due to the reaction of HCO3- to form calcium bicarbonate and magnesium bicarbonate (Chitsazan et al. 2017). The groundwater of the study area was classified according to the RSC classification system Table (6) and RSC value less than 1.25 is regarded safe for irrigation purpose. The result of the current study showed that RSC ranged between -12.50 to-10.95,-0.67 to -0.12 and 0.99-1.43 for the drinking water of Quetta,
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Pishin and Mastung substantially (Table 5). The result of the study also confirmed that water of the study area was
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safe for irrigation purposes except in case of Mastung area. Usually, the water of arid and semi-arid region is
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characterized by high RSC values resulting intosodification and salinization of the agriculture soil (Prasad and Bose 2001). Hence, the results of our study are in accordance to Bashir et al. (2017) who evaluated the quality of water of
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Sanghar Sindh, Pakistan for drinking and irrigation purpose using different statistical tools and indices. The results of their study ascertained that water of their study area was suitable for irrigation purpose with few exceptions.
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3.3.3. Sodium percentage (%)
Sodium percentage (%) in the study areas ranged from 27.37 to 29.52, 84.24 to 88.80 and 68.28 to75.31% in the
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water of the Quetta, Pishin, and Mastung, respectively (Table 5). The water of the study area was classified
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according to the standard classification of Na + (%) (Table 7) followed by many researchers (Srinivas et al. 2017; ElAziz 2017; Islam et al. 2017).
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The groundwater of the Quetta falls into the category of “Good”, whereas groundwaterof Pishin and Mastung represented “permissible” to “doubtful” category. It is evident from the result that water of the Pishin and Mastung
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possess a high percentage of sodium as compared to Quetta district. The most probable reasons for such high level of sodium in the water might be lithological sources causing the dissolution of such minerals in water and the other
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reason might be the use of agrochemicals used in agriculture activities. High level of sodium percentage (%) in soil cause devastating impacts on soil structure, aeration, and infiltration (Singaraja et al. 2014). For instance, agricultural activities carried out with more alkaline water for longer periods have been reported to decline yield of crops due to osmotic pressure caused in soil plants system by the accumulation of sodium in the soil. The high osmotic pressure in soil-plant system retards the ability of plants to take water or nutrients from soil medium ( Naseem et al. 2012).
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ACCEPTED MANUSCRIPT 3.3.4. Permeability index (PI) The permeability of the soil is greatly affected by long-term use of mineral-rich (Ca2+, Mg2+, Na+, HCO3-) water for irrigation purpose (Singh and Singh 2008). Thus, permeability index manipulates the water quality by degrading agricultural soil (Obiefuna and Sheriff2011).The long-term use of irrigation water rich in minerals reduces aeration in soil and makes it hard to plow and also retard the emergence of seedlings. Doneen developed a criterion for the
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classification of water according to the level of Permeability index. This criterion included three classes i.e. I, II and
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III. The class I and II are regarded as good for irrigation purposes with permeability index of 75 or above. The class
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III water is characterized as unsuitable for irrigation with permeability index of 25 or below it (Doneen 1975; Raju 2007). The results of the current study showed that Permeability index (PI) ranged between 27.72 to 30.01, 84.24 to
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88.80 and 85.11-91.33 meq/L for the groundwater of Quetta, Pishin and Mastung, respectively (Table 5). According to this classification groundwater of the, all the three districts was suitable for irrigation. Results of our study are in
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line with other studies (Kurdi and Eslamkish 2017; Aher and Gaikwad 2017).
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3.3.5. Magnesium hazard (MH)
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The result of the current study illustrated that magnesium hazard (MH) ranged from 83.79 to 89.40, 61.29 to 70.65 and 51.94 to69.23 meq/L for the water of Quetta, Pishin and Mastung, respectively (Table 5). According to the
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criteria developed for the classification of water (Table 6) in terms of magnesium hazard the water of the study area was found unsuitable for agriculture purposes. The high concentration of magnesium in water increases the
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alkalinity of water strongly distressing the yield of crops. Similarly, the high concentration of magnesium retards the infiltration of soil as magnesium is 50% larger than the calcium and therefore not attracted to clay particles. As a
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result of this mechanism, a lot of water is adsorbed between magnesium and clay particles reducing soil infiltration capability (Hussain et al. 2017). It is also supported by the fact that the water of the study area (Pishin and Mastung) was NaCl type reflecting the low concentration of calcium and high concentration of magnesium. The results of the current study are in accordance with several other studies reported in several other developing countries (Patel et al. 2017; Golekar et al. 2017; Padhi et al. 2017).
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ACCEPTED MANUSCRIPT 3.4. Multivariate statistical analysis
3.4.1. Cluster analysis(CA) Cluster analysis is multivariate statistical approach, used for the classification of a set of objects into groups based on their similar properties (Hu etal 2017). In the current study cluster analysis was applied on the groundwater data
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to determine similarity in the ground water compositions among the study sites. The resulting dendrogram of the
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cluster analysis (CA) grouped together the studied samples into three main clusters (Fig.5). Cluster 1 includes
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sample Q1 -Q10 (Quetta), cluster 2 includes sample P1-P20 (Pishin) whereas cluster 3 includes M1- M3 (Mastung) respectively. Water samples representing similar composition are grouped into one cluster whereas; water samples
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of different compositions were grouped into other clusters. The results of the study indicated that water of the study area varied in the composition due to differences in the level of heavy metals and other physicochemical properties
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and are clustered into three different clusters each one comprises of the water samples of one area. It is evident from the result that samples in one cluster comprise of similar hydrogeochemical properties such as major ionic
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composition and other heavy metals. The cluster analysis (CA) has commonly been used throughout the world and regarded as good tool to represent spatial variations in the groundwater hydrochemistry of aquifers (Luo et al. 2017;
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Yamazaki et al. 2017).
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Dendrogram(Wards method) 2000000 1843772 1800000 1643772
1600000
1443772
1200000
1043772
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1243772
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1000000
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Dissimilarity
Dissimilarity
1400000
600000
843772
400000
C1
C2
443772 C3
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M25 M30 M24 M21 M22 M26 M29 M23 M27 M28 P18 P11 P15 P12 P13 P19 P17 P14 P16 P20 Q4 Q8 Q9 Q6 Q7 Q1 P10 Q2 Q3 Q5
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643772
200000
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Fig.5 Dendrogram showing spatial variability among the water quality in study sites of Balochistan ( Q, P and
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M symbols are representing groundwater samples from Quetta, Pishin and Mastung, respectively).
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3.4.2. Principal component analysis
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Principal component analysis (PCA) was applied on twenty-two (22) variables on the whole water quality data set of study sites. PCA generated many principal components (PC), only those components were retained having
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eigenvalue greater than 1. The results of the principal component analysis contributed maximum four (4) components responsible for 84.93%of the total variance in the data (Table 7). The first component of the PCA was responsible for 39.67%of variances with an eigenvalue of 8.73. It includes the most significant parameters controlling the groundwater chemistry of the study area i.e. pH(0.79), EC(0.84), TDS(0.95), HCO3-(0.95), Cl− (0.91), Na+(0.92), Mg2+(0.85), K+(0.94), SO42-(0.95), Cd(-0.64), As(0.64) and F- (0.81). The second component accounted for about20.31% variance with eigenvalue 4.47. Factor 2 had a strong positive loading for Mn (0.71) and Fe(0.88)
and
negative
loading
of
NO− 3 (0.83) .Similarly, the third component accounted for 15.48% variance with eigenvalue3.41 dominated by
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ACCEPTED MANUSCRIPT positive loading of Cr (0.63), Pb (0.65) and Cu (0.64). The fourth component of the PCA was accounted for about 9.48% of total variance with an eigenvalue of 2.09 and dominated by Ca2+ (0.64), Ni (0.63) and Hg (-0.79). One of the most important significance of the PCA in the water quality analysis is the interpretation of the whole data set in a way that each variable is interpreted based on multiple or specific hydrochemicalprocess. For instance, in the current study, the first component of the PCA included the most important water quality parameters involving
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those mechanisms and relationships, governing the whole hydrochemistry of the groundwater. In the first component, the positive correlation between EC and TDS and the other major anions reveals the dominance of
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anions which might be due to mineralization of geological components of the aquifers. Meanwhile, the relationship of pH with other parameters indicates that pH is the major driver controlling the behavior of many ions such as F-,
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As, SO42-, Fe, and Cd. Similarly, the correlation among the light metals was also clearly depicted showing that these light metals might be originated from weathering of rocks employing various mechanisms such as ion exchange,
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redox potential and water-rock interaction (Hussain et al. 2017).The negative loading of the Cd in the first component indicating that it might be originated from anthropogenic sources such as brick kilns combustion and
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agricultural activities.
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The second component of the PCA included those parameters generated from geological processes such as Fe, Mn and also that generated from anthropogenic sources such as NO− 3 (Khound and Bhattacharyya, 2017).The third
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component was dominated by those parameters (Cr, Pb, Cu) originated only from anthropogenic sources such as
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coal and other low-quality fuel used in the combustion activities. The other component included mixed loading of parameters(Ca2+, Ni, Hg) released both from natural and anthropogenic sources like weathering of calcite rocks,
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combustion of coal and other low-quality fuel used in the burning activities of brick kilns.
3.4.3. Correlation matrix analysis The results from correlation matrix are presented in the Table (9) for the whole data set of the three sites of study. The results have depicted that majority of the variables have shown an erratic pattern of correlation, the significant positive correlations, showing close connection of these parameters with one another, controlling the whole hydrochemistry of the region. For instance, pH is one of the most influential parameters controlling the mobility of most of the metals in the water and soil mediums (Farooqi et al. 2009). The positive correlation between pH,
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ACCEPTED MANUSCRIPT fluoride(F-)and arsenic(As) indicates that high pH favors the mobility of these ions, for instance, at higher pH, Arsenic( As) starts to desorb from the iron oxide in the groundwater (Shakoor et al. 2015).The significant positive correlation of pH with As and F also describe that, the high concentration of these ions in the groundwater of the study area might be due to the alkalization phenomenon (Rafique et al. 2008).The positive correlation between pH andSO42-indicates that the mobility of the SO42-is also pH dependent as at low pH, SO42- is found to be immobile for
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the plant uptake (Firdous et al. 2016).Nevertheless, the negative correlation of pH with Fe, Cd, Cu, Cr and Mn
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shows that these micronutrients are only available at low pH so high pH might restrict their availability in the
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solution (Lomaglio et al.2017).
EC and TDS had shown very strong correlation with one another and also with other variables such as Na+,
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Ca2+,Mg2+, HCO3-, SO42-,Cl-and F- indicating that EC and TDS are managed by the concentration of these ions, on the other hand, it is also showing the dominance of these ions in the study area. Usually, Ca 2+, Mg2+,and HCO3- are
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the dominant ions in natural water (Singh et al. 2008). The positive correlation among HCO 3-,SO42-,As and pH suggesting oxidative dissolution of As in the groundwater which might be the main factor governing the enrichment
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of As in the groundwater of the study area (Farooqi et al. 2007).Similarly, the positive correlation among F-,HCO3-
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,SO42-, pH and Na+ favors the dissolution of F- in the groundwater. For instance, OH − and HCO3- ions are simultaneously released during the leaching and dissolution of F - in the groundwater, OH − replaces exchangeable F
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under alkaline condition (Rafique et al. 2009; Gupta and Ayoob 2016).
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The positive correlation of Na+ and Cl- shows that these trigger the water chemistry indicating much possible process involved in the hydrological process of the area (Sundaray 2010). Since, the study area is characterized as
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arid to semiarid region of the country so main possible source for the high concentration of sodium and chloride and their close relationship might be due to the excessive evaporation and the dissolution of halite rocks and other anthropogenic activities such as agricultural and brick kilns operational activities (Kumar et al. 2006; Elumalai et al. 2017). The positive correlation among various heavy metals and light metals are indicative of the same sources. The main sources of these heavy metals in the groundwater may be the agriculture (application of inorganic nitrogenous and phosphate fertilizers and animal manures) and brick kilns activities in the study area. Brick kilns use different types of low quality fuel for baking of bricks in the study area. The combustion of coal and other fuels such as wood, rubber tires and other plastic wastes are responsible for the emissions of different types of heavy metals in the
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It is evident from the results of the current study that natural, as well as anthropogenic sources both, are responsible
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for the high concentration of various light and heavy metals in the groundwater (Ćujić et al. 2017). It is briefly, concluded that groundwater chemistry of the studied area is greatly influenced both by geological as well as
Conclusion
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4.
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anthropogenic contamination
Groundwater is a significant resource for human consumption and its preservation in terms of its quality and
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availability for future generations is of utmost importance. Physicochemical properties are assessed to evaluate the
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quality of groundwater for drinking and agricultural purposes. In the current study, the groundwater quality around functional brick kilns in three districts of Balochistan was successfully evaluated through water quality index and
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multivariate statistical approaches. The results of the current study showed that majority of water quality parameters were beyond the permissible limits of WHO with few exceptions. Geochemistry of the study area display
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Na+>Mg2+>Ca2+>K+ andHCO3->SO42->Cl->F- a pattern of the dominance of the major cations and anions. The three districts have CaCl and NaCl type of water, reflecting the geology of the study area. Water quality index (WQI) also
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showed that groundwater of three districts was found to be unsuitable for drinking purpose through exhibiting poor quality. Similarly, the quality of water for irrigation was assessed in terms of irrigation quality indicators such as
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SAR, RSC, Na (%), PI and MH hazard and the results showed that groundwater of the study sites was suitable for irrigation only in terms of SAR and PI. Additionally, principal component analysis (PCA), cluster analysis (CA) and correlation matrix were found significant tools in identifying the source of pollution, spatial variability, and interrelationship among different variables, governing the hydrochemistry of groundwater through various mechanisms. The results of multivariate statistical approaches showed that groundwater of the study area is greatly influenced both from the geological composition of the area and anthropogenic activities such as agricultural and brick kilns activities carried out in the vicinity of the study area. It is briefly, concluded that water quality index (WQI) and irrigation quality indicators are useful tools in giving a comprehensive picture of the ground quality to
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ACCEPTED MANUSCRIPT the public and other concerned authorities for planning management strategies in preserving this vital source for future. Acknowledgment The first author graciously acknowledges Sardar Bahadur Khan Women’s University Quetta, Balochistan and
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Higher Education Commission (HEC) Pakistan by providing a scholarship for her Ph.D. studies.
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Funding
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This research did not receive any specific grant from funding agencies in the public, commercial or not for profit
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sectors. Conflict of interest
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The authors declare that they have no conflict of interest.
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ACCEPTED MANUSCRIPT Table 1 Physicochemical properties of the groundwater of the study area (n=30) Quetta Parameter
Range
Pishin
Mean±SD
Range
Mastung
Mean±SD
Range
Mean±SD
WHO Standard
7.99-7
7.59±0.38
7.9-6.8
7.57-0.35
7.9-7.1
7.51±0.24
6.5 -8.5
EC µS/cm
943-765
895.3±61.73
1106-999
1075.5±35.85
1567-1234
1470.5±112.46
1500
TDS (mg/L)
897-740
847.5±53.66
708-543
660.4±52.93
1076-564
815.3±180.11
1000
HCO3-(mg/L)
386-170
323.7±70.78
160-115
142.86±13.25
240-199
228.7±15.11
500
Na+(mg/L)
117-67
102.2±15.41
176-116
156.8±16.35
245-140
188.6±35.70
200
Ca2+(mg/L)
24.5-19
22.51±1.8
20-16
18.85±1.41
28-20
25.5±2.46
75
Mg2+(mg/L)
136-76
118.3±21.41
27-19
23.8±2.66
30-18
24.1±3.84
50
K+(mg/L)
7.8-2.8
5.87±1.68
2-1.3
1.72±0.187
12.6-1.4
8.009±3.86
12
Cl− (mg/L)
70-50
62.3±6.25
125-90
108.2±9.003
230-175
207.5±19.19
250
SO42-(mg/L)
274-160
45.399±1.28
188-134
168.2±15.03
189-126
161.2±22.58
250
Cd(mg/L)
0.35-0.003
0.06±0.10
0.10-0.003
0.05±0.03
0.10-0.02
0.05±0.03
0.003
Cr(mg/L)
0.09-0.01
0.04±0.02
0.10-0.02
0.052±0.03
0.20-0.13
0.16±0.03
0.05
Pb(mg/L)
0.10-0.02
0.07±0.03
0.44-0.03
0.11±0.12
0.19-0.03
0.11±0.05
0.01
Cu(mg/L)
0.09-0.031
0.05±0.02
0.06-0.03
0.04±0.01
0.97-0.01
0.14±0.3
2
Mn(mg/L)
0.91-0.02
0.12±0.27
0.76-0.05
0.21±0.24
0.20-0.12
0.16±0.03
0.4
Fe(mg/L)
1.10-0.30
0.64±0.29
0.98-0.03
0.27±0.30
0.96-0.40
0.69±0.20
0.3
Zn(mg/L)
0.41-0.01
0.11±0.12
0.26-0.01
0.09±0.08
0.26-0.01
0.09±0.08
3
NO− 3 (mg/L)
1.76-0.87
1.15±0.278
0.98-0.36
0.67±0.21
2.76-0.53
1.74±0.712
45
Ni(mg/L)
0.14-0.10
0.09±0.031
1.20-0.37
0.81±0.37
0.94-0.15
0.49±0.27
0.02
As (mg/L)
0.02-0.01
0.01±1.67
0.06-0.03
0.05±10.44
0.01-0.05
0.01±3.64
0.01
Hg(mg/L)
0.01-0.003
0.02±2.15
0.06-0.03
0.05±10.443
0.09-0.02
0.05±21.37
0.001
F- (mg/L)
1.53-0.26
0.82±0.34
1.58-0.28
0.92±0.44
1.96-0.96
1.59±0.31
1.5
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CE
IP
CR
AN
US
PT
ED
M
37
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pH
ACCEPTED MANUSCRIPT Table 2 Assignment of relative weight to the studied groundwater quality parameters Chemical Parameter
WHO standards
Weight (wi)
Relative Weight (Wi)
6.5 -8.5
4
0.05
EC
1500
4
0.05
TDS
1000
4
0.05
HCO3-
500
3
0.04
Na+
200
3
Ca2+
75
2
Mg2+
50
K+
12
Cl− SO42-
Cu
Fe
Hg
IP
CR
250
4
0.05
250
5
0.06
0.003
5
0.06
0.05
5
0.06
0.01
5
0.06
2
2
0.02
0.4
4
0.05
0.3
4
0.05
3
3
0.04
45
5
0.06
0.02
4
0.05
10
5
0.06
6
5
0.06
1.5
5
0.06
85
1
AC
As
CE
Zn
Ni
0.02
PT
Mn
NO− 3
2
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Pb
0.02 0.02
US
M
Cr
0.04
2
AN
Cd
T
pH
F− ∑
38
ACCEPTED MANUSCRIPT Table 3 Drinking quality of groundwater of the study area according to WQI classification Range
Mean±Sdt
Water type
Quetta
137.39-188.33
160.07±17.53
Poor Water
Pishin
78.27-223.26
153.63±44.32
Poor Water
Mastung
177.33-311.41
238.59±38.60
Very poor Water
Type of water
CR
Range
IP
Table 4 Water classification grading based on water quality Index (WQI)
T
Site
<50
Excellent Good
US
50-100 100-200
Poor Very Poor
AN
200-300
AC
CE
PT
ED
M
>300
39
Water unsuitable for drinking purpose
ACCEPTED MANUSCRIPT Table 5 Calculated irrigation quality indices for groundwater of the study area Quetta
Mastung
(meq/L)
Range
Mean±Std
Range
Mean±Std
Range
Mean±Std
SAR
1.50-1.89
1.89±0.18
4.40-6.03
5.63±0.49
4.30-6.59
536±0.86
RSC
(-12.50)-(-10.95)
-10±10.95
(-0.67)-(-0.12)
-0.40±0.19
0.99-1.43
1.13±0.14
Na%
27.37-29.52
29.52±1.78
65.89-72.04
70.02±1.90
68.29-75.31
71.31±2.41
PI
27.72-30.01
30.01±1.85
84.24-88.80
86.29±1.53
MH
83.79-89.40
89.40±2.61
61.29-70.65
67.66±3.07
CR US AN M ED PT CE AC
40
85.11-91.33
88.03±1.80
51.94-69.23
60.14±5.24
IP
Pishin
T
Parameter
ACCEPTED MANUSCRIPT
Table 6 Water quality classification based on SAR. RSC, PI and MH SAR(meq/L)
Wate quality
RSC(meq/L)
Water
PI(meq/L)
quality
Water
MH(meq/L)
quality
Water quality
Good
<1.25
Good
>75%
Good
<50
Suitable
6-9
Doubtful
1.25-2.5
Doubtful
25-75%
Suitable
>50
Unsuitable
>9
Unsuitable
>2.5
Unsuitable
<25%
Unsuitable
AC
CE
PT
ED
M
AN
US
CR
IP
T
0-6
41
ACCEPTED MANUSCRIPT Table 7 Water classification based on Na+ percentage (%) Water quality
< 20
Excellent
20-40
Good
40-60
Permissible
60-80
Doubtful
>80
Unsuitable
AC
CE
PT
ED
M
AN
US
CR
IP
T
Na%
42
ACCEPTED MANUSCRIPT Table 8 Factor loading of each physicochemical parameters of groundwater with their variance and eigen values Variables
F1
F2
F3
F4
0.79
-0.37
0.42
0.25
EC
0.84
0.41
0.01
0.09
TDS
0.95
0.24
0.09
0.02
HCO3-
0.95
-0.29
-0.53
-0.42
Na+
0.92
0.29
-0.03
0.10
Ca2+
0.02
0.19
-0.51
0.64
Mg2+
0.85
0.42
0.18
-0.12
K+
0.94
0.26
0.05
0.03
Cl−
0.91
0.31
-0.13
-0.05
SO42-
0.95
0.25
0.17
-0.03
0.27
0.61
0.06
-0.64
Cr
-0.55
0.50
0.63
0.06
Pb
-0.59
0.42
0.65
-0.08
0.09
0.13
0.64
0.22
-0.17
0.71
-0.50
0.17
-0.18
0.75
-0.37
0.03
-0.07
0.88
-0.24
-0.33
-0.03
-0.83
0.07
-0.41
0.05
-0.08
-0.47
0.63
0.65
-0.49
0.42
0.001
0.14
0.03
0.18
-0.79
F-
0.81
-0.48
0.33
-0.05
Eigenvalue
8.73
4.47
3.41
2.09
Variability (%)
39.67
20.31
15.48
9.48
Cumulative %
39.67
59.97
75.45
84.93
ED
M
Cd
AN
US
CR
IP
T
pH
Cu
PT
Mn Fe
CE
Zn
Ni As Hg
AC
NO− 3
43
ACCEPTED MANUSCRIPT Table 9 Results of the correlation analysis of the physiochemical parameters of the groundwater of study area Variables
pH
EC
TDS
HCO3-
Na+
Ca2+
Mg2+
K+
Cl−
SO42-
Cd
Cr
Pb
Cu
Mn
Fe
pH
1
EC
0.24
1
TDS
0.22
0.94
1
HCO3
0.57
0.96
0.93
1
Na+
0.32
0.92
0.93
0.96
1
Ca2+
0.40
0.74
0.61
0.67
-0.59
1
Mg2+
0.36
0.58
0.53
0.62
0.49
0.04
1
K+
0.06
-0.10
0.04
0.03
0.10
0.50
-0.26
1
Cl−
0.34
0.88
0.88
0.95
0.94
0.48
0.52
0.06
1
SO42-
0.79
0.95
0.93
0.97
0.98
0.47
0.55
-0.03
0.96
1
Cd
-0.54
0.15
0.33
0.05
0.14
0.12
-0.19
0.08
0.06
0.06
1
Cr
-0.67
0.11
0.03
0.01
0.10
0.11
-0.27
-0.14
0.12
0.05
0.38
1
Pb
0.04
-0.28
-0.38
-0.29
-0.19
0.23
-0.51
-0.15
-0.20
-0.20
-0.38
0.43
1
Cu
-0.58
0.11
-0.07
-0.11
-0.16
-0.24
-0.12
-0.44
-0.23
-0.15
0.29
0.65
0.28
1
Mn
-0.74
-0.07
0.04
-0.17
-0.01
-0.19
-0.57
-0.03
-0.06
0.45
0.70
0.23
0.31
1
Fe
-0.53
-0.42
-0.53
-0.50
-0.39
-0.05
-0.57
-0.11
-0.39
-0.43
-0.06
0.76
0.78
0.53
0.56
1
Zn
-0.34
0.45
0.55
0.32
E C
-0.08
NO− 3
-0.01
-0.28
-0.40
-0.20
Ni
-0.02
-0.32
-0.25
-0.09
As
0.89
-0.20
-0.07
Hg
0.87
0.30
F-
0.89
0.67
-
NO− 3
Zn
Ni
As
Hg
F-
T P
C A
I R
C S
T P
D E
U N
A
M
0.39
-0.19
-0.19
-0.02
0.35
0.41
0.46
0.25
-0.29
0.12
0.65
-0.09
1
-0.30
-0.13
0.20
0.44
-0.13
-0.27
-0.30
-0.07
-0.19
-0.20
-0.35
0.06
-0.36
1
-0.10
0.13
0.13
0.27
0.05
-0.18
-0.24
-0.03
-0.001
-0.47
-0.12
0.02
-0.53
0.43
1
0.58
0.56
0.09
-0.19
0.63
-0.06
-0.25
0.19
-0.01
-0.23
-0.12
-0.05
-0.10
-0.16
0.44
0.54
1
0.24
0.34
0.26
0.18
0.29
-0.02
0.33
0.40
-0.39
-0.53
-0.04
-0.39
-0.60
-0.46
-0.04
0.08
-0.34
-0.34
1
0.77
0.53
0.71
0.19
0.36
-0.14
0.45
0.53
-0.37
-0.58
-0.08
-0.54
-0.48
-0.56
0.05
-0.16
-0.25
-0.48
0.92
44
1
ACCEPTED MANUSCRIPT Highlights The groundwater quality was assessed in the vicinity of brick kilns in Balochistan
Elevated concentrations of heavy metals were recorded in the groundwater
Water quality index (WQI) suggested low quality for drinking purposes
Groundwater quality was found unfit for irrigation due to high MH and Na%
Natural and anthropogenic factors were responsible for low quality of groundwater
AC
CE
PT
ED
M
AN
US
CR
IP
T
45