Distance and Weightage-based Identification of Most Critical and Vulnerable locations of Surface Water Pollution in Kabul River Tributaries

Distance and Weightage-based Identification of Most Critical and Vulnerable locations of Surface Water Pollution in Kabul River Tributaries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Distance and Weightage-based Identification of Most Critical and Vulnerable locations of Surface Water Pollution in Kabul River Tributaries Muhammad Irfan, M. Mahboob Alam, Shahbaz khan, Ilyas khan, Sayed M Eldin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2349299/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2023 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Water plays a key role in the economic growth of an agricultural country. Pakistan is a farming country that uses almost 90% of its water resources for agricultural purposes. Khyber Pakhtunkhwa (KPK) province of Pakistan is blessed with extensive surface water resources. In addition to using groundwater resources for irrigation, large parts of its flat plains are irrigated with the Kabul River surface water. Due to large population growth and unregulated small/local scale industries in the region, surface water quality deteriorated with time. Which impacts people's health when polluted surface water is used for irrigation purposes. This research investigates the surface water quality of Kabul River's different tributaries and identifies the most critical locations in terms of water quality. Various physical, chemical, and biological parameters at ten sites on as many Kabul River tributaries are examined to investigate surface water quality. The distance and weightage-based identification method is used to identify the most critical and vulnerable locations for each parameter. The maximum seven critical values determined against the Bara River using the weightage-based method, i.e., 17.5, 5.95, 7.35, 27.65, 1.75, 0.35, and 10.45 for total alkalinity, sodium, total hardness, magnesium, total suspended solids, BOD, and turbidity. In comparison, the distance-based iteration method is used to identify the most vulnerable location against the specific parameter. All the results are finally compared with the interpolated values over the entire region using Kriging interpolation. The areas are mostly the same identified through the distance and weightage-based method. The results will guide the competent authority to install the monitoring station and treatment plants for specific parameters responsible for the critical location and to stop further contamination of the particular parameter near the threshold. Earth and environmental sciences/Environmental sciences Physical sciences/Engineering Surface Water Quality Total Suspended Solid Turbidity Kriging interpolation Water Quality Indicator Water Quality Management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Water is a limited and fundamental resource for life, exchanging its form in a continuous cycle between land and atmosphere (Jackson et al., ( 2001 )). While the classification of available water is done in multiple ways, the most common classification is based on the availability of water Vis a Vis its source, namely surface water, groundwater, and atmospheric water. Without going into the forms in which water is available and used in ground and atmospheric sources, we look into surface water and its use for producing agricultural products. Within the terrestrial ecosystem, the same water used for crop production is shared with the public and aquatic life (Carvalho, ( 2017 )). Surface water classifications are applied to surface water bodies, such as streams, rivers, and lakes. The general use of these waters is for drinking water supply, irrigation, fishing, industrial use, transport, and recreation (Molden, ( 2013 )). In Pakistan, surface water resources are predominantly used for agriculture. Agriculture is key to Pakistan’s gross domestic product (GDP). According to the Economic Survey of Pakistan_2022, the Agriculture sector contributes about 22.7% to the GDP, which employs 37.4% of the Labor force and 64% of exports earnings. It provides livelihoods to 62% of the country's population (Ejaz & Ahmad, ( 2017 )). Regarding water resources, agriculture is one of the biggest consumers, using over 90% of available fresh water in Pakistan (A. S. Qureshi, ( 2020 )). The heavy burden on agricultural produce for national consumption and for enhancing foreign exchange reserves through exports mandates the growers to use large amounts of fertilizers to supplement the naturally available nutrients in the soil. Which ultimately affects the groundwater quality in the long run (Daud et al., ( 2017 )). Numerous studies have established the causal relations between the rate of use of fertilizers and deterioration in groundwater quality over time (Bank, ( 2006 ); R. Qureshi & Ashraf, ( 2019 )). Unsustainable use of water, whether ground or surface, also ultimately affects groundwater quality. The connecting interface of ground and surface water makes it almost impossible not to affect one resource while the other is being exploited (Bierkens & Wada, ( 2019 )). Draining untreated or partially treated water from industries, hospitals, and other public and commercial buildings into surface water bodies is another major source of pollution in surface water (McFarland, Larsen, Yeshitela, Engida, & Love, ( 2019 )). While in the developed world, strict regulations ensure water treatment before it can be drained into surface water bodies, the same may exist on paper. However, enforcement of these important regulations is usually found wanting (Rajaram & Das, ( 2008 )). In a recent case, the high court bench of a city in Pakistan summoned relevant government authorities regarding the disposal of effluents from different kinds of industries directly into surface water bodies. The industries that were pointed out were construction material-producing, ranging from cement to steel reinforcements, pharmaceutical, and paper industries. There is also evidence of shallow groundwater contamination in India from industries of bicycles and ancillary parts, electroplating, steel, and foundries resulting in the high concentration of cyanide in groundwater (Kakar & Bhatnagar, ( 1981 )). The largest source of surface water pollution in Pakistan remains municipal wastewater, which pollutes rivers, drains, and streams downstream of large cities with organic matter, suspended solids, and surfactants. (Ilyas et al., ( 2019 )). Industrial wastewater discharges are also high, polluting surface waters with heavy metals, oil products, phenols, and other hazardous substances(Daud et al., ( 2017 ); Marsalek et al., ( 2008 )). In the case of Pakistan, the problem is not only with the quality of water but also the available water quantity. Based on generally accepted thresholds of available water in a country on per capita and per annum basis (Jimenez & Asano, ( 2008 )), Pakistan crossed the water scarcity line in 2005. If the population growth and water consumption rates by different economic sectors remain unchanged, the country will touch the whole scarcity line by 2025 (M. A. A. Khan, Ashraf, & Siddiqui, ( 2021 )). The surface water resources of Pakistan are mainly based on the flows of the Indus River and its tributaries (Kahlown & Majeed, ( 2003 )). The Indus River has a total length of 2900 kilometers (Km), and the drainage area is about 966,000 sq. In Km, five major tributaries join its eastern side, and three minor tributaries drain in the mountain region. Several small tributaries also join the Indus towards its western side. The biggest of such tributaries is River Kabul (Jamil, ( 2019 )). Kabul River can be seen in Fig. 1 , which is the western tributary of the mighty Indus River, contributes 16.5 million Acre Feet (MAF) of water to Pakistan’s complex water system (T. Malik, ( 2019 )). Before flowing into KPK province in Pakistan, the Kabul River passes through Kabul, Surobi, and Jalalabad in Afghanistan, some 25 Km (16 Miles) north of the Durand line. In KPK province, the Kabul River passes through Peshawar, Charsadda, and Nowshera. Its largest tributary is the Kunar River, which starts as the Mastuj River, flowing from the Chiantar glacier in Brughil valley in Chitral, Pakistan, and after flowing south into Afghanistan, it is met by the Bashgal River flowing from Nurestan (Ranjan & Chatterjee, ( 2020 )). In River Kabul, about 72% of the runoff occurs between May and September, and 28% occurs between October to April. The peak runoff is generally in June and July. In comparison, the peak demand for agriculture is during July and August (Armstrong et al., ( 2019 )). The Kabul River and its tributaries transport untreated sewage from the cities, towns, and villages of Afghanistan and Pakistan from Peshawar, Mardan, Khyber, Mohmand, and Malakand agencies (Yousafzai, Khan, & Shakoori, ( 2008 )). The lower sections of the river pass through the plains, which are particularly densely populated. The effluents from many industries end up in the river Kabul, either directly or through nullahs which eventually drain into the river. Of these industries, the sugar mills, distilleries, paper mills, tanneries, ghee factories, and textile mills contribute most of the water pollution hazards (Ali, Pervez, & Khan, ( 2020 )). The wastewater contains domestic, industrial, and commercial effluents, the quality of which is checked based on different laboratory tests, i.e., tests for physical, chemical, and biological parameters (Carraro et al., ( 2016 )). Further research has been done to analyze the water quality of a region based on these tests using conventional techniques and highlights the common risks and possible suitable solutions in water supply for domestic and agricultural use (Elala, Labhasetwar, & Tyrrel, ( 2011 )). The surface and groundwater quality of the Kabul River at Attock city, Punjab, has been investigated by calculating the water quality index (WQI) (Iqbal et al., ( 2009 )). The status of groundwater was compared with WHO standards, and found a negative trend in terms of nitrates and fecal microbes in the Kabul catchment (Zaryab, Noori, Wegerich, & Kløve, ( 2017 )). Another research that has been done to investigate the concentration of heavy metals in the surface water of the Kabul River found that nickel has the highest concentration, 30 times higher than the permissible limit of WHO (T. Khan, Muhammad, Khan, & Khan, ( 2011 )). This research idea was used to identify the risk of flood based on the most crucial variables of flood characteristics of Waverly city, Iowa (Sonmez & Bizimana, ( 2020 )). Pakistan Council of Research in Water Resources (PCRWR) studied water quality parameters of the upper Khyber Pakhtunkhwa region and northern areas of Pakistan. That study included Mardan, Buner, and Swat Districts of KPK, among others (Imran, Bukhari, & Gul, 2018a , 2018b ; Saeedb et al.). Although the PCRWR study did not include the districts which are the focus of this study, the results obtained, however, show similarity with the results of the PCRWR study of district Mardan which is not only close to this study’s locations but also similar kind of geological, social and industrial conditions. According to the international union for conservation of nature, Pakistan program (IUCN), the Kabul watershed houses 205, 10, 41, and 45 industrial units in Peshawar, Charsada, Nowshera, and Mardan districts, respectively, constitute more than 15% of the total industries in the region (Nafees). Almost all these industries have no treatment facilities for effluents. Nitrites from Agricultural fertilizers and untreated effluents like ammonia, Chromium, and nickel from the industries thus influence the water quality in these areas (T. Khan & Khan, ( 2019 )). All the mentioned research work has been done to check the Kabul River water quality status is based only on the water quality index and suggested a possible solution for future use. This research work aims not to highlight the risk assessment of the Kabul River catchment due to exceeding the maximum permissible values of WHO and only suggest the possible solution superficially. This research investigates the water quality of all the tributaries added to the Kabul River. Based on the water quality status, it highlights the most critical branch using the weightage and distance method and compares the results with interpolated maps in arc-GIS. The results will guide the concerned authorities to apply the remedial measure by installing the monitoring stations on critical tributaries and using the treatment procedures to overcome their long-lasting effect. Different stakeholders must launch an awareness campaign to aware the dwellers and other beneficiaries about the vulnerability of vulnerable locations. The remaining research work comprises as follows. Section 2 consists of sub-topics are; the detailed characteristics of the study area and the method used to solve the mentioned issue, followed by comparing the results through interpolated values and plotting the comparison matrix using the Pearson method. Section 3 describes the detailed problem solution with research findings, and the study's conclusion is in section 4 . 2. Material Method 2.1 Study area and Sampling Collection. The stretch of the Kabul River under study is the section from just upstream of Warsak Dam to its junction with the Indus. This area is densely populated, with much of the KPK's small industry dependent upon the water of the Kabul River and its tributaries for different purposes Imran, 2018. Most industries ultimately drain their effluents without any treatment in the Kabul River. Ironically, this is also where much of the agricultural products consumed in cities like Peshawar, Mardan, and Swat. are grown. Much concern has been raised about the quality of water in these areas Iqbal, 2009. The geographical boundary of the Kabul River basin and the areas from where the water samples were collected for this study are shown in Fig. 2 . The sampling points are shown in dots, the explanation of which is mentioned in Table 1 . All the water samples were collected from ten different tributaries in 1-liter polyethylene (PE) bottles, which were washed with deionized water before use, that drained into the Kabul River. These sample bottles were sealed and placed in a dark environment at a constant temperature range of 4–10 ⁰C to avoid contamination and the effects of light and temperature. The detail of the sampling locations with a unique ID was the tributary of Kabul River along with their latitude and longitude and their height from the mean sea level given in Table 1 . Table 1 Sampling points details of the study area Sampling Points Latitude Longitude Altitude (ft.) River ID Warsak Dam 34.16442 71.357898 1257 WD Adezai River 34.08889 71.74889 951.5 AK Naghuman River 34.12169 71.607621 1005 NG khiyali River 34.12194 71.70889 961.04 KK Jindi River 34.15629 71.734471 977 JR Bara River 33.9668 71.561619 1171 BR Pabbi River 33.97382 71.748456 1026 PR Nowshera Kallan River 34.00887 71.985346 930 NK Rustam River 34.34967 72.278996 1233 RR Indus River at Khairabad 33.89687 72.233806 875 IR All standard protocols necessary for sample collection, sample storage, sample transportation, and sample tests were followed as per relevant American Society of Testing and Materials (ASTM) guidelines given in Table 2 . A total of nine parameters were tested on the collected water quality sample under the relevant standards mentioned in the parameter section, followed by the units of each parameter, their accuracy, test standards and Food and Agriculture Organization (FAO)/WHO threshold values both for drinking and agriculture use. Table 2 Water quality test standards used along with Apparatus used and threshold values of WHO and FAO Parameters Units Apparatus Accuracy Test Standard. WHO Limits drinking water FAO Limits for Agricultural Use PH PH meter with Buffer solution ± 0.002 ASTM D1293-99 6.5–8.5 5-8.5 Color TCU Multi parameter Photometer. ASTM D1209-05 5 Unpleasant/Acceptable Taste and Odour Acceptable/Non-Acceptable ASTM E2892-15 Acceptable Unpleasant/Acceptable TSS mg/L Filter paper, beaker, Desiccators, titration assembly, vacuum pump, and conical flask. ± 0.5% ASTM D5907-18 25 120 TDS mg/L Filter paper, beaker, Desiccators, titration assembly, vacuum pump, and conical flask. ± 0.5% ASTM D5907-10 300 0-2000 Electric Conductivity µs/cm DDS-22C High Accuracy Digital Conductivity meter. ± 1.0% ASTM D1225-14 400 > 3 Dissolved Oxygen (DO) mg/L BOD bottles and other related chemicals. ± 1.0% ASTM D888-09 6.5-8 5.0–8.0 Biochemical Oxygen Demand (BOD) mg/L BOD bottles and other related chemicals. ± 1.0% ASTM D2329-68 0 80 Chemical Oxygen Demand (COD) mg/L BOD bottles and other related chemicals. ± 1.0% ASTM D1252-06 < 250 150 The samples were analyzed for their physical, chemical, and biological properties relevant to the study. Considering the pollution sources, the study area is exposed to, and the ultimate use of untreated surface water for irrigation purposes, physical parameters like pH, Total Settleable Solids (TSS), Total Dissolved Solids (TDS), Turbidity, and Electrical Conductivity (Ngabirano, Byamugisha, & Ntambi, ( 2016 )) were measured. For chemical analysis, water samples were tested for Total Alkalinity, Sodium (Na), Calcium (Ca), Magnesium (Mg), Total Hardness, Nitrite (NO2-), Sulfate (SO42-), Phosphate (PO4), Chloride (Cl-), and Arsenic (Ar) (Sehar, Naz, Ali, & Ahmed, ( 2011 )). Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and Dissolved Oxygen (DO) (Ahmed, ( 2017 )) limit were measured for biological properties. 2.2. Distance and Weightage based identification of most critical and vulnerable locations. The methodology presented here is developed to identify the most critical and vulnerable locations in polluted groundwater. We define the criticality of a certain location based on a large value exceeding the threshold defined by WHO. This way, we differentiate between the locations with observed parameter values just exceeding the threshold amount and those exceeding the threshold by a large margin. We define the vulnerability of certain locations by identifying areas closest to critical areas identified earlier in terms of their observed values. An iterative distance-based indicator is used to identify locations that need immediate intervention in terms of securing the water quality. 2.2.1. Critical location identification. Mathematical equations identify the critical location with the help of certain observed parameters, i.e., physical, chemical, and biological parameters. The threshold value is escalated by 50% and added to the original threshold value given by WHO as given in Eq. 1 . Then compare the measured values at all locations with the escalated values instead of the original threshold values to find the severity of exceedance using Eq. 4 . Indices es = escalated values by 50% i = Location of sample collection j = recorded value of water quality parameter ex = exceedance value (means near to exceeding the threshold value) Parameters \({PP}_{j}^{es}\) water quality measured value j of physical parameter PP escalated by 50% es . \({CP}_{j}^{es}\) water quality measured value j of chemical parameter CP escalated by 50% es. \({BP}_{j}^{es}\) water quality measured value j of biological parameter BP escalated by 50% es . \({PP}_{who,j}\) threshold value set by WHO for physical parameter PP \({CP}_{who,j}\) threshold value set by WHO for chemical parameter CP \({BP}_{who,j }\) threshold value set by WHO for biological parameter BP \({PP}_{ij}\) observed values j of physical parameter PP for particular location i \({CP}_{ij}\) observed values j of chemical parameter CP for particular location i \({Z}_{ij}\) exceedance threshold value Z for particular location i of sub parameter j \({II}_{ij}\) Importance indicator II for particular location i of sub parameter j \({ CL}_{j}\) critical location CL based on sub parameter j \({PP}^{ex}\) exceedance value ex of physical parameter PP \({VL}_{j}\) vulnerable location VL based on sub parameter j For a particular physical parameter, the escalated threshold value would thus be: $${PP}_{j}^{es}= \left(0.5* {PP}_{who,j}\right)+{PP}_{who,j}$$ 1 Similarly, equations 2 and 3 used for chemical and biological parameters: $${CP}_{j}^{es}= \left(0.5* {CP}_{who,j}\right)+{CP}_{who,j}$$ 2 $${BP}_{j}^{es}= \left(0.5* {BP}_{who,j}\right)+{BP}_{who,j}$$ 3 Then Eq. 4 is used to calculate the extent to which certain water quality parameter exceeds the threshold Z for all the water quality parameters, i.e., $${Z}_{ij}={PP}_{ij}-{PP}_{who,j}$$ 4 Moreover, compare it with the escalated threshold values as defined above. Depending on whether Z exceeds the escalated threshold at a certain location, we assign weights to these locations to single them out among all locations. The importance indicator ( II ) for a certain location thus gives us an idea of the extent of intervention needed to mitigate the exceedance situation at a particular location. $${II}_{ij}=\left[\begin{array}{c}\begin{array}{c}0.9* {Z}_{ij} if {Z}_{ij}>{PP}_{j}^{es}\\ \end{array}\\ 0.35* {Z}_{ij} if {Z}_{ij}< {PP}_{j}^{es} \end{array}\right]$$ 5 We finally single out the critical location for a particular water quality parameter j by choosing the location with has the maximum II value. $${ CL}_{j}=\underset{i\in \left[1..n\right],j}{\text{max}}{II}_{ij}$$ 6 Where n is the total number of locations over which the search for maximum is performed. The above methodology would work for those water quality parameters for which the desired value is not to exceed the upper threshold value defined by a regulatory body (e.g., Total Hardness). In case the desired value is not to be below than defined threshold (Dissolved Oxygen) value, we will use the search for minimum value among all locations, i.e. \({ CL}_{j}=\underset{i\in \left[1..n\right],j}{\text{min}}{II}_{ij}\) (7) Critical locations obtained for each water quality parameter thus identify locations that exceed the threshold values by a large amount. Water use from these areas should be strictly regulated, which is especially important for irrigation water use to avoid harmful chemicals entering the food chain. 2.2.1. Vulnerable Location Identification. We propose an iterative distance-based search method to identify the most vulnerable locations (VL) in terms of exceedance of threshold values. The method is used for locations that, though they have not yet crossed the threshold limit, based on the observed value, are most prone to be next in crossing the threshold values. $${VL}_{j}=\text{min}\left(abs\left|{PP}_{ij}-{PP}^{ex}\right|\right)$$ 8 $${VL}_{j}=\text{min}\left(abs\left|{CP}_{ij}-{CP}^{ex}\right|\right)$$ 9 Equations 8 and 9 inherently show the methodology's iterative nature whereby we search for locations where the observed values of different Physical, Chemical, and biological parameters are the closest to the exceedance value. The results would benefit the decision-makers in prioritizing the mitigating measure for a particular location. 2.2.2. Spatial interpolation for comparison. To compare the results obtained from the above methodology, we employ the kriging interpolation method in our study area for identifying the most critical locations. Using a 30 Meter digital elevation model of the region as external drift, we interpolate the observed values of different parameters over the entire region. 2.3. Water quality test results and discussion. Figures 3 , 4 , and 5 show the summary results of water quality tests of selected physical, chemical, and biological parameters, respectively. WHO-specified allowable/threshold limits, used as guiding limits, are depicted as red horizontal lines for each parameter. Threshold limits are “triggers” for starting mitigating efforts for bringing the recorded values to safe values and an “endpoint” for terminating mitigating efforts. The overall test results contradict the previous general findings about water quality in these areas. Almost all considered physical parameters lie below the threshold limits except electrical conductivity and dissolved oxygen. The purer the water, the lower the conductivity. Distilled water acts almost as an insulator, while salty water is an electrical conductor. Half of the selected locations exceed the threshold limits of electrical conductivity. Almost all locations exceed the dissolved oxygen values by a considerable margin. Higher dissolved oxygen values (or too low) affect aquatic life in surface water and its quality. When analyzed for chemical water quality parameters (Fig. 4 ), selected locations show mixed results. Calcium, Magnesium, and Nitrite exceeded threshold values in almost all locations. Samples taken from Bara River (BR) seem to have the worst quality of surface water in that both in Physical (TSS, BOD, Turbidity) and Chemical (Na, TH, TA) water quality parameters, we observe that it behaves differently than the rest of the locations. The reason could be that most of the small-scale industry at this location (Marble and Paper industry) drains its effluents directly into the surface water streams. Which makes the surface water at this location increasingly polluted concerning the quality parameters where other locations have reasonably acceptable values. Figure 5 , e.g., show the selected location results for biological parameters such as total and fecal coliforms. All considered locations exceed the acceptable threshold limits of WHO for these parameters. Malik et al ( 2010 ) refer to the WHO study, which says that fecal bacteria, parasites, and other microbes cause about 6000 deaths of adults and children every day, resulting in a statistic of 1.8 million deaths every year from complications caused by the presence of this kind of pollutant in water bodies (M. A. Malik, Azam, & Saboor, 2010 ). Figure 6 and 7 shows a correlation between measured physical and chemical-biological water quality parameters. These correlations were calculated to ensure that the samples were properly collected, stored, transported, and tested. Our general knowledge about the dependence of different water quality parameters on each other and their confirmation from the correlation test gives us confidence that the data collected is trustworthy. For example, the main salts generally dissolved in water are carbonates, bicarbonates, sulfates, chlorides, nitrates, and phosphates. TDS in water generally indicates a probability of higher electric conductivity (Selvakumar, Ramkumar, Chandrasekar, Magesh, & Kaliraj, ( 2017 )). We obtain a high Pearson correlation value between TDS and EC of 0.83 (Fig. 6 ). Similarly, high correlation values were obtained between measured TSS and parameters like Turbidity, TH, and COD. The Turbidity/COD and Turbidity/BOD similarly display correlation values above 0.85. 3. The Research Findings And Discussion The mathematical Eq. 1 – 3 were applied to escalate the threshold value by 50% in addition to the threshold value set by WHO. In Table 3 , the first line of each parameter is the observed value denoted by CP, PP & BP. In contrast, the second line is the escalated value addition with the WHO values of each parameter for all locations. All the exceeded values were highlighted as bold; the bold, positive values indicate that the observed values exceeded the except for dissolved oxygen. The count of highlighted values in all water quality parameters for BR, PR, RR, JR, IR, and AK are 8, 7,6,5, and 4, respectively, indicating that BR repeats the most, followed by PR. This indication will be further confirmed by applying the remaining equations. Table 3 Measured values are represented as CP/BP/PP, while the escalated values are as Z. Parameter Terms WD AK NG KK JR BR PR NK RR IR Total Alkalinity \({CP}_{ij}\) 210 180 120 114 160 250 195 200 175 165 \({Z}_{ij}\) 10 -20 -80 -86 -40 50 -5 0 -25 -35 Sodium \({CP}_{ij}\) 25.2 36.3 25.1 17.1 33.5 67 60 17.1 16.45 43.5 \({Z}_{ij}\) -24.8 -13.7 -24.9 -32.9 -16.5 17 10 -32.9 -33.5 -6.5 Calcium \({CP}_{ij}\) 62 289 62.5 76 160 206 101 76 120 275 \({Z}_{ij}\) -18 209 -17.5 -4 80 126 21 -4 40 195 Total hardness \({CP}_{ij}\) 118 136 221 143 125 221 125 143 89 180 \({Z}_{ij}\) -82 -64 21 -57 -75 21 -75 -57 -111 -20 Magnesium \({CP}_{ij}\) 45 67 45.2 77 58 229 210 77 44.45 143 \({Z}_{ij}\) -105 -83 -104 -73 -92 79 60 -73 -105.5 -7 Nitrite \({CP}_{ij}\) 13 41 0 37 42 39 34 66 38 32 \({Z}_{ij}\) 12 40 -1 36 41 38 33 65 37 31 Total Coliform \({BP}_{ij}\) 0 2 0 0 0.1 0 9 4 0.1 3 \({Z}_{ij}\) 0 2 0 0 0.1 0 9 4 0.1 3 Fecal Coliform \({BP}_{ij}\) 0 0 0 0 1 0 2 0 1 0 \({Z}_{ij}\) 0 0 0 0 1 0 2 0 1 0 Total Suspended Solids \({PP}_{ij}\) 4 23 18 8 13 30 18 22 10 18 \({Z}_{ij}\) -21 -2 -7 -17 -12 5 -7 -3 -15 -7 Electric Conductivity \({PP}_{ij}\) 380 560 360 290 460 150 800 290 450 640 \({Z}_{ij}\) -20 160 -40 -110 60 -250 400 -110 50 240 Dissolved Oxygen \({PP}_{ij}\) 17 8.6 9.5 7 12 9 12 12 6.7 12 \({Z}_{ij}\) 9 0.6 1.5 -1 4 1 4 4 -1.3 4 Biochemical oxygen demand \({PP}_{ij}\) 1 2 2 2 3 6 3 2 3 3 \({Z}_{ij}\) -4 -3 -3 -3 -2 1 -2 -3 -2 -2 Turbidity \({PP}_{ij}\) 0 0 0 1 2 16 2 1 2 1 \({Z}_{ij}\) -5 -5 -5 -4 -3 11 -3 -4 -3 -4 The exceeded values ( \({Z}_{ij}\) ) were assigned special weightage of 90 % 35% by following the condition of Eq. 5 and obtained the importance indicator values (II) for all the locations using the values of Table 3 . All the highlighted values in Table 4 are the critical values obtained using Eq. 6 . Table 4 The Critical values are highlighted as bold using the Eq. 6 –7. Location WD AK NG KK JR BR PR NK RR IR Total Alkalinity 3.5 -7 -28 -30.1 -14 17.5 -1.75 0 -8.75 -12.25 Sodium -8.68 -4.795 -8.715 -11.515 -5.775 5.95 3.5 -11.515 -11.74 -2.27 Calcium -6.3 188.1 -6.125 -1.4 28 113.4 7.35 -1.4 14 175.5 Total hardness -28.7 -22.4 7.35 -19.95 -26.25 7.35 -26.25 -19.95 -38.85 -7 Magnesium -36.75 -29.05 -36.68 -25.55 -32.2 27.65 21 -25.55 -36.94 -2.45 Nitrite 10.8 36 -0.35 32.4 36.9 34.2 29.7 58.5 33.3 27.9 Total Coliform 0 1.8 0 0 0.09 0 8.1 3.6 0.09 2.7 Fecal Coliform 0 0 0 0 0.9 0 1.8 0 0.9 0 TSS -7.35 -0.7 -2.45 -5.95 -4.2 1.75 -2.45 -1.05 -5.25 -2.45 Electric Conductivity -7 56 -14 -38.5 21 -87.5 140 -38.5 17.5 84 DO 3.15 0.21 0.525 -0.35 1.4 0.35 1.4 1.4 -0.455 1.4 BOD -1.4 -1.05 -1.05 -1.05 -0.7 0.35 -0.7 -1.05 -0.7 -0.7 Turbidity -1.75 -1.75 -1.75 -1.4 -1.05 10.45 -1.05 -1.4 -1.05 -1.4 Table 5 shows all the vulnerable values identified by the distance-based iterative objective function using equations 7 & 8 . All the observed values were subtracted from the WHO standard values except the highlighted value in Table 3 . The values that exceeded the observed values were already used for identifying critical locations, therefore not given in Table 5 , and left as an empty box. The minimum value in each row is the most vulnerable, and the location is the most vulnerable. Table 5 The vulnerable values are highlighted as bold using equations 8 & 9 . Location WD AK NG KK JR BR PR NK RR IR VL Total Alkalinity − 20 80 86 40 − 5 − 25 35 5 Sodium 24.8 13.7 24.9 32.9 16.5 − − 32.9 33.55 6.5 6.5 Calcium 18 − 17.5 4 − − − 4 − − 4 Total hardness 82 64 − 57 75 − 75 57 111 20 20 Magnesium 105 83 104.8 73 92 − − 73 105.55 7 7 Nitrite − − 1 − − − − − − − 1 Sulfate 221.86 202.84 222.09 210.38 196.03 210.38 196.03 210.38 196.03 196.03 196.03 Chloride 227 241 239 242 239 237 233 237 233 244 227 Phosphate 0.43 0.67 0.7 0.66 0.44 0.47 0.37 0.57 0.37 0.66 0.37 Arsenic 0.035 0.029 0.046 0.04 0.025 0.04 0.029 0.038 0.029 0.029 0.025 TSS 21 2 7 17 12 − 7 3 15 7 2 TDS 140 64 140 180 107 261 107 180 122 39 39 Electric Conductivity 20 − 40 110 − 250 − 110 − − 20 DO 9 0.6 1.5 − 4 1 4 4 − 4 0.6 BOD 4 3 3 3 2 − 2 3 2 2 2 COD 245 245 246 246 245 243 245 245 245.7 245 243 Turbidity 5 5 5 4 3 − 3 4 3 4 3 The locations listed in Table 6 , the second column, are the most critical in terms of exceedance margin from the threshold values. These results were obtained from the application Eq. 1 to Eq. 7 for those physical and chemical water quality parameters that exceed the threshold value. We noted that Bara River's water quality parameters behaved differently among all locations of parameter values. The table below confirms the identification of Bara River as the most critical location in at least seven water quality parameters. All these locations exceed the threshold values by considerable margins. At the same time, the locations identified in the third column are the most vulnerable due to applying equations 8 and 9 for the physical and chemical water quality parameters that nearly exceed the threshold value. These results closely match the physical reality on the ground. The Khairabad station appears the most in Table 6 , which is a location where the Kabul River, along with all its tributaries, meets the Indus River. The water quality parameters, for example, Total Suspended Solids, Sodium, Total Hardness, Magnesium, and Sulfate, are on the upper side of allowable limits. The location is vulnerable because further deterioration will change these locations to critical areas. Adezai, Jindi, and Pabbi Rivers and Warsak Dam appear twice in Table 6 ; all these locations inhabit and sustain large population densities along their banks. In addition to agriculture, small-scale industries (Ghee, Marble, Paper industries, and Auto-Repair Shops) are predominantly the means of sustenance for the local population. Table 6 Critical location and Vulnerable locations are based on Table 3 – 5 . Parameters Critical Location (CL) Vulnerable Location (VL) Total Suspended Solids BR AK Electric Conductivity PR WD Dissolved Oxygen RR AK Biochemical Oxygen Demand BR PR, JR, RR Turbidity BR PR, JR Total Alkalinity BR PR Sodium BR IR Calcium AK NK Total Hardness BR IR Magnesium BR IR Nitrite NK NG Total Dissolved Solids IR Chemical Oxygen Demand BR Sulfate BR Chloride WD Phosphate RR Arsenic JR The method mentioned in sub-section 2.2.3 was employed to compare the results obtained for critical and vulnerable locations. Figure 8 – 10 shows the areas identified by kriging interpolation as the most critical ones. The areas are mostly the same identified through the distance and weightage-based identification method explained above. Bara River is in the most critical state due to the different parameters exceeding the threshold values by a large margin. Mitigating and preventive measures must be taken at the identified location to improve the water quality parameters, especially Total Suspended Solids, BOD, Turbidity, Total Alkalinity, Sodium, Total Hardness, and Magnesium. 4. Conclusion The study developed two objective functions applied to different locations in the larger prone area of Peshawar District, Pakistan. The methodology identified the critical areas and vulnerable locations concerning the water quality part. The results show considerable skills in identifying the critical and vulnerable locations compared with already published work for the same area of water quality parameters. The developed method is intuited and easy to apply and gives results, which are enclosed tendons with the ground reality of the region. Declarations Data availability The data generated and analyzed during the current study are included in this article. The analyzed data in the form of an excel sheet are attached in the supplementary files. References Ahmed, A. M. (2017). Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs). Journal of King Saud University-Engineering Sciences, 29 (2), 151–158. Ali, Y., Pervez, H., & Khan, J. (2020). Selection of the most feasible wastewater treatment technology in Pakistan using multi-criteria decision-making (MCDM). Water Conservation Science and Engineering, 5 (3), 199–213. Armstrong, R. L., Rittger, K., Brodzik, M. J., Racoviteanu, A., Barrett, A. P., Khalsa, S.-J. S.,.. . Wilson, A. M. (2019). Runoff from glacier ice and seasonal snow in High Asia: separating melt water sources in river flow. Regional Environmental Change, 19 (5), 1249–1261. Bank, W. (2006). Reengaging in agricultural water management: challenges, opportunities, and trade-offs. Water for Food Team, Agriculture and Rural Development Department (ARD) World Bank, Washington, DC . Bierkens, M. F., & Wada, Y. (2019). Non-renewable groundwater use and groundwater depletion: a review. Environmental Research Letters, 14 (6), 063002. Carraro, E., Bonetta, S., Bertino, C., Lorenzi, E., Bonetta, S., & Gilli, G. (2016). Hospital effluents management: chemical, physical, microbiological risks and legislation in different countries. Journal of environmental management, 168 , 185–199. Carvalho, F. P. (2017). Pesticides, environment, and food safety. Food and energy security, 6 (2), 48–60. Daud, M., Nafees, M., Ali, S., Rizwan, M., Bajwa, R. A., Shakoor, M. B.,.. . Murad, W. (2017). Drinking water quality status and contamination in Pakistan. BioMed research international, 2017 . Ejaz, N., & Ahmad, M. (2017). Distortions to Agricultural Incentives in Light of Trade Policy. Elala, D., Labhasetwar, P., & Tyrrel, S. F. (2011). Deterioration in water quality from supply chain to household and appropriate storage in the context of intermittent water supplies. Water Science and Technology: Water Supply, 11 (4), 400–408. Ilyas, M., Ahmad, W., Khan, H., Yousaf, S., Yasir, M., & Khan, A. (2019). Environmental and health impacts of industrial wastewater effluents in Pakistan: a review. Reviews on environmental health, 34 (2), 171–186. Imran, S., Bukhari, L., & Gul, S. (2018a). Water Quality Assessment Report Along the Banks of River Kabul Khyber Pakhtunkhwa 2018. Pakistan Council of Research in Water Resources (PCRWR). All rights reserved by PCRWR. The authors encourage fair use of this material for non-commercial purposes with proper citation. Authors: Saiqa Imran, Lubna Naheed Bukhari and Samar Gul, Pakistan Council of Research in Water Resources, Pakistan , 45. Imran, S., Bukhari, L., & Gul, S. (2018b). Water Quality Assessment Report: Mingora City District Swat Khyber Pakhtunkhwa 2018. Pakistan Council of Research in Water Resources (PCRWR). All rights reserved by PCRWR. The authors encourage fair use of this material for non-commercial purposes with proper citation. Authors: Saiqa Imran, Lubna Naheed Bukhari and Samar Gul, Pakistan Council of Research in Water Resources, Pakistan , 40. Iqbal, U., Qasim, H., Khan, A. K., Rashid, R., Nasreen, S., Mahmood, Q., & Khan, J. (2009). Surface and ground water quality risk assessment in district Attock Pakistan. World Appl. Sci. J, 7 (8), 1029–1036. Jackson, R. B., Carpenter, S. R., Dahm, C. N., McKnight, D. M., Naiman, R. J., Postel, S. L., & Running, S. W. (2001). Water in a changing world. Ecological applications, 11 (4), 1027–1045. Jamil, M. (2019). Running Dry: Water Scarcity in Pakistan . Retrieved from Jimenez, B., & Asano, T. (2008). Water reclamation and reuse around the world. Water Reuse: an international survey of current practice, issues and needs, 14 , 3–26. Kahlown, M. A., & Majeed, A. (2003). Water-resources situation in Pakistan: challenges and future strategies. Water Resources in the South: present scenario and future prospects, 20 , 33–45. Kakar, Y., & Bhatnagar, N. (1981). Ground water pollution due to industrial effluents in Ludhiana, India Studies in Environmental Science (Vol. 17, pp. 265–272): Elsevier. Khan, M. A. A., Ashraf, I., & Siddiqui, M. T. (2021). A qualitative insight into the factors behind water scarcity in Punjab, Pakistan. Pakistan Journal of Agricultural Sciences, 58 (1). Khan, T., & Khan, H. (2019). Environmental sustainability of grey water footprints in Peshawar Basin: Current and future reduced flow scenarios for Kabul River. International Journal of Agricultural and Biological Engineering, 12 (4), 162–168. Khan, T., Muhammad, S., Khan, B., & Khan, H. (2011). Investigating the levels of selected heavy metals in surface water of Shah Alam River (A tributary of River Kabul, Khyber Pakhtunkhwa). Journal of Himalayan Earth Sciences, 44 (2), 71–79. Malik, M. A., Azam, M., & Saboor, A. (2010). Water quality status of upper KPK and northern areas of Pakistan : Pakistan Council of Research in Water Resources, Water Resources Research … Malik, T. (2019). Pak-Afghan Water Issue: A Case for Benefit-Sharing. Policy Perspectives, 16 (1), 77–98. Marsalek, J., Cisneros, B. J., Karamouz, M., Malmquist, P.-A., Goldenfum, J. A., & Chocat, B. (2008). Urban water cycle processes and interactions: urban water series-UNESCO-IHP (Vol. 2): CRC press. McFarland, A. R., Larsen, L., Yeshitela, K., Engida, A. N., & Love, N. G. (2019). Guide for using green infrastructure in urban environments for stormwater management. Environmental science: Water research & technology, 5 (4), 643–659. Molden, D. (2013). Water for food water for life: A comprehensive assessment of water management in agriculture : Routledge. Nafees, M. Central Asia Journal No. 67. Ngabirano, H., Byamugisha, D., & Ntambi, E. (2016). Effects of seasonal variations in physical parameters on quality of gravity flow water in Kyanamira Sub-County, Kabale District, Uganda. Qureshi, A. S. (2020). Groundwater governance in Pakistan: From colossal development to neglected management. Water, 12 (11), 3017. Qureshi, R., & Ashraf, M. (2019). Water security issues of agriculture in Pakistan. PAS Islamabad Pak, 1 , 41. Rajaram, T., & Das, A. (2008). Water pollution by industrial effluents in India: Discharge scenarios and case for participatory ecosystem specific local regulation. Futures, 40 (1), 56–69. Ranjan, A., & Chatterjee, D. (2020). Cutting across the Durand: Water dispute between Pakistan and Afghanistan on river Kabul. World Water Policy, 6 (2), 246–258. Saeedb, S., Naseerb, R., Mehmoodb, T., Luqmang, N., Ahmadb, H., Sadiah, H.,.. . Rafiqueb, N. Drinking water quality of various sources in Peshawar, Mardan, Kohat and Swat districts of Khyber Pakhtunkhwa province, Pakistan. Sehar, S., Naz, I., Ali, M. I., & Ahmed, S. (2011). Monitoring of physico-chemical and microbiological analysis of under ground water samples of district Kallar Syedan, Rawalpindi-Pakistan. Research Journal of Chemical Sciences __________________________________________________________ ISSN, 2231 , 606X. Selvakumar, S., Ramkumar, K., Chandrasekar, N., Magesh, N., & Kaliraj, S. (2017). Groundwater quality and its suitability for drinking and irrigational use in the Southern Tiruchirappalli district, Tamil Nadu, India. Applied Water Science, 7 (1), 411–420. Sonmez, O., & Bizimana, H. (2020). Flood hazard risk evaluation using fuzzy logic and weightage-based combination methods in geographic information system. Yousafzai, A. M., Khan, A. R., & Shakoori, A. (2008). An assessment of chemical pollution in River Kabul and its possible impacts on fisheries. Pakistan Journal of Zoology, 40 (3), 199. Zaryab, A., Noori, A. R., Wegerich, K., & Kløve, B. (2017). Assessment of water quality and quantity trends in Kabul aquifers with an outline for future drinking water supplies. Central Asian Journal of Water Research, 3 (2), 3–11. Additional Declarations No competing interests reported. Supplementary Files 3listofParametersAutosaved.xlsx FinalresultsCLVL.xlsx Cite Share Download PDF Status: Published Journal Publication published 18 Jul, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 19 May, 2023 Reviews received at journal 11 Feb, 2023 Reviewers agreed at journal 31 Jan, 2023 Reviewers agreed at journal 31 Jan, 2023 Reviewers invited by journal 31 Jan, 2023 Editor assigned by journal 26 Dec, 2022 Editor invited by journal 12 Dec, 2022 Submission checks completed at journal 12 Dec, 2022 First submitted to journal 06 Dec, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2349299","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":159519308,"identity":"74e829cc-ec94-4120-8a9e-c7713d582bfd","order_by":0,"name":"Muhammad Irfan","email":"data:image/png;base64,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","orcid":"","institution":"University of Engineering and Applied Science","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Irfan","suffix":""},{"id":159519312,"identity":"9ccc0228-4f28-4fd4-9718-860e3c21cb0a","order_by":1,"name":"M. Mahboob Alam","email":"","orcid":"","institution":"University of Science and Information Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"M.","middleName":"Mahboob","lastName":"Alam","suffix":""},{"id":159519313,"identity":"4b45107c-da99-4ec6-9260-8f2846ca312c","order_by":2,"name":"Shahbaz khan","email":"","orcid":"","institution":"University of Engineering and Applied Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shahbaz","middleName":"","lastName":"khan","suffix":""},{"id":159519316,"identity":"558797e5-a398-44d9-b327-efe0a75f1eb9","order_by":3,"name":"Ilyas khan","email":"","orcid":"","institution":"majmaah University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ilyas","middleName":"","lastName":"khan","suffix":""},{"id":159519319,"identity":"94f5fb90-f734-4edd-b016-cd99d7350111","order_by":4,"name":"Sayed M Eldin","email":"","orcid":"","institution":"Future University in Egypt","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sayed","middleName":"M","lastName":"Eldin","suffix":""}],"badges":[],"createdAt":"2022-12-06 09:29:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2349299/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2349299/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-38018-8","type":"published","date":"2023-07-18T21:42:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":30330046,"identity":"93ede245-4f62-4889-bde9-f62328505143","added_by":"auto","created_at":"2022-12-14 16:12:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":158829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLocation map of a) Pakistan, Afghanistan, and Khyber Pakhtunkhwa Province b) the \u0026nbsp;whole Kabul River basin digital elevation model.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/b0b5490c1f6559ef3d964627.jpg"},{"id":30328888,"identity":"bac70c26-cd95-4f3e-9570-73b69e511002","added_by":"auto","created_at":"2022-12-14 16:04:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":158199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSampling points locations in the study area of The Kabul River catchment.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/49362b0f59b2ec426a6663a4.jpg"},{"id":30328892,"identity":"74adfb4b-967b-42fc-9687-85af20fab49b","added_by":"auto","created_at":"2022-12-14 16:04:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResults of Physical water quality tests performed at each location\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/e2dabfc2455564f0d68d0228.png"},{"id":30328887,"identity":"1fceb342-8b29-4a44-8f36-d5a525e69898","added_by":"auto","created_at":"2022-12-14 16:04:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":111139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResults of Chemical water quality tests performed at each location\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/8455f7bb7142066c67da3ca3.png"},{"id":30328891,"identity":"4b01cb5c-c5ad-451d-aba1-a5879d9a588e","added_by":"auto","created_at":"2022-12-14 16:04:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":36821,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResults of Biological water quality tests performed at each location\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/7f4f96af08355ac58884c168.jpg"},{"id":30331470,"identity":"2b501c50-8957-4335-b481-3abfa1883e6f","added_by":"auto","created_at":"2022-12-14 16:20:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":78615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePearson correlation values between physical water quality parameters at all locations\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/186307de518a5d469d7b46d9.jpg"},{"id":30328890,"identity":"84ebb558-e32a-45c4-81b4-2e874bafc758","added_by":"auto","created_at":"2022-12-14 16:04:25","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":130081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePearson correlation values between chemical/biological water quality parameters\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/312d486b28e6093f9720f8c7.jpg"},{"id":30332118,"identity":"db048cdf-ff24-42cb-907f-9146c6dd08e6","added_by":"auto","created_at":"2022-12-14 16:28:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":255711,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInterpolated values of water quality physical parameters over the entire study area.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/862fe16303a6111162e7d8f5.png"},{"id":30328898,"identity":"d1228fa6-150c-4b61-a747-f114216b5d51","added_by":"auto","created_at":"2022-12-14 16:04:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":221158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInterpolated values of water quality chemical parameters over the entire study area.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/b764409b4de01ed6da089859.png"},{"id":30328893,"identity":"76c60a8b-92c6-4415-a91f-b82626cae4c2","added_by":"auto","created_at":"2022-12-14 16:04:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":217755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInterpolated values of water quality biological parameters over the entire study area.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/2d436b42443c8be62fffae89.png"},{"id":44737270,"identity":"0dbc54bf-3598-4833-9f7a-e4d911e32e51","added_by":"auto","created_at":"2023-10-16 22:33:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2547001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/2457d056-4459-4430-8bb4-73fca0d9f0f7.pdf"},{"id":30330047,"identity":"40bc65d0-27c9-4c04-a539-5ed463c344dd","added_by":"auto","created_at":"2022-12-14 16:12:25","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":78060,"visible":true,"origin":"","legend":"","description":"","filename":"3listofParametersAutosaved.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/4bcf3b983a5c542800b1e449.xlsx"},{"id":30328897,"identity":"c268c67d-71fd-420f-937b-4b725e1bfdce","added_by":"auto","created_at":"2022-12-14 16:04:25","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":36273,"visible":true,"origin":"","legend":"","description":"","filename":"FinalresultsCLVL.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2349299/v1/866d0d34c5d998fc58289b09.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distance and Weightage-based Identification of Most Critical and Vulnerable locations of Surface Water Pollution in Kabul River Tributaries","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWater is a limited and fundamental resource for life, exchanging its form in a continuous cycle between land and atmosphere (Jackson et al., (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)). While the classification of available water is done in multiple ways, the most common classification is based on the availability of water Vis a Vis its source, namely surface water, groundwater, and atmospheric water. Without going into the forms in which water is available and used in ground and atmospheric sources, we look into surface water and its use for producing agricultural products. Within the terrestrial ecosystem, the same water used for crop production is shared with the public and aquatic life (Carvalho, (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)). Surface water classifications are applied to surface water bodies, such as streams, rivers, and lakes. The general use of these waters is for drinking water supply, irrigation, fishing, industrial use, transport, and recreation (Molden, (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)). In Pakistan, surface water resources are predominantly used for agriculture. Agriculture is key to Pakistan\u0026rsquo;s gross domestic product (GDP). According to the Economic Survey of Pakistan_2022, the Agriculture sector contributes about 22.7% to the GDP, which employs 37.4% of the Labor force and 64% of exports earnings. It provides livelihoods to 62% of the country's population (Ejaz \u0026amp; Ahmad, (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)). Regarding water resources, agriculture is one of the biggest consumers, using over 90% of available fresh water in Pakistan (A. S. Qureshi, (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eThe heavy burden on agricultural produce for national consumption and for enhancing foreign exchange reserves through exports mandates the growers to use large amounts of fertilizers to supplement the naturally available nutrients in the soil. Which ultimately affects the groundwater quality in the long run (Daud et al., (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)). Numerous studies have established the causal relations between the rate of use of fertilizers and deterioration in groundwater quality over time (Bank, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); R. Qureshi \u0026amp; Ashraf, (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)). Unsustainable use of water, whether ground or surface, also ultimately affects groundwater quality. The connecting interface of ground and surface water makes it almost impossible not to affect one resource while the other is being exploited (Bierkens \u0026amp; Wada, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eDraining untreated or partially treated water from industries, hospitals, and other public and commercial buildings into surface water bodies is another major source of pollution in surface water (McFarland, Larsen, Yeshitela, Engida, \u0026amp; Love, (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)). While in the developed world, strict regulations ensure water treatment before it can be drained into surface water bodies, the same may exist on paper. However, enforcement of these important regulations is usually found wanting (Rajaram \u0026amp; Das, (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)). In a recent case, the high court bench of a city in Pakistan summoned relevant government authorities regarding the disposal of effluents from different kinds of industries directly into surface water bodies. The industries that were pointed out were construction material-producing, ranging from cement to steel reinforcements, pharmaceutical, and paper industries. There is also evidence of shallow groundwater contamination in India from industries of bicycles and ancillary parts, electroplating, steel, and foundries resulting in the high concentration of cyanide in groundwater (Kakar \u0026amp; Bhatnagar, (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)). The largest source of surface water pollution in Pakistan remains municipal wastewater, which pollutes rivers, drains, and streams downstream of large cities with organic matter, suspended solids, and surfactants. (Ilyas et al., (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)). Industrial wastewater discharges are also high, polluting surface waters with heavy metals, oil products, phenols, and other hazardous substances(Daud et al., (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Marsalek et al., (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eIn the case of Pakistan, the problem is not only with the quality of water but also the available water quantity. Based on generally accepted thresholds of available water in a country on per capita and per annum basis (Jimenez \u0026amp; Asano, (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)), Pakistan crossed the water scarcity line in 2005. If the population growth and water consumption rates by different economic sectors remain unchanged, the country will touch the whole scarcity line by 2025 (M. A. A. Khan, Ashraf, \u0026amp; Siddiqui, (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eThe surface water resources of Pakistan are mainly based on the flows of the Indus River and its tributaries (Kahlown \u0026amp; Majeed, (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)). The Indus River has a total length of 2900 kilometers (Km), and the drainage area is about 966,000 sq. In Km, five major tributaries join its eastern side, and three minor tributaries drain in the mountain region. Several small tributaries also join the Indus towards its western side. The biggest of such tributaries is River Kabul (Jamil, (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eKabul River can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which is the western tributary of the mighty Indus River, contributes 16.5\u0026nbsp;million Acre Feet (MAF) of water to Pakistan\u0026rsquo;s complex water system (T. Malik, (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)). Before flowing into KPK province in Pakistan, the Kabul River passes through Kabul, Surobi, and Jalalabad in Afghanistan, some 25 Km (16 Miles) north of the Durand line. In KPK province, the Kabul River passes through Peshawar, Charsadda, and Nowshera. Its largest tributary is the Kunar River, which starts as the Mastuj River, flowing from the Chiantar glacier in Brughil valley in Chitral, Pakistan, and after flowing south into Afghanistan, it is met by the Bashgal River flowing from Nurestan (Ranjan \u0026amp; Chatterjee, (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn River Kabul, about 72% of the runoff occurs between May and September, and 28% occurs between October to April. The peak runoff is generally in June and July. In comparison, the peak demand for agriculture is during July and August (Armstrong et al., (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)). The Kabul River and its tributaries transport untreated sewage from the cities, towns, and villages of Afghanistan and Pakistan from Peshawar, Mardan, Khyber, Mohmand, and Malakand agencies (Yousafzai, Khan, \u0026amp; Shakoori, (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)). The lower sections of the river pass through the plains, which are particularly densely populated. The effluents from many industries end up in the river Kabul, either directly or through nullahs which eventually drain into the river. Of these industries, the sugar mills, distilleries, paper mills, tanneries, ghee factories, and textile mills contribute most of the water pollution hazards (Ali, Pervez, \u0026amp; Khan, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eThe wastewater contains domestic, industrial, and commercial effluents, the quality of which is checked based on different laboratory tests, i.e., tests for physical, chemical, and biological parameters (Carraro et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)). Further research has been done to analyze the water quality of a region based on these tests using conventional techniques and highlights the common risks and possible suitable solutions in water supply for domestic and agricultural use (Elala, Labhasetwar, \u0026amp; Tyrrel, (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)). The surface and groundwater quality of the Kabul River at Attock city, Punjab, has been investigated by calculating the water quality index (WQI) (Iqbal et al., (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)). The status of groundwater was compared with WHO standards, and found a negative trend in terms of nitrates and fecal microbes in the Kabul catchment (Zaryab, Noori, Wegerich, \u0026amp; Kl\u0026oslash;ve, (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)). Another research that has been done to investigate the concentration of heavy metals in the surface water of the Kabul River found that nickel has the highest concentration, 30 times higher than the permissible limit of WHO (T. Khan, Muhammad, Khan, \u0026amp; Khan, (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)). This research idea was used to identify the risk of flood based on the most crucial variables of flood characteristics of Waverly city, Iowa (Sonmez \u0026amp; Bizimana, (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003ePakistan Council of Research in Water Resources (PCRWR) studied water quality parameters of the upper Khyber Pakhtunkhwa region and northern areas of Pakistan. That study included Mardan, Buner, and Swat Districts of KPK, among others (Imran, Bukhari, \u0026amp; Gul, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Saeedb et al.). Although the PCRWR study did not include the districts which are the focus of this study, the results obtained, however, show similarity with the results of the PCRWR study of district Mardan which is not only close to this study\u0026rsquo;s locations but also similar kind of geological, social and industrial conditions. According to the international union for conservation of nature, Pakistan program (IUCN), the Kabul watershed houses 205, 10, 41, and 45 industrial units in Peshawar, Charsada, Nowshera, and Mardan districts, respectively, constitute more than 15% of the total industries in the region (Nafees). Almost all these industries have no treatment facilities for effluents. Nitrites from Agricultural fertilizers and untreated effluents like ammonia, Chromium, and nickel from the industries thus influence the water quality in these areas (T. Khan \u0026amp; Khan, (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eAll the mentioned research work has been done to check the Kabul River water quality status is based only on the water quality index and suggested a possible solution for future use. This research work aims not to highlight the risk assessment of the Kabul River catchment due to exceeding the maximum permissible values of WHO and only suggest the possible solution superficially. This research investigates the water quality of all the tributaries added to the Kabul River. Based on the water quality status, it highlights the most critical branch using the weightage and distance method and compares the results with interpolated maps in arc-GIS. The results will guide the concerned authorities to apply the remedial measure by installing the monitoring stations on critical tributaries and using the treatment procedures to overcome their long-lasting effect. Different stakeholders must launch an awareness campaign to aware the dwellers and other beneficiaries about the vulnerability of vulnerable locations. The remaining research work comprises as follows. Section 2 consists of sub-topics are; the detailed characteristics of the study area and the method used to solve the mentioned issue, followed by comparing the results through interpolated values and plotting the comparison matrix using the Pearson method. Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the detailed problem solution with research findings, and the study's conclusion is in section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e"},{"header":"2. Material Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area and Sampling Collection.\u003c/h2\u003e \u003cp\u003eThe stretch of the Kabul River under study is the section from just upstream of Warsak Dam to its junction with the Indus. This area is densely populated, with much of the KPK's small industry dependent upon the water of the Kabul River and its tributaries for different purposes Imran, 2018. Most industries ultimately drain their effluents without any treatment in the Kabul River. Ironically, this is also where much of the agricultural products consumed in cities like Peshawar, Mardan, and Swat. are grown. Much concern has been raised about the quality of water in these areas Iqbal, 2009.\u003c/p\u003e \u003cp\u003eThe geographical boundary of the Kabul River basin and the areas from where the water samples were collected for this study are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The sampling points are shown in dots, the explanation of which is mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll the water samples were collected from ten different tributaries in 1-liter polyethylene (PE) bottles, which were washed with deionized water before use, that drained into the Kabul River. These sample bottles were sealed and placed in a dark environment at a constant temperature range of 4\u0026ndash;10 ⁰C to avoid contamination and the effects of light and temperature. The detail of the sampling locations with a unique ID was the tributary of Kabul River along with their latitude and longitude and their height from the mean sea level given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSampling points details of the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampling Points\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAltitude (ft.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver ID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarsak Dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.16442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.357898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdezai River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.08889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.74889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e951.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaghuman River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.12169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.607621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ekhiyali River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.12194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.70889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e961.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJindi River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.15629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.734471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBara River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.9668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.561619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePabbi River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.97382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.748456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNowshera Kallan River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.00887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.985346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRustam River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.34967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.278996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndus River at Khairabad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.89687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.233806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll standard protocols necessary for sample collection, sample storage, sample transportation, and sample tests were followed as per relevant American Society of Testing and Materials (ASTM) guidelines given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A total of nine parameters were tested on the collected water quality sample under the relevant standards mentioned in the parameter section, followed by the units of each parameter, their accuracy, test standards and Food and Agriculture Organization (FAO)/WHO threshold values both for drinking and agriculture use.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWater quality test standards used along with Apparatus used and threshold values of WHO and FAO\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApparatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTest Standard.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWHO Limits drinking water\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFAO Limits for Agricultural Use\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePH meter with Buffer solution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASTM D1293-99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5\u0026ndash;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5-8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMulti parameter Photometer.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASTM D1209-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnpleasant/Acceptable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaste and Odour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcceptable/Non-Acceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASTM E2892-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAcceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnpleasant/Acceptable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFilter paper, beaker, Desiccators, titration assembly, vacuum pump, and conical flask.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASTM D5907-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFilter paper, beaker, Desiccators, titration assembly, vacuum pump, and conical flask.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASTM D5907-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0-2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectric Conductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;s/cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDDS-22C High Accuracy Digital Conductivity meter.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASTM D1225-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissolved Oxygen (DO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBOD bottles and other related chemicals.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASTM D888-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.0\u0026ndash;8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiochemical Oxygen Demand (BOD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBOD bottles and other related chemicals.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASTM D2329-68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemical Oxygen Demand (COD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBOD bottles and other related chemicals.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASTM D1252-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe samples were analyzed for their physical, chemical, and biological properties relevant to the study. Considering the pollution sources, the study area is exposed to, and the ultimate use of untreated surface water for irrigation purposes, physical parameters like pH, Total Settleable Solids (TSS), Total Dissolved Solids (TDS), Turbidity, and Electrical Conductivity (Ngabirano, Byamugisha, \u0026amp; Ntambi, (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)) were measured. For chemical analysis, water samples were tested for Total Alkalinity, Sodium (Na), Calcium (Ca), Magnesium (Mg), Total Hardness, Nitrite (NO2-), Sulfate (SO42-), Phosphate (PO4), Chloride (Cl-), and Arsenic (Ar) (Sehar, Naz, Ali, \u0026amp; Ahmed, (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)). Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and Dissolved Oxygen (DO) (Ahmed, (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)) limit were measured for biological properties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Distance and Weightage based identification of most critical and vulnerable locations.\u003c/h2\u003e \u003cp\u003eThe methodology presented here is developed to identify the most critical and vulnerable locations in polluted groundwater. We define the criticality of a certain location based on a large value exceeding the threshold defined by WHO. This way, we differentiate between the locations with observed parameter values just exceeding the threshold amount and those exceeding the threshold by a large margin. We define the vulnerability of certain locations by identifying areas closest to critical areas identified earlier in terms of their observed values. An iterative distance-based indicator is used to identify locations that need immediate intervention in terms of securing the water quality.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Critical location identification.\u003c/h2\u003e \u003cp\u003eMathematical equations identify the critical location with the help of certain observed parameters, i.e., physical, chemical, and biological parameters. The threshold value is escalated by 50% and added to the original threshold value given by WHO as given in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Then compare the measured values at all locations with the escalated values instead of the original threshold values to find the severity of exceedance using Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndices\u003c/b\u003e \u003c/p\u003e \u003cp\u003ees\u0026thinsp;=\u0026thinsp;escalated values by 50%\u003c/p\u003e \u003cp\u003ei\u0026thinsp;=\u0026thinsp;Location of sample collection\u003c/p\u003e \u003cp\u003ej\u0026thinsp;=\u0026thinsp;recorded value of water quality parameter\u003c/p\u003e \u003cp\u003eex\u0026thinsp;=\u0026thinsp;exceedance value (means near to exceeding the threshold value)\u003c/p\u003e \u003cp\u003e \u003cb\u003eParameters\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({PP}_{j}^{es}\\)\u003c/span\u003e \u003c/span\u003e water quality measured value \u003cem\u003ej\u003c/em\u003e of physical parameter \u003cem\u003ePP\u003c/em\u003e escalated by 50% \u003cem\u003ees\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({CP}_{j}^{es}\\)\u003c/span\u003e \u003c/span\u003e water quality measured value \u003cem\u003ej\u003c/em\u003e of chemical parameter \u003cem\u003eCP\u003c/em\u003e escalated by 50% \u003cem\u003ees.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({BP}_{j}^{es}\\)\u003c/span\u003e \u003c/span\u003e water quality measured value \u003cem\u003ej\u003c/em\u003e of biological parameter \u003cem\u003eBP\u003c/em\u003e escalated by 50% \u003cem\u003ees\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({PP}_{who,j}\\)\u003c/span\u003e \u003c/span\u003e threshold value set by \u003cem\u003eWHO\u003c/em\u003e for physical parameter \u003cem\u003ePP\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({CP}_{who,j}\\)\u003c/span\u003e \u003c/span\u003e threshold value set by \u003cem\u003eWHO\u003c/em\u003e for chemical parameter \u003cem\u003eCP\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({BP}_{who,j }\\)\u003c/span\u003e \u003c/span\u003e threshold value set by \u003cem\u003eWHO\u003c/em\u003e for biological parameter \u003cem\u003eBP\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({PP}_{ij}\\)\u003c/span\u003e \u003c/span\u003e observed values \u003cem\u003ej\u003c/em\u003e of physical parameter \u003cem\u003ePP\u003c/em\u003e for particular location \u003cem\u003ei\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({CP}_{ij}\\)\u003c/span\u003e \u003c/span\u003e observed values \u003cem\u003ej\u003c/em\u003e of chemical parameter \u003cem\u003eCP\u003c/em\u003e for particular location \u003cem\u003ei\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e \u003c/span\u003e exceedance threshold value \u003cem\u003eZ\u003c/em\u003e for particular location \u003cem\u003ei\u003c/em\u003e of sub parameter \u003cem\u003ej\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({II}_{ij}\\)\u003c/span\u003e \u003c/span\u003e Importance indicator \u003cem\u003eII\u003c/em\u003e for particular location \u003cem\u003ei\u003c/em\u003e of sub parameter \u003cem\u003ej\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({ CL}_{j}\\)\u003c/span\u003e \u003c/span\u003ecritical location \u003cem\u003eCL\u003c/em\u003e based on sub parameter \u003cem\u003ej\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({PP}^{ex}\\)\u003c/span\u003e \u003c/span\u003e exceedance value \u003cem\u003eex\u003c/em\u003e of physical parameter \u003cem\u003ePP\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({VL}_{j}\\)\u003c/span\u003e \u003c/span\u003e vulnerable location \u003cem\u003eVL\u003c/em\u003e based on sub parameter \u003cem\u003ej\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFor a particular physical parameter, the escalated threshold value would thus be:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${PP}_{j}^{es}= \\left(0.5* {PP}_{who,j}\\right)+{PP}_{who,j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSimilarly, equations \u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e used for chemical and biological parameters:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${CP}_{j}^{es}= \\left(0.5* {CP}_{who,j}\\right)+{CP}_{who,j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${BP}_{j}^{es}= \\left(0.5* {BP}_{who,j}\\right)+{BP}_{who,j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThen Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is used to calculate the extent to which certain water quality parameter exceeds the threshold \u003cem\u003eZ\u003c/em\u003e for all the water quality parameters, i.e.,\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${Z}_{ij}={PP}_{ij}-{PP}_{who,j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eMoreover, compare it with the escalated threshold values as defined above.\u003c/p\u003e \u003cp\u003eDepending on whether Z exceeds the escalated threshold at a certain location, we assign weights to these locations to single them out among all locations. The importance indicator (\u003cem\u003eII\u003c/em\u003e) for a certain location thus gives us an idea of the extent of intervention needed to mitigate the exceedance situation at a particular location.\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${II}_{ij}=\\left[\\begin{array}{c}\\begin{array}{c}0.9* {Z}_{ij} if {Z}_{ij}\u0026gt;{PP}_{j}^{es}\\\\ \\end{array}\\\\ 0.35* {Z}_{ij} if {Z}_{ij}\u0026lt; {PP}_{j}^{es} \\end{array}\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWe finally single out the critical location for a particular water quality parameter \u003cem\u003ej\u003c/em\u003e by choosing the location with has the maximum \u003cem\u003eII\u003c/em\u003e value.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${ CL}_{j}=\\underset{i\\in \\left[1..n\\right],j}{\\text{max}}{II}_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003en\u003c/em\u003e is the total number of locations over which the search for maximum is performed.\u003c/p\u003e \u003cp\u003eThe above methodology would work for those water quality parameters for which the desired value is not to exceed the upper threshold value defined by a regulatory body (e.g., Total Hardness). In case the desired value is not to be below than defined threshold (Dissolved Oxygen) value, we will use the search for minimum value among all locations, i.e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ CL}_{j}=\\underset{i\\in \\left[1..n\\right],j}{\\text{min}}{II}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCritical locations obtained for each water quality parameter thus identify locations that exceed the threshold values by a large amount. Water use from these areas should be strictly regulated, which is especially important for irrigation water use to avoid harmful chemicals entering the food chain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Vulnerable Location Identification.\u003c/h2\u003e \u003cp\u003eWe propose an iterative distance-based search method to identify the most vulnerable locations (VL) in terms of exceedance of threshold values. The method is used for locations that, though they have not yet crossed the threshold limit, based on the observed value, are most prone to be next in crossing the threshold values.\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${VL}_{j}=\\text{min}\\left(abs\\left|{PP}_{ij}-{PP}^{ex}\\right|\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$${VL}_{j}=\\text{min}\\left(abs\\left|{CP}_{ij}-{CP}^{ex}\\right|\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEquations\u0026nbsp;\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e9\u003c/span\u003e inherently show the methodology's iterative nature whereby we search for locations where the observed values of different Physical, Chemical, and biological parameters are the closest to the exceedance value. The results would benefit the decision-makers in prioritizing the mitigating measure for a particular location.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Spatial interpolation for comparison.\u003c/h2\u003e \u003cp\u003eTo compare the results obtained from the above methodology, we employ the kriging interpolation method in our study area for identifying the most critical locations. Using a 30 Meter digital elevation model of the region as external drift, we interpolate the observed values of different parameters over the entire region.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Water quality test results and discussion.\u003c/h2\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show the summary results of water quality tests of selected physical, chemical, and biological parameters, respectively. WHO-specified allowable/threshold limits, used as guiding limits, are depicted as red horizontal lines for each parameter. Threshold limits are \u0026ldquo;triggers\u0026rdquo; for starting mitigating efforts for bringing the recorded values to safe values and an \u0026ldquo;endpoint\u0026rdquo; for terminating mitigating efforts.\u003c/p\u003e \u003cp\u003eThe overall test results contradict the previous general findings about water quality in these areas. Almost all considered physical parameters lie below the threshold limits except electrical conductivity and dissolved oxygen. The purer the water, the lower the conductivity. Distilled water acts almost as an insulator, while salty water is an electrical conductor. Half of the selected locations exceed the threshold limits of electrical conductivity. Almost all locations exceed the dissolved oxygen values by a considerable margin. Higher dissolved oxygen values (or too low) affect aquatic life in surface water and its quality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen analyzed for chemical water quality parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), selected locations show mixed results. Calcium, Magnesium, and Nitrite exceeded threshold values in almost all locations. Samples taken from Bara River (BR) seem to have the worst quality of surface water in that both in Physical (TSS, BOD, Turbidity) and Chemical (Na, TH, TA) water quality parameters, we observe that it behaves differently than the rest of the locations. The reason could be that most of the small-scale industry at this location (Marble and Paper industry) drains its effluents directly into the surface water streams. Which makes the surface water at this location increasingly polluted concerning the quality parameters where other locations have reasonably acceptable values.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, e.g., show the selected location results for biological parameters such as total and fecal coliforms. All considered locations exceed the acceptable threshold limits of WHO for these parameters. Malik et al (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) refer to the WHO study, which says that fecal bacteria, parasites, and other microbes cause about 6000 deaths of adults and children every day, resulting in a statistic of 1.8\u0026nbsp;million deaths every year from complications caused by the presence of this kind of pollutant in water bodies (M. A. Malik, Azam, \u0026amp; Saboor, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows a correlation between measured physical and chemical-biological water quality parameters. These correlations were calculated to ensure that the samples were properly collected, stored, transported, and tested. Our general knowledge about the dependence of different water quality parameters on each other and their confirmation from the correlation test gives us confidence that the data collected is trustworthy. For example, the main salts generally dissolved in water are carbonates, bicarbonates, sulfates, chlorides, nitrates, and phosphates. TDS in water generally indicates a probability of higher electric conductivity (Selvakumar, Ramkumar, Chandrasekar, Magesh, \u0026amp; Kaliraj, (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)). We obtain a high Pearson correlation value between TDS and EC of 0.83 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Similarly, high correlation values were obtained between measured TSS and parameters like Turbidity, TH, and COD. The Turbidity/COD and Turbidity/BOD similarly display correlation values above 0.85.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. The Research Findings And Discussion","content":"\u003cp\u003eThe mathematical Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e were applied to escalate the threshold value by 50% in addition to the threshold value set by WHO. In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the first line of each parameter is the observed value denoted by CP, PP \u0026amp; BP. In contrast, the second line is the escalated value addition with the WHO values of each parameter for all locations. All the exceeded values were highlighted as bold; the bold, positive values indicate that the observed values exceeded the except for dissolved oxygen. The count of highlighted values in all water quality parameters for BR, PR, RR, JR, IR, and AK are 8, 7,6,5, and 4, respectively, indicating that BR repeats the most, followed by PR. This indication will be further confirmed by applying the remaining equations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasured values are represented as CP/BP/PP, while the escalated values are as Z.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal Alkalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e50\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e17\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e209\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e80\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e126\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e21\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e40\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e195\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal hardness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e21\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e21\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e79\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e60\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-105.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e12\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e40\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e36\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e41\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e38\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e33\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e65\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e37\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e31\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal Coliform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({BP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e9\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFecal Coliform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({BP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal Suspended Solids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({PP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElectric Conductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({PP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e160\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e60\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e400\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e50\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e240\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDissolved Oxygen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({PP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e-1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e-1.3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBiochemical oxygen demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({PP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({PP}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e11\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe exceeded values (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{ij}\\)\u003c/span\u003e\u003c/span\u003e) were assigned special weightage of 90 % 35% by following the condition of Eq.\u0026nbsp;\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and obtained the importance indicator values (II) for all the locations using the values of Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All the highlighted values in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e are the critical values obtained using Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Critical values are highlighted as bold using the Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;7.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Alkalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e17.5\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-12.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e5.95\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-11.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-11.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e188.1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e113.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e175.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal hardness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-19.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-26.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e7.35\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-26.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-19.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-38.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-36.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-29.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-36.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-25.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e27.65\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-25.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-36.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e58.5\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Coliform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e8.1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFecal Coliform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e1.8\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e1.75\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectric Conductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e140\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e3.15\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.35\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e10.45\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows all the vulnerable values identified by the distance-based iterative objective function using equations 7 \u0026amp;\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e8\u003c/span\u003e. All the observed values were subtracted from the WHO standard values except the highlighted value in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The values that exceeded the observed values were already used for identifying critical locations, therefore not given in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and left as an empty box. The minimum value in each row is the most vulnerable, and the location is the most vulnerable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe vulnerable values are highlighted as bold using equations \u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e8\u003c/span\u003e \u0026amp; \u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Alkalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e6.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal hardness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e105.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e210.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e196.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e210.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e196.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e210.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e196.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e196.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e196.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e227\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArsenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectric Conductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e245.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e243\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe locations listed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the second column, are the most critical in terms of exceedance margin from the threshold values. These results were obtained from the application Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e to Eq.\u0026nbsp;7 for those physical and chemical water quality parameters that exceed the threshold value. We noted that Bara River's water quality parameters behaved differently among all locations of parameter values. The table below confirms the identification of Bara River as the most critical location in at least seven water quality parameters. All these locations exceed the threshold values by considerable margins. At the same time, the locations identified in the third column are the most vulnerable due to applying equations \u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e9\u003c/span\u003e for the physical and chemical water quality parameters that nearly exceed the threshold value. These results closely match the physical reality on the ground. The Khairabad station appears the most in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, which is a location where the Kabul River, along with all its tributaries, meets the Indus River. The water quality parameters, for example, Total Suspended Solids, Sodium, Total Hardness, Magnesium, and Sulfate, are on the upper side of allowable limits. The location is vulnerable because further deterioration will change these locations to critical areas. Adezai, Jindi, and Pabbi Rivers and Warsak Dam appear twice in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; all these locations inhabit and sustain large population densities along their banks. In addition to agriculture, small-scale industries (Ghee, Marble, Paper industries, and Auto-Repair Shops) are predominantly the means of sustenance for the local population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCritical location and Vulnerable locations are based on Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCritical Location (CL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVulnerable Location (VL)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Suspended Solids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectric Conductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissolved Oxygen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiochemical Oxygen Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePR, JR, RR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePR, JR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Alkalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Hardness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Dissolved Solids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemical Oxygen Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArsenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe method mentioned in sub-section 2.2.3 was employed to compare the results obtained for critical and vulnerable locations. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the areas identified by kriging interpolation as the most critical ones. The areas are mostly the same identified through the distance and weightage-based identification method explained above. Bara River is in the most critical state due to the different parameters exceeding the threshold values by a large margin. Mitigating and preventive measures must be taken at the identified location to improve the water quality parameters, especially Total Suspended Solids, BOD, Turbidity, Total Alkalinity, Sodium, Total Hardness, and Magnesium.\u003c/p\u003e "},{"header":"4. Conclusion","content":"\u003cp\u003eThe study developed two objective functions applied to different locations in the larger prone area of Peshawar District, Pakistan. The methodology identified the critical areas and vulnerable locations concerning the water quality part. The results show considerable skills in identifying the critical and vulnerable locations compared with already published work for the same area of water quality parameters. The developed method is intuited and easy to apply and gives results, which are enclosed tendons with the ground reality of the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated and analyzed during the current study are included in this article. The analyzed data in the form of an excel sheet are attached in the supplementary files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed, A. M. (2017). Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs). Journal of King Saud University-Engineering Sciences, \u003cem\u003e29\u003c/em\u003e(2), 151\u0026ndash;158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, Y., Pervez, H., \u0026amp; Khan, J. (2020). Selection of the most feasible wastewater treatment technology in Pakistan using multi-criteria decision-making (MCDM). Water Conservation Science and Engineering, \u003cem\u003e5\u003c/em\u003e(3), 199\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmstrong, R. L., Rittger, K., Brodzik, M. J., Racoviteanu, A., Barrett, A. P., Khalsa, S.-J. S.,.. . Wilson, A. M. (2019). Runoff from glacier ice and seasonal snow in High Asia: separating melt water sources in river flow. Regional Environmental Change, \u003cem\u003e19\u003c/em\u003e(5), 1249\u0026ndash;1261.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBank, W. (2006). Reengaging in agricultural water management: challenges, opportunities, and trade-offs. \u003cem\u003eWater for Food Team, Agriculture and Rural Development Department (ARD) World Bank, Washington, DC\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBierkens, M. F., \u0026amp; Wada, Y. (2019). Non-renewable groundwater use and groundwater depletion: a review. Environmental Research Letters, \u003cem\u003e14\u003c/em\u003e(6), 063002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarraro, E., Bonetta, S., Bertino, C., Lorenzi, E., Bonetta, S., \u0026amp; Gilli, G. (2016). Hospital effluents management: chemical, physical, microbiological risks and legislation in different countries. Journal of environmental management, \u003cem\u003e168\u003c/em\u003e, 185\u0026ndash;199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho, F. P. (2017). Pesticides, environment, and food safety. Food and energy security, \u003cem\u003e6\u003c/em\u003e(2), 48\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaud, M., Nafees, M., Ali, S., Rizwan, M., Bajwa, R. A., Shakoor, M. B.,.. . Murad, W. (2017). Drinking water quality status and contamination in Pakistan. \u003cem\u003eBioMed research international, 2017\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEjaz, N., \u0026amp; Ahmad, M. (2017). Distortions to Agricultural Incentives in Light of Trade Policy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElala, D., Labhasetwar, P., \u0026amp; Tyrrel, S. F. (2011). Deterioration in water quality from supply chain to household and appropriate storage in the context of intermittent water supplies. Water Science and Technology: Water Supply, \u003cem\u003e11\u003c/em\u003e(4), 400\u0026ndash;408.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIlyas, M., Ahmad, W., Khan, H., Yousaf, S., Yasir, M., \u0026amp; Khan, A. (2019). Environmental and health impacts of industrial wastewater effluents in Pakistan: a review. Reviews on environmental health, \u003cem\u003e34\u003c/em\u003e(2), 171\u0026ndash;186.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImran, S., Bukhari, L., \u0026amp; Gul, S. (2018a). Water Quality Assessment Report Along the Banks of River Kabul Khyber Pakhtunkhwa 2018. Pakistan Council of Research in Water Resources (PCRWR). \u003cem\u003eAll rights reserved by PCRWR. The authors encourage fair use of this material for non-commercial purposes with proper citation. Authors: Saiqa Imran, Lubna Naheed Bukhari and Samar Gul, Pakistan Council of Research in Water Resources, Pakistan\u003c/em\u003e, 45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImran, S., Bukhari, L., \u0026amp; Gul, S. (2018b). Water Quality Assessment Report: Mingora City District Swat Khyber Pakhtunkhwa 2018. Pakistan Council of Research in Water Resources (PCRWR). \u003cem\u003eAll rights reserved by PCRWR. The authors encourage fair use of this material for non-commercial purposes with proper citation. Authors: Saiqa Imran, Lubna Naheed Bukhari and Samar Gul, Pakistan Council of Research in Water Resources, Pakistan\u003c/em\u003e, 40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIqbal, U., Qasim, H., Khan, A. K., Rashid, R., Nasreen, S., Mahmood, Q., \u0026amp; Khan, J. (2009). Surface and ground water quality risk assessment in district Attock Pakistan. World Appl. Sci. J, \u003cem\u003e7\u003c/em\u003e(8), 1029\u0026ndash;1036.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson, R. B., Carpenter, S. R., Dahm, C. N., McKnight, D. M., Naiman, R. J., Postel, S. L., \u0026amp; Running, S. W. (2001). Water in a changing world. Ecological applications, \u003cem\u003e11\u003c/em\u003e(4), 1027\u0026ndash;1045.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJamil, M. (2019). \u003cem\u003eRunning Dry: Water Scarcity in Pakistan\u003c/em\u003e. Retrieved from\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJimenez, B., \u0026amp; Asano, T. (2008). Water reclamation and reuse around the world. Water Reuse: an international survey of current practice, issues and needs, \u003cem\u003e14\u003c/em\u003e, 3\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahlown, M. A., \u0026amp; Majeed, A. (2003). Water-resources situation in Pakistan: challenges and future strategies. Water Resources in the South: present scenario and future prospects, \u003cem\u003e20\u003c/em\u003e, 33\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKakar, Y., \u0026amp; Bhatnagar, N. (1981). Ground water pollution due to industrial effluents in Ludhiana, India \u003cem\u003eStudies in Environmental Science\u003c/em\u003e (Vol.\u0026nbsp;17, pp.\u0026nbsp;265\u0026ndash;272): Elsevier.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan, M. A. A., Ashraf, I., \u0026amp; Siddiqui, M. T. (2021). A qualitative insight into the factors behind water scarcity in Punjab, Pakistan. Pakistan Journal of Agricultural Sciences, \u003cem\u003e58\u003c/em\u003e(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan, T., \u0026amp; Khan, H. (2019). Environmental sustainability of grey water footprints in Peshawar Basin: Current and future reduced flow scenarios for Kabul River. International Journal of Agricultural and Biological Engineering, \u003cem\u003e12\u003c/em\u003e(4), 162\u0026ndash;168.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan, T., Muhammad, S., Khan, B., \u0026amp; Khan, H. (2011). Investigating the levels of selected heavy metals in surface water of Shah Alam River (A tributary of River Kabul, Khyber Pakhtunkhwa). Journal of Himalayan Earth Sciences, \u003cem\u003e44\u003c/em\u003e(2), 71\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik, M. A., Azam, M., \u0026amp; Saboor, A. (2010). \u003cem\u003eWater quality status of upper KPK and northern areas of Pakistan\u003c/em\u003e: Pakistan Council of Research in Water Resources, Water Resources Research \u0026hellip;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik, T. (2019). Pak-Afghan Water Issue: A Case for Benefit-Sharing. Policy Perspectives, \u003cem\u003e16\u003c/em\u003e(1), 77\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsalek, J., Cisneros, B. J., Karamouz, M., Malmquist, P.-A., Goldenfum, J. A., \u0026amp; Chocat, B. (2008). \u003cem\u003eUrban water cycle processes and interactions: urban water series-UNESCO-IHP\u003c/em\u003e (Vol. 2): CRC press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcFarland, A. R., Larsen, L., Yeshitela, K., Engida, A. N., \u0026amp; Love, N. G. (2019). Guide for using green infrastructure in urban environments for stormwater management. Environmental science: Water research \u0026amp; technology, \u003cem\u003e5\u003c/em\u003e(4), 643\u0026ndash;659.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolden, D. (2013). \u003cem\u003eWater for food water for life: A comprehensive assessment of water management in agriculture\u003c/em\u003e: Routledge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNafees, M. Central Asia Journal No. 67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNgabirano, H., Byamugisha, D., \u0026amp; Ntambi, E. (2016). Effects of seasonal variations in physical parameters on quality of gravity flow water in Kyanamira Sub-County, Kabale District, Uganda.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQureshi, A. S. (2020). Groundwater governance in Pakistan: From colossal development to neglected management. Water, \u003cem\u003e12\u003c/em\u003e(11), 3017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQureshi, R., \u0026amp; Ashraf, M. (2019). Water security issues of agriculture in Pakistan. PAS Islamabad Pak, \u003cem\u003e1\u003c/em\u003e, 41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajaram, T., \u0026amp; Das, A. (2008). Water pollution by industrial effluents in India: Discharge scenarios and case for participatory ecosystem specific local regulation. Futures, \u003cem\u003e40\u003c/em\u003e(1), 56\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRanjan, A., \u0026amp; Chatterjee, D. (2020). Cutting across the Durand: Water dispute between Pakistan and Afghanistan on river Kabul. World Water Policy, \u003cem\u003e6\u003c/em\u003e(2), 246\u0026ndash;258.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeedb, S., Naseerb, R., Mehmoodb, T., Luqmang, N., Ahmadb, H., Sadiah, H.,.. . Rafiqueb, N. Drinking water quality of various sources in Peshawar, Mardan, Kohat and Swat districts of Khyber Pakhtunkhwa province, Pakistan.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSehar, S., Naz, I., Ali, M. I., \u0026amp; Ahmed, S. (2011). Monitoring of physico-chemical and microbiological analysis of under ground water samples of district Kallar Syedan, Rawalpindi-Pakistan. Research Journal of Chemical Sciences __________________________________________________________ ISSN, \u003cem\u003e2231\u003c/em\u003e, 606X.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelvakumar, S., Ramkumar, K., Chandrasekar, N., Magesh, N., \u0026amp; Kaliraj, S. (2017). Groundwater quality and its suitability for drinking and irrigational use in the Southern Tiruchirappalli district, Tamil Nadu, India. Applied Water Science, \u003cem\u003e7\u003c/em\u003e(1), 411\u0026ndash;420.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSonmez, O., \u0026amp; Bizimana, H. (2020). Flood hazard risk evaluation using fuzzy logic and weightage-based combination methods in geographic information system.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYousafzai, A. M., Khan, A. R., \u0026amp; Shakoori, A. (2008). An assessment of chemical pollution in River Kabul and its possible impacts on fisheries. Pakistan Journal of Zoology, \u003cem\u003e40\u003c/em\u003e(3), 199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaryab, A., Noori, A. R., Wegerich, K., \u0026amp; Kl\u0026oslash;ve, B. (2017). Assessment of water quality and quantity trends in Kabul aquifers with an outline for future drinking water supplies. Central Asian Journal of Water Research, \u003cem\u003e3\u003c/em\u003e(2), 3\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Surface Water Quality, Total Suspended Solid, Turbidity, Kriging interpolation, Water Quality Indicator, Water Quality Management","lastPublishedDoi":"10.21203/rs.3.rs-2349299/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2349299/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eWater plays a key role in the economic growth of an agricultural country. Pakistan is a farming country that uses almost 90% of its water resources for agricultural purposes. Khyber Pakhtunkhwa (KPK) province of Pakistan is blessed with extensive surface water resources. In addition to using groundwater resources for irrigation, large parts of its flat plains are irrigated with the Kabul River surface water. Due to large population growth and unregulated small/local scale industries in the region, surface water quality deteriorated with time. Which impacts people's health when polluted surface water is used for irrigation purposes. This research investigates the surface water quality of Kabul River's different tributaries and identifies the most critical locations in terms of water quality. Various physical, chemical, and biological parameters at ten sites on as many Kabul River tributaries are examined to investigate surface water quality. The distance and weightage-based identification method is used to identify the most critical and vulnerable locations for each parameter. The maximum seven critical values determined against the Bara River using the weightage-based method, i.e., 17.5, 5.95, 7.35, 27.65, 1.75, 0.35, and 10.45 for total alkalinity, sodium, total hardness, magnesium, total suspended solids, BOD, and turbidity. In comparison, the distance-based iteration method is used to identify the most vulnerable location against the specific parameter. All the results are finally compared with the interpolated values over the entire region using Kriging interpolation. The areas are mostly the same identified through the distance and weightage-based method. The results will guide the competent authority to install the monitoring station and treatment plants for specific parameters responsible for the critical location and to stop further contamination of the particular parameter near the threshold.\u003c/em\u003e \u003c/p\u003e","manuscriptTitle":"Distance and Weightage-based Identification of Most Critical and Vulnerable locations of Surface Water Pollution in Kabul River Tributaries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-14 16:04:20","doi":"10.21203/rs.3.rs-2349299/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-05-19T11:02:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-02-11T07:29:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4793fba5-86e0-42ed-80b8-646eb6ada626","date":"2023-02-01T03:39:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0ea50cb3-6b76-42fe-bcf9-ecaa4fb4db8f","date":"2023-02-01T01:36:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-02-01T01:34:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-12-27T02:51:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-12-12T18:54:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-12-12T18:35:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2022-12-06T09:25:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"410c498f-7b43-464a-8358-e4685f3761fc","owner":[],"postedDate":"December 14th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":17652274,"name":"Earth and environmental sciences/Environmental sciences"},{"id":17652275,"name":"Physical sciences/Engineering"}],"tags":[],"updatedAt":"2023-10-16T22:31:29+00:00","versionOfRecord":{"articleIdentity":"rs-2349299","link":"https://doi.org/10.1038/s41598-023-38018-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2023-07-18 21:42:47","publishedOnDateReadable":"July 18th, 2023"},"versionCreatedAt":"2022-12-14 16:04:20","video":"","vorDoi":"10.1038/s41598-023-38018-8","vorDoiUrl":"https://doi.org/10.1038/s41598-023-38018-8","workflowStages":[]},"version":"v1","identity":"rs-2349299","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2349299","identity":"rs-2349299","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}