water quality index research paper

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A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives

Sandra chidiac.

1 Department of Agricultural and Food Engineering, School of Engineering, Holy Spirit University of Kaslik, P.O.Box 446, Jounieh, Lebanon

Paula El Najjar

2 FMPS HOLDING BIOTECKNO s.a.l. Research & Quality Solutions, Naccash, P.O. Box 60 247, Beirut, Lebanon

Naim Ouaini

Youssef el rayess, desiree el azzi.

3 Syngenta, Environmental Safety, Avenue des Près, 78286 Guyancourt, France

Water quality index (WQI) is one of the most used tools to describe water quality. It is based on physical, chemical, and biological factors that are combined into a single value that ranges from 0 to 100 and involves 4 processes: (1) parameter selection, (2) transformation of the raw data into common scale, (3) providing weights and (4) aggregation of sub-index values. The background of WQI is presented in this review study. the stages of development, the progression of the field of study, the various WQIs, the benefits and drawbacks of each approach, and the most recent attempts at WQI studies. In order to grow and elaborate the index in several ways, WQIs should be linked to scientific breakthroughs (example: ecologically). Consequently, a sophisticated WQI that takes into account statistical methods, interactions between parameters, and scientific and technological improvement should be created in order to be used in future investigations.

Introduction

Water is the vital natural resource with social and economic values for human beings (Kumar 2018 ). Without water, existence of man would be threatened (Zhang 2017 ). The most important drinking sources in the world are surface water and groundwater (Paun et al. 2016 ).

Currently, more than 1.1 billion people do not have access to clean drinking water and it is estimated that nearly two-thirds of all nations will experience water stress by the year 2025 (Kumar 2018 ).

With the extensive social and economic growth, such as human factors, climate and hydrology may lead to accumulation of pollutants in the surface water that may result in gradual change of the water source quality (Shan 2011 ).

The optimal quantity and acceptable quality of water is one of the essential needs to survive as mentioned earlier, but the maintenance of an acceptable quality of water is a challenge in the sector of water resources management (Mukate et al. 2019 ). Accordingly, the water quality of water bodies can be tested through changes in physical, chemical and biological characteristics related to anthropogenic or natural phenomena (Britto et al. 2018 ).

Therefore, water quality of any specific water body can be tested using physical, chemical and biological parameters also called variables, by collecting samples and obtaining data at specific locations (Britto et al. 2018 ; Tyagi et al. 2013 ).

To that end, the suitability of water sources for human consumption has been described in terms of Water Quality Index (WQI), which is one of the most effective ways to describe the quality of water, by reducing the bulk of information into a single value ranging between 0 and 100 (Tyagi et al. 2013 ).

Hence, the objective of the study is to review the WQI concept by listing some of the important water quality indices used worldwide for water quality assessment, listing the advantages and disadvantages of the selected indices and finally reviewing some water quality studies worldwide.

Water quality index

History of water quality concept.

In the last decade of the twentieth century, many organizations involved in water control, used the water quality indices for water quality assessment (Paun et al. 2016 ). In the 1960’s, the water quality indices was introduced to assess the water quality in rivers (Hamlat et al. 2017 ).

Horton ( 1965 ), initially developed a system for rating water quality through index numbers, offering a tool for water pollution abatement, since the terms “water quality” and “pollution” are related. The first step to develop an index is to select a list of 10 variables for the index’s construction, which are: sewage treatment, dissolved oxygen (DO), pH, coliforms, electroconductivity (EC), carbon chloroform extract (CCE), alkalinity, chloride, temperature and obvious pollution. The next step is to assign a scale value between zero and 100 for each variable depending on the quality or concentration. The last step, is to designate to each variable is a relative weighting factor to show their importance and influence on the quality index (the higher the assigned weight, the more impact it has on the water quality index, consequently it is more important) (Horton 1965 ).

Later on, Brown et al. ( 1970 ) established a new water quality index (WQI) with nine variables: DO, coliforms, pH, temperature, biochemical oxygen demand (BOD), total phosphate, nitrate concentrations, turbidity and solid content based on a basic arithmetic weighting using arithmetic mean to calculate the rating of each variable. These rates are then converted not temporary weights. Finally, each temporary weight is divided by the sum of all the temporary weights in order to get the final weight of each variable (Kachroud et al. 2019a ; Shah and Joshi 2017 ). In 1973, Brown et al., considered that a geometric aggregation (a way to aggregate variables, and being more sensitive when a variable exceeds the norm) is better than an arithmetic one. The National Sanitation Foundation (NSF) supported this effort (Kachroud et al. 2019a ; Shah and Joshi 2017 ).

Steinhart et al. ( 1982 ) developed a novel environmental quality index (EQI) for the Great Lakes ecosystem in North America. Nine variables were selected for this index: biological, physical, chemical and toxic. These variables were: specific conductance or electroconductivity, chloride, total phosphorus, fecal Coliforms, chlorophyll a , suspended solids, obvious pollution (aesthetic state), toxic inorganic contaminants, and toxic organic contaminants. Raw data were converted to subindex and each subindex was multiplied by a weighting factor (a value of 0.1 for chemical, physical and biological factors but 0.15 for toxic substances). The final score ranged between 0 (poor quality) and 100 (best quality) (Lumb et al. 2011a ; Tirkey et al. 2015 ).

Dinius ( 1987 ), developed a WQI based on multiplicative aggregation having a scale expressed with values as percentage, where 100% expressed a perfect water quality (Shah and Joshi 2017 ).

In the mid 90’s, a new WQI was introduced to Canada by the province of British Columbia, and used as an increasing index to evaluate water quality (Lumb et al. 2011b ; Shah and Joshi 2017 ). A while after, the Water Quality Guidelines Task Group of the Canadian Council of Ministers of the Environment (CCME) modified the original British Columbia Water Quality Index (BCWQI) and endorsed it as the CCME WQI in 2001(Bharti and Katyal 2011 ; Lumb et al. 2011b ).

In 1996, the Watershed Enhancement Program (WEPWQI) was established in Dayton Ohio, including water quality variables, flow measurements and water clarity or turbidity. Taking into consideration pesticide and Polycyclic Aromatic Hydrocarbon (PAH) contamination, is what distinguished this index from the NSFWQI (Kachroud et al. 2019a , b ).

Liou et al. (2003) established a WQI in Taiwan on the Keya River. The index employed thirteen variables: Fecal coliforms, DO, ammonia nitrogen, BOD, suspended solids, turbidity, temperature, pH, toxicity, cadmium (Cd), lead (Pb), copper (Cu) and zinc (Zn). These variables were downsized to nine based on environmental and health significance: Fecal coliforms, DO, ammonia nitrogen, BOD, suspended solids, turbidity, temperature, pH and toxicity. Each variable was converted into an actual value ranging on a scale from 0 to 100 (worst to highest). This index is based on the geometric means (an aggregation function that could eliminate the ambiguous caused from smaller weightings) of the standardized values (Akhtar et al. 2021 ; Liou et al. 2004 ; Uddin et al. 2021 ).

Said et al. ( 2004 ) implemented a new WQI using the logarithmic aggregation applied in streams waterbodies in Florida (USA), based on only 5 variables: DO, total phosphate, turbidity, fecal coliforms and specific conductance. The main idea was to decrease the number of variables and change the aggregation method using the logarithmic aggregation (this function does not require any sub-indices and any standardization of the variables). This index ranged from 0 to 3, the latter being the ideal value (Akhtar et al. 2021 ; Kachroud et al. 2019a , b ; Said et al. 2004 ; Uddin et al. 2021 ).

The Malaysian WQI (MWQI) was carried out in 2007, including six variables: DO, BOD, Chemical Oxygen Demand (COD), Ammonia Nitrogen, suspended solids and pH. For each variable, a curve was established to transform the actual value of the variable into a non-dimensional sub-index value.

The next step is to determine the weighting of the variables by considering the experts panel opinions. The final score is determined using the additive aggregation formula (where sub-indices values and their weightings are summed), extending from 0 (polluted) to 100 (clean) (Uddin et al. 2021 ).

The Hanh and Almeida indices were established respectively in 2010 on surface water in Vietnam and 2012 on the Potrero de los Funes in Argentina, based on 8 (color, suspended solids, DO, BOD, COD, chloride, total coliforms and orthophosphate) and 10 (color, pH, COD, fecal coliforms, total coliforms, total phosphate, nitrates, detergent, enterococci and Escherichia coli .) water quality variables. Both indices were based on rating curve- based sum-indexing system (Uddin et al. 2021 ).

The most recent developed WQI model in the literature was carried out in 2017. This index tried to reduce uncertainty present in other water quality indices. The West Java Water Quality Index (WJWQI) applied in the Java Sea in Indonesia was based on thirteen crucial water quality variables: temperature, suspended solids, COD, DO, nitrite, total phosphate, detergent, phenol, chloride, Zn, Pb, mercury (Hg) and fecal coliforms. Using two screening steps (based on statistical assessment), parameter (variable) redundancy was determined to only 9: temperature, suspended solids, COD, DO, nitrite, total phosphate, detergent, phenol and chloride. Sub-indices were obtained for those nine variables and weights were allocated based on expert opinions, using the same multiplicative aggregation as the NSFWQI. The WJWQI suggested 5 quality classes ranging from poor (5–25) to excellent (90–100) (Uddin et al. 2021 ).

Phases of WQI development

Mainly, WQI concept is based on many factors as displayed in Fig.  1 and described in the following steps:

The selection is carried out based on the management objectives and the environmental characteristics of the research area (Yan et al. 2015 ). Many variables are recommended, since they have a considerable impact on water quality and derive from 5 classes namely, oxygen level, eutrophication, health aspects, physical characteristics and dissolved substances (Tyagi et al. 2013 ).

Different statistical approach can be used for transformation, all parameters are transformed from raw data that have different dimensions and units (ppm, saturation, percentage etc.) into a common scale, a non-dimensional scale and sub-indices are generated (Poonam et al. 2013 ; Tirkey et al. 2015 ).

Weights are assigned to each parameter according to their importance and their impact on water quality, expert opinion is needed to assign weights (Tirkey et al. 2015 ). Weightage depends on the permissible limits assigned by International and National agencies in water drinking (Shah and Joshi 2017 ).

WQI is the sum of rating and weightage of all the parameters (Tripathi and Singal 2019 ).

It is important to note that in some indices, statistical approaches are commonly used such as factor analysis (FA), principal component analysis (PCA), discriminant analysis (DA) and cluster analysis (CA). Using these statistical approaches improves accuracy of the index and reduce subjective assumptions (Tirkey et al. 2015 ).

Evolution of WQI research

According to Scopus ( 2022 ), the yearly evolution of WQI's research is illustrated in Fig.  2 (from 1978 till 2022).

Evolution of WQI research per year (Scopus 2022 )

Overall, it is clear that the number of research has grown over time, especially in the most recent years. The number of studies remained shy between 1975 and 1988 (ranging from 1 to 13 research). In 1998, the number improved to 46 studies and increased gradually to 466 publications in 2011.The WQI's studies have grown significantly over the past decade, demonstrating that the WQI has become a significant research topic with the goal of reaching its maximum in 2022 (1316 studies) (Scopus, 2022 ).

Per country

In Fig.  3 , the development of WQI research is depicted visually per country from 1975 to 2022.

Evolution of WQI research per country (Scopus 2022 )

According to Scopus ( 2022 ), the top three countries were China, India and the United States, with 2356, 1678 and 1241 studies, respectively. Iran, Brazil, and Italy occupy the fourth, fifth, and sixth spots, respectively (409, 375 and 336 study). Malaysia and Spain have approximately the same number of studies, respectively 321 and 320 study. The studies in the remaining countries decrease gradually from 303 document in Spain to 210 documents in Turkey. This demonstrates that developing nations, like India, place a high value on the development of water quality protection even though they lack strong economic power, cutting-edge technology, and a top-notch scientific research team. This is because water quality is crucial to the long-term social and economic development of those nations (Zhang 2019 ).

Different methods for WQI determination

Water quality indices are tools to determine water quality. Those indices demand basic concepts and knowledge about water issues (Singh et al. 2013 ). There are many water quality indices such as the: National Sanitation Foundation Water Quality Index (NSFWQI), Canadian Council of Ministers of Environment Water Quality Index (CCMEWQI), Oregon Water Quality Index (OWQI), and Weight Arithmetic Water Quality Index (WAWQI) (Paun et al. 2016 ).

These water quality indices are applied in particular areas, based on many parameters compared to specific regional standards. Moreover, they are used to illustrate annual cycles, spatio-temporal variations and trends in water quality (Paun et al. 2016 ). That is to say that, these indices reflect the rank of water quality in lakes, streams, rivers, and reservoirs (Kizar 2018 ).

Accordingly, in this section a general review of available worldwide used indices is presented.

National sanitation foundation (NSFWQI)

The NSFWQI was developed in 1970 by the National Sanitation Foundation (NSF) of the United States (Hamlat et al. 2017 ; Samadi et al. 2015 ). This WQI has been widely field tested and is used to calculate and evaluate the WQI of many water bodies (Hamlat et al. 2017 ). However, this index belongs to the public indices group. It represents a general water quality and does not take into account the water’s use capacities, furthermore, it ignores all types of water consumption in the evaluation process (Bharti and Katyal 2011 ; Ewaid 2017 ).

The NSFWQI has been widely applied and accepted in Asian, African and European countries (Singh et al. 2013 ), and is based on the analysis of nine variables or parameters, such as, BOD, DO, Nitrate (NO 3 ), Total Phosphate (PO 4 ), Temperature, Turbidity, Total Solids(TS), pH, and Fecal Coliforms (FC).

Some of the index parameters have different importance, therefore, a weighted mean for each parameter is assigned, based on expert opinion which have grounded their opinions on the environmental significance, the recommended principles and uses of water body and the sum of these weights is equal to 1 (Table ​ (Table1) 1 ) (Ewaid 2017 ; Uddin et al. 2021 ).

Weight scores of the nine NSF-WQI parameters

Due to environmental issues, the NSFWQI has changed overtime. The TS parameter was substituted by the Total Dissolved Solids (TDS) or Total Suspended Solids (TSS), the Total Phosphate by orthophosphate, and the FC by E. coli (Oliveira et al. 2019 ).

The mathematical expression of the NSFWQI is given by the following Eq. ( 1 ) (Tyagi et al. 2013 ):

where, Qi is the sub-index for ith water quality parameter. Wi is the weight associated with ith water quality parameter. n is the number of water quality parameters.

This method ranges from 0 to 100, where 100 represents perfect water quality conditions, while zero indicates water that is not suitable for the use and needs further treatment (Samadi et al. 2015 ).

The ratings are defined in the following Table ​ Table2 2 .

Colors and definition used in the classification of pollution using NSFWQI (Roozbahani and Boldaji 2013 )

In 1972, the Dinius index (DWQI) happened to be the second modified version of the NSF (USA). Expended in 1987 using the Delphi method, the DWQI included twelve parameters (with their assigned weights): Temperature (0.077), color (0.063), pH (0.077), DO (0.109), BOD (0.097), EC (0.079), alkalinity (0.063), chloride (0.074), coliform count (0.090), E. coli (0.116). total hardness (0.065) and nitrate (0.090). Without any conversion process, the DWQI used the measured variable concentrations directly as the sub-index values (Kachroud et al. 2019b ; Uddin et al. 2021 ).

Sukmawati and Rusni assessed in 2018 the water quality in Beratan lake (Bali), choosing five representative stations for water sampling representing each side of the lake, using the NSFWQI. NSFWQI’s nine parameters mentioned above were measured in each station. The findings indicated that the NSFWQI for the Beratan lake was seventy-eight suggesting a good water quality. Despite this, both pH and FC were below the required score (Sukmawati and Rusni 2019 ).

The NSFWQI indicated a good water quality while having an inadequate value for fecal coliforms and pH. For that reason, WQIs must be adapted and developed so that any minor change in the value of any parameter affects the total value of the water quality index.

A study conducted by Zhan et al. ( 2021 ) , concerning the monitoring of water quality and examining WQI trends of raw water in Macao (China) was established from 2002 to 2019 adopting the NSFWQI. NSFWQI's initial model included nine parameters (DO, FC, pH, BOD, temperature, total phosphates, and nitrates), each parameter was given a weight and the parameters used had a significant impact on the WQI calculation outcomes. Two sets of possible parameters were investigated in this study in order to determine the impact of various parameters. The first option was to keep the original 9-parameter model, however, in the second scenario, up to twenty-one parameters were chosen, selected by Principal Component Analysis (PCA).

The latter statistical method was used to learn more about the primary elements that contributed to water quality variations, and to calculate the impact of each attribute on the quality of raw water. Based on the PCA results, the 21-parameter model was chosen. The results showed that the quality of raw water in Macao has been relatively stable in the period of interest and appeared an upward trend overall. Furthermore, the outcome of environmental elements, such as natural events, the region's hydrology and meteorology, can have a significant impact on water quality. On the other hand, Macao's raw water quality met China's Class III water quality requirements and the raw water pollution was relatively low. Consequently, human activities didn’t have a significant impact on water quality due to effective treatment and protection measures (Zhan et al. 2021 ).

Tampo et al. ( 2022 ) undertook a recent study in Adjougba (Togo), in the valley of Zio River. Water samples were collected from the surface water (SW), ground water (GW) and treated wastewater (TWW), intending to compare the water quality of these resources for irrigation and domestic use.

Hence, WQIs, water suitability indicators for irrigation purposes (WSI-IPs) and raw water quality parameters were compared using statistical analysis (factor analysis and Spearman’s correlation).

Moreover, the results proposed that he water resources are suitable for irrigation and domestic use: TWW suitable for irrigation use, GW suitable for domestic use and SW suitable for irrigation use.

The NSFWQI and overall index of pollution (OPI) parameters were tested, and the results demonstrated that the sodium absorption ratio, EC, residual sodium carbonate, Chloride and FC are the most effective parameters for determining if water is suitable for irrigation.

On the other hand, EC, DO, pH, turbidity, COD, hardness, FC, nitrates, national sanitation foundation's water quality index (NSFWQI), and overall index of pollution (OPI) are the most reliable in the detection of water suitability for domestic use (Tampo et al. 2022 ).

Following these studies, it is worth examining the NSFWQI. This index can be used with other WQI models in studies on rivers, lakes etc., since one index can show different results than another index, in view of the fact that some indices might be affected by other variations such as seasonal variation.

Additionally, the NSFWQI should be developed and adapted to each river, so that any change in any value will affect the entire water quality. It is unhelpful to have a good water quality yet a low score of a parameter that can affect human health (case of FC).

Canadian council of ministers of the environment water quality index (CCMEWQI)

The Canadian Water Quality Index adopted the conceptual model of the British Colombia Water Quality Index (BCWQI), based on relative sub-indices (Kizar 2018 ).

The CCMEWQI provides a water quality assessment for the suitability of water bodies, to support aquatic life in specific monitoring sites in Canada (Paun et al. 2016 ). In addition, this index gives information about the water quality for both management and the public. It can furthermore be applied in many water agencies in various countries with slight modification (Tyagi et al. 2013 ).

The CCMEWQI method simplifies the complex and technical data. It tests the multi-variable water quality data and compares the data to benchmarks determined by the user (Tirkey et al. 2015 ). The sampling protocol requires at least four parameters sampled at least four times but does not indicate which ones should be used; the user must decide ( Uddin et al. 2021 ). Yet, the parameters may vary from one station to another (Tyagi et al. 2013 ).

After the water body, the objective and the period of time have been defined the three factors of the CWQI are calculated (Baghapour et al. 2013 ; Canadian Council of Ministers of the Environment 1999 ):

• The scope (F1) represents the percentage of variables that failed to meet the objective (above or below the acceptable range of the selected parameter) at least once (failed variables), relative to the total number of variables. F1 = Number of failed variables Total number of variables × 100 2
• The frequency (F2) represents the percentage of tests which do not meet the objectives (above or below the acceptable range of the selected parameter) (failed tests). F2 = Number of failed tests Total number of tests × 100 3

For the cases in which the test value must not fall below the objective:

• The normalized sum of excursions, or nse , is calculated by summing the excursions of individual tests from their objectives and diving by the total number of tests (both meetings and not meeting their objectives): nse = ∑ i = 1 n excursion i number of tests 6
• F3 is then calculated an asymptotic function that scales the normalized sum of the excursions from objectives (nse) to yield a range between 0 and 100: F3 = nse 0.01 nse + 0.01 7

Finally, the CMEWQI can be obtained from the following equation, where the index changes in direct proportion to changes in all three factors.

where 1.732 is a scaling factor and normalizes the resultant values to a range between 0 and 100, where 0 refers to the worst quality and one hundred represents the best water quality.

Once the CCME WQI value has been determined, water quality in ranked as shown in Table ​ Table3Table 3

Water quality categorizations according to CCMEWQI (Kizar 2018 ; Canadian Council of Ministers of the Environment 1999 )

Ramírez-Morales et al. ( 2021 ) investigated in their study the measuring of pesticides and water quality indices in three agriculturally impacted micro catchments in Costa Rica between 2012 and 2014. Surface water and sediment samples were obtained during the monitoring experiment.

The specifications of the water included: Pesticides, temperature, DO, oxygen saturation, BOD, TP, NO3, sulfate, ammonium, COD, conductivity, pH and TSS.

Sediment parameters included forty-two pesticides with different families including carbamate, triazine, organophosphate, phthalimide, pyrethroid, uracil, benzimidazole, substituted urea, organochlorine, imidazole, oxadiazole, diphenyl ether and bridged diphenyl.

WQIs are effective tools since they combine information from several variables into a broad picture of the water body's state. Two WQIs were calculated using the physicochemical parameters: The Canadian Council of Ministers of the Environment (CCME) WQI and the National Sanitation Foundation (NSF) WQI.

These were chosen since they are both extensively used and use different criteria to determine water quality: The NSF WQI has fixed parameters, weights, and threshold values, whereas the CCME has parameters and threshold values that are customizable.

The assessment of water quality using physico-chemical characteristics and the WQI revealed that the CCME WQI and the NSF WQI have distinct criteria. CCME WQI categorized sampling point as marginal/bad quality, while most sampling locations were categorized as good quality in the NSF WQI. Seemingly, the water quality classifications appeared to be affected by seasonal variations: during the wet season, the majority of the CCME WQI values deteriorated, implying that precipitation and runoff introduced debris into the riverbed. Thus, it’s crucial to compare WQIs because they use various factors, criteria, and threshold values, which might lead to different outcomes (Ramírez-Morales et al. 2021 ).

Yotova et al. ( 2021 ) directed an analysis on the Mesta River located between Greece and Bulgaria. The Bulgarian section of the Mesta River basin, which is under the supervision of the West-Aegean Region Basin Directorate, was being researched. The goal was to evaluate the surface water quality of ten points of the river using a novel approach that combines composite WQI developed by the CCME and Self organizing map (SOM) on the required monitoring data that include: DO, pH, EC, ammonium, nitrite, nitrate, total phosphate, BOD and TSS.

The use of WQI factors in SOM calculations allows for the identification of specific WQI profiles for various object groups and identifying groupings of river basin which have similar sampling conditions. The use of both could reveal and estimate the origin and magnitude of anthropogenic pressure. In addition, it might be determined that untreated residential wastewaters are to blame for deviations from high quality requirements in the Mesta River catchment.

Interestingly, this study reveals that WQI appear more accurate and specific when combined with a statistical test such as the SOM (Yotova et al. 2021 ).

Oregon water quality index (OWQI)

The Oregon Water Quality Index is a single number that creates a score to evaluate the water quality of Oregon’s stream and apply this method in other geographical region (Hamlat et al. 2017 ; Singh et al. 2013 ). The OWQI was widely accepted and applied in Oregon (USA) and Idaho (USA) (Sutadian et al. 2016 ).

Additionally, the OWQI is a variant of the NSFWQI, and is used to assess water quality for swimming and fishing, it is also used to manage major streams (Lumb et al. 2011b ). Since the introduction of the OWQI in 1970, the science of water quality has improved noticeably, and since 1978, index developers have benefited from increasing understanding of stream functionality (Bharti and Katyal 2011 ). The Oregon index belongs to the specific consumption indices group. It is a water classification based on the kind of consumption and application such as drinking, industrial, etc. (Shah and Joshi 2017 ).

The original OWQI dropped off in 1983, due to excessive resources required for calculating and reporting results. However, improvement in software and computer hardware availability, in addition to the desire for an accessible water quality information, renewed interest in the index (Cude 2001 ).

Simplicity, availability of required quality parameters, and the determination of sub-indexes by curve or analytical relations are some advantages of this approach (Darvishi et al. 2016a ). The process combines eight variables including temperature, dissolved oxygen (percent saturation and concentration), biochemical oxygen demand (BOD), pH, total solids, ammonia and nitrate nitrogen, total phosphorous and bacteria (Brown 2019 ). Equal weight parameters were used for this index and has the same effect on the final factor (Darvishi et al. 2016a ; Sutadian et al. 2016 ).

The Oregon index is calculated by the following Eq.  9 (Darvishi et al. 2016a ):

where,n is the number of parameters (n = 8) SI i is the value of parameter i.

Furthermore, the OWQI scores range from 10 for the worse case to 100 as the ideal water quality illustrated in the following Table ​ Table4 4 (Brown 2019 ).

Average values of river water index according to OWQI index (Darvishi et al. 2016a )

Kareem et al. ( 2021 ) using three water quality indices, attempted to analyze the Euphrates River (Iraq) water quality for irrigation purposes in three different stations: WAWQI, CCMEWQI AND OWQI.

For fifteen parameters, the annual average value was calculated, which included: pH, BOD, Turbidity, orthophosphate, Total Hardness, Sulphate, Nitrate, Alkalinity, Potassium Sodium, Magnesium, Chloride, DO, Calcium and TDS.

The OWQI showed that the river is “very poor”, and since the sub-index of the OWQI does not rely on standard-parameter compliance, there are no differences between the two inclusion and exclusion scenarios, which is not the case in both WAWQI and CCMEWQI (Kareem et al. 2021 ).

Similarly, the OWQI showed a very bad quality category, and it is unfit for human consumption, compared to the NSFWQI and Wilcox indices who both showed a better quality of water in Darvishi et al., study conducted on the Talar River (Iran) (Darvishi et al. 2016b ).

Weighted arithmetic water quality index (WAWQI)

The weighted arithmetic index is used to calculate the treated water quality index, in other terms, this method classifies the water quality according to the degree of purity by using the most commonly measured water quality variables (Kizar 2018 ; Paun et al. 2016 ).This procedure has been widely used by scientists (Singh et al. 2013 ).

Three steps are essential in order to calculate the WAWQI:

Qn is the quality rating for the nth water quality parameter.

Vn is the observed value of the nth parameter at a given sampling station.

Vo is the ideal value of the nth parameter in a pure water.

Sn is the standard permissible value of the nth parameter.

The quality rating or sub index corresponding to nth parameter is a number reflecting the relative value of this parameter in polluted water with respect to its permissible standard value (Yogendra & Puttaiah 2008 ).

Wn is the unit weight for the nth parameter.

K is the constant of proportionality.

Sn is the standard value of the nth parameter.

• The overall WQI is the aggregation of the quality rating (Qn) and the unit weight (Wn) linearly (Jena et al. 2013 ): WQI = ∑ Qn Wn ∑ Wn 12

After calculating the WQI, the measurement scale classifies the water quality from “unsuitable water” to “excellent water quality” as given in the following Table ​ Table5 5 .

WAWQI and status of water quality (Yogendra and Puttaiah 2008 )

Sarwar et al. ( 2020 ) carried out a study in Chaugachcha and Manirampur Upazila of Jashore District (Bangladesh). The goal of this study was to determine the quality of groundwater and its appropriateness for drinking, using the WAWQI including nine parameters: turbidity, EC, pH, TDS, nitrate, ammonium, sodium, potassium and iron. Many samplings point was taken from Chaugachcha and Manirampur, and WQI differences were indicated (ranging from very poor to excellent). These variations in WQI were very certainly attributable to variances in geographical location. Another possibility could be variations in the parent materials from which the soil was created, which should be confirmed using experimental data. It is worth mentioning that every selected parameter was taken into consideration during calculation. Similarly, the water quality differed in Manirampur due to the elements contained in the water samples that had a big impact on the water quality (Sarwar et al. 2020 ).

In 2021, García-Ávila et al. undertook a comparative study between the CCMEWQI and WAWQI for the purpose of determining the water quality in the city of Azogues (Ecuador). Twelve parameters were analyzed: pH, turbidity, color, total dissolved solids, electrical conductivity, total hardness, alkalinity, nitrates, phosphates, sulfates, chlorides and residual chlorine over 6 months. The average WAWQI value was calculated suggesting that 16.67% of the distribution system was of 'excellent' quality and 83.33% was of 'good' quality, while the CCMEWQI indicated that 100% of the system was of ‘excellent’ quality.

This difference designated that the parameters having a low maximum allowable concentration have an impact on WAWQI and that WAWQI is a valuable tool to determine the quality of drinking water and have a better understanding of it (García-Ávila et al. 2022a , b ).

Additional water quality indices

The earliest WQI was based on a mathematical function that sums up all sub-indices, as detailed in the 2.1. History of water quality concept section (Aljanabi et al. 2021 ). The Dinius index (1972), the OWQI (1980), and the West Java index (2017) were later modified from the Horton index, which served as a paradigm for later WQI development (Banda and Kumarasamy 2020 ).

Based on eleven physical, chemical, organic, and microbiological factors, the Scottish Research Development Department (SRDDWQI) created in 1976 was based on the NSFWQI and Delphi methods used in Iran, Romania, and Portugal. Modified into the Bascaron index (1979) in Spain, which was based on 26 parameters that were unevenly weighted with a subjective representation that allowed an overestimation of the contamination level. The House index (1989) in the UK valued the parameters directly as sub-indices. The altered version was adopted as Croatia's Dalmatian index in 1999.

The Ross WQI (1977) was created in the USA using only 4 parameters and did not develop into any further indices.

In 1982, the Dalmatian and House WQI were used to create the Environmental Quality Index, which is detailed in Sect.  2.1 . This index continues to be difficult to understand and less powerful than other indices (Lumb et al. 2011a ; Uddin et al. 2021 ).

The Smith index (1990), is based on 7 factors and the Delphi technique in New Zealand, attempts to eliminate eclipsing difficulties and does not apply any weighting, raising concerns about the index's accuracy (Aljanabi et al. 2021 ; Banda and Kumarasamy 2020 ; Uddin et al. 2021 ).

The Dojildo index (1994) was based on 26 flexible, unweighted parameters and does not represent the water's total quality.

With the absence of essential parameters, the eclipse problem is a type of fixed-parameter selection. The Liou index (2004) was established in Taiwan to evaluate the Keya River based on 6 water characteristics that were immediately used into sub-index values. Additionally, because of the aggregation function, uncertainty is unrelated to the lowest sub-index ranking (Banda and Kumarasamy 2020 ; Uddin et al. 2021 ).

Said index (2004) assessed water quality using only 4 parameters, which is thought to be a deficient number for accuracy and a comprehensive picture of the water quality. Furthermore, a fixed parameter system prevents the addition of any new parameters.

Later, the Hanh index (2010), which used hybrid aggregation methods and gave an ambiguous final result, was developed from the Said index.

In addition to eliminating hazardous and biological indicators, the Malaysia River WQI (MRWQI developed in the 2.1 section) (2007) was an unfair and closed system that was relied on an expert's judgment, which is seen as being subjective and may produce ambiguous findings (Banda and Kumarasamy 2020 ; Uddin et al. 2021 ).

Table illustrated the main data of the studies published during 2020–2022 on water quality assessments and their major findings:

Advantages and disadvantages of the selected water quality indices

A comparison of the selected indices is done by listing the advantages and disadvantages of every index listed in the Table ​ Table7 7 below.

New attempts of WQI studies

Many studies were conducted to test the water quality of rivers, dams, groundwater, etc. using multiple water quality indices throughout the years. Various studies have been portrayed here in.

Massoud ( 2012 ) observed during a 5-year monitoring period, in order to classify the spatial and temporal variability and classify the water quality along a recreational section of the Damour river using a weighted WQI from nine physicochemical parameters measured during dry season. The WWQI scale ranged between “very bad” if the WQI falls in the range 0–25, to “excellent” if it falls in the range 91–100. The results revealed that the water quality of the Damour river if generally affected by the activities taking place along the watershed. The best quality was found in the upper sites and the worst at the estuary, due to recreational activities. If the Damour river is to be utilized it will require treatment prior any utilization (Massoud 2012 ).

Rubio-Arias et al. ( 2012 ) conducted a study in the Luis L. Leon dam located in Mexico. Monthly samples were collected at 10 random points of the dam at different depths, a total of 220 samples were collected and analyzed. Eleven parameters were considered for the WQI calculation, and WQI was calculated using the Weighted WQI equation and could be classified according to the following ranges: < 2.3 poor; from 2.3 to 2.8 good; and > 2.8 excellent. Rubio-Arias et al., remarked that the water could be categorized as good during the entire year. Nonetheless, some water points could be classified as poor due to some anthropogenic activities such as intensive farming, agricultural practices, dynamic urban growth, etc. This study confirms that water quality declined after the rainy season (Rubio-Arias et al. 2012 ).

In the same way, Haydar et al. ( 2014 ) evaluated the physical, chemical and microbiological characteristics of water in the upper and lower Litani basin, as well as in the lake of Qaraaoun. The samples were collected during the seasons of 2011–2012 from the determined sites and analyzed by PCA and the statistical computations of the physico-chemical parameters to extract correlation between variables. Thus, the statistical computations of the physico-chemical parameters showed a correlation between some parameters such as TDS, EC, Ammonium, Nitrate, Potassium and Phosphate. Different seasons revealed the presence of either mineral or anthropogenic or both sources of pollution caused by human interference from municipal wastewater and agricultural purposes discharged into the river. In addition, temporal effects were associated with seasonal variations of river flow, which caused the dilution if pollutants and, hence, variations in water quality (Haydar et al. 2014 ).

Another study conducted by Chaurasia et al., ( 2018 ), proposed a groundwater quality assessment in India using the WAWQI. Twenty-two parameters were taken into consideration for this assessment, however, only eight important parameters were chosen to calculate the WQI. The rating of water quality shows that the ground water in 20% of the study area is not suitable for drinking purpose and pollution load is comparatively high during rainy and summer seasons. Additionally, the study suggests that priority should be given to water quality monitoring and its management to protect the groundwater resource from contamination as well as provide technology to make the groundwater fit for domestic and drinking (Chaurasia et al. 2018 ).

Daou et al. ( 2018 ) evaluated the water quality of four major Lebanese rivers located in the four corners of Lebanon: Damour, Ibrahim, Kadisha and Orontes during the four seasons of the year 2010–2011. The assessment was done through the monitoring of a wide range of physical, chemical and microbiological parameters, these parameters were screened using PCA. PCA was able to discriminate each of the four rivers according to a different trophic state. The Ibrahim River polluted by mineral discharge from marble industries in its surroundings, as well as anthropogenic pollutants, and the Kadisha river polluted by anthropogenic wastes seemed to have the worst water quality. This large-scale evaluation of these four Lebanese rivers can serve as a water mass reference model (Daou et al. 2018 ).

Moreover, some studies compared many WQI methods. Kizar ( 2018 ), carried out a study on Shatt Al-Kufa in Iraq, nine locations and twelve parameters were selected. The water quality was calculated using two methods, the WAWQI and CWQI. The results revealed the same ranking of the river for both methods, in both methods the index decreased in winter and improved in other seasons (Kizar 2018 ).

On the other hand, Zotou et al. ( 2018 ), undertook a research on the Polyphytos Reservoir in Greece, taking into consideration thirteen water parameters and applying 5 WQIs: Prati’s Index of Pollution (developed in 1971, based on thirteen parameter and mathematical functions to convert the pollution concentration into new units. The results of PI classified water quality into medium classes (Gupta and Gupta 2021 ). Bhargava’s WQI (established in 1983, the BWQI categorize the parameters according to their type: bacterial indicators, heavy metals and toxins, physical parameters and organic and inorganic substances. The BWQI tends to classify the water quality into higher quality classes, which is the case in the mentioned study (Gupta and Gupta 2021 ). Oregon WQI, Dinius second index, Weighted Arithmetic WQI, in addition to the NSF and CCMEWQI. The results showed that Bhargava and NSF indices tend to classify the reservoir into superior quality classes, Prati’s and Dinius indices fall mainly into the middle classes of the quality ranking, while CCME and Oregon could be considered as “stricter” since they give results which range steadily between the lower quality classes (Zotou et al. 2018 ).

In their study, Ugochukwu et al. ( 2019 ) investigated the effects of acid mine drainage, waste discharge into the Ekulu River in Nigeria and other anthropogenic activities on the water quality of the river. The study was performed between two seasons, the rainy and dry season. Samples were collected in both seasons, furthermore, the physic-chemistry parameters and the heavy metals were analyzed. WQI procedure was estimated by assigning weights and relative weights to the parameters, ranking from “excellent water” (< 50) to “unsuitable for drinking” (> 300). The results showed the presence of heavy metals such as lead and cadmium deriving from acid mine drainage. In addition, the water quality index for all the locations in both seasons showed that the water ranked from “very poor” to “unsuitable for drinking”, therefore the water should be treated before any consumption, and that enough information to guide new implementations for river protection and public health was provided (Ugochukwu et al. 2019 ).

The latest study in Lebanon related to WQI was carried out by El Najjar et al. ( 2019 ), the purpose of the study was to evaluate the water quality of the Ibrahim River, one of the main Lebanese rivers. The samples were collected during fifteen months, and a total of twenty-eight physico-chemical and microbiological parameters were tested. The parameters were reduced to nine using the Principal Component Analysis (PCA) and Pearson Correlation. The Ibrahim WQI (IWQI) was finally calculated using these nine parameters and ranged between 0 and 25 referring to a “very bad” water quality, and between 91 and 100 referring to an “excellent” water quality. The IWQI showed a seasonal variation, with a medium quality during low -water periods and a good one during high-water periods (El Najjar et al. 2019 ).

WQI is a simple tool that gives a single value to water quality taking into consideration a specific number of physical, chemical, and biological parameters also called variables in order to represent water quality in an easy and understandable way. Water quality indices are used to assess water quality of different water bodies, and different sources. Each index is used according to the purpose of the assessment. The study reviewed the most important indices used in water quality, their mathematical forms and composition along with their advantages and disadvantages. These indices utilize parameters and are carried out by experts and government agencies globally. Nevertheless, there is no index so far that can be universally applied by water agencies, users and administrators from different countries, despite the efforts of researchers around the world (Paun et al. 2016 ). The study also reviewed some attempts on different water bodies utilizing different water quality indices, and the main studies performed in Lebanon on Lebanese rivers in order to determine the quality of the rivers (Table ​ (Table6 6 ).

Various research projects carried out on WQIs

As mentioned in the article (Table ​ (Table7); 7 ); WQIs may undergo some limitations. Some indices could be biased, others are not specific, and they may not get affected by the value of an important parameter. Therefore, there is no interaction between the parameters.

Moreover, many studies exhibited a combination between WQIs and statistical techniques and analysis (such as the PCA, Pearson’s correlation etc.). with a view to obtain the relation between the parameters and which parameter might affect the water quality.

In other research, authors compared many WQIs to check the difference of water quality according to each index. Each index can provide different values depending on the sensitivity of the parameter. For that reason, WQIs should be connected to scientific advancements to develop and elaborate the index in many ways (example: ecologically). Therefore, an advanced WQI should be developed including first statistical techniques, such as Pearson correlation and multivariate statistical approach mainly Principal Component Analysis (PCA) and Cluster Analysis (CA), in order to determine secondly the interactions and correlations between the parameters such as TDS and EC, TDS and total alkalinity, total alkalinity and chloride, temperature and bacteriological parameters, consequently, a single parameter could be selected as representative of others. Finally, scientific and technological advancement for future studies such as GIS techniques, fuzzy logic technology to assess and enhance the water quality indices and cellphone-based sensors for water quality monitoring should be used.

Declarations

All authors declare that they have no conflict of interest.

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Contributor Information

Sandra Chidiac, Email: [email protected] .

Desiree El Azzi, Email: moc.atnegnys@izza_le.eerised .

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Water quality indices: challenges and application limits in the literature.

1. Introduction

2. historical evolution of w q i (water quality index) concept, 3. evidence of disagreement between water quality indices results in published works, 4. disagreement using the same w q i, 4.1. different wqis lead to contradiction, 4.2. modification of the aggregation method and of the nature of variables, 4.3. test of four wqis using the same dataset, 5. discussion, 5.1. choices of variables, 5.2. the weighting methods, 5.3. the aggregation methods, 5.4. fuzzy logic, 6. conclusions, author contributions, conflicts of interest, appendix a. weighted arithmetic average, appendix a.1. horton’s index, appendix a.2. first national sanitation foundation water quality index (nsfwqi) in 1971.

Appendix A.3. Prati’s Pollution Index

Appendix A.4. First Dinius Water Quality Index (DWQI) in 1972

Appendix A.5. Method of Ramakrishaniah [ 62 ] (RWQI)

Appendix B. Weighted Geometric Average

Appendix b.1. second national sanitation foundation water quality index (nsfwqi) in 1973, appendix b.2. bhargava method.

Appendix B.3. Second Dinius Water Quality Index

Appendix C. Weighted and Unweighted Harmonic Square Average

Appendix c.1. canadian council of ministers of the environment water quality index (ccmewqi).

Appendix C.2. Oregon Water Quality Index (OWQI)

Appendix d. logarithmic aggregations, appendix d.1. the wqi proposed by tiwari and mishra (tmwqi).

Appendix D.2. New Water Quality Index Proposed by Said et al.

Appendix e. fuzzy logics (fwqi).

• determination of the quality classes for the measured variables;
• arrangement of the variables according to their classes into the four groups;
• application of membership functions ( m f ) to standardize the natural measurement scales of the quality variable into a measurement of the quality degree (membership grade). In this step, four membership functions are used. m f i is the membership function of the observed value i depending on the limits given in Table A9 and Table A10 . The reviewed membership functions from Icaga’s paper are defined as below: m f 1 = 1 for x < a 1 − [ x − a b 1 − a ] for a ≤ x ≤ b 1 0 for otherwise m f 2 = x − a b 1 − a for a ≤ x ≤ b 1 1 for b 1 ≤ x ≤ b 2 1 − [ x − b 2 c 1 − b 2 ] for b 2 ≤ x ≤ c 1 0 for otherwise m f 3 = x − b 2 c 1 − b 2 for b 2 ≤ x ≤ c 1 1 for c 1 ≤ x ≤ c 2 1 − [ x − c 2 d − c 2 ] for c 2 ≤ x ≤ d 0 for otherwise m f 4 = x − c 2 d − c 2 for c 2 ≤ x ≤ d 1 for d < x 0 for otherwise
• four rule bases are successively used: if Q V i 1 = I V or Q V i 2 = I V or … or Q V i n = I then O u t p u t = I if Q V j 1 = I I I or Q V j 2 = I I I or … or Q V j n = I I then O u t p u t = I I if Q V k = I I or Q V k 2 = I I or … or Q V k n = I I I then O u t p u t = I I I if Q V l 1 = I or Q V l 2 = I or … or Q V l n = I V then O u t p u t = I V where Q V i is the quality variable; I , I I , I I I , I V are the quality classes in conventional classification; N is the number of quality variables. In the rule bases the “or” operators are used to obtain maximum values.
• Using the fuzzy algorithm: In fuzzy algorithm, the Mamdani [ 75 ] approach is used. Fuzzy inferences of the groups are determined using grades of membership functions of the variables;
• Defuzzification of the inferences to obtain an index whose value ranges (0;100) interval using Centroid methods, which calculate the center of gravity of the output function.

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Kachroud, M.; Trolard, F.; Kefi, M.; Jebari, S.; Bourrié, G. Water Quality Indices: Challenges and Application Limits in the Literature. Water 2019 , 11 , 361. https://doi.org/10.3390/w11020361

Kachroud M, Trolard F, Kefi M, Jebari S, Bourrié G. Water Quality Indices: Challenges and Application Limits in the Literature. Water . 2019; 11(2):361. https://doi.org/10.3390/w11020361

Kachroud, Moez, Fabienne Trolard, Mohamed Kefi, Sihem Jebari, and Guilhem Bourrié. 2019. "Water Quality Indices: Challenges and Application Limits in the Literature" Water 11, no. 2: 361. https://doi.org/10.3390/w11020361

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• Published: 09 September 2022

Water quality index for assessment of drinking groundwater purpose case study: area surrounding Ismailia Canal, Egypt

• Hend Samir Atta   ORCID: orcid.org/0000-0001-5529-0664 1 ,
• Maha Abdel-Salam Omar 1 &
• Ahmed Mohamed Tawfik 2  

Journal of Engineering and Applied Science volume  69 , Article number:  83 ( 2022 ) Cite this article

7124 Accesses

19 Citations

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The dramatic increase of different human activities around and along Ismailia Canal threats the groundwater system. The assessment of groundwater suitability for drinking purpose is needed for groundwater sustainability as a main second source for drinking. The Water Quality Index (WQI) is an approach to identify and assess the drinking groundwater quality suitability.

The analyses are based on Pearson correlation to build the relationship matrix between 20 variables (electrical conductivity (Ec), pH, total dissolved solids (TDS), sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), chloride (Cl), carbonate (CO 3 ), sulphate (SO 4 ), bicarbonate (HCO 3 ), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), lead (Pb), cobalt (Co), chromium (Cr), cadmium (Cd), and aluminium (Al). Very strong correlation is found at [Ec with Na, SO 4 ] and [Mg with Cl]; strong correlation is found at [TDS with Na, Cl], [Na with Cl, SO 4 ], [K with SO 4 ], [Mg with SO 4 ] and [Cl with SO 4 ], [Fe with Al], [Pb with Al]. The water type is Na–Cl in the southern area due to salinity of the Miocene aquifer and Mg–HCO 3 water type in the northern area due to seepage from Ismailia Canal and excess of irrigation water.

The WQI classification for drinking water quality is assigned with excellent and good groundwater classes between km 10 to km 60, km 80 to km 95 and the adjacent areas around Ismailia Canal. While the rest of WQI classification for drinking water quality is assigned with poor, very poor, undesirable and unfit limits which are assigned between km 67 to km 73 and from km 95 to km 128 along Ismailia Canal.

Introduction

Nowadays, groundwater has become an important source of water in Egypt. Water crises and quality are serious concerns in a lot of countries, particularly in arid and semi-arid regions where water scarcity is widespread, and water quality assessment has received minimal attention [ 3 , 9 ]. So, it is important to assess the quality of water to be used, especially for drinking purposes.

Poor hydrogeological conditions have been encountered causing adverse impacts on threatening the adjacent groundwater aquifer under the Ismailia Canal. The groundwater quality degradation is due to rapid urban development, industrialization, and unwise water use of agricultural water, either groundwater or surface water.

As groundwater quality is affected by several factors, an appropriate study of groundwater aquifers characteristics is an essential step to state a supportable utilization of groundwater resources for future development and requirements [ 11 , 12 ]. It is important that hydrogeochemical information is obtained for the region to help improving the groundwater management practices (sustainability and protection from deterioration) [ 17 ].

Many researchers have paid great attention to groundwater studies. In the current study area, the hydrogeology and physio-hydrochemistry of groundwater in the current study area had been previously discussed by El Fayoumy [ 15 ] and classified the water to NaCl type; Khalil et al. [ 27 ] stated that water had high concentration of Na, Ca, Mg, and K. Geriesh et al. [ 21 ] detected and monitored a waterlogging problem at the Wadi El Tumilate basin, which increased salinity in the area. Singh [ 34 ] studied the problem of salinization on crop yield. Awad et al. [ 7 ] revealed that the groundwater salinity ranges between 303 ppm and 16,638 ppm, increasing northward in the area.

Various statistical concepts were used to understand the water quality parameters [ 24 , 28 , 35 ].

Armanuos et al. [ 4 ] studied the groundwater quality using WQI in the Western Nile Delta, Egypt. They had generated the spatial distribution map of different parameters of water quality. The results of the computed WQI showed that 45.37% and 66.66% of groundwater wells falls into good categories according to WHO and Egypt standards respectively.

Eltarabily et al. [ 19 ] investigate the hydrochemical characteristics of the groundwater at El-Khanka in the eastern Nile Delta to discuss the possibility of groundwater use for agricultural purposes. They used Pearson correlation to deduce the relationship between 13 chemical variables used in their analysis. They concluded that the groundwater is suitable for irrigation use in El-Qalubia Governorate.

The basic goal of WQI is to convert and integrate large numbers of complicated datasets of the physio-hydrochemistry elements with the hydrogeological parameters (which have sensitive effect on the groundwater system) into quantitative and qualitative water quality data, thus contributing to a better understanding and enhancing the evaluation of water quality [ 38 ]. The WQI is calculated by performing a series of computations to convert several values from physicochemical element data into a single value which reflects the water quality level's validity for drinking [ 16 ].

Based on the physicochemical properties of the groundwater, it should be appraised for various uses. One can determine whether groundwater is suitable for use or unsafe based on the maximum allowable concentration, which can be local or international. The type of the material surrounding the groundwater or dissolving from the aquifer matrix is usually reflected in the physicochemical parameters of the groundwater. These metrics are critical in determining groundwater quality and are regarded as a useful tool for determining groundwater chemistry and primary control mechanisms [ 18 ].

The objective of this research is to assess suitability of groundwater quality of the study area around Ismailia Canal for drinking purpose and generating WQI map to help decision-makers and local authorities to use the created WQI map for groundwater in order to avoid the contamination of groundwater and to facilitate in selection safely future development areas around Ismailia Canal.

Description of study area

The study area lies between latitudes 30° 00′ and 31° 00′ North and longitude 31° 00′ and 32° 30′ East. It is bounded by the Nile River in the west, in the east there is the Suez Canal, in the south, there is the Cairo-Ismailia Desert road, and in the north, there are Sharqia and Ismailia Governorates as shown in Fig. 1 . Ismailia Canal passes through the study area. It is considered as the main water resource for the whole Eastern Nile Delta and its fringes. Its intake is driven from the Nile River at Shoubra El Kheima, and its outlet at the Suez Canal. At the intake of the canal, there are large industrial areas, which include the activities of the north Cairo power plant, Amyeria drinking water plant, petroleum companies, Abu Zabaal fertilizer and chemical company, and Egyptian company of Alum. Ismailia Canal has many sources of pollution, which potentially affects and deteriorates the water quality of the canal [ 22 ].

Map of the study area and location of groundwater wells

The topography plays an important role in the direction of groundwater. The ground level in the study area is characterized by a small slope northern Ismailia Canal. It drops gently from around 18 m in the south close to El-Qanater El-Khairia to 2 amsl northward. While southern Ismailia Canal, it is characterized by moderate to high slope. The topography rises from 10 m to more than 200 m in the south direction.

Geology and hydrogeology

The sequence of deposits rocks of wells was investigated through the study of hydrogeological cross-section A-A′ and B-B′ located in Fig. 2 a, b [ 32 ]. Section B-B′ shows that the study area represents two main aquifers that can be distinguished into the Oligocene aquifer (southern portion of the study area) and the Quaternary aquifer (northern portion of the study area). The Oligocene aquifer dominates the area of Cairo-Suez aquifer foothills. The Quaternary occupies the majority of the Eastern Nile Delta. It consists of Pleistocene sand and gravel. It is overlain by Holocene clay. The aquifer is semi-confined (old flood plain) and is phreatic at fringes areas in the southern portion of eastern Nile Delta fringes. The Quaternary aquifer thickness varies from 300 m (northern of the study area) to 0 at the boundary of the Miocene aquifer (south of the study area). The hydraulic conductivity ranges from 60 m/day to 100 m/day [ 8 ]. The transmissivity varies between 10,000 and 20,000 m 2 /day.

a Geology map of the study area. b Hydrogeological cross-section of the aquifer system (A-A′) and geological cross-section for East of Delta (B-B′)

Groundwater recharge and discharge

The main source of recharge into the aquifer under the study area is the excess drainage surplus (0.5–1.1 mm/day) [ 29 ], in addition to the seepage from irrigation system including Damietta branch and Ismailia Canal.

Groundwater and its movements

In the current research, it was possible to attempt drawing sub-local contour maps for groundwater level with its movement as shown in Fig. 3 . Figure 3 shows the main direction of groundwater flow from south to north. The groundwater levels vary between 5 m and 13 m (above mean sea level). The sensitive areas are affected by (1) the excess drainage surplus from the surface water reclaimed areas which located at low lying areas; (2) the seepage from the Ismailia Canal bed due to the interaction between it and the adjacent groundwater system, and (3) misuse of the irrigation water of the new communities and other issues. Accordingly, a secondary movement was established in a radial direction that is encountered as a source point at the low-lying area (Mullak, Shabab, and Manaief). Groundwater movement acts as a sink at lower groundwater areas (the northern areas of Ismailia Canal located between km 80 to km 90) due to the excessive groundwater extraction. The groundwater level reaches 2 m (AMSL). The groundwater levels range between + 15 m (AMSL) (southern portion of Ismailia Canal and study area near the boundary between the quaternary and Miocene aquifers).

Groundwater flow direction map in the study area (2019)

The assessment of groundwater suitability for drinking purposes is needed and become imperative based on (1) the integration between the effective environmental hydrogeological factors (the selected 9 trace elements Fe, Mn, Zn, Cu, Pb, Co, Cr, Cd, Al) and 11 physio-chemical parameters (major elements of the anions and cations pH, EC, TDS, Na, K, Ca, Mg, Cl, CO 3 , SO 4 , HCO 3 ); (2) evaluation of WQI for drinking water according to WHO [ 36 ] and drinking Egyptian standards limit [ 14 ]; (3) GIS is used as a very helpful tool for mapping the thematic maps to allocate the spatial distribution for some of hydrochemical parameters with reference standards.

The groundwater quality for drinking water suitability is assessed by collecting 53 water samples from an observation well network covering the area of study, as seen in Fig. 1 . The samples were collected after 10 min of pumping and stored in properly washed 2 L of polyethylene bottles in iceboxes until the analyses were finished. The samples for trace elements were acidified with nitric acid to prevent the precipitation of trace elements. They were analyzed by the standard method in the Central Lab of Quality Monitoring according to American Public Health Association [ 2 ].

The water quality index is used as it provides a single number (a grade) that expresses overall water quality at a certain location based on several water quality parameters. It is calculated from different water parameters to evaluate the water quality in the area and its potential for drinking purposes [ 13 , 25 , 31 , 33 ]. Horton [ 23 ] has first used the concept of WQI, which was further developed by many scholars.

The first step of the factor analysis is applying the correlation matrix to measure the degree of the relationship and strength between linearly chemical parameters, using “Pearson correlation matrix” through an excel sheet. The analyses are mainly based on the data from 53 wells for physio-chemical parameters for the major elements and trace elements. Accordingly, it classified the index of correlation into three classes: 95 to 99.9% (very strong correlation); 85 to 94.9% (strong correlation), 70 to 84.9% (moderately), < 70% (weak or negative).

Equation ( 1 ) [ 4 ] is used to calculate WQI for the effective 20 selected parameters of groundwater quality.

In which Q i is the ith quality rating and is given by equation ( 2 ) [ 4 ], W i is the i th relative weight of the parameter i and is given by Eq. ( 3 ) [ 4 ].

Where C i is the i th concentration of water quality parameter and S i is the i th drinking water quality standard according to the guidelines of WHO [ 36 ] and Egypt drinking water standards [ 14 ] in milligram per liter.

Where W i is the relative weight, w i is the weight of i th parameter and n is the number of chemical parameters. The weight of each parameter was assigned ( w i ) according to their relative importance relevant to the water quality as shown in Table 2 , which were figured out from the matrix correlation (Pearson correlation, Table 1 ). Accordingly, it was possible assigning the index for weight ( w i ). Max weight 5 was assigned to very strong effective parameter for EC, K, Na, Mg, and Cl; weight 4 was assigned to a strong effective parameter as TDS, SO 4 ; 3 for a moderate effective parameter as Ca; and weight 2 was assigned to a weak effective parameter like pH, HCO 3, CO 3 , Fe, Cr, Cu, Co, Cd, Pb, Zn, Mn, and Al. Equation ( 2 ) was calculated based on the concertation of the collected samples from representative 53 wells and guidelines of WHO [ 36 ] and Egypt drinking water standards [ 14 ] in milligram per liter. This led to calculation of the relative weight for the weight ( W i ) by equation ( 3 ) of the selected 20 elements (see Table 2 ). Finally, Eq. ( 1 ) is the summation of WQI both the physio-chemical and environmental parameters for each well eventually.

The spatial analysis module GIS software was integrated to generate a map that includes information relating to water quality and its distribution over the study area.

Results and discussion

The basic statistics of groundwater chemistry and permissible limits WHO were presented in Table 3 . It summarized the minimum, maximum, average, med. for all selected 20 parameters and well percentage relevant to the permissible limits for each one; the pH values of groundwater samples ranged from 7.1 to 8.5 with an average value of 7.78 which indicated that the groundwater was alkaline. While TDS ranged from 263 to 5765 mg/l with an average value of 1276 mg/l. Sodium represented the dominant cation in the analyzed groundwater samples as it varied between 31 and 1242 mg/l, with an average value of 270 mg/l. Moreover, sulfate was the most dominant anion which had a broad range (between 12 and 1108 mg/l), with an average value of 184 mg/l. This high sulfate concentration was due to the seepage from excess irrigation water and the dissolution processes of sulfate minerals of soil composition which are rich in the aquifer. Magnesium ranged between 11 and 243 mg/l, with an average value of 43 mg/l. The presence of magnesium normally increased the alkalinity of the soil and groundwater [ 10 , 37 ]. Calcium ranged between 12 and 714 mg/l with a mean value of 119 mg/l. For all the collected groundwater samples, calcium concentration is higher than magnesium. This can be explained by the abundance of carbonate minerals that compose the water-bearing formations as well as ion exchange processes and the precipitation of calcite in the aquifer. Chloride content for groundwater samples varies between 18 and 2662 mg/l with an average value of 423 mg/l. Carbonate was not detected in groundwater, while bicarbonate ranged from 85 to 500 mg/l. Figures 5 , 6 , and 7 were drawn to show the extent of variation between the samples in each well.

Piper diagram [ 30 ] was used to identify the groundwater type in the study area as shown in Fig. 4 . According to the prevailing cations and anions in groundwater samples Na–Cl water type in the southern area due to salinity of the Miocene aquifer, Mg–HCO 3 water type in the northern area due to seepage from Ismailia Canal and excess of irrigation water and there is an interference zone which has a mixed water type between marine water from south and fresh water from north.

Piper trilinear diagram for the groundwater samples

Concentration of selected physio-chemical parameters

Concentration of major elements

Concentration of trace element

Concentration for 20 elements by percentage of wells (relevant to their limits of WHO for each element)

a , b WQI aerial distribution for drinking groundwater suitability for WHO ( a ) and Egyptian standards ( b )

Atta, et al. [ 5 ] revealed that the abundance of Fe, Mn, and Zn in the groundwater is due to geogenic aspects, not pollution sources. Khalil et al. [ 26 ] and Awad et al. [ 6 ] revealed that the source of groundwater in the area is greatly affected by freshwater seepage from canals and excess irrigation water which all agreed with the study.

Table 3 and Fig. 8 showed that 100% of wells for EC were assigned at desirable limits. 43.79% of wells for TDS were assigned at the desirable limit and 27.05% of them at the undesirable limits. While pH, 81.25% were assigned at the desirable limit. The percentage of wells for the aerial distribution of cations concentration assigned at desirable limits ranged between 64.6% for K, 85.45% for Mg, 68.73% for Na, and 70.8% for Ca. While the percentage of wells for the aerial distribution of cations concentration assigned at the undesirable limits ranged between 8.3% for Mg, 31.27% for Na, 14.6% for K, and 16.7% for Ca.

The percentage of wells for the aerial distribution of anions concentration assigned at desirable limits ranged between 72.9% for Cl, 66.7% for HCO 3 , and 79.2% for SO 4 . While the percentage of wells for the aerial distribution of anions concentration assigned at the undesirable limit ranged between 4.2% for Cl, 0% for HCO 3 , and 20.8% for SO 4 as shown in Table 3 and Fig. 8 .

Table 3 and Fig. 8 presented the aerial distribution concentration for 8 sensitive trace elements. The percentage of wells assigned at desirable limits ranged between 100% for (Zn, Cr, and Co), 86% for Fe, 27.3% for Mn, 77.4% for Cd, 27.2% for Pb, and 96% for Al, while the percentage of wells assigned at undesirable limits ranged between 0% for (Fe, Zn, Cr, and Co), 50% for Mn, 13.6% for Cd, 36.4% for Pb, and 4% for Al.

Figure 8 summarizes the results of the concentration for the selected 20 elements (11 physio-hydrochemical characteristics, and 9 sensitive environmental trace elements) by %wells relevant to the limits of WHO for each element.

The water quality index is one of the most important methods to observe groundwater pollution (Alam and Pathak, 2010) [ 1 ] which agreed with the results. It was calculated by using the compared different standard limits of drinking water quality recommended by WHO (2008) and Egyptian Standards (2007). Two values for WQI were calculated and drawn according to these two standards. It was classified into six classes relevant to the drinking groundwater quality classes: excelled water (WQI < 25 mg/l), good water (25–50 mg/l), poor water (50–75 mg/l), very poor water (75–100 mg/l), undesirable water (100–150 mg/l), and unfit water for drinking water (> 150 mg/l) as shown in Fig. 9 a, b. Figure 9 a (WHO classification) indicated that in the most parts of the study area, the good water class was dominant and reached to 35.8%, 28.8% was excellent water; 7.5% were poor water, 11.3% very poor water quality, and 13.3% were unfit water for drinking water. Similarly, for Egyptian Standard classification via WQI, the study area was divided into six classes: Fig. 9 b indicated that 35.8% of groundwater was categorized as excellent water quality, 34% as good water quality, 9.4% as poor water, 5.7% as very poor water, 1.9% as undesirable water and 13.3% as unfit water quality. This assessment was compared to Embaby et al. [ 20 ], who used WQI in the assessment of groundwater quality in El-Salhia Plain East Nile Delta. The study showed that 70% of the analyzed groundwater samples fall in the good class, and the remainder (30%), which were situated in the middle of the plain, was a poor class which mostly agreed with the study.

Conclusions and recommendation

This research studied the groundwater quality assessment for drinking using WQI and concluded that most of observation wells are located within desirable and max. allowable limits.

The groundwater in the study area is alkaline. TDS in groundwater ranged from 263 to 5765 mg/l, with a mean value of 1277 mg/l. Sodium and chloride are the main cation and anion constituents.

The water type is Na–Cl in the southern area due to salinity of the Miocene aquifer, Mg–HCO 3 water type in the northern area due to seepage from Ismailia Canal and excess of irrigation water and there is an interference zone which has a mixed water type between marine water from south and fresh water from north.

The WQI relevant to WHO limits indicated that 23% of wells were located in excellent water quality class that could be used for drinking, irrigation and industrial uses, 38% of wells were located in good water quality class that could be used for domestic, irrigation, and industrial uses, 11% of wells were located in poor water quality class that could be used for irrigation and industrial uses, 8% of wells were located in very poor water quality class that could be used for irrigation, 6% of wells were located in unsuitable water quality class which is restricted for irrigation use and 15% of wells were located in unfit water quality which will require proper treatment before use.

The WQI relevant to Egyptian standard limits indicated that 25% of wells were located in excellent water quality class that could be used for drinking, irrigation, and industrial uses, 43% of wells were located in good water quality class that could be used for domestic, irrigation, and industrial uses, 8% of wells were located in poor water quality class that could be used for irrigation and industrial uses, 6% of wells were located in very poor water quality class that could be used in irrigation, 6% of wells were located in unsuitable water quality class which is restricted for irrigation use and 13% of wells were located in unfit water quality which will require proper treatment before use.

The percentage of wells located at unfit water for drinking were assigned in the Miocene aquifer, and north of Ismailia Canal between km 67 to km 73 and from km 95 to km 128.

It is highly recommended to study the water quality of the Ismailia Canal which may affect the groundwater quality. It is recommended to study the water quality in detail between km 67 to 73 and from km 95 to km 128 as the WQI is unfit in this region and needs more investigations in this region. A full environmental impact assessment should be applied for any future development projects to maximize and sustain the groundwater as a second resource under the area of Ismailia Canal.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available because they are part of a PhD thesis and not finished yet but are available from the corresponding author on reasonable request.

Abbreviations

World Health Organization

• Water Quality Index

Electrical conductivity

Total dissolved solids

Bicarbonate

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Atta, H.S., Omar, M.AS. & Tawfik, A.M. Water quality index for assessment of drinking groundwater purpose case study: area surrounding Ismailia Canal, Egypt. J. Eng. Appl. Sci. 69 , 83 (2022). https://doi.org/10.1186/s44147-022-00138-9

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Evaluation of water quality index and geochemical characteristics of surfacewater from Tawang India

• Nisha Gaur 1 ,
• Arpan Sarkar 1 ,
• Dhiraj Dutta 1 ,
• B. J. Gogoi 1 ,
• Rama Dubey 1 &
• Sanjai Kumar Dwivedi 1  

Scientific Reports volume  12 , Article number:  11698 ( 2022 ) Cite this article

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Metrics details

• Biogeochemistry
• Environmental sciences

In this study,the water samples were collected from 31 sites of Tawang, Arunachal Pradesh, India (North-Eastern Himalaya), during the winter season to check the suitability of water for drinking and irrigation purposes.The study scientifically demonstrates the estimation of Water quality index (WQI) andhydrogeochemical characteristics of surface water samples by utilizing multivariate statistical methods. The main water quality parameters considered for this study were TDS, conductivity, salinity, pH, hardness, cations and anions. WQI was calculated in order to find out the deviation in the water quality parameters particularly with respect to BIS permissible limits.The major influencing factors responsible for the variation in these parameters were derived by using Principal component analysis (PCA) and Correlation matrix.To check the suitability of water for drinking purpose, hydrogeochemical facies and rock water interaction was derived by using well established methods such as Piper Plot (determine water type), WQI (Quality monitoring), and saturation index (for mineral dissolution). The results revealed that the silicate weathering was the main ionic source in comparison to carbonate weathering which is due to the higher dissolution capacity of silicate minerals.The results of the scattered plot between (Ca 2+  + Mg 2+ )–(HCO 3 ˉ + SO 4 2 ˉ) versus (Na +  + K + )–Clˉ (meq/L) highlighted thation exchange occurs between Mg 2+ and Ca 2+ ofsurface water with Na + and K + of rock /soil. This means that calcium ion was getting adsorbed, and sodium ion was getting released. The Ca 2+ –Mg 2+ –HCO 3 ˉ, Na + –HCO 3 ˉand Na + –Clˉ type of surface water suggested permanent and temporary hardness respectively in the studied region. The dominant cations of this study were Na + and Ca 2+ while the dominant anions were HCO 3 ˉ and SO 4 2 ˉ. In order to check the suitability of water sources for irrigation, parameters like, Magnesium hazard (MH), Total hardness (TH), Permeability Index (PI), Kelly Index (KI), Sodium adsorption rate (SAR), Sodium percentage (Na%), and Residual sodium carbonate (RSC) were determined. The results showed that 93% of the samples had PI score < 75, which indicates the suitability of the water for irrigation. Also the WQI calculation showed an average WQI value of 82.49, amongst which 61% samples were in the range of 0–50 being considered as good for drinking, while 39% were catageorised as unsuitable for drinking showing a value of > 50. Hence the above findings reveal that geogenic activities play a major role in influencing the water quality of Tawang region. Hence suitable water treatment technologies or methods might be used to eliminate thenon desirable elements and minerals present in surface water.

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Introduction.

For all life forms present on earth water is a basic need fulfilled from variousnatural resources. It is desired that, the water being consumed should not contain any microbes or harmful chemicals thatcan cause damage to life. Due to industrialization, surface and groundwater has been contaminated with a widevariety of pollutants.Both natural (precipitation, the geography of the watershed, atmosphere, and geology) and anthropogenic (industrial activities, domestic and/or agricultural run-off) activities determine the chemical, physical and biological composition of surface and groundwater. Water contamination leads to deterioration of water quality whichthreatens the life present on earth as well as disturbs theeconomic advancement and social success 1 , 2 .

Tawang, a small district in Arunachal Pradesh, Indialocated at an altitude of 4500 m is surrounded with high mountains, glaciers and lakes. The water requirement of local population is met from the surface water sources such as springs, lakes, ponds and rivers. At the mentioned altitude, the only propable sources of contamination are natural and geogenic activities. Few reports onwater quality of Eastern Himalayan region showed severe fecal contamination of surface water sources and the reservoir tank used by the community, leading to high health risk 3 , 4 . Additionally, the geological composition of sourrounding soil and rock-formation strongly affects the water quality, since the flowing and stagnant water comes in their close contact.Now a days hydrogeochemical studies are gaining momentum worldwide to ascertain the impact of geological strata on water quality of natural water bodies. The studies throw light on water rock interaction leading to presence of certain minerals in the water mainly derived from weathering of rocks and dissolution of minerals. The reported studies have highlighted the presence of silicates, carbonates, alkali and alkaline earth metals, heavy metals etc. based on the hydrogeochemical analysis of water sources by using different statistical approaches 5 . Hence the hydrogeochemical characterization of surface water demonstrates thelevel of minerals and ions contributing to aquifer's composition. Furthermore, the surface water is also susceptible to other forms of contamination arising from domestic activities. The constant decline of surface water quality not only affects the humans but also poses a serious threat to the ecosystems flourishing within it. Hence, to maintain the health of any water source, certain water quality indicators or parameters must be monitored regularly to keep the aquifer system performing at its best 6 , 7 . Regular monitoring of water quality is a crucial part for determining the baseline data which would help in identifying any existing problems, or any issues that could emerge in the future related to water quality.

Water quality is generally defined by its physicalapperance (colour, odour, taste), chemical (pH, turbidity, hardness, alkalinity, total solids, presence of metallic or non-metallic salts) and biological properties.Many researchers have proposed WQI in the form of a simple expression in order to represent the general quality of surface water as there are a variety of physical, chemical and biological water quality parameters 8 , 9 , 10 . It is a concise and comprehensive method to express the quality of water for different stages of usage and is commonly represented by a single number. WQI of any water sample is calculated by aggregating the values of different parameters that givesa single number which expresses the quality or contamination status of water. WQI not only can be used for the prediction of pollutants present in water but also compares the water qualities of different sources and hence decides the proper usage of water resources 11 , 12 . It is very difficult to comprehend a complex and large data matrix comprising a large number of parameters while calculating WQI. To overcome this problem and make the process less subjective, different multivariate techniques (Principal Component Analysis (PCA), factor analysis, etc.) helps in better interpretation of the results. Several parameters are monitored and analyzed simultaneously along with studying environmental issues. Ideally, PCA can reduce the dimensionality of the multivariate data set while maintaining its structure to the maximum extent. Hence, while dealing with environmental data PCA has often been used 13 .

In recent years Geographic Information System (GIS) technique is emerging as a powerful tool for storing, assessing, monitoring and displaying spatial dataof surface and groundwater quality. This tool is also effective in developing solution for water resource related problems, understanding the natural environmentand managing water resources on required scale.Additionally, for evaluation and analysis of spatial information of water resources, Inverse Distance Weighted (IDW) interpolation methodalong with GIS techniques has proved as a powerful tool. For transforming huge data sets to generate various spatial distribution maps and projections revealing trends, associations, and sources of contaminant it is an economically feasible and time-efficient technique 14 , 15 , 16 .

Therefore, in the present study, the physico-chemical and bacterial properties of surface water samples collected from different sites of Tawang were determined and compared with other related studies. Main emphasis was laid on interaction between physico-chemical parameters of water by using Principal component analysis (PCA) and correlation matrix. The selected parameters were normalized by using a z-score before PCA as it requires individual indicators to have a common unit. In the final step, the Pearson correlation analysis was done followed by water quality index calculation. These statistical analysis tools are important to understand the vital variables which affect the quality of water. For spatial evaluation of various surface water quality parameters, the GIS technique has been used.

Furthermore, the hydrogeochemical facies and hydrogeochemical signatures such as ion exchange process, rock-water interaction, and dissolution were also studied for the analysis of chemical characteristics of surface water. Conventional graphical plots such as Magnesium hazard (MH), Total hardness (TH), Permeability Index (PI), Kelly Index (KI), Sodium adsorption rate (SAR), sodium percentage (Na%), and residual sodium carbonate (RSC) have been used to determine the various hydrogeochemical processes controlling the hydrochemical characteristics of water collected from the study area. Hence this study will throw some light on the untouched surface water sources in the Tawang area and thus can act as a baseline for water quality assessment in various other districts of Arunachal Pradesh and Northeastern region of India.The ultimate objective of the present study was to highlight the interaction between geogenic factors and water and correlate it with the water quality index, so that its suitabilitycan be ascertained for potability and irrigation purpose.

The Ecological hotspot of Tawangis in the Arunachal Pradesh (latitude 91° 33’ E to 92° 26’E; longitude 27° 29’N to 27°52’N) at an altitude of 1800–3300 m above mean sea level in Eastern Himalayan Region (Fig.  1 a and b). This place is famous for its 400-year-old biggest Buddhist monastery and important pilgrim center for the followers of Buddhism. It is also famous for its natural beauty which enchants the traveller. It was observed that the maximum number of tourists visit this place during the summer season due to pleasant weather. The people of the forward location of Tawang depend on natural water sources for their daily needs. Because of the tough terrain, it is very difficult to bring fresh water from other locations so there is a need to preserve these water bodies.

Location Map of Ecological hotspot of Tawang, Arunachal Pradesh, India. ( a ) Location of Tawang on Indian map ( b ) Sample collection sites. QGIS Software Desktop 3.18.3 ( https://qgis.org/en/site/forusers/download.html ).

During winters and the rainy season (because of landslides and heavy snowfall), the survival condition is very harsh and very difficult for local people to collect potable water from a nearby area. Therefore, this area was selected to check the water quality from different natural water sites of Tawang. As the population of this area is comparatively less and no anthropogenic activity was observed so thirty-one different natural freshwater sites alongthe forward location of Tawang, Arunachal Pradesh were chosen for sample collection to ascertain thesutability of water quality for drinking and irrigation.

Methodology

The water sampling from 31different locations of Tawang district, Arunachal Pradesh was done during the winter season (December 2020) as per the standard procedures of the American Public Health Association 17 . For the collection and analysis of various water variables, standard methods were followed 17 , 18 . With the help of a global positioning system (GPS) as shown in Fig.  1 b, the sampling locations were marked. All the plastic bottles were thoroughly washed and dried before sample collection and the bottles were rinsed with water sample to be collected at the time of collection. After sample collection proper labelling was done. The latitude, longitude, and altitude of all the sampling sites along with the source were recorded during the sample collection using a GPS system (Model: Garmin GPS 72H) as mentioned in Table 1 .

Physiochemical parameters determination

After sample collection, the bottles were taken to the laboratory in an icebox to avoid any unusual change in water quality and stored at 4 °C for further analysis. Electrical conductivity, TDS, and salinity were analyzed with the help of EUTECH Instruments CD650 (Thermo Scientific, United State). A digital pH meter (EUTECH pH 610) was used to estimate pH. Turbidity meter (EUTECH TN 100) was used to determine turbidity and iron was estimated by using a UV–Vis spectrophotometer (Analytikjena SPECORD 205). The acid titration method was carried out for Bicarbonate analysis. Chloride, Nitrite, Flouride, Nitrate, and Sulfate were evaluated by using Ion Chromatography (Metrohm, 882 Compact IC plus, 858 Professional Sample Processor). Sodium, Potassium, Calcium, and Magnesium were analyzed by following the standard methods specified in IS-1500:2012 18 , 19 . All the analysis was done as per the mathods methods mentioned in APHA 20 .The Physiochemical properties and spatial distribution pattern of collected water samplesare shown in Fig.  2 a–q.

Spatial distribution map of ( a ) pH, ( b ) EC, ( c ) TDS, ( d ) Salinity, ( e ) Turbidity, ( f ) Sodium (Na + ), ( g ) Calcium (Ca 2+ ) and ( h ) Magnesium (Mg 2+ ). Spatial distribution map of ( i ) Potassium (K + ), ( j ) Iron (Fe 2+ ), ( k ) Chloride (Cl - ), ( l ) Bromide (Br - ), ( m ) Fluoride (F - ), ( n ) Nitrite (NO 2 - ), ( o ) Nitrate (NO 3 - ), ( p ) Phosphate (PO 4 3- ) and ( q ) Sulphate (SO 4 2- ) QGIS Software Desktop 3.18.3 ( https://qgis.org/en/site/forusers/download.html ).

Quality assurance and quality control (QA/QC)

Standard solutions were used for the pre-calibration of all the instruments used for all the analysis as per the company guidelines to maintain the accuracy and precision in observations. Before each in situ observation all the electrodes were properly washed with double distilled water. Conditioning of probes should be done in the sample before each use for best stabilization time. Freshely prepared buffer solutions of two different units were used for calibration of rinsed and dry pH probe. For each chemical analysis, a blank sample had been run for each chemical analysis and for each quality parameter at least three observations had been taken. Standard operating protocols were adopted for each instrument and chemical analysis with adequate safety throughout the study period. To get the accurate data/information the analytical grade chemicals and glasswares (Borocile, Merk Thermo Fishers) were used for chemical analysis. To get the linear calibration curves in Ion chromatography, the calibration was performed by running the replicate standards of different anions.

Multivariate statistic methods

Kolmogorov–Smirnov (K–S) test with the SPSS 21.0 Pro software packages (SPSS Ins., Chicago., USA) ( https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-21 ) was used to assess the normality of the water quality parameters. To compare the spatial and temporal variations of 15 water quality parameters measured in different sites of Tawang, descriptive statistical analysis including one-way Analysis of Variance (ANOVA) was performed. Tukey’s multiple range tests were performed to evaluate the large differences in mean values of water quality parameters. The Pearson’s correlation coefficient (r) was calculated to determine the correlation between variables. Principal Component Analysis (PCA)was used to analyze the spatial and temporal changes in water quality. In addition, PCA also extracts the pollution factor and recognizes pollution sources applied in water quality analysis. PCA also reduces the data sets and enables the formation of new factors. In this case, the parameters have been reduced from 15 to 13. The classification of factor loading is strong (> 0.75), moderate (0.75–0.50), and weak (0.50–0.30) relating to the absolute loading value. To consider the suitability of the data to run PCA, Kaiser–Meyer–Olkin (KMO) test was applied and if the value of the KMO test is below 0.5 it is unacceptable, between 0.5 to 0.7 is sufficient, and > 0.7 is considered as good. PAST Software 4.03 ( https://www.softpedia.com/get/Science-CAD/PAST.shtml ) was used for correlation analysis and principle component analysis. These analyses were applied as a complementary tool to describe water quality deterioration 8 , 9 , 21 , 22 .

Water quality index (WQI)

Several national and international organizations have formulated a huge number of water indices such as Weight Arithmetic Water Quality Index (WAWQI), National Sanitation Foundation Water Quality Index (NSFWQI), Canadian Council of Ministers of the Environment Water Quality Index (CCMEWQI), Oregon Water indices which are followed worldwide 23 , 24 .

Water quality index (WQI) is defined as “the grading technique that provides the combined effect of each of the water quality parameters on the overall water quality for human consumption” 25 . It is widely used to characterize the availability of potable water resources and their usefulness for domestic purposes. In the present study the weight arithmetic water quality index was determined as per the methods mentioned in the literature 26 . The mean values of analyzed 15 parameters (pH, turbidity, EC, TDS, Salinity, Clˉ, Brˉ, NO 2 ˉ, NO 3 ˉ, PO 4 3 ˉ, SO 4 2 ˉ, Na + , Ca 2+ , Mg 2+ and K + of 31 samples were included in the calculation. Depending on the water quality effect and its importance for human health, the average weight value (AW) between 1 and 5was assigned for each parameter. In the first step, the relative weight (RW) was calculated by using the Eq. ( 1 ):

The quality rating (Q i ) was calculated in the second step by dividing the measured parameters (C i ) to the permitted drinking water values (S i ) (as per WHO) and multiply by 100 as mentioned in Eq. ( 2 ):

In the third step, the Sub-indices (SI) were calculated by using Eq. ( 3 ), and WQI was calculated by using Eq. ( 4 ).

Based on the computed WQI the water was classified into five types: WQI 0–25 excellent, 26–50 good, 51–75 poor, 76–100 very poor, and > 100 unsuitable 27 , 28 .

Results and discussion

QGIS SoftwareDesktop 3.18.3 ( https://qgis.org/en/site/forusers/download.html ) was used to prepare the water quality map in this study based on the selected parameters as shown in Fig.  2 a–q. The physicochemical parameters and bacterial analysis results of collected 31 water samples from different locations of Tawangare discussed below. In this study for the reference purpose, permissible limit of different parameters studied were taken from Bureau of Indian Standards (BIS) 29 and World Health Organization (WHO) 19 .

Water quality assessment using physio-chemical parameters

The nature of the geological materials through which the surface water flows and the quality of the recharge water defines the types and concentrations of natural contaminants. A wide range of compounds (calcium, magnesium, chloride, arsenate, fluoride, nitrate, iron, etc.) may be picked up by the water when it moves through sedimentary rocks and soils. Hence, the harmful effect of these natural pollutants depends on their type and concentration 12 . The descriptive statistics for the analyzed parameters are summarized in Table 2 . The results of the K-S test showed that pH, EC, salinity, Ca 2+ , Mg 2+ and Na + was normally distributed ( p  > 0.05) while Clˉ, Brˉ, NO 2 ˉ, NO 3 ˉ, PO 4 3 ˉ, SO 4 2 ˉ, Fˉ, TDS and K + were not normally distributed ( p  < 0.05).

The acidic or basic nature of any water body is reflected by its pHwhich is one of the most important operational water quality parameters. Since it controls the solubility of various metallic contaminants, it is considered as one of the important parameters of water quality. Discharge of industrial pollutants or human waste in the nearby vicinity, or biological activity are some of the reasonsfor fluctuations in the pH value of any water body. The physico-chemical parameters of water also show a change if the pH of any water body changes due to abovementioned reasons. There are possibilities of the formation of trihalomethanes (toxic) if the pH becomes very high. In the case of alkaline pH, the shifting in pH up over 7 was observed due to the presence of alkaline earth metals that interact with soluble CO forming carbonate and bicarbonates. In the present study, the value of pH from all sources was found to be in the range of 5.96 to 7.43. As per WHO, the permissible limit for pH in drinking water is 8.5 19 , hence the pH of collected water was found to be within this limit.

The presence of all dissolved solids in water represents TDS (mg/L),along with salinity and conductivity. Additionally, the dissolved organic matter and inorganic salts such as Ca 2+ , Mg 2+ Na + , K + , HCO 3 ˉ, Clˉ and SO 4 2 ˉ in water also contribute to TDS increment . Since change in pH affectsthesolubility of suspended matter,itstrongly affects TDS which may sometimes lead to precipitation of some of the dissolved solutes.In the present study, the TDS of the collected water sample varied from 13.85 mg/L to 140.8 mg/L (mean-50.163 mg/L)as represented in Fig.  3 . The desirable limit of TDS for drinking water is < 500 mg/L which indicates that the water is in good condition. Tawang water has potable water potential andissuitable for aquatic biota in terms of this parameter. The chief causes of TDS include agricultural operations, domestic runoff, soil contamination caused by leaching, and point source water pollution discharged by industrial or sewage treatment plants 30 .

The physio-chemical parameters of 31 water samples collected from Tawang.

Electrical conductivity (EC) is the ability of water to conduct electrical current. The increase in the dissolved salt concentration increases the electrical conductivity of water and, therefore, it provides the general indication of water quality with respect to the amount of total dissolved solids in the form of cations and anions, their concentration and mobility, etc. The electrical conductivity varies with temperature as the solubility of salts which areresponsible for ionic composition is also affected by changes in temperature. Hence, in the present study, the electrical conductivity of the collected water samples was found to be in the range of 136.1 µS/cm to 1361 µS/cm as shown in Fig.  3 . The average EC value of Tawang samples collected from studied location was 525.13 µS/cm. Though this area is not near to dense urbanization still the EC value is slightly higher thanthepermissible limit (400 µS/cm) this might be due to the natural geological activities 31 .

Since salinity indicates the presence of dissolved salts, it also gives the information of water TDS and conductivity, as these three parameters are interrelated. In addition, it is a key factor limiting biota distribution. If the quantities of dissolved salts in the natural water increase it may lead to severe health issues like high blood pressure (BP) or hypertension leading way to cardiovascular diseases (CVD) 12 . In the present study as shown in Fig.  3 , the minimum salinity value was 9.51 mg/L and maximum 134.2 mg/L while the average value was 50.037 mg/L. A statistically significant difference was found between the samples ( p  < 0.05). Because of fewer human activities in this area, the water is found to be within the permissible limit.

Ions such as Clˉ and SO 4 2 ˉ in water also affect the salinity. The highest Clˉlevel was recordedat site 11 (17.766 mg/L) and the highest SO 4 2 ˉ was measured 887.54 mg/L for site 1. The results confirm that the anthropogenic activities and hot water springs present in these locations adequatelyimpact the salinity. The Clˉ and SO 4 2 ˉparameters did not show any significant difference between locations. The average value of Clˉ and SO 4 2 ˉ in the present study was 3.36 mg/L and 37.104 mg/L respectively. The Clˉion,andSO 4 2 ˉ ion present in the Tawang region were in permissible limit except for site 1(above permissible limit). In practice salinity is mostly the result of the following major inorganic ions such as Na + , Ca 2+ , Mg 2+ , K + , Clˉ, SO 4 2 ˉ , etc. Water quality is also decreased by increasing the nitrogenous and phosphorous compounds in it. The increase in these compounds might be due to the excessive use of fertilizers in farming which are mixed with the river stream by runoff due to rainy climate.This event is known as primary contamination which may lead to eutrophication giving rise to secondary contamination, threatening the biodiversity in water 12 . The highest NO 3 ˉ was recorded to be 32.15 mg/L for site 9 while the highest NO 2 ˉ was measured at sight 6 (8.86 mg/L). These two ions in 31 sites of Tawangwere in the permissible limit (50 mg/L)as per WHO. However, the PO 4 3 ˉvalues in sites 2, 26B, and 30 were 378.9,88.39 and 322.88 respectively which is above the permissible limit (40 mg/L). The fluoride ions in the Tawang region in some sites (3, 16, 23, 24, 26A, and 28) were above the permissible limit (1.5 mg/L). This might be due to fluoride-bearing minerals present in rocks and sediments. In addition, the use of pesticides in agriculture might be increasing the fluoride concentration in these sites. Table 3 shows the values of ions present in 31samples from different locations which were identified by ion chromatography.

Turbidity is the easiest measure of water quality for any human as it shows how clean or cloudy water is. Several factors affect the turbidity of any water body, and it is caused by particles (clay, silt, phytoplanktons algae, fine organic and organic matter, inorganic matter, and other microscopic organisms, etc.) which are suspended or dissolved in water that scatter light making it appear cloudy. The presence of many suspended solids indicates the high turbidity which reduces the aesthetic quality of any water source 32 . Additionally, turbidity also increases the cost of the water treatment process from different industries such as food processing, pharmaceutical, etc. The main sources of turbidity are natural (erosion from uplands, stream channel movement, etc.) or anthropogenic activities (rock blasting or digging also cause erosion). Turbidity may not be intrinsically harmful, but it interferes with disinfection during water treatment and provides a medium for microbial growth. This leads to serious consequences such as nausea, cramps, diarrhea, etc. 8 , 9 , 21 . In the present study, the water samples collected from Tawang, India showedaturbidity value of 0. This might be due to weather conditions as the samples were collected during the winter season. During this season the average rainfall in the Tawang area is 53.3 mm which is very little as compared to average rainfall during the rainy season (1723 mm). Moreover, in this season the soil leaching is very much negligible and mostly water sources in frozen condition.

The dissolved polyvalent metallic ions from sedimentary rocks, seepage, and runoff from soils are the principal natural sources of hardness in water. Hard water is very dangerous to human health as it causes many diseases such as osteoporosis, nephrolithiasis, hypertension, stroke, etc. It occurs mainly due to the salts of magnesium and calcium. Though these are the common essential mineral constituents of food,their excess intake can cause various abovementioned diseases. Hardness in water is expressed in terms of milligrams of calcium carbonates equivalent per liter 12 , 33 . Water containing calcium carbonate below 60 mg/L is considered as soft water while containing 60–120 mg/Lis moderately hard and having 120–180 mg/Lis hard, and more than 180 mg/Lis considered to bevery hard water. Therefore, in the present study, the calcium was in the range between 13 mg/L and 58 mg/L and magnesium was in the range between 2 mg/L and 32 mg/L as mentioned in Fig.  4 . According to these results, the water of the Tawang area is soft as the values comply with national and international drinking water standards such as BIS-IS-2500 (2012), WHO, and EU 18 , 19 , 29 . The mean values of Ca 2+ (33.13 ± 12.06 mg/L) and Mg 2+ (15.53 ± 8.93 mg/L) in the study area are within the permissible limit.

Calcium (Ca 2+ ), Magnesium (Mg 2+ ), Sodium (Na + ) and Potassium (K + ) of 31 water samples collected from Tawang.

One of the most abundant elements of the earth’s crust is iron. Natural or anthropogenic sources can be the cause of iron contamination in water. The iron contaminated water not only leads to staining of laundry and utensils but also reacts with tea and coffee producing black color. Iron is an essential element and structural component of hemoglobin, myoglobin,several enzymesetc, and its deficiency leads to anemia and in severe cases loss of well-being. In addition, its overdose in humans can cause severe health problems such as liver cancer, diabetes, cirrhosis of the liver, heart disease, infertility, etc. The high concentration of iron in water changes its color, taste, odor, and also corrodes water pipelines 12 , 33 . In the present study ironcontamination of the collected water samples was found to be in the range of the permissible limit.

Principle component analysis

Principle component analysis (PCA) is used to reduce the dimensions of multivariate datasets. While trying to reduce the input data dimensions, PCA retains the maximum informative value of the input data sets. PCA decreases the number of dimensions that are not correlated and summarizes the information that is dispersed in several dimensions. PCA discards the redundant and highly correlated parameters and selects the independent variables. Additionally, it also identifies the variance in terms of a small number of new pseudo-variable (Principle component) within a huge dataset of correlated variables 9 , 34 .

Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests of Sphericity have been performed to examine the suitability of the present dataset for PCA. Sampling adequacy is measured by KMO which indicates that the proportion of variance is caused by underlying Principal Components 22 , 35 . A value closer to 1 generally indicates that the data sets may be used for PCA and in this study KMO value is 0.547. If the KMO value < 0.5 then the data sets will not be useful for PCA. To examine whether the correlation matrix is an identity matrix, Bartlett’s test of Sphericity was used. All variables become unrelated making the PCA model inappropriate and unsuitable statistical tool for advanced data analysis if the correlation matrix is an identity matrix. The Null Hypothesis of Bartlett’s test assumes that there is no scope for dimensionality reduction (if the correlation matrix is the identity matrix). In the present work, the significance level (0.000) is less than 0.05 which rejects the Null Hypothesis. Therefore, it means that there is no significant relationship among the parameters. Finally, using Varimax rotation with Kaiser Normalization PCA has been carried out on normalized data and so the covariance matrix coincides with the correlation matrix (Table 4 ). SPSS software was used to carry out both tests. The % variance and cumulative variance is also represented in Table 4 . This explains more than 76% of the total variance and shows only the first six components. However, more components might be needed as the first component by itself explains less than 25% of the variance. In the standardized ratings, six PCs explain more than 76% of the total variability so these PCs have been reasonably retained to reduce the dimension further.

The z-score analysis has been performed for 15 parameters in this study. Figure  5 shows the Box and whisker plot which displays the variability in z-score values of the data points. The maximum and minimum values in the box plot are represented via vertical black lines which in most cases lie beyond the boxes. The whiskers that are not considered outliers are extended to the most extreme data points and the outliers in the box plot are plotted individually using the ‘ + ’ symbol. To obtain a set of linearly transformed scores, Z-scores are estimated which can be simplified by plotting a straight-line graph between z-scores and corresponding scores. The parameters were subjected to PCA after the estimation of z-scores. Analysis of 15 parameters would still be a very tedious and expensive job so PCA has been performed which reduced the parameters using a statistical approach. PCs are independent axes where data is projected 22 , 36 . Table 3 shows the rotated factors loading, eigenvalues, individual variance, and cumulative variance of these PCs. The 6 PCs of these parameters account for 76.24% of the total variance and have individual eigenvalues > 1.

Box plot shows the z-score variability of data.

Figure  6 shows the relation between the first 2 and 3PCs that contributes maximum to the overall variance. The direction and length of the vectors in the 2D bi-plotare represented in Fig.  6 a which indicates the contribution of each variable to the two PCs in the bi-plot. For example, on the horizontal axis, the first PCs have positive coefficients for Chloride, Fluoride, Sulphate, Bromide, EC, TDS, Salinity, and Potassium while negative coefficients for nitrate, nitrite, phosphate, pH, calcium, magnesium, and sodium. That is why 8 vectors are directed into the right half of the plot and 7 are directed to the left half of the plot. Similarly, the PC2 has negative coefficients on the vertical axis for sulfate, phosphate, salinity, calcium, magnesium, and potassium whereas has positive coefficients for the remaining 9 variables. A point for the mean values of each parameter at all of the 31 locations was also included in this 2D biplot. Therefore, their relative locations can be determined from the plot as these points are scaled with respect to the maximum score value and maximum coefficient length. If the first 2 PCs do not explain enough of the variance in the data, then the 3-dimension plot proves to be quite useful. Figure  6 b shows the 3D representation of first 3 PCs.

( a ) 2D biplot (2PCs) and ( b ) 3D plot (3PCs) of principal components.

Correlation analysis

Pearson linear correlation matrix was generated by using those parameters which were contributingamaximum of 6 PCs (> 0.35). Table 5 shows the most effective water quality parameters to define any co-variation. The obtained resultsindicateavery strong positive correlation between TDS and EC, and salinity. Salinity shows a strong positive correlation with TDS. pH shows weak and very weak positive correlation with all parameters except NO 2 ˉ. While Ca has a very weak positive correlation with NO 3 ˉ, Fˉ and pH; it has a very weak negative correlation with Clˉ, NO 2 ˉ, PO 4 3 ˉ and Br and has a moderate negative correlation with EC, TDS, and salinity. A similar very weak negative correlation is shown between K + and all the parameters except Clˉ, pH, and EC (shows very weak positive correlation). Na shows a very weak negative correlation with NO 3 ˉ, PO 4 3 ˉ, Brˉ, EC, TDS, and salinity; it has a very weak positive correlation with Fˉ, Clˉ, NO 2 ˉ and pH.Brˉ and moderate positive correlation with Fˉ and PO 4 3 ˉ and shows very weak negative correlation with NO 2 ˉ. Hence, correlation coefficients between pairs of water quality parameters concentrations indicate that Fluoride, Bromide, phosphate, and calcium values significantly correlate with pollutant parameters such as nutrient and trace elements. This suggested that these nutrients and trace elements have high values as they are significantly affecting TDS, salinity, and EC because ofthenatural geological activities in the study area.

Biological parameters for water quality assessment

A wide variety of pathogenic and non-pathogenic microorganisms are found in water bodies which may lead to unpleasant taste and odour in water. This may be served as an indicator and the main concern behind studying the microbiological quality of water. Bacteria, helminths, protozoa, and viruses are contaminants that are derived from feces, household waste, etc. Indicator organisms are generally used to analyze the microbiological quality of water and among them, coliform and E. coli are such indicators. In this work, the bacterial colony count of collected water samples from different locations of Tawang was found to be in the range of 7 CFU/ml to 20117 CFU/ml as mentioned in Table 7 . As per BIS Standards 29 the permissible limit of bacterial count in drinking water should be 0 CFU/100 ml.

Spatial distribution pattern

Figure  2 a–g represents the spatial distribution pattern of different water quality parameters in the location maps of sample collection area. Figure  2 a represents the spatial distribution pattern of the pH, which indicates that the central part along with NE-WS across the district shows the alkaline nature of surface water. On the other hand the NE-NW along with the central part represents the acidic nature of water. According to previous reports, fluoride is absorbed on a clay surface in acidic water,and desorbed in alkaline water. Hence, for fluoride dissolution, alkaline pH is more favorable 37 , 38 . The EC in the district is in the range of 136.1µS/cmto 1361µS/cm(Fig.  2 b). The TDS is also low in the central and NE-NW-SE-SW parts of the Tawang (Fig.  2 c). This shows that the EC and TDS have a significant positive correlation as evidence by the correlation matrix of the quality parameters (Table 5 ). The salinity map clearly and significantly indicates that it is within the permissible limit (600 mg/L) but unevenly distributed in the Tawang district (Fig.  2 d). A positive correlation is observed between the salinity, EC, and TDS of the surface water as mentioned in Table 5 . Most of the surface water in the spatial distribution graph showed alkaline nature which might be due to the presence of carbonatesand bicarbonates 39 .

The spatial distribution of Ca 2+ and Mg 2+ suggested varying concentrations within the permissible limit which reflects that the study area is characterized by soft surface water (Fig.  2 g and h). The study area is surrounded by rocks covered with snow, so the Ca 2+ and Mg 2+ ions present in the surface water might be due to the leaching of calcium and magnesium-bearing rocks. Ca 2+ shows a significant negative correlation with EC, TDS, and salinity. Na + is highest (within permissible limit) in the central part and some patches in the NW part of the district. It is also showing negative correlation with the TDS and salinity in the spatial distribution pattern and correlation matrix. The presence of K + is within thepermissible limit but it is covering the major portion of the district (Fig.  2 i) 40 . The spatial distribution pattern of sulphate (Fig.  2 q) and chloride (2 k) reveals that they are present within the permissible limit. The spatial distribution pattern of iron reveals its absence in the district (Fig.  2 j). Another important factor for the study area is fluoride (Fig.  2 m) which is mainly observed in the SE part of the district above the permissible limit, with a concentration of more than 10 mg/L which may lead to skeletal fluorosis 41 . Fluoride concentration in surface water depends on many factors such as temperature, pH, the solubility of fluorine bearing minerals, size, and type of geological formations, presence, and absence of complexing or precipitating ions and colloids, anion exchange capacity of water and the contact time during which the water remains in contact with the geological formations 12 , 40 , 42 .

Nitrate, nitrite, and phosphate in the surface water are mainly due to anthropogenic activities such as waste disposal, sanitary landfills, overapplication of fertilizers or improper manure management practices,etc. 43 . In this study, it was observed that the nitrate, nitrite, and phosphate are within the permissible limit which indicates absence of any such kind of activities (Fig.  2 n,o,p).

Water quality assessment using WQI

FOR A RAPID ASSESSMENT OF ENVIRONMENTAl impact,WQI can help us to decide overall water quality. WQI provides a value with a quick and understandable explanation of water quality. BIS 29 , US EPA 44 ,and WHO standard 19 parameter values were used for the calculation of WQI at different water sampling locations in Tawang, Arunachal Pradesh. The relative water quality parameters are presented in Table 6 .

In the present study, the water samples were collected during the winter season, and the calculated WQI results are mentioned in Table 7 . The average WQI value in Tawang is 82.49. The WQI results of maximum samples are in the range of 0–50 which are considered as good for drinking, while some samples are unsuitable for drinking showing value > 50. In terms of WQI, the Tawang water samples from most of the sites have good water quality and some have poor water quality. Therefore, WQI has been widely applied in the monitoring of water quality and plays a significant role in water resource management for suitable applications.

In this research, the WQI value calculated for sites 13, 14, 17, 18, and 25 was 24.58, 21.22, 23.96, 23.07 and 25.80 respectively. These results show that the water of these sites is suitable for drinking as it comes in the range of 0–25. Additionally, the WQI value calculated for site 1, 5, 6, 8, 9,11, 19, 20, 21A, 21B, 22, 26B, 29, and 31 was in the range of 26–50 which shows that the water from these sites is suitable for drinking after normal treatment. However, the WQI values in sites 2, 3, 4, 10, 16, 23, 24, 26A, 28, and 30 are more than 100. Because of the water flowfrom agricultural land and household wastewater into these sites,adecrease in water quality is observed leading to high WQI values.

Hydrogeochemical facies and rock water interaction

Based on their hydrogeochemical facies, Hill Piper trilinear diagram explains and classifies different types of water groups 45 . Figure  7 shows the uneven distribution of major ions which are plotted on the Hill-Piper trilinear diagram using Grapher Software (Grapher 16.3.410)( [email protected]). This diagram represents the major significant cations and anions responsible for the nature of surface water. It is comprised of two triangles at the base and one diamond shape at the top which categorized surface water into various six types (Ca 2+ - HCO 3 ˉ type, Na + - Clˉ type, Mixed Ca 2+ - Mg 2+ - Clˉtype, Mixed Ca 2+ -Na + - HCO 3 ˉ type, Na + - HCO 3 ˉ type and Ca 2+ - Clˉtype) 46 . A critical evaluation of the diagram reflects that majority of the samples (50%)fall under Ca 2+ - HCO 3 ˉtype and 10% of the samples showed Ca 2+ -Clˉ type. The study was conducted during winter season and from the results it is clear that weathering of rocks and precipitation are the major processes occurring in the surface water environment. Hydrochemistry of the investigated samples represents that the alkaline earth > alkali metals and weak acid > strong acidic anions. Major cations are present in order, No Dominant type ˃ Mg 2+ of the mean abundance while anions are present in mean abundance order of HCO 3 ˉ˃SO 4 2 ˉ. Hence, it can be concluded that the surface water in this study is polluted due to natural activities or rock-water interaction. Similar type of studies have been carried out to find the hydrogeochemistry and water quality ofRewalsar Lake of Lesser Himalayan,which showed that the alkaline earth surpass the alkaline metal and weak acid exceed to strong acid 5 , 47 .

Hill Piper trilinear diagram.

The geochemical processes occurring in the surface water of the study area was further verified by Durov’s plot (Grapher16.3.410, [email protected]) 48 . This diagramconsistsof two ternary plotswhich are plotted against anions and cations of interest (data are normalized to 100%). The Durov plot reveals the relationship and properties of large sample groups while clustering the data points indicating the samples with similar chemical composition 49 . Figure  8 shows the Durov’s plot which evaluate the water type from geochemical process that affect the surface water. In this study, most of the surface water samples are plotted in field 5 which indicates that water exhibits simple dissolution or mixing (no dominant of cation or anion). Some of the samples fall in field 8 which indicates that the surface water has undergone reverse ion exchange with water minerals. Few of the surface water samples are plotted in field 6 which indicates the probable mixing or uncommon dissolution influences (SO 4 2- dominant or anion discriminant and Na dominant). In recent research on Parbati river overall water quality, hydrogeochemical characteristics and other chemical parameters were assessed. The results of Durov’s plot of this study are in line with the present research 50 .

Durov plot illustrating hydrochemical processes involved in surface water/groundwater in different locations of Tawang area.

Hydrogeochemistry of water is altered and affected by different natural processes like evaporation, precipitation, rock weathering and combination of all these processes. These processes can be deciphered using Gibb’s diagram which is characterized by three chief zones: rock weathering dominance, precipitation, and evaporation. It is formed by plotting between the ratio of TDS vs Clˉ/(Clˉ + HCO 3 ˉ) and TDS vs Na + / (Na +  + Ca 2+ ) for anions and cations respectively whereby all ions are expressed in meq/L.It is well known that the reactions occurinsurface water and essential minerals of the water play a vital role towards water quality. Additionally, it is also useful in knowing the primary mechanism of ion contribution in surface water 46 , 51 .

The Gibbs plot of surface water samples collected from Tawang area in winter season has been shown in Fig.  9 a and b. The samples collected from this area is prevalent with rock-water interaction and precipitation. Therefore, in plot 10a precipitation dominates over rock -water and evaporation while in plot 10b, rock- water interaction dominates over evaporation and precipitation. Hence, in this study the two primary factors that influenced the surface water chemistry are weathering of rock forming minerals and rainwater intrusion into the aquifer. The samples which lie in the evaporation zone are the indicative of the water influenced by sea water and in this study the samples have fallen into precipitation or rock- water dominant zone. While if any sample falls outside these three mentioned zones, then might be due to any anthropogenic activities.

Gibbs diagrams ( a ) cations: TDS vs Na + /Na +  + Ca 2+ ) ( b ) anions: TDS vs Cl/(Cl -  + HCO 3 - )reveals water chemistry controlling mechanism.

Earlier studies on different water bodies in Himalayan regions also confirmed rock dominance as a main factor for controlling ionic composition 47 , 52 , 53 . The Himalayan water bodies are surrounded by rocks so the water percolation through the rocky lithology and longtime rock water interaction may result in high solute concentration. Furthermore, the less ionic composition in this study reflects the precipitation dominance which might be due to precipitation and melting of ice at higher altitude of Himalayan region 52 .

Figure  10 is the modified version of Durov plot and Piper Plot in which the two equilateral triangles are omitted. In case of Hill Piper plot, the type of water is determined on the data plot when the milliequivalent percentage of major anions and cations are plotted in each triangle irrespective of their triangular field. The central diamond field which provides the overall character of water is the extension of the triangular field. In contrast, the Chadha diagram is plotted between the difference in the alkaline earth (Ca 2+  + Mg 2+ ) and alkali metals (Na +  + K + ) on X axis and the difference in weak acid anions (CO 3 2-  + HCO 3 ˉ) and strong acid anions (Clˉ + SO 4 2 ˉ) on Y axis. Depending on the size of the scale chosen the plot can be square or rectangular field which has all the advantage of diamond- shaped field of Hill- Piper diagram. The rectangular field is divided into eight sub fields to represent the primary character of water. The eight sub fields are as follows: (1) Alkaline earths > alkali metals. (2) Alkali metals > alkaline earths. (3) Weak acidic anions > strong acidic anions. (4) Strong acidic anions > weak acidic anions. (5) Alkaline earths and weak acidic anions > alkaline metals and strong acidic anions respectively. This type of water has temporary hardness and in Chadha’s plot the position of the data points represents Ca 2+ –Mg 2+- HCO 3 ˉ-type, HCO 3 ˉ dominant Ca 2+ –Mg 2+ -type or Ca 2+ –Mg 2+ dominant HCO 3 ˉ -type waters. (6)Alkaline earths > alkali metals and strong acidic anions > weak acidic anions. This type of water has permanent hardness and during irrigation usage it does not deposit residual carbonate. The datapoints in the Chadha’s plot represents Ca 2+ –Mg 2+ -dominant Clˉ-type, Ca 2+ –Mg 2+ –Clˉ-type or Clˉ-dominant Ca 2+ –Mg 2+ -dominant Clˉ-type waters. (7) Alkali metals > alkaline earths and strong acidic anions > weak acidic anions. This type of water creates salinity problem and the data points in the Chadha’s plot represents Clˉ -dominant Na + -type, Na + -dominant Clˉ-type, Na + –Clˉ-type, or Na 2 SO 4 -type waters. (8) Alkali metals > alkaline earths and weak acidic anions > strong acidic anions. Residual sodium carbonate deposit and foaming problem occurs in such type of waters. The data points in the Chadha’s plot represents HCO 3 ˉ-dominant Na + -type, Na + –HCO 3 ˉ-type or Na + -dominant HCO 3 ˉ-type waters 54 , 55 . In present study, the points in the Chadha’s plots lies in different fields (1 and 2) and showing Clˉ -dominant Na + -type, Na + -dominant Clˉ-type, Na + –Clˉ-type, Na 2 SO 4 –Ca 2+ –Mg 2+ –HCO 3 ˉ-type, HCO 3 ˉ dominant Ca 2+ –Mg 2+ -type or Ca 2+ –Mg 2+ dominant HCO 3 ˉ-type waters. Most of the samples indicated nature of surface water in the Tawang area as hard (TH > 75) and influence permanent and temporary hardness 47 . The data points set in some location near field 5 and 7 suggested that alkaline earths and weak acidic anions exceedsalkali metals and strong acidic anions zone.

Chadha’s Plot for hydrogeochemical facies.

Figure  11 a–i shows the statistical analysis of certain pairs of parameters to elucidate the hydrologic processes. The concentration of Ca 2+ (R 2  = 0.0399), Na + (R 2  = 0.0159)and Mg 2+ (R 2  = 0.0081) in surface water have shown weak association with HCO 3 ˉ concentration. Similarly, HCO 3 ˉ + Clˉ have weak association with Na +  + Mg 2+  + Ca 2+ ions in the surface water (R 2  = 0. 0476). However, Mg 2+ , Ca 2+ and Na + have also showed the weak association amongst them (Mg 2+ vs Ca 2+ , R 2  = 0. 0591 and Ca 2+ vs Na 2+ , R 2  = 0. 0005). Additionally, the weak association between the cations in the surface water with Clˉ was observed. Similar to this work surface water and groundwater of river Munda Basin was studied and the results are in line with the present study 56 .

Hydrologic processes, statistical analyses of the correlations between ( a ) Ca 2+ vs HCO 3 - ( b ) Na 2+ vs. HCO 3 - ( c ) Mg 2+ vs. HCO 3 - ( d ) HCO 3 -  + Cl - vs. Na +  + Mg 2+  + Ca 2+ ( e ) Mg 2+ vs. Ca 2+ ( f ) Na 2+ vs. Cl - ( g ) Ca 2+ vs. Na 2+ ( h ) Ca 2+ vs. Cl - & ( i ) Ma 2+ vs. Cl - .

Schoeller’s index is also used to study ion exchange that occurs between the surface water and the host environment. To interpret the ion exchange behavior between the surfacewater and the host environment Eqs. ( 5 ) and ( 6 ) are used. This process is also known as chloro-alkaline indices and all the ions are expressed in meq/L. Depending on the Na + and K + exchange from water with Mg 2+ and Ca 2+ in rock/soil, or vice versa, CAI-I and CAI-II values may be positive or negative. If the Chloro-alkaline indices value is positive, it means Na + and K + exchange occurs in water with Mg 2+ and Ca 2+ while if it yields negative value this means ion exchange occurs between in Mg 2+ and Ca 2+ surface water and Na + and K + in rock/soil 57 , 58 , 59 . In the present study as shown in Fig.  12 , all the samples (100%) have generated negative values which revealed a presence of reverse ion exchange controlling surface water chemistry as well as rock-water interaction.

Scatter diagram shows the variation of chloro- alkaline indices of surface water samples in the study area.

Process controlling the surface water chemistry

The dissolution of different parent materials yield different ion combinations, so the geological formation occurring within the study area can be assigned to the process that influence the surface water in the Tawang area aquifer. Na + and K + are originating from silicate weathering while Ca 2+ and Mg 2+ are from carbonate weathering before evaporation. Additionally, SO 4 2- and Clˉ originate from evaporation while HCO 3 ˉ from carbonate silicates. Studies have been done by many researchers which prove that in water system calcite, dolomite, anhydrite and gypsum weathering and dissolution are very predominant processess.Anthropogenic activity to some extent also generates these parameters. The physiochemical characteristics are also affected by the dissolution of evaporites 47 , 60 .

The scatter plots were used to understand the source of major ions and ion exchange process affecting the surfacewater of Tawang area. These plots compared the different parameters in equivalent concentration (meq/L) 60 . Figure  13 a shows the scatter plot of (Ca 2+  + Mg 2+ ) versus (HCO 3 ˉ + SO 4 2 ˉ) which results in close to 1:1 equiline and highlighted that the water samples from different locations of Tawang has calcite, gypsum dissolution, anhydrite, and dolomite. Meanwhile, reverse ion exchange and carbonate weathering is demonstrated by the abundant amount of Ca 2+  + Mg 2+ while silicate weathering can be suggested by the presence of (HCO 3 ˉ + SO 4 2 ˉ) over (Ca 2+  + Mg 2+ ). This result revealed that all samples show dominance (HCO 3 ˉ + SO 4 2 ˉ) over (Ca 2+  + Mg 2+ ) indicating that silicate weathering is a reaction that affect the chemistry of the surface water samples in different location of Tawang. Furthermore, in two locationsthe reverse ion exchange is occurring due to excessive (Ca 2+  + Mg 2+ ) over (HCO 3 -  + SO 4 2- ) 52 .

Scatter plots indicating sources of different parameters in surface water of Tawang area.

Meanwhile Fig.  13 b shows that the plot for (Ca 2+  + Mg 2+ ) vs HCO 3 ˉrepresents that the water samples have fallen below the 1:1 equiline which suggested that in all the locations silicate weathering impacts waterdominantly.If these points fall on or above the divider line, it indicates that HCO 3 ˉ in the surface water is controlled by alkaline earth metals as well as carbonate lithology 47 , 52 , 56 . Moreover, (SO 4 2 ˉ + Cl - ) vs HCO 3 ˉscattered plot is shown in Fig.  13 c in which SO 4 2-  + Cl - has shown dominance over HCO 3 - at lower concentration range. Similarly, in Fig.  13 d a scattered plot was plotted between (SO 4 2 ˉ + Clˉ) vs (Na +  + K + ) and it was found that the water sample points are above 1:1 line 55 .

The points plotted againstalkaline earth metals vs total cations in Fig.  13 e have fallen above and below the equiline but the maximum number of locations of water samples are below 1:1 line. This might be due to the presence of higher amount of Na +  + K + with more dissolved solids. The high (Na +  + K + )/total cation (50%) and low (Ca 2+  + Mg 2+ )/(Na +  + K + ) (9.9%) means water chemical composition is greatly affected by silicate weathering along with less influence of carbonate dissolution. Additionally, if the soil is more influenced with alkalinity this could result in more (SO 4 2 ˉ + Clˉ) and from these findings it can be concluded that the soil source might be having Na 2+ SO 4 2 ˉ and K 2+ SO 4 2 ˉ. Figure  13 f shows the scattered plot of (Na +  + K + ) vs total cation and the higher ratio of (Na +  + K + ) present in the surface water due to silicate weathering or alkaline soil 47 , 56 . Furthermore, the Fig.  13 g shows the points between Clˉversus Na + to assess the impact of halite dissolution towards the surface water chemistry, whereby all the samples have fallen below the 1:1 equiline. Thus, it can be concluded that the source of Na + is silicate weathering which may be from Na-plagioclaseas mentioned in previous studies 61 .

Another primary process of salination that occurs in the surface water system is depicted using SO 4 2 ˉvs. Ca 2+ plot as shown in Fig.  13 h. In this plot the points are below 1:1 line which suggest that the gypsum dissolution is less in water samples. A positive relation of (Ca 2+  + Mg 2+ ) vs. Na + was observed in Fig.  13 i and it is used to assess the impact of ion exchange on surface water chemistry. In the present study the points are present on both sides of the 1:1 equiline indicative of both ion exchange and reverse ion exchange to occur in water. If the (Na + /Clˉ) vs EC (µS/cm) plot will yield a horizontal line,then it has been suggested that the evaporation process is occurring in the water system. If the Na + /Clˉ is nearly equal to 1, sodium will be a product of halite dissolution whereas Na + /Clˉ > 1 indicative of ions emitted from silicate minerals weathering. In the present work the Na + /Clˉmolar ratio is in the range of 5.2–417.2 as shown in Fig.  13 j which is greater than 1, indicative of silicate weathering as an indicative of Na + release to the surfacewater 61 .

Water suitability forirrigation purpose

Water quality must be monitored to maintain soil fertility and better crop output. The low quality water shows harmful impact on heavy clayed soil but can be used for irrigation of sandy and permeable soil through which chemical may pass deep down 47 . To know the suitability of water for irrigation certain parameters like Magnesium hazard (MH), Total hardness (TH), Permeability index (PI), Kelly Index (KI), Sodium adsorption rate (SAR), Sodium percentage (Na%) and Residual sodium carbonate (RSC) are used.

Szabolcs and Darab proposed a formula as mentioned in Table 8 for the calculation of Magnesium hazard (MH) in meq/L. According to this formula if MH < 50 meq/L then the water is suitable for irrigation while if MH > 50 meq/L then the water is unsuitable for irrigation 62 . In the present study as shown in Fig. 14a, 80% of water samples from different locations have MH scores within the safe limit of 50 meq/L indicating suitability for irrigation. While 20% of water samples have MH score > 50 meq/L which means these water samples have more Mg 2+ over Ca 2+ which adversely affect the soil quality leading to poor irrigation yield. In most of the surface water the state of equilibrium is maintained by the Ca 2+ and Mg 2+ ions,however if the in-equilibrium occurs between these two ions the soil will become alkaline due to high Mg 2+ concentration in water and subsequently leads to reduction in crop yield. Additionally, in highly saline and predominantly sodium dense water, Mg 2+ ions will negatively impact soil structure.

Due to precipitated Ca 2+ and Mg 2+ ions in water, the quality of permanent and temporary hardness can be deduced from action of soap on it. Calcium carbonate is the reason for temporary hardness in water which can be removed byheating,whilethepermanent hardness is due to the presence of both Ca 2+ and Mg 2+ ions andcan be removed by ion exchange. In the present study, the total hardness (TH) was calculated as per Toddmentioned in Table 8 and is expressed in mg/L. The total hardness in this study ranges from 73.75 ± 0.5 to 272.39 ± 0.08 mg/L as shown in Fig.  14 b whereby its optimal limit is 80–100 mg CaCO 3 /L.

( a ) Magnesium hazard and ( b ) Total hardness of the water samples collected from different locations of Tawang.

The long-term use of Na + , Ca 2+ , Mg 2+ and HCO 3 ˉ rich water affects the soil permeability. To find out the suitability of water Doneen used the Permeability index (PI) and classified irrigation water in three classes which are Class-I, Class-II and Class-III. Figure  15 (Grapher16.3.410, [email protected]) shows the classification of irrigation water based on permeability index calculated as per the formula mentioned in Table 8 . According to this classification only Class-I and Class-II types of water are suitable for irrigation due to 75% or more maximum permeability score while Class-III is not suitable for irrigation due to 25% maximum permeability. In the present research work, 48% of the samples falls in Class-I and 45% of the water samples falls in Class-II, indicating that the water is good for irrigation purpose. However, 7% of water sample falls into Class-III, indicating that water is not suitable for irrigation.

Irrigation water classification based on Permeability index.

Kelly index (KI)is also another useful method to classify water for irrigation. It can be calculated using the following formula as mentioned in Table 8 . According to this index, KI < 1.0 is indicative of good water for irrigation while KI > 1.0 is indicative of bad water and unsuitable for irrigation 47 , 52 . In the present study the 58% of water samples obtained from different location of Tawang has yielded KI values < 1.0 while 42% of them yielded KI value > 1.0. Hence, 58% of water samples from different location is suitable for irrigation.

Sodium adsorption rate (SAR) is a parameter to calculate the correlation between soluble divalent cations (Ca 2+ and Mg 2+ ) and Na + . If SAR value is higher, it means the Na + concentration is higher with respect to Ca 2+ and Mg 2+ . An alkaline soil is developed from high Na + concentration and reduced soil permeability. The SAR is determined by using formula mentioned in Table 8 and based on this the irrigation water is classified into four alkali categories: S1: low (0–10), S2: medium (10–18), S3: high (18–26) and S4: very high (> 26) 63 . In the present study as shown in Fig.  16 (Grapher 16.3.410, [email protected]), the SAR values in the study area ranges from 0.95 to 5.03 and as per Richard’s classification all the samples fall into the low sodium hazard and low to very high salinity hazard. These results support the findings of KI. Therefore, it can be concluded that the maximum water samples from different location of Tawangare suitable for irrigation purpose.

Classification of irrigation water using Wilcox diagram.

Sodium is deemed as an important cation in water irrigation due to its ability to reduce soil fertility, as high cation concentration shows negative effect on plant growth. The sodium percentage in this study was also calculated by using Wilcox formula as mentioned in Table 8 64 . According to BIS the maximum acceptable limit of Na% is 60% and above that limit it is harmful for growth of plants. Additionally, the higher concentration of sodium increases the hardness in soil leading to reduction in soil permeability 47 . Hence, based on the Table 8 , 20% of the samples lie in good category, 48% in permissible while 32% in doubtful in terms of their suitability for irrigation. Additionally, a graph has been plotted between Na% and EC as per Wilcox to determine the water suitability for irrigation and from Fig.  17 it was found that 94% of samples are excellent to good for agricultural usages. The remaining 6% of the samples lies in good to permissible category and can be used for irrigation purpose. From these results it can be concluded that for irrigation purpose water should have high concentration of Ca 2+ and Mg 2+ ions and lesser Na + .

Surface water rating using Sodium Percentage and Electrical Conductivity.

The residual sodium carbonates (RSC) are another method to check water irrigation property. RSC can be calculated using the difference in sum of carbonate and bicarbonate to the sum of calcium and magnesium in water as mentioned in Table 8 . The excess amount of carbonate and bicarbonate altered the soil physical property either by increasing its salinity or itself get precipitated leading to decrease in soil fertility. The high concentration of bicarbonate precipitate Ca 2+ and Mg 2+ in water and reduce their amount that ultimately leads tohigh concentration of CO 3 2- and HCO 3 ˉ in the solution expressed as HCO 3 ˉ hazard. RSC is also used to assess the relationship between alkaline earths with weak acids to assess the water quality for irrigation. If weak acid > alkaline earths, then soil permeability is damaged, as alkaline earths become precipitated in the soil. Additionally, excessive carbonate and bicarbonate in water also intrude alkaline earth over the permissible limit and finally affect the agricultural crop. The RSC value is categorized into safe for irrigation (< 1.25 epm), fair quality water (1.25–2.5 epm) and unsuitable for irrigation (> 2.5 epm) 47 , 52 , 56 .In this study, 99% of the surface water samples have been categorized as safe for irrigation while the remaining 1% unsuitable for irrigation. The negative RSC values indicates that the calcium and magnesium are partially precipitated.

In the present study, the water quality of 31 different locations of Tawangduring the winter season were evaluated from the viewpoint of its suitability for drinking and irrigation. For calculation of water quality, 15 parameters were selected represented by pH, TDS, anions, cations, EC, salinity, turbidity for PCA analysis. Different statistical toolssuch as PCA and coorelation analysis were used in this paper to derive the relationship between different parameters of water as mentioned below.

In the present study, the average WQI value is found to be 82.49. The WQI results of 61% samples are in the range of 0–50 which are considered as good for drinking, while 39% are unsuitable for drinking showing value > 50. In terms of WQI, the Tawang water samples from most of the sites have good water quality except some which show poor water quality.

Hill piper plot showed that the alkaline earth dominates alkali metal and weak acid exceeds strong acid. This plot also showed that the Ca 2+ –HCO 3 ˉ, Na + –HCO 3 ˉ and Na + –Clˉ type of water is dominant in this area.Gibbs plot also revealed that rock weathering is the main process which controls the surface water The dominance due to precipitation was also observed, which may be due to continuous outflow of surface water thus having short rock-water interaction. The silicate weathering is found to be dominant process rather than carbonate weathering due to rich silicate minerals lithology. For increasing concentration of alkali metals in the surface water of Tawang, reverse ion exchange process has played an active role. Both Na + and Ca 2+ are observed as dominant cations while HCO 3 ˉ and SO 4 2 ˉ as dominant anions. The values in the chloro-alkaline indices in this study were negative indicating the exchange of Ca 2+ and Mg 2+ ions by Na + and K + ions of rock material.

The scatter plot of Ca 2+  + Mg 2+ vs total cation and Na +  + K + vs total cation has specified the silicate weathering as a dominant source of major ions. Furthermore, the scattered plot of (Ca 2+  + Mg 2+ ) vs (HCO 3 ˉ + SO 4 2 ˉ), (Ca 2+  + Mg 2+ ) vs HCO 3 ˉ and (SO 4 2 ˉ + Clˉ) vs HCO 3 ˉindicated that both silicate weathering and reverse ion exchange processes have played an important role in geochemical reactions.

The parameters like MH, KI, PI, SAR, Na% and RSC have shown that the water from different locations of Tawang is suitable for irrigation. Some water samples were found to be hard with permanent and temporary hardness. However 93% of the samples have shown PI score < 75, which indicates the suitability of the samples for irrigation.

Hence this study has thrown some light on the water quality of untouched surface water sources of theTawang area. The study has revealed the suitability of most of the water samples for human consumption as well as irrigation. From the detailed water quality analysis and geochemical characteristics it can be inferred that those surface water sources lying at an altitude of maximum 4465 m in the studied area have not been exposed to any kind of anthropogenic activities. Hence the obtained results are in coorelation with natural activities only, which make it even more valuabledatabase asset for future references.

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The authors want to acknowledge the Government of Arunachal Pradesh and Indian Army for their support during sample collection.

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Nisha Gaur, Arpan Sarkar, Dhiraj Dutta, B. J. Gogoi, Rama Dubey & Sanjai Kumar Dwivedi

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Dr. N.G.–Microbiological Testing of the sample. Mr. A.S.–Water sample collection and Field work. Mr. D.D.–Physico chemical testing of the sample and statistical analysis of the data. Dr. B.J.G.–Geo-location collection and GIS based data compilation. Dr. R.D.– Writing of the Manuscript. Dr. S.K.D.–Overall Guidance and final editing of the manuscript.

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Gaur, N., Sarkar, A., Dutta, D. et al. Evaluation of water quality index and geochemical characteristics of surfacewater from Tawang India. Sci Rep 12 , 11698 (2022). https://doi.org/10.1038/s41598-022-14760-3

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DOI : https://doi.org/10.1038/s41598-022-14760-3

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A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives

Affiliations.

• 1 Department of Agricultural and Food Engineering, School of Engineering, Holy Spirit University of Kaslik, P.O.Box 446, Jounieh, Lebanon.
• 2 FMPS HOLDING BIOTECKNO s.a.l. Research & Quality Solutions, Naccash, P.O. Box 60 247, Beirut, Lebanon.
• 3 Syngenta, Environmental Safety, Avenue des Près, 78286 Guyancourt, France.
• PMID: 37234131
• PMCID: PMC10006569
• DOI: 10.1007/s11157-023-09650-7

Water quality index (WQI) is one of the most used tools to describe water quality. It is based on physical, chemical, and biological factors that are combined into a single value that ranges from 0 to 100 and involves 4 processes: (1) parameter selection, (2) transformation of the raw data into common scale, (3) providing weights and (4) aggregation of sub-index values. The background of WQI is presented in this review study. the stages of development, the progression of the field of study, the various WQIs, the benefits and drawbacks of each approach, and the most recent attempts at WQI studies. In order to grow and elaborate the index in several ways, WQIs should be linked to scientific breakthroughs (example: ecologically). Consequently, a sophisticated WQI that takes into account statistical methods, interactions between parameters, and scientific and technological improvement should be created in order to be used in future investigations.

Keywords: Ground water; Surface water; Water quality index (WQI); Water quality parameters.

© The Author(s), under exclusive licence to Springer Nature B.V. 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Conflict of interest statement

Conflict of interestAll authors declare that they have no conflict of interest.

Phases of WQI development

Evolution of WQI research per…

Evolution of WQI research per year (Scopus 2022)

Evolution of WQI research per country (Scopus 2022)

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