In the present study, the suitability of groundwater for drinking and irrigation purpose were analyzed. A total of 65 groundwater samples were collected and analyzed for relevant water quality parameters from Sirdala block of Nawada district Bihar (India). To estimate the WQI for the drinking purpose, various parameters such as pH, TDS, TH, AS and major ions (Ca2+, Mg2+, Na+, K+, Cl, F, HCO3, NO3, SO42−) were considered. The WQI for irrigation, various irrigation indices namely soluble sodium percent (Na%), sodium adsorption ratio (SAR), residual sodium bicarbonate (RSCB), permeability index (PI), magnesium hazards ratio (MHR), Kelly's ratio (KR), potential salinity (PS), Cl: HCO3, Mg(2+): Ca(2+) and Na+: Ca(2+) were employed and discussed in addition with Wilcox and USSL diagram. Plots, namely Gibbs ratio and Piper diagram, were used to understand chemical characteristics of groundwater. The WQI for drinking purpose was observed between 57.67 and 929.90 in which most samples (70.76%) were unsuitable for drinking. However, the WQI for irrigation purpose varied from 87.88 to 434.67 in which 50% of samples were suitable. Gibb's plots determined in this study reveal that evaporation-precipitation was the dominant factor that influences hydrochemistry. Piper plots suggest that Na-HCO3 water type was major hydrochemical facies in groundwater.

  • The Piper diagram is applied to explore the dominancy of hydrochemical facies.

  • Water quality parameters except for EC, TDS, , and are within the acceptable limits for drinking water quality.

  • WQID shows a predominance of poor water quality in the area.

  • Indices PI, MHR, KR, Na%, and PS are within the acceptable limit for irrigation purposes.

  • WQII shows a predominance of no restriction to slight restriction classes.

Graphical Abstract

Graphical Abstract
Graphical Abstract

The availability of freshwater is a prime resource for socioeconomic development of any region. Groundwater is a major source of freshwater for many countries. The main reasons for the augmented reliance on groundwater sources are the scarcity of surface water and the failure of rainfall (Sharma et al. 2016). Nearly 65% of groundwater in the world is used for drinking, 20% for agricultural and livestock, and 15% for industrial and mining activity (Adimalla et al. 2020; Salehi et al. 2018). In India, approximately 89% of groundwater is withdrawn for irrigation, 9% for domestic, and 2% for industrial use (Margat & van der Gun 2013; Sutradhar & Mondal 2021). Water scarcity has become an increasingly severe issue, for regions where annual precipitation is less than 500 mm (Sharma et al. 2016). In recent years, intensive agricultural activity, a rapidly growing population, high rate of urbanization coupled with indiscriminate utilization groundwater of have raised the withdrawal of groundwater, resulting in deterioration of quality and quantity (Janardhana Raju et al. 2011; Ahmad et al. 2019; Sutradhar & Mondal 2021). Groundwater quality is an essential issue since it impacts agricultural development and human health productivity. The presence of toxic chemicals in groundwater affects crop productivity by altering available nutrients and soil fertility. Water-related disease kills more than 3.4 million people annually and thus making them the leading cause of death worldwide (Chaudhary & Satheeshkumar 2018). Thus, deterioration of groundwater quality should be minimized in order to provide quality water for drinking and irrigation purposes. Efficient water resource management could be done easily using the Geographic Information System (GIS) (Ghorbani Nejad et al. 2017; Anbarasu et al. 2020; Hao et al. 2020; Xiao et al. 2020; Chaudhry et al. 2021).

In the 1960s, Horton developed the first water quality index (WQI) model, using 10 water quality parameters (Horton 1965). The WQI is a tool that can deliver a number on the behalf of several water parameters and thus reflect the quality of water (Verma et al. 2020; Sutradhar & Mondal 2021; Uddin et al. 2021). Determination of the WQI is composed of several steps: parameter selection, calculation of single-value dimensionless indices, weight assignment, and finally, a single-value WQI is created by an aggregation function employing all water quality parameters, sub-indices, and weighting factors. Generally, hydrochemical parameters, namely major cations, anions, and heavy metals, are considered for the drinking water quality index (WQID). In case of the irrigation water quality index (WQII), several irrigation indices such as Na%, SAR, RSCB, PI, MHR, KI, PS, and , and were used.

The objective of the study is to provide information that controls the groundwater chemistry of the selected area of the Nawada district so that the best use of groundwater could be done for drinking and irrigation purposes. In the present study, the water quality index, the irrigation index, and the correlation among them have been analysed. Hydrochemical characterization was also done based on various diagrams such as Piper and Gibb's diagram to understand the groundwater chemistry.

Study area

Sirdala block of Nawada district, Bihar is located between 24° 32′ 58.72″ and 24° 45′ 46.69″ North latitude, 85° 17′ 47.97″ to 85° 28′ 58.65″ East longitude, and occupies 246.742 km2, accounting for 9.89% of the district's geographical area (Figure 1). The area observes 1,037 mm annual rainfall which is a potential source of groundwater recharge. The climate is hot and dry, with winter temperatures ranging from 16 to 4 °C and summer temperatures reaching up to 46 °C (CGWB 2013).
Figure 1

Location map and sampling points of the study area.

Figure 1

Location map and sampling points of the study area.

Close modal

During the pre-monsoon period, 65 water samples were collected from the Sirdala block, from existing hand pumps, and tube wells having various depths and covering entire portions of the study area. The geographical location (latitude and longitude) of the sampling point was recorded and the same is presented in Figure 1. Samples were collected 5 min after the start of the pump and stored in polyethylene bottles of 100 mL capacity that were initially washed with nitric acid and rinsed thoroughly with deionized water. Immediately after collection, physical parameters, namely pH, TDS, and salinity are measured using the portable digital meter (PSCTestr35, Eu-tech Instruments). From each source, two sets of the sample one for heavy metals and other for major anion–cation were collected. For heavy metals determination, collected samples were preserved with nitric acid so that pH of the sample could be less than 2 and stored in a cool and dry place till the analysis. ICP-OES was employed for the determination of heavy metals. The second sets of samples were analysed for major cations ( and anions using ion-chromatograph (Metrohm). The suitability of groundwater for drinking and irrigation purposes was determined with the help of water quality index. ArcGIS 10.3 software was used to create a spatial map of the study area. Thematic layers of the relevant elements are obtained using Inverse Distance Weighting (IDW) feature of ArcGIS 10.3. IDW calculates missing values merging other adjacent sample values, owing to the facts that adjacent values are closer than the distant ones. USSL and Wilcox diagrams were created using Diagrammes software, Gibbs, and scatter plots were created using MS Excel 2019, while the Piper diagram was created using Rock Works15 software.

Water quality index

A composite grading known as the WQI was used to assess the quality of groundwater for drinking and irrigation purposes. The main goal of the WQI is to convert huge and complex water quality data into a single quality index (Ramakrishnaiah et al. 2009; Varol & Davraz 2015). These data are of direct interest to the general public because it aids in a better understanding of water quality (Aly et al. 2015; Adimalla et al. 2018). The entire computation is based on standard limits given by many organizations such as BIS, ICMR, and WHO for water quality parameters and irrigation indices. A total of 13 water quality parameters are considered for the determination of WQID. Whereas in the case of WQII calculation, 10 irrigation indices were considered.

Procedure for the determination of WQID/WQII following steps were considered:

Step 1. Assignment of weight for individual parameter

Initially, parameters under consideration were assigned suitable weightage as per their relative importance in the overall quality of water for drinking (Table 1) and irrigation purposes (Table 2), as measured by the percentage of the sample falling within the standard limit. For 0–20, 21–40, 41–60, 61–80, and 81–100% of samples within the standard limit, weights of 5, 4, 3, 2, 1 are applied to the quality parameter (Ramakrishnaiah et al. 2009; Raychaudhuri et al. 2014; Sutradhar & Mondal 2021; Sarkar et al. 2022). Thus, weights allotted to each parameter vary from 1 to 5, signifying good to poor water quality.

Table 1

Weight assignment based on percentage sample fall within the standard limits and their relative weight for WQID calculation (Ramakrishnaiah et al. 2009; Aly et al. 2015; Adimalla et al. 2018; Sutradhar & Mondal 2021)

ParametersStandard limitsSample within the standard limit
Weight (wi)Relative weight (Wi)
NumberPercentage
pH 8.5 65 100 0.038462 
TDS 500 7.69 0.192308 
 75 65 100 0.038462 
 30 56 86.4 0.038462 
 150 58 89.2 0.038462 
 12 63 96.92 0.038462 
 1.5 51 78.5 0.076923 
 250 58 89.3 0.038462 
 45 51 78.5 0.038462 
 200 65 100 0.038462 
 244 17 26.15 0.153846 
TH 300 60 7.6 0.038462 
As 0.01 65 100 0.038462 
ParametersStandard limitsSample within the standard limit
Weight (wi)Relative weight (Wi)
NumberPercentage
pH 8.5 65 100 0.038462 
TDS 500 7.69 0.192308 
 75 65 100 0.038462 
 30 56 86.4 0.038462 
 150 58 89.2 0.038462 
 12 63 96.92 0.038462 
 1.5 51 78.5 0.076923 
 250 58 89.3 0.038462 
 45 51 78.5 0.038462 
 200 65 100 0.038462 
 244 17 26.15 0.153846 
TH 300 60 7.6 0.038462 
As 0.01 65 100 0.038462 
Table 2

Weight assignment based on percentage sample fall within the standard limits, relative weight for WQII calculation (Raychaudhuri et al. 2014; Adimalla et al. 2018; Nageswara Rao et al. 2021)

ParametersStandard limitsSample within the standard limit
Weight (wi)Relative weight (Wi)
NumberPercentage
Na% 60 44 67.7 0.076923 
SAR 10 65 100 0.038462 
RSCB 51 78.5 0.076923 
PI 25 0.192308 
MHR 50 12 18.5 0.192308 
KR 22 33.8 0.153846 
PS 47 72.3 0.076923 
Cl:HCO3 65 100 0.038462 
Mg:Ca 65 100 0.038462 
Na:Ca 30 46.2 0.115385 
ParametersStandard limitsSample within the standard limit
Weight (wi)Relative weight (Wi)
NumberPercentage
Na% 60 44 67.7 0.076923 
SAR 10 65 100 0.038462 
RSCB 51 78.5 0.076923 
PI 25 0.192308 
MHR 50 12 18.5 0.192308 
KR 22 33.8 0.153846 
PS 47 72.3 0.076923 
Cl:HCO3 65 100 0.038462 
Mg:Ca 65 100 0.038462 
Na:Ca 30 46.2 0.115385 
Table 3

Standard limits of water quality parameter and its health impact

ParameterLimitProposed organisation
% sample within the limitAdverse health effect produces when exceeding the limit
BISICMRWHO
pH 6.5–8.5 ✓  ✓ 100 Eyes and skin irritation, irritation of the mucous membranes, and aggravation of skin conditions 
TDS 500 ✓   7.69 Cause Gastrointestinal problems, Gastrointestinal irrigation, laxative effect 
TH 300 ✓ ✓  92.31 Artery calcification, urine concretions, kidney or bladders, or stomach problems 
Ca2+ 75 ✓   100 Hypercalcemia, improper brain and heart functioning, stone in the kidneys or bladder as well as discomfort in the urinary tract 
Mg2+ 30 ✓   86.15 Laxative effect and hypermagnesemia 
Na + 150    92.31 Hypertension and coronary heart disease 
K+ 12   ✓ 96.92 Hyperkalaemia may cause a heart attack, and a laxative effect on the neurological and digestive systems. 
 244 ✓   26.15 Metabolic and respiratory acidosis 
Cl 250 ✓ ✓ ✓ 89.23 Indigestion eczema, heart problem, dry and itchy skin, asthma and should cause cancer 
 45 ✓ ✓  78.54 Methemoglobinemia in bottle-fed infants 
 200 ✓  ✓ 100 Cause irritation in the gastrointestinal tract, diarrheal dehydration, laxative effect 
F- 1.5   ✓ 26.15 Cause fluorosis of the teeth and skeleton 
ParameterLimitProposed organisation
% sample within the limitAdverse health effect produces when exceeding the limit
BISICMRWHO
pH 6.5–8.5 ✓  ✓ 100 Eyes and skin irritation, irritation of the mucous membranes, and aggravation of skin conditions 
TDS 500 ✓   7.69 Cause Gastrointestinal problems, Gastrointestinal irrigation, laxative effect 
TH 300 ✓ ✓  92.31 Artery calcification, urine concretions, kidney or bladders, or stomach problems 
Ca2+ 75 ✓   100 Hypercalcemia, improper brain and heart functioning, stone in the kidneys or bladder as well as discomfort in the urinary tract 
Mg2+ 30 ✓   86.15 Laxative effect and hypermagnesemia 
Na + 150    92.31 Hypertension and coronary heart disease 
K+ 12   ✓ 96.92 Hyperkalaemia may cause a heart attack, and a laxative effect on the neurological and digestive systems. 
 244 ✓   26.15 Metabolic and respiratory acidosis 
Cl 250 ✓ ✓ ✓ 89.23 Indigestion eczema, heart problem, dry and itchy skin, asthma and should cause cancer 
 45 ✓ ✓  78.54 Methemoglobinemia in bottle-fed infants 
 200 ✓  ✓ 100 Cause irritation in the gastrointestinal tract, diarrheal dehydration, laxative effect 
F- 1.5   ✓ 26.15 Cause fluorosis of the teeth and skeleton 
Table 4

Statistics of water quality parameter and irrigation indices

VariableUnitsMinimumMaximumAverageStd. deviation
pH No unit 6.88 8.03 7.33 0.24 
TDS Mg/L 334 1,660 785.83 288.35 
 mg/L 6.02 68.26 19.75 10.41 
 mg/L 0.02 81.77 20.72 15.61 
 mg/L 21.93 205.7 80.69 40.25 
 mg/L 47.56 2.76 7.03 
 mg/L 0.27 8.06 1.32 1.35 
 mg/L 3.95 799.81 91.21 125.06 
 mg/L 8,752.28 242.26 1,210.76 
 mg/L 2.33 118.88 27.97 26.98 
 mg/L 640 293.95 112.22 
TH mg/L 23.25 506.93 134.57 85.26 
As mg/L 0.037 3.5 0.36 0.6 
SAR meq/L 1.27 15.32 3.35 2.18 
RSC meq/L −3.25 9.11 2.13 2.03 
Na% meq/L 31.99 94.08 57.15 13.29 
KR meq/L 0.45 15.88 1.79 2.13 
PI meq/L 45.71 139.53 95.77 19.57 
PS meq/L 0.15 23.27 2.86 3.75 
MHR meq/L 0.24 79.37 59.17 15.11 
TH meq/L 23.25 506.93 134.57 85.26 
CAI – 1 meq/L 21.57 22.14 −1.16 6.41 
CAI – 2 meq/L −7.49 21.37 1.89 3.66 
GIB-1 meq/L 0.04 0.29 0.24 
GIB-2 meq/L 0.56 0.96 0.76 0.09 
RSCB meq/L −1.45 9.45 3.83 1.89 
Cl: meq/L 4.91 0.46 0.69 
Mg2+:Ca2+ meq/L 0.01 3.85 1.76 0.97 
Na+:Ca2+ meq/L 1.27 22.99 4.49 4.02 
VariableUnitsMinimumMaximumAverageStd. deviation
pH No unit 6.88 8.03 7.33 0.24 
TDS Mg/L 334 1,660 785.83 288.35 
 mg/L 6.02 68.26 19.75 10.41 
 mg/L 0.02 81.77 20.72 15.61 
 mg/L 21.93 205.7 80.69 40.25 
 mg/L 47.56 2.76 7.03 
 mg/L 0.27 8.06 1.32 1.35 
 mg/L 3.95 799.81 91.21 125.06 
 mg/L 8,752.28 242.26 1,210.76 
 mg/L 2.33 118.88 27.97 26.98 
 mg/L 640 293.95 112.22 
TH mg/L 23.25 506.93 134.57 85.26 
As mg/L 0.037 3.5 0.36 0.6 
SAR meq/L 1.27 15.32 3.35 2.18 
RSC meq/L −3.25 9.11 2.13 2.03 
Na% meq/L 31.99 94.08 57.15 13.29 
KR meq/L 0.45 15.88 1.79 2.13 
PI meq/L 45.71 139.53 95.77 19.57 
PS meq/L 0.15 23.27 2.86 3.75 
MHR meq/L 0.24 79.37 59.17 15.11 
TH meq/L 23.25 506.93 134.57 85.26 
CAI – 1 meq/L 21.57 22.14 −1.16 6.41 
CAI – 2 meq/L −7.49 21.37 1.89 3.66 
GIB-1 meq/L 0.04 0.29 0.24 
GIB-2 meq/L 0.56 0.96 0.76 0.09 
RSCB meq/L −1.45 9.45 3.83 1.89 
Cl: meq/L 4.91 0.46 0.69 
Mg2+:Ca2+ meq/L 0.01 3.85 1.76 0.97 
Na+:Ca2+ meq/L 1.27 22.99 4.49 4.02 
Table 5

Classification of groundwater on the basis of hardness (Todd & Mays 1980)

TH as CaCO3CategorizationNo. of sample% of sample
<75 Soft 12 18.4 
75–150 Slight hard 46 70.7 
150–300 Hard 12 18.4 
> 300 Very hard 7.6 
TH as CaCO3CategorizationNo. of sample% of sample
<75 Soft 12 18.4 
75–150 Slight hard 46 70.7 
150–300 Hard 12 18.4 
> 300 Very hard 7.6 

Step 2. Relative weight calculation using Equation (1)
formula
(1)
where wi is the allotted weight of each water quality parameter/indices; Rwi is the relative weight of each water quality parameter/indices; n is the total number of parameters selected for WQI calculations.
Step 3. Calculation of quality rating scale for individual parameters using Equation (2)
formula
(2)
where is the quality rating; is the concertation of parameters observed in sample analysis/computed value of indices; is the standard limit for each water quality parameter or indices either for drinking/irrigation purposes.
Step 4. The WQI calculation using Equation (3)
formula
(3)

The WQI developed for drinking water and irrigation water are designated as WQID and WQII, respectively.

Geochemical characterization governing groundwater chemistry

Gibbs ratio

The Gibbs diagram is a popular approach for determining the link between water composition and aquifer lithological properties of the area. The boomerang-shaped Gibbs diagram illustrates three separate fields: precipitation dominance, rock dominance, and evaporation precipitation. It may be obtained by plotting Gibbs ratios I and II on the x-axis and TDS on the y-axis (Gibbs 1970). Gibbs ratios I and II may be, calculated using Equations (4) and (5), respectively:
formula
(4)
formula
(5)
where concentration of the ions is expressed in meq/L. Gibbs diagrams I and II show that the majority of the sample lies in the evaporation precipitation zone (Figure 2). Thus, groundwater chemistry is principally controlled by evaporation precipitation (Subba Rao 2002; Srinivasamoorthy et al. 2008).
Figure 2

Gibbs diagram (a) ((Na/Na + Ca) vs. TDS) and (b) ((Cl/(Cl + HCO3) vs. TDS).

Figure 2

Gibbs diagram (a) ((Na/Na + Ca) vs. TDS) and (b) ((Cl/(Cl + HCO3) vs. TDS).

Close modal

Piper diagram

The Piper diagram (Figure 3) indicates a plot between concentration of major cations and anions and helps to understand the hydro-geochemical process which governs groundwater chemistry of the area. The triangular plot of anions shows clear dominancy (66.67%) of bicarbonate in groundwater. The abundance of bicarbonate indicates study area fall under frequent recharge zone (Subba Rao 2007). The remaining 21.67% samples were found chloride (Cl) type, indicating possible anthropogenic pollution. The major sources of Cl are seawater extrusion, industrial pollution, etc.; however, the area under study is far away from the coastal area and also have negligible industrial activity (Tyagi & Sarma 2020). Furthermore, the physico-chemical character of the groundwater reveals that Ca– and Na + K were dominant types of water facies. From the diamond plot it was observed that weak acids dominate strong acids and alkaline earth dominates over alkalis. Such types of water facies are responsible for temporary hardness (Thakur et al. 2016). Ca–Mg–HCO3 (present in 45% of the sample) is the dominant water facies found in the studied area, followed by Na–K–, Ca–Mg–Cl– and Na–K–Cl– with 23.34, 21.67, 9.99% of the total sample, respectively. The cations plots reveal that majority of the samples (%) in the central part is No dominant type followed by alkali dominance type (Na + K). The water sample which falls closer to the alkali type (Na + K) may be related to volcanic aquifer accentuate enhancement in K derived from the alkaline-potassic rocks (Sappa et al. 2014). 18.46% samples are categorised as magnesium type facies and describe Ca–Mg–HCO3 based cation–anion exchange process. This process may be originated from the successive dissolution due to the action of frequent recharge (Subba Rao 2007).
Figure 3

The Piper diagram of major ions data in groundwater, Sirdala block, Nawada district of Bihar.

Figure 3

The Piper diagram of major ions data in groundwater, Sirdala block, Nawada district of Bihar.

Close modal
Figure 4

Correlation between (a) magnesium and total hardness and (b) calcium and total hardness.

Figure 4

Correlation between (a) magnesium and total hardness and (b) calcium and total hardness.

Close modal

Chloro-alkaline index or the Schoeller index

The chemical reaction in which ion-exchange occurs during movement/residence of groundwater through host rock may be represented by chloralkaline index (CAI-I and CAI-II) proposed by Schoeller (1997). The CAI-I and CAI-II were calculated using the following Equations (6) and (7) where concentration of all ions is expressed in meq/L:
formula
(6)
formula
(7)

The ion exchange phenomena are classified as chloroalkaline equilibrium or disequilibrium based on the above two indices. The presence of a positive value of index suggests a direct base-exchange reaction with an equilibrium state in which sodium and potassium from the groundwater exchange with calcium and magnesium in the rocks. Similarly, a cation–anion exchange reaction with a choro-alkaline disequilibrium situation is referred to as a negative value of ion exchange. In the present study, the value obtained from CAI-I and CAI-II shows that about 40 and 78.65% of the sample have positive index values suggesting that direct base exchange where sodium and potassium in groundwater exchange with calcium and magnesium of the rocks, inferring that choro-alkaline balance. In aquifer, when alkali rock minerals are replaced with calcium and magnesium ions (Ca2+ +Mg2+ > HCO3) indicates base exchange of hardened water. However, when calcium and magnesium exchanged with alkali rock (HCO3 > Ca2+ + Mg2+), indicates base exchange softened water, but in study area scarcity of softened water indicates positive ionic ratio of indexes and chemistry of area controlled by anion–cation exchange process (Schoeller 1997; Thakur et al. 2016).

Suitability of groundwater for drinking purposes

Groundwater is observed as a primary source of drinking water in the Sirdala block. The chemical constituents of drinking water play an important role to determine its suitability as it affects human health. Table 3 presents the impact of chemical constituents on human health along with their standard limits. It is observed that pH of the study area varies between 6.5 and 8.5 and found within standard limit as per BIS and WHO (Table 4). Drinking water should have a low concentration of dissolved solids, that results in decreased electrical conductivity. Electrical conductivity of the study area, ranges from 437 to 2,360 μS/cm, thus indicating 100% of the samples are exceeding standard limit (300 μS/cm) as per ICMR. Total hardness is the measure of the concentration of divalent cation in water and also it is a critical parameter to assign the water suitability for drinking. Consumption of water with a high concentration of total hardness may pose serious health effects such as calcification of arteries, urinary concentration, diseases of kidneys, and stomach disorders. Figure 4 indicates a correlation between total hardness-calcium and total hardness-magnesium with R2 values of 0.9579 and 0.7429, respectively. Hardness may be classified into four categories, namely soft, slightly hard, hard, and very hard on the basis of concentration (Todd & Mays 1980). Table 5 presents the categorization of hardness and observed that only 18.46% of the sample are soft, 70.7% of the sample are slightly hard, 18.4% of the sample are hard and 7.6% of the sample are very hard. It means that only 18.4% sample is within the standard limit. The calcium concentration in the study area ranges from 6.014 to 68.26 mg/L, which is within the standard limit (75 mg/L), with an average value of 19.75 mg/L. The standard limit of is 150 and 12 mg/L. In the study area concentration of and ranged from 21.93 to 205.7 mg/L and 0 to 47.56 mg/L, with an average value 80.69 and 2.76 mg/L, respectively (Table 4). Total dissolved solids (TDS) also indicate the suitability of water for drinking, irrigation, and industrial purpose (Sharma et al. 2016). TDS concentration in the study area were observed in the range 334–1,660 mg/L with average value of 785.83 mg/L. 92.30% sample exceed the standard limits of TDS for drinking purposes (Table 1). Consumption of high TDS water may cause gastrointestinal irritation, cause laxative effects, etc. in humans (Sharma et al. 2016). Bicarbonate () concentration was observed between 0 and 640 mg/L, with average value 293.95 mg/L. Bicarbonate concentration in drinking water should be limited to 224 mg/L as per BIS. 73.85% sample exceeds the standard limit. Chloride () in groundwater ranged from 3.95–799.81 mg/L, with an average value of 91.21 mg/L (Table 4). Chlorides at high concentration add salty test and may cause health problems (McCarty 2004). concentration in groundwater ranged from 2.33 to 118.88 mg/L, with average value of 27.97 mg/L. concentration of groundwater ranged from 0 to 8,752.28 mg/L. The presence of elevated amount of may indicate indiscriminate use of chemical fertilizer in agricultural practices in study area (Zapata et al. 2014). 19.70% of samples exceed the standard limit (Table 3). Excessive nitrate levels in portable water cause methemoglobinemia in new-borns (Sharma et al. 2016). Fluoride in groundwater ranged from 0.27 to 8.06 mg/L, with average value of 1.32 mg/L in the study area (Table 4). Majority 78.5% of the sample having fluoride content less than 1.5 mg/L, which indicates safe for drinking (Table 1).

Drinking water quality index

WQID is a number that indicates overall quality of water for drinking purposes and thus may prevent many diseases related to water consumption. The spatial distribution of WQID is shown in Figure 5. For the preparation of the spatial distribution map, IDW features of ArcGIS 10.3 were employed. On the basis of WQID (Table 6), drinking water has been grouped into five categories: Excellent, good, poor, very poor, and unsuitable (Yin et al. 2020; Sutradhar & Mondal 2021). The southern part of the study area was found good (13.84%) water quality for drinking purposes; however, the eastern part was quite poor (70.76%) to very poor (12.30%) in quality. WQID of groundwater ranged from 56.76 to 929.90 with an average of 161.5 and only nine water sample found fit for drinking purposes. The majority of the sample was found poor class of water, thus suggesting suitable treatment before its use for drinking (Table 6).
Table 6

Classification based on the WQI for drinking purposes

WQI valueAppropriateness of water for drinking purposesNo. of the samples that fall into a different category% of sample
<50 Excellent 
50–100 Good 13.84 
101–200 Poor 46 70.76 
201–300 Very poor 12.30 
> 300 Inappropriate 3.07 
WQI valueAppropriateness of water for drinking purposesNo. of the samples that fall into a different category% of sample
<50 Excellent 
50–100 Good 13.84 
101–200 Poor 46 70.76 
201–300 Very poor 12.30 
> 300 Inappropriate 3.07 
Figure 5

Spatial distribution of the WQID.

Figure 5

Spatial distribution of the WQID.

Close modal

Suitability of groundwater for irrigation purposes

The mineralogical content of the irrigation water has a significant impact on crop yield. The presence of too many dissolved ions in irrigation water may stymie crop yield. The study area belongs to the sub-tropical to sub-humid monsoon climate, and the wealth of the study area depends on agriculture (CGWB 2013). Rainfall irregularity increased the dependency of farmers on groundwater for irrigation. Thus, proper management of irrigation water should be a priority. Several indices and chemical parameters are utilized to study the mechanism that influences groundwater chemistry and to determine the quality of irrigation water. These indices are, Na%, SAR, MHR, KR, PS, PI, RSBC, Cl: , Mg2+: Ca2+, Na+: Ca2+ etc. used with USSL diagram and Wilcox diagram.

Irrigation water quality indices

Na%

Sodium is a key parameter employed to determine the suitability of groundwater for irrigational use and is determined by Equation (8). Sodium forms a chemical bond with soil and reduces water transport capacity resulting in hard and dry soil. Sodium also combines with carbonate and chloride which results in the formation of alkaline and saline soils and retards crop growth (Todd & Mays 1980). If irrigation water contains an elevated amount of sodium ions, it replaces calcium and magnesium ions from clay particles through the base-exchange process (Rao et al. 2017):
formula
(8)
where concentration of ions is expressed in meq/L.
Based on the Na%, groundwater is classified into five class: excellent, good, permissible, doubtful, and unsuitable (Table 7). Soluble sodium present in the study area ranged from 31.99 to 94.08 meq/L, and the average concentration in groundwater was found to be 57.15 meq/L (Table 4). The Wilcox (1955) diagram (Figure 6(b)) of Na% versus electrical conductivity shows that 53.38% of the sample comes under the excellent to satisfactory category; 18.46% of the sample fall under the satisfactory to permissible category; 20% of the sample fall under the permissible to doubtful category; 3.07% of the sample fall under the doubtful to unsuitable category; 5.09% of the sample fall under the unsuitable category for irrigational use.
Table 7

Groundwater categorization based on irrigation indices (Nageswara Rao et al. 2021)

Parameter/IndicesValuesCategoryNo. of samples% of sample
EC <250 Excellent 
 250–750 Good 12.3 
 750–2,250 Permissible 55 84.6 
 2,250–300 Doubtful 3.0 
 >3,000 Unsuitable 
Na% <20 Excellent 
 20–40 Good 6.1 
 40–60 Permissible 40 61.5 
 60–80 Doubtful 18 27.6 
 >80 Unsuitable 13 20 
TDS <450 Safe 3.0 
 450–2,000 Moderate 63 96.9 
 >2,000 Unsuitable 
SAR <10 Excellent 64 98.4 
 10–18 Good 1.5 
 18–26 Doubtful 
 >26 Unsuitable 
RSCB <5 Safe 52 80 
 5–10 Marginal 13 20 
 >10 Unsuitable 
PI >75% Good 55 84.6 
 25–75 Slightly good 10 15.4 
 <25 Unsuitable 
MHR <50 Suitable 13 20 
 >50 Unsuitable 52 80 
KR <1 Suitable 21 32.4 
 >1 Unsuitable 44 67.6 
PS <3 Safe 47 72.3 
 >3 Unsuitable 18 27.6 
Parameter/IndicesValuesCategoryNo. of samples% of sample
EC <250 Excellent 
 250–750 Good 12.3 
 750–2,250 Permissible 55 84.6 
 2,250–300 Doubtful 3.0 
 >3,000 Unsuitable 
Na% <20 Excellent 
 20–40 Good 6.1 
 40–60 Permissible 40 61.5 
 60–80 Doubtful 18 27.6 
 >80 Unsuitable 13 20 
TDS <450 Safe 3.0 
 450–2,000 Moderate 63 96.9 
 >2,000 Unsuitable 
SAR <10 Excellent 64 98.4 
 10–18 Good 1.5 
 18–26 Doubtful 
 >26 Unsuitable 
RSCB <5 Safe 52 80 
 5–10 Marginal 13 20 
 >10 Unsuitable 
PI >75% Good 55 84.6 
 25–75 Slightly good 10 15.4 
 <25 Unsuitable 
MHR <50 Suitable 13 20 
 >50 Unsuitable 52 80 
KR <1 Suitable 21 32.4 
 >1 Unsuitable 44 67.6 
PS <3 Safe 47 72.3 
 >3 Unsuitable 18 27.6 
Figure 6

Groundwater classification for irrigation purposes: (a) USSL diagram and (b) Wilcox diagram.

Figure 6

Groundwater classification for irrigation purposes: (a) USSL diagram and (b) Wilcox diagram.

Close modal

Sodium adsorption ratio

The SAR is a relative ratio of Na+ concentration to that of Ca2+ and Mg2+ concentration present in the groundwater. In most of the soil, Ca2+ and Mg2+ are in exchangeable form. Na+ replaces Ca2+ and Mg2+ in soil when Na+-rich water is continuously used for irrigation. Thus, an increase of exchangeable Na+ in the soil leads to deflocculating soil structure and promotes compaction, thereby impairing the permeability of the soil and leading to crop reduction (Kaur et al. 2017; Roy et al. 2018). As per SAR categorization, water grouped into four classes for irrigation and the percentage of samples falling under each group is presented in Table 7. The SAR of the water sample is calculated using Equation (9), in which the concentration of ions is expressed in meq/L:
formula
(9)

SAR values in the study area were found to be 1.27–15.32 meq/L with an average value of 3.35 meq/L. All of the samples fall under the excellent and good categories. Form Figure 6(a), USSL plots show that the majority of the sample in the study area belongs to C2S1 and C3S1 categories and suggests the cultivation of medium- to high-salt-tolerant crops. Furthermore, suitable measures should be taken to avoid waterlogging to counter the negative impact on the permeability of the soil (United States Salinity Laboratory Staff 1954).

Residual sodium bicarbonate

Groundwater with higher amounts of carbonates and bicarbonates than calcium and magnesium may generate sodium bicarbonates. Excess sodium adsorption in the soil results in excessive residual sodium carbonate, which degrades soil structure and lowers soil permeability. RSCB is calculated using Equation (10):
formula
(10)
where concentration of ions is expressed in meq/L.

Water having RSBC < 5 meq/L is suitable, from 5 to 10 indicates slightly suitable, and >10 indicates unsuitable for irrigation (Gupta & Gupta 1987). The negative sign in RSCB value indicates water is suitable for irrigation. RSCB values vary between −1.45 and 9.45 meq/L in the study area with an average value of 3.83 meq/L (Table 4). Majority (80%) of the sample in groundwater was found safe, and 20% marginal for irrigation purposes (Table 7).

Permeability index

PI is the criteria that determines the suitability of groundwater for irrigational uses. It indicates water movement ability through the soil and influences the permeability of the soil. Doneen (1964) classified irrigation water into three groups as per PI values. PI of irrigation water may be calculated using Equation (11):
formula
(11)
where concentration of ions is expressed in meq/L.

Permeability >75% falls under class I. Groundwater falling under this class is suitable for irrigational use. Groundwater having permeability ranging from 25 to 75% is slightly suitable for irrigation and falls under class II. Permeability <25% is unsuitable for irrigation and falls under class III. The PI of the study area varies from 45.71 to 193.52%, denoting, classes I and II of PI (Table 4). According to PI classification, 84.6% sample were suitable for irrigation, and 15.40% samples were found under the slightly suitable category for irrigation (Table 7).

Magnesium hazards ratio

In groundwater, alkaline earth metal, namely calcium and magnesium, are generally found in an equilibrium condition. Calcium and magnesium both divalent ions are the essential nutrients for the crop growth. They also decide physical properties of soil, namely friability and aggregation. The elevated concentration of calcium and magnesium ions may increase salinity, and also decrease the phosphorus binding capacity of the soil (Al-Shammiri et al. 2005; Joshi et al. 2009). MHR for irrigation water may be determined using Equation (12) (Szabolcs & Darab 1964):
formula
(12)
where concentration of all ions is expressed in meq/L.

The literature suggests that MHR should be lower than 50 for good soil and better crop yield (Szabolcs & Darab 1964). In the present study, MHR values ranged from 0.24 to 79.37 mg/L (Table 4). Only 20% of the sample of groundwater is below 50, which indicates safe for irrigation use (Table 7).

Kelly's ratio

Kelly (1940) developed a factor for the evaluation of water suitability for irrigation purposes and calculated using Equation (13):
formula
(13)
where concentration of ions is expressed in meq/L.

It is desirable to have KR < 1 for irrigation purposes. KR value in the study area varies from 0.45 to 15.88 meq/L (Table 4). Nearly 33.85% sample was found less than 1, hence showing majority of the sample is unsuitable for irrigation purposes (Table 7).

Potential salinity

PS is also an important water quality parameter based index, which is used for the classification of water for irrigational uses (Doneen 1964). Water having PS less than 3 meq/L is suitable for irrigation. The variation of potential salinity of the entire area is calculated using Equation (14) and represented in Figure 7:
formula
(14)
Figure 7

Spatial distribution of various indices: (a) SAR, (b) RSC, (c) Na %, (d) KR, (e) PS, (f) MHR, (g) PI, and (H) RSCB.

Figure 7

Spatial distribution of various indices: (a) SAR, (b) RSC, (c) Na %, (d) KR, (e) PS, (f) MHR, (g) PI, and (H) RSCB.

Close modal

PS in groundwater was found to be 0.15–23.27 meq/L with an average value of 2.86 meq/L (Table 4). Majority of groundwater samples (72.31%) were found to have a PS value less than 3, indicating safe for irrigation use (Table 7).

Irrigation water quality index

The WQII values for irrigation use are categorized into four groups, namely none, slight, moderate, and severe; based on the constraints during utilisation (Table 8). Figure 8 presents the spatial distribution map for WQII. It shows that north-west part of the study area falls in none (no restriction), south-east part falls in slight restriction categories. Three patches in the central region of the study area are observed as moderate and one patch was in a severe restriction category. The irrigation WQI of groundwater was 87.88–434.67 with an average of 165.70. 50.67% samples have WQII value less than 150, thus falls under the group ‘none’. It indicates that approximately 50% tube-wells are suitable for irrigation purposes (Table 8).
Table 8

Categorization of the WQI for irrigation purposes

WQI valueRestrictionNo. of samples that fall into a different category% of sample
<150 None 33 50.67 
150–300 Slight 30 46.15 
300–450 Moderate 
>450 Severe 3.07 
WQI valueRestrictionNo. of samples that fall into a different category% of sample
<150 None 33 50.67 
150–300 Slight 30 46.15 
300–450 Moderate 
>450 Severe 3.07 
Figure 8

Spatial distribution of the WQII.

Figure 8

Spatial distribution of the WQII.

Close modal

Correlation and comparison between irrigation indices for WQII

Scatter plots of correlation matrix have been created using the Pearson correlation matrix to study the relationship between different irrigation indices, namely SAR, Na%, KR, PI, PS, MHR, and RSCB. Table 9 reveals that SAR is significantly correlated with KR, Na with R2 values 0.928, 0.837, respectively. Similarly, Na% showed a positive correlation, KR with R2 0.748, moderately correlated PI with R2 0.608, and weak and negatively correlated with PS, MHR. KR low correlated PI with R2 = 0.496, RSCB with R2 = 0.375 and negatively correlated with PS, MHR. Spatial distributions of the indices are plotted to obtained comparative view and variation over the study area (Figure 7).

Table 9

Correlation matrix of irrigation indices for irrigation water suitability

IndicesSARNa%KRPIPSMHRRSCB
SAR       
Na% 0.837      
KR 0.928 0.748     
PI 0.376 0.608 0.496    
PS 0.107 −0.108 −0.096 −0.557   
MHR −0.367 −0.396 −0.531 −0.356 0.053  
RSCB 0.405 0.222 0.375 0.330 −0.099 0.315 
IndicesSARNa%KRPIPSMHRRSCB
SAR       
Na% 0.837      
KR 0.928 0.748     
PI 0.376 0.608 0.496    
PS 0.107 −0.108 −0.096 −0.557   
MHR −0.367 −0.396 −0.531 −0.356 0.053  
RSCB 0.405 0.222 0.375 0.330 −0.099 0.315 

Suitability assessment of groundwater samples (collected from the Sirdala block of Nawada district, Bihar, India) was studied for their utilisation as drinking and irrigation water. For this purpose, 14 various water quality parameters (pH, TDS, TH, As, , ) and 10 (Na %, SAR, RSCB, PI, MHR, KI, PS, , and ) irrigation indices were analysed. The study indicated the following:

  • The Piper diagram revealed that alkaline earth exceeds alkali earth, and weak acid exceeds storage acid in the groundwater chemistry. Gibbs plots I and II show that evaporation precipitation is the principal factor controlling the geochemistry of the study area.

  • Groundwater of the study area was Ca–,Ca–Mg–, Na–K– (locally mixed types), and Ca–Mg–Cl– type.

  • Out of 14, five drinking water parameters were found within the standard limit; however, WQID of only 13.84% samples were found good for drinking purpose.

  • Na+ was observed as the major cation followed by Ca+, Mg+, and K+. In case of anions, was the major ion followed by Cl > > .

  • A significant correlation was found between SAR–KR (0.928), SAR–Na% (0.837), RSC-RSCB (0.778), RSC-PI (0.771), and KR–Na% (0.748).

  • Irrigation indices, such as PI (>25 in 100% of the sample), MHR (>50 in 81.15% of the sample), KR (>1 in 66.155 of the sample), %Na (>60, in 32.30%), PS (>3, in 27.69% of the sample) showed a high value in the groundwater sample, indicating partially unsuitable for irrigational uses.

  • As per WQII analysis, approximately 50% samples were found suitable for irrigation use.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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