Groundwater quality assessment using the WQI and GIS mapping: Suitability for drinking and irrigation usage in the Sirdala block of Nawada district

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 In India, approximately 89% of groundwater is withdrawn for irrigation, 9% for domestic, and 2% for industrial use (Margat & van der Gun 2013; Sutradhar & Mondal 2021).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 (WQI_D). In case of the irrigation water quality index (WQI_I), 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.

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.

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 WQI_D. Whereas in the case of WQI_I calculation, 10 irrigation indices were considered.

Procedure for the determination of WQI_D/WQI_I 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 WQI_D calculation (Ramakrishnaiah et al. 2009; Aly et al. 2015; Adimalla et al. 2018; Sutradhar & Mondal 2021)

Parameters .	Standard limits .	Sample within the standard limit .		Weight (wi) .	Relative weight (Wi) .
		Number .	Percentage .
pH	8.5	65	100	1	0.038462
TDS	500	5	7.69	5	0.192308
	75	65	100	1	0.038462
	30	56	86.4	1	0.038462
	150	58	89.2	1	0.038462
	12	63	96.92	1	0.038462
	1.5	51	78.5	2	0.076923
	250	58	89.3	1	0.038462
	45	51	78.5	1	0.038462
	200	65	100	1	0.038462
	244	17	26.15	4	0.153846
TH	300	60	7.6	1	0.038462
As	0.01	65	100	1	0.038462

Parameters .	Standard limits .	Sample within the standard limit .		Weight (wi) .	Relative weight (Wi) .
		Number .	Percentage .
pH	8.5	65	100	1	0.038462
TDS	500	5	7.69	5	0.192308
	75	65	100	1	0.038462
	30	56	86.4	1	0.038462
	150	58	89.2	1	0.038462
	12	63	96.92	1	0.038462
	1.5	51	78.5	2	0.076923
	250	58	89.3	1	0.038462
	45	51	78.5	1	0.038462
	200	65	100	1	0.038462
	244	17	26.15	4	0.153846
TH	300	60	7.6	1	0.038462
As	0.01	65	100	1	0.038462

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Table 3

Standard limits of water quality parameter and its health impact

Parameter .	Limit .	Proposed organisation .			% sample within the limit .	Adverse health effect produces when exceeding the limit .
		BIS .	ICMR .	WHO .
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

Parameter .	Limit .	Proposed organisation .			% sample within the limit .	Adverse health effect produces when exceeding the limit .
		BIS .	ICMR .	WHO .
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

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Table 4

Statistics of water quality parameter and irrigation indices

Variable .	Units .	Minimum .	Maximum .	Average .	Std. 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	0	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	0	8,752.28	242.26	1,210.76
	mg/L	2.33	118.88	27.97	26.98
	mg/L	0	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	1	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	0	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

Variable .	Units .	Minimum .	Maximum .	Average .	Std. 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	0	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	0	8,752.28	242.26	1,210.76
	mg/L	2.33	118.88	27.97	26.98
	mg/L	0	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	1	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	0	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

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The WQI developed for drinking water and irrigation water are designated as WQI_D and WQI_I, respectively.

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).

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).

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.

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).

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 >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).

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).

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).

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).

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).

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, WQI_D 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 WQI_I 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.