The present study aimed to assess the impact of anthropogenic stressors on the physico-chemical characteristics and water quality of the River Ganga employing a GIS-based approach in the middle Gangetic Plain at Patna, India. After the survey, sand mining, bridge construction, and disposal of untreated domestic and sewage wastes were selected as major anthropogenic stressors. A total of 48 samples were collected in pre-monsoon and post-monsoon seasons of 2022 and were analyzed for 16 physico-chemical parameters, namely water temperature (WT), pH, electrical conductivity (EC), total dissolved solids (TDS), turbidity, dissolved oxygen (DO), biological oxygen demand (BOD), total hardness (TH), Ca2+, Mg2+, Na+, K+, Cl- and SO42− ions, following standard protocols. The WQI was calculated using the Weighted Arithmetic Water Quality Index (WAQWI) method and spatial maps were created using ArcGIS software. The result revealed significant seasonal variation in several physico- chemical parameters except for Ca2+, K+ and TA (p > 0.05). ANOVA revealed significant variation for BOD and COD at Ghagha and Triveni, whereas for nitrate at Gai Ghat reference and impact sites, respectively. The Water Quality Index (WQI) revealed a deterioration in water quality by 60% in post-monsoon season. HCA revealed that the WQI was mostly governed by TDS, TH, TA, and EC.

  • The study highlights the impact of anthropogenic stressors on the River Ganga water quality in Patna India.

  • Sand mining, sewage disposal and bridge construction activities were selected as major anthropogenic stressors.

  • Significant seasonal variation was observed for several physico-chemical parameters.

  • Water quality deteriorated from pre-monsoon to post-monsoon season.

  • WQI of the River Ganga is primarily governed by TDS, TH, TA and EC.

Rivers are crucial water bodies, constituting approximately 0.49% of the world's freshwater resources (Okpoli & Iselowo 2019; Wang et al. 2023). They act as ecological corridors by supporting biodiversity and promoting species migration, genetic diversity, and gene flow essential for the resilience of ecosystems (Ding et al. 2022; Jing et al. 2023). They serve as lifelines, providing essential water for agriculture, industry, and trade, driving economic growth, and prosperity. Living Planet Report (2020) reported that the river ecosystem is home to approximately 10% of the biodiversity of which one-quarter is under critically endangered status. Anthropogenic disturbances such as urbanization, industrialization, agriculture, and improper waste disposal, have a considerable impact on river water quality, disrupting the delicate balance of natural ecosystems and pose significant threats to the health and integrity of rivers. Nearly 80% of the sewage and industrial wastes is dumped worldwide without proper treatment in the river bodies. In the last 50 years, more than 85% of rivers of the world have degraded considerably with respect to water quality, quantity and ecological status (Sheng et al. 2019). Yellow River in China has been affected highly by sedimentation and pollution leading to the extinction of more than 30% of the fish species. Hill et al. (2017) reported that 46% of the rivers and streams in the USA are under poor biological status with very high content of nutrients such as nitrogen and phosphorus.

The River Ganga, also known as the Ganges, holds a unique and profound place in the hearts of millions, making it one of the holiest and most revered rivers globally. The Ganga, spanning around 2,525 km in length, begins its journey from the Gangotri glacier situated in the Himalayas (Prabhakar 2020). As it meanders through northern India, it profoundly influences the lives of countless people along its path (Ansari & Kumar 2022). Moreover, it serves as a source of irrigation, and drinking water for millions of people, especially in the cities and towns located along its banks (Pandit et al. 2019; Divya et al. 2023). Additionally, the river supports diverse ecosystems, harboring unique flora and fauna, making it an ecological treasure (Varma & Jha 2023). The middle stretch of the River Ganga covers the districts of Eastern Uttar Pradesh, and Bihar (Sulaiman et al. 2023). Patna, the capital of Bihar, holds strategic significance as an important region within the Middle Gangetic Plain, situated gracefully on the southern banks of the River Ganga (Sulaiman et al. 2021; Sulaiman et al. 2023). Currently, the river stretch faces several anthropogenic disturbances such as sand mining, bridge construction and the discharge of untreated domestic wastes (Prabhakar 2020). Additionally, industries located near the banks of the River Ganga discharge their wastes into the river body altering the river water quality (Roy & Shamim 2020). Several government schemes such as the Ganga Action Plan, and Namami Gange have been introduced by the central government to ameliorate the quality of river water in the last few decades yet out of 12,000 m3/d (million litres per day) sewage generated, 3,000 m3/d is directly discharged into the main stem of the River Ganga (NMCG 2022). Therefore, continuous monitoring of the river water quality is essential to assess its suitability for various purposes.

The physico-chemical parameters are valuable tools for monitoring river water quality as they provide insights into various physical and chemical characteristics of the water such as several cations, anions and nutrients (Sila 2019; Jain et al. 2022). However, the limits may vary for each parameter and alone they cannot provide a clear scenario of water quality. The Water Quality Index (WQI) is a numerical method utilized to evaluate and convey the comprehensive water quality of a specific water body, including rivers, lakes, or reservoirs (Horton 1965). It combines various data on physico-chemical parameters to generate a single value, providing a clear representation of the water's overall health (Boyacioglu 2007). WQIs offer a standardized and accessible approach to summarize complex water quality information, facilitating evaluation against established water quality standards (Chabuk et al. 2023). The effectiveness of the WQI in assessing water quality has been validated through multiple studies conducted globally, and in India, focusing on different rivers (Jomet Sebastian et al. 2013; Anima & Chandrakala 2017; Lkr et al. 2020; Nong et al. 2020; Braga et al. 2022; Qi et al. 2022; Pant et al. 2023).

However, combining the traditional WQI methods along with modern geospatial tools enhances the quality of water assessment. Geographical Information Systems (GIS) play a pivotal role in this integration, offering advanced capabilities to analyze and visualize spatial data related to water quality, identify pollution sources, and monitor the changes over time (Zafar et al. 2022). By harnessing the power of spatial mapping in conjunction with WQI, a more comprehensive and spatially-informed approach to water quality assessment is achieved, aiding in sustainable water management and conservation efforts (Ali 2020). Although, several authors have used the combined approach of WQI and GIS in other stretches of the River Ganga (Shil et al. 2019; Wu et al. 2020; Ramachandran et al. 2021; Krishan et al. 2022; Chabuk et al. 2023) limited study has documented the spatial variation in the water quality, and its controlling parameters in the middle stretch of the River Ganga at Patna, India (Rai et al. 2011; Sandhu et al. 2011; Singh et al. 2017; Satya & Narayan 2018; Singh & Kumar 2021; Alam et al. 2023). In addition, a survey of the major anthropogenic stressors prior to the sampling has been a drawback in previously reported studies. Therefore, the present study was designed to fill this knowledge gap and evaluate the spatio-temporal variation in water quality due to the impact of anthropogenic stressors in the River Ganga at Patna, Bihar.

Study area

The area under study is a 36-km long segment of the main stem of the River Ganga between Digha and Fatuha in the middle Gangetic Plain at Patna. Four sampling sites were selected, namely Digha Ghat, Ghagha Ghat, Gai Ghat, and Triveni Ghat, based on major anthropogenic disturbances in the area, as represented in Figure 1. Sampling was conducted with respect to both impact and reference sites. The point of anthropogenic disturbance was chosen as the impact site, and locations 50 m upstream, downstream, and on the left bank from that point were considered as the reference sites.
Figure 1

Map showing the study area.

Figure 1

Map showing the study area.

Close modal

Sample collection and analysis

In the present study, a total of 48 water samples were collected from the main stem of the River Ganga in the months of April–May (pre-monsoon) and October–November (post-monsoon) in 2022. Sampling was done following the standard procedure as prescribed by APHA (2012). Water samples were collected in acid-washed HDPE (high-density polyethylene) bottles. These bottles were transported to the Environment Biology Laboratory, Department of Zoology, Patna University, Patna in an ice-box at 4 °C. Samples were analyzed for several physico-chemical parameters such as water temperature (WT), pH, electrical conductivity (EC), total dissolved oxygen (TDS), turbidity, dissolved oxygen (DO), biochemical oxygen demand (BOD), TH, Ca2+, Mg2+, Na+, K+, Cl, NO3− and . In the field, on-site measurements were taken for WT, pH, EC, DO, and total alkalinity (TA). pH and EC were determined using the portable meter, while DO was analyzed through Winkler's Iodometric method, and TA was assessed using an acid titrimetric method The remaining parameters were analyzed in the laboratory following the standardized procedure outlined in APHA (2012). Major cations such as Ca2+ and Mg2+ ion was quantified using the EDTA titrimetric method, while sodium (Na+) and potassium (K+) were measured using a flame photometer. Nitrate () levels were determined using the phenol disulfonic acid method, and sulfate () levels were assessed using the turbidimetric method.

Calculation of the WQI

The WQI is a widely recognized and efficient tool for evaluating water quality (Ewaid et al. 2020; Aydin et al. 2021). It simplifies complex data by combining multiple physico-chemical parameters into a single numerical value through standardized rating curves (Wu et al. 2020; Han et al. 2022). This approach facilitates easy interpretation, making it valuable for assessing water quality for drinking purposes and aiding decision-making in water management, benefiting policy-makers, managers, and the public (Ustaoğlu et al. 2020; Kothari et al. 2021). In the present study, WQI was estimated using Weighted Arithmetic Water Quality Index (WAWQI) as proposed by Tiwari & Mishra (1985) in Equation (1).
formula
(1)
In the given context, Wn represents the relative unit weight of the nth water quality parameter, and Qn denotes the quality rating of the same parameter. Both the relative unit weight (Wn) and quality rating (Qn) for each parameter can be calculated using the following expressions, referred to as Equations (2) and (3), respectively.
formula
(2)
In this context, the constant of proportionality, K, is determined by the equation K = 1/∑1/Vs.
formula
(3)
where Vn represents the value of the respective physico-chemical parameter obtained after analysis, while Vi stands for the ideal parameter value, where Vi equals 0 for all parameters except for pH (Vi = 7) and DO (Vi = 14.6). Additionally, Vs denotes the standard permissible value for the nth water quality parameter. The calculation of relative weight and WQI for the Digha upstream during the pre-monsoon period is presented in Table 1.
Table 1

Calculation of the WQI value for the Digha upstream sampling site during pre-monsoon 2022

ParametersVs1/VsWnVnQnWn*Qn
pH 8.5 0.117647 0.236618 8.6 101.1765 23.94016 
EC 750 0.001333 0.002682 400 53.33333 0.143022 
TH 200 0.005 0.010056 226 113 1.136357 
TDS 500 0.002 0.004023 150 30 0.120675 
Ca2+ 75 0.013333 0.026817 33.978 45.304 1.214903 
Mg2+ 30 0.033333 0.067042 16.071 53.57 3.591426 
Na+ 200 0.005 0.010056 70.7 35.35 0.355489 
K+ 12 0.083333 0.167604 10 83.33333 13.96703 
TA 200 0.005 0.010056 176 88 0.884951 
Cl 250 0.004 0.008045 39.03 15.612 0.125599 
 200 0.005 0.010056 54.83 27.41667 0.275709 
TUR 0.2 0.40225 120 48.27005 
 45 0.022222 0.044694 0.09 0.2 0.008939 
 Σ 1/Vs0.497203   94.03431 
 K = 2.011252     
ParametersVs1/VsWnVnQnWn*Qn
pH 8.5 0.117647 0.236618 8.6 101.1765 23.94016 
EC 750 0.001333 0.002682 400 53.33333 0.143022 
TH 200 0.005 0.010056 226 113 1.136357 
TDS 500 0.002 0.004023 150 30 0.120675 
Ca2+ 75 0.013333 0.026817 33.978 45.304 1.214903 
Mg2+ 30 0.033333 0.067042 16.071 53.57 3.591426 
Na+ 200 0.005 0.010056 70.7 35.35 0.355489 
K+ 12 0.083333 0.167604 10 83.33333 13.96703 
TA 200 0.005 0.010056 176 88 0.884951 
Cl 250 0.004 0.008045 39.03 15.612 0.125599 
 200 0.005 0.010056 54.83 27.41667 0.275709 
TUR 0.2 0.40225 120 48.27005 
 45 0.022222 0.044694 0.09 0.2 0.008939 
 Σ 1/Vs0.497203   94.03431 
 K = 2.011252     

Note: Unit in mg/L, except EC (μS/cm) and pH.

Statistical and spatial analyses

Several important statistical techniques such as t-test, ANOVA, Tukey test, and hierarchical cluster analysis (HCA) were performed with the help of Microsoft Excel 2021 and SPSS ver. 22.0. The t-test was used to understand seasonal differences between physico-chemical parameters and WQI at a 95% level of significance. ANOVA was implied to determine the effect of anthropogenic stressors between respective impact and reference sites whereas the Tukey test was used to pinpoint specific sites which showed significant differences.

HCA was applied to group the parameters into similar clusters based on their similarities. During HCA, the variables were graphically clustered into dendrograms using Ward's method and squared Euclidean distance as a measure of similarity (Dar et al. 2021; Luo et al. 2021). Spatial analysis of the physico-chemical parameters was done with the help of ArcGIS ver (10.5) using the Inverse Distance Weighted (IDW) interpolation technique. IDW interpolation is a widely utilized spatial analysis method for predicting values at unmeasured locations based on nearby data points (Gunarathna et al. 2016). By using linear-weighted combinations of sample points, IDW effectively estimates values for specific cells, demonstrating its versatility and user-friendly nature (Ajaj et al. 2018). One of its notable strengths lies in its ability to handle missing data and produce smooth output, making it highly suitable for diverse applications in geography and environmental studies (Yang et al. 2020). Due to its intuitive nature, efficiency, and robustness against outliers, IDW remains a favored choice in spatial analysis (Gnanachandrasamy et al. 2015; Zafar et al. 2022).

Spatio-temporal variation in physico-chemical parameters

The statistical summary of the Physico-chemical parameters is represented in Table 2.

Table 2

Summary of the physico-chemical parameters

Pre-monsoon
Post-monsoon
ParameterMin.Max.MeanStd. devMin.Max.MeanStd. dev
pH 8.30 8.70 8.56 0.11 7.5 8.30 8.02 0.19 
EC 300 400.00 150.25 193.46 400 600.00 443.75 60.92 
TDS 157.00 279.00 222.13 27.18 184 396.00 256.63 44.46 
TH 138.00 180.00 153.13 9.80 144 248.00 173.63 24.47 
Ca2+ 29.93 42.07 34.28 2.65 15.371 43.69 29.93 8.83 
Mg2+ 14.61 18.51 16.65 1.21 12.42 31.17 21.20 5.51 
Na+ 24.10 77.00 62.76 11.22 10 74.70 41.16 13.12 
K+ 8.50 14.00 10.19 1.27 6.9 26.50 9.66 5.02 
TA 170.00 270.00 189.88 23.73 116 348.00 193.63 70.59 
Cl 27.02 43.04 37.47 3.54 11.011 36.04 25.10 5.02 
 22.50 58.00 36.05 9.78 23.3 34.00 29.99 3.03 
 0.04 0.18 0.10 0.04 0.06 0.47 0.19 0.13 
TUR 5.8 22 10.44 5.05 20.3 55.7 33.82 10.10 
DO 5.65 10.9 7.7 1.12 6.2 5.56 0.33 
BOD 1.2 4.5 2.75 1.9 4.6 3.12 0.84 
COD 8.2 31.8 17.3 6.43 12.4 35.4 21.3 6.15 
Pre-monsoon
Post-monsoon
ParameterMin.Max.MeanStd. devMin.Max.MeanStd. dev
pH 8.30 8.70 8.56 0.11 7.5 8.30 8.02 0.19 
EC 300 400.00 150.25 193.46 400 600.00 443.75 60.92 
TDS 157.00 279.00 222.13 27.18 184 396.00 256.63 44.46 
TH 138.00 180.00 153.13 9.80 144 248.00 173.63 24.47 
Ca2+ 29.93 42.07 34.28 2.65 15.371 43.69 29.93 8.83 
Mg2+ 14.61 18.51 16.65 1.21 12.42 31.17 21.20 5.51 
Na+ 24.10 77.00 62.76 11.22 10 74.70 41.16 13.12 
K+ 8.50 14.00 10.19 1.27 6.9 26.50 9.66 5.02 
TA 170.00 270.00 189.88 23.73 116 348.00 193.63 70.59 
Cl 27.02 43.04 37.47 3.54 11.011 36.04 25.10 5.02 
 22.50 58.00 36.05 9.78 23.3 34.00 29.99 3.03 
 0.04 0.18 0.10 0.04 0.06 0.47 0.19 0.13 
TUR 5.8 22 10.44 5.05 20.3 55.7 33.82 10.10 
DO 5.65 10.9 7.7 1.12 6.2 5.56 0.33 
BOD 1.2 4.5 2.75 1.9 4.6 3.12 0.84 
COD 8.2 31.8 17.3 6.43 12.4 35.4 21.3 6.15 

Note: Unit in mg/L, except EC (μS/cm) and pH.

Water temperature

WT is a crucial factor that profoundly influences the biological processes and interactions of organisms in an aquatic ecosystem (Srivastava et al. 2016; Chaudhary et al. 2017). The spatial distribution map of WT revealed significant seasonal variation, where river WT ranged from 28 to 31.7 °C in pre-monsoon and 22–24.9 °C during post-monsoon season (Figure 2(a)) (Table 3). Lower WT during post-monsoon can be attributed to increased cloud cover which reduces the intensity of sunlight reaching the water surface (Mal et al. 2022). ANOVA analysis indicated no significant difference between EC values at respective reference and impact sites (Table 4).
Table 3

Results of t-test between pre- and post-monsoon seasons

ParametersDegree of freedom (DF)t-stat valueP(Tt) two-tailt-critical two-tail
pH 15 10.11648029 0.000 2.13145 
EC 15 −2.78151795 0.013 2.13145 
WT 15 17.83472546 0.000 2.13145 
TDS 15 −5.161629342 0.000 2.13145 
TUR 15 −15.8907833 0.000 2.13145 
DO 15 7.959059111 0.000 2.13145 
BOD 15 −3.661139552 0.002 2.13145 
COD 15 −10.01183781 0.000 2.13145 
TH 15 −4.221349072 0.000 2.13145 
Ca2+ 15 1.803499198 0.091 2.13145 
Mg2+ 15 −3.024132098 0.008 2.13145 
Na+ 15 9.259558296 0.000 2.13145 
K+ 15 0.504226581 0.621 2.13145 
TA 15 −0.215181977 0.832 2.13145 
Cl 15 12.00061061 0.000 2.13145 
 15 2.245301601 0.040 2.13145 
 15 −2.930949130 0.010 2.13145 
ParametersDegree of freedom (DF)t-stat valueP(Tt) two-tailt-critical two-tail
pH 15 10.11648029 0.000 2.13145 
EC 15 −2.78151795 0.013 2.13145 
WT 15 17.83472546 0.000 2.13145 
TDS 15 −5.161629342 0.000 2.13145 
TUR 15 −15.8907833 0.000 2.13145 
DO 15 7.959059111 0.000 2.13145 
BOD 15 −3.661139552 0.002 2.13145 
COD 15 −10.01183781 0.000 2.13145 
TH 15 −4.221349072 0.000 2.13145 
Ca2+ 15 1.803499198 0.091 2.13145 
Mg2+ 15 −3.024132098 0.008 2.13145 
Na+ 15 9.259558296 0.000 2.13145 
K+ 15 0.504226581 0.621 2.13145 
TA 15 −0.215181977 0.832 2.13145 
Cl 15 12.00061061 0.000 2.13145 
 15 2.245301601 0.040 2.13145 
 15 −2.930949130 0.010 2.13145 
Table 4

Results of ANOVA

Site
ParameterDigha Ghat
Ghagha Ghat
Gai Ghat
Triveni Ghat
F valueP valueF valueP valueF valueP valueF valueP valueF-critical
pH 0.0394265 0.9880414 0.1238938 0.941186 0.017778 0.996257 0.6721311 0.612478 6.591382 
EC 0.4789491 0.6666667 0.6151 0.2 0.891448 0.106911 6.591382 
WT 0.0230303 0.9945251 0.0310078 0.991551 0.023061 0.994514 0.0240964 0.99415 6.591382 
TDS 0.1810636 0.9040604 2.7884193 0.173566 2.919201 0.163757 0.8686303 0.527198 6.591382 
TUR 0.3773337 0.7752878 0.4994912 0.702485 0.0708 0.972551 0.7241613 0.588221 6.591382 
DO 0.1345015 0.9344676 0.4436322 0.734857 0.213071 0.882709 0.4793495 0.713975 6.591382 
BOD 3.8736595 0.1118858 64.079314 0.000774 1.754258 0.294328 19.434117 0.007561 6.591382 
COD 4.5438008 0.0888641 7.1353383 0.043962 0.791401 0.558705 22.105587 0.005951 6.591382 
TH 0.0250696 0.9938015 1.1654024 0.426539 6.444444 0.051845 0.6214178 0.637368 6.591382 
Ca2+ 1.8947368 0.271705 0.5055231 0.699085 0.3907 0.766969 1.3035585 0.388788 6.591382 
Mg2+ 1.2995885 0.3898047 1.1194737 0.440247 0.116953 0.94552 0.3115865 0.817319 6.591382 
Na+ 0.0214462 0.9950681 3.2192344 0.144147 3.136342 0.1492 0.3421163 0.797585 6.591382 
K+ 0.0105152 0.9982777 2.3551733 0.213124 0.594049 0.651328 2.1769392 0.233362 6.591382 
TA 0.2 0.8914479 0.4137591 0.752813 0.589927 0.653463 4.8729733 0.080068 6.591382 
Cl 0.0399454 0.9878142 0.7956107 0.556923 0.866659 0.527972 0.0602929 0.978087 6.591382 
 0.9603352 0.4928503 3.0709457 0.153374 1.091054 0.449043 0.0510056 0.98271 6.591382 
 0.537727 0.6812480 1.88127  0.273761 8.794872 0.031032 1.039187 0.465746 6.591382 
Site
ParameterDigha Ghat
Ghagha Ghat
Gai Ghat
Triveni Ghat
F valueP valueF valueP valueF valueP valueF valueP valueF-critical
pH 0.0394265 0.9880414 0.1238938 0.941186 0.017778 0.996257 0.6721311 0.612478 6.591382 
EC 0.4789491 0.6666667 0.6151 0.2 0.891448 0.106911 6.591382 
WT 0.0230303 0.9945251 0.0310078 0.991551 0.023061 0.994514 0.0240964 0.99415 6.591382 
TDS 0.1810636 0.9040604 2.7884193 0.173566 2.919201 0.163757 0.8686303 0.527198 6.591382 
TUR 0.3773337 0.7752878 0.4994912 0.702485 0.0708 0.972551 0.7241613 0.588221 6.591382 
DO 0.1345015 0.9344676 0.4436322 0.734857 0.213071 0.882709 0.4793495 0.713975 6.591382 
BOD 3.8736595 0.1118858 64.079314 0.000774 1.754258 0.294328 19.434117 0.007561 6.591382 
COD 4.5438008 0.0888641 7.1353383 0.043962 0.791401 0.558705 22.105587 0.005951 6.591382 
TH 0.0250696 0.9938015 1.1654024 0.426539 6.444444 0.051845 0.6214178 0.637368 6.591382 
Ca2+ 1.8947368 0.271705 0.5055231 0.699085 0.3907 0.766969 1.3035585 0.388788 6.591382 
Mg2+ 1.2995885 0.3898047 1.1194737 0.440247 0.116953 0.94552 0.3115865 0.817319 6.591382 
Na+ 0.0214462 0.9950681 3.2192344 0.144147 3.136342 0.1492 0.3421163 0.797585 6.591382 
K+ 0.0105152 0.9982777 2.3551733 0.213124 0.594049 0.651328 2.1769392 0.233362 6.591382 
TA 0.2 0.8914479 0.4137591 0.752813 0.589927 0.653463 4.8729733 0.080068 6.591382 
Cl 0.0399454 0.9878142 0.7956107 0.556923 0.866659 0.527972 0.0602929 0.978087 6.591382 
 0.9603352 0.4928503 3.0709457 0.153374 1.091054 0.449043 0.0510056 0.98271 6.591382 
 0.537727 0.6812480 1.88127  0.273761 8.794872 0.031032 1.039187 0.465746 6.591382 

Bold values signify ‘significant difference’, i.e. p value less than 0.05.

Figure 2

Spatial map showing seasonal variation in physico-chemical parameters of the River Ganga in the pre-monsoon (PRM), and post-monsoon (POM): (a) WT, (b) pH, (c) EC, (d) TDS, (e) turbidity, (f) DO, (g) biological oxygen demand, (h) COD, (i) TH, (j) calcium, (k) magnesium, (l) sodium, (m) potassium, (n) total alkalinity, (o) chloride, and (p) sulfate.

Figure 2

Spatial map showing seasonal variation in physico-chemical parameters of the River Ganga in the pre-monsoon (PRM), and post-monsoon (POM): (a) WT, (b) pH, (c) EC, (d) TDS, (e) turbidity, (f) DO, (g) biological oxygen demand, (h) COD, (i) TH, (j) calcium, (k) magnesium, (l) sodium, (m) potassium, (n) total alkalinity, (o) chloride, and (p) sulfate.

Close modal

pH

The spatial distribution map of pH revealed significant seasonal variation in pH, where river water was alkaline in nature during the pre-monsoon season while slightly alkaline during the post-monsoon season (Figure 2(b)) (Table 3). A study conducted by Prabhakar (2020) on a similar stretch of the River Ganga analyzing physico-chemical parameters showed higher pH at Digha and Gai ghat during the pre-monsoon season. Lower pH during the post-monsoon period can be attributed to increased minerals and ions in the water due to the erosion of soils and rocks in the river catchment (Prasad et al. 2020; Kumar & Singh 2021a, 2021b). Analysis of Variance (ANOVA) was carried out between each reference and impact sites of Digha Ghat, Ghagha Ghat, Gai Ghat and Triveni Ghat, respectively, which revealed no significant variations (Table 4).

Electrical conductivity

Figure 2(c) illustrates the spatial distribution of EC in the study area during the pre-monsoon and post-monsoon seasons. A Student's t-test indicated a significant difference between in EC values of the two seasons (t = −2.78; p = 0.013). EC ranged from 300 to 400 μS/s in pre-monsoon to 400−600 μS/s during post-monsoon.

Higher EC values were observed in the eastern stretch of the river during both seasons, which could be attributed to the confluence of River Punpun with the River Ganga at Triveni Ghat, Patna (Figure 1). The increased surface runoff and soil erosion during the monsoon season significantly contribute to the elevated EC levels observed during the post-monsoon season (Mondal et al. 2016; Niloy et al. 2021; Kumar et al. 2023). Furthermore, ANOVA analysis revealed no significant difference between the respective reference and impact sites (Table 4).

Total dissolved solids

During the pre-monsoon season, total dissolved solids (TDS) levels ranged from 157.3 to 278.2 mg/L, whereas in the post-monsoon season, they varied from 184.3 to 394.1 mg/L. The spatial map displayed elevated TDS levels in the middle and lower sections of the river stretch during both seasons, with significant seasonal variations (Figure 2(d)) (Table 3). A dolphin survey conducted by BUDKO (2017) along the same 36 km stretch of the River Ganga between 2014 and 2017 reported TDS levels ranging from 177 to 281 mg/L, with a mean value of 232 mg/L. High TDS values in the post-monsoon season might be due to increased surface off and sedimentation during the monsoon period (Seth et al. 2016; Zafar et al. 2022).

Turbidity

The spatial distribution map of turbidity showed significant variation between pre-monsoon and post-monsoon seasons (t = −15.89, p = 0.000) with higher values in the post-monsoon season. The eastern downstream portion of the river stretch has higher turbidity during both seasons which may be contributed by River Punpun which is classified as a Priority-V polluted river stretch (Revised Action Plan for Restoration and Conservation of River Punpun, 2018) (Figure 2(e)). The higher turbidity levels observed during the post-monsoon season in the Ganga River can be attributed to the preceding heavy rainfall during the monsoon, which leads to increased soil erosion from the catchment area, surface runoff, and potential flooding (Monir et al. 2011; Kumar & Singh 2021a, 2021b). Previous studies conducted in Ganga have reported a similar trend in turbidity values during pre-monsoon and post-monsoon (Prabhakar 2020; Shankar Ram et al. 2022). ANOVA revealed no significant variations among respective impact and reference sites (Table 4).

Dissolved oxygen

DO plays a crucial role in sustaining diverse aquatic life and is also a key factor in assessing the impact of waste discharge on a water body (Matta et al. 2018). The spatial distribution map revealed significant spatial variation in DO values during both season (Figure 2(f)) (Table 3). The downstream stretch of the study area marked the lowest DO values during both seasons which might be due to the confluence of the Punpun River with the Ganga at Triveni Ghat (Figure 1). DO values were lower in post-monsoon season which might be caused by higher turbidity that leads to decreased light penetration and ultimately low photosynthetic activity (Sader 2017; Li et al. 2021). Malik et al. (2021) reported high DO levels ranging from 7.2 to 8.9 mg/L during the summer season in the upper Ganga stretch at Uttarakhand. Another study conducted by Bhutiani et al. (2016) at Haridwar showed DO values ranging between 6.9 and 8 mg/L. ANOVA revealed no significant variations in DO values among respective impact and reference sites (Table 4).

Biochemical oxygen demand

The biochemical oxygen demand (BOD) is a pivotal parameter extensively used in water quality assessment to quantify the level of organic pollution present in freshwater bodies (Trombadore et al. 2020; Sarkar et al. 2022). BOD values ranged from 1.6 to 4.5 mg/L during pre-monsoon and 1.9–4.6 mg/L during the post-monsoon season with significant seasonal differences (Table 3). The spatial map revealed a similar trend during both seasons with higher values at the middle and the eastern stretch of the River Ganga (Figure 2(g)). ANOVA showed a significant difference between reference and impact sites at Ghagha Ghat (F = 64.07; p = 0.000) and Triveni Ghat (F = 19.43; p = 0.007). Tukey test revealed significant variations between GH1, GH2 (p = 0.001), GH1, GH3 (p = 0.003), GH2, GH4 (p = 0.002) and GH3, GH4 (p = 0.012) whereas between TG1, TG2 (p = 0.028), TG1, TG3 (p = 0.043), TG2, TG4 (p = 0.011) and TG3, TG4 (p = 0.017), respectively. Both these sites have similar anthropogenic disturbances in the form of discharge of untreated sewage wastes which contribute to such elevated BOD levels (Ghosh & Kumar 2022).

Chemical oxygen demand

The chemical oxygen demand (COD) test is employed to quantify the total amount of organic matter in water, encompassing both soluble and particulate forms, thereby playing a crucial role in assessing water pollution status (Matta et al. 2020). The Student's t-test revealed significant seasonal variation between pre-monsoon and post-monsoon (t = −10.01; p = 0.000) but the spatial variation trend was similar to that of BOD (Figure 2(h)). A previous study conducted by Prabhakar (2020) on the similar stretch of the River Ganga reported that COD values ranged from 6.8 to 24.5 mg/L where the highest mean value was observed at the Gai ghat impact site (17.5 mg/L). The ANOVA yielded statistically significant differences between the reference and impact sites at both Ghagha Ghat (F = 7.135, p = 0.040) and Triveni Ghat (F = 22.10, p = 0.005). Tukey test showed significant difference between GH1, GH2 (p = 0.045) in contrast to the differences between TG1, TG2 (p = 0.013), TG1, TG3 (p = 0.027), TG2, TG4 (p = 0.010) and TG3, TG4 (0.020), respectively.

Total hardness

Figure 2(i) illustrates the spatial variation in TH levels during the pre-monsoon and post-monsoon seasons. Notably, higher TH values were observed in the central portion of the river stretch, with significant differences between the two seasons, as summarized in Table 3. During the pre-monsoon season, 25% of the samples were categorized as moderately hard, whereas in the post-monsoon season, only 6.25% of the samples fell into the moderately hard category. The remaining samples in both seasons were classified as hard. The TH levels in 2022 were 24.75% higher compared to 2017 (Budko 2017), and there was a 40.34% increase in TH levels from 2020 to 2022 (Prabhakar 2020). The results from the ANOVA analysis indicated that there were no significant differences in TH values between the impact and reference sites (Table 4).

Ca2+ ion

The spatial map of Ca2+ ion revealed elevated concentrations at the middle stretch of the river during the pre-monsoon season, while during the post-monsoon season, higher concentrations were observed in the Eastern and Western portions of the river (Figure 2(j)). A similar range has been noted for Ca2+ ion in the River Ganga from various locations of Uttarakhand, Uttar Pradesh and Bihar (Bhardwaj et al. 2010; Haritash et al. 2016; Azam et al. 2018). The results of the t-test indicated that there was no significant seasonal difference in Ca2+ ion levels (Table 3). Similarly, the ANOVA showed no notable difference between the Ca2+ ion concentration at the respective impact and reference sites (Table 4).

Mg2+ ion

Mg2+ ion ranged from 14 to 18 mg/L and 12.7–30.9 mg/L during pre-monsoon and post-monsoon seasons, respectively, exhibiting significant seasonal variation (Table 3). The spatial map revealed increased Mg2+ ion levels in the middle and lower eastern stretch during post-monsoon (Figure 2(k)). BUDKO (2017) reported low Mg2+ ion concentration in the Patna stretch ranging from 4 to 14.69 mg/L. Similar observations were noted by other studies conducted in different stretches of the River Ganga (Bhardwaj et al. 2010; Haritash et al. 2016; Azam et al. 2018). The results of ANOVA did not show any significant differences between respective reference and impact sites (Table 4).

Na+ ion

Na+ ion showed a significant seasonal difference between pre-monsoon and post-monsoon periods (Table 3). The spatial map revealed higher Na+ levels in the eastern and western stretches of the study area during pre-monsoon, while lower values were observed in the western portion during post-monsoon (Figure 2(l)). However, all the samples were within the BIS (2012) drinking water limit of 200 mg/L. Prabhakar (2020) reported Na+ ion values ranging from 54 to 78 mg/L at different reference and impact sites for sand mining at Patna. Similar results were obtained by another study carried out by BUDKO (2017), which showed that Na+ ion ranged from 12 to 78 mg/L during 2014–2017. ANOVA revealed no significant difference between the respective impact and reference sites (Table 4).

K+ ion

K+ ion ranged from 8.5 to 14 mg/L and 6.9–26 mg/L during pre-monsoon and post-monsoon season, respectively, and did not exhibit any significant seasonal variation (Table 3). Spatial map revealed low K+ ion level in the eastern stretch during both the season with comparative higher values in the middle stretch (Figure 2(m)). During pre-monsoon, 25% samples exceeded the desirable limit of 10 mg/L as stated by BIS (2012) which decreased to 12.5% in the post-monsoon season. The results of ANOVA did not show any significant difference between respective impact and reference sites (Table 4).

Total alkalinity

During the pre-monsoon season, TA levels ranged from 170 to 270 mg/L, whereas in the post-monsoon season, they varied from 116 to 348 mg/L. The spatial map displayed elevated TA levels in the mid to lower sections of the river stretch during both seasons, and did not show any significant seasonal variations (Figure 2(n)) (Table 2). During pre-monsoon, 18.75% samples exceeded the desirable limit of 200 mg/L for TA as set by BIS (2012) which increased to 31.25% in the post-monsoon season. Increased carbonate and silicate weathering process might contribute to elevated TA levels in the post-monsoon season (Naseema et al. 2013; Sharma et al. 2014; Zafar et al. 2022). ANOVA revealed no significant difference between the respective impact and reference sites (Table 4).

Chloride (Cl)

The spatial map of Cl ion revealed elevated concentrations at the western stretch of the river during the pre-monsoon season, while during the post-monsoon season, higher concentrations were observed in the Eastern and middle portions of the river stretch (Figure 2(o)). However, none of the samples exceeded the desirable limit of 250 mg/L as set by BIS (2012). Similar result have been noted for Cl ion in the River Ganga from various locations of Uttarakhand, Uttar Pradesh, and Bihar (Shukla & Arya 2018; Lata et al. 2020; Tiwari et al. 2022). The results of the t-test indicated that there was significant seasonal difference in Cl ion levels (Table 3). Similarly, the ANOVA showed no notable difference between the Cl ion concentration at the respective impact and reference sites (Table 4).

Sulfate (SO42-)

ion showed a significant seasonal difference between pre-monsoon and post-monsoon periods (Table 3). The spatial map revealed higher ion levels in the eastern and western stretches of the study area during pre-monsoon, while higher values were observed in the entire middle stretch during post-monsoon (Figure 2(p)). However, all the samples were within the BIS (2012) drinking water limit of 200 mg/L. ANOVA revealed no significant difference between the respective impact and reference sites (Table 4).

Nitrate (NO3-)

The concentration of ions exhibited variation among sites, with elevated levels observed at Triveni Ghat and Ghagha Ghat, both of which are impacted by the disposal of domestic sewage waste. ion showed a significant seasonal difference between pre-monsoon and post-monsoon season (Table 3) with notably higher values during post-monsoon. ANOVA indicated a significant difference between reference and impact sites at Gai Ghat (F = 8.79; p = 0.03), and Tukey test further identified significant variations specifically between GH1 and GH2 (p = 0.04). Siddiqui & Pandey (2019) conducted nutrient assessment of the River Ganga and reported ion variation from 0.08 to 0.4 mg/L across the entire stretch. Another study conducted by Tiwari et al. (2022) compared multidecadal assessment of environmental variables and reported fluctuations in Nitrate from 0.1 to 0.2 mg/L during the last two decades.

Evaluation of water quality using the WQI

The WAQWI method (Horton 1965) was used to calculate the WQI seasonally for different sampling locations during 2022. The classification of river water, based on the obtained WQI values, is presented in Table 4. During the pre-monsoon period, 37.5% of the samples fall into the ‘very poor’ class, while the remaining samples are categorized as ‘unsuitable for drinking purposes.’ However, in the post-monsoon period, all the samples are classified under unsuitable for drinking purpose (Figure 3). The upstream and left bank sites showed comparative lower WQI values when compared to impact and downstream sites. Triveni impact showed the highest WQI value during both the season. The confluence of River Punpun with the River Ganga at Triveni Ghat, Fatuha might be the reason behind it as untreated sewage wastes are directly dumped in River Punpun (Revised Action plan for Restoration and Conservation of River Punpun, 2018). The t-test revealed significant seasonal differences in the WQI values during pre-monsoon and post-monsoon (t-stat = −15.15; p = 0.000). ANOVA was estimated between respective reference and impact sites and did not show any significant differences.
Figure 3

Variation in the WQI of the River Ganga during pre-monsoon and post-monsoon seasons.

Figure 3

Variation in the WQI of the River Ganga during pre-monsoon and post-monsoon seasons.

Close modal

Association among water quality parameters and the WQI

A HCA was conducted to classify the primary water quality variables into distinct groups based on their similarities. By doing so, the analysis aims to identify variables that likely share a common source or origin. A total of three major clusters can be depicted from Figure 4. The result revealed that the pH of river water is mostly governed by K+ and ions. The common association of both these ions suggest their anthropogenic origin mostly from fertilizers. The second cluster includes major cations and anions along with turbidity. It suggests common geogenic origin from rocks. The final cluster shows that WQI is closely associated with TDS followed by TH, TA and EC. These parameters majorly govern the water quality of the study area.
Figure 4

HCA of physico-chemical parameters using the Ward Linkage method.

Figure 4

HCA of physico-chemical parameters using the Ward Linkage method.

Close modal

Rivers are vital freshwater resources that play a crucial role in supporting ecosystems and human societies. However, their degrading water quality has become a significant challenge for mankind. The present study sheds light on the detrimental impact of anthropogenic stressors on the physico-chemical characteristics and water quality of the sacred River Ganga in the Middle Gangetic Plain at Patna, Bihar. Utilizing a GIS-based underutilized approach, the study effectively evaluated spatial and temporal variations in water quality, revealing notable seasonal fluctuations in various physico-chemical parameters. The river water quality varied from very poor to unsuitable for drinking during both the season suggesting that prior to treatment, water cannot be used for drinking purposes. HCA underlines the importance of statistical techniques to better interpret physico-chemical data and reveals parameters that govern the WQI in the study area. The surveillance of the stressing factors, their impact on the various physico-chemical parameters, and the incorporation of GIS-based approaches in water quality assessment have certainly enhanced the monitoring standards. In addition, the study can be beneficial to such densely populated regions across the globe, especially to those that are undergoing the process of rapid urbanization. Overall, the research contributes valuable information to the scientific community and policymakers, urging them to prioritize conservation and restoration measures to safeguard the ecological integrity of the River Ganga.

The authors are thankful to the members of the Department of Zoology, Patna University for their support toward this research.

No funding was obtained for this study.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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