Assessing drinking and irrigation water quality in a highly altered subtropical river in India using hydro-chemical indices

Abstract

River water pollution and the subsequent degradation of water quality for irrigation and drinking are reported worldwide, especially in tropical regions with excess population pressure. The present study intends to investigate irrigation and drinking water quality and assess their suitability in the subtropical Damodar River in India using hydrochemical indices during pre-monsoon (PRM), monsoon (MON), and post-monsoon (POM) periods. The water quality index (WQI) results reveal that the river’s water is unsuitable for drinking, as 68.92% (52.95% in PRM, 86.54% in MON, and 66.88% in POM) of samples are found to be unfit for consumption in the temporal dimension. However, in the spatial dimension, the percentage of unsuitable water samples is primarily high near the village of Mujher Mana station, with 97.20% of samples (97.87% in PRM, 97.91% in MON, and 95.83% in POM) deemed unfit for drinking. This suggests the Damodar River water in MON and near the village of Mujher Mana needs treatment before drinking. The study’s findings from the irrigation hazards indices and the local farmers’ feedback indicate that the river water is suitable for irrigation use. Moreover, SAR, %Na, KR, and PS are high at Mujher Mana village, RSC at Raniganj downstream (Ds), PI at Barakar, and MAR at Durgapur upstream (Us) in terms of spatial extent. The ANOVA test indicates a significant variation in river water quality across different spatio-temporal dimensions in the study area. Water pollution is mainly attributed to the discharge of untreated industrial and urban effluents directly into rivers, without undergoing water treatment. Therefore, it is imperative to address the issue promptly to reinstate the river water quality.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-37030-y.

Introduction

River water contamination is a global environmental issue caused by both geological and anthropogenic factors¹. Many rivers and streams are significantly polluted² worldwide. River water pollution mainly emerges from weathering of rock and soil (geological factors), industrial, agricultural, and mining activities, domestic effluents and disposal (anthropogenic factors)^3,4. River water is used for numerous purposes in India, like cleaning, irrigation, drinking, bathing, and industrial applications⁵. In less developed countries, especially in rural populations, river water is still used for drinking⁶. An elevated concentration of pollutants in drinking water leads to various diseases, diarrhoea, cholera, hepatitis A, typhoid, dysentery, and skin conditions⁷. Moreover, even a high concentration of a single parameter can harm people’s health; for instance, elevated levels of arsenic in drinking water can lead to arsenicosis with long-term exposure. Furthermore, river water is an important source of irrigation, but using contaminated river water in agricultural fields adversely affects crop health and production^8,9. Therefore, assessing river water quality has become increasingly important for human health, as well as irrigation and sustainable development.

Water pollution in India has reached a critical point; for example, the Yamuna River in India has reached this point and is categorized as ‘E’¹⁰. Significant pollution affects almost every river system in the country. The National Environmental Research Institution (NEERI) in Nagpur has determined that around 70% of water in India is contaminated¹¹. For example, the Damodar River is recognised as one of the most polluted rivers¹². This situation is primarily due to numerous heavy industries and mining operations in the basin, along with a high population density that heavily relies on agriculture¹³. Moreover, De et al.¹⁴ demonstrated that the urban-industrial effluents of Durgapur contain various harmful pollutants released into the river through several canals¹⁵. A comprehensive water quality analysis conducted by CIFRI¹⁶ during 1992–1995 reported a decline in many biotic communities within the river, including plankton, fish, fisheries, and in situ bioassays. Senapati et al.¹⁷ estimated the BOD level of the Damodar River using the Artificial Neural Network approach. Besides, Hoque et al.⁶ assessed the carcinogenic risk (CR) and found a moderate degree of risk to human health for individuals residing in the Damodar River Basin (DRB). Pareek et al.¹⁸ also examined the effects of Ghaggar water pollution on human health, focusing on metrics such as dissolved oxygen (DO), biochemical oxygen demand (BOD), total dissolved solids (TDS), and several hydrogeochemical indicators. Furthermore, Hoque et al.⁴ observed that the surface and bottom waters of the Damodar River exhibited low to medium salinity levels and a low degree of salt hazard. Sarkar and Islam⁸ demonstrated differences in irrigation water quality between the upper and lower reaches of the river using an analysis of variance (ANOVA) test, which is widely used in water quality analysis because of its reliability¹⁹.

Numerous research studies have been conducted on the contamination of the Damodar River water^4,14. Some investigations have assessed the suitability of the river water for irrigation and drinking from a spatial perspective, while a few studies have examined the spatio-temporal changes in water quality. However, no systematic study has evaluated the river water quality for irrigation and drinking purposes within a spatio-temporal context. Thus, the novelty of this study would be the long-term spatio-temporal analysis of both irrigation and drinking water suitability. The DRB in the Asansol to Durgapur area has experienced significant environmental stress due to rapid industrialisation and urbanisation. Urban-industrial effluents discharged into the river pose a growing threat to its water quality. Therefore, the suitability of the river water for consumption or irrigation is questionable, especially for individuals residing in adjacent communities. Hence, the study area has been selected based on the high concentration of contaminated effluents originating from the Durgapur–Asansol urban-industrial zone and mining, and intensive agricultural practice areas, which are predominantly discharged into the DRB. Moreover, the current study is unique in utilising a dataset from 2011 to 2023, particularly in a stretch that is prone to urban water pollution. This study aims to (1) examine the drinking and irrigation water quality of the DRB using the water quality index and irrigation hazard indices, and (2) depict the spatial and seasonal variations in water quality using the ANOVA test along with the key driving forces. This research will rely on robust spatio-temporal data by applying statistical and geospatial methods. The outcomes of this study could contribute to achieving several sustainable development goals (SDG 6: clean water, sanitation, and hygiene, and SDG 14: life below water) for the DRB and other similar regions worldwide.

Datasets and methodology

Study area

The Damodar River is a significant tributary of the Bhagirathi-Hooghly River and is 592 km long²⁰. It was known as the Sorrow of Bengal because of its extensive flooding and devastation, but the river has been somewhat tamed due to dam constructions on the river systems. The river originates at Khamarpat Hill of Chotanagpur plateau near Chandwa village in Latehar district^21,22, above ~ 10 m from mean sea level. This plateau is part of the western section of the peninsular shield and the river descends into the plain area in the eastern region. The river under consideration exhibits a complex network of tributaries and sub-tributaries, including Barakar, Konar, Bokaro, Khadia, Bhera, Jamuria, Ghari, and Haharo. The DRB is a funnel-shaped sub-basin of the Bhagirathi-Hooghly River (Fig. 1). The basin covers about 23,370.98 km² area²³ in Jharkhand (73.7%) and West Bengal (26.3%) states²⁴. The basin is geographically distributed throughout multiple districts such as Hazaribagh, Ramgarh, Kodarma, Giridih, Dhanbad, Bokaro, Chatra, Latehar, Ranchi, Jamtara, Deoghar and Lahardanga in Jharkhand and Bardhaman, Hooghly, Howrah, Bankura, and Purulia in West Bengal. The upper portion of the river valley region is characterized by the coarse, gritty soil resulting from the weathering process of pegmatite, quartz veins, and conglomeratic sandstones. This soil is further mixed with rock fragments and sandy soil. The lower section of the river is characterized by alluvial soil. The basin experiences an approximate annual precipitation of 1400 mm and most rainfall occurs during the MON season, spanning from June to August. The population residing in the basin accounted for approximately 14.66 million in 2001 and about 17.31 million individuals in 2011²⁵. The DRB is rich in mineral resources (coal, fire clay, bauxite, mica, and limestone) and the valley is home to large-scale mining and industrial activity.

Fig. 1.

Study area with sampling stations, (a) Location of the DRB in the Eastern part of India, (b) Drainage network of the DRB with dams and barrages, (c) Water samples collection stations (Note: Sample locations from 1–11 are mentioned in Table 1 based on WBPCB data; Source: Fig. 1a–b were created by the authors using ArcGIS software- version 10.2 with the base layer from https://goto.arcgisonline.com/maps/World_Imagery; Fig. 1c was created by the authors based on European Space Agency (ESA) Sentinel-2 imagery- 45Q dated 01 January 2020 using https://livingatlas.arcgis.com/landcover/).

The construction of reservoirs, barrages, check dams, and canals has decreased in the typical river flow patterns. The perturbation of flow rate, resulting in reduced velocity, has deleterious effects on the ecological dynamics of aquatic communities. Besides the domestic effluents, the river receives pollution from coal mining areas and thermal power plants. These sources of pollution severely affect the quality of the river. The river, particularly in its middle and lower sections, has been identified as the most polluted^26,27. In these sections, the Panchet and Maithon dams, and the Durgapur barrage play a significant role in controlling flow. The river is interspersed with natural drains known as Nallah. The northern nallahs include Nunia, Singaran, and Tamla. The southern nallah is referred to as kadamda. Numerous industrial and municipal drainage systems directly or indirectly connect to the river. Specifically, the research encompassed the middle segment of the river, spanning from Barakar (located at 23°44′34.52″N latitude and 86°48′32.48″E longitude) to Burdwan (located at 23°13′35.90″N latitude and 87°49′04.21″E longitude), covering a distance of approximately 82.1 km, which is the most polluted river segment of the Damodar. Moreover, in this stretch, 11 water quality monitoring stations of the West Bengal Pollution Control Board (WBPCB) are located in the Damodar River.

Datasets

The present study has examined the Damodar River water quality in the temporal and spatial dimensions to assess suitability for irrigation and drinking based on the hydrochemical data obtained from the West Bengal Pollution Control Board²⁸. The monthly average water quality dataset of eleven available monitoring stations of the WBPCB on the river is considered for the present study (Table 1). Based on the data availability, the river water quality data were collected from February 2011 to January 2023 at six stations, i.e., (station-1) Barakar at Asansol (Water intake point), (2) Damodar at Dishergarh village (Nr. Jharkhand-West Bengal border), (3) Damodar at Ds of IISCO after 3rd outfall at Dhenna village, (4) Damodar at Narainpur after the confluence of Nunia nallah, (5) Damodar near Mujher Mana village after the Tamla nallah, and (11) Water intake point for Burdwan town. The remaining five stations’ data were collected from February 2014 to January 2023 at—(station-6) Damodar at Andal Ds, (7) Damodar at Andal Us, (8) Damodar at Asansol Us, (9) Damodar at Durgapur Us, and (10) Damodar at Raniganj Ds. Hence, a total of 47 water sample data were collected for pre-monsoon (PRM), 48 sample data for monsoon (MON), and another 48 samples for post-monsoon (POM) period for the six stations, i.e. stations 1–5 & 11. Likewise, a total of 35 water sample data were collected for PRM (February-May), 36 sample data for MON (June–September), and another 36 samples for the POM (October-January) period for the other five stations, i.e., stations 6–10. Further, the local people’s perceptions regarding using the river water for irrigation and drinking, as well as its impact on irrigation and human health, have been taken into account. The survey was carried out using a detailed questionnaire (Table SQ1). A total of 40 representative households have also been selected from these 4 locations using a purposive sampling method for understanding the present condition of human health and irrigation.

Table 1.

Details of water quality monitoring stations and season-wise distribution of water samples.

Station (Sl. No)	Station name	Latitude	Longitude	Year of observation	Season-wise distribution of water samples
					PRM	MON	POM
1	Barakar at Asansol (water intake point)	23°44′34.52″	86°48′32.48″	2011–2023	47	48	48
2	Damodar at Dishergarh village (near Jharkhand-West Bengal border)	23°41′04.34″	86°49′21.76″		47	48	48
3	Damodar at Ds of IISCO after 3rd outfall at Dhenna village	23°38′48.78″	86°53′44.67″		47	48	48
4	Damodar at Narainpur after the confluence of Nunia nallah	23°35′32.62″	87°05′38.77″		47	48	48
5	Damodar near Mujher Mana village after the confluence of Tamla nallah	23°28′12.14″	87°19′05.74″		47	48	48
6	River Damodar at Andal Ds	23°33′07.75″	87°12′54.32″	2014–2023	35	36	36
7	River Damodar at Andal Us	23°33′54.47″	87°09′53.30″		35	36	36
8	River Damodar at Asansol Us	23°37′36.02″	86°57′51.77″		35	36	36
9	River Damodar at Durgapur Us	23°28′41.10″	87°18′06.66″		35	36	36
10	River Damodar at Raniganj Ds	23°34′27.79″	87°07′25.79″		35	36	36
11	Water intake point for Burdwan town	23°13′35.90″	87°49′04.21″	2011–2023	47	48	48

Based on WBPCB (2022)²⁸.

Water quality standards were maintained as per the American Public Health Association (APHA) standard procedures because they test a wide range of water quality and recognise each element in a single category for all types of river water (Table 2). Furthermore, parameters like pH and temperature are measured onsite i.e., at monitoring stations, using a probe and a meter. Other physico-chemical parameters used in this investigation were tested in the Durgapur regional laboratory run by WBPCB. The test parameters, such as DO, BOD, and COD, and water samples were carried in ice bags and stored at 4°C, and tests were mainly completed within 72 h for quality control²⁸. Table S1 shows the detailed summary of field tests and laboratory procedures for the 22 water-quality parameters listed, which follow the APHA standards. Further, the accuracy of the analytical technique was measured using charge ion balance error (CBE) calculated using Eq. 1²⁹.

1

where TC and TA are total cations and anion concentrations, respectively (in mg/l).

Table 2.

Analytical water testing methods and APHA standard.

Parameter	Method	APHA number, 24th edition (2023)
NO3− (mg/l)	Automated cadmium reduction method	4500-NO3− -F
NH3 (mg/l)	Automated phenate method	4500-NH3-G
Total Phosphorus (mg/l)	Manual digestion and flow injection analysis for Total Phosphorus	4500-P-H
Total Suspended Solids (mg/l)	Total Suspended Solids dried at 103–105 °C	2540-D
EC (µS/cm)	Laboratory method	2510-B
Turbidity (NTU)	Nephelometric method	2130-B

It was found that ~ 93% of samples (N = 1383) were below the CBE threshold value of 10%, indicating its reliability for further analysis³⁰.

Methodology

The present study adopted a systematic methodological design to evaluate river water quality for drinking purposes and to analyse irrigation hazards (Fig. 2). Moreover, water quality indices and statistical techniques have been used to reveal the water quality for drinking and irrigation use in the spatio-temporal dimension.

Fig. 2.

Methodological flow chart adopted for the current study.

Measurement of the suitability of drinking water

In the present work, 22 different water quality parameters including ammonia-N (NH₃-N), biochemical oxygen demand (BOD), electrical conductivity (EC), dissolved oxygen (DO), nitrate–N (NO₃⁻), pH, calcium (Ca²⁺), chloride (Cl⁻), chemical oxygen demand (COD), fluoride (F⁻), magnesium (Mg²⁺), phosphate-P (PO₄³⁻), potassium (K⁺), sodium (Na⁺), sulphate (SO₄²⁻), total alkalinity (TA), total dissolved solids (TDS), total hardness (TH) as CaCo3, total suspended solids (TSS), turbidity, bicarbonate (HCO₃⁻) and carbonate (CO₃²⁻) have been used to measure and analyse the suitability of the Damodar River water quality for drinking use. The river water suitability for drinking use has been calculated using the weighted arithmetic water quality index method³¹ based on the standard permissible limits of the parameters for drinking use as per the Bureau of Indian Standards (BIS)³². The water quality index (WQI) for drinking water suitability evaluation was computed using Eqs .2–4³³.

2

where

3

Here, denotes quality rating of the n^th water parameter; for observed value of the n^th water parameter; for standard parameter value within the permissible limit; for ideal predicted value of the n^th parameter. Regarding pH, the ideal value is 7 and for DO it is 14.6 mg/I but for all other parameters, it is zero³⁴.

After this calculation, the unit weight of the water quality parameter was calculated using Eq. 4.

4

stands for the relative weight of the parameter; for the standard value of the parameter; for the coefficient for proportionally.

Measurement of the irrigation hazard

Several irrigation suitability measuring indices, such as sodium adsorption ratio (SAR), percentage of sodium (%Na), Kelly ratio (KR), and residual sodium carbonate (RSC), permeability index (PI), potential salinity (PS) and magnesium adsorption ratio (MAR), have been used worldwide for evaluating irrigation water quality. The Damodar River water for irrigation use has also been evaluated based on various irrigation hazard indices, including sodicity, alkalinity, salinity, and magnesium hazard.

Sodicity hazard

1. Sodium adsorption ratio (SAR)

The SAR is used to assess sodium hazard levels in irrigation water⁴. It is also applied to measure salinity hazard using the electrical conductivity⁹. Bicarbonate and carbonate ions in the irrigation water increase the permeability hazard quantified by SAR. The SAR in irrigation water relies on the calcium (Ca²⁺), magnesium (Mg²⁺) and sodium (Na⁺), concentrations and their ionic exchange. It was computed using Eq. 5³⁵.

5

where Ca²⁺, Mg²⁺ and Na⁺ are the calcium, magnesium and sodium concentrations in the river water, respectively.

• b.Percentage sodium (%Na)

Measuring the percentage of sodium is important for determining the irrigation water quality⁴. A high %Na concentration impacts the soil permeability and plant communities’ growth that depends on sodium (Na⁺), potassium (K⁺), calcium (Ca²⁺) and magnesium (Mg²⁺) concentrations and their ionic exchange^4,36. It was computed using Eq. 6⁸.

6

where Na⁺, K⁺, Ca²⁺ and Mg²⁺ are the sodium, potassium, calcium and magnesium concentrations in the river water, respectively.

• c.Kelly ratio (KR)

The Kelly ratio (KR) is the ratio of sodium (Na⁺), calcium (Ca²⁺) and magnesium (Mg²⁺). This ratio indicates the degree and possible impact of sodium on irrigation water quality³⁷. It measures the sodium concentration in irrigation water quality. KR was calculated using Eq. 7³⁸.

7

where Na⁺, Ca²⁺ and Mg²⁺ are the sodium, calcium and magnesium concentrations in the river water, respectively.

Alkalinity hazard

1. Residual sodium carbonate (RSC)

It is the common measure for assessing the alkalinity hazard and a high RSC concentration reduces the soil permeability⁴. It shows bicarbonate (HCO₃⁻) and carbonate (CO₃²⁻) concentrations compared to calcium (Ca²⁺) and magnesium (Mg²⁺) concentrations. It was computed using Eq. 8³⁹.

8

where HCO₃⁻, CO₃²⁻, Ca²⁺ and Mg²⁺ are bicarbonate, carbonate, calcium and magnesium in river water concentrations, respectively.

• b.Permeability index (PI)

Doneen⁴⁰ developed this index to measure the water suitability for irrigation and categorised the water into three classes, i.e. class-I (PI > 75%), class-II (25% < PI < 75%), and class-III (PI < 25%) as good, suitable, and unsuitable for irrigation, respectively. This index indicates the ability of irrigation water to impact soil permeability⁸. It was computed using Eq. 9⁴¹.

9

where Na⁺, Ca²⁺, Mg²⁺ and HCO₃⁻ are the sodium, calcium, magnesium and bicarbonate concentrations in the river water, respectively.

Salinity hazard

1. Potential salinity (PS)

This indicates the suitability of water based on the concentration of insoluble salt. It is also used to evaluate the irrigation water quality, which was calculated using Eq. 10³⁷.

10

where Cl⁻ is the chloride and SO₄²⁻ is the sulphate concentration in the river water.

Magnesium hazard (MH)

1. Magnesium adsorption ratio (MAR)

It is measured to identify magnesium hazards in irrigation water and was calculated using Eq. 11⁴².

High magnesium concentration in irrigation water impacts soil structure and reduces soil infiltration rates, which affects soil quality and decreases crop production³⁶.

11

where Mg²⁺ is magnesium and Ca²⁺ is calcium, the concentrations in the water samples.

Statistical analysis

Analysis of variance (ANOVA) test ANOVA test is a statistical tool that determines the difference between two or more factors by testing for significance⁴. In the present study, a one-way ANOVA test was used to represent significant differences in spatial (upstream to downstream) and temporal (PRM, MON, and POM) irrigation hazard and drinking indices using Eq. 12.

12

where F measures the ANOVA test coefficient, MSB denotes the mean squared error of between-group variance, and MSW for the mean squared error of within-group variance. If the computed F value is greater than the tabulated F value, the null hypothesis is rejected and the alternative hypothesis is accepted. In the present study, a one-way ANOVA test has been computed using MS Excel (version 2010).

Bi-variate and multivariate analysis In the present investigation, hydrochemical parameters’ bi-variate correlations (r) are depicted with a heat map prepared in a Python environment (Jupyter Notebook). These Pearson’s correlation coefficients show the degree of association with various parameters. Multivariate analyses are really crucial in the domain of hydrochemical investigation and determining the factors controlling the hydrochemical evolution for the DRB. We have used principal component analysis (PCA) with varimax rotation methods to maximise variation for easier interpretation of the effects of the factors (irrigation and drinking water indices) on the hydrochemical behaviour of the Damodar River. The PCA is calculated using Eq. 13, following Hotelling⁴³.

13

where Z stands for the component score; α for component loading; X for the measured value of variables; i for the component number; j for the sample number; m for the total number of variables.

Similarly, we have used hierarchical cluster analysis (HCA) to cluster the water quality monitoring stations based on the level of water pollution as reflected through the various irrigation and water quality indices⁴⁴. We have used SPSS (version 13) to run the PCA and HCA for the present analysis.

Results

General hydro-chemical characterisation of water quality parameters

In the present investigation, it is found that the concentrations of BOD, EC, DO, NO₃⁻, pH, Ca²⁺, COD, Mg²⁺, Na⁺, TA, TDS, TH, HCO₃⁻, and CO₃²⁻ were high in the PRM period. However, the concentrations of Cl⁻, F⁻, PO₄³⁻, K⁺, SO₄²⁻, TSS and turbidity were high in MON, and only the NH₃-N concentration was high in POM based on the mean values of the physicochemical parameters. Moreover, from the spatial variation of surface water quality variables, it is also observed that the content of physico-chemical parameters was high at Mujer Mana village, followed by Raniganj, and low at Barakar, followed by Disergarh.

Furthermore, the physicochemical parameters have also been measured according to the permissible levels for drinking water^32,33. It was noted that the higher percentages of DO, COD, and turbidity concentrations in surface water samples surpassed their permissible levels across all seasons and sites. In contrast, the concentrations of NO₃⁻, Ca²⁺, Cl⁻, Mg²⁺, Na⁺, SO₄²⁻ and TH remained within their permissible limits over the statio-temporal dimension. Additionally, a very high percentage of NH₃-N, EC, and PO₄³⁻ concentrations in water samples at the Mujer Mana station exceeded their permissible limits compared to the other stations during all seasons.

The twenty-two hydrochemical parameters show a degree of correlation ranging from very poor to very strong (Fig. 3). For example, sodium is positively correlated with potassium, TDS and conductivity with a correlation (r) > 0.6; while total hardness is also positively correlated with calcium and magnesium with a higher correlation coefficient (r > 0.7). The CO₃²⁻, HCO₃⁻ and total alkalinity are perfectly corrected with one another (Fig. 3). Similarly, the two nitrogen species—ammonia and nitrate also exhibit a strong positive relation (r = 0.5). However, most of the parameters show a relatively feeble positive correlation (r < 0.5) (Fig. 3). The parameters also exhibit negative correlations. For example, DO maintains a feeble negative correlation with most of the hydro-chemical parameters, while pH is negatively correlated with phosphate, sulphate, potassium and turbidity (Fig. 3).

Fig. 3.

Heatmap showing the correlation among the water quality parameters.

Water quality index for drinking purposes

River water quality has been assessed for drinking use based on the WQI and demarcated into five categories: excellent (0–25), good (25–50), poor (50–75), very poor (75–100), and unfit for consumption (> 100). It is found that the WQI values ranged from 98.32 to 1018.76 with a mean value of 211.72 in the PRM, 378.67 to 917.06 with a mean value of 478.04 in MON, and 163.78 to 848.27 with an average value of 262.99 in the POM period (Table S2). Based on the average values, the WQI values are high in the MON period. The average CVs of 83.39%, 91.77% and 11.34% are found in all periods, respectively. Hence, based on the average values, the WQI value and its variability are high in the MON period. Besides, the highest average WQI values of 1018.76 in PRM, 917.06 in MON, and 848.27 in the POM period are observed near Mujher Mana village station (Table S2 and Fig. 4). The spatial dimension shows the lowest average WQI values of 98.32 in PRM at Barakar, 378.67 in MON at Narainpur, and 163.78 in POM at Raniganj (Fig. 4).

Fig. 4.

Spatial and seasonal variation of the Damodar River water quality in terms of water quality index (WQI).

Additionally, only 1.09% of the samples fall into the excellent category, 16.85% are classified as good, 16.63% are considered poor, 12.47% are rated very poor, and 52.95% are categorised as unfit for consumption during the PRM period (Table S3). In MON, 1.71% of samples fall into the good category, 4.70% are poor, 7.05% are very poor, and a massive 86.54% are under the unfit category in MON. However, about 7.48% are under the good category, 16.88% are poor, 8.76% are very poor, and 66.88% are under the unfit category in the POM. Thus, it is found that most samples (86.54% in MON, 66.88% in POM, and 52.95% in PRM) in all seasons fall into the unfit category for consumption, indicating that the water in this particular stretch of the river (Barakar to Burdwan) is unsuitable for drinking. In addition, based on the average values, the river water is less appropriate for consumption during the MON period. In the MON rainy period, the highest percentage of samples (86.54%) comes under the unsuitable class compared to other seasons. Therefore, the WQI data demonstrate that the river water quality deteriorates temporally during the MON season and is inferior spatially near the hamlet of Mujher Mana compared to other stations.

Suitability of river water for irrigation

This study employs several irrigation hazard indicators to assess the appropriateness of river water for irrigation purposes. The irrigation hazard has been evaluated based on the sodicity, alkalinity, salinity, and magnesium hazards.

Sodicity hazard

1. Sodium adsorption ratio (SAR)

The SAR serves as an indication of salt risk, influencing soil permeability and agricultural yield. Elevated salt levels in irrigation water affect soil permeability and overall water salinity. The SAR has been evaluated, revealing a range of 0.78 to 1.76 with an average of 0.97 during the PRM, 0.77 to 1.62 with an average of 0.91 during the MON, and 0.70 to 1.71 with an average of 0.95 in the POM period within this research region. The research reveals that the SAR is elevated during the PRM period, followed by the remaining periods, based on the average SAR values in the region examined. In the spatial dimension, Mujher Mana village station exhibits the highest SAR values of 1.76, 1.62, and 1.71 in all seasons, respectively. However, the lowest SAR values of 0.78 in PRM and 0.77 in MON are observed at Dishergarh village and 0.70 in POM at Barakar at Asansol station (Fig. 5a, Table S4). Further, irrigation water has been classified based on the categorical classification of SAR values and found that all the samples (100%) in all seasons lay under excellent categories, indicating water suitability for irrigation use (Table S5). Thus, these results portray that the SAR is high in the PRM compared to others in the temporal dimension and is also high near Mujher Mana village among the other stations in the spatial dimension. Additionally, according to the SAR classification, the river water is suitable for irrigation.

Fig. 5.

Spatio-temporal distribution of sodicity hazard, (a) Sodium adsorption ratio (SAR) and (b) Percentage of sodium (%Na).

• b.Percentage of sodium (%Na)

In the present work, it is found that the %Na in the surface water sample varies from 28.98 to 44.49 with a mean value of 32.58 in the PRM, 29.26 to 38.24 with a mean value of 31.49 in the PRM and 7.49 to 43.34 with a mean value of 29.77 in the POM period. It is observed that the %Na is high in PRM according to the mean values of %Na. Additionally, the %Na levels are consistently high near Mujher Mana village station across all seasons, as shown in the spatio-temporal analysis (Fig. 5b and Table S6). In contrast, the %Na levels are low at Durgapur Us during PRM, Andal Ds in MON, and Andal Us in POM. Moreover, it is also found that 2.63% of samples are in the excellent category, 83.15% are in the good category, and 14.22% are permissible for irrigation use in the PRM season based on the categorisation of irrigation water. For the MON, 7.15% of the samples are excellent, 78.35% are good, 14.1% are permissible, and 0.4% are doubtful. For the POM period, 84.19% of samples lie in the good category and 15.81% in the permissible category (Table S7). These results indicate that, in terms of %Na, most samples fall into the good category across all seasons and are suitable for irrigation use. Furthermore, the sodicity hazard related to %Na is higher in the PRM season compared to other seasons. It is also high near Mujher Mana village compared to other stations in spatial dimensions.

• c.Kelly’s ratio (KR)

The KR is utilised to assess the appropriateness of river water for agricultural purposes. The computed KR value during the PRM period varies from 0.38 to 0.79, with a mean value of 0.47. The range is 0.38 to 0.62, with an average of 0.44 during the MON season, and 0.38 to 0.76, with an average of 0.46 in the POM season. In the time dimension, the PRM has the highest mean values, followed by the other periods. Simultaneously, in the spatial dimension, it remains elevated near Mujher Mana village station during all seasons. The concentration of the measured parameter is minimal at station Andal Ds during both the PRM and MON periods, as well as at station Barakar during the POM period (Fig. 6a and Table S8). Furthermore, KR is categorised according to the appropriateness of irrigation water, where KR > 1 signifies that the river water is detrimental and inappropriate for irrigation, while KR < 1 denotes the contrary. The KR > 1 indicates an excess concentration of Na⁺ in water³⁷. The KR categorisation indicates that 87.09%, 97%, and 97.43% of water samples are deemed appropriate for irrigation during the PRM, MON, and POM periods, respectively (Table S9). Consequently, these findings demonstrate that the river water is appropriate for agriculture. Furthermore, the sodicity danger, measured by KR, is elevated during the PRM season compared to other seasons, particularly near the Mujher Mana station in spatial dynamics.

Fig. 6.

Spatio-temporal distribution of (a) Kelly’s ratio (KR) and (b) Residual sodium carbonate (RSC) .

Alkalinity hazard

1. Residual sodium carbonate (RSC)

The alkalinity hazards are quantified by the RSC index, which impacts agricultural development due to contaminated water reaching its roots. The computed RSC values vary from 2.10 to 2.94, averaging 2.39 during the PRM period; from 1.85 to 2.66, averaging 2.22 during the MON; and from 2.01 to 2.46, averaging 2.26 during the POM. Additionally, elevated coefficients of variation of 61.59, 73.64, and 75.12 are seen in all periods, respectively. The RSC is elevated in the PRM period, followed by the other phases, according to the mean values of RSC. Furthermore, it is elevated at Raniganj Ds during the PRM and POM periods and at Asansol Us during the MON in terms of spatial dynamics (Fig. 6b and Table S10). Furthermore, the water suitability for irrigation is demarcated into three classes based on the RSC values, i.e., RSC > 1.25 meq/1 as suitable, 1.25 < RSC < 2.5 meq/1 as doubtful and RSC > 2.5 meq/1 as unfit. It is found that 14.84% of samples are under the good category, 57.47% are doubtful and 27.69% are inappropriate for irrigation use in the PRM based on the categorisation of the RSC. While 29.25% of samples are under the “good” category, 42.73% are doubtful and 28.02% are unsuitable in MON. In POM, 23.52% of samples are under the good category, 45.72% are doubtful and 30.76% are unsuitable (Table S11). Thus, maximum samples in all seasons lie from doubtful to unsuitable, indicating unsuitability for irrigation use. Moreover, the alkalinity hazard, measured by RSC, is the highest during the PRM season compared to other seasons and at the Raniganj Ds station compared to other stations in the study area.

• b.Permeability Index (PI)

The PI is a significant index used for assessing the appropriateness of irrigation water⁴⁵. The PI values of the water samples vary from 71.77 to 84.66, with an average of 78.06 in the PRM; from 72.04 to 88.05, with an average of 78.97 in the MON; and from 71.81 to 83.31, with an average of 78.08 in the POM period. According to its mean values throughout time, it is highest during the MON, followed by the other periods. Nonetheless, the levels are elevated in Barakar station throughout the PRM and POM periods and at Dishergarh village during the MON, as seen in the spatial extension (Fig. 7a and Table S12). According to the threshold values of PI for irrigation appropriateness, 60.87% of samples are classified as excellent, 33.18% as moderate, and 5.95% as unsuitable during the PRM season. During the MON, 55.79% of samples are classified as excellent, 38.94% as moderate, and 5.27% as inappropriate. Furthermore, 68.17%, 30.12%, and 1.72% of samples fall into the excellent, moderate, and inappropriate categories, respectively, during the POM season (Table S13). Analysis reveals that the majority of samples throughout all seasons fall into the excellent category, suggesting that the river water is appropriate for irrigation throughout the year. Furthermore, according to the PI, the permeability hazard is elevated during the MON season compared to other seasons, with Barakar exhibiting the highest risk among the other stations in terms of spatial extent.

Fig. 7.

Spatio-temporal distribution of (a) Permeability index (PI) and (b) Potential salinity (PS).

Potential salinity (PS)

The PS is a significant index for assessing salinity hazards, used to assess the appropriateness of irrigation water. The current investigation reveals that the PI values of the river water samples vary from 0.57 to 1.96, with an average of 1.04 during the PRM; from 0.76 to 2.21, averaging 1.27 in the MON; and from 0.59 to 2.11, averaging 1.07 in the POM period. Consequently, PS is elevated during the MON, followed by the POM and PRM periods, according to mean values in the temporal dimension. A large coefficient of variation of 111.72% is seen during the MON season. PS levels are elevated at the station near Mujher Mana village throughout PRM and POM periods, as well as at Anadal Us during the MON, as shown in the spatial extension (Fig. 7b and Table S14). Additionally, it was determined that 97.16%, 95.37%, and 95.01% of samples fall into the excellent-to-good category during all periods, respectively (Table S15). Consequently, most samples throughout all seasons fall within the excellent to good category, indicating the appropriateness of river water for irrigation throughout the year. Furthermore, the salt risk is elevated during the MON compared to other seasons, and the station near Mujher Mana village is included among the other stations in the spatial context.

Magnesium adsorption ratio (MAR)

The magnesium hazard has been evaluated in the current study utilising the MAR, revealing a range of 34.73 to 40.89, with an average of 37.24 during the PRM period; 34.12 to 39.58, with an average of 37.65 in the MON; and 30.40 to 40.76, with an average of 36.62 in the POM period. Consequently, the MAR is elevated during the MON season, followed by the other periods, as shown by the mean values in the temporal analysis (Fig. 8 and Table S16). Moreover, in the spatial dimension, it remains elevated in Durgapur Us across all seasons. Furthermore, a MAR value of less than 50 signifies water’s appropriateness for irrigation, whereas a MAR value beyond 50 denotes unsuitability for irrigation. According to the categorisation, 93.21%, 91.23%, and 94.87% of water samples in all periods, respectively, fall within the acceptable range (Table S17). Consequently, most samples demonstrate their appropriateness for irrigation throughout all seasons. The findings suggest that the magnesium danger is elevated during the MON season compared to other seasons and is more pronounced in Durgapur within the spatial context of the research region.

Fig. 8.

Spatio-temporal distribution of magnesium adsorption ratio (MAR).

Spatio-temporal variations in water quality

This research uses a one-way ANOVA test to illustrate spatio-temporal variations in surface water quality. The ANOVA findings indicate a considerable variation in irrigation water quality indicators, such as %Na, KR, PI, SAR, and WQI, throughout all seasons in the spatial dimension. Nevertheless, indicators such as MAR and RSC show no significant variations across all seasons. Moreover, PS exhibits a substantial variation in spatial dimensions between the PRE and POM seasons (Table 3). Moreover, the ANOVA test about the temporal change of water quality indicates that there is no significant difference in irrigation water quality indices, but a significant difference is seen in the WQI. Consequently, our findings show a substantial variation in the water quality of the Damodar River across both spatial and temporal dimensions.

Table 3.

One-way ANOVA test for describing the spatio-temporal variation in river water quality.

Indices	Seasons	Spatial (upstream–downstream variation)				Temporal (seasonal variation)
		F	P-value	F-critical	Remarks	F	P-value	F-critical	Remarks
%Na	PRM	17.54169	5.4E−27	1.85	Alt	2.550247	0.07843	3.0022	Null
	MON	3.761536	7.01E−05		Alt
	POM	16.88943	4.33E−26		Alt
KR	PRM	18.71658	9.93E−29		Alt	2.257235	0.10502		Null
	MON	4.309593	9.05E−06		Alt
	POM	18.32874	2.85E−28		Alt
MAR	PRM	1.209155	0.282583		Null	0.718309	0.48776		Null
	MON	0.880905	0.551058		Null
	POM	1.734196	0.070703		Null
PI	PRM	3.91797	3.98E−05		Alt	0.714229	0.48975		Null
	MON	6.952782	3.58E−10		Alt
	POM	3.573956	0.00014		Alt
PS	PRM	15.90951	1.54E−24		Alt	2.048381	0.12933		Null
	MON	1.121745	0.343775		Null
	POM	19.59494	3.91E−30		Alt
RSC	PRM	1.139958	0.330414		Null	1.084802	0.33826		Null
	MON	0.94537	0.491023		Null
	POM	0.354417	0.964975		Null
SAR	PRM	33.87754	7.19E−49		Alt	2.289325	0.10172		Null
	MON	17.64581	2.97E−27		Alt
	POM	32.66723	1.11E−47		Alt
WQI	PRM	18.5314	1.86E−28		Alt	35.4479	1E-15		Alternative
	MON	4.043647	2.46E−05		Alt
	POM	9.095177	9.58E−14		Alt

The null hypothesis (H0: all group means are equal) and the alternative hypothesis (H1: at least one group mean is different). The results show that ‘Null’ indicates acceptance of H0, i.e., there is no significant variance among the stations/seasons, while ‘Alt’ (Alternative) indicates the rejection of H0 and acceptance of H1, i.e., a significant difference in water exists at a significance level of α = 0.05; the degree of freedom (n−1) is 10 for the spatial dimension and 2 for the temporal dimension.

Association among the hydrochemical indices and controlling factors

The Principal Component Analysis (PCA) portrays that two stages of the PCA (PC-I and PC-II) are almost equally important, accounting for > 80% explanation (PC-I:41.82 and PC-II: 38.36) after varimax rotation (Table 4).

Table 4.

Total variance explained.

Component	Initial Eigenvalues			Rotation sums of squared loadings
	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %
1	5.500	68.748	68.748	3.345	41.816	41.816
2	.914	11.425	80.173	3.068	38.356	80.173
3	.685	8.566	88.739
4	.431	5.391	94.130
5	.190	2.378	96.508
6	.144	1.805	98.313
7	.104	1.295	99.608
8	.031	.392	100.000

Extraction method: principal component analysis.

It indicates a strongly developed system of correlation at the two stages. At the first stage (PC-I), PI, MAR and RSC dominate the system with their higher positive factor loading > 0.85 (Table 5). However, at the PC-II, SAR dominates the system with a higher positive loading (> 0.8), followed by the KR, WQI and PS (Table 5). These factors underscore the role of the river pollution induced mainly by the urbanisation and industrialisation in the DRB. The magnesium, RSC and permeability factors emerge as the major pollution indicators at the initial stages, while the higher sodium, potassium concentrations drive the poorer water quality in the second stage of the PCA.

Table 5.

Rotated component matrix (a).

	Component
	1	2
%Na	.586	.635
KR	.558	.793
MAR	.862	.289
PI	.911	.251
PS	.062	.730
RSC	.870	.327
SAR	.445	.826
WQI	.398	.754

Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalisation. a Rotation converged in 3 iterations.

The component plot shows that there is a close association among the PI, RSC and MAR whole another major cluster of the indices is constituted by SAR, KR, WQI, and %Na (Fig. 9a). However, PS formed an outlier in this system. Moreover, the hierarchical cluster analysis (HCA) depicts that there are two clusters formed at a relatively closer distance of around 1 (Fig. 9b). These clusters are located mainly in the upper Damodar River. The first clusters of station area formed by the station Ids. 1, 2. 3 and 8, while the other cluster is formed by Ids. 4,6,7,9,10, and 11 (Fig. 9b). However, station Id. 5 located at Mujher Mana behaves as an outlier in the system. This is due to the higher pollution load at this middle stretch of the river induced by the urban industrial concentrations in and around the Damodar’s middle stretch. The lower section of the Damodar (Station Id. 11) near Burdwan depicts relatively lesser pollution loads due to the sag effect or distance decay effect from Station 5 (Fig. 9b).

Fig. 9.

Dynamics of the hydrochemical indices, (a) PCA component plot showing the association among the major indices controlling the hydrochemical evolution, (b) HCA showing the clustering of the water quality monitoring stations based on the nature of the water pollution.

Discussion

The current research evaluates the water quality of the Damodar River for drinking and irrigation purposes using WQI and irrigation hazard indices. The analysis of hydro-chemical parameters exhibits the concentration of BOD, EC, DO, NO₃⁻, pH, Ca²⁺, COD, Mg²⁺, Na⁺ , TA, TDS, TH, HCO₃⁻, and CO₃²⁻ in PRM, Cl⁻, F⁻, PO₄³⁻, K⁺, SO₄²⁻, TSS and turbidity in MON, and NH₃-N in POM were high. However, in the spatial dimension, it is observed that the concentration of physico-chemical parameters is high at Mujer Mana village, followed by Raniganj and low at Barakar, followed by Disergarh, compared to the other stations. The higher concentration of physico-chemical parameters is observed at Mujer Mana village in all the seasons due to urban-industrial effluents from Durgapur discharge into the river through Tamla Nallah. The WQI analysis demonstrates that there are significant seasonal fluctuations in the middle section of the Damodar River. The highest WQI score is observed in the MON season in the temporal dimension and at Mujher Mana village in the spatial dimension. The hydrogeochemical study reveals that the majority (86.54%) of samples are unsuitable for consumption during the MON due to elevated levels of turbidity. In contrast, the proportion of unsuitable samples is somewhat lower during the PRM and POM periods, standing at 52.95% and 66.88% respectively. Turbidity and phosphate are major factors contributing to the unsuitability of the river water for drinking and the elevated WQI. Bora and Goswami⁴⁶ reported a similar outcome when evaluating the water quality of the Kolong River in Assam, India. This phenomenon may be attributed to precipitation during the MON season, which facilitates surface runoff. Consequently, the combined discharge of agricultural and industrial effluents accumulates in the river water bodies. It is noteworthy that the sodicity hazard, as measured by the SAR, exhibits the highest level at Mujher Mana during all three seasons. The RSC indicates unsuitability for irrigation, as most samples fall under the doubtful to unsuitable category across all seasons. Furthermore, it is noted that the majority of samples exhibit favourable characteristics for irrigation applications, as determined by the evaluation of several indices, including SAR, %Na, KR, PI, PS, and MAR within the designated study region. The spatial and temporal variations in irrigation and drinking water quality as depicted by ANOVA (Table 3) exhibit two important facets of the hydrochemical analysis- (1) WQI varies both spatially and temporally implying the delicate changes in drinking water quality in space and time, and (2) most of the irrigation hazard indices vary spatially but not temporally because year wise changes are not as such an extent to impact the irrigation hazards thresholds. In brief, spatial variations in irrigation and drinking water quality in various seasons are noteworthy because of the large stretches of the Damodar River with visible differences in land use and effluent discharge, as mentioned in the study area section. Hoque et al.⁴⁷ noted that the middle reach is more polluted compared to the others due to the presence of elevated concentrations of industrial effluents. The findings of our study are corroborated by the research conducted by Gautam et al.⁴⁸ in assessing the appropriateness of Ghaghra River water quality for agricultural use in India. Similarly, Muthusamy et al.⁴⁹ identified various salts and cations (e.g. sodium, magnesium) as responsible for the pollution of irrigation and drinking water.

Water quality, especially river water quality, drastically changes from one river to another river or between the same river’s upper, middle and lower courses. In the Dan River basin, water quality rapidly changes in the middle and lower sections of the river because of the influence of the urban areas⁵⁰. Similarly, our study shows that the Damodar River basin faces similar problems i.e. middle and lower sections are more polluted by the pollution of coal mining areas and thermal power plants and domestic effluents, industries, domestic sewage, agricultural runoff etc. Moreover, domestic wastewater and cremation activities can increase ammonium ions in river water⁵¹. However, the study of Uddin et al.⁵² showed that the coastal area rivers are affected by ocean salinity, which was not suitable for drinking and irrigation purposes in all seasons. In this study, the concentrations of NO₃⁻, Ca²⁺, Cl⁻, Mg²⁺, Na⁺, SO₄²⁻ and TH remained within their permissible limit, but all sites of the river exceeded the permissible limit of DO, COD. Contrary to our findings, Tsirkunov et al.⁵³ found that the concentration of NO₃⁻ and SO₄²⁻ in the river has increased because of atmospheric deposition. Overall analysis of our study shows that the water quality in this study area is very poor in the monsoon season, but moderate to good condition in the pre- and post-monsoon season. Contrary to our findings, Sarkar and Islam⁸ showed that the Churni River has good quality in the monsoon season compared to pre and post-monsoon because of the dilution effect.

The findings from a questionnaire survey further corroborate the notion that river water is not suitable for drinking purposes. In a study conducted by Shrivastava et al.⁵⁴, a comparable outcome was observed regarding the Patalganga River, specifically its adequacy for drinking purposes and the preservation of human health. Similar health hazards are also reported for river basins affecting children and infants^55–57. The survey carried out using a detailed questionnaire (Table SQ1) found that about 92.5% of people living near the Damodar River use river water for their daily needs. Among them, 77.5% use river water for bathing, 15% for drinking, 32.5% for irrigation, 47.5% for washing clothes, and 12.5% for domestic needs. In comparison to other seasons, the quality of river water during the MON period is relatively less acceptable for consumption. During the MON season, a significant proportion of industrial wastewater effluent mixes with river water⁵⁸. When comparing the Mujher Mana station to other nearby stations, it becomes evident that the surface water is inappropriate for drinking purposes. The Damodar River watershed is characterised by the presence of coalfields and industries, resulting in the natural mixing of heavy chemical discharges with the river water. Respondents from Durgapur mentioned a canal known as the Durgapur Gas Canal, which discharges industrially polluted water into the Damodar River near the Durgapur barrage; when this canal releases polluted water, the river becomes contaminated. People from Raniganj claimed that contaminated water from Asansol mingled with the Damodar during the MON season. Some respondents stated that humans bathe animals in the river, refuse accumulates, burials take place on the river’s banks, and domestic wastewater drains into the river; all these contribute to pollution. Additionally, activities such as land burning, burial mounds, and washing animals in the river further contaminate its water. The local populace exhibits a lack of formal education and a limited understanding of the issues regarding river water contamination. The government assisted local residents by providing filtered water, but this water is also pumped from the river, filtered, and distributed through pipelines. Most people residing within the study region utilise filtered water for drinking. Based on the feedback from participants, it is worth mentioning that bathing in surface water during the MON period is generally considered beneficial. However, it is noted that younger individuals may experience a slight susceptibility to skin ailments, including itchiness. Nevertheless, the impact of this issue is deemed relatively minimal.

The present study bears several implications for regional planning and the development of policy frameworks aimed at mitigating water pollution and regulating the water quality of the Damodar River, which will help agricultural practices and better human health and hygiene. First, there is a need to develop more reservoirs equipped with filtration systems to treat industrial effluent⁵⁹ and prevent untreated water⁶⁰ from mixing directly with the river water. Second, the government must implement regulations and restrictions on industries and residents who engage in the disposal of pollutants into rivers or drains. Specifically, measures should be taken to address the contamination of river water by industrial effluents and the impact on animals that utilise these water sources for bathing. Additionally, the government should impose fines for any instances where burning grounds or burial mounds are established on riverbeds. Third, a comprehensive public awareness initiative should be carried out in each village situated near the river. The purpose of this program would be to educate residents about the detrimental effects of polluted river water and to inform them about the various ways they can contribute to mitigating this pollution. Fourth, following the coal extraction process, the land becomes barren and susceptible to erosion. Consequently, the eroded particles combine with river water, leading to pollution as they mix with harmful minerals. To mitigate this erosion and safeguard the river water, it is recommended to fill the resulting hollows with alternative materials. Additionally, afforestation efforts should be undertaken in the upper regions of these areas. Fifth, to minimise environmental impacts, it is advisable to reduce the application of fertilisers and pesticides in agricultural areas, particularly those located close to riverbeds. Finally, it is worth noting the presence of major industrial entities such as the Indian Iron and Steel Company (IISCO) of Burnpur and the Durgapur Steel Plant (DSP). The constant monitoring of these industries and their effluents is essential. These policy recommendations can be explored through a detailed diagnostic survey in the study region, employing a participatory approach involving all local stakeholders. The DRB urgently needs SDG 6 for clean water, sanitation, and hygiene, which these recommendations may help achieve. Moreover, the deteriorating environmental flow conditions with reduced biodiversity and aquatic life can be saved, which may promote SDG 14, i.e., life below water, for the sustainable development of the DRB.

This research has some limitations. To demonstrate the spatial fluctuations in river water quality, a comprehensive analysis of the whole river is essential for thorough hydrochemical knowledge; however, only a chosen segment is examined in this research. The absence of long-term temporal data constrains time series analysis. The third issue pertains to the inability to evaluate the impact of sewage and industrial effluent treatment on water quality. Future endeavours may pursue modelling drinking and irrigation water quality, which is not addressed in the current study. Machine Learning (ML) methodologies such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) for forecasting and synthesising results may represent a significant aspect of future research. A diagnostic survey, including historical, contemporary, and prospective hydrochemical analyses, may be crucial for future regional assessments in the DRB and other places.

Conclusions

The current study aims to evaluate the spatio-temporal dynamics of drinking and irrigation surface water quality. The results indicate that the Damodar River water was found unfit for drinking, especially during the MON season when industrial and farming effluents combine with the river water. The water quality is poor at Mujher Mana village, where Asansol-Durgapur urban-industrial effluents discharge into the river through the Tamla nallah. The WQI exhibited substantial spatio-temporal variations. However, the river water is appropriate for irrigation use, per irrigation indices including SAR, %Na, KR, PI, PS, and MAR.

Regarding spatial distribution, SAR, %Na, KR, and PS are high at Mujher Mana village; RSC is raised at Raniganj Ds; PI is high at Barakar; and MAR is significant at Durgapur Us. As per the ANOVA test findings, WQI portrays spatio-temporal variations, while the irrigation hazards indices show only variation in the spatial dimensions. The area is well-known for its industries and large population, whose effluents and domestic emissions add to the Damodar River’s pollution. The study emphasises the need to tackle water pollution to ensure its fitness for drinking and irrigation purposes and maintain the health and well-being of the local community. The study results may benefit diverse planners and stakeholders in developing sustainable river restoration plans and water resource management for agricultural and drinking purposes using the participatory approach involving the local communities. Improved wastewater treatment, strict enforcement of effluent standards, continuous monitoring, and community awareness programs may also be taken up for the betterment of water quality and the sustainable development of water resources. A detailed diagnostic survey is required for site-suitability analysis for the construction of the sewage treatment plants (STPs) in the DRB. A wider spatio-temporal sampling design would provide more valuable insights and deserves further investigation. The study findings may be advantageous for other comparable regions of the world to comprehend the river pollution and water quality depletions for the sustainable development of this critical region.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Aznarul Islam: Conceptualisation, Data curation, Formal analysis, Investigation, Methodology; Writing original draft; Md. Mofizul Hoque, Tahamina Nasrin: Methodology; Writing manuscript; Abu Reza Md. Towfiqul Islam, Subodh Chandra Pal: Original draft preparation, reviewing and editing; Mohd Sayeed Ul Hasan, Venkatesan Madha Suresh: Data curation, Methodology, Writing original draft; Edris Alam: Funding, review and editing; Zakir Md Hossain: Formal analysis; Manuscript writing, reviewing and editing.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.