Assessment of River Water Quality During a Religious Mass Bathing Festival at Sangam, Prayagraj

Introduction

Water is one of the most important natural resource which is not only essential for supporting life, but also plays a crucial role in agricultural, industrial and economic development. However, extensive human population growth, unplanned and uncontrolled industrial activities as well as heedless environmental intervention has led to deterioration of various surface water bodies. Rivers are often considered as a major source of fresh water for different human needs including drinking, bathing, washing and other irrigational and industrial activities ¹. Besides this, river water is also considered to have importance in various religions and traditions for attainment of peace through cleansing of mind and soul.

Usually, the towns located on both sides of a river release several untreated industrial, agricultural and urban runoffs into it ². Taking things aside, there are myriad of agents which not only alter the physicochemical nature of water, but also worsen the water quality, like discarding wastes directly in water, defecating along the banks, cremation of dead bodies and religious mass bathing. Thus, an array of parameters including pH, electrical conductance, total dissolved solids, alkalinity, hardness, dissolved oxygen, biochemical oxygen demand, chlorides and nitrates aids in estimating the extent of contamination ³. Several government agencies and other regulatory bodies such as the World Health Organization (WHO) have established thresholds for these parameters in order to monitor and prevent pollution of rivers and other freshwater resources. However, estimating the degree of contamination using these parameters, yields enormous complex data and the individual interpretation of these parameters often leads to uncertainty ⁴. Therefore, the water quality index (WQI) serves as an appropriate tool to effectively represent the bulk information of these parameters and to reduce it into a single dimensionless value that helps in complete understanding of water for its human consumption and other industrial and agricultural purposes ⁵. Thus, WQI helps in expressing the complete data in a lucid manner that can be easily communicated to policy makers as well as common citizens ⁶. Multivariate statistical techniques such as multiple linear regression (MLR) may also be used to minimize bulk water quality data and to define the essential parameters that explain the variation in WQI, without losing the original information ^7,8.

Prayagraj city, formerly known as Allahabad, in the state Uttar Pradesh of Northern India, is known for the confluence point of two sacred rivers Ganga and Yamuna, consequently making it a venue for various large-scale religious and cultural programs ⁹. Each year lakhs of devotees participate in different religious gatherings at Sangam, Prayagraj for mass bathing and offering prayers and flowers. Even though water resource contamination causes roughly 80% human diseases ¹, there has been a lack of research regarding the quality of river water during religious mass bathing festivals, especially at this particular site of India.

Present manuscript aims to find out how mass bathing affects the water quality at Sangam, Prayagraj during the Magh Mela festival and defines a mathematical expression for WQI using multiple linear regression.

Materials and Methods

Sampling and analysis

Prayagraj city (25.4358ºN 81.8463ºE) is among the ancient most cities of India with immense religious importance for Hindus, because of the confluence of two sacred rivers Ganga and Yamuna ⁹. The city is often known as “Tiratharaj” in the language Hindi, which means “king of all pilgrimages”, and is a major site for various religious and cultural programs ¹⁰. The city is well connected to other important cities Lucknow and Kanpur via the National Highway (NH-2). Figure 1 shows the map of Prayagraj city, indicating the location of the sampling site.

Figure 1: Map of Prayagraj showing the sampling site Click here to View figure

Samples were collected from the above-mentioned location (25º25′31.026″N 81º53′15.198″E) during the Magh Mela festival from 10 January to 9 February, 2020. Table 1 shows the list of religious mass baths held during this period. Collection of samples was performed during the evenings on the mentioned mass bathing dates and a day before these bathing dates, using sterile polyethylene bottles from a depth of 20 cm below the surface of water ². The collected samples were immediately stored in portable ice-box and were refrigerated in laboratory at temperature below 4ºC until analysis ¹¹. The physicochemical parameters were analyzed in agreement with the standard protocols given by American Public Health Association ¹².

Table 1: List of religious mass baths during Kumb Mela festival.

Date	Occassion
10 January 2020	Paush Purnima
15 January 2020	Makar Sankranti
24 January 2020	Mauni Amavasya
30 January 2020	Basant Panchami
09 February 2020	Maghi Purnima

 

Evaluation of Water Quality Index (WQI)

WQI assists as a valuable tool to measure the overall contamination status of water and its adequacy for specific human needs. The approach is based on normalization of bulk water quality data and transforming it into a single value, which can represent the whole data in more simplified manner ^13,14. This technique was initially developed by Horton in 1965 ¹⁵ and was further modified by Brown in 1970 ¹⁶. In recent decades, a number of methods have been developed for evaluating WQI ⁴. Several recent studies have used the weighted arithmetic method for determining WQI for different Indian rivers ^{1, 5, 14, 17, 18}.

The present manuscript employs the weighted arithmetic procedure for estimation of WQI at the sampling site using ten most common parameters including pH, electrical conductance (EC), total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), chlorides (Cl) and nitrates (NO₃), making use of following equation:

Where Q_i and W_i are the quality rating scale and relative unit weight of each water quality parameter respectively.

where V_i is the experimental value of the ith parameter, V₀ is its ideal value in case of pure water [V₀ = 0 for every parameter, except for pH (7.0) and DO (14.6)] and V_s is the recommended standard permissible value for this parameter.

The unit weight (W_i) is evaluated by the formula

where k is the proportionality constant, which may be calculated as

Statistical Analysis

The obtained data was statistically analyzed with help of IBM SPSS (version 20.0) and Microsoft Excel (version 2010). Statistically significant difference between the data sets was analyzed using the parametric Student’s t-test as well as the non-parametric Mann-Whitney U test. Multiple linear regression (MLR) analysis was used to investigate relationship between the dependent variable and the predictor variables. Following equation was used for the MLR models:

Y= B₀ + B₁X₁ + B₂X₂ + B₃X₃ + ^… + B_nX_n

where Y is the independent variable, B₀ is the intercept on y axis and B₁, B₂, B₃,…., B_n are the coefficients for the predictor variables X₁, X_2, X₃,….., X_n respectively. The accuracy of the models was measured using the regression coefficient (R²).

The multi-collinearity criteria was observed with help of correlation coefficients between the predictor variables, which were lower than 0.8 ⁸. The auto-correlation in the data for MLR models was investigated by Durbin-Watson test. Any value for Durbin-Watson static near 2.0 and within the range 1.5 – 2.5 was assumed to indicate no auto-correlation.

Results and Discussion

Measurement of physico-chemical parameters

Table 2 illustrates the overall descriptive statistics of various parameters for river water at Sangam, during the Magh Mela festival. The average measurements of pH, TDS, TA, TH, COD, BOD, DO, Cl^¯, NO₃^¯ and EC were 7.7, 456.7, 185.9, 160.9, 10.9, 4.1, 7.6, 34.0, 0.2 mg/l and 231.7 µS cm^-1 respectively.

Table 2: Overall descriptive statistics of various physico-chemical parameters

Parameter	Mean ± SD (n = 10)	Range	Desirable value 19
pH	7.7 ± 0.46	6.9 – 8.3	6.5 – 8.5
Electrical Conductance EC (µS cm-1)	231.7 ± 25.10	196.4 – 262.0	250-750
Total dissolved solids TDS	456.7 ± 92.76	328.2 – 573.1	500
Total alkalinity TA as CaCO3	185.9 ± 96.19	54.7 – 305.5	200
Total hardness TH as CaCO3	160.9 ± 29.75	109.0 – 210.3	200
Chemical oxygen demand COD	10.9 ± 3.15	6.2 – 16.8	–
Biochemical oxygen demand BOD	4.1 ± 2.5	0.9 – 8.6	2
Dissolved oxygen DO	7.6 ± 2.32	3.9 – 11.7	6
Chloride	34.0 ± 12.75	17.6 – 49.9	250
Nitrate	0.2 ± 0.18	0.0 – 0.5	45

Except EC and pH all values are in mg/l

Comparison of these values with the Bureau of Indian Standards (BIS) limits, reveals that BOD and DO levels were relatively higher while the chloride and nitrate concentrations were substantially lower than the recommended permissible limits. Higher BOD levels suggest accumulation of excess microbes due to release of human excretory matter, use of soaps, detergents and offerings of flowers and incense ¹⁰. The other parameters such as EC, TDS, TA and TH were well within the recommended range. Similarly the existence of excess phytoplanktons might explain the elevated DO levels ²⁰.

Throughout the sampling duration, the pH readings indicated a slightly alkaline nature due presence of carbonates, bicarbonates and hydroxyl ions on account of erosion of limestone bedrock and certain other alkaline contaminants from domestic effluents as well as due to mass bathing activity during the festival. Nearly similar range of values for pH and DO were earlier reported at different sites of Prayagraj ¹.

Correlation of parameters

The mutual relationship between the parameters was investigated with help of Pearson’s correlation matrix which has been depicted in Table 3. A glance at this table reveals that the parameters pH and DO exhibit negative correlation with rest of the parameters. The study also demonstrated a significantly strong correlation (r = 0.841, p < 0.01) between pH and DO indicating enhanced dissolution of oxygen at higher pH on account of unpropitious bacterial growth.

Table 3: Correlation matrix of physico-chemical parameters.

	pH	EC	TDS	TA	TH	COD	BOD	DO	Cl	NO3
pH	1
EC	-.514	1
TDS	-.820**	.443	1
TA	-.734**	.264	.937**	1
TH	-.428	-.023	.447	.505	1
COD	-.712**	.350	.636*	.634*	.742*	1
BOD	-.878**	.674*	.866**	.726*	.455	.703**	1
DO	.841**	-.763*	-.831**	-.682*	-.411	-.718**	-.745**	1
Cl	-.827**	.384	.989**	.526	.486	.891*	.880**	-.825**	1
NO3	-.809**	.813**	.760*	.632	.358	.863*	.939**	-.903**	.746*	1

^* Correlation is significant at 0.05 level (2-tailed)

^** Correlation is significant at 0.01 level (2-tailed)

Similarly, it was noticed that TDS exhibited significantly higher positive correlation with TA and Cl, suggesting major contribution of hydroxide, bicarbonate, carbonate and also chloride ions towards the overall TDS of the surface water. Among other parameters, the variation in TH values (160.9 ± 29.75) is comparatively low and thus TH exhibits relatively weak correlation with other parameters.

Assessment of river water quality using WQI

The river water quality at Sangam was determined in terms of weighted arithmetic water quality index (WQI) using 10 selected parameters viz. pH, EC, TDS, TA, TH, COD, BOD, DO, Cl^¯ and NO₃^¯. The evaluation of WQI involves transformation of each water quality parameter having different dimension to a common scale by assigning a unit relative weight W_i ⁵. The standard permissible values (V_s) and relative unit weights (W­_i) of various parameters used for calculation of WQI have been illustrated in Table 4.

Table 4: Standard permissible values (Vs) and relative unit weights (Wi) of various parameters

Parameters	Permissible Value (Vs)*	Relative unit weight (Wi)
pH	8.5	0.195516
EC	750	0.002216
TDS	500	0.003324
TA	120	0.013849
TH	300	0.00554
COD	10	0.166188
BOD	5	0.332377
DO	7	0.237412
Cl	250	0.006648
NO3	45	0.036931

 

The overall status of water quality was decided on basis of WQI values viz, excellent (0 – 25), good (26 – 50), poor (51 – 75), very poor (76 – 100) and not suitable for drinking (> 100) respectively ^{1, 7}. The overall mean value of WQI observed during the Magh Mela festival was 80.07, suggesting very poor quality of river water. Table 5 shows a comparison of weighted arithmetic WQI values obtained in present study with those reported in other studies for different Indian rivers. The table reveals that the WQI values obtained in present study lies well within the range of values reported in other studies.

Table 5: Comparison of WQI values with other studies in India.

S. No.	Region		mean WQI* (min – max)	Reference
1	River Ganga, Prayagraj, Uttar Pradesh		103.72 (86.20 – 157.69)	1
2	River Sabarmati, Gujrat		74.49 (19.30 – 98.62)	17
3	River Ganga, Haridwar, Uttrakhand		64.83 (56.28 – 73.05)	6
4	River Kolong, Assam		72.23 (49.25 – 169.20)	5
5	River Narmada, Madhya Pradesh		45.74  (9.90 – 260.20)	22
6	River Gomti, Lucknow, Uttar Pradesh		69.50 (42.90 – 101.90)	14
7	River Tungabhadra, Karnataka		79.73 (38.60 – 156.70)	23
8	River Sabarmati, Gujrat		282.52 (139.87 – 647.01)	24
9		Sangam, Prayagraj, Uttar Pradesh	80.07 (44.37 – 121.75)	Present Study

^*reported values are weighted arithmetic water quality index

Effect of mass bathing

Although mass bathing is considered a sacred tradition infused with several religious beliefs, however due to unintentional negligence of devotees, it poses a substantial threat to the water quality. Defecation, washing of clothes, use of detergent, soaps, fluoridated toothpastes and offering of milk, oil, flowers and incense have mainly degrade water during mass baths ²⁵. In order to investigate the immediate effect of mass bathing on water quality, the measurement of water quality parameters and the evaluation of WQI were performed on respective bath dates as well as the non-bath dates i.e., one day preceding the corresponding bath dates.

Table 6 illustrates the effect of mass bathing on various water quality parameters. It is evident from the table that the mass bathing significantly altered the parameters pH, TDS, TA, BOD, DO and Cl¯ (p < 0.05). Due to excess accumulation of bioorganic matter and microbes, mass bathing caused significant reductions in the values of pH and DO ¹⁰, while the parameters TDS, TA, BOD and Cl¯ increased significantly (p < 0.05). Thus the overall quality of river water was affected, consequently, the average WQI during the bath dates (95.65 ± 19.84) was significantly higher (p < 0.05) than that on corresponding non-bath dates (64.49 ± 14.65).

Table 6: Effect of mass bathing activity on water quality at Sangam

Parameter	Before bath	On bath day	p-value
			t-test	Mann-Whitney
	mean ± SD	mean ± SD		U test
pH	8.1*± 0.21	7.4 ± 0.35	0.008	0.012
EC (µS cm-1)	228.4 ± 27.30	235.0 ± 25.4	0.704	0.754
TDS	373.3 ± 33.1	540.1* ± 29.69	0	0.009
TA	99.5 ± 36.74	272.4* ± 27.92	0	0.009
TH	146.9 ± 26.00	174.9 ± 28.72	0.145	0.117
COD	9.1 ± 2.25	12.7 ± 3.07	0.073	0.094
BOD	2.4 ± 1.23	5.9* ± 2.33	0.025	0.028
DO	9.1* ± 2.04	6.2 ± 1.67	0.042	0.047
Cl	22.4 ± 3.66	45.6* ± 4.14	0	0.009
NO3	0.1 ± 0.12	0.3 ± 0.20	0.101	0.082
WQI	64.49 ± 14.65	95.65* ± 19.84	0.024	0.028

Except EC and pH all values are in mg/l

^*Statistically significant difference (p < 0.05)

The pattern of variation of WQI on successive mass baths has been depicted in Figure 2.

Figure 2: Variation of WQI with successive mass baths Click here to View figure

The figure reveals that the WQI values increased gradually and the maximum value was attained on third mass bath i.e., Mauni Amavasya on 24 January 2020. Thereafter a steady decrease was observed in the WQI values. The reason for observing highest WQI on Mauni Amavasya, may be attributed to the fact that this bath date is especially considered auspicious and holy, thus attracting a huge crowd of devotees ²⁵.

Multiple linear regression (MLR) analysis

The present manuscript utilizes MLR in order to establish a mathematical relation to express the dependence of WQI on different water quality parameters by assuming a linear relationship between the response and the predictor variables. The relevancy of the MLR model was examined by the coefficient of determination (R² value). Any cognizable autocorrelation in data was detected using the Durbin – Watson test. In order to scrutinize the most influential parameters, the variation of WQI was observed with respect to each parameter with help of scatterplots and corresponding R² values. Figure 3 displays the plots of WQI as a function of parameters COD, BOD and DO. The R² values obtained for these plots were > 0.8 suggesting the importance of these variables in deciding the overall WQI of the river water at Sangam.

Figure 3: WQI plotted as a function of COD, BOD and DO Click here to View figure

Moreover, as evident from Table 3 these variables are mutually not too highly correlated (r < 0.8). Therefore, an MLR model was framed using these variables as predictors. The details of the model including the R² value, Durbin – Watson statistic, the ANOVA results and the values of coefficients have been illustrated in Table 7.

Table 7: Details of MLR model

MODEL SUMMARY
Predictors	R	R Square	Adjusted R Square	Std. Error	Durbin Watson
COD, BOD, DO	.999	.999	.995	.61141	2.336
ANOVA RESULTS
	Sum of Squares	df	Mean Square	F	Sig.
Regression	4858.614	3	1619.538	4332.412	.000
Residual	2.243	6	.374
Total	4860.857	9
COEFFICIENTS
	Unstandardized Coefficients		Standardized Coefficients	t	Sig.
	B	Std. Error	Beta
(Constant)	63.903	3.426		18.655	.000
COD	1.509	.106	.205	14.230	.000
BOD	5.121	.247	.560	20.760	.000
DO	-2.812	.279	-.281	-10.097	.000

 

As evident from the table, the R² value and the Adjusted R² value for the model were 0.999 and 0.995 respectively which clearly points out that the model accurately predicted the WQI values. The Durbin – Watson statistic for the model was 2.336 i.e., well within the range 1.5 – 2.5 and close to the value 2.0, suggesting absence of any significant autocorrelation in the data. Moreover, the predictor variables used in the regression model exhibited high statistical significance (p < 0.01). Figure 4 (a) shows the normal probability plot obtained for the model revealing a comparison between model’s predicted WQI values and the observed WQI values. Examination of this plot indicated that the model clearly followed the experimental data. The predicted values were either slightly higher or slightly low, but the overall difference remained very small.

Figure 4: (a). Normal probability plot, (b). Scatterplot of predicted values of WQI versus observed values. Click here to View figure

Similarly, the R² value 0.9995 for the plot between predicted values and observed values of WQI in Figure 4 (b) further confirms this fact. Hence, the variation in WQI values was significantly explained by the predictor variables COD, BOD, DO and the water quality index may be expressed by following equation (R² = 0.999):

WQI = 63.903 + 1.509COD + 5.121 BOD – 2.812 DO

Conclusion

Sangam, the confluence point of rivers Ganga and Yamuna, is one of the India’s most important holy sites. However, on account of unintentional negligence and inadequate sanitization, the gathering of huge crowds at this site, results in degradation of the water quality. Samples were brought from Sangam during the Magh Mela festival in the year 2020. WQI score was employed to measure the status of these samples. Among the different parameters, BOD and DO exceeded the prescribed Indian limits. The parameters pH and DO were highly correlated mutually as well as with other parameters, while TH exhibited relatively poor correlation with the other parameters. As a consequence of mass bathing, the parameters pH and DO decreased whereas TDS, TA, BOD and Cl¯ increased significantly. The overall mean WQI was 80.07 reflecting a very poor status of the water. The average WQI on bath days was significantly higher than that for non-bath days and the highest value of WQI was observed on the auspicious bath day, Mauni Amavasya, due to huge gathering of devotees. According to MLR analysis, the variation of WQI throughout the bath festival may be described quantitatively using the parameters COD, DO and BOD. Certain measures by the local authorities, such as construction of appropriate sanitation facilities, waste management and general awareness program regarding importance of sanitation can substantially improve the river water status.

Acknowledgement

The authors declare no conflict of interest associated with this manuscript. The present study did not receive any financial support. Authors gratefully acknowledge Vice-Chancellor, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, UP, India, for providing necessary facilities. Authors are also thankful to Mr. Manish Kumar Pandey, Analytical Division, Biotech Park, Lucknow, UP, India, for providing technical support during the analysis of samples.

Conflict of Interest

There is no conflict of interest.

Funding Sources

There is no funding source.

References

1. Sharma, P.; Meher, P.K.; Kumar, A.; Gautam, Y.P.; Mishra, K.P. Sustainability of Water Quality and Ecology, 2014, 3, 67-76.
   CrossRef
2. Kamboj, N.; Kamboj, V. Water Science,2019, 33(1), 65-74.
   CrossRef
3. Rahmanian, N.; Ali, S.H.B.; Homayoonfard, M.; Ali, N.J.; Rehan, M.; Sadef, Y.; Nizami, A.S. Journal of Chemistry, 2015, 2015, https://doi.org/10.1155/2015/716125.
   CrossRef
4. Kadam, A.K.; Wagh, V.M.; Muley, A.A.; Umrikar, B.N.; Sankhua, R.N. Modeling Earth Systems and Environment, 2019, 5(3), 951-962.
   CrossRef
5. Bora, M.; Goswami, D.C. Applied Water Science, 2017, 7(6), 3125-3135.
   CrossRef
6. Bhutiani, R.; Khanna, D.R.; Kulkarni, D.B.; Ruhela, M. Applied Water Science, 2016, 6(2),107-113.
   CrossRef
7. Ewaid, S.H.; Abed, S.A. The Egyptian Journal of Aquatic Research, 2017, 43(2),117-122.
   CrossRef
8. Ewaid, S.H.; Abed, S.A.; Kadhum, S.A. Environmental Technology & Innovation, 2018, 11, 390-398.
   CrossRef
9. Dwivedi, S.; Chauhan, P. S.; Mishra, S.; Kumar, A.; Singh, P. K.; Kamthan, M.; Nautiyal, C. S. Environmental Monitoring and Assessment, 2020, 192(4), 1-15.
   CrossRef
10. Srivastava, R.K.; Sinha, A.K.; Pande, D.P.; Singh, K.P.; Chandra, H. Environmental Toxicology and Water Quality: An International Journal, 1996, 11(1),1-5.
    CrossRef
11. APHA (American Public Health Association), Standard Methods for the Examination of Water and Wastewater, 2012, 27^th Ed. Washington, DC.
12. Tyagi, S.; Sharma, B.; Singh, P.; Dobhal, R. American Journal of Water Resources, 2013, 1(3), 34-38.
    CrossRef
13. Goel, P.; Saxena, A.; Singh, D.S.; Verma, D. Curr. Sci., 2018, 114, 650-654.
    CrossRef
14. Horton, R.K. Journal of Water Pollution Control Federation, 1965, 37(3), 300-306.
15. Brown, R.M.; McClelland, N.I.; Deininger, R.A.; Tozer, R.G. Water and Sewage Works, 1970, 117(10).
16. Shah, K.A.; Joshi, G.S. Applied Water Science, 2017, 7(3), 1349-1358.
    CrossRef
17. Dutta, S.; Dwivedi, A.; Kumar, M.S. Environmental Monitoring and Assessment, 2018, 190(12), 718-734.
    CrossRef
18. BIS (Bureau of Indian Standards) 10500, Specification for Drinking Water. Indian Standards Institution, New Delhi, 2012,pp. 1–5.
19. Fouzia, I.; Amir, K. World Journal of Fish and Marine Sciences, 2013, 5(3), 322-334.
20. WHO, Guidelines for Drinking-Water Quality. World Health Organization, Geneva, Switzerland, 2011, 216, 303-304.
21. Gupta, N.; Pandey, P.; Hussain, J. Water Science, 2017, 31(1), 11-23.
    CrossRef
22. Ranjith, S.; Shivapur, A.V.; Kumar, P.S.K.; Hiremath, C.G.; Dhungana, S. International Journal of Innovative Technology and Exploring Engineering, 2019, 8(9), 247-253.
23. Khatri, N.; Tyagi, S.; Rawtani, D.; Tharmavaram, M. Sustainable Water Resources Management, 2020, 6(6), 1-11.
    CrossRef
24. Shukla, S.; Gupta, S. Journal of Research in Engineering and Technology, 2013, 4(10), 313-319.

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