Current World Environment

Introduction

Water, the most precious resource of the planet earth assumes vital importance for sustainable environmental health in the present century because of its persistent exploitation across the globe. The deterioration of groundwater and surface water quality is alarmingly high.¹ Typically water gets contaminated by various factors such as the population explosion, rapid urbanization and agricultural development, which are continuously contributing to the degradation of the water sources. This environmental resource degradation has been noticed for increased disease morbidity and mortality in various regions (Jain et al., 2007 ² and Adimalla, 2018 ³). The goal seven of Millennium Development Goals set by United Nations, to provide access of safe drinking water to half the population across the world has been met, much before the scheduled time of 2015.⁴ Interestingly, about 663 million people across the world still lack access to quality drinking water.⁵ The Sustainable Development Goal six, Clean water and Sanitation, aims for providing access to safe and affordable drinking water for all by the year 2030.⁶

Contaminated water sources lead to debilitating water-borne diseases afflicting human health. The polluted waters transmit diseases such as cholera, shigellosis, viral hepatitis, campylobacteriosis, various skin diseases, gall bladder and kidney stones, other gastrointestinal diseases and different types of cancers (Smith, 2018).⁷ As per the World Health Organization (WHO) 2017 estimates, about 1.1 billion people across the globe are drinking unsafe water. Moreover, a vast majority of diarrheal diseases in the world (88%) are attributable to the unsafe water, sanitation and hygiene.⁸ Approximately 3.1% of annual deaths (1.7 million) and 3.7% of the annual health burden i.e. disability-adjusted life years [DALYs] worldwide (54.2 million) are attributable to unsafe water, sanitation and hygiene (WHO, 2015).⁹ Provision of safe water and sanitation services are the topmost priority in many parts of the world and have been the implicit part of policy matter of almost all the countries. However, there still exists a huge challenge for the developing countries especially the lower income group ones, to overcome the hurdles in this provision.

Water Quality Index (WQI) as proposed by the National Sanitation Foundation of America is a composite indicator value of quality of water (Brown et al., 1970).¹⁰ Though WQI is extensively used in most of the countries with slight modification (Hallock, 2002¹¹; Veerbhadram, 2005¹²), its use in India is rare. Solan district of Himachal Pradesh has a mountainous topography in the northwestern Himalayan region, is urbanizing/ industrializing at a fast rate. The high disease burden of Solan has necessitated the monitoring and the surveillance of the water sources of the area. Henceforth, the present research was conducted to assess the seasonal physical-chemical parameters of water as well as to estimate the Water Quality Index (WQI) and also the quality of the water intended for drinking purposes.

Material and Methods

The physical-chemical assessment of drinking water sources falling in all five health blocks namely Arki, Dharampur, Chandi, Nalagarh and Syri was undertaken as per the following methodology:

A complete listing of Public Health Institutes (PHIs) falling under these blocks was made from the records of the office of the Chief Medical Officer, Department of Health and Family Welfare, Solan, HP. Thereafter the incidence of diarrhoea for each PHI was tabulated for five years (2012-2016). In order to select the PHIs further, the commonly used standard stratification method of construction of strata outlined by Singh and Sukhatme (1969)¹³ was employed. The frequency of diarrhoeal incidence of each PHI was tabulated. Thereafter, the statistical cumulative cube root method i.e. Cum.3√f, was employed for constructing four strata from the frequency distribution table i.e low, moderate, high and very high. Thereafter the total 49 PHIs were accordingly allocated with optimum stratification under the class intervals of low (0- 602), moderate (603- 1202), high (1203- 2402) and very high (2403 and above) incidence areas strata. The approximation statistics were employed and accordingly, 1, 6, 11 and 7 PHIs were found under the strata low, moderate, high and very high category respectively. Out of these categories, a single PHI of low and seven PHIs of very high incidence strata were selected as the regions for the assessment of water quality of the drinking water sources. Accordingly, Primary Health Centre (PHC) Kurgal was selected under the low incidence category and Civil Hospital (CH) Arki, Community Health Centre (CHC) Kunihar, PHC Baddi, CH Kandaghat, Employee’s State Insurance (ESI) Hospital Parwanoo, CHC Nalagarh and Regional Hospital (RH) Solan were selected for the study under the high incidence category. Thereafter, a cross-sectional Knowledge, Attitude and Practice survey (KAP) was conducted in these selected PHIs.

The Sample Size for the KAP Survey

A total of 180 patients (25 per PHI) admitted, were selected randomly for the KAP survey. The sample size was calculated by using the following standard Formulae for cross-sectional surveys

Sample size = Z_1-α/2 ² p (1-p)/d² = 163

• Adding 10% non response, sample size = 180
• Per site sample size= 180/8 = 23, rounded off to 25

here

• Z_{1-α/2 =} Standard normal variate {at 5% type 1 error (P< 0.05)}. and is 1.96
• p is an expected proportion in population (12.1 %, the probability of diarrhoea in Solan as per the NFHS- 4 report)
• d is absolute error or precision (5 %).

An interviewer-administered semi-structured pilot-tested questionnaire was used for the patients admitted in the selected PHIs. This was utilized for identifying all the water sources of the study area. Thereafter on the basis of Selective Random Sampling technique, a representative number of water bodies i.e. 80 sources (by taking ten water sources from each of the eight sites), were selected.

Table 1: Drinking water standards recommending Agencies and unit weights. (All values except pH and Electrical conductivity are in mg/l).

Sl. No	Parameters	Standards	Recommending agency	Unit weight
1	pH	6.5- 8.5	ICMR, BIS	0.219
2	Electrical conductivity	300	ICMR	0.371
3	Total Dissolved Solids	500	ICMR, BIS	0.0037
4	Biological oxygen demand	5	ICMR	0.3723
5	Dissolved oxygen	5	ICMR, BIS	0.3723
6	Calcium	75	ICMR, BIS	0.025
7	Magnesium	30	ICMR, BIS	0.061
8	Chloride	250	ICMR	0.0074
9	Nitrate	45	ICMR, BIS	0.0412

Water Sampling

The grab samples of the surface and groundwater of the selected sources falling in the very high and low incidence area of water-borne diseases were collected in acid-washed one-litre plastic bottles by following the standard procedure (Rice, 2017).¹⁴ In the case of dug wells, the samples were collected by lowering the bottle at depth of about one foot below the water surface. The temperature was recorded in situ with the help of a mercury thermometer. The collected samples were transported to the laboratory after appropriate labelling and examined for pH, EC, TDS and BOD immediately. The remaining samples were stored in a refrigerator in the laboratory at 4ËšC for subsequent analysis. The pH of the water was determined microprocessor-based pH meter (Model 510 of EIA make). Electrical conductivity (expressed in µS cm^-1) and total dissolved solids (mg l^-1) were determined by using microprocessor-based conductivity/ TDS meter (Model- 1601 of EIA make). BOD was determined by using the BOD system Oxi-direct (Aqualytic make). Calcium, magnesium, nitrate were determined photometrically with Spectroquant Pharo 300 (Merk make) and expressed in mg l^-1. Chloride was estimated (mg l^-1) by US standard method (EPA 325.1). The method was analogous to APHA-4500-Cl-E. Heavy metals viz. the trace elements like As, Cd, Cr, Pb, Zn and Hg were determined (mg l^-1) by using Inductively Coupled Plasma model 6300 duo of Thermo make. The physical and chemical parameters of potable water were discussed by comparing with CPCB (Centre Pollution Control Board), WHO, BIS and ICMR standards. Chemical Oxygen Demand (COD), expressed in mg l^-1, was estimated by the method analogous to EPA 410.4 US Standard method 5220 D, and ISO 15705.

Water Quality Index Calculations

The weighted arithmetic index method (Brown et al., 1970)¹⁰ has been used for calculation of WQI of the water bodies. The quality rating or sub-index (q_n) calculations were done by employing the following formulae:

q_n = 100[V_n – V_iv ] / [S _n - V_iv ]

Here, there are n water quality parameters and quality rating or sub-index (q_n) corresponding to the n^th parameter is a number reflecting the relative value of this parameter in the polluted water with respect to its standard permissible value. The expressions mentioned in the formulae denote the following:

q_n = Quality rating for the n^th Water quality parameter

V_n = Estimated value of the n^th parameter at a given sampling station

S_n = Standard permissible value of the n^th parameter

V_iv = Ideal value of n^th parameter in pure water (0 for all other parameters except the parameter pH and Dissolved Oxygen which have ideal values of 7.0 and 14.6 mg l^-1 respectively).

Unit weight was calculated by a value inversely proportional to the recommended standard value S_n of the corresponding parameter.

Wn = K/ S_n

Wn = unit weight for the n^th parameters

S_n = Standard value for the n^th parameters

K = Constant for proportionality

The overall Water Quality Index was calculated by aggregating the quality rating with the unit weight linearly.

WQI = Σ q_n Wn / Σ Wn

The rating of water quality using this method is given in Table 2 (Chatterji and Raziuddin, 2002).¹⁵

Table 2: Water Quality Rating as per Weight Arithmetic Water Quality Index Method

WQI value	Rating of Water Quality	Grading
0 - 25	Excellent water quality	A
26 - 50	Good water quality	B
51- 75	Poor water quality	C
76 - 100	Very poor water quality	D
> 100	Unsuitable for drinking purpose	E

Statistical Analysis

The data emanating from the experiment conducted was subjected to statistical analysis through Factorial Randomized Design (RBD) and the significance of each treatment was calculated (Cochran and Cox, 1964).¹⁶ 

Results and Discussion

The perusal of data illustrated in Table 3 shows the mean seasonal variation in the physical parameters of water across the eight selected regions. TDS varied significantly with the seasons, the regions and with the interaction of seasons with the regions of the water sources. Whereas EC varied significantly with both the seasons and the regions, pH and temperature were affected only by the regions of the water sources. pH: The pH of water sources ranged from 6.92 to 7.43, which was however in the normal range and also within the permissible limits prescribed by BIS. The higher values recorded at Arki may be ascribed to low anthropogenic activities of the region in the terms of industrialization. The comparatively lower values observed for Kunihar, Baddi, Kandaghat, Nalagarh and Solan regions may be explained by the dense population and the presence of industries in these regions releasing more of CO₂ into the water and thus decreasing the pH, similarly reported by Kashyap et al., (2015).¹⁷ Temperature: The highest values of temperature at high disease burden region Nalagarh may be ascribed to the relatively plain terrain and lesser vegetation of the region. Moreover, a larger proportion of pollutants entering in the form of industrial effluents, organic matter from domestic, agricultural waste and sewage may also be increasing the water temperatures.

Table 3: Seasonal variation in physical parameters of drinking water sources in Solan 2017.

Parameter		pH			Temperature (ËšC)			Electrical Conductivity  (µS cm -1)			Total dissolved solids (mg dl -1)
Disease burden area Season		M*	PM**	Mean	M	PM	Mean	M	PM	Mean	M	PM	Mean
High Incidence	Arki	7.48	7.39	7.43	28.83	28.83	28.83	233.95	132.06	183	63.21	31.55	47.38
	Baddi	7.18	7.21	7.19	28.77	28.77	28.77	254.17	198.6	226.38	97.56	61.08	79.32
	Kandaghat	7.3	7.09	7.19	28.13	28.35	28.24	309.26	146.07	227.66	34.04	15.83	24.93
	Kunihar	7.05	7.18	7.11	27.89	28.42	28.15	378.2	259.5	318.85	84.03	43.08	63.55
	Nalagarh	6.93	7.15	7.04	29.09	29.4	29.24	414.3	210.9	312.6	86.59	41.16	63.87
	Parwanoo	7.05	6.8	6.92	29.4	27.84	28.62	268.76	199.66	234.21	130.71	31.32	81.01
	Solan	7.02	6.83	6.93	27.96	28.59	28.27	542.5	286.8	414.65	0.67	3.81	2.24
Low Incidence	Kurgal	7.37	7.23	7.3	27.84	27.88	27.86	189.01	113.8	151.4	72.6	35.5	54.05
Mean		7.17	7.11	7.14	28.48	28.51	28.46	323.76	193.42	258.59	71.17	32.92	52.04
C.D. (0.05)		0.22			0.84			77.19			23.75
Region Season		NS			NS			38.59			11.87
Reagon X Season		NS			NS			NS			33.6

* Monsoon, **Post monsoon

The lowest values of temperature noticed at Kurgal, the low disease burden region may be ascribed to the presence of hilly terrain enriched with vegetation, the findings in line with a study by Sanalkumar et al., (2014).¹⁸ Electrical Conductivity (EC): Solan region recorded the highest EC values (286.80 µS cm^-1), probably to the developmental activities like construction of roads and buildings, improper disposal of garbage and sewage leading to high amounts of dissolved inorganic substances in ionized form, as also reported by Kerketta et al., (2013).¹⁹ The lowest values recorded at Kurgal, the low disease burden region may be ascribed to the relatively low temperatures prevailing in this region, as also reported by Oyem et al., (2014).²⁰ The EC recorded during monsoon (323.76 µS cm^-1) exceeded the ICMR standard value of 300 µS cm^-1and was significantly higher than the post-monsoon status (193.42 µS cm^-1), due to more runoff causing greater dissolution of ionic pollutants into the water in the monsoon months.

Total Dissolved Solids

Parwanoo, a high disease burden region, recorded the highest TDS (81.01 mg l^-1), along with statistically at par high disease burden regions of Baddi, Nalagarh and Kunihar. Higher TDS may be ascribed to the presence of lots of industries which potentially pollute water sources through their solid and liquid wastes, as also noted in another study by Rana et al. (2016).²¹ Monsoon months also exerted a significant influence on the TDS of water sources (71.17 mg l^-1). However, the TDS values observed were well below the BIS prescribed limits of 500 mg l^-1.

The data depicted in Table 4 shows the seasonal and area wise variation detected in the chemical parameters. Parameters such as COD, BOD and Turbidity varied significantly across the regions and with the seasons. Moreover, the interaction of the disease burden regions with the seasons of the year also exerted significant influence in the variations in DO and COD.

Table 4: Seasonal variation in chemical parameters of drinking water sources in Solan 2017.

Parameter		Dissolved Oxygen  (mg dl -1)			COD (mg dl -1)			BOD (mg dl -1)			Turbidity  (mg dl -1)
Disease burden area Season		M*	PM**	Mean	M	PM	Mean	M	PM	Mean	M	PM	Mean
High Incidence	Arki	8.04	8.18	8.11	225.7	78.6	152.15	15.1	9.4	12.25	6.4	2.5	4.45
	Baddi	7.68	7.85	7.76	192.8	39.8	116.3	21.4	10.7	16.05	2.6	1.4	2
	Kandaghat	7.46	7.41	7.43	148	167	157.5	25.4	12	18.7	6.3	1.9	4.1
	Kunihar	8.21	8.2	8.2	81	102.5	91.75	18.4	9.5	13.95	8.2	1.9	5.05
	Nalagarh	7.71	7.56	7.64	85.3	66.2	75.75	32	14.5	23.25	2.2	1.5	1.85
	Parwanoo	7.78	7.61	7.7	106.4	91.9	99.15	19	8.9	13.95	5.5	1.4	3.45
	Solan	9.09	8.03	8.56	133.8	100.1	116.95	21.9	11.9	16.9	4.1	1.5	2.8
Low Incidence	Kurgal	7.7	8.33	8.01	499.7	174	336.85	20.6	10.3	15.45	3.3	1.3	2.3
Mean		7.95	7.96	7.95	184.08	102.51	143.29	21.72	10.9	16.31	4.82	1.67	3.24
C.D. (0.05)		0.43			83.49			5.71			1.59
Region Season		NS			41.74			2.86			0.78
Reagon X Season		0.61			118.07			NS			2.25

* Monsoon, **Post monsoon

Dissolved Oxygen

The highest values of DO at Solan and statistically at par region Kunihar may be ascribed to the greater dissolution of oxygen in the water, attributed to the low temperatures prevailing in this region. The lowest values of DO noticed at Kandaghat region may be ascribed to the high altitude of the region, a similar finding reported by Tareq et al., (2013).²² However, the values observed were well above the BIS desirable limits of 5 mg l^-1. Irrespective of the disease burden regions, highest DO was 9.09 mg l^-1 noticed at Solan in monsoon and the lowest of 7.41 mg l^-1 at Kandaghat during post-monsoon months. However, the values observed were well above the BIS desirable limits of 5 mg l^-1. COD: The highest values of COD at Kandaghat (157.50 mg l^-1) may be due to the fact that most of the water sources being in the form of open streams have more of organic matter from the point and non-point sources getting mixed with them due to runoff. On the contrary, the majority of the water sources of Nalagarh region (with lowest COD of 75.75 mg l^-1) are either the hand pumps or the bore wells. Henceforth these are not prone to surface runoff and have less of organic matter and the lowest values of COD thereafter. Mahananda et al., (2010)²³ have also reported similar findings. The COD detected in the months of monsoon and post-monsoon was 135.50 and 91.54 l^-1 respectively. However, the values observed in the present study were well below the BIS prescribed limits of 250 mg l^-1. BOD: The highest values of BOD at Nalagarh may be ascribed to more growth of weeds etc. in some of the water bodies and lack of cleanliness of these sources, causing more of organic matter after the decay of such vegetative matter, as also being revealed in the KAP survey undertaken in the present study. However, the values observed were well above the ICMR permissible levels of 5 mg l^-1.

Table 5: Seasonal variation in chemical parameters of drinking water sources in Solan 2017


The data illustrated in Table 5 shows the variations in the concentrations of calcium, magnesium, nitrate, chlorides and lead. The regions and the seasons exerted significant influence on the amounts of calcium, magnesium, chlorides and lead. The interaction of regions with seasons did not affect any of these parameters.

Calcium

Ca was detected in the range of 37.2- 122.9 mg l^-1. The highest concentration of Ca at Kunihar (122.9 mg l^-1) may be ascribed to the presence of large amounts of limestones in the region as also reported by Bacciottini et al., (2004).²⁴ The values observed in high disease burden regions of Kunihar and Nalagarh were above the BIS permissible levels of 75 mg l^-1. Whereas, the values observed during monsoon (71.29 mg l^-1) and post-monsoon (53.77 mg l^-1) were below the permissible levels.

Magnesium

Mg was detected in the range of 14.25- 30.61 mg l^-1. The highest concentration of Mg at Kunihar may be ascribed to the types of rocks in these areas. The values detected were very well below the BIS permissible levels of 100 mg l^-1.

Nitrate

The highest concentration at Kandaghat may be probably due to using of inorganic nitrogenous fertilizers and manures in the region. However, the values detected were very well below the BIS permissible levels of 45 mg l^-1. Higher contents observed during monsoon were due to the point and non-point sources of pollution, hastened further by the runoff phenomenon, as also reported by Mueller et al., (2013).²⁵

Chloride

The highest concentration of chloride at Solan and Kunihar may be ascribed to the mixing of sewage generated from the domestication of milk-producing animals such as cows, buffaloes, goats etc in the region, as also reported by Interlandi et al., (2003).²⁶ The land use patterns especially the sewage systems and the livestock management in this region, contributes the high hydrological fluxes of chloride ions, making them pass through the underlying strata into the groundwater, as also reported by Chauhan et al., (2014 ).²⁷ However, the values detected were below the BIS permissible limits of 250 mg l^-1.

The data presented in Table 6 elicits the seasonal and temporal variation of the various trace elements of water such as mercury, arsenic, zinc, cadmium and chromium. Whereas lead concentrations in water significantly varied with the seasons and the regions of the water sources, Hb and Cr concentrations showed variation with respect to the seasons, regions and the interaction of seasons with regions. On the other hand the concentrations of As, Zn and Cd were neither affected by the seasons and the regions and nor by the interaction of these two factors.

Lead

The highest concentration of lead at Parwanoo and Solan may be ascribed to a large number of cable manufacturing units and plastic stabilizer factories in these regions, as also reported by Cobbina et al., (2015).²⁸ The values detected were higher than the BIS permissible limits of 0.01mg l^-1.

Mercury

Irrespective of the regions, the relatively high values of mercury recorded in Arki region (0,09 mg l^-1) during the post-monsoon season may be probably due to mineral deposits getting leached into the ground strata and eventually finding its way into the water aquifer. The highest concentration of mercury at Baddi and Kandaghat region may be ascribed to the widespread mining activities undertaken in the area as also reported by Kim et al., ( 2015).²⁹ No mercury was detected in the water sources of Arki, Nalagarh, Parwanoo and Solan.

Arsenic

The high contents observed at Parwanoo may be attributed to their geogenic source of origin, probably from the type of rocks present in this region. The study findings are in line with Shankar et al., (2014).³⁰ The values detected were well below the BIS permissible limits of 0.05 mg l^-1.

Zinc

The highest concentration of lead at Nalagarh region may be ascribed to a large number of alloys and steel galvanizing industries and tannery plants in the region, as also has been reported in other studies in Bangladesh (Hasan et al., 2017).³¹ Excessive use of insecticides in farming practices in this region may also be leading to higher concentrations of zinc in water sources. However, the values detected were below the BIS permissible limits of 15 mg l^-1.

Cadmium

All the water sources were free of Cd contents except Solan region which recorded 0.001 mg l^-1of Cd. However, the values detected were well below the permissible limits. Contents detected may be due to the effluent waste from automobile industries and building construction material, as also reported by Machado et al., (2017).³²

Chromium

The highest concentration of chromium at Parwanoo and Solan region may be ascribed to runoff mixed with automobile industry wastes, paints and dyes being used in extensive building materials, and steel works along with Cr- electroplating industries. Kandaghat and Kunihar regions, predominantly rural areas and without industries, also had higher contents. These rural areas exhibit the presence of chemical-based agriculture. Use of pesticides and insecticides leads may be leading to higher chromium contents of water, as also reported by Repula et al., (2012).³³

Table 6: Seasonal variation in chemical parameters of drinking water sources in Solan, 2017

Table 7: Water Quality Index of drinking water sources in Solan 2017

Disease burden	Area	Monsoon	Post monsoon	Mean
High incidence	Arki	119.87	84.18	102.02
	Baddi	160.93	94.15	127.54
	Kandaghat	182.52	96.08	139.29
	Kunihar	151.54	94.38	122.96
	Nalagarh	219.78	114.3	167.04
	Parwanoo	142.3	81.22	111.76
	Solan	177.45	102.05	139.75
Low incidence	Kurgal	83.75	78.58	81.17
Mean		154.77	93.12
C.D. (0.05)
Season 13.96
Region 27.92
Season x Region 39.48

Water Quality Index

The regions of the water sources, the seasons and the interaction of the regions with the seasons had a significant influence on the water quality index of the drinking water sources (Table 6). Nalagarh, the high disease burden region, had the water sources which were unsuitable for drinking purposes (Grade E) with WQI of 167.04 followed by statistically at par region of Solan (139.75) and Kandaghat (139.29). The other high disease burden regions of Baddi, Kunihar, Parwanoo and Arki also had the sources which were unsuitable for drinking purposes. Even the water sources of the low disease burden region Kurgal were having the water of very poor quality with the WQI of 81.17. The deterioration of water quality was significantly more in a monsoon with water unsuitable for drinking purposes (WQI- 157.77) as compared to the post-monsoon season having a very poor quality of water (WQI- 93.12). The unsuitable drinking water quality of the sources in Nalagarh and Solan may be ascribed to the ill effects of industrialization in congested urban areas where the rampant discharge of industrial wastes and effluent is not only entering the water sources through runoff but probably has also leached down and contaminated the various groundwater sources even. Kandaghat region is predominantly a rural area with heavy usage of chemical-based farming which may be polluting the water sources, as also reported by Chauhan et al., (2015).²⁷ The region of Kurgal had water sources which were predominantly unprotected and were always prone to external environmental pollutants and contaminants. The runoff phenomenon, more in monsoon further deteriorated the water quality of these sources, as also has been reported by Hasan et al., (2017)³¹ and Machado et al., (2017).³²

Conclusion

Rampant industrial pollutants deteriorating water quality is a matter of great concern and warrants for utmost public action. The ill effect of urbanization and industrialization on the water quality has been clearly highlighted in the present study. Improper sewage disposal, unchecked industrial effluents entering the water sources, may be sometimes due to runoff phenomenon and the unprotected nature of natural water sources itself, is leading to the poor water quality of drinking water sources of the district. This may be leading to the higher diarrhoeal disease burden observed in the region. Last but not the least, poor knowledge, attitude and practice of the people in respect to aspects of the management of water sources need intervention so as to protect these precious natural resources.

Acknowledgements

We highly acknowledge the study participants of the KAP survey who were instrumental in identifying the various drinking water sources.

References

1. Adimalla N., Li P., Venkatayogi S. Hydrogeochemical evaluation of groundwater quality for drinking and irrigation purposes and integrated interpretation with water quality index studies. Environmental Processes, 2018; 5(2): 363-383. Available from: https://doi.org/10.1007/s40710-018-0297-4.
   CrossRef
2. Jain S.K., Agarwal P.K., Singh V.P. Hydrology and water resources of India. 2007. Springer, Netherlands: 7-13.
3. Adimalla N. Groundwater quality for drinking and irrigation purposes and potential health risks assessment. A case study from the semi-arid region of south India. Exposure and health, 2018. Available from: https://doi.org/10.1007/s12403-018-0288-8.
   CrossRef
4. United Nations. 2014. The Millennium Development Goals Report.1^st ed. [ebook] United Nation. Available from: http://www.un.org/millenniumgoals/2014%20MDG%20report/MDG%202014%20English%20web.pdf. [Accessed 17 Jan. 2015].
5. WHO/UNICEF. 2017. Joint Monitoring Programme. Progress on Drinking Water, Sanitation and Hygiene. 2017 update and SDG baselines. Geneva, World Health Organization and United Nations Children's Fund. Licence: CC BY-NC-SA 3.0 IGO.
6. WHO/UNICEF. 2018. Core questions and indicators for monitoring WASH in health care facilities in the Sustainable Development Goals. Geneva, World Health Organization and United Nations Children's Fund. Licence: CC BY-NC-SA 3.0 IGO.
7. Smith R.S. Classification of water-related disease. Water and Health. Chapter in Encyclopedia of Life Support Systems. 21p. Available from: http://www.eolss.net/Eolss-sampleAllChapters.aspx. [Accessed 3 March. 2018].
8. Pruss-Ustun A., Wolf J., Corvalan C., Bos R., Neira M. (eds). 2016. Preventing disease through healthy environments. A global assessment of the burden of disease from environmental risks. World Health Organization, Maria publications, France. 147p.
9. WHO. 2015. Global Health Estimates 2015 Summary Tables. Global YLL By Cause Age And Sex, 2000-2015. Geneva, World Health Organization.
10. Brown R.M., McClelland N.I., Deininger R.A., Tozer R.G. A water quality index- Do we care? Wat. Sewage. 1970; Wks: 339-343.
11. Hallock D. 2002. A water quality index for ecology’s stream monitoring program. Department of Ecology Publications, Washington State Department of Ecology, Olympia. Available from: http://www.ecy.wa.gov/bibio/0203052.html.
12. Veerabhadram K. Mapping of Water Quality Index (WQI) using Geographical Information System (GIS) as a decision supporting system tool. Available from: http://www.gisdevelopment.net/application/environment/water/ma03123abs.htm. [Accessed 22 September. 2017].
13. Singh R., Sukhatme B.V. Optimum Stratification. Annals of the Institute of Statistical Mathematics, 1969; 21(1):515-528.
    CrossRef
14. Rice E.W., Baird R.B., Eaton A.D., Clesceri L.S. (eds). 2012. Standard methods for the examination of water and wastewater. 22^thedn. APHA, AWWA and WEF. Washington D.C. 1496p.
15. Chatterji C., Raziuddin M. Determination of water quality index (WQI) of a degraded river in Asansol industrial area, Raniganz, Burdwan, West Bengal. Nature, Environment and Pollution Technology. 2002;1(2):181-189.
16. Cochran G.C., Cox G.M. 1964. Experimental Designs. Asia Publishing House, Bombay. 611p.
17. Kashyap R, Verma KS and Bhardwaj SK. Pollution potential assessment of Markanda river around Kala Amb industrial town of Himachal Pradesh India. 2015. International journal of science and nature, 6(4): 606-612.
18. Sanalkumar M.G., Thara S., Bini B., Salu V.S. Seasonal fluctuations in the pollution indicators microorganisms and aquatic insects in the Vettiyar segment of river Achankovil. International Journal of Science and Research. 2014; 4(11):6-14.
19. Kerketta P., Baxia S.L., Gora R.H., Kumari S., Roushan R.K. Analysis of physicochemical properties and heavy metals in drinking water from different sources in and around Ranchi, Jharkhand, India. Veterinary World. 2013; 6(7):370-375.
    CrossRef
20. Oyem H.H., Oyem I.M., Usese A.I. Iron, manganese, cadmium, chromium, zinc and arsenic groundwater contents of Agbor and Owa communities of Nigeria. Springerplus. 2015; 4:104.
    CrossRef
21. Rana A, Bhardwaj SK and Thakur M. 2016. Surface water quality and associated aquatic insect fauna under different land use in Solan (district Solan), Himachal Pradesh. Indian Journal of Ecology, 43(1): 58-64.
22. Tareq S.M., Rahaman M.S., Rikta S.Y., Nazrul Islam S.M., Sultana M.S. Seasonal variation in water quality of Ganges and Brahmaputra river, Bangladesh. Jahangirnagar University Environmental Bulletin. 2013; 2:71-82.
    CrossRef
23. Mahananda M.R., Mohanty B.P., Behera N.R. Physico-chemical analysis of surface and groundwater of Bargarh district, Orissa, India. International journal of recent research and applied sciences. 2010; 2(3):284-295.
24. Bacciottini L., Tanini A., Falchetti A., Masi L., Franceschelli F., Pampaloni B., Giorgi G., Brandi M.L. Calcium bioavailability from calcium-rich mineral water, with some observations on methods. Journal of clinical gastroenterology. 2004; 38(9):761-766.
    CrossRef
25. Mueller D.K., Helsel D.R. 2013. Nutrients in the Nation’s Waters – Too Much of a Good Thing? U.S. Geological Survey Circular 1136. United States Government Printing Office, Denver. 31p. Available from: http://pubs.usgs.gov/circ/circ1136/circ1136.html.
26. Interlandi S. J., Crockett C. S. Recent water quality trends in the Schuylkill River, PA, USA: a preliminary assessment of the relative influences of climate, river discharge and suburban development. Water Res. 2003; 37:1737–1748.
    CrossRef
27. Chauhan A., Verma S.C. Impact of Agriculture, Urban and Forest land use on physicochemical properties of water- a review. International journal of current microbiology and applied sciences. 2015; 4(10):18-22.
28. Cobbina S.J., Duwiejuah A.B., Quansah R., Obiri S., Bakobie N. Comparative assessment of heavy metals in drinking water sources in two small – scale mining communities in Northern Ghana. International Journal of Environmental Research and Public Health. 2015; 12: 10620-34.
    CrossRef
29. Kim M.K., Lee Y.M., Zoh K.D. Spatial and Temporal variation of total mercury and methylmercury in a lacustrine wetland in Korea. Environmental science and pollution research international. 2015; 22(9): 6578-89.
    CrossRef
30. Shankar S., Shanker U., Shikha. Arsenic contamination of groundwater: a review of sources, prevalence, health risks and strategies for mitigation. The Scientific World Journal. 2014; 304524;18p.
31. Hasan M., Begum L., Hosain S., Poddar P., Chowdhury A., Aliv F. Study on heavy metals (Zinc and Lead) in drinking water of Tannery area, adjacent areas and outside village areas. Journal of Environmental and Analytical Toxicology.2017; 7:433.
    CrossRef
32. Machado K.S., Ferreira P.A., Rizzi J., Figueira R., Froehner S. Spatial and temporal variation of heavy metals contamination in recent sediments from Barigui river basin, South Brazil. Environment pollution and climate challenge. 2017; 1:108.
    CrossRef
33. Repula C.M., Quinaia S.P., Campos B.K., Ganzarolli E.M., Lopes M.C. Accumulation of chromium and lead in bryophytes and pteridophytes in a stream affected by tannery wastewater. Bulletin of Environmental Contamination and toxicology. 2012;88:84-88.
    CrossRef