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Journal of Asian Earth Sciences: X 8 (2022) 100108
Available online 27 June 2022
2590-0560/© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Study of hydrochemical and geochemical characteristics and solute uxes
in Upper Ganga Basin, India
M.K. Sharma
*
, Pradeep Kumar , Parul Prajapati, Kunarika Bhanot , Udita Wadhwa,
Garima Tomar, Rakesh Goyal , Beena Prasad, Babita Sharma
National Institute of Hydrology, Roorkee 247667, Uttarakhand, India
ARTICLE INFO
Keywords:
Hydrochemical
Hydrogeochemical
Upper Ganga Basin
C ratio
Principal component analysis
Solute ux
ABSTRACT
Himalayan rivers are considered the most sensitive of all the ecosystems to the impact of climate change. In the
present investigation, hydrochemical processes controlling the meltwater chemistry of the rivers Bhagirathi,
Alaknanda and Ganga in the Upper Ganga Basin, India have been studied simultaneously creating a large
database for the rst time. For this purpose, an extensive water quality assessment in Upper Ganga Basin has
been carried out by collecting water samples from all three rivers on monthly basis from September 2016 to May
2018 and analysing these samples for hydro-chemical parameters. Hydro-chemical characteristics revealed that
sulphide oxidation and carbonation- the two proton producing reactions govern the chemical weathering pro-
cesses pertaining in the rivers. One of the most peculiar ndings of the study is the dominance of carbonate
dissolution in the whole stretch of River Alaknanda, while the dominance of sulphide oxidation in River Bha-
girathi upto Dabrani revealing the continuum of Gangotri glacial processes followed by carbonate dissolution
upto Haridwar. The principal component analysis further supports this weathering process in the basin.
1. Introduction
Water is very essential compound for lives, it’s also known as the
elixir of life. Mountains play a critical role in the water cycle, storing
water during the cold season, and releasing it as melt during the dry
season (Casassa et al., 2009). Most of the large river systems of the world
originate from mountains, storing fresh water in the form of snow and
ice. Since ancient times, rivers are one of the paramount natural re-
sources for human development. Moreover, rivers are considered the
most sensitive of all the ecosystems to the impacts of climate change,
both directly and indirectly by the combination of various other
stressors (Pandey et al., 2014).
Generally, glaciers are the sources of a large river system. Along with
meltwater, glaciers also deliver a huge amount of sediment. In the
glacial areas, freshwater interacts with sediments in a cold environment
and is further transported downstream, through the mountain systems
and plains. The acquisition of solute includes the chemical weathering of
rocks occurring in the glacial environment (Kumar et al., 2009). In a
glacial system, the discharge variation also has a direct implication on
the sediment dynamics characteristics. This sediment dynamics char-
acteristic is also associated with the development and progression of the
subglacial zone as this zone is the main contributor of glacial sediments
(Thayyen et al., 1999).
The Asian rivers are reported to be the greatest transporter of sedi-
ments with only rivers Ganga and Brahmaputra delivering about 329
and 597 MT of suspended sediments respectively to the Bay of Bengal
annually, which is about 7% of the global annual sediment ux (Sharma
et al., 2019). The Himalayan Mountain range has young geology which
is the reason for the large sediment load (Pandey et al., 1999). Chemical
weathering of active layer sediments is largely the reason for increased
solute uxes in the proglacial zone (Sharma et al., 2021). The sediment
outow from the Gangotri glacier system plays an important role in
solute acquisition during sediment-meltwater interaction and thereby in
controlling the hydro-chemical behaviour of meltwater of the Gangotri
glacier (Sharma et al., 2019).
Geo-chemical conditions have a marked inuence on the surface
water quality. Hydro geochemical studies explain the relationship of
water chemistry to aquifer lithology for water quality. Such a relation-
ship would help not only to explain the origin and distribution of dis-
solved constituents but also to illuminate the factors controlling the
surface water chemistry (Singh et al., 2012, 2014). Rock weathering is
the dominant factor in the overall hydrochemical characteristics. The
* Corresponding author.
E-mail address: mks.nihr@gov.in (M.K. Sharma).
Contents lists available at ScienceDirect
Journal of Asian Earth Sciences: X
journal homepage: www.sciencedirect.com/journal/journal-of-asian-earth-sciences-x
https://doi.org/10.1016/j.jaesx.2022.100108
Received 10 October 2021; Received in revised form 19 June 2022; Accepted 23 June 2022
Journal of Asian Earth Sciences: X 8 (2022) 100108
2
major ion chemistry of river water is governed by weathering process in
the drainage basin, minor contributions from cyclic sea salt, atmospheric
provision (from terrestrial, marine and anthropogenic sources) of
chemical constituents and pollution (Sarin et al., 1992; Singh and Has-
nain, 1998). The possible link between Himalayan uplift and Cenozoic
climate change has resulted in natural weathering and continual
geochemical processes (Chakrapani and Saini, 2009; Singh and Hasnain,
1998).
The plain and peninsular drainages of the Ganga are dominated by
saline-alkaline soils containing Na
2
CO
3
and NaHCO
3
. The contribution
of their higher solubility is signicant to the dissolved ions budget of the
river waters which was earlier considered as the part of silicate weath-
ering resulting in an overestimation of silicate weathering and hence the
CO
2
consumption in the Ganga System. Three times higher silicate
erosion rate was observed in the mountainous catchment of the Ganga
compared to its plain catchment and thereby indicating the importance
of mountain versus plain erosion of the Ganga on rest. This emphasizes
the role of higher physical erosion in contributing to the higher chemical
erosion in the hilly terrain compared to higher temperature and resi-
dence times in the plain catchment (Chatterjee and Singh, 2022).
The most susceptible water bodies to pollution are rivers because
they carry a large amount of industrial and municipal wastewater along
with fertilizers in the form of runoff from agricultural land (Khan et al.,
2016a; Raghav and Shrivastava, 2016). Upper Ganga Basin in the Hi-
malayan region is generally observed as an unpolluted region. The
Ganga water in upstream, midstream and downstream regions are used
not only for drinking and irrigation purposes but the Ganga water is also
used for pilgrimage activities, worshipping of deities and holy bath. The
regular discharge of pollutants can disturb the ecology of the river sys-
tem diminishing the self-purication capacity of the river Ganga,
because the entire upper stretch (upto Haridwar) covers maximum pil-
grim sites visited by many devotees throughout the year (Dimri et al.,
2021). Discharge of sewage into the Ganga is responsible for 75% of its
pollution with millions of litters of sewage generated per day in the
towns along the Ganga (Das, 2011). Due to the increasing problem of
deterioration of river water quality, constant monitoring of a river sys-
tem is required to evaluate the effects of environmental factors on water
quality for proper utilization and sustainable development of the
resource (Dixon and Chiswell, 1996, Khan et al., 2016a, 2016b, 2017,
and 2018, Ahiarakwem and Onyekuru, 2011). Effective management of
surface water is possible by identifying potential pollution sources and
using multivariate statistical techniques on extended matrix data (Lee
et al., 2001, Adams et al., 2001; Khan et al., 2020) and have been used to
identify natural and anthropogenic sources of pollution in surface waters
(Singh et al., 2004, 2005).
In the Himalayan region, the Bhagirathi and Alaknanda rivers
contributing high sediment and dissolved ux meet at Devprayag and
ow in the name of the Ganges. Although a number of studies on hydro-
chemical behaviour have been carried out by different workers covering
different reaches of the river Ganga basin for a small period of investi-
gation (Ahmad and Hasnain, 2001; Chakrapani, 2005; Khan et al.,
2016a, 2016b, 2017, and 2018; Hasnain and Thayyen, 1999; Kumar
et al., 2009; Matta, 2014; Mata et al., 2015, 2018a, 2018b, 2020a,
2020b; Pandey et al., 2014; Sarin et al., 1989, 1992; Sharma et al., 2019;
2021; Sharma and Sharma, 2016; Singh and Hasnain, 1998, 2002; Singh
et al., 2012, 2014, 2015; Thayyen et al., 1999; Trivedi et al., 2010) but
no study is available covering whole Upper Ganga basin for a longer
period of investigation creating a large database of water quality.
Keeping in view the importance of water quality of the Himalayan River
Ganga, a comprehensive study of spatial and temporal hydro-chemical
characteristics and hydrogeochemical processes controlling the hydro-
chemistry of Upper Ganga Basin has been discussed in the present paper
and compared the solute uxes of river Bhagirathi, Alaknanda and
Ganga.
2. Material and methods
2.1. Study area
The Ganga, one of the largest river systems on the globe, originates at
Gomukh near the Gangotri Glacier at an elevation of 3800 m and tra-
verses a length of 2525 km through eleven states of northern and eastern
India until it meets the sea in the Bay of Bengal. Gangotri glacier is
situated in the Uttarkashi district of Uttarakhand state in the central
Himalaya and lies between 30 43′22′’ to 30 55′49′’N and 79 4′41′’ to
79 16′34′’E. The Upper Ganga basin refers to the two main headwaters
in the Himalayas- the Bhagirathi and the Alaknanda river system. The
river Bhagirathi ows from the Gangotri glacier at Gomukh and the river
Alaknanda rises at the conuence of the Satopanth and Bhagirath
Kharak glaciers in Uttarakhand. The Alaknanda and Bhagirathi unite at
Devprayag to form the mainstream known as the Ganga which cuts the
Himalayas to emerge at Rishikesh. It then ows a few kilometres further
downstream and debouches onto the proximal part of the Ganga at
Haridwar, a town of immense cultural and religious signicance (Fig. 1).
The Bhagirathi basin has three main sub-basins, namely, Bhagirathi
River, Bhilangana River and Asiganga River sub-basin. It has a total
catchment area of 8847 km
2
. Bhagirathi River ows for 217 km until it
reaches Devprayag (elevation 475 m) where it meets the Alaknanda
River. The average rainfall in the region varies between 1000 to 2500
mm of which 50–80% falls during the monsoon period between June
and September. It has an average gradient of 6.44%. The Bhagirathi
River basin experiences strong climatic seasonal variations, which is also
reected in the monthly variation in stream ows.
The Alaknanda basin is in the eastern part of the Garhwal Himalaya.
The Alaknanda is considered to rise at the conuence and foot of Sato-
panth and Bhagirath Kharak glaciers. The major rivers in the basin are
Alaknanda, Mandakini, Nandakini, Pinder, Dhauliganga and Bir-
ahiganga. The total catchment area of the basin is about 12587 km
2
. The
river Alaknanda runs a total 224 km distance before conuence with
Bhagirathi at Devprayag. The Alaknanda basin receives heavy snowfall
for about 3–4 months during winter at places above 2000 m altitude.
The high rainfall variation can also be attributed to high ranges in
altitude between different places. The climate in Alaknanda basin varies
from sub-tropical to alpine.
The geochemical cycling of elements is mainly governed by rivers
which carry the elements, separated from soil and rocks during weath-
ering and erosion. The Bhagirathi basin is characterised by a rapid
waterfall, cascade and lower gradient relief in many places. The route of
river Bhagirathi comprises Central Crystalline rocks primarily consisting
of schists, micaceous quartzites, calc-silicates, amphibolites, gneisses,
granites, slates and phyllites. The middle and lower reaches of the river
have an abundance of limestone and dolomite and before conuence
with Alaknanda, it traverses through phyllites and micaceous graywack.
The Alaknanda river basin which ows through the Central Crystalline
zone is characterised by both sedimentary and highly metamorphosed
gneissic rocks. Along its length, the river comes in contact with lime-
stones, marbles and quartzitic sequences of the Tejam and Berinag
Formations. Before its conuence with the Bhagirathi, the stream passes
through the limestone and dolomite-having Uttarkashi Formation and
the phyllite and micaceous greywackes of the Chandpur Formation
(Singh and Hasnain,1998).
2.2. Water quality sampling and analysis
Thirteen sampling sites have been selected for water quality assess-
ment in Upper Ganga Basin and monitoring is carried out on monthly
basis from September 2016 to May 2018. Details of the sampling loca-
tions are given in Table 1. Four sites on river Bhagirathi, six sites on river
Alaknanda and three sites on river Ganga have been selected for water
quality sampling. 21 water samples were collected from each site on a
monthly basis and total of 273 water samples were collected during the
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
3
investigation period, out of which 84 water samples from river Bhagir-
athi, 126 water samples from river Alaknanda and 63 water samples
from river Ganga were collected. One litre water sample was collected
from each selected site in clean polyethylene bottles and preserved by
adding an appropriate reagent. Water samples were collected using the
grab sampling method and immediately electrical conductivity (EC) and
pH values were measured in the eld using a portable conductivity and
pH meter (make: HACH, model: HQ40 −D −Multimodel meter).
Samples for dissolved oxygen were preserved with alkali azide followed
by MnSO
4
. Water samples were ltered through a 0.45 µm membrane
lter paper by hand operated vacuum pump for further analysis. All the
samples were stored in sampling kits maintained at 4 ◦C and brought to
the laboratory for detailed physico-chemical analysis.
Various water quality parameters monitored include pH, EC, Total
Suspended Solids (TSS), Alkalinity, Hardness, Major Cations (Na
+
, K
+
,
Ca
2+
, Mg
2+,
NH
4
+
), Major Anions (HCO
3
–
, Cl
-
, SO
4
2-
, NO
3
–
, PO
4
3-
) and De-
mand Parameters (DO, BOD, COD) and analysed following standard
methods (APHA, 2012; BIS, 2012). DO was estimated by the Winkler
titration method. BOD was determined by 5 days of incubation at 20 ◦C
followed by Winkler titration method for DO estimation and COD was
measured by the closed reux method. Filtered melt water samples
collected from rivers Bhagirathi, Alaknanda and Ganga were analysed
for major cation (Na
+
, K
+
, Ca
2+
, Mg
2+
, NH
4
+
), as well as major anion
(HCO
3
–
, Cl
-
, SO
4
2-
, NO
3
–
, PO
4
3-
) using Metrohm make Ion Chromatograph
(IC) model 930 (for anions and cations) and HCO
3
–
was determined by
using potentiometric auto titrator model 888 systems. An overall pre-
cision expressed as percent relative standard deviation (RSD) for Ca, Mg,
Na, K, F, Cl, SO
4
and NO
3
were <5 %. All chemicals used in the study
were obtained from Merck, India and were of analytical grade. Ultra-
pure water was used throughout the study.
The ionic balance between inorganic positive and negative charges
in natural water was used to quantify the precision of the measurement.
In the Upper Ganga Basin, the total positive charge of the water
(T
Z
+
=Na
+
+K
+
+Ca
2+
+Mg
2+
) in meq/L and the total negative charge of
the water (T
Z
−
=HCO
3
−
+SO
4
2-
+Cl
−
+NO
3
−
) in meq/L maintained a
roughly balanced relationship, and the normalized inorganic charge
balance (NICB (%) =100 ×((T
Z
+
-T
Z
-
)/(T
Z
+
+T
Z
−
)) was calculated. Overall
data reproducibility for cations and anions was within ±5% indicating
the high accuracy of the data.
2.3. Statistical analysis
2.3.1. Data processing
A univariate and multivariate normal distribution are required for
Fig. 1. Map showing sampling site locations on River Bhagirathi, Alaknanda and Ganga in the Study Area.
Table 1
Details of Sampling Locations for Water Quality Monitoring.
S.No. Site Code Location Stream
1. B1 Gangotri Bhagirathi
2. B2 Dabrani Bhagirathi
3. B3 Uttarkashi Bhagirathi
4. B4 Devprayag Bhagirathi
5. A1 Joshimath Alaknanda
6. A2 Karnprayag Alaknanda
7. A4 Karnprayag Alaknanda
8. A5 Rudraprayag Alaknanda
9. A7 Rudraprayag Alaknanda
10. A8 Devprayag Alaknanda
11. G1 Devprayag Ganga
12. G2 Rishikesh Ganga
13. G3 Haridwar Ganga
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
4
the best outcomes of the statistical multivariate methods like PCA (Zhou
et al., 2007; Oppong and Agebedra, 2016; Marin et al., 2018). Shapiro
Wilk‘s (Shapiro and Wilk, 1965) and Royston’s tests (Royston, 1983)
were used to verify the univariate and multivariate normality condi-
tions, respectively. Pearson Correlation Coefcient was used in this
multivariate analysis because water quality data (20 water quality pa-
rameters viz., pH, EC, TDS, TSS, DO, BOD, COD, alkalinity, hardness,
HCO
3
, Cl, SO
4
, NO
3
, PO
4
, F, Na, K, Ca, Mg and NH
4
for River Bhagirathi,
Alaknanda, Ganga) was having non-normal distribution and this corre-
lation method is best suitable for reducing deviation of variables (Marin
Celestino et al. 2019). Based on Royston’s test, the dataset has a non-
normal distribution. To achieve a normal like distribution, the original
set of variables was transformed using a logarithmic transformation
(natural logarithm). To approach the best conditions of the multivariate
analysis, feature scaling on the database was done using standardization
(or Z-score normalization). Standardization minimizes the variance in
variables and protects dissimilarity metrics such as the Euclidian dis-
tance from being severely inuenced (Davis and Sampson, 1986). The
Kolmogoroy – Smirnov (K-S) test was used to assess how well the
transformed variables were adjusted to the normal log distribution
(Rizvi et al 2015; Muangthong and Shrestha, 2015; Marin Celestino
et al., 2019; Castillo et al., 2021). The precision and acceptability of the
data for PCA were assessed by using the Kaiser Normalization.
2.3.2. Principal component analysis
The relationship between the hydrochemical characteristics of the
Upper Ganga Basin was assessed through correlation matrix and was
analysed through principal component analysis. PCA is a powerful and
most popular multivariate statistical technique applied to analyse the
variance of a set of inter-correlated variables and to extract information
from the data table thus transforming it into a new set of orthogonal
variables called principal components thereby decreasing the dimen-
sionality of the dataset (Abdi and Williams, 2010; Ravikumar and
Somashekar, 2017). The new variables known as principal components
(PCs) are the linear combinations of the initial variables. The values
associated with these new variables are known as factors. The retained
factors were rotated orthogonally using Varimax Rotation developed by
Kaiser (Kaiser, 1958). Varimax rotation aims at simplication of the
interpretation of each component that has a small number of large
loadings and a large number of zero loadings. PCA was applied sepa-
rately on the standardized and normalized hydrochemical data of River
Bhagirathi, River Alaknanda and River Ganga. The uncorrelated prin-
cipal components obtained through an eigen value analysis of the cor-
relation matrix produced loadings associated with each component
through Varimax Rotation with Kaiser normalization. All the statistical
analysis was conducted using IBM SPSS Statistics version 28.
3. Results
3.1. Hydro-chemical data of Upper Ganga basin
The water samples collected from thirteen sampling sites of the River
Ganga System in Uttarakhand Himalayas for the period of September
2016 to May 2018 were analysed. The spatio-temporal distributions of
the physicochemical parameters are given in Fig. 2 (a-k) and S1 (a-d) in
Supplementary material and Table 2. The pH value varied from 6.3 to
8.6, 6.8 to 8.3 and 7.3 to 8.9 for rivers Bhagirathi, Alaknanda and Ganga
respectively show that the river waters were slightly alkaline. The pH for
all of the samples of the upper Ganga basin was well within the limits
prescribed by BIS (2012) and WHO (2011) for various uses of water
including drinking and other domestic supplies. Most of the sites are
characterized by low EC values varying from 63.7 to 228 µS/cm with an
average value of 138.2 µS/cm, from 62.6 to 245 µS/cm with an average
value of 176 µS/cm and from 104 to 270 µS/cm with average value 171
μ
S/cm in river Bhagirathi, river Alaknanda and river Ganga respectively.
The TSS value varied from 0.2 to 2837 mg/L with an average value of
168 mg/L in river Bhagirathi, from 0.08 to 1953 mg/L with an average
value of 98 mg/L in river Alaknanda and from 0.26 to 743.8 mg/L with
average value 79.9 mg/L in river Ganga. The maximum value was
noticed in the monsoon season in the river water of Bhagirathi at
Gangotri.
The demand parameters viz., Dissolved Oxygen (DO), Biochemical
Oxygen Demand (BOD) and Chemical Oxygen Demand (COD), are the
most important scale parameters to indicate the quality level of a river in
water quality assessment. The DO varied from 7.0 to 11.3 mg/L with an
average value of 9.8 mg/L, from 8.0 to 12.9 mg/L with an average value
of 9.8 mg/L and from 8.1 to 11.5 mg/L with an average value 9.5 mg/L
in the river water of river Bhagirathi, river Alaknanda and river Ganga
respectively.
BOD ranged from 0.3 to 2.2 mg/L with an average value of 1.2 mg/L,
from 0.3 to 3.4 mg/L with an average value of 1.39 mg/L and from 0.5 to
3.2 mg/L with an average value 1.4 mg/L while COD ranged from 2.1 to
45.7 mg/L with average value 12.8 mg/L, from 2.08 to 41.1 mg/L with
average value 12.6 mg/L and from 2.1 to 37 mg/L with average value
12.7 mg/L in the water of rivers Bhagirathi, Alaknanda and Ganga
respectively. COD is the amount of oxygen required to oxidize inorganic
matter to organic. COD values were higher than the maximum criteria
limit of 10 mg/L for the river for drinking purposes prescribed by CPCB
at almost all the sites of river Bhagirathi and river Alaknanda. COD is
inuenced by the discharge of domestic sewage and tourist activities
may be considered a sizeable contributor to inorganic and organic
waste.
Total hardness in the water may be attributed to the presence of
bicarbonates, sulphates, chlorides and nitrates of calcium and magne-
sium. Total hardness varied from 21.5 to 101.3 mg/L with an average
value of 57.7 mg/L, 10 to 111 mg/L with an average value of 86.2 mg/L
and from 48.2 to 123.1 mg/L with an average value of 77.9 mg/L in
rivers Bhagirathi, Alaknanda and Ganga respectively.
Bicarbonate varied from 8.8 to 78.7 mg/L, 32.4 to 102.4 mg/L and
41.5 to 114.7 mg/L while the average concentration of HCO
3
was
observed 39.7, 74 and 71.9 mg/L in the water of river Bhagirathi, Ala-
knanda and Ganga respectively. Sulphate concentration varied from 12
to 56.7 mg/L with an average value of 29.3 mg/L, 3.7 to 41.2 mg/L with
an average value of 31.8 mg/L, and from 8.7 to 31.5 mg/L with an
average value of 20.2 mg/L in river Bhagirathi, Alaknanda and Ganga
respectively.
Chloride concentration varied from 0.1 to 2.9 mg/L with average
value 1.1 mg/L, from 0.12 to 6.33 mg/L with average value 1.51 mg/L
and from 0.05 to 4.0 mg/L with average value 1.6 mg/L while nitrate
concentration was observed to vary from 0.02 to 3.8 mg/L with average
value 0.9 mg/L, from 0.261 to 2.5 mg/L with average value 1.24 mg/L
and from 0.011 to 3.84 mg/L with average value 1.43 mg/L in water of
river Bhagirathi, Alaknanda and Ganga respectively.
Calcium was the dominant cation and the average concentrations of
calcium were observed at 17, 24.3 and 33.15 mg/L, while the second
dominant cation was magnesium, with average values noticed at 3.8,
6.15 and 5.41 mg/L in rivers Bhagirathi, Alaknanda and Ganga
respectively. Average sodium values were 2.8, 3.19 and 3.17 mg/L while
average potassium values were observed at 2.3, 2.5 and 2.32 mg/L for
river Bhagirathi, river Alaknanda and river Ganga respectively. The
major anions (HCO
3
, Cl, NO
3
and SO
4
) and cations (Na, K, Ca and Mg) at
all the sites were observed under permissible limits as suggested by BIS
(2012).
4. Discussion
4.1. Solute chemistry of Upper Ganga basin
The observed pH reects that the water of the upper Ganga basin was
slightly acidic to alkaline in nature (6.3–8.9). The minimum value was
observed in the river water of Bhagirathi at Gangotri because of the
formation of carbonic acid resulting from the reaction between carbon
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
5
(a) (b)
(c) (d)
(e) (f)
(g) (h)
(i) (j)
(k)
Fig. 2. Temporal variation of major anions and cations in the water of river Bhagirathi, river Alaknanda and river Ganga.
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
6
dioxide trapped under ice (Trivedi et al. 2010). Pandey et al. (1999) also
reported the neutral to alkaline (7.1–8.3) nature of river Bhagirathi like
most of the other Himalayan streams. The value of pH was observed to
be moderate and remain slightly alkaline during the study period at all
the sampling sites of river Alaknanda. The same nding was observed by
Sharma and Sharma (2016) for the river Alaknanda. EC is an excellent
estimation of dissolved ions and nutrient load in water samples. EC
varied between 62.6 and 270 µS/cm with maximum value at Haridwar
in river Ganga. EC was found more in the Ganga water samples as
compared to rivers Bhagirathi and Alaknanda which indicates high
dissolution of minerals and salts along the river continuum.
DO varied from 7.0 to 12.9 mg/L in the samples of the upper Ganga
basin, although the maximum value of DO was observed at Devprayag in
river Alaknanda. The higher concentrations of DO were recorded during
the winter season mainly due to low turbidity and temperature and
minimum during summer and monsoon seasons. Similar ndings were
noticed by Sharma and Sharma (2016). The DO rapidly depletes by the
discharge of oxygen demanding waste.
BOD ranged from 0.3 to 3.4 mg/L, however, COD was found to vary
between 2.08 and 45.7 mg/L. The maximum COD values were found in
winter at Dabrani and the minimum in summer. The variation in the
COD can be caused by the release of untreated sewage and agricultural
waste at some locations in the stretch of the upper Ganga basin by
habitation along the river. The average values of COD were higher than
the maximum criteria limit of 10 mg/L for rivers for drinking purposes
prescribed by Central Pollution Control Board (CPCB) at almost all the
sites of the upper Ganga basin except Gangotri.
TDS varied between 40.06 and 172.8 mg/L and higher values of TDS
were found in the river water of Ganga at Haridwar. The concentration
of TDS in River Ganga is more than the other rivers of the upper Ganga
basin. High values of TDS at Haridwar suggested the contamination of
river water possibly due to industrial wastewater, domestic sewage,
agricultural run-off and bathing or dumping the waste material into the
river water by tourists.
Due to land erosion with runoff, high TSS values were observed in
the monsoon season. Out of thirteen sites, a maximum value of 2837
mg/L was observed at Gangotri in July 2017. Chakrapani and Saini
(2009) also reported the highest value of TSS in the monsoon season.
Rainfall and higher average air temperature increased discharge and
thereby sediment transport also accounts for increased TSS values
(Haritashya et al., 2006). These results conrm that central Himalayan
glaciers produce the highest TSS which is dependent on meteorological
conditions over the glacier and the amount of resultant runoff (Kumar
et al., 2018). The river water at most of the sites has higher TSS values
above the prescribed criteria limit of 10 mg/L for drinking purpose for
rivers by CPCB (Table 2).
Hardness is due to the multivalent cations and anions in the water
samples. The hardness range was found to be from 9.6 to 123 mg/L and
the value of total hardness was observed minimum in monsoon season
and the value decreased in post-monsoon season. A similar nding has
been observed by Chakrapani and Saini (2009) in the seasonal samples
of river Bhagirathi showing average calcium concentration lower in the
monsoon season. The maximum value was observed at Dabrani. Agri-
cultural runoff, urban discharge, and clothe washing at open drains are
responsible for a signicant increase in hardness. None of the samples
has hardness value above the acceptable limit of 200 mg/L for drinking
purposes (BIS 2012).
The dissolved ionic species of the river water are the products of
atmospheric inputs and weathering over the drainage basin (Pandey
et al., 1999). The abundance of protons (H
+
) may have a signicant role
in controlling the rate and magnitude of solute acquisition in glacier
meltwaters. Major anion chemistry of the meltwater would indicate the
source of H
+
ion, nature and extent of chemical weathering, thereby
solute acquisition. Major ion chemistry of the Ganga source waters–the
Bhagirathi, Alaknanda and Ganga have been attempted to assess the
chemical weathering processes and concluded the domination of the
weathering of carbonate rocks by carbonic and sulphuric acids in the
high-altitude Himalaya. Meltwater chemistry is found to be governed by
the coupled reaction involving sulphide oxidation and carbonate
dissolution in Ganga headwaters (Sharma et al., 2019). A minimum
value of the dissolved concentration of ions are possibly due to different
weathering intensities generating meltwater at different discharge
values (Kumar et al., 2009). The result revealed that the major ion
chemistry of surface water of the river system is inuenced by seasonal
mineral dissolution and rock weathering reactions.
The anions for all sampling sites and seasons were observed to be in
decreasing order of HCO
3
–
>SO
4
2-
>Cl
-
>NO
3
–
. A similar trend was reported
by Singh et al. (2015). Bicarbonate was the dominant anion on average
accounting for 48.7%, during the study period. On average, SO
4
2−
accounted for 47.7 %, chloride for 2.4 % and nitrate for 1.15 % of the
total anions during the period of investigation. The two major anions
HCO
3
–
and SO
4
2-
in surface water are mainly derived from the dissolution
of atmospheric CO
2
in water and the oxidation of sulphides (Singh and
Hasnain, 1998).
Table 2
Hydro-chemical data of Upper Ganga Basin.
Water quality
parameter
Criteria
limit
(For river)
Observed values across 13 sampling sites
B-1 B-2 B-3 B-4 A-1 A-2 A-4 A-5 A-7 A-8 G-1 G-2 G-3
TSS, mg/L <10 mg/L 499.7 71.8 78.0 21.8 81.40 97.56 80.31 84.30 144.65 103.59 77.8 76.4 85.4
TDS, mg/L <500 mg/L 77.0 104.1 94.3 78.5 123.07 111.64 113.18 110.80 105.67 112.48 89.2 109.4 130.7
pH 6.5 – 8.5 7.13 7.51 7.69 7.6 7.91 8.02 7.98 7.915 7.89 7.925 7.85 7.9 8.13
EC, µS/cm <1000 µS/
cm
120 162 147 123 192 174 177 173 165 176 139 170 204
DO, mg/L ≥5 mg/L 10.03 10.235 9.71 9.37 9.68 9.805 9.705 9.635 9.882 9.976 9.612 9.547 9.388
BOD, mg/L ≤3 mg/L 1.125 1.039 1.432 1.028 1.35 1.2775 1.275 1.610 1.3775 1.439 1.075 1.532 1.475
COD, mg/L <10 mg/L 9.484 15.038 13.858 12.72 10.45 12.254 10.92 16.00 12.42 13.75 11.004 11.898 15.308
Alkalinity, mg/L <200 mg/L 13.86 35.512 42.16 38.50 56.24 61.91 63.781 62.71 59.38 64.17 48.191 59.315 69.760
Hardness, mg/L <200 mg/L 47.11 69.64 61.81 52.13 86.19 80.35 81.78 74.86 75.18 79.55 61.85 77.33 94.67
Cl, mg/L <250 mg/L 0.417 1.153 1.518 1.411 1.326 0.893 0.954 1.4956 1.137 1.5154 1.334 1.549 1.927
SO
4
, mg/L <200 mg/L 38.712 38.34 23.59 16.71 31.84 21.42 18.57 18.27 16.88 17.22 15.54 19.83 25.16
NO
3
, mg/L <45 mg/L 0.5182 0.821 1.017 1.1422 1.237 0.9842 1.096 1.124 1.1223 1.244 1.1362 1.4393 1.705
PO
4
, mg/L 0.0625 0.0168 0.0020 0.0036 0.0079 0.004 0.1265 0.1736 0.0110 0.0093 0.0053 0.0077 0.0079
F, mg/L <1 mg/L 0.4172 0.272 0.274 0.224 0.111 0.1924 0.169 0.1816 0.207 0.175 0.209 0.202 0.188
Na, mg/L <50 mg/L 2.172 2.990 3.235 2.669 3.189 2.679 2.632 2.948 2.751 3.049 2.645 3.182 3.67
K, mg/L <10 mg/L 2.329 2.159 2.574 2.191 2.306 2.3005 2.292 2.497 2.377 2.549 2.268 2.268 2.4199
Ca, mg/L <75 mg/L 14.01 19.47 18.37 16.05 24.25 22.92 23.36 22.63 22.07 23.36 18.62 22.10 26.26
Mg, mg/L <30 mg/L 3.147 5.048 3.887 3.168 6.14 5.73 5.7 5.53 4.88 5.16 3.743 5.39 7.081
NH
4
, mg/L <0.65 mg/
L
0.112 0.167 0.144 0.153 0.306 0.117 0.132 0.179 0.154 0.2014 0.242 0.155 0.137
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
7
The anions were observed in decreasing order of SO
4
2-
>HCO
3
–
>
Cl
-
>NO
3
–
at Gangotri and Dabrani however, at a lower stretch of river
Bhagirathi, the trend was observed to be HCO
3
–
>SO
4
2-
>Cl
-
>NO
3
–
. A
similar trend of HCO
3
–
>SO
4
2-
>Cl
-
>NO
3
–
was reported for river Alaknanda
and after the conuence of river Ganga by Singh et al. (2015). The
average concentration of major anions HCO
3
–
, Cl
-
, SO
4
2-
and NO
3
–
were
observed to be 0.65 meq/L, 0.031 meq/L, 0.611 meq/L and 0.014 meq/L
respectively for the water samples of river Bhagirathi during the period
of investigation. Although the average concentration of major anions for
river Alaknanda was found at 1.19 meq/L, 0.033 meq/L, 0.417 meq/L
and 0.018 meq/L respectively, thereafter for river Ganga was observed
to be 1.18 meq/L, 0.045 meq/L, 0.42 meq/L and 0.023 meq/L respec-
tively. Average concentration of major cations Na
+
, K
+
, Ca
2+
and Mg
2+
,
was observed to be 0.12 meq/L, 0.059 meq/L, 0.848 meq/L and 0.312
meq/L respectively for river Bhagirathi, 0.122 meq/L, 0.06 meq/L, 1.13
meq/L and 0.445 meq/L respectively for river Alaknanda and for river
Ganga 0.137 meq/L, 0.059 meq/L, 1.16 meq/L and 0.44 meq/L
respectively.
Quantitatively the anion chemistry indicates that sulphate and bi-
carbonate were the dominant anions, accounting for 30 to 73% and 24.9
to 65.0% of the total anion respectively and Cl−Cl
-
and NO−
3 NO
3
accounted for 1.12 to 3.38 % and 0.8 to 1.53 % respectively along the
river of Bhagirathi and is given in Fig. 3a. For the river Alaknanda, bi-
carbonate and sulphate accounted for 61 to 75.4% and 20.8 to 35.6%
respectively. While chloride and nitrate accounted for 1.3 to 2.4% and
0.94 to 1.2% respectively (Fig. 3b). Along the river Ganga average
contribution of major anions HCO
3
–
, SO
4
2-
, Cl
-
and NO
3
–
were observed at
70.7%, 25.19%, 2.66% and 1.37% respectively almost constant at all
Fig. 3. Distribution of major anions and cations for river Bhagirathi, river Alaknanda and river Ganga.
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
8
sites (Fig. 3c). Sulphate content is acquired in the glacier meltwater by
the dissolution of sulphate minerals viz: gypsum and anhydrite, sulphide
oxidation driving subglacial chemical weathering reactions. The
possible sources of chloride can be attributed to domestic waste, water
discharge, chlorination of public water supplies, and fertilizer applica-
tion (Zhang et al.,2007).
The order of concentration of cations varied as
Ca
2+
>Mg
2+
>Na
+
>K
+
. Hasnain and Thayyen (1999) also reported a
similar nding in the Dokriani glacier. A similar trend was observed in
the Ganga headwaters (Sarin et al., 1992; Sharma et al., 2019; Singh
et al., 2012, 2014). Among cations, (Ca
2+
+Mg
2+
) dominance accounted
for 86.5%, 89.76% and 88.6% of total cations for rivers Bhagirathi,
Alaknanda and Ganga respectively. Calcium was the dominant cation on
average accounting for 63.5%, 65% and 63.9% for rivers Bhagirathi,
Alaknanda and Ganga respectively. On average, Mg
2+
accounted for
23.1%, 24.71% and 24.7% followed by Na
+
8.8%, 6.8% and 7.9% and
then K
+
4.67%, 3.44 % and 3.5% of total cation for river Bhagirathi,
Alaknanda and Ganga respectively. Conclusively, the cations Ca
2+
and
Mg
2+
were the dominant cations in the water samples collected from the
upper Ganga basin. The amount of calcium in natural water depends on
the rock type. The higher concentration of Ca
2+
might be due to
geographic location and religious human activities. Silicates, pyroxenes,
amphiboles, and feldspar which are igneous rock minerals are also the
principal source of calcium in Ganga water (Chakrapani and Saini,
2009). Mg was found commonly lower than calcium concentration. Its
positive correlation with HCO
3
–
and SO
4
2-
indicates their common source
from the dissolution of carbonates. The order of concentration of cations
in the meltwaters of Gangotri varied as Ca
2+
>Mg
2+
>Na
+
>K
+
.
Calcium was the dominant cation accounting for 61.8 to 64.7%,
64.21 to 65.9% and 62.49 to 65.7% during the study period for the river
Bhagirathi, Alaknanda and Ganga respectively. Mg
2+
accounted for 21.2
to 26.49%, 23.3 to 25.85% and 21.8 to 27.12% followed by sodium from
8.02 to 9.48%, 6.19 to 7.24% and 7.42 to 8.2% respectively and po-
tassium 3.65 to 5.79%, 3.12 to 3.7% and 2.96 to 4.16% respectively of
total cations during the period of investigation for the river Bhagirathi,
river Alaknanda and river Ganga respectively. The value of Mg was
found commonly to be lower than calcium concentration. This obser-
vation is quite similar to earlier results reported by (Chakrapani and
Saini, 2009; Pandey et al., 1999; Sarin et al., 1989) for the Alaknanda,
Bhagirathi and major Himalayan rivers of Uttarakhand. Na and K were
less dominant ions in Bhagirathi and Ganga water. Hasnain and Thayyen
(1999) also observed similar ndings in the Dokriani glacier. The
maximum concentration of major cations was observed in winter and
the minimum value of these cations in the monsoon season. In the Ganga
headwater, Na
+
and K
+
are mainly derived from igneous and meta-
morphic rocks of the Central Crystalline rocks. Micas, orthoclase (KAl-
Si
3
O
8
) and albite are common parental minerals for Na
+
and K
+
release
in the Ganga headwater, which may react with water and carbonic acid
and accumulate clay minerals in the sediments (Singh and Hasnain,
2002).
The average ratio of (Ca +Mg) to (Na +K) in the meltwater was
calculated to be 6.75, 9.17 and 8.08 for river Bhagirathi, river Ala-
knanda and river Ganga respectively indicating dominancy of (Ca +Mg)
over (Na +K). The high input of (Ca +Mg) to the Tz
+
(total cations) was
found to be 0.865, 0.897 and 0.884 for river Bhagirathi, river Alaknanda
and river Ganga respectively showing that hydro geochemistry of
Gangotri glacier meltwater is mainly regulated by carbonate
weathering.
4.2. Hydrogeochemical characteristics of Upper Ganga basin
The hydro-chemical data was processed to study the hydro-
geochemical characteristics of the Upper Ganga Basin [Fig. 4(a-f) and S2
(a-l) in Supplementary material]. The plotted sample points of all the
water samples fall above 1:1 equiline in the scatter plot between (Ca +
Mg) and Tz
+
[Fig. 4a and S2(a and g) in Supplementary material]
showing a high contribution of (Ca +Mg) to the (Tz
+
) total cations
(Gibbs, 1970). “red dots” located at the start and end of the line in Fig. 4
(a-f) and S2(a-l) in Supplementary material represents equiline.
A high positive correlation was observed with an average (Ca +Mg)/
Tz
+
ratio of 0.87, 0.898 and 0.775 for rivers Bhagirathi, Alaknanda and
Ganga respectively. The plot between (Na +K) and Tz
+
demonstrates
low input of (Na +K) to the total cations (Tz
+
) indicated the small input
of ions from silicate weathering [Fig. 4c and S2(c and i) in Supple-
mentary material]. Na
+
, K
+
and dissolved silica in the drainage basin are
mainly derived from the weathering of silicate minerals, with clay
(a) (b) (c)
)e()d( (f)
Fig. 4. Scatter plots for River Bhagirathi.
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
9
minerals as by-products. Similar ndings from silicate weathering were
also reported by other researchers (Ahmad and Hasnain, 2001). Ca
2+
,
Mg
2+
and SO
4
2-
may be derived either from the weathering of carbonates
or the dissolution of evaporates [Fig. 4b and S2(b and h) in supple-
mentary material]. Under natural conditions (Ca +Mg)/HCO
3
equiva-
lent ratio from carbonate weathering is one. Fig. 4d and S2(d and j) in
the Supplementary material show that some of the samples were close to
the 1:1 line, indicating their source was carbonate weathering. The plot
of (Ca +Mg) v/s HCO
3
for most of the samples in the study area in-
dicates an excess of Ca +Mg over HCO
3
suggesting that a portion of the
(Ca +Mg) has to be balanced by other anions like SO
4
and/or Cl
(Paudyal et al., 2016). The plot of (Ca +Mg) v/s HCO
3
+SO
4
is a major
indicator to identify the ion exchange process activated in the study
area. Plot of (Ca +Mg) v/s HCO
3
+SO
4
for river Bhagirathi, Alaknanda
and Ganga shows that most of the plotted points clusters around the 1:1
equiline and some fall in HCO
3
+SO
4
indicating the ion exchange
process which may be due to the excess of HCO
3
+SO
4
. The scatter plots
between Ca +Mg and HCO
3
+SO
4
show good correlation, wherein all
points are on the equiline. This attributes that a portion of HCO
3
+SO
4
is
balanced by cations derived from silicate rock weathering (Singh et al.,
2014) [Fig. 4f and S2(f and l) in the Supplementary material]. A low
ratio of Na
+
to Cl
-
indicates a low contribution from atmospheric pre-
cipitation and evaporates dissolution and negates the possible impact of
atmospheric pollution on the glacier water (Kumar et al., 2009) [Fig. 4e
and S2(e and k) in the Supplementary material]. Gibbs plot shows that
almost all collected surface water samples from rivers Bhagirathi, Ala-
knanda and Ganga fall in rock dominance zone [Fig. 5(a–c)] suggesting
precipitation induced chemical weathering along with the dissolution of
rock-forming minerals. Few samples are away from this zone reecting
the contribution of the anthropogenic activity responsible for the
chemical composition of surface water in the study area.
4.3. Chemical weathering
The relative importance of two proton producing reactions −sul-
phide oxidation and carbonation, reects the dominance of the two
major sources of aqueous proton driving chemical weathering reactions.
The detail of the C ratio calculation is provided in the supplementary
material. The average C ratio for meltwater of Bhagirathi River at
Gangotri and Dabrani was found at 0.254 and 0.465 respectively
implying that protons derived from the reaction of sulphide oxidation
[Fig. 6(a–c)]. While at Uttarkashi and Devprayag, the C ratio was
calculated 0.63 and 0.684 respectively, which indicates coupled reac-
tion involving the protons derived from dissolution and dissociation of
atmospheric CO
2
additionally with oxidation of sulphide minerals con-
trols the chemistry of meltwater [Fig. 6(a and c)]. Here it is clearly
revealed the meltwater chemistry of river Bhagirathi upto Dabrani is
dominated by sulphate i.e. oxidation of sulphide further indicating the
inuence of Gangotri glacier in continuum upto Dabrani and thereafter
upto Haridwar, it is dominated by bicarbonate i.e. dissolution and
dissociation of atmospheric CO
2
. C ratio was observed more than 0.5
almost constant at all sites for river Alaknanda showing that carbonate
dissolution and pyrite oxidation are dominant in controlling the chem-
istry of water in river Alaknanda of the upper Ganga basin (Fig. 6b).
4.4. Seasonal variations of major ion chemistry and solute uxes
In the present study, discharge in Upper Ganga Basin was measured
at Devprayag of river Bhagirathi, river Alaknanda and at Rishikesh of
river Ganga. Daily discharge of river Bhagirathi, river Alaknanda and
river Ganga for a rising limb of the hydrograph (May −June), peak ow
(July) and falling limb (August −September) was observed to be 419
m
3
/s, 1518.7 m
3
/s and 1960 m
3
/s respectively. Chemical constituent
concentration-ow relationships turned out to be successful in recent
years to support the relative chemical contributions to the river from
regular inputs pertaining to rain (Bowes et al. 2015). From the
Fig. 5. Gibbs plots for River Bhagirathi, Alaknanda and Ganga.
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
10
Fig. 6. C-ratio for river Bhagirathi, river Alaknanda and river Ganga.
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
11
relationship between ion concentrations and river ow at different sites
it can be accomplished that all the constituents exhibited a dilution of
concentration with the increasing ow of the river. More or less all the
chemical constituents show a logarithmic relationship with the ow
[Fig. 7(a–h) and S3(a–d) in Supplementary material]. However, few
exceptions are visible particularly in the river Ganga for the ions NO
3
,
NH
4
and PO
4
(Fig. S3b in Supplementary material and Fig. 7e and 7f
respectively). These ions show variations from the general trend as their
concentration increased with an increase in discharge of the river. PO
4
however reportedly suffered this deviation in river Alaknanda as well
(Fig. 7f).
Suspended sediment concentration is directly linked with the
discharge variation in glacial meltwater. The highest TSS values were
recorded in the monsoon season at all sites. Himalaya facilitates the
(a) (b)
)d()c(
(e) (f)
(g) (h)
Fig. 7. Relationship of the concentration of anion and cations with ow in river Bhagirathi, Alaknanda and Ganga.
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
12
annual discharge peak in the month of June and contributes a large
proportion of the annual runoff. This shows that TSS presented a fair
positive correlation with discharge variation, an increase in the value of
discharge, TSS also increases. The percentage of the monsoon runoff
components varies across the elevations in the river basin as lower el-
evations receive higher precipitation than higher elevations. Suspended
sediment and turbidity increase due to soil erosion caused by altered
stream grade, aquatic organisms and transports a large nutrient ux
(Prathumratana et al., 2008). The ux of various water quality constit-
uents is given in supplementary information in Table S1, Fig. 8(a-i) and
S4(a-d) in Supplementary material. It can be clearly seen from Fig. 8
(a–i) and S4(a–d) that the ux of almost all the parameters follows the
same trend as that of discharge.
To study the relationship between discharge and uxes, plots are
drawn between discharge and uxes of TSS, TDS, HCO
3
, SO
4
, Cl, NO
3
,
PO
4,
Ca, Mg, Na, K and NH
4
in river water of Bhagirathi, Alaknanda and
Ganga and are given in Fig. 9(a-h) and S5(a-d) in Supplementary ma-
terial. It can be inferred from these plots that ux for almost all pa-
rameters is highly correlated with the discharge of river Bhagirathi
while for river Alaknanda and river Ganga the correlation values are
signicantly low. The best correlation has been shown by TDS (r
2
=
0.89) for the river Bhagirathi (Fig. 9a).
However, for PO
4
, river Alaknanda comparatively depicts a signi-
cant correlation (r
2
=0.45) rather than for river Bhagirathi (Fig. 9f).
Apart from TDS, the other parameters are also signicantly correlated
with discharge revealing r
2
values as 0.82 (SO
4
), 0.66 (Na), 0.86 (Mg),
0.82 (HCO
3
), 0.58 (NO
3
) and 0.83 (K) (Fig. 9d, Figure S5b in Supple-
mentary material, Fig. 9h, 9c, 9e and Figure S5c in Supplementary
material respectively). Amongst all the water quality constituents, NH
4
ux shows a poor correlation with discharge at all three rivers
(Figure S5d in Supplementary material).
4.5. Statistical analysis of the hydrochemical data
The statistical analysis of the hydrochemical data of river water
samples (River Bhagirathi, River Alaknanda and River Ganga) has been
carried out by correlation coefcient to assess the mechanism of solute
acquisition processes in the Upper Ganga Basin. The intercorrelation
between twenty water quality parameters viz., pH, EC, TDS, TSS, DO,
BOD, COD, alkalinity, hardness, HCO
3
, Cl, SO
4
, NO
3
, PO
4
, F, Na, K, Ca,
Mg and NH
4
for Rivers Bhagirathi, Alaknanda and Ganga are given in
Tables S2, S3 and S4 in Supplementary material respectively. The rela-
tionship among each of the hydrochemical parameters showed positive
and negative correlations and the text in bold represents signicant
relation at p <0.01.
4.5.1. River Bhagirathi
A strong positive correlation of HCO
3
with Cl (r =0.84) and Na (r =
0.85) suggested the release of these ions from rock weathering through
silicate weathering (Supplementary Table S2). A positive correlation of
HCO
3
was also observed with Ca (r =0.87) in the Bhagirathi River water
indicating the production of HCO
3
–
ions from dissolved CO
2
and release
of cations from rock minerals thereby suggesting carbonate weathering
as shown in the following reactions:
CaCO
3
+CO
2
+H
2
O ↔ Ca +2HCO
3
.
Calcite
(Ca, Mg, Na, K) silicates +H
2
CO
3
↔ Ca +Mg +Na +K +H
4
SiO
4
+
HCO
3
+Clay minerals
CaMg (CO
3
)
2
+2H
2
CO
3
↔ Ca +Mg +4HCO
3
Dolomite
It is revealed from the C ratio that hydrochemistry upto Dabrani is
governed by sulphide oxidation and afterwards by carbonation. The
occurrence of this trend is attributable to the prevailing glacier phe-
nomenon (Sharma et al., 2019). HCO
3
is the main ion responsible for
alkalinity in natural water which is reected by the correlation coef-
cient (r =1.00). There is a strong positive correlation of hardness
observed with EC (r =0.98), TDS (r =0.98), Ca (r =0.96) and Mg (r =
0.91). These results indicated the dependency of hardness on ions
particularly Ca and Mg. A high positive relation between Na and Cl (r =
0.91) has also been observed which indicates the dissolution of salt
preferably from halite during the summer season.
4.5.2. River Alaknanda
The relationship between different hydrochemical parameters for
River Alaknanda has been interpreted by a correlation matrix as shown
in Supplementary Table S3. A correlation coefcient of 1.0 was obtained
for HCO
3
with alkalinity. The other parameters which showed a good
positive correlation with HCO
3
are EC (r =0.41), Ca (r =0.75) and Mg
(r =0.74). The dependency of hardness on calcium and magnesium is
also contemplated in the water of Alaknanda. The correlation co-
efcients 0.75 and 0.74 were obtained for hardness with Ca and Mg
respectively. EC showed a strong correlation with alkalinity (r =0.84),
Ca (r =0.91) and Mg (r =0.91). Moreover, Cl showed a positive cor-
relation with both Na (r =0.80) and K (r =0.74).
4.5.3. River Ganga
The correlation coefcient matrix of hydrochemical parameters for
River Ganga is shown in Supplementary Table S4. The values of the
coefcients showed some interrelations between the parameters. A
strong positive correlation occurred for EC with alkalinity (r =0.97),
hardness (r =0.98), HCO
3
(r =0.97), Ca (r =0.92) and Mg (r =0.96).
Both alkalinity and hardness showed a correlation with Ca and Mg and
are also strongly related to each other (r =1.0). A correlation coefcient
of 0.91 between Na and Cl was also observed indicating the dissolution
of the salt preferably at Haridwar or Rishikesh, which may have
precipitated during the summer season. The signicant concentration of
Ca, Mg and SO
4
in the stream of River Ganga is very well supported by
the correlation coefcients existing among them. Their presence in the
river water is mainly attributed to the rock-forming minerals, calcite and
dolomite.
On applying the correlation coefcient analysis to the hydrochemical
constituents of Rivers Bhagirathi, Alaknanda and Ganga, it was observed
that the variables are dependent on each other. It can be inferred that the
most prominent correlations in all the three river systems existed
amongst hardness, EC, HCO
3
, Ca and Mg. The impact of halite dissolu-
tion during summer preferably at Haridwar and Rishikesh is signi-
cantly supported by the correlation between Na and Cl.
4.6. Principal component analysis (PCA)
The multivariate analysis was applied to twenty water quality pa-
rameters viz., pH, EC, TDS, TSS, DO, BOD, COD, alkalinity, hardness,
HCO
3
, Cl, SO
4
, NO
3
, PO
4
, F, Na, K, Ca, Mg and NH
4
for river Bhagirathi,
Alaknanda and Ganga. The details of the principal component analysis
has already been discussed in Material and methods section. The eigen
value analysis of the correlation matrix yields factors which lie
orthogonal to one another within a multidimensional space. The factors
are uncorrelated to each other, described by means of their loadings/
correlation with original variables and ranked in order of the amount of
the total variance they explain. The loading close to 1.0 is an indicative
of a strong correlation between two variables while loading of zero in-
dicates that the two variables are uncorrelated.
4.6.1. River Bhagirathi
Table 3 gives the eigen values, percent of total variance explained
and cumulative percent of the total variance. The chemical character-
istics of the river water samples can be interpreted on the basis of ve
principal components (Table 4). The rst principal component accounts
for 50% of the total variance and major contributions have been shown
by hardness and TDS. Thus, this factor is called the hardness factor. The
other dominating variables contributing their loadings are Mg, Ca,
HCO
3
, Na, alkalinity and SO
4
. Apart from these variables, another factor
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
13
(a) (b)
(c) (d)
(e) (f)
(g) (h)
(i)
Fig. 8. Variation of discharge, uxes of major anion and cation in river water of Bhagirathi, Alaknanda and Ganga.
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
14
which adds to this factor is COD. The second principal component which
accounts for 11% of the total variance has been greatly acknowledged by
potassium and chloride factor. The third principal component accounts
for 8% of the total variance. This component can be identied as the
hydronium ion factor. The release of H
+
ions into the stream supports
the phenomena of carbonate weathering occurring in the Upper Ganga
Basin. The fourth principal component accounts for 6% of the total
variance and is identied as the DO factor. The fth principal compo-
nent accounts for 5% of the total variance and is mainly characterised by
nitrate ions. The nitrate ion is chiey responsible for the anthropogenic
activities occurring in the region. Thus, this component is identied as
the nitrate factor or the anthropogenic factor.
4.6.2. River Alaknanda
For River Alaknanda, the hydrochemical characteristics are
explained by the rst ve principal components which explain 76% of
the total variance (Table 3). The rst principal component accounts for
46% of the total variance and the dominating variables covered are
(a) (b)
(c) (d)
(e) (f)
(g) (h)
Fig. 9. Relationship of uxes of anion and cations with discharge in river Bhagirathi, Alaknanda and Ganga.
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
15
HCO
3
, alkalinity, Ca, K and EC. Hence, this factor is called the bicar-
bonate factor. The second principal component accounts for 10% of the
total variance and this is characterised by sulphate ion. Thus, this could
be called as sulphate factor. The third principal component accounts for
7% of the total variance and is highly loaded on ammonium and a trivial
loading on chloride. Thus, this is recognised as ammonium factor. The
fourth principal component accounts for 6% of the total variance. The
highest loading on potassium is observed in this component. The fth
principal component explains 5% of the total variance and is highly
loaded on uoride (Table 4). Hence, this factor is called uoride factor.
4.6.3. River Ganga
For river Ganga, the hydrochemical characteristics are explained by
the rst ve principal components which accounts for 91% of the total
variance (Table 3). The rst principal component accounts for 47% of
the total variance and is mainly characterised by uniform loading of
hardness, HCO
3
, alkalinity, EC, Ca and Mg. Due to the highest loading of
hardness, this factor can be classied as a hardness factor. The high
positive loading of Ca and HCO
3
indicates a greater contribution of these
ions towards hardness. The second principal component which accounts
for 16% of the total variance is classied as uoride factor for the
maximum loading of uoride. Na and Cl also contribute signicantly to
this factor. The third principal component explains 8% of the total
variance and is highly loaded on K with low loading of NO
3
. Fluoride
and potassium mark their presence in the streams of Ganga through
weathering of mineral rocks, biotite and tourmaline in the Upper Ganga
Basin (Tewari et al., 2019). The fourth principal component accounts for
7.3% of the total variance and is positively loaded on pH. This factor is
called as hydrogenion factor. Carbonate weathering releases proton thus
affecting the pH balance of the river. The fth principal component
explains 7.1% of the total variance and the highest loading on BOD is
observed in this component along with high negative loading on DO
(Table 4). This factor also has slight positive loading on COD. Clearly,
PC5 establishes an inverse relationship between DO and the remaining
two demand parameters. This factor explains the presence of organic
contaminants in the river.
Thus, the principal component analysis provides an insight into the
dominant loadings of water quality constituents in all the three river
systems. The presence of ions viz., Na and Cl in the streams of River
Bhagirathi and Ganga through rock weathering is well explained by
their corresponding loadings as well as through their strong correlation.
However, K loading was observed in the water of River Bhagirathi,
Alaknanda and Ganga. The highest loadings of HCO
3
, Ca, Mg, hardness
and alkalinity in the river streams are accountable to the weathering
phenomena. The loadings of pH and NO
3
were predominantly observed
for River Bhagirathi and River Ganga.
5. Conclusions
The contribution of Himalaya rivers originating from snow and
glacier elds of higher Himalaya play an important role in controlling
the solutes levels in the river Ganges. As these mountain waters with a
signicant amount of snow, glacier meltwaters and rainfall is charac-
terised by low ionic concentrations and play a major role in diluting the
high solute load emanating from the Ganga plain catchments. Hence any
change in the quality and quantity of the Himalayan tributaries of River
Ganga under the climate change regime will impact the quality pa-
rameters of River Ganga. Hence a clear understanding of the charac-
teristics and process driving the chemical enrichment of the glacial
meltwater and its instream modication in the high altitude region is
essential for evaluating the role of the Himalayan component of the
Ganga river system in maintaining the quality of the Ganges water. In
this perspective, the study of hydrochemical and geochemical charac-
teristics of the Upper Ganga Basin is necessary at regular intervals. The
present investigation revealed that the nature of meltwater is slightly
acidic and physicochemical parameters were within permissible limits
at almost all sampling points. However, COD (2.1–45.7 mg/L) and TSS
(0.08–2837 mg/L) values were above the prescribed criteria limit of 10
mg/L for drinking purposes for river by CPCB at almost all sampling
sites. Bicarbonate and sulphate were observed as dominant anions in the
rivers. One of the most peculiar ndings of the study is that the melt-
water chemistry of river Bhagirathi upto Dabrani is dominated by sul-
phate i.e. oxidation of sulphide further indicates the inuence of
Gangotri glacier in continuum upto Dabrani and thereafter upto Har-
idwar, it is dominated by bicarbonate i.e. dissolution and dissociation of
atmospheric CO
2
. This aspect is further supported by the C ratio. C ratio
showed that protons derived from the reaction of sulphide oxidation in
the upper stretch of river Bhagirathi while in the lower stretch, coupled
reactions involving dissolution and dissociation of atmospheric CO
2
and
oxidation of sulphide are the major contributor of proton production.
A high contribution of calcium and magnesium was observed over
the total cation. The hydrogeochemical study revealed the minor
contribution of silicate weathering and ion exchange process controls
the major chemistry. Gibbs plot suggested precipitation induced chem-
ical weathering along with the dissolution of rock-forming minerals.
From the study of the relationship between discharge and uxes, it can
be inferred that ux for almost all parameters is highly correlated with
the discharge of river Bhagirathi while for river Alaknanda and river
Table 3
Eigen values based on correlation matrix of Rivers Bhagirathi, Alaknanda and Ganga.
S.
No.
River Bhagirathi River Alaknanda River Ganga
Eigen
Value
Percent of
total variance
Cumulative percent
of variance
Eigen
Value
Percent of
total variance
Cumulative percent
of variance
Eigen
Value
Percent of
total variance
Cumulative percent
of variance
1 10.10 50.539 50.539 9.352 46.762 46.762 9.546 47.729 47.729
2 2.212 11.061 61.601 1.981 9.906 56.668 3.308 16.539 64.268
3 1.614 8.070 69.671 1.495 7.477 64.145 1.645 8.224 72.492
4 1.275 6.374 76.045 1.335 6.673 70.818 1.467 7.337 79.829
5 1.050 5.251 81.296 1.061 5.303 76.121 1.423 7.116 86.944
6 0.949 4.745 86.041 0.943 4.717 80.837 0.928 4.642 91.587
7 0.756 3.778 89.819 0.863 4.316 85.153 0.579 2.893 94.479
8 0.598 2.990 92.810 0.669 3.345 88.498 0.360 1.798 96.278
9 0.572 2.862 95.671 0.564 2.822 91.320 0.267 1.334 97.612
10 0.381 1.907 97.578 0.470 2.351 93.672 0.240 1.198 98.810
11 0.152 0.760 98.338 0.379 1.897 95.569 0.096 0.480 99.290
12 0.117 0.583 98.920 0.342 1.710 97.279 0.068 0.339 99.629
13 0.101 0.503 99.424 0.268 1.339 98.617 0.049 0.246 99.875
14 0.054 0.270 99.694 0.120 0.600 99.217 0.016 0.081 99.957
15 0.033 0.165 99.859 0.074 0.372 99.589 0.006 0.028 99.985
16 0.016 0.082 99.941 0.053 0.267 99.856 0.003 0.013 99.998
17 0.009 0.043 99.984 0.022 0.110 99.966 0.000 0.002 100.000
18 0.003 0.016 100.000 0.007 0.034 100.000 – – –
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
16
Ganga the relationship is signicantly low. Principal component anal-
ysis revealed that ve principal components are sufcient to explain the
hydrochemical behaviour of these rivers.
CRediT authorship contribution statement
M.K. Sharma: Investigation, Conceptualization, Methodology,
Writing – original draft. Pradeep Kumar: Investigation, Data curation,
Writing – review & editing. Parul Prajapati: Analysis work, Data pro-
cessing, Manuscript writing. Kunarika Bhanot: Software, Data Pro-
cessing, Manuscript writing. Udita Wadhwa: Analysis work. Garima
Tomar: Analysis work, Data Processing. Rakesh Goyal: Investigation.
Beena Prasad: Analysis work. Babita Sharma: Analysis work, Data
Processing, Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgement
This research work is a part of NMSHE project titled “Integrated
Hydrological Studies for Upper Ganga Basin up to Rishikesh” sponsored
by Department of Science & Technology, Govt. of India, New Delhi vide
DST Sanction No. DST/SPLICE/CCP/NMSHE/TF-4/NIH/2015 −G and
hereby acknowledged. The authors are thankful to the Director, Na-
tional Institute of Hydrology, Roorkee for providing analytical facilities
and nancial support for carrying out this work.
Data availability statement
The corresponding author conrms that he had full access to all the
data used in the study and takes responsibility for the integrity of the
data and the accuracy of the data analysis. Discharge data used in the
study was provided by Central Water Commission, Ministry of Jal
Shakti, Govt. of India, New Delhi and is thankfully acknowledged. The
analytical data that support the ndings of this study will be available
from the corresponding author upon reasonable request.
Appendix A. Supplementary material
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jaesx.2022.100108.
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Varimax rotated component loadings with Kaiser Normalization of Rivers Bhagirathi, Alaknanda and Ganga.
River Bhagirathi River Alaknanda River Ganga
Variables Principal Component Variables Principal Component Variables Principal Component
PC1 PC2 PC3 PC4 PC5 PC1 PC2 PC3 PC4 PC5 PC1 PC2 PC3 PC4 PC5
TDS 0.944 0.205 0.079 −0.050 0.044 Alkalinity 0.956 0.104 0.025 0.004 0.173 Hardness 0.982 0.098 0.075 0.083 0.093
EC 0.944 0.206 0.078 −0.049 0.046 HCO
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Ca 0.895 0.182 0.246 −0.012 0.013 TDS 0.768 0.526 0.180 0.110 0.066 EC 0.944 0.216 0.204 0.086 0.071
Alkalinity 0.782 0.398 0.366 −0.136 0.158 EC 0.768 0.527 0.179 0.111 0.066 TDS 0.944 0.216 0.204 0.087 0.071
HCO
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4
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SO
4
0.701 −0.212 −0.250 0.101 −0.215 TSS −0.204 −0.678 −0.135 0.017 −0.434 F 0.052 0.835 −0.032 0.244 0.024
TSS −0.527 0.196 −0.425 0.437 −0.448 Mg 0.651 0.672 0.068 0.119 0.177 Na 0.458 0.748 0.410 0.133 0.088
K 0.401 0.813 −0.015 −0.036 −0.028 NH
4
−0.024 0.090 0.910 −0.156 −0.116 Cl 0.299 0.746 0.543 0.088 −0.117
Cl 0.591 0.611 0.243 −0.270 0.144 Cl 0.495 0.112 0.737 0.204 0.093 K 0.207 0.278 0.867 0.176 0.090
pH 0.143 0.068 0.898 −0.012 0.072 pH −0.132 −0.107 0.062 −0.938 −0.042 NO
3
0.548 −0.136 0.737 −0.059 −0.034
DO −0.137 −0.131 −0.013 0.959 0.077 F 0.245 0.078 −0.081 0.059 0.898 pH 0.444 0.066 −0.033 0.814 −0.124
NO
3
0.039 0.050 0.071 0.078 0.959 BOD 0.257 0.031 0.007 0.038 −0.003 COD −0.025 0.369 0.355 0.772 0.241
F 0.409 0.271 −0.055 −0.091 0.029 DO −0.056 −0.014 −0.007 −0.131 0.045 BOD 0.082 0.182 0.119 0.049 0.830
NH
4
0.105 0.117 −0.090 0.005 0.041 PO
4
−0.040 −0.104 −0.010 0.028 −0.028 DO −0.268 −0.250 0.026 0.250 −0.614
PO
4
−0.176 −0.099 −0.137 0.099 −0.036 NO
3
0.179 0.139 0.122 −0.028 −0.042 NH
4
−0.316 0.438 0.207 −0.399 −0.607
BOD −0.021 0.030 0.019 −0.001 −0.171 COD 0.419 0.072 0.042 0.140 0.207 PO
4
0.039 0.210 −0.035 −0.029 0.092
M.K. Sharma et al.
Journal of Asian Earth Sciences: X 8 (2022) 100108
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