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In the present study, the four estuaries were selected from the South Gujarat region to appraise the impact of industrial pollution in the estuarine water samples. The study focused on the tidal variation of nutrients, which disclosed that concentrations of NO2-N, NO3-N, NH4-N, TN, and reactive silicates were higher in low-tide whereas pH, salinity, and dissolved oxygen were higher in high-tide water samples. The results of high BOD and low DO expose the anthropogenic inputs in these estuaries during the low-tide. The results of physico-chemical and nutrients parameters of water showed that the pollution level is strongly influenced by tidal and seasonal changes. Pearson's correlation matrix and principal component analysis (PCA) are applied to a hydrological and hydrographical dataset for finding the spatial-temporal variation during the tidal difference. This study suggested that there is an impact of industrial pollution and anthropogenic inputs on the estuarine water of the study area.
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( Received 10 February 2020; Accepted 28 February 2020; Date of Publication 29 February 2020 )
WSN 143 (2020) 79-102 EISSN 2392-2192
Elucidation of tidal spatial-temporal variation
of physico-chemical and nutrient parameters
of estuarine water at South Gujarat
Nisheeth C. Desai1,*, Nipul B. Kukadiya1, Jignasu P. Mehta1,
Dinesh R. Godhani1, Jayendra Lakhmapurkar2 and Bharti P. Dave3
1Department of Chemistry, (DSTFIST sponsored Department) Mahatma Gandhi Campus,
Maharaja Krishnakumarsinhji Bhavnagar University, Bhavnagar 364 002, Gujarat, India
2Gujarat Ecology Society (GES) Subhanpura, Vadodara - 390002, India
3School of Sciences, Indrashil University, Kadi, Gujarat, India
*E-mail address: dnisheeth@gmail.com
ABSTRACT
In the present study, the four estuaries were selected from the South Gujarat region to appraise
the impact of industrial pollution in the estuarine water samples. The study focused on the tidal variation
of nutrients, which disclosed that concentrations of NO2N, NO3N, NH4N, TN, and reactive silicates
were higher in low-tide whereas pH, salinity, and dissolved oxygen were higher in high-tide water
samples. The results of high BOD and low DO expose the anthropogenic inputs in these estuaries during
the low-tide. The results of physico-chemical and nutrients parameters of water showed that the
pollution level is strongly influenced by tidal and seasonal changes. Pearson’s correlation matrix and
principal component analysis (PCA) are applied to a hydrological and hydrographical dataset for finding
the spatial-temporal variation during the tidal difference. This study suggested that there is an impact of
industrial pollution and anthropogenic inputs on the estuarine water of the study area.
Keywords: Estuary water, Physico-chemical parameters, Nutrients, Tidal variation, Principal
component analysis
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1. INTRODUCTION
Estuaries are important coastal ecosystems, having a confluence of fresh and marine
environments that create a salinity gradient from inner to the outer estuary [1]. These prominent
zones regulating material fluxes from terrestrial to the ocean [2], which carried river nutrient
loads and therefore, it is the most significant for the ecosystem [3]. This zone receives a
significant amount of freshwater, particulates, nutrients, dissolved organic matter, suspended
matter and contaminants from surface-dwelling, exchange resources and liveliness with the
open ocean. Residential, recreational and mechanized developments (such as marinas) are
usually located right on the waterfront with supporting structures such as an embankment create
the contamination in these ecosystems.
Industrial and anthropogenic interpolations into the estuarine areas resulted in the
discharge of partially treated and untreated wastewater into the insubstantial ecosystem. Among
these, the discharge from chemical, paper, pharmaceutical, and food product based industries
are considered as paramount sources of inorganic, organic pollutants and heavy metals into the
water column [4-6]. The inorganic pollutants in seawater corollary from the decomposition of
agricultural organic pollutants, which are because of the excess use of nutrients during
cultivation [7-11]. This coastal water leads to the eutrophication process, which is the most
common impact of human activities, industrial and coastal development [12-14].
Socioeconomic development in South Gujarat and precipitous industrialization led to the
emergence of many industries near rivers. These industries are utilizing freshwater according
to need and conveniently disposing of the wastewater either into a river or in the estuaries
depending upon their locations. The quality of estuarine water is found to deteriorate in the
present study area [15]. The rivers of Gujarat are bearing the impact of industrial pollution due
to the heavy industrialization, which generates an enormous quantity of toxic and hazardous
wastes [16]. The industrial cluster of Vapi GIDC, central effluent treatment plant (CETP) and
other GIDC(s) are located in the vicinity of rivers and estuaries in the south Gujarat region. The
environment by surroundings is polluted as a result of the discharge of industrial waste. Vapi is
an industrial town, which is listed in the world's top 10 polluted cities [17]. The results of
Dudani et al. [18] showed the impact of industrial pollution on the estuaries and overall of the
health of the mangrove ecosystem.
The main aim of the present study to the assessment of the spatial and temporal variation
of physico-chemical parameters, nutrients and anthropogenic inputs in the estuarine regions of
south Gujarat.
2. EXPERIMENTAL
2. 1. Material and methods
2. 1. 1. Study area overview
The South Gujarat estuarine habitats situated at 21.6683 20.1531 N latitude and 72.5451
72.7428 E longitude. Four estuaries of south Gujarat were selected to explore the pollution
status of that region (Fig. 1). (1) Varoli estuary: It is located in Umargam and location 20.21163
N and 72.75619 E were selected for sample collection and used as the least polluted zone. (2)
Damanganga estuary (20.41241 N and 72.84033 E): The Damanganga River originates from
the Sahyadri hills in Maharashtra and ending in the Arabian Sea near Daman. It is considered
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as one of the most populated areas in the South of Gujarat [19]. Several reports published
elsewhere [19-20] indicates the amount of pollution load in this area. (3) Kolak estuary
(20.46548 N and 72.8574 E): The Kolak River originates from Saputara hills near Valvari and
meets the Arabian Sea. Zingde et al. [21] reported the water quality of the Kolak river way back
in the 1980s and suggested high pressures on engineering and anthropogenic activities. (4) Par
(20.5341 N and 72.8881 E): The Par originates from Sahyadri hills of Satpura Range, flows
towards the west and joins the Arabian Sea.
Figure 1. Sampling locations (1) Varoli (2) Damanganga, (3) Kolak and (4) Par estuaries
of South Gujarat, India
2. 1. 2. Methodology
The study carried out for two successive years in three different seasons, i.e. pre-monsoon
(May), post-monsoon (November), and winter (March) between May-2015 to April 2017. The
estuarine water samples were collected seasonally using 5 L Niskin sampler in low tide and
high tide periods. The estuarine water samples were collected and stored as per prevailing
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protocols. The physico-chemical variables like temperature, pH, salinity, conductivity, and
TDS were measured “in situ” by using a portable Cyber-Scan 650; Eutech Thermo Fisher
Scientific, USA. Turbidity was measured in situ by Eutech TN100 portable turbidity meter
with a resolution of 0.01 NTU. APHA [22] was used for the analysis of Dissolved oxygen (DO)
and BOD. The nutrients (i.e. NO2-N, NO3-N, NH4-N, PO4-P, and SiO4-Si), the samples were
filtered through 0.45 µm pore size cellulose nitrate membrane filter and analyzed as per
protocols reported by earlier [23-24].
2. 1. 3. Statistical analysis
The statistical analysis was accomplished by using SPSS (version 20.0) software. The
relationship between the physico-chemical variables and nutrients can provide important
information on the trend of each parameter during the tidal difference [25]. Pearson’s
correlation coefficients and its significant level were determined in order to understand the
spatial-temporal variation of the nutrients and physico-chemical parameters due to tidal
variation. The principal component analysis (PCA) is an important factor in analyzing the
estuarine water quality behaviors due to tidal variation.
3. RESULTS AND DISCUSSION
The estuarine environment is exposed to various changes in physico-chemical properties
due to the continuous mixing of freshwater with marine water. Assessing water quality is very
important in determining the quality of the ecosystem [26].
3. 1. Assessment of physico-chemical water quality parameters
pH is known as the key variable in water since many properties, processes and reactions
are pH-dependent. In the estuarine water, the pH range was from 7.8 to 8.3 and it is due to the
buffering capacity of the seawater [27]. It was reported that pH 5 to 9 is not directly harmful to
aquatic life but such changes can make many common pollutants more toxic in nature [28]. The
pH of the water was varied from 6.87 to 8.08 during the low tide and was varied from 7.17 to
8.11 during the high-tide. The average values of pH were 7.64 ±0.38, 7.69 ±0.21, 7.54 ±0.25
and 7.54 ±0.15 for the low-tide samples and it was 7.77 ±0.33, 7.64 ±0.21, 7.70 ±0.20 and 7.80
± 0.24 for high tide water sample in the Varoli, Damanganga, Kolak, and Par respectively (Fig.
2a). CO2 uptake by planktons leads to more dissolution of CO2 that generates carbonic acid
[29] in the winter season and hence the pH values were lower in winter as compared to other
seasons. The pH showed negative correlation with NO3N (r = 0.631, p<0.01) and TN
(r = 0.493, p<0.05) for the low-tide samples (Table 1) and it also showed negative correlation
with NO3N (r = 0.633, p<0.01) for the high-tide samples (Table 2). The pH and NO3N has
no direct influence, but pH variation may alter the degree of solubility and kinetics of other
chemical reactions of oxygen compounds so that, it can release oxygen radicals or reduced form
that favors either the oxidized form of nitrogen or the reduced form of nitrogen [30].
Salinity is an indicator of a freshwater inroad into the seawater of estuaries and extrusion
of tidal water in the inland water bodies. The average values of salinity in the surface water
samples of Varoli, Damanganga, Kolak and Par estuaries were 35.30 ±2.034 (ppt), 22.095
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±11.27 (ppt), 21.74 ±4.89 (ppt) and 32.54 ±5.33 (ppt) during low-tide and the average salinity
values were 36.46 ±1.429 (ppt), 31.86 ±5.23 (ppt), 34.0 ±2.44 (ppt) and 35.37 ±2.29 (ppt) in
high-tide respectively (Fig. 2b). The salinity showed positive correlation with dissolved oxygen
(r = 0.491, p<0.05), reactive silicate (r = 0.910, p<0.01) and it was negatively correlated with
BOD (r = 0.608, p<0.01), NO2N (r = 0.815, p<0.01), NO3N (r = 0.769, p<0.01), NH4N
(r = 0.456, p<0.05), TN (r = 0.822, p<0.01) and PO4P (r = 0.456, p<0.05) in the low-tide
water samples (Table 1). The salinity showed a negative correlation with all nutrients except
phosphate in high-tide surface water samples (Table 2). The nutrients were negatively
correlated with salinity in each season which was in accordance to work done by Iwata et al
[31]. The high values of salinity in the low-tide samples in the pre-monsoon seasons is attributed
to the removal of freshwater through the evaporation mechanism [32]. The lowest value of
salinity was noticed for the post-monsoon season during the low-tide period, which may be due
to the fact that a very high influx of freshwater received by the estuary. Similar results have
been registered for Cochin estuaries [33] which, validates our observations.
Conductivity is often used as an alternative measure of dissolved solids and it has direct
correlation with dissolved solids for a specific body of water. The conductivity was varied from
15.53 to 57.58 mS/cm in the low-tide and 36.11 to 59.86 mS/cm in the high-tide. The average
values of conductivity (mS/cm) at Varoli (52.75 ±2.895 mS/cm), Damanganga (35.44 ±15.89
mS/cm), Kolak (34.259 ±7.04 mS/cm) and Par (49.309 ±7.32 mS/cm) respectively in the low-
tide samples and it was found at Varoli (54.74 ±2.57 mS/cm), Damanganga (48.643 ±7.57
mS/cm), Kolak (51.407 ±3.36 mS/cm) and Par (53.16 ±3.15 mS/cm) in the high-tide samples
respectively (Fig. 2c). The conductivity was positively correlated with DO (r = 0.483, p<0.05),
negatively correlated with BOD (r = 0.583, p<0.05), NO2N (r = 0.796, p<0.01), NO3N
(r = 0.729, p<0.01), NH4N (r = 0.480, p<0.05), TN (r = 0.794, p<0.01), PO4P (r = 0.443,
p<0.05) and silicate (r = 0.917, p<0.01) in the low-tide samples. The conductivity showed
positive correlation with turbidity (r = 0.450, p<0.05) and negatively correlated with NO2N
(r = 0.567, p<0.01), NO3N (r = 0.549, p<0.01), NH4N (r = 0.674, p<0.01) TN (r = 0.539,
p<0.01) and silicates (r = 0.878, p<0.01) in the high-tide samples (Table 1 and 2). The negative
correlation of conductivity with all nutrients in low-tide and high-tide was in the congruence
with the results reported elsewhere [31, 34].
Conductivity is the measurement of the ability of water to the content of dissolved ionic
salts in the water. It is often used as an alternative measure of dissolved solids and it has direct
correlation with dissolved solids for a specific body of water. The conductivity was varied from
15.53 to 57.58 mS/cm in the low-tide and 36.11 to 59.86 mS/cm in the high-tide. The average
values of conductivity (mS/cm) at Varoli (52.75 ±2.895 mS/cm), Damanganga (35.44 ±15.89
mS/cm), Kolak (34.259 ±7.04 mS/cm) and Par (49.309 ±7.32 mS/cm) respectively in the low-
tide samples and it was found at Varoli (54.74 ±2.57 mS/cm), Damanganga (48.643 ±7.57
mS/cm), Kolak (51.407 ±3.36 mS/cm) and Par (53.16 ±3.15 mS/cm) in the high-tide samples
respectively (Fig. 2c).
The conductivity was positively correlated with DO (r = 0.483, p<0.05), negatively
correlated with BOD (r = 0.583, p<0.05), NO2N (r = 0.796, p<0.01), NO3N (r = 0.729,
p<0.01), NH4N (r = 0.480, p<0.05), TN (r = 0.794, p<0.01), PO4P (r = 0.443, p<0.05)
and silicate (r = 0.917, p<0.01) in the low-tide samples. The conductivity showed positive
correlation with turbidity (r = 0.450, p<0.05) and negatively correlated with NO2N (r = 0.567,
p<0.01), NO3N (r = 0.549, p<0.01), NH4N (r = 0.674, p<0.01) TN (r = 0.539, p<0.01)
and silicates (r = 0.878, p<0.01) in the high-tide samples (Table 1 and 2).
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Fig. 2 (a)
Fig. 2 (b)
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Fig. 2 (c)
Fig. 2 (d)
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Fig. 2 (e)
Fig. 2 (f)
Figure 2. Spatial-temporal variation of physico-chemical parameters (2a) pH, (2b) salinity,
(2c) conductivity, (2d) turbidity, (2e) DO and (2f) BOD in the surface water during tidal
fluctuation at South Gujarat estuaries.
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Table 1. Pearson Correlation Matrix for the seawater quality parameter during the low-tide.
Parameter
pH
Salinity
Conductivity
DO
BOD
NO2-N
TN
Phosphate
R.silicates
pH
1
Salinity
0.180
1
EC
0.173
0.995**
1
Turbidity
-0.036
0.077
0.068
DO
0.078
0.491*
0.483*
1
BOD
0.133
-0.608**
-0.583**
-0.473*
1
NO2-N
-0.342
-0.815**
-0.796**
-0.404*
0.562**
1
NO3-N
-0.631**
-0.769**
-0.729**
-0.432
0.456*
0.784**
NH4-N
-0.016
-0.456*
-0.480*
-0.545*
0.375
0.398
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**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Statistical evaluation is done by SPSS (20.0) software
Table 2. Pearson Correlation Matrix for the seawater quality parameter during the high-tide.
TN
-0.493*
-0.822**
-0.794**
-0.421
0.497*
0.921**
1
Phosphate
-0.231
-0.456*
-0.443*
-0.478*
0.497*
0.495*
0.436
1
R.silicates
-0.006
0.910**
-0.917**
-0.397
0.632**
0.646**
0.701**
0.473*
1
pH
EC
Turbidity
DO
BOD
NO2-N
NO3-N
NH4-N
TN
Phosphate
R.silicates
1
0.247
0.282
1
0.250
0.450*
1.
-0.150
0.15
-0.01
1
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**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Statistical evaluation is done by SPSS (20.0) software
The negative correlation of conductivity with all nutrients in low-tide and high-tide was
in the congruence with the results reported elsewhere [31, 34].
The increasing level of turbidity in the water resulted in the hindrance of penetrating light
and this occurrence damaged the aquatic life and also deteriorates the quality of surface water.
In the monsoon season, heavy soil erosion and suspended solids from sewage and fresh rainy
water increased the turbidity, which has a confrontational effect on the aquatic life [35].
Estuaries are usually more turbid than marine and riverine waters owing to the input of sediment
from rivers, the occurrence of dense populations of phytoplankton, and the asset of tidal currents
that prevent fine particles to settle down [12]. The Gulf of Khambhat accumulates a heavy
inflow of sediments during monsoon season due to the seven major rivers are ending here [36].
The turbidity levels fluctuated from15.9 to 1210 (NTU) during low-tide and 32.4 to 491 (NTU)
in high-tide respectively.
-0.246
-0.36
-0.28
-0.15
1
-0.208
-0.567**
-0.415*
-0.17
0.34
1
-0.633**
-0.555*
-0.41
-0.28
0.40
0.824**
1.
-0.186
-0.674**
-0.557*
-0.13
0.16
0.17
0.3
1
-0.02
-0.539**
-0.20
-0.37
0.00
0.721**
0.853**
0.21
1
-0.28
0.08
0.03
-0.14
0.39
0.541**
0.607**
-0.16
0.39
1
0.00
-0.878**
-0.29
-0.507*
0.33
0.527*
0.45
0.43
0.629**
-0.01
1
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The average turbidity in the Varoli (128.31 ±72.48 NTU), Damanganga (108.55 ±107.31
NTU), Kolak (471.5 ±386.86 NTU) and Par (415.43 ±125.68 NTU) during the low-tide and in
the high-tide, 179.28 ±137.01 NTU (Varoli), 122.93 ± 69.93 NTU (Damanganga), 200.56
±149.57 NTU (Kolak) and 240.51 ± 76.17 NTU (Par) respectively (Fig. 2d). The turbidity was
positively correlated with NH4N (r = 0.561, p<0.01) during the low-tide and showed negative
correlation with NO2N (r = 0.41, p<0.05) and NH4N (r = 0.557, p<0.01) in high-tide
samples (Table 1 and 2). The high values of turbidity in the study area during low-tide periods
may be attributed to runoff, soil erosion, industrial effluent and muddy flats around estuaries.
Dissolved oxygen (DO) is an important constituent of water and its concentration in water
is an indicator of prevailing water quality and the ability of the water body to maintain a
judicious aquatic life. The DO divulges the changes that occur in the biological parameters due
to the aerobic or anaerobic phenomenon and indicates the condition of the river water for the
purpose of the aquatic as well as human life [37]. The DO was varied from 0.648 to 7.78 (mg/L
O2) in the low-tide samples, whereas it was varied between 2.77 to 8.76 (mg/L O2) in the high-
tide samples. The average concentration of DO (mg/L O2) during the low-tide were 5.83 ±1.32,
3.36 ±0.78, 3.49 ±1.36 and 4.0 ±0.76 and the average values in the high-tide were 6.97 ±1.31,
5.52 ±1.06, 5.23 ±0.88 and 6.01 ±1.06 for Varoli, Damanganga, Kolak and Par estuaries
respectively (Fig. 2e).
DO was positively correlated with salinity (r = 0.491, p<0.05) and was negatively
correlated with BOD (r = 0.473, p<0.05), NO2N (r = 0.404, p<0.05), NO3N (r = 0.432,
p<0.05), NH4N (r = 0.545, p<0.05) TN (r = 0.421, p<0.05) and PO4P (r = 0.478, p<0.05)
in the low-tide samples (Table 1). The lower values of DO in low-tide might be due to industrial,
domestic wastage and also the influence of salinity, temperature, conductivity, currents, and
upwelling tides lead to such changes [38]. The negative correlation of DO with nutrients and
BOD might be due to the industrial and domestic effluents released into the region as these are
the main sources of oxidizable organic matter [39-40]. The results of DO suggested that the
lowest value was beyond the acceptable limits for aquatic life in Kolak, Damanganga, and Par
stations. This may be in consequence of the inputs of untreated industrial effluents, domestic
sewage, and tidal effect. Zingde et al. [21, 41, 42] have reported the very low values of DO for
these estuaries. Several other reports suggested that industrial effluent discharged in
Damanganga, Kolak and Par estuaries resulted in the death of fish and aquatic animals and
found at the bank of rivers [43-45].
Biochemical Oxygen Demand (BOD) is a lively water quality parameter since it provides
an index to evaluate the effect of discharged wastewater on the receiving environment. The
increasing level of BOD suggested that the water column is contaminated by organic and
nutrients substances inputs in estuaries, especially during the low-tide where the estuarine water
intrudes to seawater. The values of BOD varied between 0.42 to 386.42 mg/L in the low-tide
and was varied from 1.28 to 178.2 mg/L in the high-tide. The average values of BOD 1.51
±0.80 mg/L (Varoli), 280.6 ±84.47 mg/L (Damanganga), 187.34 ±100.8 mg/L (Kolak) and
70.98 ±6.48 mg/L (Par) in the low-tide samples. It was 2.12 ±0.60 mg/L(Varoli), 94.12 ±45.55
mg/L(Damanganga), 63.07 ± 55.33 mg/L (Kolak) and 12.06 ±15.58 mg/L (Par) in the high-tide
samples (Fig. 2f). Zingde et al. [21, 41] have also found higher values BOD in these estuaries.
The results of BOD suggested that the high pollution load in these estuaries has an antagonist
effect on the coastal and marine network. High BOD level indicates a decline in DO because
the oxygen exists in the water was being consumed by the bacteria leading to the inability of
fish and other aquatic organisms to persist in the river. BOD found above permissible limits
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[46] in the water samples of Damanganga and Kolak, which showed that these estuaries are
under high anthropogenic pressure.
Fig. 3 (a)
Fig. 3 (b)
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Fig. 3 (c)
Fig. 3 (d)
Figure 3. Spatial-temporal variation of nutrients; (3a) NO2-N, (3b) NO3-N, (3c) NH4-N and
(3d) TN in the surface water during tidal fluctuation at South Gujarat estuaries.
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Fig. 4 (a)
Fig. 4 (b)
Figure 4. Spatial-temporal variation of nutrients (4a) inorganic phosphate and (4b) reactive
silicates in the surface water during tidal fluctuation at South Gujarat estuaries.
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3. 2. Assessment of nutrients
The nitrogen cycle involves elementary dissolved nitrogen oxides such as (i) NO3¯ and
(ii) NO2¯ and reduced forms like (i) NH4+ and (ii) NH3 are playing an important role in
sustaining the aquatic life in the marine world. The concentrations of these three major elements
are characteristically higher in estuaries than in the open ocean. The domestic and industrial
effluents and run-off are the main sources of macro-elements, while the atmosphere and marine
waters may also contribute to it in minor amounts.
Nitrate (NO3N) is one of the most important markers of pollutions in water and is the
highest oxidized form of nitrogen. The most important source of nitrogen is the biological
oxidation of organic nitrogenous substances derived from sewage and industrial wastewater or
produced indigenously in the water [47]. Zepp [48] observed that variation in nitrate and its
reduced inorganic mixtures are predominantly the consequences of biologically activated
reactions. The concentration of NO3–N was oscillating between 8.23 to 70.68 µM in the low-
tide and it is varied between 8.92 to 55.35 µM in the high-tide. The average concentration of
NO3N 14.60 ±2.32 µM (Varoli), 38.28 ±21.68 µM (Damanganga), 28.76 ±12.27 µM (Kolak)
and 21.11 ±8.14 µM (Par) in the low-tide samples and 11.86 ±2.35 µM (Varoli), 30.21 ±15.52
µM (Damanganga), 19.17 ±7.0 µM (Kolak) and 16.58 ±3.62 µM (Par) in high-tide samples
(Fig. 3b). The highest concentration of NO3–N was recorded 70.68 µM in the Damanganga for
post-monsoon and the minimum was noticed 8.23 µM in the pre-monsoon for Par. The NO3N
exhibited positive correlation with DO and other nutrients; NO2N (r = 0.784, p<0.01), TN
(r = 0.871, p<0.01), PO4P (r = 0.449, p<0.05), silicate (r = 0.670, p<0.01) and negatively
correlated with pH, salinity and DO in the low tide (Table 1). The low-tide and high-tide results
showed similar trends. Quick absorption by phytoplankton and enhancement by surface run-off
resulted in a large-scale spatial-temporal variation of nitrate in the coastal region of the Gulf of
Khambhat. The results of Edokpayi et al. [49] revealed a similar pattern for nutrient presence
in this region.
Nitrite (NO2N) is an intermediate in the oxidation process of ammonia to nitrate in the
nitrogen cycle. Many industrial, domestic and sewage effluents are rich in ammonia can lead to
increase nitrite concentrations in receiving waters. Nitrite is toxic to aquatic life comparatively
at low concentrations. The values of NO2N were altered 0.75 to 61.71 µM for the low-tide
samples and it was between 0.26 to 41.69 µM for the high tide samples. The average
concentration of NO2–N was 5.13 ±3.53 µM (Varoli), 34.57 ±22.89 µM (Damanganga), 23.38
±14.49 µM (Kolak) and 5.17 ±5.12 µM (Par) for the low-tide samples and 2.07 ±1.90 µM
(Varoli), 15.88 ± 14.9 µM (Damanganga), 4.85 ±2.15 µM (Kolak) and 3.02 ±1.59 µM (Par) for
the high-tide samples (Fig. 3a). The highest concentration of NO2–N was 61.71 µM in winter
and the lowest was 1.13 µM in the Damanganga estuary in the low-tide samples for pre-
monsoon. The negative correlation with salinity (r = 0.815, p<0.01) suggested that during the
low-tide the concentration of NO2N increased. This may be attributed to the industrial effluent
and domestic wastage inputs in these estuaries.
Ammonia is present in terrestrial and marine environments where the plants and animals
were expelled ammonia. It is produced by the decay of organisms and by the commotion of
living micro-organisms [50]. Ammonium ion (NH4+) represented 80% of dissolved inorganic
nitrogen (DIN) and its highest values are always associated with freshwater invasion [33].
Sankaranarayanan and Qasim [51] suggested that the three-dimensional and time-based
variation in ammonia concentration might also be due to its oxidation to other forms or
reduction of nitrates to lower forms in coastal waters.
World Scientific News 143 (2020) 79-102
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The concentration of ammonicalnitrogen (NH4–N) was varied from 1.05 to 30.78 µM in
the low-tide samples and altered between 1.08 to 5.22 µM for the high-tide samples. The
average concentration of NH4–N was 2.38 ±1.01 µM (Varoli), 10.56 ±5.63 µM (Damanganga),
(10.78 ±9.36 µM (Kolak) and 4.303 ±3.32 µM (Par) for low-tide and 2.47 ±0.57 µM (Varoli),
3.35 ±1.49 µM (Damanganga), 3.01 ±1.46 µM (Kolak) and 2.90 ±0.95 µM (Par) for high-tide
respectively (Fig. 3c). The maximum value of NH4–N was noticed 30.78 µM in the Par during
low-tide in the pre-monsoon and minimum concentration was obtained 1.05 µM in the Varoli
during low-tide for post-monsoon. The NH4N positively correlated to turbidity and was
negatively correlated with salinity, conductivity and DO for the low-tide samples and was
negatively correlated with salinity, turbidity, and conductivity for the high-tide samples.
Total nitrogen (TN) is the measure of all forms of nitrogen (organic and inorganic). The
importance of nitrogen in the aquatic environs is patchy according to the relative amounts of
the forms of nitrogen present, be it ammonia, nitrite, nitrate, or organic nitrogen. The
concentration of TN was ranging from 28.66 to 152.36 µM in the low-tide and was varied from
24.22 to 110.98 µM in the high-tide samples. The average concentration of TN was 36.15 ±4.02
µM (Varoli), 103.17 ±47.59 µM (Damanganga), 71.03 ±28.18 µM (Kolak) and 48.02 ±10.52
µM (Par) for the low-tide and average concentration was 30.77 ±3.77 µM (Varoli), 69.92
±30.45 µM (Damanganga), 42.62 ±6.96 µM (Kolak) and 40.75 ±11.21 µM (Par) for the high-
tide (Fig. 3d). The concentration of TN was highest in the Damanganga (152.36 µM) and lowest
(24.22 µM) in the Par in the winter season respectively. There is all-encompassing evidence
that an increase in nitrogen loads are linked to eutrophication in the estuaries [52] and displayed
an impact on aquatic life and microorganism.
Phosphate in coastal waters depends upon its concentration in the freshwater that mixed
with the seawater [53]. Inorganic phosphate is the most readily accessible form of uptake during
photosynthesis in the aquatic ecosystem and enrichment of phosphate causes eutrophication,
which leads to aggregation with algal blooms, resulting in the depletion of DO level in estuaries.
The concentration of inorganic phosphate (PO4–P) was varied between 0.788 to 10.22 µM and
0.45 to 5.63 µM in the low-tide and high-tide samples respectively. The average concentration
of PO4P was 2.26 ± 0.98 µM (Varoli), 4.16 ±2.28 µM (Damanganga), 5.48 ±2.82 µM (Kolak)
and 2.13 ±0.93 µM (Par) was noticed during the low-tide and average values were 1.71 ±0.80
µM (Varoli), 3.18 ±1.34 µM (Damanganga), 2.25 ±0.50 µM (Kolak) and 1.77 ±0.77 µM (Par)
(Fig. 4a) in the high-tide. Industrial effluents, as well as domestic wastage released around
Damanganga, Silvasa, Vapi GIDC, Valsad GIDC, and CETP at Vapi and other creaks located
around industries, maybe the major contributors of phosphate into south Gujarat estuarine
environment. Liu et al. [54] have reported that seawater serves as the main source of phosphate
in the estuarine and coastal waters except for those receives freshwater contaminated with
industrial and domestic waste containing detergents as well as waste from an agro field rich
with phosphate-phosphorous fertilizers and pesticides. The noticeable seasonal deviation in the
phosphate concentration might be due to various processes like adsorption and desorption of
phosphate and buffering action of sediments under varying conservational conditions [55].
The silicate is one of the important nutrients that regulate the phytoplankton distribution
in the estuaries and also useful for other living organisms in the estuarine area. The
concentration of reactive silicate (SiO4Si) ranging from 21.56 to 131.18 µM in the low-tide
and was altered between 23.75 to 102.72 µM in the high-tide samples respectively. The average
concentration of silicate was in the Varoli (40.50 ±7.76 µM), Damanganga (95.92 ±32.80 µM),
Kolak (101.71 ±9.96 µM) and Par (61.37 ±28.99 µM) in the low tide and the average values of
World Scientific News 143 (2020) 79-102
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reactive silicate were found in the Varoli (29.91 ±4.47 µM), Damanganga (57.15 ±29.02 µM),
Kolak (42.51 ±6.80 µM) and Par (40.45 ±7.40 µM) during the high-tide (Fig. 4b). The highest
concentration of silicate was found in the samples of Damanganga (131.18 µM) in the post-
monsoon. The variation of silicate in coastal water is influenced by the physical mixing of
seawater with freshwater, adsorption into sedimentary particles, chemical interaction with clay
minerals, co-precipitation with humic components and biological removal by phytoplankton,
especially by diatoms and silicoflagellates [56]. Silicate showed a negative correlation with
salinity and in the low-tide salinity decreased and the concentration of silicate exceedingly
increased. The main source of silicates in these coastal water regions is the entry of silicates
through land drainage, which is richened in the weathered silicate material [57].
3. 3. Principal components analysis (PCA)
Principal components analysis (PCA) has been used on a correlation matrix of rearranged
data to explain the structure of the underlying dataset and to identify the unobservable, latent
pollution sources. PCA of water quality parameters and nutrient measurements derived from
the low-tide and the high-tide samples and data suggested that there were three composite
variables (hereafter PC1, PC2, and PC3). Twelve parameters were used in PCA such as pH,
salinity, conductivity, turbidity, DO, BOD, NO2-N, NO3-N, NH4-N, TN, phosphate and reactive
silicates. The PCA suggested the percentage of alterability of PC1 (57.41 %,), PC2 (17.58%)
and PC3 (9.58%) for low-tide samples and is depicted in Fig.5 and summarized in Table 3. PC1
exhibited a positive loading of BOD, NO2-N, NO3-N, TN, phosphate, reactive silicates and had
a negative correlation with pH, salinity, conductivity and DO.
Figure 5. PCA diagram for low tide behavior of water quality parameters
World Scientific News 143 (2020) 79-102
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Table 3. Loading of water quality parameters on the principal component at low tide
Extraction Method: Principal Component Analysis
Component Matrixa a. 3 components extracted
The data of PC1 flourished the loading pattern of dissolved nutrients and BOD. A positive
correlation of BOD with nutrients is attributed to inputs of industrial and sewage wastage in
estuarine waters. PC2 revealed a positive loading of turbidity, NH4-N and had a negative
association with pH and DO, whereas PC3 showed a positive loading for pH, which suggests
that contribution of pH variability in water depends only on PC3. The percentage of the
unpredictability of PC1 (53.50 %,), PC2 (19.92%) and PC3 (9.62%) for the high-tide samples
and is depicted in Fig. 6 and a set of data presented in the Table 4.
Variable
Low tide
Principal component
1
2
3
pH
-0.516
-0.299
0.721
Salinity
-0.958
0.104
-0.192
Conductivity
-0.943
0.085
-0.245
Turbidity
-0.028
0.946
0.173
DO
-0.612
-0.597
0.146
BOD
0.723
-0.037
0.219
NO2-N
0.896
-0.167
-0.082
NO3-N
0.812
-0.323
-0.311
NH4-N
0.488
0.702
0.380
TN
0.875
-0.289
-0.123
Phosphate
0.628
0.422
-0.411
Reactive silicate
0.859
-0.177
0.196
Eigenvalue
7.463
2.286
1.246
Variance (%)
57.411
17.585
9.585
Cumulative
57.411
74.996
84.581
World Scientific News 143 (2020) 79-102
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PC1 showed a high positive loading of BOD, NO2-N, NO3-N, NH4-N, TN, reactive
silicates and had a negative relationship with salinity, conductivity, turbidity and DO. PC2
flaunted a positive loading of phosphate, salinity, conductivity and had a negative correlation
with NH4-N, pH and DO, whereas PC3 had a positive loading for pH, DO and BOD. The
negative relationship between nutrients and DO was observed in the PCA may be due to the
consumption of large amounts of oxygen by organic matters [58-59]. The comparison of PCA
during the low-tide and high-tide suggested that the water quality parameters and loading trend
had quite a similar pattern but NH4-N, phosphate and turbidity loading pattern was found
different in both situations.
Table 4. Loading of water quality parameters on the principal component at high tide
Extraction Method: Principal Component Analysis
Component Matrixa a. 3 components extracted.
Variable
High tide
Principal component
1
2
3
pH
-0.372
-0.418
0.762
Salinity
-0.926
0.361
-0.023
Conductivity
-0.918
0.380
-0.041
Turbidity
-0.653
0.187
0.065
DO
-0.595
-0.246
0.343
BOD
0.671
0.073
0.469
NO2-N
0.763
0.457
0.332
NO3-N
0.753
0.536
-0.140
NH4-N
0.640
-0.549
-0.393
TN
0.839
0.413
0.076
phosphate
0.235
0.942
0.110
Reactive silicate
0.856
-0.193
0.148
Eigenvalue
6.956
2.59
1.251
Variance (%)
53.504
19.923
9.625
Cumulative
53.504
73.427
83.052
World Scientific News 143 (2020) 79-102
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Figure 6. PCA diagram for high tide behavior of water quality parameters
4. CONCLUSIONS
The study provides seasonal, tidal and spatial-temporal of hydrological regime of the
estuarine bio-network of the South Gujarat region. During the monsoon season, the salinity of
estuarine water reduced due to the high incursion of the freshwater into the seawater. The major
nutrients showed significant seasonal transformations in the concentration levels and in some
cases, tidal variations were also witnessed. Similarly, DO, BOD and other water quality
indicators showed dissimilarities in the different seasons. The present investigation also showed
that the physico-chemical properties of the coastal water of the South Gujarat estuarine region
were emphatically affected by freshwater inflow and industrial waste influx, especially during
the low-tide. PCA and Pearson’s correlation coefficient showed that very little freshwater input
during non-monsoon seasons and high nutrient input from sewage and industrial discharges and
other point sources of pollution have caused localized problems of the water quality of the
estuaries of South Gujarat. The study area was under heavy pressure of industrial waste and
high anthropogenic activities.
Acknowledgment
Authors are gratified to the UGC, New Delhi and Department of Science and Technology, New Delhi (DST-FIST
SR/FST/CSI-212/2010) for financial support under the NON-SAP and DST-FIST programs, respectively. This
work was supported by the Earth Science Technology cell (ESTC), Ministry of Earth Science-Government of
India, [MoES/16/06/2013-RDEAS Dated 11.11. 2014].
World Scientific News 143 (2020) 79-102
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