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Deteriorating water quality of rivers is of major concern in India; this is especially true for rivers being used as drinking water sources. One such river considered in this study is the Patalganga, which is located about 60 km from Mumbai and is a significant source of water supply for Panvel, Alibaug and Rasayani. This paper aims to determine the polluting sources responsible for the poor water quality of the Patalganga River and to suggest a scientifically sound water quality management plan to improve the same. A total of 14 water samples from different point sources of pollution were collected and tested for physico-chemical parameters (pH, temperature, DO, BOD, COD, TSS, TDS, EC, PO4 3- , NO3-N and NH3-N), metals (As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn) and microbiological parameter using World Health Organization (WHO) and the Bureau of Indian Standards (BIS) standards. Based on, the water quality at most of the sampling stations was found to be unsuitable for drinking. Hierarchical cluster analysis (HCA) classified the 14 sampling stations into three clusters. The HCA identified a uniform source of parameters (physico-chemical and nutrients) for all the sampling stations, excluding two sampling stations (7 and 12) that exhibited anomalous concentrations. Furthermore, as per the WQI, the water quality status of Patalganga River fell under good category, except at the sampling station 7 and 12 where the water quality index were bad (49) and medium (51) category, respectively, and were totally unfit for drinking purpose. Water quality management plan specific to the individual sites has been delineated in the paper.
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International Journal of Scientific Research in Environmental Sciences, 3(2), pp. 0071-0087, 2015
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ISSN: 2322-4983; ©2015; Author(s) retain the copyright of this article
Full Length Research Paper
Water Quality Management Plan for Patalganga River for Drinking Purpose and
Human Health Safety
Asheesh Shrivastava*, Shalini A Tandon, Rakesh Kumar
National Environmental Engineering Research Institute, 89-B, Dr. A.B. Road, Worli, Mumbai 400 018, India
* Corresponding Author: Email:, Tel.: +91-022-24973521; Fax: +91-022-24936635
Received 27 October 2014; Accepted 02 February 2015
Abstract. Deteriorating water quality of rivers is of major concern in India; this is especially true for rivers being used as
drinking water sources. One such river considered in this study is the Patalganga, which is located about 60 km from Mumbai
and is a significant source of water supply for Panvel, Alibaug and Rasayani. This paper aims to determine the polluting
sources responsible for the poor water quality of the Patalganga River and to suggest a scientifically sound water quality
management plan to improve the same. A total of 14 water samples from different point sources of pollution were collected
and tested for physico-chemical parameters (pH, temperature, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N and NH3-N),
metals (As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn) and microbiological parameter using World Health Organization (WHO)
and the Bureau of Indian Standards (BIS) standards. Based on, the water quality at most of the sampling stations was found to
be unsuitable for drinking. Hierarchical cluster analysis (HCA) classified the 14 sampling stations into three clusters. The HCA
identified a uniform source of parameters (physico-chemical and nutrients) for all the sampling stations, excluding two
sampling stations (7 and 12) that exhibited anomalous concentrations. Furthermore, as per the WQI, the water quality status of
Patalganga River fell under good category, except at the sampling station 7 and 12 where the water quality index were bad (49)
and medium (51) category, respectively, and were totally unfit for drinking purpose. Water quality management plan specific
to the individual sites has been delineated in the paper.
Keywords: Classification, Cluster, Metals, Nutrients, Patalganga River, Water quality, Water quality index
Universally, requirement for freshwater will continue
to rise significantly over the coming decades to meet
the needs of increasing populations, growing
economies, changing lifestyles and evolving
consumption patterns. This will greatly amplify the
pressure on limited natural resources and ecosystems.
Unsafe water and sanitation account for almost one
tenth of the global burden of disease (Fewtrell et al.,
2007). Total 768 million and 2.5 billion people in the
world are living without access to clean water and
proper sanitation, respectively (WHO, 2002; WHO
and UNICEF, 2013a). According to the World
Commission on water for the 21st century, more than
half of the world‟s major rivers are depleted and
contaminated to the extent that they threaten human
health and poison the surrounding ecosystems
(Interpress, 1999). Contaminated drinking water can
cause various diseases such as typhoid fever,
dysentery, cholera and other intestinal diseases (Udoh,
1987; Adeyemi, 2004; Dixit and Shanker, 2009).
In developing countries, about 1.8 million people,
mostly children, die every year as a result of water
related diseases (Payen, 2011; Onda et al., 2012; Wolf
et al., 2013; WHO, 2006; WHO, 2011;
WHO/UNICEF, 2013a). Anthropogenic activities
have resulted in a significant decrease in surface water
quality of aquatic systems in watersheds (Anhar et al.,
1998; Mohd Kamil et al., 1997a; 1997b; May et al.,
2006). In India, rivers are an important source of
water, as many Indian cities are situated on the banks
of the rivers. Untreated discharge of pollutants into a
river from domestic sewers, storm water discharges,
industrial wastewaters, agricultural runoff and other
sources can have short-term as well as long-term
effects on the water quality of a river system (Singh,
2007; Varghese et al., 2011; Rai et al., 2012; Giri and
Singh, 2014). Total 80% of the water in India has
become polluted due to the discharge of untreated
domestic sewage and partially-treated industrial
effluents into the natural water source (Ensink et al.,
2009; CPCB 2007a). High levels of pollutant input in
river water systems cause an increase in biological
oxygen demand (BOD), chemical oxygen demand
Shrivastava et al.
Water Quality Management Plan for Patalganga River for Drinking Purpose and Human Health Safety
(COD), total dissolved solids (TDS), total suspended
solids (TSS), metals such as Cd, Cr, Ni and Pb, and
fecal coliforms (Mohd Kamil,1991; Sangodoyin,
1991; Chatterjee et al., 2000; Adekunle and Eniola,
The correlation coefficients (r) between the water
quality parameters were calculated in order to indicate
the nature and the sources of the polluting substances
(Bajpayee et al., 2012). Hierarchical cluster analysis
(HCA) was used to reduce the number of variables
into a small number of indices while preserving the
relationship present in the original data. The
application of HCA helps identify the vital
components or factors which account for most of the
variances of a system (Ouyang et al., 2006; Shrestha
and Kazama, 2007). Various techniques have been
used for quality assessment of surface waters,
amongst which use of water quality indices is one of
most acceptable methods (Nikoo et al., 2011). A water
quality index based on some very important
parameters can provide a single indicator of water
quality. The general WQI was developed by Brown et
al., (1970) and improved by Deininger for the Scottish
Development Department (1975). It is one of the most
effective ways to communicate water quality (Ott,
1978; Pesce and Wunderlin, 2000; Prakirake et al.,
2009; Taner et al., 2011).
The Patalganga River is located between the
Western Ghats and the Arabian Sea. It is an important
source of drinking water and industrial raw water for
the nearby villages and industries, respectively. The
sewage from the towns and villages along the river is
directly disposed into the river without any treatment.
Mainly textile, pharmaceuticals and dye intermediate
manufacturing industries are located in the catchment
of the Patalganga River. It is, therefore, of vital
importance to monitor the water quality parameters of
the Patalganga River to ascertain whether the water
quality is still suitable for various purposes. So, far no
systematic study has been undertaken to assess the
water quality of Patalganga River. The increased
anthropogenic activities in the catchment area will
normally influence water quality downstream and this
will impact on water treatment steps required to
ensure safe water. Therefore, this study envisaged (i)
to quantitative determination of some of the physico-
chemical parameters, microbial status and metals (As,
Cd, Co, Cu, Cr, Fe, Mn, Ni, Pb, and Zn) content of the
Patalganga River water along its 17 km stretch and to
compare the values with the drinking water standards
recommended by the World Health Organization
(WHO) and the national agency, Bureau of Indian
Standards (BIS) (ii) to evaluation of river water
quality using correlation coefficient (r) and water
quality index (WQI) (iii) to apply a hierarchical
cluster analysis for better interpretation of river water
quality data (iv) to provide a water quality
management plan for Patalganga River for drinking
purpose to minimize the health risks.
2.1. Study Area and Sampling
The Patalganga valley is surrounded by the Karnala
ridge, the Matheran ridge and the Sahyadri ranges.
The Patalganga River originates from the hilly range
of Sahyadri and formed by the tailrace water released
from the Tata Hydro Power Station near Khopoli; it
then flows to the west through Khopoli city, Khalapur
region, and ultimately joins the Arabian Sea at the
Dharamtar creek. It is located across 18°48'0" N and
73°4'0" E and at an elevation of 7 m above sea level.
The catchment area of Patalganga River is 338 km2.
The river serves as the southern boundary of the
Mumbai Metropolitan Region and is also the
boundary between Panvel and Khalapur regions. The
stretch from Khopoli (from Tata Hydropower) up to
the Chawane weir is the fresh water zone, whereas in
the stretch beyond the Chawane weir, tidal influence
is observed. Many industries have been established in
the vicinity of the said river. The sampling stations of
the study area have been described in Table 1 and
depicted in Figure 1. The river water samples were
collected in pre-cleaned, acid washed jerry cans from
the river Patalganga at fourteen sampling stations
located at the river and inlets (sampling stations 5, 7
and 12) during the winter season of 2013 and was
later stored in a refrigerator below 4 °C until used. For
orthophosphate determination, the samples were
collected in glass bottles in order to avoid adsorption
on the walls of polyethylene containers. The glass
bottles were previously soaked in diluted HNO3 and
then rinsed with deionized water. The mean value of
each sampling station was considered for river water
quality assessment.
2.2. Analytical Methods
On site measurement and laboratory analyses were
carried out as per standard methods. On site
measurement included fixation of dissolved oxygen
(DO), electrical conductivity (EC) and temperature.
Dissolved oxygen (DO), biochemical oxygen demand
(BOD), chemical oxygen demand (COD), total
suspended solids (TSS), total dissolved solids (TDS),
total solid (TS), ammoniacal - nitrogen (NH3-N),
nitrate nitrogen (NO3-N), orthophosphate (PO43-), oil
and grease and E. Coli were analyzed as per APHA
Standard Methods (APHA, AWWA, WEF, 1995). For
metal analysis the samples were preserved with the
addition of 2 ml/l HNO3 to avoid precipitation of the
International Journal of Scientific Research in Environmental Sciences, 3(2), pp. 0071-0087, 2015
metals. For the analysis of metals samples were
digested with 5 ml of di-acid mixture (10 ml HNO3 +
5 ml HClO4) on a hot plate and filtered by Whatman
No. 42 filter paper and made up the volume to 50 ml
by double distilled water for analysis of metals using
Inductively Coupled Plasma Optical Emission
Spectrophotometer (Optima 4100 DV ICP-OES)
(APHA, 1995).
Table 1: Description of the Sampling Stations
Gagangiri (upstream): Origin of Patalganga River, background river water quality. This water comes from
Tata Hydro power station with no apparent human habitation in the stretch from the Tata Hydro power
station and Gagangiri Ashram.
Gagangiri (downstream): Washing and bathing activities at the Ashram were observed. The Ashram is
equipped with 37 toilets catering to the needs of 5000 regular visitors. During the peak period the number of
visitors increases to 100,000. The untreated sewage directly flows into the Patalganga River.
Chemical manufacturing industry upstream.
Chemical manufacturing industry downstream.
Burning Ghat : River water near the cremation ground mixes with the untreated municipal waste water from
west Khopoli. Khopoli does not have an operational sewage treatment plant. The mean flow rate 70~75
Hanuman Temple (backside water) : The river water was oily as seen from the steps side, further on in its
vicinity sewage water from the public toilets mixes with the river water and flows below the bridge between
the Hanuman temple and the Masjid.
Drain : After the Hanuman Temple. Domestic and public toilet wastewater is discharged untreated directly
into the river. The mean flow rate 30~35 mld.
Near Masjid : Beyond the drain.
Esamba Phata : The river receives water from industries on either side.
Savroli Phata: Impact of washing, bathing and tankers washing activities.
Kharsundi Bandhara : Reservoir
Kharsundi industrial area: The wastewater comes from various industries. The mean flow rate 40~45 mld.
Vayal (Raw water): The Vayal weir is made for arresting the river water. Intake point for drinking water
supply to Navi Mumbai and JNPT. The river water is taken in through a small channel into the pumping
station via the screens, pumping 120 MLD of raw river water to the Bhokarpada water treatment pant.
Vayal (treated water): Used for drinking purpose.
2.3. Statistical Analysis
Pearson Correlation Coefficient was used to determine
the relationships between observed water quality
characteristics. A Microsoft Excel add-in module
XLSTAT 4.3 was used to carry out the hierarchical
cluster analysis (HCA). The calculation of WQI was
made as per National Sanitation Foundation.
3.1. Physico-chemical Parameters
Degradation of water quality negatively affects the
accessibility of water for humans and increasing
financial costs for human beings, and diminishing
species diversity and abundance of resident
communities. These changes in environmental quality
can be associated with changes in water quality
parameters (UN GEMS/Water Programme, 2006).
Therefore, assessment of the water samples for
pollution has been made by comparing assessed
values of all the physico-chemical parameters with the
corresponding standards prescribed for drinking water
by various organizations such as World Health
Organization (WHO) and the Bureau of Indian
Standards (BIS), as detailed in Table 2. Water
temperature is one of the most important physical
characteristics of aquatic systems. As water
temperature rises, the rate of photosynthesis increases,
thereby providing adequate amounts of nutrients
(FOEN 2011). The water temperature values were
found to be within the permissible limit of the WHO.
pH is important to quantify the health of a river
since the water is used by the public for drinking
purpose (Sharma and Kansal, 2011). The river water
exhibited a near neutral pH (7.0 to 7.5) and was well
within the acceptable range given by WHO and BIS
for drinking water. The conductivity apparently
increased at sampling stations 7 (1683 µS/cm) and 12
(1082 µS/cm) due to domestic and industrial
wastewaters inflow, respectively. However, this, too,
was well within the acceptable range given by BIS
and WHO for drinking water. The TDS content of
water samples collected at the selected stations ranged
between 22-1128 mg/L, which is well below the limit
Shrivastava et al.
Water Quality Management Plan for Patalganga River for Drinking Purpose and Human Health Safety
value of 500 mg/L (WHO 1984) acceptable for
potable use, except at stations 7 (1128 mg/L) and 12
(725 mg/L). In the absence of a suitable potable water
source, the permissible limits for TDS as per WHO
and BIS are 2000 mg/L and 2100 mg/L, respectively.
The steady increase in TDS and conductance indicates
that water is contaminated due to discharge of
domestic and industrial wastewaters.
Dissolved Oxygen (DO) measures the amount of
gaseous oxygen dissolved in an aqueous solution.
Oxygen gets into water by diffusion from the
surrounding air, by aeration and photosynthesis. As a
DO levels drop below 5.0 mg/L, aquatic life is put
under stress (Liu et al., 2009; Li and Bishop, 2004).
DO was much above the desired value (5 mg/L) as per
WHO and BIS guidelines for drinking water quality at
all the sampling stations due to significant turbulence
in the river waters. The highest and lowest DO values
were found from the sample stations 1 and 12,
respectively. The Biological oxygen demand (BOD) is
a measure of organic carbon loading in the water
system that exerts a high level of biological oxygen
demand to the system (Sullivan et al., 2010).
Generally, unpolluted waters typically have BOD
values of 2 mg/L or less, and those receiving
wastewaters may have values up to 10 mg/L or more,
while COD in unpolluted surface waters range from
20 mg/L or less to greater than 200 mg/L in waters
receiving effluents (Agbaire et al., 2009; Garg et al.,
2010; Utang and Akpan, 2012). If effluents with high
BOD levels are discharged into a river, it will
accelerate bacterial growth and consume the dissolved
oxygen in the river (Kulshrestha and Sharma, 2006;
Kumar and Chopra, 2012). Total eleven sampling
stations showed high BOD concentrations above the
permissible limit for drinking water (WHO 1998) with
the peaks at sampling stations 7 (65 mg/L) and 12 (50
mg/L). These are the areas where direct anthropogenic
influence and the discharge of untreated municipal
wastewater have been noticed. Unusually low DO and
high BOD values were observed at sampling stations
7 and 12. The COD concentrations were found to be
more than WHO permissible limit (10 mg/L) at all the
sampling stations with peaks being observed at
sampling stations 7 (122 mg/L) and 12 (76 mg/L)
(Figure 2). Direct discharge of untreated domestic and
industrial wastewater into the river was responsible
for the high organic pollution, and led to very high
BOD and COD values in the downstream sampling
The NO3-N concentration in surface water is
generally low, but can reach high levels from
agricultural runoff, or from contamination by human
or animal wastes (WHO 1998). NO3-N concentrations
were within the permissible limits at all the sampling
stations. However, NH4-N values were astronomical
when compared with the WHO standard at all the
sampling stations, except at sampling station 1 (0.08
mg/L). Generally, the high concentration of NH4-N
causes a problem with taste and odour of water apart
from toxicity to aquatic lives. Unusually high NH4-N
concentration was reported at sampling station 7 (5.41
mg/L) due to domestic and public toilet wastewater is
discharged untreated directly into the river water. The
high NH4-N concentration at sampling station 14
(drinking water) reflects deterioration of water
quality, which requires additional wastewater
treatment technology (Metcalf and Eddy 2003).
Moreover, drinking water containing more than 0.2
mg/L of ammonia drastically decreases the
disinfection efficiency. Symptoms of NH4-N
poisoning are restlessness, dullness, weakness, muscle
tremors profuse salivation, vocalization, lung edema,
tonic-clonic convulsion, and finally death by heart
failure (Markesbery et al., 1984; Camargo and
Alonso, 2006; Majumder et al., 2006; Ojosipe, 2007).
The high NO3-N with low amount of NH4-N enhanced
the self-purification activities of surface water, by
increasing the rate of nitrification-denitrification
transformation process in river water (Li and Bishop,
2004). The PO43- values reported were well within the
tolerable limits. PO43- is rarely found in high
concentrations in waters as it is actively taken up by
macrophytes and algae. However, high concentrations
of PO43- can show the presence of contamination and
are largely responsible for eutrophic conditions
(WHO, 1998).
Oil and grease in the aquatic environment may be
damaging in a variety of ways. Even at low
concentrations, oil and grease may be toxic to aquatic
life, reduce dissolved oxygen, and alter the usability
and aesthetics of a water body (Khan et al., 2006).
Additionally, oil and grease may interfere with
aerobic and anaerobic biological processes and lead to
decreased wastewater treatment efficiency. Recent
monitoring indicates that oil and grease concentrations
ranged from nil to 19.6 mg/L. Especially, sampling
stations 6 (19.6 mg/L) showed high oil and grease
concentrations compared with BIS standard due to
inflow of temple wastewaters.
E. coli is the traditional bio-indicator of sewage
pollution in aquatic ecosystems and determination
revels vital information regarding water quality
(Wright et al., 2004; Ram et al., 2008). Samples from
sample stations 2 to 13 showed presence of E. coli.
Hence, the data show that the river water is
completely unfit for drinking purposes unless given
proper treatment.
International Journal of Scientific Research in Environmental Sciences, 3(2), pp. 0071-0087, 2015
Fig. 2: Concentrations of (A) Dissolved Oxygen; (B) Biological Oxygen Demand; (C) Chemical Oxygen Demand at 14
Different Sampling Stations
Shrivastava et al.
Water Quality Management Plan for Patalganga River for Drinking Purpose and Human Health Safety
3.2. Metals Analysis
Metals enter the river from a variety of sources, which
can be either natural or anthropogenic (Bem et al.,
2003; Wong et al., 2003; Adaikpoh et al., 2005; Akoto
et al., 2008). Usually, in unaffected environments, the
concentrations of most of the metals are very low and
are mostly derived from the mineralogy and
weathering of rocks (Karbassi et al., 2008). Rivers in
urban areas have also been associated with water
quality problems due to the practice of discharge of
untreated domestic and industrial waste into the water
bodies, which lead to an increase in the level of metal
concentrations in the river water (Rim-Rekeh et al.,
2006; Iqbal et al., 2006; Khadse et al., 2008; Juang et
al., 2009; Jumbe et al., 2009; Venugopal et al., 2009;
Sekabira et al., 2010). Metals are non-degradable and
can accumulate in the human body, causing damage to
the nervous system and internal organs (Lee et al.,
2007; Lohani et al., 2008).
The concentration trend of different metals in the
river water and the maximum values for metals in
water have been prescribed by the WHO and BIS, and
shown in Table 3. The As concentrations were
observed high compared with WHO and BIS
standards at sampling stations 2, 6, 8 and 12 due to
inflow of domestic and industrial wastewaters,
respectively. The adverse effects of chronic exposure
to drinking arsenic water on human body are
cardiovascular disease, neurological effects, chronic
lung disease, reproductive disease, adverse renal
affects, developmental abnormalities, hematological
disorders, diabetes mellitus and cancers of skin, lung,
liver, kidney and bladder. In dose-response manner;
the children who use the drinking water with high
arsenic concentration (> 0.05 mg/L) execute lower
performance than those children, using drinking water
with low arsenic (<0.005 mg/L) (Wasserman et al.,
2004; WHO, 2006; Rakib and Bhuiyan 2014). The Cd
and Co concentrations were reported nil at all the
sampling station. The concentrations of Cr, Cu, Ni, Pb
and Zn in the river water were quite low and found
within the WHO and BIS permissible limits. The
concentrations of Fe and Mn were found highest at
sampling station 7 (1.18 mg/L) and 11 (0.19 mg/L),
respectively. The high Fe concentrations could be
attributed to anthropogenic activities and land runoff.
The high level (> 200 mg/L) of Fe can cause
hemochromatosis with symptoms such as chronic
fatigue, arthritis, heart disease, cirrhosis, thyroid
disease. The Fe concentration in water causes
conjunctivitis, choroiditis, and retinitis if it contacts
and remains in the tissues (Huang, 2003; Kayode et
al., 2006). The presence of high concentration of Fe
may also increase the hazard of pathogenic organisms;
since most of them need Fe for their growth (Tiwana
et al., 2005; Anonymous, 2008). Industrial activities
were predominantly responsible for the high
concentrations of Mn in river waters. Mn
concentration over 0.1 mg/L lead to adverse impact on
water coloration, metallic taste, odor problem,
turbidity, biofouling and corrosion, and staining of
laundry and plumbing fixture. The elevated amounts
of Mn may cause apathy, irritability, headache,
insomnia as well as gastrointestinal irritation and
respiratory disease (Apostoli et al., 2000; Roccaro et
al., 2007; Rygel, 2006; Menezes-Filho et al., 2011). In
the worst form, it may lead to a permanent
neurological disorder. However, exposure to Mn from
drinking water is normally substantially lower than
intake from food (USEPA, 2004). Although, in
general, the relative high metals concentrations were
observed at mid-stream as compared to upstream and
downstream, it can be attributed to inflow of industrial
and household wastewater in river stream.
The region has many small-scale as well as large
industrial units located close to the Patalganga River
that use toxic metals for various products and
discharge their effluents directly or indirectly into the
river. Apart from it, its tributaries also pass through
some of the most industrialised belts and carry
effluents that ultimately drain into the Patalganga
River, increasing the load of toxic metals. The
samples from various industrial units contained few
metals above permissible limits of WHO and BIS for
drinking water while some metals such as Cd and Co
were totally absent. The Pb was found above the
permissible limit only in one industrial effluent (a
perfume manufacturing industry). Drinking water
picks up Pb pollution from several sources such as
household paint, vehicle exhausts and industrial
wastes. Pb builds up in the human body over many
years and can damage the brain, red blood cells and
kidneys. It is an accumulative metabolic poison that
affects behavior, as well as the hematopoietic,
vascular, nervous, renal, and reproductive systems of
the human body (USEPA, 2005; Moore and
Ramamoorthy, 1984; Nadeem-ul-Haq et al., 2009;
Singare et al., 2012).
4.1. Correlation Coefficient
In the present study, correlation coefficient was used
to identify the highly correlated water quality
parameters. This can help in selecting the treatments
to minimize pollutants in river water (Joarder et al.,
2008). Simple correlation coefficient (r) computed
between physicochemical properties in Patalganga
River is presented in Table 3. There was no significant
correlation between water temperature and the other
International Journal of Scientific Research in Environmental Sciences, 3(2), pp. 0071-0087, 2015
physical parameters except DO (r = 0.67; p < 0.01).
Water temperature correlated negatively with the DO
and positively with TDS and SS, with the latter
showing a positive correlation with BOD. The EC,
TDS, SS and TS displayed positive strong correlation
with all the parameters except DO and Oil and grease.
Strong positive correlation of EC with BOD (r = 0.94;
p < 0.01) and COD (r = 0.88; p < 0.01) supported the
presence of wastewater coming from industries as the
chief causative factor for aquatic pollution (Dike et al.
2013). It is clear from the results that the DO was
negatively correlated with all the variables and was
not positively correlated with any of the studied
parameters. The DO exhibited negative correlation
with BOD (r = − 0.81; p < 0.01), COD (r = − 0.68; p <
0.05) and oil and grease (r = − 0.27) decrease in DO
concentration is linked with oxidation of re-suspended
organic matter (Kriest and Oschlies, 2013). Negative
correlation between DO and NO3-N (r = 0.67; p <
0.01) was observed due to high discharge, which
increases concentration of DO in the interstitial water
because of increased turbulence that reduces the
anoxic environment required for denitrification
(Lansdown et al., 2012). The NO3-N showed
significant positive correlation with BOD (r = 0.93; p
< 0.01) suggests the addition of these nutrient to
Patalganga River from organic waste and sewage
discharge. Strong positive correlation between TDS
and NO3-N (r = 0.99; p < 0.01) might indicate that the
pre-dominant fraction of the nitrogen species are
present in dissolved form instead of particulate
nitrogen (Charkhabi and Sakizadeh, 2006). The PO43-
showed strong negative correlation with DO (r =
0.81) with significant differences p < 0.01, while
positive correlation with BOD (r = 0.92, p < 0.01) and
COD (r = 0.84; p < 0.05). NO3-N shows strong
correlation with NH4-N (r = 0.93; p > 0.01), such high
correlation indicating contamination of the river water
from point sources, i.e., industrial, sewage and animal
wastes (Maitera et al., 2010).
The SS showed significant positive and negative
correlation with NH4-N (r = 0.97; p > 0.01), BOD (r =
0.79; p > 0.01) and DO (r = − 0.54; p > 0.05),
respectively. It was due to that SS can adsorb many
organic matters and microorganisms (Ling et al.,
2002). The Oil and grease showed positive correlation
with all parameters except water temperature (r =
0.16) and DO (r = − 0.27) (see table 4). The high
concentration of oil and grease reduce dissolved
oxygen in the river water and alter the usability and
aesthetic values of the water (Khan et al., 2006).
Table 4: Correlation Coefficient of Various Parameters in Patalganga River Water
W Tem
W Tem
** p = 0.01 level; * p = 0.05 level
Shrivastava et al.
Water Quality Management Plan for Patalganga River for Drinking Purpose and Human Health Safety
4.2. Cluster Analysis
Hierarchical cluster analysis (HCA) was used to
analyze the water quality data for spatial and temporal
differences. The HCA was applied to a subgroup of
the dataset to evaluate their usefulness to classify the
river water samples, and to identify suitability for
drinking water purpose. Guler et al. (2002) described
hierarchical cluster analysis as “an efficient means to
recognize groups of samples that have similar
chemical and physical characteristics”. The distance
cluster represents the degree of association between
elements. The lower the value on the distance cluster,
the more significant is the association. HCA is the
most common approach, which provides intuitive
similarity relationships between any one sample and
the entire data set, and is generally illustrated by a
dendrogram (McKenna, 2003). To classify the water
quality in sampling stations and to determine the
source of pollution, HCA with Ward method,
Euclidean distance based on the standardized mean of
the physico-chemical parameters were used.
According to the thirteen parameters, HCA
categorized fourteen sampling stations into three
distinctive clusters described based on pollution
magnitude as clean, slightly polluted, and polluted.
Examination of Figure 3 shows the identification of
three major branches in the dendrogram, labeled A, B
and C. These were identified as major cluster groups
because the linkage distance at which they combine
with each other is relatively large, indicating that there
are relatively large Euclidean distances between the
samples. Group A consists of 12 sampling stations
while groups B and C are represented by sampling
stations 7 and 12, respectively. The group A was
further divided into two subgroups A‟ and A‟‟. The
subgroup A' mainly represented upstream area and
low nutrient concentrations compared to A" while A"
represented mainly industrial wastewaters. The
upstream area of rivers is less influenced by human
activities. Therefore, the condition of river water was
slightly clean and optimized. The group B (sampling
station 7) was influenced by the inflow of domestic
and public toilets wastewaters. Finally, the group
C (sampling station 12) could be influenced by the
extensive inflow of industrial wastewaters.
Eventually, the result denotes that HCA is an effective
technique to assess and classify river water in the
Patalganga River case study. At the same time, it is
significant to a large extent to authorities and decision
makers to know the latest information on the river
which guide them in the optimal strategy
establishment in which sampling stations can be
Table 5: Classification of Water Quality Index
Very bad
4.3. Water Quality Index
Based on the WQI an assessment was made whether
the river water was acceptable for domestic use and
drinking purpose. For this reason, this analysis is
extremely necessary. Also, people living in these
areas can determine from which part of the river they
can draw the best quality water (Adak et al., 2001).
Water quality has been assessed using Water Quality
Index (WQI) developed by the U.S. National
Sanitation Foundation Water Quality Index (NSF
WQI) in 1970. This index has been widely tested on
field and applied to data from a number of different
geographical areas all over the world in order to
calculate Water Quality Index (WQI) of various water
bodies. Critical pollution parameters were considered
(Sharifinia et al., 2013) for computing WQI.
Expression for NSF WQI is given by
NSF WQI = ∑ Wi Ii
Where Ii is the sub-index for ith water quality
parameters; Wi is the weight (in terms of importance)
associated with ith water quality parameter; p is the
number of water quality parameters.
The water quality index uses a scale from 0 to 100
to rate the quality of the water, with 100 being the
highest possible score. The classification criteria
standards based on NSF WQI are given in Table 5.
The computed overall WQI was 100 and can,
therefore, be categorized into five types “excellent” to
“very bad”. The results obtained from this study
revealed that WQI of the Patalganga River waters falls
under the range of 43 to 78 (Figure 4). The study area
WQI assessment showed that water quality of river
waters falls under “Good category with the majority
of the sampling stations having water quality with
WQI in the range of 70 to 83 and need to be treated
before its use. Sampling stations 7 and 12 showed
“Bad” and “Medium” category, respectively, and were
totally unfit for drinking purpose.
International Journal of Scientific Research in Environmental Sciences, 3(2), pp. 0071-0087, 2015
Fig. 3: Dendrogram from Hierarchical Cluster Analysis of Sampling Stations of Patalganga River
Socio economic development is clearly linked to
access to safe drinking-water. Environmental,
economic and social policies associated with waste
management are mostly inadequate and insufficient,
resulting in increasing deterioration of the
environment (Mara, 2003; Goldar and Banerjee, 2004;
Hutton et al., 2007). The poor management of river
water has resulted in a major shift in the quantity and
quality of water and altered ecosystems, limiting the
benefits available for human that depend on them.
Improving the water quality in the Patalganga River is
possible, but requires interventions in both domestic-
municipal and industrial sectors. River pollution can
be controlled by considering multiple options such as:
(a) Installation of STPs and using the treated
wastewater for irrigation and ground water recharge -
as the flow rate of the river is high ex-situ water
purification by anchored PhytoRid will be useful
(Kumar et al., 2010). The high NH4-N concentration
in drinking water requires additional wastewater
treatment technology such as biological nitrification
or physicochemical processes (such as ion exchange,
membrane filtration, air stripping and ozonation).
Shrivastava et al.
Water Quality Management Plan for Patalganga River for Drinking Purpose and Human Health Safety
(b) Waste segregation at source, localized
recycling, localized/community level
vermicomposting - in case of temple wastewater
management awareness creation, and employment of
small temporary plastic nets for solid waste removal
are important steps.
(c) Regular monitoring for checking and improving
the management practice.
(d) Public awareness and participation through
media and organizing public programs for spreading
the message effectively is essential.
(e) Low cost sanitation complexes to prevent open
(f) A separate truck washing terminal and
treatment of wastewater produced. Use of oil spill
control methods.
(g) Water quality laws and regulations need to be
enforced effectively. Creation of no development zone
about 500 m on the either side of the river.
Based on the cluster analysis, and on comparing the
water quality parameters with national and
international standards of parameters from 14
sampling stations, the sampling stations were divided
into three major groups to reduce the number of
sampling sites to ease future monitoring exercises.
Three groups and one subgroup were generated from
HCA method. Subgroup A‟ reflects the low physico-
chemical, microbial and metals concentration.
Subgroup A‟‟ is mainly affected by industrial
wastewaters. Group B is mainly influenced by
domestic and public wastewaters. Group C reflects the
characteristic of industrial wastewater. Based on WQI,
it could be inferred that water quality at these above
mentioned sampling stations are “Good”, “Medium”
and “Bad” category and can only be used for drinking
after conventional treatment and disinfection. The
results suggest that anthropogenic activities have had
significant effects on water quality in the river.
From this classification, it is possible to plan for
optimum sampling strategies that can reduce the
number of sampling points during assessment and the
affiliated recurring cost during environmental
monitoring. It was observed that the main causes of
deterioration in water quality were high interference
of anthropogenic activities, lack of proper sanitation,
and industrial and domestic wastewater inflow. This
work may assist the decision makers in the pollution
control of the Patalganga River where the WQI and
clustering process gives an effective overview about
the locations where intensified monitoring activity and
control measures are required. A specific management
plan involving all stakeholders will help improve and
maintain the river water quality.
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Dr. Kumar is a Director Grade Scientist in Mumbai Zonal Lab of NEERI, Mumbai. Dr. Rakesh Kumar
received M.Tech in Environment Science and Engineering from IIT, Bombay and PhD in
Environmental Engineering from Nagpur University, India in 1994. He has published more than 65
papers in international journals and 43 papers in national refereed journals with 2 books and 9 chapters
in different books. He has 8 patents to his credit. Additionally, he has published several technical
reports on research/consultancy conducted for government agencies and private industries. Dr.Rakesh
Kumar has about 26 years of experience in the field of environmental science and engineering. He has
received 9 awards for his outstanding contribution to Environmental Science & Engineering.
Dr Shalini A Tandon is a scientist in National Environmental Engineering Research Institute (A
Council of Scientific and Industrial Research Institute), Mumbai. She obtained her PhD in
Environmental Sciences from Indian Agricultural Research Institute, New Delhi. Her major field of
study is Environmental monitoring and waste water recycling.
Dr. Asheesh Shrivastava is a Research Associate in Mumbai Zonal Lab of NEERI, Mumbai. Dr.
Asheesh Shrivastava received his doctorate in Environmental Science from Kyoto University, Kyoto,
Japan in 2010. He is recipient of the Japanese Government Scholarship (MEXT). His current research
interest includes environmental monitoring and restoration of water bodies by natural process.
... Preserving and ensuring the sustainable use of surface water resources can contribute towards the implementation of Sustainable Development Goals (SDGs 6) [9]. The increasing population, economic growth, and change in lifestyle cause an increase in the requirement of fresh water, which amplifies the pressure on limited water resources [10]. The surface water resources are at risk of contamination because of rapid industrialization, urbanization, extensive agriculture activities, mining and population growth [5,11]. ...
... This can be justified with lower anthropogenic activities like mining, industrial activities during COVID-19-induced lockdown. (9) Low heavy metal (10) Low heavy metal (10) Low heavy metal (10) Low heavy metal (9) 2017 Low heavy metal (9) Low heavy metal (11) Moderate heavy metal (17) Moderate heavy metal (14) Low heavy metal (10) 2018 Low heavy metal (10) Low heavy metal (10) Moderate heavy metal (15) Moderate heavy metal (16) Low heavy metal (9) 2019 Low heavy metal (10) Low heavy metal (10) Low heavy metal (10) Low heavy metal (9) Moderate heavy metal (13) 2020 Low heavy metal (10) Low heavy metal (10) Low heavy metal (9) Low heavy metal (9) Low heavy metal (9) ...
... This can be justified with lower anthropogenic activities like mining, industrial activities during COVID-19-induced lockdown. (9) Low heavy metal (10) Low heavy metal (10) Low heavy metal (10) Low heavy metal (9) 2017 Low heavy metal (9) Low heavy metal (11) Moderate heavy metal (17) Moderate heavy metal (14) Low heavy metal (10) 2018 Low heavy metal (10) Low heavy metal (10) Moderate heavy metal (15) Moderate heavy metal (16) Low heavy metal (9) 2019 Low heavy metal (10) Low heavy metal (10) Low heavy metal (10) Low heavy metal (9) Moderate heavy metal (13) 2020 Low heavy metal (10) Low heavy metal (10) Low heavy metal (9) Low heavy metal (9) Low heavy metal (9) ...
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Considering the well-documented impacts of land-use change on water resources and the rapid land-use conversions occurring throughout Africa, in this study, we conducted a spatiotemporal analysis of surface water quality and its relation with the land use and land cover (LULC) pattern in Mokopane, Limpopo province of South Africa. Various physico-chemical parameters were analyzed for surface water samples collected from five sampling locations from 2016 to 2020. Time-series analysis of key surface water quality parameters was performed to identify the essential hydrological processes governing water quality. The analyzed water quality data were also used to calculate the heavy metal pollution index (HPI), heavy metal evaluation index (HEI) and weighted water quality index (WQI). Also, the spatial trend of water quality is compared with LULC changes from 2015 to 2020. Results revealed that the concentration of most of the physico-chemical parameters in the water samples was beyond the World Health Organization (WHO) adopted permissible limit, except for a few parameters in some locations. Based on the calculated values of HPI and HEI, water quality samples were categorized as low to moderately polluted water bodies, whereas all water samples fell under the poor category (>100) and beyond based on the calculated WQI. Looking precisely at the water quality’s temporal trend, it is found that most of the sampling shows a deteriorating trend from 2016 to 2019. However, the year 2020 shows a slightly improving trend on water quality, which can be justified by lowering human activities during the lockdown period imposed by COVID-19. Land use has a significant relationship with surface water quality, and it was evident that built-up land had a more significant negative impact on water quality than the other land use classes. Both natural processes (rock weathering) and anthropogenic activities (wastewater discharge, industrial activities etc.) were found to be playing a vital role in water quality evolution. This study suggests that continuous assessment and monitoring of the spatial and temporal variability of water quality in Limpopo is important to control pollution and health safety in the future.
... The findings are examined in light of the outcomes of other employees [7]. Shrivastava et al (2015) determined the polluted sources responsible for the poor water quality of the Patalganga River and suggested a scientifically sound water quality management plan to improve the quality. A total of 14 water samples from different points were collected and physicochemical parameters, metals and microbiological parameters were tested using WHO and the BIS standards. ...
... A total of 14 water samples from different points were collected and physicochemical parameters, metals and microbiological parameters were tested using WHO and the BIS standards. Based on the water quality, most of the sampling stations were found to be unsuitable for drinking [8]. ...
Conference Paper
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The aim of this project is to analyze and predict the quality of river water for daily usage and agricultural purpose. Water is one of the most essential elements of nature that contributes to perform biological operations of all living bodies on earth. The quality of water impacts directly on living bodies. Change in water quality causes great damage to the living species. Through this project we have schemed to detect the alteration earlier so that crucial steps can be undertaken to prevent impending losses. Taking advantage to the Gradient Boosting Model (GBM), the water quality was examined and forecasted. With the help of automatic water parameter measuring tools, samples were collected from numerous rivers of Bangladesh. The GBM was instructed utilizing the samples collected from year 2013 to 2019. The model functions using specified arguments. The model evaluates the water quality and anticipates the change that demonstrates the future water quality. The findings suggest that the model's expected values and actual values are in excellent agreement and the future change in water quality has been reported correctly.
... The samples were taken to the laboratory and analyzed for the following parameters: pH, temperature, dissolved oxygen biochemical oxygen demand , total hardness, dissolved solids, total solids, nitrate, chloride, conductivity total bacterial count, and total coliform count. On site measurement and laboratory analyses were carried out as per standard methods [51,52] . ...
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A study on the quality of the water consumed and on the determinants of the prevalence of typhoid fever was carried out in two health zones (Kadutu and Miti-Murhesa) of the province of south Kivu from June to September 2021.The study aimed at contributing to the advancement of scientific knowledge that can be used in the control of water-borne disease crisis such as typhoid fever in rural areas of the DRCongo. The study carried out was of the analytical cross-sectional type with primary data collected from field using a semi-structured questionnaire. Water samples were collected and analyzed for bacteriological and physicochemical quality using standard procedures. The generalized linear model (GLM) with Gaussian identity model and or with a logarithmic link function, was applied to investigate socio-economic and environmental factors likely influencing the knowledge of respondents about the causes and health consequences of the current prevalence of typhoid fever in the health zones of Kadutu and Miti-Murhesa. The results indicated that the greater majority of the respondents did not perceive the water they drink as possible source of diseases. In rural health zones, sanitary condition was generally poor, refuse disposal and toilet system still primitive. Most households from rural health zone do not treat the water they drink as they were using open and unprotected toilet systems, which Included open pit toilet, bush method, or use of lake/river banks. Urban communities with that had better social and economic facilities were less exposed to risks of typhoid fever, particularly during early rains of the rainy season. There were significant differences (p<0.05) between the sites (health zone) as to the level of knowledge of the type of water consumed and its influence on the prevalence of typhoid fever. There was significant (P<0.05) variability in the values of the physico-chemical properties of the water consumed in the surveyed health zones, although values were in conformity with WHO standards for the Africa for potable water. Germs isolated in the water samples indicated bacterial pollution of water consumed by the public in the two health zones. The causative germ of typhoid fever was found being influenced by the sources where the samples were collected. In the Kadutu health zone, the level of knowledge of respondents of the determinants of the prevalence of the typhoid fever was positively influenced by the age (GLM : Z= 3.33, P<0.001), negatively influenced by the type of health zone where the respondent lived (GLM : Z= -4.94, P<0.001), the respondent’s neighborhood of residence environment (GLM : Z= -3.78, P<0.001), the sex (GLM : Z= -3.53, P<0.001), the level of study (GLM : Z= -3.69, P<0.05), and to the fact that the respondent does or not wash the containers (GLM : Z= -4.45, P<0.001).. In the Miti-Murhesa health zone, the perception of the factors that determine the prevalence of typhoid fever was reported for being influenced negatively by whether or not to consume the food prepared a day before (GLM : Z= -4.32, P<0.05), and positively influenced by the civil status (GLM : Z= 2.11, P<0.05), the type of water consumed (GLM : Z= 2.82, P<0.001), the type of treatment applied to the drinking water (GLM : Z= 3.20, P<0.001). Overall, the result showed that the proposed water by the national water corporation company (REGIDESO) for human consumption was in the process of degradation. The results showed that the mean values recorded for physico-chemical parameters among the domestic water sources were within stipulated limits of WHO for safe drinking water, but not within REGIDESO standards. Due to the levels of microbes in the water, the water quality used by the population can be regarded as of poor quality. Thus, important measures should be taken by the health authorities to slow down the current process on order to reduce the future emergency and burden of the of water-borne diseases in rural and urban areas of South-Kivu, eastern DRCongo.
... Sagar et al. (2015) reported the physico-chemical parameters for testing water. Shrivastava et al. (2015) reported the water quality management plan for Patalganga River for drinking purpose and human health safety, which is located 60 km from Mumbai and is a significant source of water supply for Panvel, Alibaug and Rasayani. Various technical research papers on the assessment of water quality of different areas have been presented Danha et al. (2015) worked on physico-chemical analysis and fish pond conservation in Kano State, Nigeria, Elegbede et al. (2015) reported the effect of water quality characteristics of fish population of the lake Volta, Ghana, Zafar et al. (2015) analysed water and soil quality parameters of shrimp and prawn farming in the southwest region of Bangladesh. ...
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Phytoplanktons are floating microscopic autotrophs and consisting mainly members of Clorophyceae, Cyanophyceae and Bacillariophyceae and algae like green flagellates. While the zooplanktons. The studies on phytoplankton are the subject of great interest because of their role as primary producers in an aquatic ecosystem. The qualitative and quantitative studies of those species may provide the knowledge of water quality and capacity of water to sustain heterotrophic communities. The present study was carried out in the Bansagar Dam, Shahdol (M.P.) during the period of November 2014 to October 2015. The objective of this study was to identify the group or species of phytoplanktons and zooplanktons to know the biological status and productivity potential of the dam. The higher density of Chlorophyceae group showed the good biological status of the dam. The Cyanophyceae 2234org/l, 41.51% Chlorophyceae 1906org/l, 35.42%, Bacillariophyceae 1156org/l, 21.48% and Euglenophyceae 85org/l, 1.57% annual densities and their composition of percentage were recorded respectively during study period. The average annual density and of composition of zooplanktonic group are as Rotifera 1044 org/l, 47.78% Copepoda 1587 org/l, 20.62%, Protozoa 1044org/l, 13.56%, Cladocera 932org/l, 12.11% and Ostracoda 455org/l, 5.91% during the study period.
... The higher concentrations of ammonia are generally found in polluted waters (Shrivastava et al., 2015). The basins presented significant variation in their ammonia concentrations, maximum being recorded at site C (240.91±7.04 ...
... The extent of spatial heterogeneity in RWSS coverage was assessed by agglomerative hierarchical cluster analysis (HCA). Application of HCA is well documented in water resources assessment studies (Chaudhuri and Ale 2015;Chaudhuri and Roy 2017;Hadjisolomou et al. 2018;Shrivaastava, Tandon, and Kumar 2015). In the present context, the governing idea was to 'generalise' states based on similarity (or dissimilarity) in RWSS coverage (for both 40 lpcd and 55 lpcd) so as to identify underlying zones (of high/low coverage). ...
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Sustainable delivery of drinking water of adequate quantity/quality sits at the core of rural development paradigms worldwide. The overarching goal of this study was to assess operational performance of rural water supply services (RWSS) in India to help authorities understand challenges/shortfalls vis-à-vis opportunities. Data on habitation-level coverage, aggregated by states between 2013 and 2018, were obtained from the National Rural Drinking Water Programme (NRDWP) database, against two water supply norms, namely, 40 lpcd and 55 lpcd (litres per capita per day). Results indicate that certain states are faring better (providing full coverage to over 90% habitations) while others are lagging (e.g., the north-eastern region, and Kerala and Karnataka in the South, for both norms). Several states yet fail to provide 55 lpcd to over half of their rural habitations. Overall, RWSS is marked by high spatial heterogeneity, inequality and recurrent slip-backs (decline in year-to-year habitation coverage) that thwart the basic motto of NRDWP— Har Ghar Jal (Water for All). Ground-level experience reveals a mismatch between theoretical systems’ output (40 lpcd and 55 lpcd) and on-site delivery, and highly intermittent services. Moreover, frequent scheme failure/abandonment adds to systems’ uncertainties and water users’ plight. A multitude of operational/organisational flaws, associated with government waterworks bodies, at different levels of systems’ hierarchy, limit RWSS operational performance. To that end, the concluding section argues for a demand-driven RWSS model (bottom-up systems’ governance) and highlights the core tenets of the same that call for integration of environmental, social, cultural, ethical and political perspectives in RWSS systems’ thinking/design.
Coastal areas are densely populated due to socioeconomic benefits and in turn also have a greater demand for fresh water. This ever-increasing demand for fresh water can be met by coastal aquifers, which act as large reservoirs of freshwater. Excessive and unmanaged pumping from coastal aquifer allows the salt water to flow inward encroaching on the voids created by the pumping of freshwater. This phenomenon is called saltwater intrusion. To stop the saltwater intrusion, an optimal pumping strategy needs to be adopted. Simulation models are generally linked with an optimization algorithm to develop an optimal pumping strategy for management of saltwater intrusion. Sharp interface based simulation models are often used which are computationally inexpensive but lacks in prediction accuracy, as it does not incorporate the effects of dispersion and diffusion. Density dependent simulation models include the effect of dispersion and diffusion, but have a very high computational budget in evaluating an optimal pumping strategy. To overcome above-mentioned limitation a new methodology is developed, where a density dependent model is used in conjunction with a sharp interface model to derive an optimal density ratio, such that interface obtained using this density ratio implicitly accommodated the effect of dispersion and diffusion in a sharp interface model. The performance of the developed methodology is evaluated for three hypothetical scenarios of saltwater intrusion. The performance evaluation results show the applicability of the methodology for management of saltwater intrusion while maximizing fresh water pumping in coastal aquifers.
Deteriorating the water quality of river is a major concern in India. This is especially true for river being used as drinking water sources. The water quality of river changes with time and development in the surrounding and upstream area. Therefore, it is necessary to determine the various water quality parameters of river as its presence and amount reflect in water treatment plants and in the surrounding areas which are dependent on river water only. One such river considered in the study is the Tapi river near Surat, Gujarat. The aim of the study is to determine the status of water quality in the Tapi river throughout the year. Surat city is dependent on the Tapi river for its drinking water needs. There are several intake structures constructed to take-off the raw river water. Our study area contains five intake structures from Valak to Rander on Tapi river from where water samples were collected. For assessment of WQI of Tapi river twelve physico-chemical parameters of river water were used. WQI is determined for monsoon, post-monsoon and pre-monsoon from the assessed water quality parameters of the Tapi river. The overall trend of results shows that the river water quality is very poor at stations Valak, Mota Varachha and Rander, while the water quality is poor at stations Sarthana and Katargam. The study indicates that water quality is continuously degrading from monsoon to post-monsoon and from post-monsoon to pre-monsoon which makes it unsuitable for drinking and fish culture.KeywordsWater quality indexPhysico-chemical parametersDrinking waterSurat cityTapi river
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In this work, we have successfully synthesized ZrO2 nanoparticles (NPs) using Ficus benghalensis (FB) leaf extract via simple microwave-assisted method. Silver NPs were deposited on the surface of ZrO2 through photocatalytic reduction. The synthesized ZrO2 and Ag-ZrO2 photocatalysts were characterized through X-ray Diffraction (XRD), UV–Vis Diffuse Reflectance Spectroscopy (DRS), Fourier Transform-Infrared Spectroscopy (FT-IR), High-Resolution Transmission Electron Microscopy (HR-TEM), Photoluminescence (PL), and Brunauer–Emmett–Teller (BET) surface area. From the aforesaid characterization of the materials, it is revealed that synthesized Ag NPs are crystalline in nature with the face-centered cubic structure (FCC), while ZrO2 NPs have both monoclinic and tetragonal phases. TEM images indicate that both ZrO2 and Ag-ZrO2 nanocomposite have spherical shape with the particle size of 20 and 15 nm, respectively. The optical properties were obtained using UV–Vis DRS which showed a decrease in the band gap energy of ZrO2 due to surface plasmon resonance (SPR) effect of Ag NPs. A lower in PL intensity of Ag-ZrO2 compared to that of ZrO2 NPs confirms the suppression of recombination rate of excited electron–hole pairs ultimately resulted into high photocatalytic activity. BET analysis shows that all the nanocomposites have higher surface area than pure ZrO2. The pure ZrO2 and Ag-ZrO2 show the efficient photocatalytic activity towards the methylene blue (MB) and methyl orange (MO). Ag-ZrO2 (1.0 wt.%) shows 21% increment in photocatalytic activity as compared to pure ZrO2 within 160 min under UV–Vis light.
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Fractionation studies on the sediment samples provide valuable inrormation on the nature of the metals bound to the sediments. Thirty three sediment samples of River Adyar were collected during two seasons and speciation study was carried out. The industrial and domestic effluents are directed into the river course at many points in the middle and lower of the Adyar River. To ascertain the extent of heavy metal pollution in the bed sediments of the river, total metal content and speciation were evaluated. The summation of the metal recoveries in the sequential extractions was found to be within ±10 % of the total metal content. The mobility factor was evaluated which represents the exchangeable and Carbonate fractions in the sediments. Risk Assessment Code was estimated and the results reveal the extent of risk associated with the heavy metals in the sediments in various stations. The resultjs of speciation shows that Cu and Ni fells in the high risk category at certain stations of the middle and lower part of the river. Except Cr, Fe and Zn, all other heavy metals studied show medium risk with regard to RAC The effect of monsoon on the concentration of the metals in various fractions had been studied and the significance of seasonal effect is determined using t-test.
In four consecutive seasons along 9 stations, water parameters such as TDS, pH, temperature, DO, BOD, COD, TOC, TP, NH4+, TN and NO3- were determined on the Siahroud River southwest of the Caspian Sea in northern Iran. The results indicated higher TDS values in some parts of the river due to the agriculture and residential activities. The addition of ammonia fertilizers in the paddy fields is one of the major causes for the higher NH4+ in the downstream sites. Total phosphorous (TP) and total nitrogen (TN) levels in the river were mainly in the organic forms. Factor analysis showed that agriculture and urban activities were the major pollutant sources. Four zones were identified by cluster analysis, suggesting local pollution sources or the accumulation of pollution effects downstream.
Problem statement: The objective of this research was to evaluate the degree of heavy metal contamination in lakes and the extent to whic h the sediment quality of the lakes of Bangalore city has deteriorated. Approach: In this study, heavy metals such as Cd, Co, Cu, Cr , Mn, Pb, Ni and Zn in lake bed sediments were analyzed using comparative sediment quality guidelines from various derived criteria. The selection of sampling points was based upon inflow and outflow regions of the lakes; geographical proximity of industrial units i n relation to their effluent discharges; proximity of residential sites located on the banks of the wetla nd systems; drainage patterns and accessibility towards the lakes. Digestion and analysis of the sa mples were done by microwave-assisted digestion and atomic absorption spectrophotometry respectivel y. Results: The extent of sediment quality deterioration was more pronounced in Cu (203.50 ppm) and Ni (97.64 ppm) followed by Pb (206.0 ppm) and Cd (8.38 ppm). Cr (96.70 ppm) failed a single sediment quality guideline while Zn (220.0 ppm), Mn (176.0 ppm) and Co (47.7 ppm) remained within the safety levels of sediment quality guidelines prescribed for the study. The Sediment G eo-accumulation Index showed that Co, Cu and Pb showed moderate levels of pollution while the Pollu tion Load Index (PLI) between heavy metals in the lakes produced the following outputs: Ni > Pb > Cd > Cu > Cr > Co > Zn > Mn. Conclusion: This study proves that the level of sustained metal cont amination of the fragile urban wetlands has not receded even after the recent urban wetlands rejuve nation works were completed. This prolonged presence in excessive levels of the studied heavy m etals in the bed sediments casts doubt on the choic e and effectiveness of the any mitigation measures in the long run.