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Seasonal dynamicity of environmental variables and water quality index

in the lower stretch of the River Ganga
To cite this article before publication: Chakresh Kumar et al 2021 Environ. Res. Commun. in press https://doi.org/10.1088/2515-7620/ac10fd

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Page 1 of 42 AUTHOR SUBMITTED MANUSCRIPT - ERC-100510.R1

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3 1 Seasonal dynamicity of environmental variables and water quality index in the lower
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5 2 stretch of the River Ganga
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3 Chakresh Kumar1,¥, Anwesha Ghosh1,2¥, Yash1,¥, Manojit Debnath3 and Punyasloke
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10 4 Bhadury1, 2 *
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Integrative Taxonomy and Microbial Ecology Research Group, Department of Biological
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14 6 Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur-741246,
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16 7 Nadia, West Bengal, India.

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9 Centre for Climate and Environmental Studies, Indian Institute of Science Education and
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Research Kolkata, Mohanpur-741246, West Bengal, India.

Department of Botany, Panskura Banamali College (Autonomous), Panskura R.S., Purba

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38 17 *Corresponding author: pbhadury@iiserkol.ac.in; +913361360000 extn 1154
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3 25 Abstract
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6 26 Rapid human pressure in semi-urban and urban areas along with increasing industrial activities
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27 has resulted in release of untreated sewage and other forms of pollutants into major rivers
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9 28 globally including in the Ganga. In this study, nine sites represented by 59 stations along the

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11 29 lower stretch of the River Ganga were monitored seasonally to understand the effect of
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13 30 monsoonal precipitation on environmental variables and Water Quality Index (WQI). Sampling
14 31 was undertaken in pre-monsoon, monsoon and post-monsoon seasons (2019). In situ surface
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16 32 water temperature, dissolved oxygen, pH, total dissolved solids (TDS) and electrical conductivity

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18 33 (EC) were measured along with dissolved nutrients and Chlorophyll-a. Both pH and DO were
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34 strongly influenced by monsoon and affected WQI. TDS was higher in point source (PS) stations
21 35 during pre-monsoon (113-538 ppm) compared to surface water (SW) stations (113-248 ppm)
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with strong influence of monsoon (PS-27.4-310.3 ppm; SW-27-68.9 ppm). Dissolved nutrients
including nitrate and o-phosphate concentration showed significant seasonal variation and
influenced monsoonal precipitation. In PS stations across studied seasons dissolved nitrate
concentration varied from 26.33-646 µM while in SW the range was from 21.22-148.06 µM. In
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30 40 the studied sites, higher concentration of dissolved nutrients in PS stations reflected the release
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32 41 of untreated municipal and industrial sewage directly into the river. The effect of precipitation
33 42 and resulting environmental variables was clearly evident on biological variable (concentration
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35 43 of Chl-a) with observed values in PS stations (0-21 mg/L) which were lesser compared to SW
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37 44 stations (0-89.3 mg/L) during monsoon.. Non-metric multidimensional scaling revealed three
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45 distinct clusters with greater overlap between PS and SW stations in monsoon. The WQI values
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40 46 (14-52) determined for lower stretch of Ganga revealed very poor water quality in majority
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42 47 stations and monsoonal precipitation did not have any influence on the observed trends.
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3 53 Abbreviations
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6 54 PS-Point Source EC-Electrical conductivity
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8 55 SW-Surface water WQI-Water quality index
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56 DO-Dissolved Oxygen TDS-Total dissolved solid
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13 57 Stn- Station SPM-Suspended particulate matter
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15 58 MLD- Millions of liter per day SWT- Surface water temperature
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18 59 AT- Air temperature
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Keywords: Ganga; water quality index; dissolved nutrients; dissolved oxygen; monsoon

Running title: Water quality of the lower stretch of Ganga

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1
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3 64 Introduction
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6 65 Rivers represent one of the major resources of freshwater for sustenance of human populations
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8 66 globally. River water is one of the principal sources for drinking and other purposes including
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67 agriculture, industrial and recreational activities (Chen et al., 2006; Anawar and Chowdhury,
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11 68 2020). However, due to increasing human pressure many riverine systems globally are
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13 69 increasingly reeling from pollution. Thus to develop an effective water management, there is a
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15 70 need to understand the scale of pollution, develop and quantify indices to track pollution and also
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71 to understand spatio-temporal changes of pollution in rivers. Such approaches have been
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18 72 attempted in major riverine systems globally including in Asia such as the Mekong River (Fu et
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20 73 al., 2012) and Yangtze River (Zhang et al., 2014).
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The growing influx of population into semi-urban and urban areas of India are putting pressure
on the natural resources including on major rivers such as the Ganga (Tare et al., 2013). The
River Ganga sustains populations residing along the river basin including in the cities of
Haridwar, Kanpur, Allahabad, Varanasi, Patna and Kolkata (Joshi et al., 2009; CPCB, 2013;
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78 Agarwal, 2015). In the state of West Bengal (India), the flow of river Ganga is regulated by the
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31 79 Farakka BarrageThe river splits downstream of the barrage; lower stretch is known as
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33 80 Bhagirathi-Hooghly which flows down right and on the left the Padma River enters into
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35 81 Bangladesh.
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82 On both the banks of the lower stretch of Ganga, intense semi urban to urban localities and
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39 83 industrial zones have developed over time including growth of towns and cities of Kalyani,
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41 84 Tribeni, Barrackpore, Kolkata and Diamond Harbour before it meets the coastal Bay of Bengal.
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43 85 On a daily basis, release of untreated municipal sewage, agricultural runoffs, untreated effluents
44 86 from small industries as well as ongoing social, tourism and religious activities affect the water
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46 87 quality of the river Ganga (Muduli et al., 2021). It is already well known that municipal sewage
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48 88 and industrial effluents are major contributors to pollution in the River Ganga (Das, 2011;
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89 Dwivedi et al., 2018; Kanuri et al., 2020).
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52 90 As per CPCB report (2014), 8250 million L/day (MLD) of wastewater is generated from human
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54 91 settlements residing along the banks of river Ganga. Of this, 2550 MLD was reported to be
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56 92 directly discharged into the river without any treatment. It was also reported that 25 Class-I
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3 93 towns (population>100000) in the three states of Uttar Pradesh, Bihar and West Bengal in India
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5 94 as 75% of all point-source (PS) pollution were contributed by these 25 towns (NRCD, 2009).
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7 95 Several steps including establishment of sewage treatment plants (STPs) under Ganga Action
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96 Plans I and II (GAP) were implemented.. However, even with the existing STPs which were set
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10 97 up in the state of West Bengal, the lower stretch of the Ganga continues to report high faecal
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12 98 coliform counts as these could only treat 1208.80 MLD sewage. Between 2007 and 2011, this
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14 99 stretch of Ganga River (Bhagirathi-Hooghly) flowing between Dakshineswar and Diamond
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100 Harbour recorded higher faecal coliform count compared to the rest of the entire upstream of the
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17 101 river. It has been also found that 51 of the 64 STPs were functional with only 60% capacity
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19 102 (CPCB, 2013).
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22 103 In the Gangetic Plains of West Bengal, seasonal precipitation that spans over the months of July
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to September and often stretching to October, plays a major role in influencing water quality of
the Ganga River. Surface waters are most vulnerable as they are continuously exposed to
disposal of wastewater (Singh et al., 2004). Municipal and industrial wastewater that originate
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107 from PS affect the river continuously,whereas surface runoff vary seasonally and influenced by
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30 108 seasonal precipitation. Variation in seasonal precipitation, surface runoff, interflow and
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32 109 groundwater seepage are some of the factors that ultimately influence river discharge (Vega et
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34 110 al., 1998) and may also affect the pollutants (Anawar and Chowdhury, 2020).
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111 Water Quality Index (WQI) represents an efficient method for assessment of water quality
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38 112 required for different human activities including bathing, irrigation, and industrial usages. WQI
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40 113 approach summarizes the effects of number of water quality parameters in a single unit-less
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42 114 value (Horton, 1965) and thus can provide holistic idea for aquatic bodies including in riverine
43 115 systems. Moreover, WQI can also help policy makers and managers to come up with
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45 116 comprehensive river basin management plans by incorporating water quality and possible uses of
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117 the water based on the inferred conditions (Bordalo et al., 2001; Kannel et al., 2007). By late
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118 70’s, more than 20 WQI were developed (Ott, 1978). Some of the most frequently used WQI are
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50 119 US National Sanitation Foundation Water Quality Index (NSFWQI; Brown et al., 1970), British
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52 120 Columbia Water Quality Index, BCWQI (Zandbergen and Hall, 1998), Oregon Water Quality
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54 121 Index, OWQI (Cude, 2001) and the Canadian Water Quality Index (Canadian Council of
55 122 Ministers of the Environment (CCME) 2001). In India, pioneering work on WQI by Bhargava
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3 123 (1983a,b,c) established a ‘WQI number’ (ranging from 0 for highly/extremely polluted to 100 for
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5 124 absolutely unpolluted water) to represent the integrated effects of parameters that result in
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7 125 pollution load. Bhargava’s WQI described the “effect of weight of each variable (pollution
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126 parameter) in the sensitivity function values of the various pollution variables relevant to a
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10 127 particular use” (Sharma and Kansal, 2011).WQI can be determined on the basis of various
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12 128 physical, chemical and biological parameters. The Central Pollution Control Board (CPCB) of
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14 129 India has used the information inferred from WQI to classify surface water into five classes
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130 ranging from A to E (A- drinking water source without conventional treatment but with
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17 131 chlorination, B-organized outdoor bathing, C-drinking water source with conventional treatment,
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19 132 D-propagation of wildlife and fisheries, E-Irrigation, industrial cooling and controlled disposal)
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21 133 to conclude the possible use of water for various human activities (CPCB, 1978). However, WQI
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approach has not been evaluated in the lower stretches of River Ganga (Bhagirathi-Hooghly)
while taking into account the effect of seasons including monsoonal precipitation. The
Bhagirathi-Hooghly has been referred to as lower stretch of River Ganga throughout the
remaining part of this study.
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30 138 In light of the available information, we hypothesize that seasonal precipitation has no influence
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32 139 on improving the water quality index (WQI) of the lower stretch of the River Ganga and local
33 140 factors solely determine ecosystem level health of the region. To test this hypothesis, the
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35 141 objectives of the present study was to i) determine environmental parameters of the lower stretch
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37 142 of Ganga with a focus onheavilyurbanized and industrialized sites ii) to determine the WQI of
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143 the studied sites and iii) to determine the influence of seasonal precipitation on the WQI of sites
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40 144 along the lower stretch of Ganga.
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43 145
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45 146 Materials and Methods
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147 Study sites
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50 148 This study was conducted along the lower stretch of River Ganga beginning from Kalyani
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52 149 (22°59’45.3”N 88°25’ 13.2”E) up to Kolkata (22°33’ 2.2”N, 88°19’ 27.6”E). The studied stretch
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53 150 spans across a distance of approximately 50 kms. Nine sites were selected located on both sides
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55 151 of the river bank for sampling namely Kalyani (Kal), Tribeni (Trv), Halisahar (Hal), Naihati
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3 152 (Nai), Chandannagar (Cdn), Palta (Pal), Barrackpore (Brk), Dakshineswar (Dak) and Kolkata
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5 153 (Kol) (Fig.1). Each site is characterized by dense population pressure and industrial activities
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7 154 which results in release of both domestic and industrial wastes into River Ganga through outlets.
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155 At each site, several stations were selected after careful observation based on number of visible
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10 156 municipal and industrial sewage outlets, canals dumping untreated sewage as well as other
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12 157 factors such as the frequent use of river bank for bathing, religious and social purposes and close
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14 158 proximity of agricultural lands to river bank. Each such point was classified as Point Source
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159 (PS). These stations could be easily distinguished from the surrounding water of the river based
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17 160 on observable water colour. Owing to high concentration of particles in the PS, a clear zone
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19 161 could be demarcated around these stations. Due to slow water movement, mixing and hence
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21 162 dispersal of particles from PS is slow. The area representing surface water in the river away from
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PS was classified as Surface Water (SW). The water color of SW was visibly different from the

Kalyani (22°59’45.3”N, 88°25’13.2”E) is a township spanning an area of 29.14 km2 with a

human density of 3500/km2 (Census, 2011). It is a Class II town and located on the eastern bank
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30 167 of Ganga. The township of Kalyani houses a number of industries including tyre manufacturing,
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32 168 industrial gas suppliers, paper mills and brick kilns. Small boats ferry people from one bank of
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169 the river to the other side resulting in release of oil in river from time to time. The area is visibly
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35 170 polluted by dumps of solid waste along the river bank including plastic debris. There is dense
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37 171 growth of water hyacinth (Eichhornia crassipes) along the bank of the river. Local village
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172 dwellers use water from the river for bathing and both household chores. They also conduct
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40 173 fishing at small scale near the river bank. Kalyani has two STPs with a combined capacity to
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42 174 treat 17 MLD (Kalyani Block B2, B3- 11 MLD, Kalyani Town Area- 6 MLD) (Mukherjee,
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44 175 2016). A STP of 21 MLD capacity has been also sanctioned in Kalyani under Namami Gange
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176 Mission. In total, six stations were sampled comprising of 2 PS and 2 SW and these are
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47 177 designated as Kal_Stn1_PS, Kal_Stn1_SW, Kal_Stn2_PS, Kal_Stn2_SW, Kal_Stn3, Kal_Stn4.

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49 178 The stations of Kalyani have the abbreviation of Kal. The station nomenclature approach has
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51 179 been also followed in rest of the sites.
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53 180 Tribeni (22°59’N, 88°25’E) is situated on the Ganga River bank opposite to Kalyani. This
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55 181 place is famous for religious congregations and thousands of people dip into the river at
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3 182 Benimadhav Ghat. Apart from its religious significance, Tribeni has a number of jute mills,
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5 183 cotton textiles, a tissue factory, brick kilns and paper factories. Additionally, there is a ‘bathing
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7 184 ghat’ and a crematorium. In total eight stations were sampled comprising of four PS and four
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185 SW. The stations of Tribeni have the abbreviation of Trv.
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11 186 Naihati (22°53’24”N, 88°25’12”E) located on the bank of Ganga covers an area of 11.55 km2
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13 187 and has a population density of 19000/km2 (Census, 2011). Naihati is a vibrant industrial area
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188 which includes production factories of paints, pigments, varnishes and associated products.
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16 189 There is innumerable jute mills located along the river bank. Additionally, this area witnesses an

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18 190 active fish market which is dependent on local production. In order to cater to the transport
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20 191 needs, there is an active ferry service between both banks of the river. Naihati has 5 big drains
21 192 (Thana Khal, Haran Majumdar Khal-I, Haran Majumdar Khal-II, Keorapara Khal, Muktarpur
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Khal), among which only Thana Khal is connected to the Naihati STP. Naihati has 265km of
open drainage and 0.66 km of underground drainage which together with the above-mentioned
big drains open into the Bhagirathi-Hooghly. In total six stations were sampled at Naihati
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196 comprising of three PS and three SW stations. The stations of Naihati have the abbreviation of
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30 197 Nai.
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198 Halisahar (22°56’49”N, 88°25’6.2”E) is situated on the east bank of the Ganga. It covers an
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34 199 area of 8.29 km2 and has a population density of 15000/km2 (Census of India, 2011).
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36 200 Industrialization along the river bank started in early 20thcentury and includes jute, paper and
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38 201 pulp mills. Halisahar has surface drains spanning length of 127 km and 76 km of unsurfaced
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39 202 drains that carry municipal wastewater into the River. Sanitation is achieved through usage of
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41 203 septic tanks (Gayen et al., 2015). A STP of 16 MLD capacity has been sanctioned under
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43 204 Namami Gange mission. In total six stations were sampled at Halisahar comprising of three PS
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45 205 and three SW stations. The stations of Halisahar have the abbreviation of Hal.
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47 206 Chandannagar (22°52’12”N, 88°22’48”E) has an area of 19 km2 with a population density of
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49 207 8800/km2 (Census of India, 2011). This township is famous for small scale and cottage
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208 industries that were set up even before the French colonized the region. Apart from a jute mills,
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52 209 several handloom set-ups, brick kilns, well lining manufacturers line the bank of the river. In
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54 210 total 6 stations were sampled comprising of three PS and three SW. The stations of
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56 211 Chandannagar have the abbreviation of Cdn.
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212 Palta (22°47’4.92”N, 88°21’53.68”E) is a small township located along the east side of the
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5 213 River. The Palta Water Works, now known as the India Gandhi Water Treatment Plant supplies
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7 214 potable surface water to Kolkata. It spans over about 19km2 area and is the first intake point for
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9 215 generation and supply of water from the river. It has a capacity to generate of 260 million

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10 216 gallons of water every day (Chatterjee, 2014). Four stations were sampled at Palta consisting of
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12 217 two PS and two SW stations. The stations of Palta have the abbreviation of Pal.
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218 Barrackpore (22°45’36”N, 88°22’12”E) has an area of 10.61 km2 with a population of
16 219 14000/km2 (Census of India, 2011). It has a number of large industrial setups including jute,

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18 220 engineering works, paper and cotton mills along the river bank. A STP of 24 MLD capacity is
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20 221 being presently set up under Namami Gange Mission. In some of the sampling stations dense
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222 growth of water hyacinth is present. In total six stations consisting of three PS and three SW
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were sampled. The stations of Barrackpore have the abbreviation of Brk.

Dakshineswar (22°39’19.55”N, 88°21’28.3”E) is one of the most important religious sites for
Hindus and is visited by people from all over the world. The temple is situated on the river bank
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29 226 as a result of which thousands of individual bath in the surrounding river water on a daily basis.
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31 227 Two large scale industries including a matchstick company and a paper mill is located along the
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33 228 river bank. In Dakshineswar, six stations comprising of three PS and three SW were sampled.
34 229 The stations of Dakshineswar have the abbreviation of Dak.
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37 230 Kolkata (22°34’21.36”N, 88°21’50.04”E), a megacity located on the bank of Ganga has an
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231 area of 206.08 km2 and population density of 22000/km2 (Census of India, 2011). Along with
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40 232 the huge population pressure that is dependent on the Ganga for daily water requirements, this
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42 233 region is also India’s oldest and second largest industrial area. Industries include those of jute,
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44 234 engineering and cotton textiles, transport and tertiary industry, chemical manufacturing, iron
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235 and steel manufacturing, tanneries, and food product industries. The bathing Ghats of Kolkata
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47 236 are used heavily for bathing. Along with this, there is intricate ferry movement across both the
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49 237 banks of the river that transport thousands of people each day. The city produces 750 million
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51 238 litres of wastewater and sewage everyday that is treated at the East Kolkata Wetlands, which is
52 239 the world’s only fully functional organic sewage management system. Apart from that, five
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54 240 functional STPs (Garden Reach, Bangur, South Suburban, Bagha Jatin and Hatisur) and 73
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56 241 drainage pumping station (as per the Kolkata Municipal Corporation;
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3 242 https://www.kmcgov.in/KMCPortal/jsp/KMCDrainageHome.jsp) handle the wastewater of the city.
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5 243 A total of eleven stations were sampled consisting of six PS and five SW. The stations of
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7 244 Kolkata have the abbreviation of Kol.
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9 245 Sampling activities

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12 246 The sampling activities were designed as per the Guidelines of Water Quality Monitoring,
13 247 CPCB. The CPCB document specifies a pre-monsoon sampling once a year followed by
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15 248 sampling every three months for perennial rivers. Sampling was conducted monthly during pre-
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17 249 monsoon (March-May, 2019), monsoon (June-September, 2019) and post-monsoon (October –
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250 December, 2019) across all the studied sites. Owing to strong influence of the south-western
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20 251 monsoons on freshwater flow in the lower stretch of Ganga during monsoon season, increase in
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22 252 freshwater input along with water flow rate are expected to have a strong influence on
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concentration and forms of dissolved nutrients and in turn on the water quality. Run off from
terrestrial sources would also alter the dynamicity in terms of forms and concentrations of
dissolved nutrient in the river. In post-monsoon, average water volume would increase but due to
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29 256 decreased flow rate, residence time of dissolved nutrients will be expected to vary. However,
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257 both these factors would not be at play during pre-monsoon. Surface water samples were
32 258 collected from 0.5 m depth using wide mouthed pre-cleaned HDPE amber bottles of 1 L capacity
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34 259 (Tarsons, India) and immediately fixed with 4% buffered formaldehyde (Merck, India) in case of
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36 260 PS and SW stations across all studied sites spanning over the three seasons. Further, 50 mL of
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261 surface water samples were collected from 0.5 m depth in triplicates to estimate dissolved
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39 262 nutrients.
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42 263 Measurement of in-situ environmental parameters
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44 264 In-situ environmental parameters were measured in triplicates at each sampling station by using
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46 265 hand held instruments. Measured environmental parameters included air temperature (AT) and
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266 surface water temperature (SWT) (Digi-sense RTD meter 20250-95, single Input thermometer
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49 267 with NIST-Traceable Calibration), dissolved oxygen (DO; Oakton DO 6+, Eutech Instrument
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51 268 Pte Ltd., Singapore), pH (Oakton pH 5+, Eutech Instrument Pte Ltd., Singapore), electrical
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53 269 conductivity and TDS (EC; HM digital EC/TDS/TEMP COM-100 Myron L Company). All
54 270 instruments were calibrated in the laboratory followed by in the field to obtain accurate results.
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3 271 Total depth of the water was measured using a graduated yardstick and was noted only for
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5 272 stations where the bottom sediment could be attained.
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273 Estimation of dissolved nutrients
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10 274 Collected surface water samples were analysed immediately for dissolved nutrients. All samples
11
12 275 were pre-filtered using a 0.45 µm 25 mm nitrocellulose filter paper (Merck Millipore, Germany).
13 276 Inorganic nutrients were analyzed as specified by the Guidelines of Water Quality Monitoring,
14
15 277 CPCB which states that inorganic nutrients including nitrate, nitrite and phosphate be measured
16

us
17 278 to determine WQI for surface water of perennial rivers. From filtered water samples, dissolved
18
279 nitrate (Finch et al., 1998), dissolved ortho-phosphate (Strickland and Parsons, 1972) and
19
20 280 dissolved nitrite (Strickland and Parsons, 1972) concentrations were determined using a UV-Vis
21
22 281 spectrophotometer (U2900, Hitachi Corporation, Japan). All estimations were performed in
23
24
25
26
27
28
282

283
triplicates.
an
Estimation of suspended particulate matter (SPM) and Chlorophyll-a (Chl-a)
dM
29 284 Surface water samples were filtered in 0.45 µm 47 mm nitrocellulose filter paper (Merck
30
31
285 Millipore, Germany) and sediment collected on the filter paper was dried overnight at 60˚C. The
32 286 difference between the wet weight and dry weight of the filter paper was considered as the SPM
33
34 287 (Harrison et al., 1997). For estimation of Chl-a pigment, surface water samples were filtered
35
36 288 through a 0.45 µm 47 mm nitrocellulose filter paper. The filter paper was kept overnight in 90%
37
38
289 acetone (Merck, India) at 4°C in dark. After 16 hours of incubation, the samples were
pte

39 290 centrifuged for 10 min at 5000 rpm at 4°C and the extract was scanned in a UV-Vis
40
41 291 spectrophotometer at 665 nm, 645 nm, and 630 nm (Strickland and Parsons, 1972).
42
43
44 292 Estimation of total hardness and total alkalinity
45
46
293 Surface water samples were filtered on 0.45µm 47 mm nitrocellulose filter paper and total
ce

47
48 294 hardness was estimated using a pre-calibrated kit following manufacturer’s protocol (Labard
49
50 295 Aquasolve, Labard Instruchem Pvt Ltd, India). Total alkalinity was measured by titration method
51
52 296 using 0.02 N H2SO4 as titrant and bromocresol green as an indicator. Following reaching end
Ac

53
297 point, the indicator changed colour from blue to yellow.
54
55
56 298 WQI Calculation
57
58 11
59
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1
2
3 299 Four parameters namely DO, pH, EC and dissolved nitrate were selected for calculation of WQI
4

pt
5 300 of the studied stations. These parameters could significantly vary between the PS and SW
6
7 301 stations and also showed a strong seasonal variation owing to the nature of the stations. Any
8
302 variation in these parameters would directly influence the functional capacity of the resident
9

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10 303 biological communities including microbes, thereby strongly altering water quality, ecosystem
11
12 304 processes and in turn deteriorate overall ecosystem level health. Additionally, these parameters
13
14 305 were chosen as per the general monitoring parameters for perennial rivers outlined in the
15
306 Guidelines for Water Quality Monitoring document specified by CPCB. WQI was calculated
16

us
17 307 using the River Ganga Index of Ved Prakash et al., (1990 as cited in Abbasi and Abbasi, 2012)
18
19 308 which is based on the formula:
20
21 𝑝
22
23
24
25
26
27
28
309

310
311
an
𝑊𝑄𝐼 = ∑ 𝑊𝑖𝐼𝑖
𝑖=1

Where Ii denotes subindex for the ith water quality parameter, Wi is the weight associated with
ith water quality parameter, and p is the number of water quality parameters. Water quality
dM
29 312 criteria for categories B (organized outdoor bathing), C (drinking water source with
30
31 313 conventional treatment), D (propagation of wildlife and fisheries) and E (irrigation, industrial
32
33 314 cooling, and controlled disposal) were considered in this study.
34
35 315
36
37
38 316 Statistical analyses
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39
40 317 Two way Analysis of Variance (ANOVA) was performed to statistically test observed variation
41
42 318 in environmental parameters between PS and SW and among the three studied seasons. A non-
43
319 metric multidimensional scaling (nMDS) ordination plot was generated using Euclidean
44
45 320 dissimilarity in vegan version 2.5-5 (Oksanen et al., 2018) in R-3.5.3 to check for clustering
46
ce

47 321 patterns among the studied stations.

48
49
50 322 Results
51
52 323 Seasonal and spatial variation of environmental parameters
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53
54
324 In-situ parameters
55
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1
2
3 325 The measured environmental parameters of all studied stations are given in Table S1. The AT
4

pt
5 326 range in pre-monsoon (25.6- 41.3°C) was higher than monsoon (24.3-36.3°C) and post-
6
7 327 monsoon (19.8-32.8°C) seasons across all the studied stations representing PS and SW. SWT
8
9 328 values showed seasonal trend like AT (pre-monsoon: 22.4-35°C, monsoon: 22.1-34.6°C, post-

cri
10
11 329 monsoon: 19.2-24.3°C). Both AT and SWT did not show any significant difference between PS
12 330 and SW but was significantly different between the sampling seasons (p>0.001) (Fig.2a).
13
14
15 331 For the PS stations DO ranged from 2.4 to 6.8 mg/L in pre-monsoon season. The PS stations of
16

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332 Dakshineswar and Kolkata exhibited lowest recorded values of DO whereas higher values were
17
18 333 recorded in Tribeni and Halisahar. Except for Kol_Stn6_PS, all stations of Kolkata recorded DO
19
20 334 values >4 mg/L (Table S1). In monsoon, DO values decreased for the PS stations of Halisahar,
21
22 335 Kolkata, Tribeni and Kalyani with DO > 7 mg/L recorded in Chandannagar. For all PS stations,
23
24
25
26
27
28
336
337
338
an
DO values in post-monsoon increased to > 4mg/L. This trend was not observed for the SW
stations. In pre-monsoon, DO values ranged from 3.9-7.53 mg/L which remained similar in
monsoon (4.2-7.3 mg/L) and post-monsoon (5.4-8.9 mg/L). The recorded DO values were found
dM
29 339 to be significantly different between PS and SW stations and in studied seasons (p>0.001) (Fig.
30 340 2b).
31
32
33 341 The recorded pH values during pre-monsoon in the PS stations ranged from 6.4-8.7 with lowest
34
342 values recorded in Dakshineswar as well as Kolkata and highest values in Naihati. In monsoon,
35
36 343 pH ranged from 6.8 to 8 with the lowest values recorded in Kolkata, Naihati and Kalyani and
37
38 344 highest in Naihati (Table S1). In post-monsoon, pH ranged from 5.3-9.3 with the lowest values
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39
40 345 recorded in Dakshineswar and highest in Tribeni. The pH values of SW stations showed trends
41 346 similar to PS stations. In pre-monsoon, pH ranged from 6.83 to 8.75 with Kolkata and Kalyani
42
43 347 showing low pH and Naihati and Halisahar showing high pH. In monsoon, the pH ranged from
44
45 348 6.43- 7.4 with lowest in Barrackpore and highest in Kolkata. In post-monsoon, pH ranged from
46
349 6.6 to 9.3 with lowest in Naihati and highest in Tribeni. The pH of water was not significantly
ce

47
48 350 different between PS and SW but showed significant difference between seasons (p>0.001)
49
50 351 (Fig.2b). During monsoon, water pH for most stations of PS and SW ranged between 7 and 8.
51
52 352 The electrical conductivity (EC) of water exhibited wide variability between the stations (Fig.2c
Ac

53
54 353 and Table S1). In pre-monsoon, EC ranged from 113-808 µS/cm for PS stations which decreased
55
56 354 in monsoon (68.7-353 µS/cm) but subsequently increased in post-monsoon (194-676 µS/cm).
57
58 13
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1
2
3 355 Recorded EC values in SW was similar to PS. Values in pre-monsoon ranged from 172-242
4

pt
5 356 µS/cm, monsoon ranged from 128-146 µS/cm and post-monsoon ranged from 170-306 µS/cm
6
7 357 (Table S1). In pre-monsoon, between the PS stations, EC showed a wide range of values that
8
358 were significantly different (p>0.001). EC was also significantly different between the PS and
9

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10 359 SW stations and also between the studied seasons (p>0.001) (Fig.2c).
11
12
13 360 Total dissolved solids (TDS) ranged from 113-538 ppm in pre-monsoon across the PS stations
14 361 whereas in monsoon it ranged from 27.4-310.3 ppm and in post-monsoon it was 93.7-321 ppm
15
16 362 (Table S1). TDS recorded in the SW stations showed similar trend to values recorded in PS. In

us
17
18 363 pre-monsoon, TDS ranged from 113-248 ppm which showed a marked decrease in monsoon (27-
19
20
364 68.9ppm) and post-monsoon seasons (78-133 ppm). The recorded TDS values were significantly
21 365 different between the PS and SW stations (p>0.001) and between the studied seasons (p>0.001)
22
23
24
25
26
27
28
366

367
368
(Fig.2c).

an
Total suspended particulate matter (SPM) ranged from 3.2-395 mg/L in pre-monsoon for PS
stations which increased to 31.1-609.3 mg/L in monsoon and 24.3-618.4 mg/L in post-monsoon
dM
29 369 (Table S1). For the SW, values ranged from 3.2-357 mg/L in pre-monsoon, 43.5-752.8 mg/L in
30
31
370 monsoon and 12.3-162.9 mg/L in post-monsoon. SPM load in the surface water was significantly
32 371 higher in monsoon compared to the other seasons (p>0.001) but not between PS and SW stations
33
34 372 of the studied sites (Fig.2c).
35
36
373 The measured total alkalinity in PS stations ranged from 138.2-386.7mg/L CaCO3 in pre-
37
38 374 monsoon, 46.7-400 mg/L CaCO3 in monsoon and 136-330 mg/L CaCO3 in post-monsoon (Table
pte

39
40 375 S1). In SW, the values ranged from 106.7-293.3mg/L CaCO3 in pre-monsoon, 96.7-160 mg/L
41
42 376 CaCO3 in monsoon and 160-210 mg/L CaCO3 in post-monsoon. The obtained values indicated
43 377 significant difference between PS and SW (p>0.001) and between the seasons (p>0.001)
44
45 378 (Fig.2c).
46
ce

47
379 The values for total hardness ranged from 125-250 ppm CaCO3 in pre-monsoon, 50-250 ppm
48
49 380 CaCO3 in monsoon and 125-275 ppm CaCO3 in post-monsoon representing the PS stations. In
50
51 381 SW, total hardness values ranged from 100-175 ppm CaCO3 in pre-monsoon, 50-150 ppm
52
Ac

53 382 CaCO3 in monsoon and 125-200 ppm CaCO3 in post-monsoon. Total hardness showed
54 383 significant difference between PS and SW and also between the seasons (p>0.001) (Fig.2c).
55
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58 14
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1
2
3 384 Profiles of Chl-a pigment
4

pt
5
6 385 The concentration of Chl-a pigment ranged from 1.6-32.8 mg/L in pre-monsoon across the PS
7
386 stations; lowest values recorded in Naihati (Nai_Stn3_PS) and Tribeni (Trv_Stn2_PS) (Table
8
9 387 S1). The profiles further changed during the monsoon season (0-21 mg/L). In monsoon, Chl-a

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10
11 388 pigment could not be detected in three PS stations namely Hal_Stn2_PS, Hal_Stn3_PS,
12
13 389 Trv_Stn1_PS, and Nai_Stn3_PS based on the adopted methodology. In the post-monsoon season
14 390 Chl-a concentration for PS stations ranged between 6.9-32.8 mg/L. For SW stations, the values
15
16 391 ranged from 1.8-53.5 mg/L in pre-monsoon, 0-89.3 mg/L in monsoon and 18.9-54.3 mg/L in

us
17
18 392 post-monsoon. During monsoon season, in four SW stations namely, Hal_Stn2_SW,
19
20
393 Trv_Stn2_SW and Trv_Stn3_SW, Chl-a pigments were not detected. The observed
21 394 concentrations of Chl-a were found to be significantly different between PS and SW stations
22
23
24
25
26
27
28
395

396

397
Concentration of dissolved nutrients an
(p>0.05) and between the seasons (p>0.001) (Fig.2a).

In PS stations, dissolved nitrate concentration ranged between 32.32-646 µM in pre-monsoon,

dM
29
398 60-335.56 µM in monsoon and 26.33-491.51 µM in post-monsoon (Table S1). In pre-monsoon,
30
31 399 lowest concentration was found in Naihati site (Nai_Stn2_PS) and highest in Dakshineswar
32
33 400 (Dak_Stn3_PS). In monsoon, lowest concentration of nitrate was found in Hal_Stn3_PS and
34
35 401 highest concentration in Nai_Stn3_PS. For the post-monsoon, lowest concentration was
36
402 encountered in Kol_Stn1_PS and highest in Dak_Stn3_PS. In SW stations, dissolved nitrate
37
38 403 ranged from 21.22-138.22 µM in pre-monsoon, 66.11-111.94 µM in monsoon and 24-148.06
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39
40 404 µM in post-monsoon. In pre-monsoon, lowest concentration of nitrate was encountered in the
41
42 405 station Cdn_Stn2_SW while highest was in Dak_Stn2_SW. In monsoon, lowest concentration
43 406 was found in Trv_Stn4_SW and highest in Kol_Stn1_SW. During post-monsoon lowest
44
45 407 concentration was found in Kol_Stn1_SW and highest in Hal_Stn1_SW. The observed variation
46
408 in dissolved nitrate concentrations were significantly different between the PS and SW stations
ce

47
48
409 (p>0.001) and also between the studied seasons (p>0.001) (Fig.2a).
49
50
51 410 The dissolved nitrite concentrations were generally found to be low in pre-monsoon (0-34.6 µM)
52
Ac

53 411 in the PS stations (Table S1). In pre-monsoon dissolved nitrite was not detected in five PS
54 412 stations namely Brk_Stn2_PS, Cdn_Stn2_PS, Kol_Stn2_PS, Kol_Stn4_PS, Kol_Stn5_PS,
55
56 413 Nai_Stn2_PS, Nai_Stn1_PS, and Nai_Stn3_PS. As part of this study, dissolved nitrite was found
57
58 15
59
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1
2
3 414 to be high in only one station, namely, Kol_Stn6_PS during the pre-monsoon season. In
4

pt
5 415 monsoon, dissolved nitrite ranged from 0.1-6.7 µM while in post-monsoon the values ranged
6
7 416 between 0.1-3.9 µM. The trend was similar in the SW stations. Estimated dissolved nitrite
8
417 concentration ranged from 0-3.6 µM in pre-monsoon, 0.15-3.97 µM in monsoon and 0.04-3.32
9

cri
10 418 µM in post-monsoon. Dissolved nitrite concentrations did not show significant difference
11
12 419 between the sites or between the stations (Fig.2b).
13
14 420 The o-phosphate concentration ranged between 0.62-31.52 µM in pre-monsoon, 3.95-276.58 µM
15
16 421 in monsoon and 1.07-117.1 µM in post-monsoon for PS stations. In pre-monsoon, o-phosphate

us
17
18 422 was found to be highest in one of the PS stations of Halisahar site (Hal_Stn2_PS) and lowest in
19
20
423 the Palta site (Pal_Stn1_PS). In one of the stations in Kolkata (Kol_Stn2_PS), o-phosphate was
21 424 beyond detection limit. In monsoon, lowest concentration was encountered in a PS station of
22
23
24
25
26
27
28
425
426
427
428
an
Chandannagar (Cdn_Stn3_PS) and highest in a station of Naihati (Nai_Stn3_PS). In post-
monsoon, o-phosphate concentration was lowest in Kalyani (Kal_Stn1_PS) and highest in the
same site (Nai_Stn1_PS). For the SW stations, in pre-monsoon concentrations were in the range
of 0.06-11.12 µM, 2.89-22.63 µM in monsoon and 0.67-60.79 µM in post-monsoon. In pre-
dM
29
30 429 monsoon, o-phosphate was lowest in Cdn_Stn3_SW and highest in Kol_Stn5_SW. For the
31
32 430 monsoon, lowest concentration was found in Cdn_Stn1_SW and highest in Trv_Stn4_SW. In
33 431 post-monsoon, lowest concentration was found in Brk_Stn1_SW and highest in Cdn_Stn3_SW.
34
35 432 The observed concentrations were significantly different between PS and SW stations (p>0.001)
36
37 433 and across studied seasons (p>0.05) (Fig.2b).
38
pte

39 434 Water quality index (WQI)

40
41
42 435 Four environmental parameters namely pH, DO, EC and dissolved nitrate were used to calculate
43 436 WQI and assign into B, C, D and E categories as per CPCB. In Kolkata, calculated WQI ranged
44
45 437 from 24-44 in pre-monsoon, 27-36 in monsoon and 24-44 in post-monsoon (Fig.3). This showed
46
438 that the water quality of Kolkata PS and SW stations shifted from bad in pre-monsoon to very
ce

47
48
439 bad during monsoon and marginal changes in post-monsoon. No major change in WQI values
49
50 440 were encountered in Kol_Stn4_SW between pre-monsoon and post-monsoon seasons. However,
51
52 441 the WQI value of this station did decrease in monsoon (Fig.3). In case of Kol_Stn5_SW of
Ac

53
54 442 Kolkata there was no observable change in WQI values irrespective of the season of sampling. In
55 443 Dakshineswar, WQI ranged from 24-41 in pre-monsoon, 30-33 in monsoon and 47-51 in post-
56
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1
2
3 444 monsoon. The WQI values indicated very bad water quality in pre-monsoon and monsoon for
4

pt
5 445 two stations namely, Dak_Stn3_PS and Dak_Stn3_SW in Dakshineswar. Both these stations
6
7 446 exhibited WQI values indicative of bad condition in pre-monsoon which changed to very bad in
8
447 monsoon. However, the WQI values of remaining stations of Dakshineswar ranged between
9

cri
10 448 medium to good condition during post-monsoon. In Barrackpore, WQI values in pre-monsoon
11
12 449 ranged from 34-40 while in monsoon it ranged from 35-38 and subsequently changed to 33-42 in
13
14 450 post-monsoon. In Barrackpore, Brk_Stn2_PS exhibited bad water quality across all the three
15
451 seasons (WQI 33-35). For Palta site, WQI values in pre-monsoon were 14 while in monsoon it
16

us
17 452 was 34-40 and in post-monsoon ranged from 36-52. In Palta, water quality of studied stations
18
19 453 remained very poor throughout pre-monsoon and marginally improved in monsoon with the
20
21 454 exception of one station (Pal_Stn2_SW). In post-monsoon, the water quality further improved
22
23
24
25
26
27
28
455
456
457
458
an
through remained in the bad category except for Pal_Stn1_SW where the water was of medium
to good condition based on WQI value. For the Chandannagar stations, WQI values ranged
between 32-42 in pre-monsoon, 32-34 in monsoon and 34-44 in post-monsoon. All the PS
stations of this site exhibited bad quality in both pre-monsoon and monsoon seasons (Fig.3).
dM
29
30 459 For Naihati, WQI values were found to range between 28-48 in pre-monsoon, 35-56 in monsoon
31
32 460 and 30-40 in post-monsoon. In two stations of Naihati, Nai_Stn2_SW and Nai_Stn3_SW the
33 461 water quality was relatively better compared to rest of the stations. The WQI values for Halisahar
34
35 462 stations exhibited bad to very bad water quality irrespective of the season of sampling (pre-
36
37 463 monsoon: 27-38; monsoon: 30-35; post-monsoon: 33-47) (Fig.3). This type of WQI trends were
38
464 also observed in Triveni and Kalyani PS and SW stations across the studied seasons (Fig.3).
pte

39
40
41 465 Statistical analysis
42
43 466 The nMDS ordination plot showed three distinct clusters (Fig.4). The stations could be
44
45 467 distinguished on the basis of pre-monsoon, monsoon and post-monsoon. There was greater
46
468 overlap between the PS and SW stations in monsoon season. Both in pre-monsoon and post-
ce

47
48
469 monsoon, the clusters formed by PS and SW stations could be distinguished. As also seen by
49
50 470 two-way ANOVA, the influence of monsoon on the environmental parameters was greater than
51
52 471 the type of station.
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53
54 472
55
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1
2
3 473 Discussion
4

pt
5
6 474 The lower stretch of the River Ganga that flows through West Bengal and is commonly referred
7
475 to as Bhagirathi-Hooghly has ‘very poor’ water quality. Unchecked metal contamination
8
9 476 originating from industrial effluents including tanneries along with rapid increase of coliform

cri
10
11 477 count has rendered the river water unfit for usage (Aktar et al., 2010; Bhardwaj and Singh, 2011;
12
13 478 Sengupta et al., 2014; Biswas et al., 2015; Pandey et al., 2017). Therefore, the main objective of
14 479 this study was to understand the effect of seasonal influences including precipitation on
15
16 480 environmental variables and resulting consequences on WQI. To address these questions, nine

us
17
18 481 sites along 50 km stretch of the River Ganga were selected. Such baseline information on
19
20
482 environmental variables and WQI had been clearly lacking for this stretch of the River Ganga
21 483 which could be immensely important for river basin management and beyond such as the Ganga-
22
23
24
25
26
27
28
484
485
486
487
an
Brahmaputra-Meghna delta systems. Large variation in environmental parameters was expected
in lower stretch of Ganga due to huge amount of freshwater input during the monsoon season
from the South-western monsoons (Ghosh et al., 2018). In 2019 the year of sampling,
collectively lower stretch of Ganga received approximately 3600 mm of rainfall from South-
dM
29
30 488 western monsoons (Indian Meteorological Department). For example, the average precipitation
31
32 489 in monsoon was much higher (1148.2 mm) compared to the other seasons of 2019 (pre-
33 490 monsoon: 226.8 mm; post-monsoon: 215.5 mm) (data not shown). The studied PS and SW
34
35 491 stations across selected sites distinctly showed the influence of seasonal precipitation on air
36
37 492 temperature with significantly lower temperatures being recorded in monsoon (24.3-36.3°C)
38
493 compared to pre-monsoon (25.6- 41.3°C). Subsequent influence of air temperature on SWT
pte

39
40
494 profiles, in particular during monsoon season was clearly evident as part of the study. The
41
42 495 observed influences of monsoonal precipitation compared to other seasons were visible in many
43
44 496 of the measured environmental variables. For example, DO values decreased in a number of
45
46 497 stations representing a number of sites including Halisahar, Kolkata, Tribeni and Kalyani during
ce

47 498 monsoon season and improved during the post-monsoon (Table S1). Seasonal variation in DO
48
49 499 appeared to be strongly influenced by monsoonal precipitation (as seen by two-way ANOVA)
50
51 500 and negatively correlated with SWT. This could result from possible change in water depth and
52
501 mixing during monsoon that can influence the atmosphere-water interchange of oxygen as
Ac

53
54 502 reported before (de la Paz et al., 2007).
55
56
57
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1
2
3 503 The studied PS and SW stations across selected sites distinctly showed the influence of seasonal
4

pt
5 504 precipitation on air temperature with significantly lower temperatures being recorded in
6
7 505 monsoon (24.3-36.3°C) compared to pre-monsoon (25.6- 41.3°C). Subsequent influence of air
8
9 506 temperature on SWT profiles, in particular during monsoon season was clearly evident as part of

cri
10 507 the study. The observed influences of monsoonal precipitation compared to other seasons were
11
12 508 visible in many of the measured environmental variables. For example, DO values decreased in a
13
14 509 number of stations representing a number of sites including Halisahar, Kolkata, Tribeni and
15
16
510 Kalyani during monsoon season and improved during the post-monsoon (Table S1). Seasonal

us
17 511 variation in DO appeared to be strongly influenced by monsoonal precipitation (as seen by two-
18
19 512 way ANOVA) and negatively correlated with SWT. This could result from possible change in
20
21 513 water depth and mixing during monsoon that can influence the atmosphere-water interchange of
22
23
24
25
26
27
28
514

515
516
an
oxygen as reported before (de la Paz et al., 2007).

The current study was focused in developing seasonal baseline information for environmental
variables in order to reliably calculate WQI and thus understand the seasonal trends of pollution
dM
517 representing approximately 50 km stretch of the lower stretch of the River Ganga. The lower
29
30 518 stretch of the river flows through four districts of West Bengal, namely, Nadia, North 24
31
32 519 Parganas, Hooghly and Kolkata. Such baseline information on environmental data and WQI with
33
520 respect to seasonal scales have been clearly lacking for the lower stretch of this river. Eleven
34
35 521 sites were selected to study the major environmental variables at a seasonal scale. Large
36
37 522 variation in environmental parameters was expected in lower stretch of Ganga due to huge
38
523 amount of freshwater input during the monsoon season from the South-western monsoons
pte

39
40 524 (Ghosh et al.,2018).Collectively, the lower stretch of Ganga received approximately 3600 mm of
41
42 525 rainfall from South-western monsoons in 2019 (India Meteorological Department). Due to heavy
43
44 526 local precipitation and increased flow of freshwater from upstream of the river, residence time of
45
46
527 dissolved nutrient concentrations would be strongly influenced as reported in other freshwater
ce

47 528 systems (Maya and Babu, 2007; Wu et al., 2016).

48
49 529 The studied PS and SW stations across selected sites distinctly showed the influence of seasonal
50
51 530 precipitation on air temperature with significantly lower temperatures being recorded in
52
531 monsoon compared to other studied seasons. Subsequent influence of air temperature on surface
Ac

53
54 532 water temperature was clearly evident as part of the study. The observed variation in SWT would
55
56 533 in turn influence other environmental parameters including dissolved nutrients and resulting
57
58 19
59
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1
2
3 534 consequences for biological communities such as photosynthetic organisms (expressed in terms
4

pt
5 535 of Chl-a concentrations). For example, the average precipitation in monsoon was much higher
6
7 536 (1148.2 mm) compared to the other seasons of 2019 (pre-monsoon: 226.8 mm; post-monsoon:
8
537 215.5 mm) (data not shown). Additionally, heavy precipitation during monsoon could act in
9

cri
10 538 cohort resulting in complex dynamicity within the lower stretch of Ganga.
11
12 539
13
14 540 Pearson’s correlation coefficient distinctly showed negative influence of SWT on concentration
15
541 of dissolved nutrients including nitrate, nitrite and o-phosphate as well as other environmental
16

us
17 542 parameters including DO. Such variation can alter the water quality by influencing individual
18
19 543 WQI parameters. Seasonal variation in DO appeared to be strongly influenced by monsoonal
20
21 544 precipitation (as seen by two-way ANOVA) and negatively correlated with SWT. This could
22
23
24
25
26
27
28
545
546
547
548
an
result from possible change in water depth and mixing during monsoon that can influence the
atmosphere-water interchange of oxygen as reported before (de la Paz et al., 2007). In this study
it was also found that PS stations exhibited lower DO concentrations compared to SW stations.
The low DO values highlight that the PS stations are more anthropogenically influenced (e.g.
dM
29 549 pollutants) compared to SW stations. Increased pollutants might inhibit physical dissolution of
30
31 550 oxygen from the atmosphere into the water by inhibiting surface interactions. Additionally, it
32
33 551 might be a deterrent to light penetration to the water which in turn would impact the resident
34
552 phytoplankton communities that play a major role in generation of oxygen (de la Paz et al.,
35
36 553 2007). Concentration of DO in surface water hence acts as an appropriate proxy for water
37
38 554 quality. CPCB mandates that DO levels should be > 5 mg/L for category B and > 4 mg/L for
pte

39
40 555 category D. Though for most studied PS stations, DO levels were > 4 mg/L, some stations
41 556 showed DO as low as 2 mg/L (e.g. Dakshineswar, Naihati and Kalyani). It would therefore be
42
43 557 critical to set-up long term monitoring of these stations in order to understand the underlying
44
45 558 causes of observed low DO concentrations. Such links between DO and high pollution levels
46
559 have been also observed in other rivers in India (Chatterjee et al., 2010). Incidentally, data from
ce

47
48 560 1987 collected from sites including Tribeni, Halisahar, Naihati, Palta, Barrackpore and
49
50 561 Dakshineswar report DO levels in close range to values found in 2019 (Mukherjee et al., 1993).
51
52 562 This indicates that the studied sites might be receiving high levels of pollutants from the
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53 563 industrial units and therefore more coordinated efforts are required to improve the quality of
54
55 564 water in these sectors.
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3 565 The average pH observed in the lower stretch of Ganga is lesser than the pH observed upstream
4

pt
5 566 of the river in Uttar Pradesh (Tiwari et al., 2016) but exhibit trends similar to previous reports
6
7 567 (Kar et al., 2008). Unlike DO, pH was not found to vary significantly between PS and SW
8
568 stations. Moreover, seasonal precipitation did not have a strong influence on pH as confirmed by
9

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10 569 Pearson’s correlation coefficient. Overall in studied sites, the PS stations exhibited marginally
11
12 570 lower pH values compared to SW stations. This could result from decomposition of organic
13
14 571 matter present in untreated water which is directly getting discharged from municipal drains
15
572 (Girija et al., 2007). The recorded pH values remained within permissible limits except for
16

us
17 573 Dak_Stn2_PS during all the studied seasons. In Dak_Stn2_PS, pH values ranged from 6.8-
18
19 574 7.7during the study period. This station (Dak_Stn2_PS) is located near the mouth of a big
20
21 575 sewage drain that opens into the river directly and regular discharge of foul smelling black
22
23
24
25
26
27
28
576
577
578
579
an
tainted water has been observed throughout the study period. Specific substances drained into the
river could result in low pH (5.3) in Dak_Stn2_PS which could have also influenced other
measured environmental parameters including EC. Other than this station, surface water of all
studied stations has pH within the range of 6.5-8.5 and adhered to the pH criterion of WQI for
dM
29 580 Categories B, C, D and E.
30
31 581
32
33 582 Owing to the vast stretch of the Indo-Gangetic plain and extensive use of the plains as fertile
34
583 agricultural lands, irrigation is largely dependent on the water supply from the river. The CPCB
35
36 584 has a mandate to show that water for irrigation purposes should have EC values lower than 2250
37
38 585 mhos/cm. Water with high EC negatively impacts crop productivity by causing physiological
pte

39
40 586 drought-like conditions (Joshi et al., 2009). The water with EC values less than 250 µmhos/cm is
41 587 considered suitable for the purpose of irrigation but can become unsafe if the EC value reaches
42
43 588 exceeds >750 µmhos/cm. As part of this study, the EC values recorded in the lower stretch of the
44
45 589 Ganga were found to be lower than values reported from upstream of the river (Tiwari et al.,
46
590 2016). Studies focused on the sectors of Ganga representing Allahabad, Kanpur and Varanasi
ce

47
48 591 have recorded EC > 500 µmho/cm. Studies in Allahabad, Kanpur and Varanasi have recorded
49
50 592 EC to be > 500 µmho/cm which could possibly result from the high pollution levels in these
51
52 593 areas. Thus EC could act as a suitable proxy to gauge water pollution along Ganga. In the studied
Ac

53 594 sites, EC values were lower in the SW stations than the PS stations. But the EC in the PS stations
54
55 595 decreased substantially during monsoon season which might be a result of dilution effect due to
56
57
58 21
59
60
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1
2
3 596 huge freshwater input. This was also observed in WQI studies in other Bhagirathi-Hooghly
4

pt
5 597 stations (Kar et al., 2008) and in Brahmaputra (Girija et al., 2007). Furthermore, mixing of water
6
7 598 also appears to decrease EC values as the SW stations which lie in close proximity to PS stations
8
599 show lower EC values than the PS stations. This indicates that mixing of water due to horizontal
9

cri
10 600 flow velocity could disperse the ions and help improve water quality. The relatively higher EC
11
12 601 values in the downstream stations including Kolkata may be due to salt water intrusion from Bay
13
14 602 of Bengal. High EC values also coincided with high TDS in the studied stations as also shown by
15
603 Pearson’s correlation efficient.
16

us
17 604
18
19 605 The TDS values in the PS stations in lower stretch of Ganga were substantially lesser than values
20
21 606 recorded upstream (Tiwari et al., 2016). Water with TDS >450 mg/L is considered good whereas
22
23
24
25
26
27
28
607
608
609
610
an
with values < 2000 mg/L is unsuitable for irrigation. TDS remained higher in the PS stations
than SW but the recorded values were lower in monsoon than pre-monsoon and post-monsoon.
The effect of monsoon precipitation on the observed TDS values across the sites was clearly
evident in this study. Reduced water flow during the dry months of pre-monsoon can result in
dM
29 611 accelerated sedimentation and thereby increase TDS (Qader, 1998; Mokhlesur et al., 2000).
30
31 612 Additionally, higher TDS in PS stations possibly indicates presence of small sized particles (>
32
33 613 2µm) that is being released into the river water from multiple sources including municipal
34
614 sewage and industrial effluents. In addition to urban anthropogenic impacts, intense local
35
36 615 agricultural activities which give rise to variability in surface run-off also strongly influences
37
38 616 TDS values (Tafangenyasha and Dube, 2007; Kalavathy et al., 2011). Both factors in cohort
pte

39
40 617 could result in consistently higher TDS values observed across PS seasons during pre-monsoon
41 618 and post-monsoon. Presence of TDS in water essentially helps to maintain cell density and
42
43 619 thereby high TDS concentrations could shrink the cells in size and influence survivability
44
45 620 (Southard et al., 2006). Polluted stretches along Narmada River in India and Potrero de los Funes
46
621 River in Argentina also report similar TDS values indicating the possible link between pollution
ce

47
48 622 and TDS (Jindal et al., 2010; Gupta et al., 2017). Similar to TDS, load of SPM in the studied
49
50 623 stations was also impacted by monsoonal precipitation. Faster river flow and increased
51
52 624 freshwater input results in greater mixing of water with the underlying sediment resulting in high
Ac

53 625 SPM load in the water column. Seasonal precipitation in the upstream of the River Ganga washes
54
55 626 down more sediment to the lower stretches and also contributes to the SPM during monsoon.
56
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1
2
3 627 High SPM load affects light attenuation and thereby affects the density and distribution of
4

pt
5 628 phytoplankton cells (e.g. He et al., 2017). This could affect the DO concentrations in the water
6
7 629 (Verma et al., 1984) and in particular have a cascading effect on higher trophic levels in the
8
630 Ganga. Hence, rapid changes of SPM in the surface water could also adversely impact WQI
9

cri
10 631 including at seasonal scales across the studied stations of Ganga.
11
12 632
13
14 633 Seasonal and site specific changes in total alkalinity and its subsequent affects have been studied
15
634 in many rivers in India including Brahmaputra (Girija et al., 2007), Narmada (Gupta et al.,
16

us
17 635 2017), Damodar (Chatterjee et al., 2010) and Gomti (Singh et al., 2004). In the current study,
18
19 636 total alkalinity was lower in monsoon compared to both pre-monsoon and post-monsoon
20
21 637 seasons. Precipitation often adds acidic compounds to the surface water in the form of acid rain
22
23
24
25
26
27
28
638
639
640
641
an
or by causing greater dissolution of atmospheric carbon dioxide and airborne pollutants such as
sulphur and nitrogen oxides (Farley, 2004). Measured values indicated high alkalinity at PS
compared to SW stations. Alkalinity is contributed by presence of carbonates, bicarbonates and
hydroxides in the water and is often contributed by untreated industrial effluents and residential
dM
29 642 waste coming from construction sites. Total alkalinity is also strongly influenced by presence of
30
31 643 water plants. Low alkaline water could be corrosive in nature (Ohlorgge, 2004), indicating poor
32
33 644 water quality and making it unfit for usage. Dissolved divalent ions including calcium,
34
645 magnesium, strontium, iron and manganese released from industries also impact total hardness
35
36 646 along with alkalinity. Consequently, PS stations showed higher total hardness compared to SW
37
38 647 in the lower stretch of the Ganga. Total hardness observed in the studied stations was similar to
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39
40 648 values and trends recorded in the upstream of the River Ganga including in Kanpur, Allahabad
41 649 and Varanasi (Tiwari et al., 2016) and River Gomti (Singh et al., 2004) but is higher than rivers
42
43 650 such as Damodar (Chatterjee et al., 2010). The recorded values indicated lower values in
44
45 651 monsoon compared to other seasons both in upstream Ganga (Trivedi et al., 2010; Tiwari et al.,
46
652 2016).
ce

47
48 653 Due to heavy local precipitation and increased flow of freshwater from upstream of the river,
49
50 654 residence time of dissolved nutrient concentrations would be strongly influenced as reported in
51
52 655 other freshwater systems (Maya and Babu 2007; Wu et al., 2016). Seasonal precipitations also
Ac

53 656 alter the concentrations of dissolved macronutrients including nitrate and o-phosphate and
54
55 657 micronutrients such as nitrite. International standards allow a phosphate concentration limit of
56
57
58 23
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1
2
3 658 0.1 mg/L (US EPA, 1986). Surface run-off from agricultural fields adds nitrogen and phosphorus
4

pt
5 659 to the surface water due to rampant usage of NPK fertilizers in the fields. Subsequently, the
6
7 660 concentration of o-phosphate was monsoon compared to pre-monsoon and post-monsoon.
8
661 Phosphates are also enhanced due to degradation of dissolved organic matter and from leaching
9

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10 662 of the bed rock. Human and animal waste along with untreated industrial effluents also releases a
11
12 663 large amount of phosphate into the surface water which is indicated by the significantly higher
13
14 664 concentrations of o-phosphate recorded in the PS stations compared to SW. High o-phosphate
15
665 concentrations could explain the high water plant density noted in most PS stations as plant
16

us
17 666 growth is enhanced in presence of o-phosphate (Donnelly et al., 1998). High o-phosphate
18
19 667 concentrations also result in eutrophication from growth of algae and macrophytes resulting in
20
21 668 poor DO and bad water quality (Rutherford et al., 1991; Davie, 2003; Bellos and Sawidis, 2005).
22
23
24
25
26
27
28
669
670
671
672
an
High o-phosphate concentration resulting in poor water quality has been observed in both rivers
of India including in Brahmaputra (Girija et al., 2007), other stretches of Ganga (Tiwari et al.,
2016), Gomti (Singh et al., 2004), Mahanadi (Panda et al., 2006) and Narmada (Gupta et al.,
2017). Nitrogen (nitrite and nitrate) is also added to surface water from agricultural activities
dM
29 673 (Nas and Berktay, 2006). The effects of increased concentration of nitrogen in the water are
30
31 674 similar to that of o-phosphate resulting in decreased dissolved oxygen levels and eutrophication.
32
33 675 BIS and WHO specify a limit of 45 mg/L nitrogen in the water. All studied stations of the lower
34
676 stretch of Bhagirathi-Hooghly had dissolved nitrogen concentrations much lower than the
35
36 677 specified safe limits. In the PS stations, highest nitrogen concentrations were observed in post-
37
38 678 monsoon (average 10 mg/L) compared to monsoon (6 mg/L) and pre-monsoon (5 mg/L) whereas
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39
40 679 in SW stations, monsoon had highest nitrogen concentrations (5 mg/L) compared to pre-
41 680 monsoon (3 mg/L) and post-monsoon (4 mg/L). The PS stations also had marginally higher
42
43 681 nitrogen concentrations than SW stations. This is possibly due to addition of nitrogenous
44
45 682 compounds from municipal and industrial effluents. At the same time, concentration of nitrogen
46
683 in the surface water during monsoon would be controlled by critical balance between increased
ce

47
48 684 terrestrial run-off and dilution in higher water volume (Causse et al., 2015). Concentrations of
49
50 685 dissolved nutrients in turn control the abundance of biological communities in the surface water.
51
52 686 Chl-a was used as a proxy to understand the abundance of phytoplankton in the surface water of
Ac

53 687 studied stations of Ganga. It was found that the concentration of Chl-a in PS stations was
54
55 688 significantly lower than that of pre-monsoon and post-monsoon. This could be due to higher
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1
2
3 689 SPM during monsoon that affects light attenuation and subsequently makes the environment
4

pt
5 690 unfavourable for higher phytoplankton density (Northcote et al., 2005; Hu et al., 2007).
6
7 691 However, in the SW stations, monsoon season recorded higher Chl-a concentrations which could
8
692 result from higher nutrient concentrations at these stations, lower SPM and TDS that could allow
9

cri
10 693 greater light penetration at the SW stations compared to PS. Additionally, the lower
11
12 694 concentration of Chl-a in PS stations could indicate lower phytoplankton density and hint toward
13
14 695 possible unfavourable conditions owing to very bad water quality at these stations. Indeed,
15
696 phytoplankton diversity was found to be lower in PS stations compared to SW stations (data not
16

us
17 697 shown). Therefore, Chl-a as well as other biological indices can be used as a proxy to study
18
19 698 water quality and understand the relationship with other key environmental variables including
20
21 699 effect of monsoonal precipitation.
22
23
24
25
26
27
28
700
701
702
703
an
The use of environmental parameters and biological community information can provide deep
insights into the quality of the water and in turn can help towards understanding health of the
river ecosystem. Four environmental parameters including DO, pH, EC and dissolved nitrate
were used to calculate WQI of the lower stretch of the Ganga. The WQI values indicated bad
dM
29 704 water quality at most of the studied stations. PS stations showed significant worse water quality
30
31 705 than the SW stations. Furthermore, WQI values indicated the deteriorating quality of water with
32
33 706 monsoon at most stations such as in Kolkata, Dakshineswar, Chandannagar, Halisahar, Triveni
34
707 and Kalyani. This could be due to flushing of water which influenced many of the environmental
35
36 708 parameters as well as concentration of macronutrients. During post-monsoon, water quality was
37
38 709 seen to improve in Dakshineswar site based on WQI values. However, collectively, the lower
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39
40 710 stretch of Ganga appears to represent bad water quality that is unfit for usage under Category B,
41 711 C, D and E of CPCB. The calculated WQI values are at par with previous dataset spublished
42
43 712 from the lower stretch of Bhagirathi-Hooghly. Kar et al. (2017) reported WQI within the range
44
45 713 of 56-78 from bathing ghats of Howrah and North24 Parganas. The authors also reported very
46
714 poor water quality, especially from Dakshineswar, a site popularly used for bathing purposes,
ce

47
48 715 indicating the direct influence of anthropogenic activities on WQI. Similar results were obtained
49
50 716 using artificial neural network analysis where the authors reported poor water quality alone the
51
52 717 lower stretch of Bhagirathi-Hooghly due to pollutants from both domestic and industrial sources
Ac

53 718 (Sinha and Das, 2014). The obtained WQI also falls within the same range as reported from the
54
55 719 upper stretches of River Ganga including in Haridwar, where an eleven year study indicated poor
56
57
58 25
59
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1
2
3 720 water quality with WQI ranging between 51-74 (Bhutiani et al . 2014). Assessment in Rishikesh
4

pt
5 721 has also indicated poor water quality (Upadhyay and Chandrakala, 2017). The authors attributed
6
7 722 the poor water quality to discharge of sewage, solid and liquid wastecontaminates and other
8
723 organic waste materials.Poor water quality in the River Ganga, including in upstream sites in
9

cri
10 724 Uttar Pradesh andUttarakhand has resulted in the spread of exotic invasive fish species such as
11
12 725 Cyprinus carpio (European carp), Oreochromis niloiticus (Nile tilapia) and other catfishes and
13
14 726 hindered the growth and survival of populations of other native fishes (Bhutiani et al.2014;
15
727 Tiwari et al., 2016). Rapid changes in water quality therefore appear to have cascading effects on
16

us
17 728 the health of the ecosystem and also on human lives that are directly dependent on the river for
18
19 729 survival. The WQI values deduced in this study could be monitored in depth across larger spatial
20
21 730 and temporal scales with integration of biological entities and subsequently modeled using deep
22
23
24
25
26
27
28
731
732
733
734
Prasad et al., 2020; Aldhyani et al., 2020).

Conclusions
an
learning approaches including artificial intelligence to control water pollution (Venkata Vara
dM
29 735 Overall, this study for the first time provides much needed seasonal data of environmental
30
31 736 parameters representing more than 50 km of the lower stretch of Ganga (point source and surface
32
33 737 water stations) which can be used as benchmark for future long-term studies. The study clearly
34
738 shows that monsoonal precipitation has a distinct influence on key environmental parameters
35
36 739 such as DO, TDS and biological variables such as Chl-a. Moreover, in this study for the first
37
38 740 time WQI was evaluated and highlight that the lower stretch of this river represents deteriorating
pte

39
40 741 water quality, albeit with some exceptions in some sites (e.g. Dakshineswar). However, the effect
41 742 of monsoonal precipitation on improving WQI values were not evident highlighting local factors
42
43 743 shape the observed trends. The study also justifies the need to adopt robust scientific approaches
44
45 744 to clean the Ganga river basin and develop long-term ecological monitoring programs to assess
46
745 the lower stretch of this river. Most importantly, the WQI developed for the Ganga River can
ce

47
48 746 prove to be useful for monitoring and improving basins and deltaic systems comprising of major
49
50 747 rivers of South Asia.
51
52 748
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53 749
54
55 750
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1
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3 751 Acknowledgements
4

pt
5 752 This work is supported by Department of Science & Technology (Govt. of India) grant
6
7 753 [DST/TM/WTI/2K16/124] awardedto Punyasloke Bhadury. The authors thank the River Traffic
8
754 Police of Kolkata Police for assistance during conducting sampling in Kolkata sector of the River
9

cri
10 755 Ganga. The authors would like to thank Manish Sutradhar for help with some of the sampling.
11
12 756
13
14 757
15
758 Data availability statement
16

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17
18 759 The data that support the findings of this study are included within the article and supplementary
19
20
760 file.
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22 761
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762

763
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dM
29 764
30
31
765
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33
34 766
35
36 767
37
38
768
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41 769
42
43
770
44
45
46 771
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47
48 772
49
50
51 773
52
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53 774
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1
2
3 776 References
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6 777 Abbasi, T. and Abbasi, S.A., 2012. Water quality indices. Elsevier
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980 Assess., 186, pp 5285-5295.
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38 982 Figure Legends
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40 983 Fig. 1 Map of the study area showing locations of sites in the lower stretch of River Ganga
41 984 representing Kalyani (22°59'45.3"N 88°25'13.2"E) to Kolkata (22°33'2.2"N, 88°19'27.6"E). The
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985 star sign denotes location of stations. The blue pins show the location of the studied sites.
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44 986 Fig. 2(a) Box plot showing seasonal variations in AT, SWT, Chlorophyll and nitrate
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46 987 concentrations recorded from the studied stations along the lower stretch of River Ganga
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988 Fig. 2(b) Box plot showing seasonal variations in DO, pH, nitrite and o-phosphate
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49 989 concentrations recorded from the studied stations along the lower stretch of River Ganga
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51 990 Fig. 2(c) Box plot showing seasonal variations in TDS, EC SPM, Hardness and Total Alkalinity
52 991 recorded from the studied stations along the lower stretch of River Ganga
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54 992 Fig. 3 Seasonal variations in WQI of studied stations along the lower stretch of River Ganga.
55 993 Corresponding colours indicate the water quality
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6 996 Ganga.
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999 Table S1 Environmental parameters as recorded in PS and SW stations of the lower stretch of
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