case study of water pollution in ganga river

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• Published: 04 June 2020

Potential Impacts of Climate and Land Use Change on the Water Quality of Ganga River around the Industrialized Kanpur Region

• Sneha Santy 1 ,
• Pradeep Mujumdar   ORCID: orcid.org/0000-0002-9493-8328 1 , 2 &
• Govindasamy Bala 1 , 3  

Scientific Reports volume  10 , Article number:  9107 ( 2020 ) Cite this article

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The heavily industrialised Kanpur region is the most polluted stretch of the Ganga river because of excessive pollutant discharge from the industries. Agricultural runoff along with climate change further adds to the pollution risk in this industrialised stretch of Ganga. In this paper, we analyse the potential impacts of climate change and land use change on the water quality in this stretch under hypothetical scenarios using the water quality model, QUAL2K. Water quality indicators of Dissolved Oxygen (DO), Biochemical Oxygen Demand, ammonia, nitrate, total nitrogen, organic-, inorganic- and total phosphorous and faecal coliform are assessed for eight climate change and six land use land cover scenarios. Eutrophic conditions are observed in this stretch of the river for all scenarios, implying severe impacts on aquatic life. DO is identified as the most sensitive indicator to the climate change scenarios considered, while nutrients and faecal coliform are more sensitive to the land use scenarios. Increase in agricultural land area leads to larger nutrient concentration while increase in built-up area causes an increase in faecal coliform concentration. Results from this hypothetical study could provide valuable guidance for improving the water quality of the Ganges in future climate change and land use change scenarios.

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Introduction

Ganga river pollution is one of the most discussed topics on river water quality in the past few years. The uncontrolled discharge of domestic sewage without treatment, excessive pollutant discharge from the industries, agricultural runoff, etc have made the river highly polluted 1 . Ganga Action Plan (GAP) was launched in 1986 with an objective of restoring water quality to ‘Bathing class’ 2 . Under GAP, several sewage treatment plants are constructed, common effluent treatment plants are constructed in places where more industries are situated, and centralized monitoring systems are made compulsory for individual industries. While these actions have contributed to improve the water quality, it is still a long way before the quality can be restored to the bathing class standards 2 . As per year 2011 records, out of the 764 grossly polluting industries discharging into Ganga river, 487 industries are from the Kanpur region 3 . Therefore, the industrialised stretch of Ganga river immediately upstream and downstream of Kanpur city can be considered as the most polluted stretch of the Ganga river. The major industries contributing to pollution of the Ganga river are tannery, sugar & distillery, pulp and paper mills 3 . Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), solids, Total nitrogen (TN), Chromium, sulphide, sulphate and chloride are the major pollutants from these industries 4 , 5 , 6 , 7 . In addition, a significant portion of the catchment area of the river comprises of agricultural land and hence nutrient pollution (nitrogen (N) and phosphorous (P)) also becomes important 8 . The effluent disposal standards are kept constant throughout the year. Thus, the quality of the river water may get deteriorated during low flow periods even though industries are disposing their effluents at the prescribed safe limits 9 . Due to climate change, it is likely that low flows may get reduced further making the water quality worse. In addition, increasing temperatures may have varying effects on water quality indicators.

In recent studies, quantification of non-point sources (NPS), mainly due to agricultural runoff has been performed using flux method 10 , mean concentration method 11 and SWAT model 12 , 13 , 14 . The export coefficient method 15 which gives non-point source pollution from all land cover types is adopted here for representing NPS in this study. Recent studies indicate that socio-economic factors such as population, urbanisation and sewage treatment have a major role in water quality compared to the climatic factors 16 . Reduction in low flow and an increase in high flow, together with increase in water temperature are projected for the future time period (2071–2100) on a global scale 17 . Flow rate changes have been found to have more impact than stream temperature changes on water quality in streams 18 . DO is found to decrease for hypothetical climate change scenarios of increasing air temperature and decreasing streamflow in a study on Tunga Bhadra river using the water quality simulation model QUAL2K 19 .

Land use Land cover (LULC) and water quality relationship have been also analysed using Pearson regression analysis and multiple regression analysis in several studies 20 , 21 , 22 , 23 . These studies show that forest area has the lowest nitrate discharge, while agricultural lands have the largest 24 . Large urban areas are associated with large increases in BOD, COD and Total Suspended Solids (TSS), and higher value of BOD and COD in the dry season 25 . This has been confirmed in a study on the U-tapao river, Thailand 26 . Nitrogen loading could increase for combined scenarios of climate change and land use for late winter, while the response could be mixed for summer and spring where both impacts are in the opposite direction 27 . Combined impact studies on groundwater also reveal a greater pollution risk in future 28 . Water quality assessment of Ganga river using different water quality indices 29 , 30 , 31 shows a poor water quality in many places. Recently, several studies have assessed the impact of climate change and socio-economic change on nitrogen and phosphorus flux in the Ganga basin using a process based Integrated catchment (INCA) model 32 , 33 , 34 , 35 , 36 , 37 . These studies find that the concentrations of nitrate, ammonia and phosphorus would decrease with increase in flow predicted for future SRES A1B climate change scenario. Socio-economic factors, sewage treatment plant capacity and effluent water quality are also found to have large impact on water quality.

Thus, past studies have shown that climate change and LULC can affect water quality substantially and the sensitivity is highly catchment dependent. The objective of our study is to quantify the individual contribution of climatic and land use parameters on water quality in the highly industrialised region of Ganga river. We also analyse the water quality for the combined climate change and land use land cover scenarios. A water quality simulation model, QUAL2K, is used for our investigations. For the quantification of non-point source pollution, we use the export coefficient method as it can be used to quantify pollution from all land use types. The water quality for the 7-day low flow with a return period of 10 years (7Q10) is the base condition for all analysis. The water quality in this base case 7Q10 will be compared to other scenarios in this study. Eight hypothetical scenarios of changes in air temperature and streamflow are constructed. A simple linear regression model is used to estimate stream temperature from air temperature corresponding to each of the scenarios. Change in water quality indicators - DO, BOD, ammonia, nitrate, total nitrogen, organic phosphorus, inorganic phosphorus, total phosphorus and faecal coliform (FC) - relative to the base condition is evaluated for each scenario. For the analysis of sensitivity to land use, historical LULC change in the catchment area contributing to the reach is considered. For the assessment of the impact of land-use change, six hypothetical land-use scenarios are considered. Only direct conversion of one land cover to another is considered to study the individual impacts. Impacts on water quality is assessed by estimating percentage change of water quality for all scenarios. For combined impact studies, water quality is analysed by changing both climatic and land use parameters.

The novelty of the work lies in modelling the combined effects of agricultural pollution, industrial pollution and climate change on water quality in a small, but representative, stretch of a highly industrialised region of the Ganga basin. The inferences drawn from such modelling studies would be immensely helpful for policy makers in identifying hotspots for making corrective interventions.

Ganga river is the largest river of India with a catchment area of 8,61,404 sq. km. River Bhagirathi and Alaknanda join at Devprayag to form the Ganga river. The total length of the river is 2525 km. It flows through 5 Indian states, namely, Uttarakhand, Uttar Pradesh, Bihar, Jharkhand and West Bengal. The main tributaries of the river are Yamuna, Ramganga, Gomti, Ghaghara, Gandak, Damodar, Kosi and Kali-East. The river supports a population of approximately 500 million people, providing water for sustaining livelihoods and irrigation of crops. Ganga river is also home for a large number of species of flora-fauna. About 2400 MW of hydropower is generated from it.

The river stretch considered for this study has a length of 238 km (Fig.  1(a) ). It is divided into two reaches based on the differing hydraulic characteristics: Ankinghat - Kanpur and Kanpur- Shahzadpur. The schematic diagram of the study area is shown in Fig.  1(b) and the drain data is given in Table  S1 .The major drains joining river at Kanpur are Ranighat drain (KD1), Sisamau nala (KD2), Bhagwatdas nala (KD3), Golaghat nala (KD4), Satti chaura (KD5) and Permiya drains (KD6). They carry waste water with high ammonia, nitrate concentration, and are contaminated with faecal coliform 3 . The Loni drain (UD1) and City jail drain (UD2) meeting river at Unnao carry high BOD and faecal coliform. Shetla bazar (JD1), Wazidpur drain (JD2) and Bhuriyaghat drain (JD3) joining the river at Jajmau cause major pollution of BOD, ammonia, nitrate, solids, phosphorus and faecal coliform 3 . Pandu river which is a house for Panki Thermal power plant drain (PR1), ICI drain (PR2), Gandanala (PR3), COD nala (PR4) and Halwa Khanda Nala (PR5), meets the Ganga river at Raebarelli with high ammonia and faecal coliform loading 3 .

( a ) Ganga basin with study area Ankinghat to Shahzadpur highlighted ( b ) Schematic diagram of the study area (KD: Kanpur drain; UD: Unnao drain; JD: Jajmau drain; PR: Pandu river; NPS: non-point source pollution).

The water quality data, river cross section, manning’s n, low flow data are obtained from Central Water Commission (CWC), Lucknow. The air temperature data at 1-degree grid is obtained from India Meteorological Department (IMD). The 30 m ASTER Digital Elevation Model is obtained from United States Geological Survey (USGS). Land use land cover data of 1:250,000 for the years 2005–06, 2010–11 and 2015–16 is obtained from the National Remote Sensing Centre (NRSC), Hyderabad. Data for evaporation, dew point temperature, cloud cover, wind speed at 0.25° grid is obtained from The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA Interim (ERAI). The pollutant load data from the drains are obtained from Central Pollution Control Board (CPCB) reports.

Scenario construction

For investigating the potential changes in water quality in the future, eight highly hypothetical climate change scenarios and 6 LULC scenarios are considered. T0FLOW10, T0FLOW20, T1FLOW0, T1FLOW10, T1FLOW20, T2FLOW0, T2FLOW10 and T2FLOW20 are the climate change scenarios considered for the study (Table  S2 ), where the number followed by ‘T’ indicates the °C rise in air temperature and the number followed by ‘FLOW’ indicates the percentage reduction in the hydrological variable, streamflow. The water temperature is modelled using linear regression and the inputs for each scenario are given in Table  S3 . Six hypothetical scenarios of LULC are formulated: (1)10WAS2AGR, (2)20WAS2AGR, (3)30WAS2AGR, (4)10WAS2BLD, (5)20WAS2BLD and (6)10WAS2FOR (Table  S4 ), where WAS2AGR, WAS2BLD and WAS2FOR represent conversion of wasteland to agriculture, built-up and forest respectively and the numbers preceding these terms represent the percentage of wasteland getting converted.

Water quality model calibration

The water quality simulation model QUAL2K is used in this study. A detailed description of the model, calibration and validation is given in Supplementary Text S1. The 7Q10 flow and the water quality (corresponding to 2016 low flow) at Ankinghat station is given as the head water boundary condition to the model for baseline analysis (T0FLOW0). QUAL2K model is calibrated for the 2016 low flow period by changing the rate parameters and validated for 2012–2015 low flow. The range of rate parameters is taken from the literature 38 , 39 , 40 , 41 and the water quality at the downstream locations of each run is compared with the water quality station data obtained from CWC. The set of rate parameters which gives the minimum RMSE between measured and modelled water quality is chosen for setting up the model. The non-point source pollution is quantified using export coefficient method (Supplementary Text S2). The data from 2005 to 2015 is used to optimize the export coefficient value for the pollutants nitrate, ammonia, phosphorus, faecal coliform and BOD. The range of export coefficient values not available in the literature is estimated using trial and error approach. Knowing the export coefficients(kg/Ha/yr) and the area of each land use type (Ha), the pollutant load (kg/yr) is calculated.

The average of diffuse flow for Ankinghat-Kanpur and Kanpur-Shahzadpur for the low flow period of 2005–2016 is used for estimating the concentration of non-point source pollutant for the baseline analysis (T0FLOW0). The resulting concentration and flow are given as diffuse source input to QUAL2K. The climatic variables such as dew point temperature, evaporation, cloud cover and wind speed data for different station points are obtained by using the nearest gridded data available from ERA Interim Reanalysis ECMWF daily dataset. All station points use the same value for the meteorological variables. Wind speed is calculated as the resultant of U wind component and V wind component (Table  S5 ). The reach rates calibrated for low flows can be used for 7Q10 analysis 42 (Supplementary Text S3); therefore, all rates are kept same for water quality analysis using 7Q10. The climate change scenarios, land use land cover scenarios and combined scenarios are compared with the historical 7Q10 analysis. The water quality indicators considered are DO, BOD, ammonia, nitrate, total nitrogen, organic phosphorus, inorganic phosphorus, total phosphorus and faecal coliform. Suspended solids are not, however, considered because of lack of data at station points for calibration.

Results and Discussion

Historical climate and land use analysis.

Historical water quality analysis carried out using the indicators such as minimum, mean and maximum DO, BOD, nitrate and ammonia (Fig. S1 (a),(b) and (c)) for Ankinghat, Kanpur and Shahzadpur, clearly indicates the reduction in DO and increase in BOD, nitrate and ammonia concentration for Ankinghat and Kanpur. The water quality is not found to change much for Shahzadpur. The average annual percentage change in streamflow for Ankinghat from 1968–1990 to 1991–2012 is −13.7% (Fig.  2(a) ). For the Ganga basin, upstream portion consists of snow cover which can lead to increase in flow during summer season due to snow melt in a warmer climate. However, barrage constructed in the upstream of Ankinghat offsets that impact on flow at Ankinghat station, leading to reduced streamflow during summer season with warming. The air temperature plot from 1980 to 2016 (Fig.  2(b) ) shows a positive trend. The average monthly temperature has increased for monsoon season and winter, with little change for summer (March and April) and decrease for May and June. The maximum increase of maximum air temperature is 1.37 °C for February and maximum increase of minimum air temperature is 1.46 °C for December (Fig. S2).

Historic analysis of ( a ) streamflow and ( b ) air temperature of Ankinghat for the period 1980–2016.

Agriculture is the predominant land cover type in both the reaches Ankinghat-Kanpur and Kanpur-Shahzadpur (Fig.  3 ). We find that built-up area and agricultural area are increasing over the years for both the reaches with built-up area having highest percentage increase. The forest area has slightly reduced from 2005 to 2010 and slightly increased from 2010 to 2015. The waste land area has significantly reduced from 2005 to 2010 and further reduced in 2015. The water body area has slightly reduced from 2005–2015 for Ankinghat-Kanpur reach, while it slightly decreases during 2005–2010 and slightly increases during 2010–2015 for Kanpur- Shahzadpur reach. The major area land cover conversion has occurred from waste land to built-up and agricultural area. The areas for each LULC classes for both reaches are given in Table  S6 .

( a ) LULC map for Ankinghat- Shahzadpur reach for 2005, 2010 and 2015 ( b ) Percentage change in each of the land use class between 2005 and 2010 and between 2005 and 2015 ( c ) Percentage distribution of each LULC class in 2005, 2010 and 2015.

Water quality for the design low flow (7Q10)

The export coefficient optimised for the study area is given in Supplementary Table  S7 . It may be noted that, significant amount of nitrate, ammonia, phosphorus, BOD and faecal coliform are exported from agricultural land and water bodies. From forest land, moderate export values are obtained for all water quality indictors considered. For the built-up area, very high export coefficient is estimated for faecal coliform and significant export coefficients for nutrients. The export coefficient values for all water quality parameters considered from waste land is minimal. The calibrated rate parameters for QUAL2K are shown in Supplementary Table  S8 . Other parameters are given default values. The Calibration and validation results of QUAL2K model is given in Figs. S3, S4, S5 and Table  S11 . R 2 value for the validation period across all parameters is 0.6 and for individual parameters the R 2 values are 0.7 (DO), 0.6 (BOD), 0.5 (FC), 0.6 (Nitrate) and 0.5 (TP).

The starting point of DO profile (Fig.  4(a) ) for this study is Ankinghat (238 km) and the zone that follows is a reaeration zone, where DO levels could increase. There is a decrease in DO corresponding to point loads at Kanpur, Jajmau and Pandu river confluence. Soon after the loading points, DO content of the river decreases because of deoxygenation. The two critical points identified are immediate downstream of Jajmau and 70 km upstream of Shahzadpur DO increases again after critical points as reaeration dominates in this region. Hence, Kanpur is in the deoxygenation zone and Shahzadpur is in the reaeration zone. BOD gets reduced from Ankinghat and suddenly increases at Kanpur due to drains carrying pollutants joining the river and again increases abruptly at the successive loading points, Jajmau and Pandu river confluence. It can be noticed that the critical DO corresponds to the maximum BOD point.

( a ) DO & BOD profile ( b ) Ammonia, nitrate & TN profile ( c ) Organic P, inorganic P & TP profile and ( d ) FC profile along the river stretch (with Ankinghat at 238 km to Shahzadpur at 0 km) in the study area.

Figure  4(b) shows the mean profile of ammonia, nitrate and total nitrogen (TN) for the 7Q10 flow. Except for Unnao drains, all other drains carry significant ammonia and nitrate loading. Abrupt increases can be noted in ammonia (Fig.  4(b) ) at Kanpur, Jajmau and Raebareilly where the drains join. The highest ammonia concentration simulated is 7.7 mg/L (at Jajmau), which again corresponds to the critical DO point. The plot for nitrate shows very small abrupt increases at the places where drains are joining. While comparing the values of ammonia and nitrate, we find that ammonia is getting converted to nitrate. Total nitrogen plot is similar to that of ammonia plot.

Figure  4(c) shows the profile of organic (org), inorganic (inorg) and total P (TP) for the study area. Only Jajmau drains carry P loading, so the main loading is from non-point source. The non-point loading is large in Ankinghat- Kanpur reach compared to other. In our study area, P mainly comes from fertilizers used in agricultural land. The organic P increases in first reach and slightly gets diluted when Kanpur drains joins, followed by a slight increase (not significantly noticeable) when Jajmau drains join, followed by a small dilution where Pandu river joins. Highest organic P is 1.8 mg/L. From the plot, transition of organic P to inorganic P can be noticed. Total Phosphorus value greater than 20 µg/L indicates the trophic state ‘Eutrophic’, pointing to the water body being well nourished. Profile plot (Fig.  4(c) ) clearly indicates that the entire study area is eutrophic.

Figure  4(d) shows the profile of FC for the study area. It can be noted that the entire stretch is heavily polluted with FC, with major contribution from drains joining the river. FC should be 0 MPN (Most Probable Number)/100 mL for drinking water use and it should be less than 500 MPN/100 mL for bathing use. The minimum value of FC observed at Ankinghat and Shahzadpur is ~3000 MPN/100 mL, which is much higher than 500MPN/100 mL indicating that the entire stretch is not suitable for bathing purpose.

Climate change scenarios

The percentage change in water quality for the eight climate change scenarios at Kanpur and Shahzadpur is shown in Figs.  5 and 6 respectively. It is found that DO is reduced with reduction in streamflow for both the stations. This can be attributed to reduction in dilution of pollutants leading to high oxygen demand and resulting in lower DO. The points on Y-axis of the two figures also show the change with respect to temperature change alone and it clearly indicates that for Kanpur station, DO is reduced with temperature increase and for Shahzadpur station, DO increases with temperature. This is because Kanpur is in a zone where deoxygenation dominates reaeration and with increase in temperature, both the rates increases, but the net is a slight increase in deoxygenation leading to reduction of DO. Shahzadpur is in a region where reaeration dominates deoxygenation and with increase in temperature, there is a net increase in reaeration leading to an increase in DO. It may be noted that for scenarios of increased temperature and reduced streamflow, DO is very low for Kanpur while it is increased for Shahzadpur station. For the two critical points identified (Jajmau downstream and Pandu river downstream) DO is the lowest for the scenario T2FLOW20. Therefore, scenario T2FLOW20 can be considered as the critical climate change scenario for critical locations 19 (normally downstream of pollutant loading points) and scenario T0FLOW20 for the points on reaeration zone (locations far from loading points). Maximum percentage reduction of 0.8% in DO for Kanpur is simulated for scenario T2FLOW20, and maximum percentage reduction (3%) for Shahzadpur is simulated for scenario T0FLOW20. With increase in temperature alone, rate of reaction increases and BOD is reduced. For scenarios of increased temperature and reduced streamflow, BOD is found to be increasing, which agrees with other studies 19 . The maximum percentage increase in BOD of 16% and 13%, respectively, for Kanpur and Shahzadpur is simulated for scenario T0FLOW20 where the flow is reduced by 20% but there is no temperature change. With an increase in temperature, pathogen concentration is reduced due to higher pathogen decay rate. Maximum reduction in FC for Kanpur and Shahzadpur is 12% and 23% respectively. The faecal coliform growth has been found to be reduced with increase in temperature on laboratory scale 43 and model based studies on Lis river 44 . Tropical rivers show an increase in FC with reduction in mean annual rainfall 45 . With reduction in streamflow, concentration of FC increases and maximum increase in concentration is 10% for Kanpur for the scenario of streamflow change alone. For Shahzadpur, the percentage increase due to streamflow reduction is very small due to smaller loading and the net of combined effect is reduction in concentration.

Percentage change in DO, BOD, FC, ammonia, nitrate, TN, organic P, inorganic P, and TP for climate change scenarios considered in this study for Kanpur. Blue, red and green lines indicate ( a ) DO, BOD and FC ( b ) ammonia, nitrate and TN ( c ) organic P, inorganic P and TP respectively. Solid lines, dashed line and dotted lines show scenarios 1–2, 3–5 and 6–8 respectively (Table  S3 ).

Percentage change in DO, BOD, FC, ammonia, nitrate, TN, organic P, inorganic P and TP for the hypothetical climate change scenarios considered in this study for Shahzadpur. Blue, red and green lines indicate ( a ) DO, BOD and FC ( b ) ammonia, nitrate and TN ( c ) organic P, inorganic P and TP respectively. Solid lines, dashed line and dotted lines show scenarios 1–2, 3–5 and 6–8 (Table  S3 ) respectively.

With an increase in temperature, there is very slight reduction in ammonia at Kanpur, while the concentration is significantly reduced at Shahzadpur station (20% for T2FLOW0). For a reduction in streamflow, concentration of the pollutant increases with a maximum of 15.8% at Kanpur and 12.4% at Shahzadpur (T0FLOW20). The changes in sensitivity of ammonia in both stations is due to difference in pollution load, the load at Kanpur being approximately double that of Shahzadpur. The nitrate concentration is significantly reduced at both stations with increase in temperature. Maximum reduction in nitrate is simulated for scenario T2FLOW0 for both stations with a value of 8%. Nitrate removal efficiency is found to increase with an increase in temperature in polluted rivers 46 . With reduction in streamflow, nitrate concentration increases. But for scenario of increased temperature and reduced streamflow, net change is a reduction in nitrate with respect to base value except for scenarios T0FLOW10 &T0FLOW20. The maximum increase in nitrate concentration is simulated for scenario T0FLOW20 at both stations with maximum value of 4%. Studies on Thames river project a reduction of nitrate in 2050 due to reduced runoff and increased denitrification 47 .

With an increase in temperature, Organic Phosphorus value is depleted due to improved reaction rates with temperature. The maximum reduction in concentration is simulated for Shahzadpur and is approximately 12% for scenario T2FLOW0. With reduction in streamflow, the concentration of organic P increases up to 15% for Kanpur and 9% for Shahzadpur. The combined effects of temperature and streamflow change considered results in better water quality than the individual change of streamflow alone. The increase in concentration for streamflow reduction is large for Kanpur, because load at Kanpur is very large compared to Shahzadpur. Organic phosphorus is converted to Inorganic Phosphorus, and with an increase in temperature, the rate at which this conversion occurs increases and inorganic P concentration increases and also with reduction in streamflow the concentration again increases, leading to very large concentration in P for combined scenario at Kanpur. The maximum increase of inorganic Phosphorus modelled for Kanpur is 32% which corresponded to scenario T2FLOW20. The inorganic P load of Shahzadpur is larger than Kanpur. For Shahzadpur, with temperature increase the concentration is reduced and with streamflow reduction concentration increases and maximum concentration increase for Inorganic Phosphorus is simulated for individual scenario of streamflow change and is 4%. Maximum percentage increase in TP is for scenario T0FLOW20 and it is 17% for Kanpur and 4% for Shahzadpur. With reduction in streamflow, there is significant increase in concentration, while the temperature sensitivity is small especially at Kanpur. Phosphorus loading is not projected to change significantly in the future, when mean climate change from 8 GCMs are imposed on Michigan lake 48 . However, P is found to reduce with increasing temperature. The percentage change in each water quality parameter per °C increase in temperature and per 10% reduction in streamflow is given in Supplementary Table  S9 .

Land use land cover scenarios

There is hardly any change in DO for various LULC scenarios considered for Kanpur (Fig.  7 ). For Shahzadpur DO slightly decreases from scenario 10WAS2AGR to 30WAS2AGR, corresponding to increase in agricultural land area. This can be attributed to the corresponding increase in nutrient concentration with increase in agricultural land area. Maximum percentage reduction in DO obtained for scenario 30WAS2AGR is 0.2%. BOD is found to increase from scenario 10WAS2AGR to 30WAS2AGR, with a maximum percentage increase of 1.4% and 0.2% at Kanpur and Shahzadpur respectively. Also, there is an increase in ammonia concentration with increase in built up land area (0.5% and 0.2% for Kanpur and Shahzadpur). The least ammonia concentration is obtained for scenario 10WAS2FOR. Similar trend is noted for nitrate, TN, organic P, inorganic P and TP. Maximum percentage increase in nitrate for 30WAS2AGR at Kanpur and Shahzadpur is 0.4% and 0.3% respectively. Maximum percentage increase in TN for 30WAS2AGR at Kanpur and Shahzadpur is 0.9% and 0.5% respectively. Increase in agricultural area results in an increase of TN and nitrate concentration in a study conducted in Japan 49 . Organic P, inorganic P and TP are found to be more sensitive to LULC scenarios. Maximum percentage increase in organic P, inorganic P and TP for 30WAS2AGR is 2.1%, 1.9% and 2.1% respectively for Kanpur and 3%, 2.5% and 2.5% respectively for Shahzadpur. Also, the concentration increases for scenario 10WAS2BLD and 20WAS2BLD with maximum percentage increase of 0.7% and 0.9% at Kanpur and Shahzadpur for scenario 20WAS2BLD. For scenario 10WAS2FOR, organic P, inorganic P and TP are found to reduce, with 0.1% at Kanpur and 0.2% at Shahzadpur. For FC, concentration increases from scenario 10WAS2AGR to 30WAS2AGR with a percentage increase of 2.4% and 2% at Kanpur and Shahzadpur respectively. And there is a sharp increase for scenario 10WAS2BLD and 20WAS2BLD and hardly any increase is simulated for scenario 10WAS2FOR. The maximum percentage increase is simulated for scenario 20WAS2BLD, with an increase of 5% and 4% at Kanpur and Shahzadpur respectively. High urbanization and reduction in forestland results in increased faecal coliform 45 . Overall, the LULC analysis shows that agricultural land use leads to more degraded water quality in terms of all parameters considered. Built-up land also leads to significant pollution, mainly FC. Forest land is the one which leads to a better water quality than others. The percentage change in water quality per 10 4 Ha area LULC conversion is given in Supplementary Table  S10 .

Percentage change in DO, BOD, FC, organic P, inorganic P, TP, ammonia, nitrate and TP for LULC scenarios (Table  S5 ) for ( a ) Kanpur and ( b ) Shahzadpur.

Combined climate and LULC scenarios

Except for DO and Inorganic Phosphorus, the climate scenario which leads to declined water quality is the scenario T0FLOW20. Also, in terms of DO and inorganic P at Shahzadpur, least water quality is simulated for the same climate scenario, while for DO and inorganic P at Kanpur, the least water quality is simulated for the scenario T2FLOW20. Except for FC, least water quality is simulated for LULC scenario 30WAS2AGR. For faecal coliform, the worst scenario is LULC scenario 20WAS2BLD. The combined and the individual scenario are compared for 9 water quality parameters considered and one plot per parameter is shown in Fig.  8 . DO of the river determines its health and its value less than 4 mg/L is not considered safe for aquatic life. DO greater than 5 mg/L is the criteria required for bathing use. DO has approximately same value up to the first loading point, after that DO varies in the order of scenarios 7Q10 > LULC > climate > combined (Fig.  8(a) ). LULC scenarios do not affect DO significantly, therefore 7Q10 & LULC and climate & combined scenarios have similar values. The DO of Jajmau is below 4 mg/L even for base analysis (7Q10), which further decreases for other scenarios. The second critical point has a DO of 4.3 mg/L for base analysis with 7Q10, while for the climate and combined scenarios DO is found to drop to below 4 mg/L making it unfit for aquatic life 50 . It can be also noticed that DO decreases to less than 5 mg/L downstream of Kanpur and regains back only at 25 kms upstream of Shahzadpur, resulting in a river stretch failing to meet bathing standards in low flow periods. BOD indicates pollution in the river and BOD greater than 3 mg/L is not suitable for bathing purpose 50 . The entire stretch considered for the study has a BOD above 3 mg/L, with high BOD in the reach from Kanpur and downstream. BOD, ammonia, nitrate, TN, organic P, inorganic P, TP concentration varies in the order of scenarios, 7Q10 < LULC < climate < combined. The maximum BOD value simulated is 29 mg/L at Jajmau downstream for the combined scenario (Fig.  8(a) ). Unlike other parameters, influence of climate and land use on FC shows high spatial variability (Fig.  8(b) ). The first abrupt increase is downstream of Kanpur, at Unnao and Jajmau loading and second one downstream of Pandu river confluence. In the upstream reach of Kanpur, the water quality decreases progressively with scenarios of 7Q10, LULC, climate and combined effect. From downstream of Kanpur to upstream of Pandu river, water quality decreases in the order of scenarios: climate change, combined effect, 7Q10 and LULC. In the downstream of Pandu river, it varies as in the upstream of Kanpur region. Ammonia nitrogen concentration of 1200 µg/L or greater is lethal to aquatic life 50 . From Fig.  8(c) it can be noted that the limit is exceeding for the entire stretch considered. The sudden increase in ammonia corresponds to the point source inputs, with maximum ammonia concentration of 9102 µg/L at Jajmau downstream for the combined scenario.

( a ) DO and BOD profile ( b ) FC profile ( c ) Ammonia, nitrate and TN profile ( d ) Organic P, inorganic P and TP profile for combined and individual scenarios. River profile shown is from Ankinghat (238 km) to Shahzadpur (0 km).

Individual impacts of climate change, and land cover change on water quality indicators such as DO, BOD, ammonia nitrogen, nitrate nitrogen, total nitrogen, organic phosphorus, inorganic phosphorus, total phosphorus and faecal coliform are analysed for the heavily industrialised stretch of the Ganga river in India. Eight highly hypothetical climate change scenarios and six LULC scenarios are used to examine the impacts, using the QUAL2K water quality model. Ganga river is found to be polluted in the heavily industrialised stretch of Kanpur for the low flow periods, because of industrial effluents and agricultural runoff. The hypothetical climate change and land use land cover scenarios considered in this study lead to higher pollution. DO of the critical points is reduced in the climate change scenarios. Except for DO, other water quality parameters are improved with temperature increase due to increased reaction kinetics at higher temperature. Streamflow reduction is a serious problem as it results in larger concentration of pollutants.

Increase in agricultural land area results in higher pollution, especially of nutrients (nitrogen and phosphorous) which can lead to eutrophication. Increase in built-up land area results in higher FC pollution. DO is, however, not found to vary much with LULC scenarios considered here. The scenario with more forestland conversion results in a better water quality. It is also found that the effects of climate change and land use change on water quality parameters add nearly linearly (Supplemental Text S4). A comparison of baseline (7Q10) with climate change scenarios, land use change scenarios and a combination of climate change and land use change shows that the water quality parameters considered here progressively deteriorate with the 7Q10, LULC, climate change and combined scenarios, except for DO which varies in reverse order. The analysis shows that entire stretch is not suitable for bathing purposes in all cases. The nutrient levels in the river shows that it is highly prone to eutrophication for all the scenarios considered. Also, the river reach downstream of Kanpur and Pandu river confluence doesn’t support aquatic life in all scenarios considered. Therefore, climate change or land use land cover change is likely to aggravate the present Ganga river pollution by industries and agricultural runoff, in the absence of any mitigative action.

Our study uses highly idealised scenarios for inferring the impacts of climate change, land use and their combined effect on water quality of Ganga river in the stretch considered. Also, the study provides an assessment of individual contribution of temperature rise and streamflow change, LULC change to agricultural, built up and forest areas on water quality, which is helpful for the policy makers and pollution control authorities to take suitable actions for a pollution free Ganga river. Projections of future climate change from GCMs and LULC will provide more realistic insight to the problem. Also, the analysis presented here, if combined with waste load allocation models 51 will help policy makers evaluate various options to mitigate the Ganga river pollution. A better water temperature model, instead of a regression model used here is likely to give more accurate results.

Our study includes only water quality analysis during low flow period, where the water quality especially DO become critical. The Ganga Action Plan focusses on reduction in pollution load on Ganga river and hence, the industrial load and sewage load are subjected to change in the future which brings uncertainty to our water quality analysis. Also, missing values of some parameters at some stations, model and parameter uncertainty could bring in additional uncertainty to our results. Nevertheless, the qualitative findings of this study will not be altered when more realistic climate change and land use change scenarios and improved inputs to QUAL2K are considered. An investigation of the seasonality of water quality and extreme high flow events and an analysis in religious gathering areas such as Haridwar and Benaras could form the future scope of the study.

Data availability

The data that support the findings of this study are available from Ministry of Water Resources, Government of India but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

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Acknowledgements

We thank the Chief Engineer, Upper Ganga Basin Organisation, Central Water Commission, Lucknow for providing very useful data on streamflow, water quality and hydraulic characteristics for the river stretch studied, India Meteorological Department for providing temperature data, National Remote Sensing Centre, Hyderabad for providing us land use land cover data. United States Geological Survey for providing DEM data, the European Centre for Medium-Range Weather Forecasts for providing atmospheric data. We also thank the Divecha Centre for Climate change, IISc, Bangalore for providing Grantham Fellowship to the first author.

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Sneha Santy developed the model, made computational runs and prepared first draft of the manuscript including figures. Mujumdar conceptualised the model, facilitated data collection and edited the manuscript. Bala helped in developing climate change scenarios and review of manuscript.

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Santy, S., Mujumdar, P. & Bala, G. Potential Impacts of Climate and Land Use Change on the Water Quality of Ganga River around the Industrialized Kanpur Region. Sci Rep 10 , 9107 (2020). https://doi.org/10.1038/s41598-020-66171-x

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Pollution and india's living river.

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Note on This Case Study

Global anthropogenic—or human caused—climate change has deeply impacted the ways that religions are practiced around the world.  At the same time, religions have also played major roles in framing the issue among their believers.  Some Hindus work tirelessly to change their habits and mitigate human impact on the climate.  Others ignore the crisis, or do not believe in Hindu environmentalism.  Read this case study with this in mind: the Hindus described here show a range of reactions to climate change, but all of them are Hindu.

As always, when thinking about religion and climate change, maintain a focus on how religion is internally diverse, always evolving and changing, and always embedded in specific cultures.

While Hinduism is a global religion, most Hindus—nearly one billion—live in India. In fact, Hindu goddesses are often a part of the Indian geographical landscape. This includes the deified river: the Ganges.

The Ganges River, also known as Ma Ganga (or Mother Ganges), flows from the glaciers of the Himalayas and crosses much of the subcontinent before flowing into the Indian Ocean. The religious origins of this goddess are varied, and devotees of different Hindu gods often believe in different stories about her. One of the more common stories comes from followers of the god Shiva. Many Shiva devotees believe Mother Ganges offered to descend to earth to purify the burning coals of the ancestors of the Hindu sage Bhagiratha. However, she was concerned that her fall from the cosmic realm would destroy the earth, so Shiva offered to catch her in his hair. Her waters ran in rivulets through his hair and onto the earth, where she purified the remains.

The Ganges River is therefore not only a waterway, but a goddess from heaven. Thus, many Hindus believe that the river has incredible healing powers. It is a common belief that bathing in the Ganges washes away a person’s bad karma and is like being in heaven. Some Hindus even believe that being brushed by a breeze which contains a single drop of the Ganges will absolve the impurities of multiple lifetimes. To most Hindus, dying in the holy city of Varanasi, on the banks of the Ganges, is said to result in moksha—a release from the endless cycle of suffering and rebirth. It is estimated that 32,000 corpses are cremated each year in Varanasi, after which their ashes are given to the Ganges. Others who cannot afford cremation simply wrap and float the body down the river. To access her healing waters, Hindus travel from all over the world on pilgrimages, often filling containers with water to bring back to their homes for rituals or healings. In fact, the largest gathering of human beings in the entire world regularly occurs on the banks of the river at the city of Allahabad. Every 12 years, the city hosts the Kumbh Mela, a religious festival during which the central ritual is bathing in the Ganges to achieve moksha. In 2001, over 30 million pilgrims attended, making it the largest gathering in human history. Unfortunately, the river has also become one of the most polluted bodies of water in the entire world, due to India’s exploding population and rapid industrialization. Over 450 million people live in the Ganges river basin, and human waste is the cause of most of the pollution. Almost five billion liters of sewage flow into the river every day, only a quarter of which is treated. By Varanasi, the Ganges is an open sewer. Fecal bacteria at this point is 150 times higher than the safe level for bathing, let alone drinking. Over 300,000 Indian children die annually from drinking contaminated water.  Industrial effluent also pollutes the river, particularly from tanneries in Kanpur. Indian industries dump nearly a billion liters of waste into the river daily. Climate change has worsened the problem: water flow has decreased as Himalayan glaciers shrink. 

In fact, many Hindus continue to bathe in or even drink the Ganges regularly. Confident in the healing powers of the divine river, they believe nothing could compromise the purity of their goddess. For them, Mother Ganges exists to wash away the impurities and pollution of earth and thus can cleanse herself. Major cleanup efforts are thus a waste of money and effort. Some governments and industries have taken advantage of these beliefs, and have used confidence in the cleansing power of the Ganges to justify continuing to pollute the river. Other Hindus acknowledge the problem, but lay blame on Muslims.  Because cattle are holy to many Hindus, Kanpur’s polluting tanneries—which create leather from cowhides—are all owned by Muslims.  Many Muslims claim that they have been unfairly persecuted by Hindu nationalists, who they say would rather persecute Muslim businesses than address more expensive sewage issues.

In March 2017, as cleanup efforts continued to fail, the High Court of Uttarakhand state confirmed the deified status that Hindus have long given the river. They issued a judgment that the Ganges and the Yamuna river—a Ganges tributary—are “living entities” which are entitled to human rights. Those caught polluting the river could thus be charged with assault or even murder. A few days later, activists sought murder charges against several politicians on behalf of the Yamuna River, sections of which are no longer able to support life. However, on July 7, 2017, the Supreme Court of India struck down Uttarakhand state’s ruling, arguing that treating the rivers as living entities was impractical. The Ganges is still revered as a living goddess by Hindus across the world, but an effective solution to its pollution remains elusive.  Hinduism Case Study – Climate Change  2018

Additional Resources

Primary sources:, secondary sources:.

•    BBC in-depth reporting on “India’s Dying Mother”: http://bbc.in/2vBdlH3  •    BBC video on the religious and geographic origins of the Ganges: http://bit.ly/2fnnhgD  •    NPR report on the Ganges as a legal “living entity”: http://n.pr/2sj02Ge  •    Financial Times video on pollution in the Ganges: http://bit.ly/2vyigrY  •    The Guardian video on pollution in the Yamuna River: http://bit.ly/2uAEIfD  •    PBS video on the Kumbh Mela festival: http://to.pbs.org/1EnPeeb  •    National Geographic video on cremations at the Ganges: http://bit.ly/2wo0SUm      

Discussion Questions

This case study was created by Kristofer Rhude, MDiv ’18, under the editorial direction of Dr. Diane L. Moore, faculty director of Religion and Public Life.

• 1.  World Religion Database, ed. Todd M. Johnson and Brian A. Grim (Boston: Brill, 2015).
• 2.  Kelly D. Alley, On the Banks of the Ganga: When Wastewater Meets a Sacred River, (Ann Arbor: University of Michigan Press, 2002), 56-60; David Kinsley, Hindu Goddesses: Visions of the Divine Feminine in the Hindu Religious Tradition, (Berkeley: UC Press, 1986), 188-189.
• 3.  Kinsley Hindu Goddesses, 191, 193-4; Justin Rowlatt, “India’s Dying Mother,” BBC News, (London), May 12, 2016. http://bbc.in/21TmEJ6 
• 4.  Linda Davidson and David Gitlitz, Pilgrimage: from the Ganges to Graceland: An Encyclopedia, (Santa Barbara: ABC-CLIO, 2002), 322-3.
• 5.  Rowlatt, “India’s Dying Mother”; George Black, “What it Takes to Clean the Ganges,” The New Yorker, Jul. 25, 2016. http://bit.ly/29PUsCy
• 6.  Krishna N Das, “India’s Holy Men to Advise Modi’s Ganges River Cleanup,” Reuters, (New Delhi), June 12, 2014. http://reut.rs/2vnJFKN 
• 7.  Rowlatt, “India’s Dying Mother.”; Black, “What it Takes to Clean the Ganges.”; Das, “India’s Holy Men.”
• 8.  Alley, On the Banks of the Ganges, 237; Kinsley, Hindu Goddesses, 191; Rowlatt, “India’s Dying Mother”; 
• Amrit Dhillon, “The Ganges: Holy River from Hell,” The Sydney Morning Herald, Aug. 4, 2014. http://bit.ly/2vQwWn6
• 9.  Black, “What it Takes to Clean the Ganges.” 
• 10.  Michael Safi, “Murder Most Foul: polluted Indian river reported dead…,” The Guardian (Delhi), July 7, 2017. http://bit.ly/2tTIGU3
• See more Christianity Case Studies
• See more Climate Change Case Studies

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Ganga River: A Paradox of Purity and Pollution in India due to Unethical Practice

D C Jhariya 1 and Anoop Kumar Tiwari 2

Published under licence by IOP Publishing Ltd IOP Conference Series: Earth and Environmental Science , Volume 597 , National Conference on Challenges in Groundwater Development and Management 6-7 March 2020, NIT Raipur, India Citation D C Jhariya and Anoop Kumar Tiwari 2020 IOP Conf. Ser.: Earth Environ. Sci. 597 012023 DOI 10.1088/1755-1315/597/1/012023

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1 Assistant Professor, Department of Applied Geology, National Institute of Technology Raipur, Chhattisgarh-492010, India

2 Assistant Professor, Department of Humanities and Social Sciences, National Institute of Technology Raipur, Chhattisgarh-492010, India

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In India, the river Ganga is believed as a goddess, and people worship it. Despite all the respect for the river, the river's condition is worsening, and we Indians are unable to maintain the purity of the river. The Ganga is a river of faith, devotion, and worship. Indians accept its water as "holy," which is known for its "curative" properties. The river is not limited to these beliefs but is also a significant water source, working as the life-supporting system for Indians since ancient times. The Ganga river and its tributaries come from cold, Himalayan-glacier-fed springs, which are pure and unpolluted. But when the river flows downgradient, it meets the highly populated cities before merging into the Bay of Bengal. From its origin to its fall, its water changes from crystal clear to trash-and sewage-infested sludge. Thousands of years passed since the river Ganga, and its tributaries provide substantial, divine, and cultural nourishment to millions of people living in the basin. Nowadays, with the increasing urbanization, the Ganges basin sustains more than 40 percent of the population. Due to the significant contribution of the growing population and rapid industrialization along its banks, river Ganga has reached an alarming pollution level.

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Materials and methodology, results and discussion, conclusions, acknowledgements, data availability statement, drinking water quality assessment of river ganga in west bengal, india through integrated statistical and gis techniques.

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Syed Yakub Ali , Sangeeta Sunar , Priti Saha , Pallavi Mukherjee , Sarmistha Saha , Suvanka Dutta; Drinking water quality assessment of river Ganga in West Bengal, India through integrated statistical and GIS techniques. Water Sci Technol 15 November 2021; 84 (10-11): 2997–3017. doi: https://doi.org/10.2166/wst.2021.293

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An attempt has been made to assess the water quality status of the lower stretch of river Ganga flowing through West Bengal for drinking using integrated techniques. For this study, 11 parameters at 10 locations from Beharampur to Diamond Harbour over nine years (2011–2019) were considered. The eastern stretch of Ganga showed a variation of Water Quality Index (WQI) from 55 to 416 and Synthetic Pollution Index (SPI) from 0.59 to 3.68 in nine years. The result was endorsed through a fair correlation between WQI and SPI (r 2 > 0.95). The map interpolated through GIS revealed that the entire river stretch in the year 2011, 2012, and 2019 and location near to ocean during the entire period of nine years were severely polluted (WQI > 100 or SPI > 1). Turbidity and boron concentration mainly contribute to the high scores of indices. Further, the origin of these ions was estimated through multivariate statistical techniques. It was affirmed that the origin of boron is mainly attributed to seawater influx, that of fluoride to anthropogenic sources, and other parameters originated through geogenic as well as human activities. Based on the research, a few possible water treatment mechanisms are suggested to render the water fit for drinking.

The study provides a base line assessment of the water quality of river Ganga for drinking.

Water quality was marked as polluted and unfit for drinking.

The seawater influx, geogenic and anthropogenic activities were assessed as the major sources of pollution.

Water treatment technologies were suggested to render the water fit for drinking.

It will be helpful to formulate appropriate management strategies.

River Ganga, internationally known as The Ganges, is one of the major rivers in India and plays a pivotal role in sustaining the lives of millions of people both physically as well as spiritually. It was declared as the ‘National River of India’ and drains approximately one-fourth of the Indian territory. It flows through Uttarakhand, Uttar Pradesh, Bihar, Jharkhand and West Bengal, covering a length of 2,525 km, and further drains into the Bay of Bengal. The towns on the river banks support about 37% of the urban population.

Over a period of time, the quality of water of the river has deteriorated due to pollution from various point and non-point sources resulting from increasing population density, rapid industrialization, unplanned urbanization, and rising living standards. Untreated industrial effluents, enormous amounts of municipal sewage, agricultural runoff, open defecation, and other waste, including polythenes and dead bodies, are being released into the river indiscriminately, irrespective of its carrying capacity. Despite the alarming level of pollution, untreated water is still used for various purposes, which may impact human health. Therefore, water quality assessment is essential as the initial step towards generating public awareness and aiding the planners and government authorities to conserve and manage the water bodies.

Water quality assessment through quantification of various physico-chemical parameters is very difficult to understand in its primary form and barely represents the actual scenario. Therefore, an assessment tool is required to integrate all the parameters scientifically to represent water quality through a single numerical value. Among many assessment tools used for water quality analysis, the Water Quality Index (WQI) is the most popular indexing tool that integrates all the parameters while comparing with the standards recommended by the government authorities to safeguard human health ( Ewaid et al. 2020 ; Nong et al. 2020 ). It was developed by Horton (1965) , in which arithmetic weighting was used with multiplicative variables ( Horton 1965 ; Avvannavar & Shrihari 2008 ; Kumar & Dua 2009 ; Lkr et al. 2020 ). Further, many modifications were done according to requirement, and many new indices were developed, such as the National Sanitation Foundation-WQI (NSFWQI), Oregon Water Quality Index, Canadian Council of Ministers of the Environment-WQI (CCME-WQI), British Columbia Water Quality Index (BCWQI), Overall Index of Pollution (OIP), WQI by Bhargava, etc. ( Poonam et al. 2013 ). These indices were also applied to assess the water quality of the river systems in India, including various stretches of river Ganga ( Samantray et al. 2009 ; Sharma & Kansal 2011 ; Saha et al. 2012 ; Shah & Joshi 2017 ; Shukla et al. 2017 ; Mitra et al. 2018 ; Ghosh et al. 2019 ; Kamboj & Kamboj 2019 ; Lkr et al. 2020 ).

Another promising tool, the Synthetic Pollution Index (SPI), was also evaluated in this study to validate the pollution level assessed by WQI. It was developed by Ma et al. (2009) to assess the impact of pollutants on the water quality and was later adopted in various studies due to its simplicity ( Solangi et al. 2019 ; Hui et al. 2020 ; Sunar et al. 2020 ).

Although the indices depict water quality, the numbers do not make the general public visualize the water quality status. Therefore, integration of the data set with a geographic information system (GIS) can help in illustrating the water quality status of river Ganga to the general public, policy makers or stakeholders. This will make the research output more approachable and understandable to the policy makers and general public. Pollution source identification through various statistical techniques such as factor analysis (FA) and cluster analysis (CA) also helps the policy makers and government to control water pollution ( Bhatti et al. 2019 ; Saha & Paul 2019a ).

Since river Ganga is listed as the sixth most polluted river in the world ( Panigrahi & Pattnaik 2019 ), a number of studies have been carried out to asesss the water quality of river Ganga ( Tiwary et al. 2005 ; Mishra 2010 ; Bhutiani et al. 2016a , 2016b ; Shukla et al. 2017 ; Kamboj & Kamboj 2019 ; Matta et al. 2020 ). However, the use of WQI and SPI together for evaluation of the water quality of Ganga river is limited. Therefore, the present study aims to assess the water quality of river Ganga flowing in the state of West Bengal, India based on the monitoring data of the environmental regulatory board of West Bengal during 2011–2019 by using integrated techniques. The result was validated through a fair correlation between WQI and SPI (r 2 > 0.95), which authenticates the extent to which the water is suitable for drinking. This represents a pioneer study to assess the drinking water quality using these integrated techniques in the mentioned stretch of the river. Further, the pollution sources are identified and thereby water treatment techniques to safeguard the human health are proposed. This base line assessment of drinking water quality of lower stretch of river Ganga may assist the policy makers to formulate appropriate management strategies for pollution abatement of the river.

This study is conducted in the lower stretch of River Ganga, flowing in the state of West Bengal, India, between 87°55′24.315″ E, 24°48′20.227″N and 87°46′10.382″E, 21°41′28.733″N ( Figure 1 ). A total of ten sampling locations were selected for this study, which were strategically decided by Central Pollution Control Board (CPCB) for effective monitoring of the river ( Table 1 ). The river basin experiences subtropical humid climate with three distinct seasons, namely cold, hot dry season, and monsoon, with an average rainfall of about 1,500 mm. The geology comprises newer alluvium (Holocene sediments) as well as older alluvial (Middle-Upper Pleistocene sediments). Extensive pollution resulting from anthropogenic activities such as sewage as well as untreated industrial effluents discharge, large scale idol immersion, mass bathing during religious festivals, disposal of dead bodies etc. has caused massive deterioration in the water quality of the river. This stretch also receives more than 87 MLD (millions of litres per day) waste water from 22 grossly polluting industries, where the chemical industry discharges about 70% of the total waste water, followed by the pulp-paper industry with 20% discharge ( CPCB 2013 ). The remaining percentage includes other industries such as distillery; food, dairy and beverage; sugar; textile and bleaching and dyeing.

Details of monitoring station of river Ganga in the West Bengal Stretch ( CPCB 2013 )

Map showing study area and sampling locations.

In spite of these polluting activities, this stretch always remains as the primary source of drinking water in many cities of Murshidabad, Baharampur, Bardhaman, Nadia, Hooghly, North 24 Parganas, Kolkata, and Howrah districts. Moreover, Kolkata has three water works, namely Palta, Garden Reach and Baranagar, where a total of 1,136 million litres/day of river water is treated and supplied to the habitats ( UNU 2000 ). Similarly, Howrah has two water works, Padhmapukur and Serampore, supplying about 270 million litres of water/day ( UNU 2000 ). Therefore, water quality assessment and documentation of the management practices are required to safeguard human health.

Standards recommended by WHO and unit weightage of the parameters used for WQI and SPI

All parameters are units are in mg/L except pH, TH (mg/L as CaCO 3 ). TH: total hardness.

Categorization of WQI and SPI for estimation of water quality

The critical value of SPI was evaluated by assigning V o of the water quality parameter as the recommended standard, beyond which the water is unsuitable for drinking. By integrating all the water quality polluting parameters selected in the study with the unit weightage through Equation ( 4 ), the critical value estimated was 1. Further, the categorization was done based on four equal quartiles ( Table 3 ).

Geospatial assessment

Source identification.

The Kaiser Normalization (PC with eigen values >1) along with varimax rotation was followed in this study for better interpretability of results. Moreover, Bartlett's test of sphericity and Kaiser–Meyer–Olkin (KMO) test were also performed to test the significance of these multivariate statistics. PC > 0.5 showed most significant correlated variables, which helps in identification of anthropogenic or geogenic sources.

All statistical analyses were performed using software SPSS 21.0 version.

Physico-chemical analysis

In the present study, a detailed analysis of physico-chemical parameters is conducted for assessment of water quality of the mentioned stretch of river Ganga comprising ten locations as illustrated in box plot ( Figure 2 ) displaying the six-number summary (minimum, first quartile, mean, median, third quartile, and maximum) of the data set from the years 2011–2019. Apart from this, one way analysis of variance (ANOVA) was performed using Tukey test, with 95% level of confidence ( α :0.05) ( Table 4 ) to confirm the significant difference between the sampling sites. The descriptive statistics of all the parameters considered in this study for the year 2011–2019 are presented in Table 5 .

Test of ANOVA for the parameters within the years (2011–2019) and sampling locations

Digits in bold represent statistical significance.

Descriptive statistics of physico-chemical parameters of all the sampling locations in the lower stretch of River Ganga from 2011 to 2019

The concentrations of all parameters are in mg/L, except pH: unitless, turbidity: NTU, TH (total hardness): mg CaCO 3 /L.

Box plot of (a) ammonia (mg/L), (b) boron (mg/L), (c) chloride (mg/L), (d) pH, (e) fluoride (mg/L), (f) nitrate (mg/L), (g) Temperature (°C), (h) sodium (mg/L), (i) sulfate (mg/L), (j) TDS (mg/L), (k) total hardness (mg CaCO 3 /L), (l) Turbidity (NTU).

The pH of the river ranged from 7.31 to 8.15 ( Figure 2(d) ), which follows the drinking water quality standard recommended by WHO (7.0–8.5). The ANOVA ( Table 4 ) showed that there is no significant statistical difference of pH between the sampling locations, which may be due to low annual variation in free CO 2 ( Gupta et al. 2017 ). Moreover, the alkaline nature is due to the influx of domestic and industrial waste water into the river and photosynthetic algae activities that consume CO 2 dissolved in water ( Driche et al. 2008 ). This result is similar to the study of Sarkar et al. (2007) for Hoogly River (lower trench of Ganga River), which showed the pH variation from 7.2 to 8.9. The (TDS) in the study area ranged from 158 to 3,066 mg/L ( Figure 2(j) ), wherein the majority of sampling locations fit in the prescribed limit of WHO (600 mg/L) except Diamond Harbour. The high amount of TDS at Diamond Harbour is due to sea water intrusion during high tide, as it is located 70 km upstream from the sea. Similar result was reported by Sarkar et al. (2007) , where the maximum TDS concentration at Diamond Harbour was given as 2.55 ppt. The turbidity of the river varied from 43.6 to 350.82 Nephelometric Turbidity Units (NTU) ( Figure 2(l) ) and the highest value was found in Diamond Harbour, which showed a significant difference ( p < 0.05) from the other locations. Turbidity results from the presence of suspended particles such as clay, silt, organic matter, plankton and other microscopic organisms in the water ( Grobbelaar 2009 ). The clarity of water decreases due to the presence of these suspended particles that get deposited in the water. Total hardness (TH) ranged from 88 to 950 mg CaCO 3 /L ( Figure 2(k) ), where all the locations were within the drinking water standard limits of 200 mg CaCO 3 /L as recommended by WHO ( WHO 2017 ), except Diamond Harbour (950 mg CaCO 3 /L). Sodium concentration in the river water was significantly high ( p < 0.05) in Diamond Harbour, with a mean range from 310 to 992 mg/L ( Figure 2(h) ), whereas in other sampling locations it was within the drinking water standard limit of 200 mg/L, recommended by WHO ( WHO 2017 ). Hypertension and cardio-metabolic diseases may result from the intake of excessive sodium in drinking water along with normal dietary sodium consumption ( Nwankwo et al. 2020 ), although no prominent relation of the level of sodium to hypertension can be affirmed conclusively and therefore no health-based guideline is available in this regard ( WHO 2003 ). Moreover, major anions such as chloride and sulfate were also significantly high ( p < 0.05) in Diamond Harbour due to its vicinity to the estuary, with maximum concentration of 1,511.47 mg/L ( Figure 2(c) ) and 211.13 mg/L, respectively ( Figure 2(i) ). The occurrence of the major ions in river water is mainly from geogenic sources such as weathering of minerals ( Asare-Donkor et al. 2018 ). However, the mean ammoniacal nitrogen (NH 3 -N) showed a significantly high level at Serampore as compared to the other locations. It ranged from 0 to 0.53 mg/L ( Figure 2(a) ), which was below the permissible limit for drinking water standard recommended by WHO (1.5 mg/L). Ammonia enters into the aquatic environment via direct means such as municipal effluent discharge and excretion of nitrogenous waste from animals and indirect means such as nitrogen fixation, air deposition and runoff from agricultural lands ( EPA 2013 ). In addition, the nitrate in Ganges river mainly comes from domestic sewage, industrial wastewater, agriculture fertilizer and aquaculture ( Sarkar et al. 2007 ). Low values of nitrate nitrogen (mean ranging from 0.25 to 1.11 mg/L) were also observed in all the sites ( Figure 2(f) ), which may be due to utilization by phytoplankton and other primary producers. Boron concentration in the study area ranged from 0 to 0.39 mg/L ( Figure 2(b) ). The ANOVA reflected no significant difference ( p > 0.05) between the locations. However, maximum boron concentration was found in Dakhineswar followed by Garden Reach and Diamond Harbour. The significant increase in the boron content of surface water can be attributed to the input of waste water with washing agents containing borate compounds or leaching from sediment or rock containing borates or borosilicates ( WHO 1998 ). The fluoride concentration (mean ranged from 0.17 to 0.74 mg/L) ( Figure 2(e) ) showed no significant difference ( p > 0.05) among all the sampling locations. Although small concentrations of these ions are necessary for the human system, when present at high concentration in drinking water they may cause various acute or chronic diseases.

The values of these parameters cannot discretely conclude the extent of deviation of the river water quality from drinking standard. Therefore, this study was conducted to assess the status of the river water quality applying WQI and SPI, and the pollution sources were identified through multivariate statistical tools, thereby suggesting appropriate treatment measures required to render the water suitable for drinking.

Water quality assessment through WQI and SPI

WQI was evaluated for nine consecutive years (2011–2019) for the mentioned stretch of river Ganga in Eastern India. The results of WQI ( Figure 3 ) and SPI ( Figure 4 ) were interpolated through IDW geostatistics using ArcGIS for better interpretability of results. The WQI of the eastern stretch of Ganga from Beharampur to Diamond Harbour ranged from 107 to 416 in 2011, 117 to 289 in 2012, 85 to 346 in 2013, 115 to 259 in 2014, 80 to 258 in 2015, 55 to 378 in 2016, 67 to 182 in 2017, 57 to 427 in 2018 and 104 to 272 in 2019. The water quality over the mentioned time period was not at all found to be suitable for drinking without advanced treatment. A similar result was obtained for SPI, which ranged from 0.93 to 3.47 in 2011, 1.17 to 2.64 in 2012, 0.85 to 3.30 in 2013, 1.15 to 2.62 in 2014, 0.80 to 2.50 in 2015, 0.94 to 3.68 in 2016, 0.67 to 1.73 in 2017, 0.59 to 4.27 in 2018 and 1.04 to 2.62 in 2019. Figures 3 and 4 indicated that the entire river stretch was severely polluted (WQI > 100 or SPI > 1) in the years 2011, 2012 and 2019. However, the water quality improved in the stretches surrounding the sampling locations: S3 (Triveni) in 2013, S5 (Sreerampur); S6 (Dakhineswar) and S9 (Uluberia) in 2015, S2 (Nawadip); S3 (Triveni); S4 (Palta) and S5 (Sreerampur) in 2016, S2 (Nawadip); S5 (Sreerampur) and S7 (Shivpur) in 2017, S5 (Sreerampur) and S7 (Shivpur) in 2018, although it continued to remain polluted (WQI: 75–100 or SPI: 0.75–1), which signified that the usage can only be achieved with primary as well as secondary treatment. Moreover, the water quality improved in a few sampling locations across the stretches around S1 (Beharampur) in 2016, S6 (Dakhineswar) and S8 (Garden Reach) in 2017, S1 (Beharampur); S6 (Dakhineswar); S8 (Garden Reach) and S9 (Uluberia) in 2018, indicating the usage of water for drinking by primary treatment followed by disinfection. However, the water quality again deteriorated in 2019. The sampling site S10 (Diamond Harbour) showed the worst water quality throughout the entire period of nine years due to intrusion of sea water from Bay of Bengal during high tides. Since the river flows further downstream through numerous towns and cities to enter the state of West Bengal, it seemingly acquires more pollution load before draining into Bay of Bengal, as obtained in this study. Sharma et al. (2014) in their study demonstrated through the application of WQI that Ganges river at various locations in Allahabad stretch showed inferior water quality for drinking purpose.

Water quality index (WQI) of the river Ganga in eastern India for suitability assessment for drinking in subsequent years (2011–2019).

Synthetic pollution index (SPI) of the river Ganga in eastern India to validate the suitability for drinking in subsequent years (2011–2019).

The higher values of WQI or SPI in these stretches of Ganga were attributed to turbidity and boron concentration. Further, the relation between WQI and SPI was evaluated through regression model ( Table 6 ). The result confirmed the significant good relation between these two indexing tools, with r 2 > 0.9 in all the years.

Regression relation between WQI and SPI for the years 2011–2019

Pollution source identification

The WQI and SPI both affirmed the water quality of river Ganga as polluted, which requires treatment for drinking. However, the application of the treatment methodology is not feasible until the pollution sources are identified. Therefore, identification of pollution sources was done through multivariate statistics, FA supported by CA. The FA in this study follows Kaiser Normalization, where PCs greater than 1 were retained. It reduces the data set to four PCs, with a cumulative variance of 87.12 and KMO value of 0.792 > 0.5, affirming its statistical significance ( Table 7 ). The result of Principal Component Analysis (PCA) is presented as a component loading plot in Figure 5 . The components are indicated by colour code ( Figure 5 ). The first component (PC1) comprised Cl − , SO 4 2− , TDS, TH, Na + and turbidity, with an eigen value of 5.07 and maximum variance of 50.74. Around 35.5% of the samples have Na/Cl > 1, indicating silicate weathering from the alluvium deposits of Gangatic plains ( Frings et al. 2015 ), around 22.2% of samples have Na/Cl between 0.86 and 1, indicating seawater intrusion ( Shammi et al. 2017 ), while the rest originates from anthropogenic sources. The sulphates originate from the sedimentary sulphur containing minerals such as dolomite and gymsum as geogenic sources apart from other industrial and sewage effluents ( Sarin et al. 1989 ; Chakrapani & Veizer 2006 ; Dwivedi et al. 2018 ). TH comprises Ca 2+ , Mg 2+ , CO 3 2− and HCO 3 − , which dilutes in the water from the silicate, calcite and dolomite weathering as well as from the mass bathing and washing with detergents, discharge of industrial effluents and domestic sewage ( Sarin et al. 1989 ; Nath et al. 2017 ). The second component, PC2, is attributed to NH 3 -N and F − , with a variance of 13.66. The agricultural runoff, untreated industrial effluents, and domestic and municipal fresh sewage increase the level of fluoride and nitrate in the river water ( Khullar 2004 ; Mandal et al. 2010 ; Dubey & Ujjania 2013 ; Sankhla & Kumar 2018 ). The third component (PC3) comprised only boron (B), with a variance of 11.56. The origin of B may be attributed to the sea water intrusion during the tides, which can also be acknowledged by its high concentration in the location S10 (Diamond Harbour). The content of boron decreases in the stretch of river with increase in distance from the Bay of Bengal. Moreover, the content of boron is also evident in different parts of Bay of Bengal, which intrude in to fresh water during high tides ( Saxena et al. 2004 ; Gupta & Gupta 2015 ; Shammi et al. 2017 ; Danish et al. 2019 ). The fourth component (PC4) comprised NO 3 − -N only, with approximately the same variance as PC3, 11.15 and eigen value of 1.11. The nitrate is derived from the oxidation of ammonia in sewage, agricultural runoff, industrial effluents, etc. by autotrophic bacteria ( Indirani 2010 ; Deshmukh 2013 ; Dubey & Ujjania 2013 ; Mitra et al. 2018 ). Therefore, the presence of nitrate in water reveals that the water got polluted a long time ago. The result of PCA is also shown as a component loading plot, where the same colour indicates same component(s) ( Figure 5 ). This result is supported by cluster analysis, which was performed by wards linkage and euclidian distance measure ( Figure 6 ). The dendrogram shows close linkage between SO 4 2− , TDS, Cl − , Na + , TH and turbidity as the first group, which is attributed to geogenic and anthropogenic sources. On the other hand, NH 3 -N and F − are present in the second group, which might be attributed to anthropogenic activities. NO 3 − -N, which might be derived from ammoniacal sources, forms the third linkage, and boron as fourth linkage is attributed to sea water intrusion from Bay of Bengal.

Components of factor analysis to estimate the pollution sources

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in five iterations; digits in bold represent strong component loading.

Component loading plots of factor analysis (FA) to estimate the origin of pollutants.

Dendrogram for physio-chemical parameters of river Ganga (2011–2019).

Proposed water quality management practice

The water needs proper treatment such as screening/straining followed by primary sedimentation. Since the water showed high level of turbidity, coagulation-flocculation can be incorporated after primary sedimentation in the treatment plant as a vital practice. Here, aluminium-sulphate (alum), a cost effective and widely available coagulant, can be used to lower the turbidity. Higher amounts of boron can be treated using polymer coagulants such as polyvinyl alcohol, glucoheptanamide derivatives of poly(amidoamine) and poly(ethyleneimine), poly(glycidyl methacrylate) and poly-N,N′-diallyl morpholinium bromide modified with hydroxyethylaminoglycerol, hydroxyethylaminoglycerol functionalized to poly(glycidyl methacrylate) and poly(4-vinyl-1,3-dioxalan-2-one-co-vinyl acetate), alkyl monol, diol or triol containing polyethylenimines ( Wolska & Bryjak 2013 ). Apart from this, defluoridation of water can be done using most effective and conventional treatment with lime and alum (Nalgonda technique). This step should be followed by secondary sedimentation with optimum sedimentation time. Finally, before using the water for drinking, disinfection should be done through chlorination and filtration.

Apart from this, direct disposal of sewage in Ganga should be checked. The wastewater should at first be treated in Sewage Treatment Plants through necessary steps before discharging it into the river. Solid waste dumping should be prevented in the river to improve the water quality. Cremation as well as devotional activities such as idol immersion etc. should be checked in the river banks. The use of river water for domestic activities such as washing clothes, utensils etc. with detergents should be limited. The main source of pollution of river Ganga is the industries, which should be regularly monitored for zero discharge of wastewater and the efficiency of the effluent treatment plants. These measures can contribute towards ameliorating water pollution and restoring the water quality of the divine river Ganga.

This study may lead the way towards future research for validation of these treatment technologies for rendering Ganga river water potable. Apart from this, quantitative assessment of a few important parameters such as heavy metals, pesticides, etc. might be evaluated on a monthly basis for better assessment of the river water. This information can also be focused for further assessment of drinking water quality in the lower stretch of the river.

This constitutes a pioneer study for water quality assessment of the eastern stretch of one of the most polluted rivers in India, the Ganga (flowing through the State of West Bengal) by the application of WQI and SPI, and the data set is integrated with GIS for depicting the status of pollution in a better manner. The water quality assessed through these integrated techniques affirmed that the mentioned stretch of River Ganga is polluted and not suitable for drinking without appropriate treatment. A few parameters such as boron and turbidity were identified as critical polluting parameters responsible for deterioration in the water quality. The multivariate statistical techniques (FA and PCA) indicated that the presence of boron is attributed to sea water intrusion; whereas turbidity originates from silicate weathering from the alluvium deposits of Gangetic plains as well as anthropogenic activities (such as mass bathing and washing with detergents, discharge of industrial effluents and domestic sewage). Based on the water quality status and origin of the polluting factors, this study also suggested various treatment techniques. The outcome of this study and the suggested techniques can be used by the regulatory authority for documentation of management strategies and framing of effective water quality management plans to combat the pollution of the mighty river Ganga.

We would like to thank West Bengal Pollution Control Board for the online data for water quality of river Ganga. We would also like to thank Dr Supriyo Goswami (IEST Shivpur) for valuable contribution.

All relevant data are included in the paper or its Supplementary Information.

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Pollution of River Ganga, Case Study

Pollution of the Ganges (or Ganga), the largest river in India, poses significant threats to human health and the larger environment. Severely polluted with human waste and industrial contaminants, the river provides water to about 40% of India's population across 11 states, serving an estimated population of 500 million people which is more than any other river in the world. Today, the Ganges is considered to be the sixth-most polluted river in the world. Raghubir Singh, an Indian photographer, has noted that no one in India spoke of the Ganges as polluted until the late 1970s. However, pollution has been an old and continuous process in the river as by the time people were finally speaking of the Ganges as polluted, stretches of over six hundred kilometres were essentially ecologically dead zones. A number of initiatives have been undertaken to clean the river but failed to deliver as desired results. After getting elected, India's Prime minister Narendra Modi affirmed to work in cleaning the river and controlling pollution. Subsequently, the Namami Gange project was announced by the government in the July 2014 budget.[10] An estimated Rs 2,958 Crores (US$460 million) have been spent until July 2016 in various efforts in cleaning up of the river. Background of the case Ganga is a trans-boundary river of Asia flowing through India and Bangladesh. It is one of the most sacred rivers to the Hindus and a lifeline to a billion Indians who live along its course. One of the most populated cities along its course is Kanpur. This city has a population of approx. 29.2 lakhs (2.9 million). At this juncture of its course Ganga receives large amounts of toxic waste from the city´s domestic and industrial sectors, particularly the leather tanneries of Kanpur. In 1985, M.C. Mehta filed a writ petition in the nature of mandamus to prevent these leather tanneries from disposing off domestic and industrial waste and effluents in the Ganga river. This writ petition was bifurcated by the Supreme Court into two parts known as Mehta I and Mehta II. Mehta I [ M.C. Mehta v. Union of India , [1987] 4 SCC 463]: Proceedings and Orders passed before final judgment: In this petition the petitioner requested the court to request the Supreme Court (the Court) to restrain the respondents from releasing effluents into the Ganga river till the time they incorporate certain treatment plants for treatment of toxic effluents to arrest water pollution. At the preliminary hearing the Court directed the issue of notice under Order I Rule 8 of the CPC, treating this case as a representative action by publishing a small gist of the petition in the newspapers calling upon all the industrialists, municipal corporations and the town municipal councils having jurisdiction over the areas through which the river Ganga flows to appear before the Court and to show cause as to why directions should not be issued to them. In pursuance of this notice many industries and local authorities appeared before the Supreme Court. The Court highlighted the importance certain provisions in our constitutional framework which enshrine the importance and the need for protecting our environment. Article 48-A provides that the State shall endeavor to protect and improve the environment and to safeguard the forests and wild life of the country. Article 51-A of the Constitution of India, imposes a fundamental duty on every citizen to protect and improve the natural environment including forests, lakes, rivers and wild life. The Court stated the importance of the Water (Prevention and Control of Pollution) Act, 1974 (the Water Act). This act was passed to prevent and control water pollution and maintaining water quality. This act established central and stated boards and conferred them with power and functions relating to the control and prevention of water pollution. Section 24 of the Act prohibits the use of the use of any stream for disposal of polluting matter. A stream under section 2(j) of the Act includes river, water course whether flowing or for the time being dry, inland water whether natural or artificial, sub-terrene waters, sea or tidal waters to such extent or as the case may be to such point as the State Government may by the notification in the official gazette may specify. The Act permits the establishment of Central Boards and State Boards. Section 16 and Section 17 of the Act describe the power of these boards. One of the functions of the State Board (the Board) is to inspect sewage or trade effluents, plants for treatment of sewage and trade effluents, data relating to such plants for the treatment of water and system for the disposal of sewage or trade effluent.

What is a Trade Effluent?

Mehta ii (m.c. mehta v. union of india decided on 12th january, 1988).

• (iii) the collection and removal of sewage, offensive matter and rubbish and treatment and disposal thereof including establishing and maintaining farm or factory:
• (vii) the management and maintenance of all Mahapalika waterworks and the construction or acquisition of new works necessary for a sufficient supply of water for public and private purposes.
• (viii) guarding from pollution water used for human consumption and preventing polluted water from being so used.

The Court also relied on Section 251, 388, 396, 398, 405 and 407 of the Adhiniyam which provide provisions for disposal of sewage, prohibition of cultivation, use of manure, or irrigation injurious to health, power to require owners to clear away noxious vegetation and power of the Mukhya Nagar Adhikari to inspect any place at any time for the purpose of preventing spread of dangerous diseases. These provisions deal with the duties of the Nagar Mahapalika or the Mukhya Nagar Adhikari appointed under the Adhiniyam with regard to the disposal of sewage and protection of the environment. These provisions governing the local bodies indicate that the Nagar Mahapalikas and the Municipal Boards are primarily responsible for the maintenance of cleanliness in the areas of their jurisdiction. The Court also relied on the provisions of the Water Act which provide the meaning of pollution, sewage effluent, stream and trade effluents. Sections 3 and 4 of the Water Act provide for the establishment of the Central and State Boards. A State Board was constituted under Section 4 of the Water Act in the State of Uttar Pradesh. Section 16 of the Water Act sets out the functions of the Central Board and Section 17 of the Water Act lays down the functions of the State Board. The functions of the Central Board are primarily advisory and supervisory in character. The Central Board is also required to advise the Central Government on any matter concerning the prevention and control of water pollution and to co-ordinate the activities of the State Boards. The Central Board is also required to provide technical assistance and guidance to the State Boards, carry out and sponsor investigations and research relating to problems of water pollution and prevention, control or abatement of water pollution. The functions of the State Board are more comprehensive. In addition to advising the State Government on any matter concerning the prevention, control or abatement of water pollution, the State Board is required among other things:

• to plan a comprehensive programme for the prevention, control or abatement of pollution of streams and wells in the State and to secure the execution thereof;
• to collect and disseminate information relating to water pollution and the prevention, control or abatement thereof;
• to encourage, conduct and participate in investigations and research relating to problems of water pollution and prevention, control or abatement of water pollution;
• to inspect sewage or trade effluents, works and plants for the treatment of sewage and trade effluents;
• to review plans, specifications or other data relating to plants set up for the treatment of water, works for the purification thereof and the system for the disposal of sewage or trade effluents or in connection with the grant of any consent as required by the Water Act;
• to evolve economical and reliable methods of treatment of sewage and trade effluents, having regard to the peculiar conditions of soils, climate and water resources of different regions and more especially the prevailing flow characteristics of water in streams and wells which render it impossible to attain even the minimum degree of dilution; and
• to lay down standards of treatment of sewage and trade effluents to be discharged into any particular stream taking into account the minimum fair weather dilution available in that stream and the tolerance limits of pollution permissible in the water of the stream, after the discharge of such effluents.

Sections 20, 21 and 23 of the Water Act confer power on the State Board to obtain information necessary for the implementation of the provisions of the Water Act, to take samples of effluents and to analyze them and to follow the procedure prescribed in connection therewith and the power of entry and inspection for the purpose of enforcing the provisions of the Water Act. Section 24 of the Water Act prohibits the use of stream or well for disposal of polluting matters etc. contrary to the provisions incorporated in that section. Section 32 of the Water Act confers the power on the State Board to take certain emergency measures in case of pollution of stream or well. Where it is apprehended by a Board that the water in any stream or well is likely to be polluted by reason of the disposal of any matter therein or of any likely disposal of any matter therein, or otherwise, the Board may under Section 33 of the Water Act make an application to a court not inferior to that of a Presidency Magistrate or a Magistrate of the first class, for restraining the person who is likely to cause such pollution from so causing. The Court relied on a common law principle which states that Municipal Corporation can be restrained by an injunction in an action brought by a riparian owner who has suffered on account of the pollution of the water in a river caused by the Corporation by discharging into the river insufficiently treated sewage from discharging such sewage into the river. In the case of Pride of Derby and Derbyshire Angling Association v. British Celanese Ltd [3], the Derby Corporation admitted that it had polluted the plaintiffs fishery by discharging into it insufficiently treated sewage. According to the Derby Corporation Act, 1901 it was under a duty to provide a sewerage system, and that the system which had accordingly been provided had become inadequate solely from the increase in the population of Derby. The Court noted that M.C. Mehta is not a riparian owner. Nevertheless he is a person interested in protecting the lives of the people who make use of the water flowing in the river Ganga. Therefore, his right to maintain the petition cannot be disputed. The nuisance caused by the pollution of the river Ganga was held to be a public nuisance. Final judgment:

• The Court directed the Kanpur Nagar Mahapalika to take appropriate action under the provisions of the Adhiniyam for the prevention of water pollution in the river. It was noted that a large number of dairies in Kanpur were also polluting the water of the river by disposing waste in it. The Supreme Court ordered the Kanpur Nagar Mahapalika to direct the dairies to either shift to any other place outside the city or dispose waste outside the city area.
• Kanpur Nagar Mahapalika was ordered to increase the size of sewers in the labour colonies and increase the number of public latrines and urinals for the use of poor people.
• Whenever applications for licenses to establish new industries are made in future, such applications shall be refused unless adequate provision has been made for the treatment of trade effluents flowing out of the factories.

The above orders were made applicable to all Nagar Mahapalikas and Municipalities which have jurisdiction over the area through which the Ganga river flows. In addition to this, the Supreme Court further relied on Article 52A (g) on the Constitution of India, which imposes a fundamental duty of protecting and improving the natural environment. The Court order that:

• It is the duty of the Central Government to direct all the educational institutions throughout India to teach at least for one hour in a week lessons relating to the protection and the improvement of the natural environment including forests, lakes, rivers and wildlife in the first ten classes.
• The Central Government shall get text books written for the said purpose and distribute them to the educational institutions free of cost. Children should be taught about the need for maintaining cleanliness commencing with the cleanliness of the house both inside and outside, and of the streets in which they live. Clean surroundings lead to healthy body and healthy mind.

Training of teachers who teach this subject by the introduction of short term courses for such training shall also be considered. This should be done throughout India. Written By:- Navnit Kumari

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International Conference on Advances and Innovations in Recycling Engineering

AIR 2021: Recent Advances in Recycling Engineering pp 51–70 Cite as

Assessment of Human Intervention on Ecology of River—A Case Study

• Damini Rana 12 , 13 &
• Namita Joshi 12  
• Conference paper
• First Online: 17 September 2022

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4 Citations

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 275))

Rivers being the natural resources, it is our duty to conserve them and protect them. But for protecting them we need to understand the parameters which are involved in polluting them. Ganges, being the holy river is mostly polluted. Hence, we need to protect it. Therefore, the need for the study is to revive the river ecology by converting them into a scientific method that will protect the river ecology. This study aims to assess the river Ganga's variations in water quality from Rishikesh to Haridwar, Uttarakhand, India. The data is collected from the five different sites lying in the route of Rishikesh to Haridwar. A scientific investigation was conducted in order to determine the influence of human interference in the ecology of river Ganga. Tapovan (Site-1), Raiwala (Site-2), Bhimgoda (Site-3), Devpura (Site-4) and Jagjeetpur (Site-5) were selected as the study and sampling sites which gives an indication of stricter implementation, management, monitoring and strengthening public awareness to safeguard river Ganga water quality.

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Department of Environmental Sciences, Kanya Gurukula Campus, Gurukula Kangri (Deemed to be University), Haridwar, Uttarakhand, India

Damini Rana & Namita Joshi

Central Laboratory, Uttarakhand Pollution Control Board, Dehradun, India

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Department of Hydromelioration, Tashkent Institute of Irrigation and Agricultural Mechanisation Engineers, Tashkent, Uzbekistan

Akmalov Shamshodbek Baxtiyarovich

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Rana, D., Joshi, N. (2023). Assessment of Human Intervention on Ecology of River—A Case Study. In: Siddiqui, N.A., Baxtiyarovich, A.S., Nandan, A., Mondal, P. (eds) Recent Advances in Recycling Engineering . AIR 2021. Lecture Notes in Civil Engineering, vol 275. Springer, Singapore. https://doi.org/10.1007/978-981-19-3931-0_4

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