case study of ganga river

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Ganga Pollution Case: A Case Study

This article is written by Abhinav Anand , a student pursuing B.A.LL.B(Hons.) from DSNLU, Visakhapatnam. The article deals with the Ganga pollution case and the peruses into reasons behind the pollution. It also discusses some of the schemes of the government to purify the river and critically analyses its impact. It further suggests changes that should be done to make the effective implementation.

Table of Contents

Introduction

Water Pollution has become a global crisis. The perennial threat of the water crisis is exacerbating because of uncontrolled and unbalanced development of the allied sectors such as industries and agriculture. According to the reports of NITI Aayog, 21 major Indian cities, including Delhi will completely run out of groundwater. This article deals with reasons behind the pollution of the river Ganga and it examines the effective measures taken by the government. It also suggests changes to expedite the cleaning process of the river.

Reasons behind the Pollution of Ganga

There are 4600 industries in Uttarakhand out of which 298 are seriously polluting industries. There are many industries which have not taken permission from the Uttarakhand pollution control board for their operations and they started their operation based on the advisory of the government in which the government exempted certain classes of industries from taking permission. The sewage treatment and advanced technology for the treatment of the wastes are not used despite government strict regulations.

Sewage is an important source of pollution and contributes 75% to the pollution caused by all sources of pollution. Urban development of different sizes contributes to sewage pollution in the river. The considerable efforts by the Ganga Action Plan are not able to improve the situation.

The report says that despite the failure of the Ganga Action Plan there is no disapproval on the part of the citizens as well as their representative living in urban areas on the banks of the river. The failure is on the part of the government agencies responsible for the effective implementation of the plan. 

The urban citizens residing near the river show a lack of interest in the cleanliness of the river. The representatives of the urban areas are not receiving enough complaints from the citizens and as a result, they refrain from raising this issue to the higher authorities. Based on the analysis done by the independent authorities, the political parties show reluctance to increase the taxes because they may lose the support of their voters. The taxes will help the authorities to have financial validity. The Kanpur Nagar Nigam has to pay operation and management taxes to the Uttar Pradesh Jal Nigam for the operation and maintenance of the services in the Ganga Action Plan. 

However, the Kanpur Nagar Nigam is unable to collect taxes from the users of the services of Ganga Action Plan to pay to the Uttar Pradesh Jal Nigam. So, the government directly transfers the money to the Uttar Pradesh Jal Nigam by cutting the share of the Kanpur Nagar Nigam. 

It has been contended that the decentralisation of funds and functionaries will help in improving the condition of the governance at Urban Level. But, it is evident that the urban local bodies are neither motivated nor passionate to do the assigned duty. 

Municipal Corporation

These are the following factors contributing to the waste in the river:

The use of plastic by people at large and its improper disposal ultimately reach in the river. Plastic pollution has been considered as one of the significant reasons for the pollution in the river. The government has failed in the implementation of Management and Sewage Waste Rules to curb the menace of plastic pollution.

The state should declare a complete ban on the use of plastic. The authorities pay no attention to the rampant use of plastics and the improper treatment of wastes before releasing them in the river. The pollution level of water has exponentially risen because of plastic wastes. The Tribunal while dealing with the matter of pollution on the ghats has banned the use of plastic in the vicinity of ghats.

However, the ban imposed by the tribunal has no effect on the ground level and the plastics are used rampantly. The plastic bags can be replaced by the jute bags which are nature friendly.

The Ghats are also one of the major sources of pollution in the river. Ganga is one of the important parts of our Indian culture due to which different kinds of pujas and other religious tasks are performed on the ghats, and the materials used are disposed of in the river. The materials are non-decomposable, highly toxic and hence pollute the river. 

Agriculture Waste

Agricultural water pollution includes the sediments, fertilizers and animal wastes. The unbalanced use of inorganic fertilizers and other fertilizers have immensely contributed to water pollution. The fertilizers rich in nitrates create toxic composition after reaching several other entities. Large quantities of fertilizers, when washed through the irrigation, rain or drainage to the river, and pollutes the river. The fertilizers rich in nitrate content are used to get more productivity from the land. This led to pollution in the entire food chain wherever the by-product of the produce is consumed. When these fertilisers wash away due to rain or other factors and pollute the river.

Effective Measures by Government to stop the Pollution

Ganga Action Plan

The Ganga Action Plan was started in 1986 for control of water pollution in the river Ganga. The main function of this plan was to make Ganga River free from the pollution from the disposal of waste from the cities settled on the banks of the river. The plan was to make Ganga pollution free from Rishikesh to Kolkata. The central pollution control board had prepared a plan of 5 years in 1984 to make Ganga pollution-free. The central Ganga authority was formed in 1985 and a Ganga action plan was launched in 1986 to make the Ganga pollution free. 

The first phase of the Ganga action plan was inaugurated by late Rajiv Gandhi at Rajendra prasad ghat of Banaras. The National Protection Agency was constituted for its implementation. During the first phase of Ganga Action Plan 256 schemes of 462 crores were undertaken in Uttar Pradesh, Bihar and West Bengal. Special stations have been created to check the quality of water.

The experts from Bharat Heavy Electricals Limited and National Environment Engineering Research Institute were appointed to check the quality of the water. Despite so much effort, the Ganga action plan failed miserably and crores of money were spent on the Ganga action plans. The failure of such a big plan has led to economic pollution.

The government launched the second phase of the Ganga Action Plan in 2001 wherein the central pollution board, central public works department and public works department are the bodies to carry out the plan. 

Namami Ganga Programme

A flagship Namami Ganga Programme was launched under separate union Water Ministry created under river rejuvenation programme. The project aims to integrate Ganga conservation mission and it is in effect to clean and protect the river and gain socio-economic benefits by job creation, improved livelihoods and health benefits to the population that is dependent on the river.

The key achievement of the Namami Ganga projects are:

• Creating sewage treatment capacity- 63 sewerage management project under implementation in the states of Uttarakhand, Uttar Pradesh, Bihar and West Bengal. 12 sewerage management projects launched in these projects.
• Creating riverfront development: 28 riverfront development projects and 33 entry-level projects for construction, management and renovation of 182 ghats and 118 crematoria has been initiated.
• River surface cleaning: The river surface cleaning is the collection of solid floating waste on the ghats and rivers.after collection, these wastes are pumped into the treatment stations.
• Public Awareness: Various activities such as seminars, workshops and conferences and numerous activities are organised to aware the public and increase the community transmission.
• Industrial Effluent Monitoring: The Grossly Polluting Industries monitored on a regular basis. Industries are following the set standard of the environmental compliances are checked. The reports are sent directly to the central pollution control board without any involvement of intermediaries.

Suggestions

These are the following suggestion for making the existing machinery robust to expedite cleanliness process of the Ganga:

Development of a comprehensive and basic plan

We need to develop a plan by which we can reach the problem in a holistic way. The already devised plans involve many intermediaries wherein the transparency factor is cornered and only paper works are shown to the people at large. 

The strategy should be formulated for different areas according to their demand. The people having apt knowledge of that area should be involved to know the actual problem of pollution in the river. A thorough check should be done and a customer-friendly platform should be formed wherein the views of every individual should be considered.

Measurement of the quality

The apt instruments are required to measure the quality of the water. We have many schemes for the cleanliness of the Ganga but the officials assigned the duty of measuring the quality of water either have authoritarian pressure or lack of knowledge to assess the quality of water. The quality of water should be measured by a recognised testing agency. Further, the research should be made to evolve better machinery for precision in quality measurement.

Getting the institutions right

The main task is to get the involved institution on the right path. The river cleaning task demands leadership, autonomy and proper management. The cities need to be amended. Ultimately they will be the custodians of the networks developed for the cleanliness process. Many cities have weak financial powers and their revenue generation is also weak so they should be given extra incentives. An awareness campaign should be launched in small cities where people have no idea about the pollution of the river and how it affects the environment. 

Engaging and mobilising all the stakeholders

The inhabitants of the river Ganga are people, elected representatives, and the religious leaders who consider the river as a pious and clean river. The mass awareness campaign can launch only when these people will be under sound financial conditions. So, if a portion is invested in these people, then it will help to develop their thinking on a large scale. 

A similar situation has arisen in Australia where the government has invested 20% of the funds in creating mass awareness among the people for the cleanliness of the Murray river basin. It has shown a great impact on the productivity of the programmes implemented in Australia. So, when we promote all the stakeholders in one or the other way we can see a holistic development in the situation.

Rejuvenation requires equal attention to quality and quantity

The rejuvenation of rivers requires quality and quantity at the same time. The old adage of “ solution to pollution is dilution” should be kept in mind while making any kind of plan. 

The improvement of water quality in Ganga during the Kumbh Mela is the result of the release of water barrage of the water upstream. The water in the upper stream is used in the agriculture process by the respective states. So, if the water is released on a regular basis it will also help to improve the quality of the water and reduce the pollution level in the water. 

Ganga is considered a pious river in the religious scriptures. The current situation demands holistic accountability from the authorities and people to make it clean. The global image is projected by the cleanliness of our rivers. The river Ganga is a part of our culture and it is our duty to maintain its sanctity. The government should formulate a more stringent policy to develop the quality of the water in the river. The environmental laws should be strictly followed and the violators should be punished. 

•   https://www.theigc.org/blog/ganga-pollution-cases-impact-on-infant-mortality/

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Restoring India’s holiest river

A long snout lined with sharp teeth slides above the water, followed by a broad back with a triangular fin. Then it is gone.

Spotting Ganga river dolphins in Northern India is tough. The muddy water hides them well and they are shy around boats. But their rarity is mostly down to the massive degradation of their home.

Now, sightings of the endangered dolphins and other wildlife are rebounding, conservationists say, thanks to a concerted effort to restore India’s most sacred river, which is an economic lifeline for more than 500 million people and supports countless species.

For nine years, cities along the Ganga have worked to stanch the flow of pollution into the river while reviving landscapes along the waterway and its tributaries. The scope and early successes of the initiative, called Namami Ganga, have led to its selection as a World Restoration Flagship . Awarded under the UN Decade on Ecosystem Restoration , the accolade recognizes ambitious efforts to revive the natural world, which is labouring under a triple crisis of climate change, nature and biodiversity loss, and pollution and waste.

“Restoring the Ganga will safeguard both the natural systems and human societies that the river has nurtured for millennia,” said Leticia Carvalho, Head of the United Nations Environment Programme’s  (UNEP’s) Marine and Freshwater Branch. “Appreciating and caring for all of our rivers, and their links to oceans and seas, will be vital to improving people’s lives while also tackling the greatest challenges facing our planet.”

The Ganga flows 2,500 km from the Himalayas to the Bay of Bengal. Its basin covers a quarter of India and houses more than 40 percent of its 1.4 billion people. It accounts for more than one-quarter of national freshwater resources. Some 40 percent of the country’s economic output is produced here.

But India’s rapid economic progress and burgeoning population have taken a heavy toll on the river and its tributaries. Urbanization, industrialization and extraction for irrigation have seriously degraded and depleted the water and the land along its banks.

“Maybe the last 20, 25 years we have found that the water quality has come down drastically," said G. Asok Kumar, Director General of the National Mission for Clean Ganga, which is implementing the Namami Gange programme. “That's where the wake-up call came in."

Stopping pollution

In 2014, the Indian government unveiled an action plan that included investments of more than US$4 billion to clean up the Ganga. Much of the funding is going into preventing sewage and industrial effluent from pouring into the river untreated. New treatment plants are to handle 5 billion litres of wastewater every day.

A goal is to improve the water quality in major cities such as Haridwar, Kanpur and Varanasi, including at the riverside temples where millions seek good fortune and absolution from sin through a dip in the Ganga, or bathe the dead before cremating them and spreading their ashes in the water.

Another focus has been on planting and growing native trees along watercourses, which helps prevent pollutants and sediment from entering the river and stores millions of tons of climate-harming carbon. So far, some 30,000 ha of land in the basin has been returned to forest, the government says. The 2030 target is 135,000 ha.

To further reduce the pollution and overextraction of river water, the government is promoting sustainable farming.

Farmers are being encouraged to replace chemical fertilizer and pesticides with more natural options, such as alternatives made from cow dung and plant extracts, or by ploughing in cover crops. Such approaches can also boost soil’s ability to retain moisture.

Kumar said farmers’ fears of falling yields have proven unfounded. "Nature is helping them to increase the productivity of the soil, rejuvenating it, making it more organic and reducing water consumption."

Engaging communities

Another arm of the initiative has sought to improve public awareness and has engaged hundreds of organizations and communities in ecosystem conservation and restoration. This includes fisherfolk who are as dependent on the health of the river as the dolphins.

"We have already started seeing results," said Goura Chandra Das, a conservationist who monitors the river’s wildlife in order to inform restoration efforts. “After we have spoken to local communities, whenever dolphins get stuck in their fishing nets, they've made special efforts to safely rescue the dolphins from their nets and release them into the wild."

The dolphins, believed to number a few thousand at most, are just one of the estimated 25,000 species of plant and animal found in the Ganga basin , including 143 aquatic animals. Other key species for conservation include softshell turtles and otters. The hilsa shad, a prized food fish, has reportedly returned to several parts of the river system.

This week delegates at the United Nations Water Conference , a landmark summit on the state of the world’s freshwater resources, highlighted the successes of the Namami Gange programme. It was hailed as a case study for other countries struggling with river pollution.

Kumar hopes similar efforts will be rolled out in other basins in India and beyond.

“The biggest learning that this Namami Gange project gives us is that nothing is impossible,” Kumar said. “This will be a tremendous hope for the next generation because water is going to be a very vital resource."

About the UN Decade on Ecosystem Restoration

The United Nations General Assembly has declared the years 2021 through 2030 the UN Decade on Ecosystem Restoration . Led by the UN Environment Programme and the Food and Agriculture Organization of the UN, together with the support of partners, it is designed to prevent, halt, and reverse the loss and degradation of ecosystems worldwide. It aims at reviving billions of hectares, covering terrestrial as well as aquatic ecosystems. A global call to action, the UN Decade draws together political support, scientific research and financial muscle to massively scale up restoration.

What is a World Restoration Flagship? 

Countries have already promised to restore 1 billion hectares – an area larger than China – as part of their commitments to the Paris climate agreement , the Aichi targets for biodiversity, the Land Degradation Neutrality targets and the Bonn Challenge . However, little is known about the progress or quality of this restoration. Progress of all 10 World Restoration Flagships will be transparently monitored through the Framework for Ecosystem Restoration Monitoring, the UN Decade’s platform for keeping track of global restoration efforts.

With the World Restoration Flagships, the UN Decade on Ecosystem Restoration is honouring the best examples of large-scale and long-term ecosystem restoration in any country or region, embodying the 10 Restoration Principles of the UN Decade.

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• Open access
• Published: 30 September 2021

Modified hydrologic regime of upper Ganga basin induced by natural and anthropogenic stressors

• Somil Swarnkar 1 ,
• Pradeep Mujumdar 1 &
• Rajiv Sinha 2  

Scientific Reports volume  11 , Article number:  19491 ( 2021 ) Cite this article

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• Environmental impact

Climate change and anthropogenic activities pose serious threats to river basin hydrology worldwide. The Ganga basin is home to around half a billion people and has been significantly impacted by hydrological alterations in the last few decades. The increasing high-intensity rainfall events often create flash flooding events. Such events are frequently reported in mountainous and alluvial plains of the Ganga basin, putting the entire basin under severe flood risk. Further, increasing human interventions through hydraulic structures in the upstream reaches significantly alter the flows during the pre-and post-monsoon periods. Here, we explore the hydrological implications of increasing reservoir-induced and climate-related stressors in the Upper Ganga Basin (UGB), India. Flow/sediment duration curves and flood frequency analysis have been used to assess pre-and post-1995 hydrological behaviour. Our results indicate that low and moderate flows have been significantly altered, and the flood peaks have been attenuated by the operation of hydraulic structures in the Bhagirathi (western subbasin). The Alaknanda (eastern subbasin) has experienced an increase in extreme rainfall and flows post-1995. The downstream reaches experience reservoir-induced moderate flow alterations during pre-and post-monsoon and increasing extreme flood magnitudes during monsoon. Furthermore, substantial siltation upstream of the reservoirs has disrupted the upstream–downstream geomorphologic linkages.

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Introduction.

Since 1901 the global average surface temperature has risen by 0.89° due to direct and indirect impacts caused by human activities on earth system processes 1 , 2 . In turn, global warming has significantly impacted the local and regional hydrological cycle worldwide 3 , 4 , 5 . Significant variability in rainfall frequency and magnitude due to changing hydrometeorological conditions has been reported across the globe 6 , 7 , 8 , 9 . As a result, severe flooding to drought conditions have become more frequent and have significantly impacted socio-economic activities in different parts of the world 10 . In addition, direct human activities such as changes in land cover, surface & groundwater withdrawal, and operations of hydraulic structures have also significantly altered the river basin hydrology in several regions 11 , 12 .

In general, dams and reservoirs play a significant role in attenuating flood peaks, frequency, duration and magnitude globally, particularly low flows 13 , 14 . Further, dams and reservoirs have also disrupted the sediment delivery to the downstream reaches, causing alteration in river channel morphology and downstream sediment starvation 15 , 16 . Consequently, river deltas are sinking at unprecedented rates worldwide 17 . Apart from the hydrological, ecological, and societal stresses caused by these large dams and reservoirs, previous researchers have also questioned their economic viability 18 , 19 .

In India, rivers largely govern freshwater resources and are considered as the lifeline of the nation. More than 70% of the rural population depends upon freshwater resources for irrigation and agricultural demands fulfilled by several large and small rivers in India 20 . However, it has been observed that the changing climatic conditions in recent decades have significantly increased the extremity of severe droughts and devastating flooding events in several parts of the country 21 , 22 , 23 . The Himalayan regions are one of the worst affected regions in the recent decades 24 , 25 , 26 , 27 . Further, several major Himalayan Rivers, particularly the Ganga River basin, are regulated by more than 300 hydraulic structures (planned, commissioned and under construction) to harness the hydropower and cater for agricultural water demands 28 , 29 . As a result, the upstream–downstream linkages of hydrological, geomorphological and ecological processes in the Himalayan River basins are severely impacted 30 , 31 , 32 , 33 , 34 . Therefore, river practitioners and scientists need to understand the implications of hydrological modifications caused by changing climate and anthropogenic activities.

In the Himalayan regions of the Ganga basin, several studies have been done to assess the impacts of land use land cover change 35 , 36 , sediment dynamics 37 , 38 , 39 , climate change hazards 24 , 25 , 26 , 40 , heavy metal 41 , water quality 42 and glacier meltwater contribution 43 . Nevertheless, detailed studies focusing on hydrological alterations caused by these hydrological structures and changing climatic conditions are currently lacking. Therefore, in this work, we select the Upper Ganga Basin (UGB) to assess the role of changing climatic conditions and increasing human activities on stream flows. The available hydrological dataset at different gauging stations was used to perform the hydrological analysis for pre-and post-dam conditions in the UGB. We first estimate the changing magnitudes of flows at those locations where large hydraulic structures were built before 1995 in the UGB. We further investigate the hydrological changes owing to changing climatic conditions and operations of hydraulic structures. Finally, we have assessed how these upstream hydrological modifications altered the hydrological regimes of the downstream regions of the UGB. The inferences drawn from the present study would be immensely useful for sustainable river basin management.

The Ganga River has two major tributaries in the upper mountainous region. The western tributary, the Bhagirathi, originates from the Gangotri glacier (30.92° N, 79.08° E) at an elevation of about 4023 m. The eastern tributary, the Alaknanda, originates from the Satopanth glacier (30.79 N, 79.37 E), an elevation of about 4600 m. At Devprayag, both tributaries join to form the Ganga. Here, we have selected the Upper Ganga Basin (UGB) up to Rishikesh (area 21,000 km 2 ) for this study (Fig.  1 a). In the Bhagirathi basin, there are four dams, namely Maneri Stage 1, Maneri Stage 2, Tehri and Koteshwar dam (Figs.  1 a, 2 a), and most of these became operational before 2010. There are only two dams in the Alaknanda basin, namely Tapovan and Srinagar, which became operational after 2015 (Fig.  1 a). Further, the Pashulok barrage is present downstream of Rishikesh. Furthermore, around 37 small and large dams are planned in the UGB (Fig.  2 b), 11 in the Bhagirathi and 26 in the Alaknanda basin 44 (Fig.  2 c,d). The mean annual rainfall in the UGB ranges from 840 to 1990 mm (Fig. S1 a). Almost 70% of the UGB receives 1000–1250 mm of rain annually, except for small patches in the western and eastern parts where more than 1500 mm of the mean annual rainfall occurs (Fig. S1 a). Overall, the Indian Summer Monsoon (ISM) contribution across the UGB varies from 51 to 86% (Fig. S1 b), leading to significant hydrological variabilities across the basin (Fig.  1 b).

(a) Digital Elevation Model (DEM) of the UGB. The major stream network is also shown in the map using magenta lines. The CWC stations and dams are illustrated using filled green circles, orange and yellow triangles, respectively. Parallel black lines show the Pashulok barrage, located below the Rishikesh. Further, the name of dams and corresponding opening year is also shown in the side table. The QGIS Version 3.2 ( https://qgis.org/en/site/forusers/download.html ) was used to generate this figure. (b) Flow duration curves (FDCs) for the period 1970–2010. The solid and dashed blue, red and green FDC lines are shown for Bhagirathi (Uttarkashi and Tehri), Alaknanda (Joshimath and Rudraprayag) and downstream stations (Devprayag and Rishikesh), respectively.

(a) Hydraulic structures in the UGB. The green and orange filled circles are showing the hydraulic structures commissioned pre-and post-2010 on the map. All these hydraulic structures’ circle sizes vary according to their hydropower generation capacity (MW). Further, A, B, C and D classes are defined based on each reservoir’s hydropower generation capacity. (b) The map shows planned hydraulic structures for the near future in the UGB. The QGIS Version 3.2 ( https://qgis.org/en/site/forusers/download.html ) was used to generate these figures. Furthermore, the bar plots show the current hydraulic structures and planned hydraulic structures for the near future in (c) the Bhagirathi and (d) the Alaknanda basin. The hill shade map of the UGB is used in the background.

Methodology

We used daily rainfall, discharge and suspended sediment load datasets for understanding the hydrological characteristics of the UGB. The details of the input dataset used in this study are listed in Table S1 (Supplementary). The exceedance probabilities were estimated in rainfall intensities, flows and sediment at each hydrological station. These estimates are depicted using the rainfall exceedance probability analysis, flow duration curve (FDC) and sediment duration curve (SDC). The daily rainfall, discharge, and suspended sediment datasets were divided into two periods—(1) 1971–1994 (pre-1995), and (2) 1995–2010 (post-1995). This temporal division was done based on the fact that the construction of a large hydraulic structure, i.e., Tehri dam (total capacity 4000 million cubic meters), started in 1995 in the Bhagirathi basin, and the frequency of flash flooding events increased in the UGB after 1995 (see Table S2 ). Hence, the anthropogenic and climate-induced alterations in the hydrology of the UGB could be captured by comparing the pre-1995 and post-1995 FDCs and SDCs. The FDCs and SDCs differences (in percentages) that showed for post-1995 were estimated with reference to pre-1995 FDCs and SDCs for all the gauge stations (see Figs. S3 and S4 in Supplementary). Further, the daily rainfall, discharge and sediments dataset for the 1971–2015 period at six stations are used for the hydrological analyses (Fig.  1 a and Table S1 ). The first five years, i.e., 1971–1975, were selected in the Bhagirathi (at Uttarkashi and Tehri) and Alaknanda basin (at Joshimath and Rudraprayag) for initial reference conditions. However, the initial reference conditions at Devprayag and Rishikesh were considered to be 1976–1980 due to the unavailability of discharge and sediments data for the 1971–1975 period. In addition, 5-yearly rainfall magnitudes, FDCs and SDCs were also estimated and compared with each station’s reference condition to assess the temporal hydrological variations. The differences (in percentage) in 5-yearly rainfall magnitudes, FDCs and SDCs were calculated by subtracting selected 5-year periods with the initial reference condition and plotted on the 2D-contour plot for each station.

We also used the frequency analysis of extreme flows (annual maximum discharge) with the Generalized Extreme Value Type-1 (Gumbel) distribution 45 at each station to estimate extreme discharge between 10- and 100-year return periods for pre-and post-1995. The Gumbel distribution for each station was selected based on Akaike Information Criterion (AIC) by comparing five widely used distributions, namely, (1) Lognormal, (2) Gamma, (3) Gumbel, (4) Weibull, and (5) Generalized Extreme Value (GEV; Table S3 ). The scale and location parameters of the Gumbel distribution were estimated using the Maximum Likelihood Estimation (MLE) method using the ‘FAmle’ package ( https://github.com/tpetzoldt/FAmle ) in R programming (see Fig. S5 in Supplementary). The differences between pre-and post-1995 extreme flows at different return periods were estimated and compared for all six gauging stations of the UGB (see Fig. S9 in Supplementary). It should be noted here that the credible extrapolation interval in flood frequency analysis is generally up to twice the record length. Hence, the 95% confidence bounds were also assessed and plotted in the return level graph for pre-1995, post-1995 and whole time series at each station of the UGB (see Figs. S6 , S7 and S8 in Supplementary).

Results and discussion

Pre-and post-1995 hydrological scenarios.

The UGB has experienced a widespread increase in high-intensity rainfall events after 1995 (Fig.  3 a,b). These are statistically increasing (p < 0.05) trends predominantly in the Alaknanda basin (Fig.  3 b). It is also noted that the Alaknanda basin has been experiencing a rising trend of high-intensity rainfall events compared to the Bhagirathi basin since 1995. The observed records also suggest an increase in extreme flooding events in the UGB (Fig.  3 c,d and Table S2 ). A total of 9 and 25 extreme flooding events are reported for the two basins together during the pre-and post-1995 period, respectively. The Bhagirathi basin has experienced 2 and 11 extreme flooding events during the pre-and post-1995 period (Fig.  3 c and Table S2 ). The Alaknanda basin has undergone 7 and 14 extreme flooding events during the pre-and post-1995 period (Fig.  3 d and Table S2 ). In terms of temperature, the Bhagirathi and the Alaknanda basins show statistically significant increasing trends. However, these increasing trends detected by the statistical tests are likely driven by the step-change that occurred between pre-and post-2000, possibly suggesting a shift in the instrumentation (Fig. S2 a,c). Further, there is no step-change or significant trend detected in the maximum temperature for both the basins (Figs. S2 b,d).

(a) Pre-and (b) post-1995 average 95th percentile rainfall magnitudes for 1970–2019. The different sizes of green filled circles represent the increasing Sen’s slope for the 95th percentile rainfall events at the 5% significance level. There is one orange-filled circle present in the Bhagirathi basin, which shows decreasing Sen’s slope for the 95th percentile rainfall events at a 5% significance level. (c,d) Shows bar plots 95th percentile rainfall of the Bhagirathi and Alaknanda basins for each year between 1970 and 2019. The blue and red bars show pre-and post-1995 annual cumulative rainfall magnitudes. The horizontal grey line shows the mean value of 95th percentile rainfall for the pre-and post-1995 period in both the bar plots. The mean (µ) and standard deviation (σ) of annual rainfall are also shown in both figures. Based on the available literature (see Table S2 in Supplementary Section), the extreme flooding events are also mentioned for the corresponding plots of the Bhagirathi and Alaknanda River basins.

In the Bhagirathi basin, the difference of post-and pre-1995 FDCs suggests a substantial reduction of up to 80% in very low flows (> 90% exceedance probability) at Uttarkashi (Fig.  4 and Table S4 ). The 5-yearly rainfall magnitude differences suggest around 50–100% reduction in low and moderate magnitude rainfall events from the reference period (Fig.  5 a). Further, the 5-yearly differences of FDCs reveal around 60–90% decline in low and moderate flows at Uttarkashi (Fig.  5 b). Coincidently, upstream of Uttarkashi, the Maneri Stage 1 dam construction was started in the 1960s, and this dam became operational in 1984 (Fig.  1 a). Therefore, a very sharp reduction in the low and moderate flows from the reference condition can be directly correlated to the operation of the Maneri Stage 1 dam. However, a decrease in the magnitude of low and moderate rainfall after 1991 (Fig.  5 a) further attenuated the low and moderate flows at Uttarkashi station (Fig.  5 b). Furthermore, the Maneri Stage 2 dam, located immediately downstream of the Uttarkashi, became operational in 2008 (Fig.  1 a), and this might have also started influencing the hydrology at Uttarkashi since then.

Difference between post-and pre-1995 flow duration curves (FDCs). These differences (%) are plotted for Uttarkashi (blue line), Tehri (dashed blue line), Joshimath (red line), Rudraprayag (dashed red line), Devprayag (green line) and Rishikesh (green dashed line) stations of the UGB. The division between high and moderate (at 20%) and moderate and low (at 70%) flows are shown by dashed black and dashed red vertical lines.

5-yearly differences in rainfall and flow duration curves (FDCs) are plotted using 2d contour plot for each station. The high (20% <), moderate (20–70%) and low (> 70%) flows are divided by black vertical lines. (a) The Uttarkashi station shows 90% reduction in low rainfall. The high rainfall slightly increased (up to 10% since 1986). (b) The Uttarkashi station shows up to 90% reduction (1981–1985) in low flows. The high flows also decreased up to 30%. (c) The Tehri station shows a reduction of 100% in low rainfall throughout the period. There is no anomalous behaviour observed in low and moderate rainfall magnitudes after 2005 at Tehri. (d) The Tehri station shows a reduction of up to 50% in low flows until 1990. After 2000, the reduction in low flows up to 85% is also appeared here. The high flows increased by 50% after 2005. The moderate flows have been increased up to 80% after 2005. (e) The Joshimath station shows increasing high, low and moderate rainfall magnitudes after 1996. The high and low magnitude rainfalls are increased up to 30% and 50%, respectively. (f) The Joshimath station shows a reduction from 20 to 70% in low and moderate flows. The high flows increased by 20% from the reference condition. (g) The Rudraprayag station shows increasing high magnitude rainfall by 30% from 1996. The moderate rainfall magnitudes have also slightly increased post-1995. (h) The Rudraprayag station shows a 10–20% reduction in all flows until 1995. The high flows have been increased 20–40% after 1995. The high rainfall magnitudes have increased up to 10% at (i) Devprayag and (k) Rishikesh stations. However, the high rainfall magnitudes have increased steeply (up to 30%) after 2005 at Devprayag than Rishikesh. The low and moderate rainfall magnitudes have decreased from the reference period at both stations. However, the percentage reductions in low and moderate rainfall magnitudes are slightly higher for the Rishikesh (up to 50%) than Devprayag (20% to 30%). The major changes in low and moderate flows up to 80% and 40% appeared at (j) Devprayag and (l) Rishikesh after 2005.

In contrast, at Tehri, the flows between 30 and 85% exceedance probability (moderate to low flows) have increased by 80% in post-1995 (Figs.  4 , 5 d) with reference to pre-1995. The difference in the FDCs of pre-and post-1995 FDCs suggests that the moderate and low flows increased rapidly downstream of the Tehri dam after becoming operational (Fig.  4 ). Additionally, the very low flows (> 90% exceedance probability) have decreased by 90% at Tehri (Fig.  5 d). The 5-yearly differences in rainfall magnitudes suggest that the moderate and low rainfall magnitudes decreased significantly after 1991 (30% to 100%; Fig.  5 c). The magnitude of very high rainfall (< 10% exceedance probability) has increased up to 30% at Tehri. In comparison, the characteristics of high and moderate flows behaviour after 1995 do not match those of high and moderate rainfall magnitudes (Fig.  5 c,d). Such anomalous hydrological behaviour of the Bhagirathi River at Tehri strongly suggests alteration of flow regime caused by the Tehri dam operation. Hence, the existence of dams in the Bhagirathi basin has reduced the extreme flows and floods downstream. Further, the moderate and low flows have significantly increased up to 125% post-2005. These abrupt increments and decrements in the flows are not observed anywhere in the UGB (Figs.  4 , 5 ). Besides, the upstream (Uttarkashi) and downstream (Tehri) stations in the Bhagirathi behave differently. These distinct and abrupt hydrological behaviour indicate the significant impact of Maneri and Tehri dams in modifying the water outflux from the Bhagirathi basin.

In the Alaknanda basin, there was no dam before 2010 (Fig.  1 a). The Srinagar dam and Tapovan dam became operational in 2015 and 2020, respectively 46 . Thus, the possibility of anthropogenically altered river flow due to reservoir operation can be ruled out before 2010. The differences in the post-and pre-1995 FDCs suggest that the high and moderate flows increased at Joshimath and Rudraprayag (Fig.  4 and Table S4 ). These differences are more predominant at Rudraprayag (up to 40%). In particular, the 5-yearly differences of FDCs from their reference condition also reveal that the high flows increased significantly after 1995 at both locations (Fig.  5 f,h). High flows at Rudraprayag show an increasing trend until 2010. However, a sudden increase (up to 100% or doubled) in high flows is observed between 1995 and 2005 for Joshimath station. The 5-yearly rainfall differences suggest increasing high magnitude rainfall after 1995 at Joshimath (up to 150%) and Rudraprayag (up to 50%; Fig.  5 e,g). It is also evident that the increasing trends (p < 0.05) of high rainfall intensities (95th percentile) have doubled (0.6 mm/y in pre-1995 and 1.2 mm/y in post-1995) and are more widespread in the Alaknanda basin (Fig.  3 b). Therefore, we argue that the increase in the high flows is linked to increasing intensities of high-intensity rainfall events in the Alaknanda basin. Further, the reported extreme events strongly suggest an increase of extreme rainfall linked to flooding events in this basin (Fig.  3 d), which have doubled (7 events in pre-1995 and 14 events in post-1995). These observations indicate that the changing climatic conditions, remarkably increasing trends of high-intensity rainfall events primarily controlled the hydrology of the Alaknanda basin until 2010. However, after the opening of the Srinagar dam (in 2015) and the Tapovan dam (in 2020; Fig.  1 a), the current flows might have been anthropogenically modified in addition to the impact of changing climatic conditions.

In the downstream reaches, high and very low flows (20% < and > 90% exceedance probability) are governed by increasing and decreasing flows from the Alaknanda and Bhagirathi basins, respectively (Fig.  4 ). However, the moderate and low flows (20–90% exceedance probability) at Devprayag and Rishikesh are predominately influenced by the moderate flows coming out from the Tehri (Bhagirathi). The 5-yearly FDCs differences at Devprayag and Rishikesh further suggest a substantial increase in moderate and low flows (> 40%), particularly after 2005 (Fig.  5 j,l and Table S4 ). However, there are no such substantial increments observed in the moderate and low rainfall magnitudes at both downstream stations (Fig.  5 i,k). These patterns strongly correlate with Tehri’s post-2005 moderate and low flows fluctuations (Fig.  5 d). Therefore, these observations suggest that the Tehri dam's water flux increased the moderate and low flows at Devprayag and Rishikesh since 2005, although these fluctuations became more significant post-2010 (Fig.  5 j,l).

Further, sediment duration curves (SDCs) suggest that high sediment fluxes are nearly similar for downstream stations. However, moderate and low sediment fluxes are an order of magnitude higher for the Devprayag and Rishikesh stations (Fig.  6 a). These differences indicate that a significant amount of sediments has been deposited between Devprayag and Rishikesh, possibly due to reduced inflow. The post- and pre-1995 differences suggest that high sediment fluxes (50% < exceedance probability) have decreased up to 50% at both locations (Fig.  6 b). These differences indicate that a considerable part of high-magnitude sediment flux is deposited upstream of Devprayag (possibly in the Tehri and Maneri reservoirs; Fig.  1 a) and not reaching the main channel downstream. Moderate and low sediment fluxes (> 50% exceedance probability) have increased tremendously at Devprayag (up to 260%) and Rishikesh (up to 70%; Fig.  6 b). These incredibly increasing amounts can be linked to sediment reworking caused by abrupt behaviour of moderate and low flows at Devprayag and Rishikesh governed by the reservoir-induced increase of moderate and low flows (Figs.  4 , 5 d,j,l). Therefore, these observations strongly suggest that the Tehri dam in the Bhagirathi basin plays a crucial role in determining the hydrological variability of the downstream UGB region.

(a) Sediment duration curves (SDCs) of Devprayag (blue) and Rishikesh (red) for the period 1970–2015. The high sediment fluxes (20% <) are comparable for both stations. However, the peak sediment fluxes are slightly higher for the Devprayag station. Further, the moderate (20–70%) and low sediment fluxes (> 70%) are an order of magnitude higher for Devprayag than the Rishikesh station. (b) Difference (%) between pre-and post-1995 SDCs of Devprayag (blue) and Rishikesh (red) stations. The peak sediment flows have been increased (120%) at Devprayag. In contrast, the peak sediment fluxes have been decreased (− 25%) at Rishikesh. Furthermore, the moderate sediment fluxes have also been reduced (up to 50%) at both stations. However, moderate and low sediment fluxes greater than 50% and 55% exceedance probability are increased from their pre-1995 values at both stations.

Role of natural and anthropogenic stressors on changing extreme flows

Frequency analysis of extreme flooding events suggests that the UGB has experienced contrasting responses due to natural and anthropogenic forcing. For instance, at Uttarkashi and Tehri, the Bhagirathi basin exhibits a total reduction of extreme flows at different return periods. Around -14.5%, -17.9% and -21.3% reductions are observed in the magnitude of 10, 50 and 100-year return period floods at Uttarkashi (Fig.  7 a,b and Table S4 ). In comparison, around -7.3%, -2.5% and -1.1% reductions are observed in the magnitude of 10, 50 and 100-year return period floods at Tehri (Fig.  7 b). Such decreasing extreme flows in the Bhagirathi basin are primarily governed by two major factors: (1) presence of small and large hydraulic structures such as Maneri Stage 1, Maneri Stage 2, Tehri and Koteshwar dam (Fig.  1 a), and (2) no significantly increasing or decreasing trends in the high-intensity rainfall events (Fig.  3 a,b).

(a) Extreme flows at different return periods at the six stations of the UGB. The Rishikesh (downstream station) and Joshimath (upstream Alaknanda basin) stations show the highest and lowest extreme flows at different return periods. The standard errors of the scale and location parameters of the Gumbel distribution are used to predict the error bound and shown using shaded regions around each return level curve. The details of 95% confidence bounds around the prediction of return level for each station are given in Supplementary. (b) Post-and pre-1995 differences of extreme flows at different return periods for different stations.

In contrast, the Alaknanda river at Joshimath and Rudraprayag show an increase of extreme flows at different return periods. For instance, around 1.5%, -0.5% and -1.1% differences are observed in the magnitude of 10-, 50-and 100-year return period floods at Joshimath (Fig.  7 b and Table S4 ). In comparison, around 15%, 9.6% and 7.9% increments are observed in the magnitude of 10-, 50-and 100-year return period floods at Rudraprayag (Fig.  7 b and Table S4 ). Therefore, the extreme flows and flooding events in the Alaknanda basin (particularly at Rudraprayag) are primarily governed by two major factors: (1) no hydraulic structures present before 2010 (Fig.  1 a), and (2) widespread increasing high-intensity rainfall in this basin (Fig.  3 a,b,d). Further, the oldest dam, Maneri Stage 1, has been operational since 1984 in the Bhagirathi basin, whereas the Srinagar dam and Tapovan dam in the Alaknanda became operational in 2015 and 2020, respectively. Therefore, we argue that the increasing number of hydraulic structures after 2015 has also impacted the extreme flows of the Alaknanda basin.

The downstream stations of the UGB behave differently when we compare the pre-and post-1995 extreme flows at different return periods. For instance, we document an increment of 10.3%, 17.5% and 19.7% in the magnitude of 10-, 50- and 100-year floods at Devprayag in the post-1995 period (Fig.  7 b and Table S4 ). However, the Rishikesh station records an -18.1% reduction in the magnitude of 10-, 50- and 100-year floods in the post-1995 period (Fig.  7 b and Table S4 ). A reduction in extreme flow magnitudes is possibly because of flow reduction caused by the Pashulok barrage downstream of the Rishikesh station. We have also observed a significant reduction in high magnitude stream flows at Rishikesh than Devprayag station (Fig.  5 j,l). The post-1995 extreme flows have decreased in the Bhagirathi basin but increased in the Alaknanda (Fig.  7 b). Therefore, a rise in extreme flooding events at Devprayag station is primarily governed by the changes in hydrometeorological conditions in the Alaknanda basin. The widespread increase in high-intensity rainfall in the Alaknanda basin and the reservoir-induced flow alterations are the primary drivers of these changes in observed extreme flow at Devprayag and Rishikesh (Fig.  3 a–c).

It is also observed that the downstream (Rudraprayag) region of the Alaknanda shows an incremental difference of up to 15% in the extreme flows (Fig.  7 b) which makes the entire downstream Alaknanda basin vulnerable to extreme flooding events in the near future. One such event was reported recently (in February 2021) near Joshimath, which destroyed the Tapovan dam 47 . Downstream of Rishikesh, the Ganga River debouches into the alluvial plains (Fig.  1 a), where several populous cities are situated. Therefore, these are the vulnerable regions where around 20% increase in extreme flooding events (at Devprayag) might enhance the flood risk manifold. Further, the Pashulok barrage downstream of Rishikesh was constructed in 1980 based on the past extreme flow information until then. However, the changing climatic conditions in the Alaknanda basin, and hence, an increase of 10–20% in extreme flows, might severely affect the operations of such structures.

Increasing anthropogenic activities and their future impacts

Overall, these hydrological analyses indicate that the flow in the Bhagirathi basin has been anthropogenically modified owing to the presence of several large and small dams (Figs.  1 a, 4 , 5 b,d). In particular, low and moderate flows, which occur primarily during pre- (Jan-May) and post-monsoon (Oct-Dec) periods, are significantly impacted (Figs.  4 , 5 b,d). The Alaknanda was a free-flowing river before the Srinagar dam was commissioned in 2015 (Figs.  1 a, 4 , 5 f,h), followed by the Tapovan dam in 2020 (Fig.  1 a,b). However, our data records could not capture these hydrological alterations at Joshimath and Rudraprayag in the Alaknanda (see Table S1 ). Recent hydrological records can be further used to verify these hydrological changes. We have demonstrated that the present low and moderate flows coming out from Devprayag and Rishikesh (downstream of the UGB) are entirely modified anthropogenically (Figs.  4 , 5 j,l). The interventions have severely affected the upstream and downstream hydrology and geomorphology of the Bhagirathi basin (Figs.  5 j,l, 6 a,b).

Around 11 and 26 additional dams of different power generation capacities in the Bhagirathi and Alaknanda basin, respectively, are planned 44 (Fig.  2 b–d). These planned hydraulic structures will be located on several small and large tributaries of the UGB (Fig.  2 b). These structures are likely to impact the low and moderate flows of the UGB further, as demonstrated in the case of the Bhagirathi basin. Additionally, the increasing number of dams will also influence the sediment transport processes across the UGB (Fig.  6 b). Further, a significant increase in the high magnitude flows is also observed in the Alaknanda River basin and Devprayag (Fig.  7 a,b). The impact of changing climatic conditions are more predominant in the Alaknanda basin (Fig.  3 a,b). Our extreme frequency analysis also suggests an increase in the magnitude of extreme flows for different return periods in the Alaknanda basin (Fig.  7 a,b). Further, the observed records indicate an increase in the frequency of extreme flood events in the UGB, especially in the Alaknanda basin (Fig.  3 a,b). During the flash flood event at Joshimath in February 2021 47 , high discharges were quickly managed because of the lean condition of the mainstream. However, if this event had occurred during the monsoon season, it could pose a severe flood risk in the downstream regions. In the past, the UGB region also witnessed the June 2013 Kedarnath disaster when rainfall magnitudes crossed a 111-year return period and produced a massive flood in the monsoon period 26 (Table S2 ). Thus, the changing extremity of streamflow in the UGB poses serious impacts on the hydraulic structures that need critical assessment and design modifications.

Conclusions

The Ganga River is the lifeline for close to half a billion people in the northern Indian region. During the twentieth century, the hydrology of the basin has been significantly modified owing to increasing anthropogenic interventions and changing climatic conditions. In particular, the upper Ganga basin (UGB) has witnessed modifications in the flow regime owing to several small and large hydraulic structures, particularly in the Bhagirathi basin (western tributary). In contrast, the Alaknanda basin (eastern tributary) has experienced increasing magnitudes of extreme rainfall events from 1970 to 2019. Therefore, the flow modifications in these basins have been influenced by different factors. Our results suggest that the reduction in rainfall magnitudes, Maneri dam in upstream and Tehri dam in downstream exert primary controls on the flows in the Bhagirathi. As a result, low and moderate flows increased at Tehri by 125%. In addition, the post-1995 extreme flows at different return periods have decreased by -21.3% in the Bhagirathi basin. Further, the Alaknanda basin was a free-flowing river until 2015. The extreme flows at different return periods have increased by 8–15% in the Alaknanda basin, primarily because of increasing high-intensity rainfall events post-1995. Therefore, the Alaknanda basin has witnessed some extreme flash flood events in recent years. Simultaneously, the downstream reaches experience anthropogenically modified low and moderate flows that are attributed to Tehri and other dams during pre-and post-monsoon months.

Our results further indicate that a significant amount of sediments transported during high flows are trapped in the Tehri and Maneri reservoirs in the Bhagirathi basin. Therefore, hydraulic structures have significantly disrupted the upstream–downstream geomorphologic linkages, thereby impacting the channel morphology in the downstream reaches as observed in several regions 48 , 49 , 50 , 51 . Furthermore, several hydraulic structures such as the Pashulok barrage were designed based on analysis of past extreme floods. However, the increasing magnitude of extreme flows (10-20%), particularly at Devprayag, might also affect the functioning of the Pashulok barrage during peak monsoon periods. The downstream regions also experience reservoir-induced flow increments during pre-and post-monsoonal months. Overall, the results obtained from this work should help in sustainable river basin management and encourage more serious work toward a better understanding of hydrology, ecology, and geomorphology in the UGB.

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Acknowledgements

The first author acknowledges the National Postdoctoral Fellowship (NPDF) grant (PDF/2020/000496) received from the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India. The second author acknowledges the support received through the JC Bose Fellowship (Number, JCB/2018/000031). The funding received from the Ministry of Earth Sciences (MoES), Government of India, through the project, “Advanced Research in Hydrology and Knowledge Dissemination”, Project No.: MOES/PAMC/H&C/41/2013-PC-II, is gratefully acknowledged. We also acknowledge the India Meteorological Department (IMD) for the high-resolution daily gridded rainfall & temperature datasets and Central Water Commission (CWC) for the daily discharge & sediment datasets provided for this work.

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S.S. analysed the hydrological records, applied the hydrological analysis and prepared the first draft of the manuscript, including figures. P.M. conceptualised the hydrological analysis, reviewed and edited the manuscript. R.S. helped to develop the hydrological analysis, reviewed and edited the manuscript.

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Swarnkar, S., Mujumdar, P. & Sinha, R. Modified hydrologic regime of upper Ganga basin induced by natural and anthropogenic stressors. Sci Rep 11 , 19491 (2021). https://doi.org/10.1038/s41598-021-98827-7

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DOI : https://doi.org/10.1038/s41598-021-98827-7

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India’s effort to clean up sacred but polluted ganga river.

Fred de Sam Lazaro Fred de Sam Lazaro

Sarah Clune Hartman Sarah Clune Hartman

• Copy URL https://www.pbs.org/newshour/show/indias-long-term-effort-to-clean-up-pollution-in-sacred-ganga-river

The Ganga River, known as the Ganges under British rule, is one of the most revered waterways in the world -- and also among the most polluted. Stretching from the Himalayan foothills to the Bay of Bengal, it provides water to nearly half a billion people, more than any other river in the world. Special correspondent Fred de Sam Lazaro reports from India on the latest efforts to clean the river.

Read the Full Transcript

Notice: Transcripts are machine and human generated and lightly edited for accuracy. They may contain errors.

Amna Nawaz:

The Ganga River, known as the Ganges under British rule, is one of the most revered waterways in the world, and also one of the most polluted.

It provides water for nearly half-a-billion people, more than any other river in the world, stretching from the foothills of the Himalayas to the Bay of Bengal.

Special correspondent Fred de Sam Lazaro reports from Varanasi, India, on the latest efforts to help clean the river.

Fred de Sam Lazaro:

In Hinduism, the Ganges, or Ganga, is sacred, a river that has nourished an ancient civilization since its beginning.

Today, the Ganges Basin, the river and its tributaries, takes in 11 states, plus the capital region of Delhi. In all, some 400 million people, on farms, in factories and in households, rely on it for life, livelihood and spiritual sustenance.

In the holy city of Varanasi, temples draw throngs of believers, who float oil lamps and marigolds.

Pirbhadra Tiwari (through translator):

It's our faith that brings us here. It's like nectar to me.

They take ritual baths, dips, even small sips, while reciting prayers to heal the body, to clean the soul.

Vishwambhar Nath Mishra is an engineer by training. He also heads the 500-year-old Sankat Mochan temple.

Vishwambhar Nath Mishra:

Whatever suffering we have, seems to just take away all the suffering.

So the river can be the source of happiness and contentment.

Definitely.

Many believers seek to have their ashes sprinkled in the river. Thousands of bodies are burned here. Many, however, are not fully cremated.

These practices stress the river, but they are only a small part of what it endures. By far, the most toxic pollution of this river is probably the least visible, unless you happen upon drainage canals like this one, which discharge millions every day of gallons of raw, untreated sewage.

Experts link pollution in the Ganga and other rivers to India's high rate of waterborne illnesses, which kill an estimated 1.5 million children each year. Researchers have also discovered the emergence of so-called superbugs in Ganges water samples, bacteria resistant to most commonly used antibiotics.

Prime Minister Narendra Modi, devoutly Hindu and allied with Hindu nationalist groups, represents Varanasi in Parliament and has made the river's cleanup a signature issue for his government. In this video posted online by his office, Modi vows to jump-start the effort, which has languished for decades.

The $3 billion dollar cleanup program began in 2015, but Mishra, citing the continued pollution, among other things, says it's shown little progress.

Now, I think red tape-ism is the biggest cause for it.

Red tape-ism?

Pollution control officials in Varanasi say new capacity is coming online that will treat much of the daily effluent.

But Rajiv Mishra, who heads the prime minister's national clean Ganga project, says there is no quick solution.

Rajiv Ranjan Mishra:

It's a very long-term thing. People always think, like, when it will be clean? I mean, that question has no meaning.

He say it will take years to bring together the competing interests and jurisdictions across an area one-and-a-half times the size of Texas.

And while the public supports a cleanup, Mishra says many don't perceive a grave threat to a river that they feel can withstand anything.

People will say, there can be some dirty things in the river, there may be some pollution, but the river remains pure. So that's a strength, as well as a challenge, for us.

He says there needs to be a shift in perception and even in some rituals. Electric crematoria have been built as an alternative to the traditional and less efficient wood-burning pyres.

And there also are smaller campaigns another to raise public awareness, like one effort which recycles flowers. Directly or indirectly, tons of these chemically treated flowers find their way into the river. They are now turned into incense sticks, which are sold near the temples.

However, the most effective way to cleanse the river — also the biggest challenge — would be to restore its natural flow. After dams, industrial and agricultural use, Mishra says there's a lot less water left for cities like Varanasi.

Imagine a person, if you take out 70 percent, 50 percent of the blood from someone's body, what will happen to it?

Restoring the river's natural flow will require sacrifice from all users, Mishra says. It's a political challenge that will become even more difficult given climate change.

Himalayan glaciers that feed this region's major rivers are receding. Rainy seasons are getting shorter and dry spells longer.

For now, Arunabha Ghosh, who heads a Delhi-based think tank, gives the government's effort a low grade.

Arunabha Ghosh:

You have a financing problem. You have a manpower problem. And, most importantly, I would say, you still have a governance architecture problem.

And if we don't fix those basic things, then you won't be able to truly transform, because the idea was, if you can fix this, you know, which — because what happens to the Ganga has a kind of also social resonance.

For now, many eyes are on Varanasi, from the prime minister to the canoeing balladeer who shuttles tourists along the Ganga. She is sacred, he sings. Stop throwing trash into her.

For the "PBS NewsHour," this is Fred de Sam Lazaro in Varanasi, India.

And Fred's reporting is in partnership with the Under-Told Stories Project at the University of St. Thomas in Minnesota.

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Fred de Sam Lazaro is director of the Under-Told Stories Project at the University of St. Thomas in Minnesota, a program that combines international journalism and teaching. He has served with the PBS NewsHour since 1985 and is a regular contributor and substitute anchor for PBS' Religion and Ethics Newsweekly.

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MC MEHTA VS UNION OF INDIA: GANGA POLLUTION CASE

• Post author: Team LawFoyer
• Post published: 19 June 2021
• Post category: Case Analysis
• Post comments: 1 Comment
• Reading time: 21 mins read

By:- Saumya Patel

In the Supreme Court of India

Man’s development is often seen to happen at the cost of the environment. From improper disposal of non-biodegradable items to large industrial discharge, everything takes a toll on the environment without which human life cannot thrive. The Ganga, which is regarded as the most sacred river in India, has now become a recipient of huge amounts of domestic and industrial waste. The case revolves around the discharge of harmful industrial effluents into the river.

The case analysis aims to scrutinize the background, facts, issues raised, arguments of both sides and highlighted concepts in the case and mentions them as succinctly as possible.

Man’s development is usually seen to happen at the value of the environment. From improper disposal of non-biodegradable things to massive industrial discharge, everything takes a toll on the surroundings which the human life cannot thrive. The Ganga, that is thought to be the foremost sacred stream in Republic of India, has become a recipient of enormous amounts of domestic and industrial waste. This case revolves throughout the discharge of harmful industrial effluents into the Ganga river.

Introduction

Emanating from the range, the Ganga flows south and so eastward and drains itself into the Bay of Bengal area. Ganga has been the lifeline of the many civilizations in India. Kanpur is one of the major city located on the banks of  Ganga and discharges an enormous quantity of waste into the watercourse. The most waste product from this city is that the industrial/trade effluents from the animal skin (leather) trade.

The waste aqua from this trade contains “putrescible organic and harmful inorganic material” once discharged within the water can wipe out the amount of dissolved oxygen within the waterbody and can result in the death of aquatic life and would cause harm to people’s who consume this water. The case was concerned by the Supreme Court via a writ petition filed by the renowned attorney, Shri MC Mehta who is considered as a pioneer within the field of environmental law and it was found that a lot of industries on the banks of the Ganga watercourse were discharging their effluents into the watercourse even without primary treatment of a similar. The case is instead referred to as the Ganga Pollution case, Kanpur Leather Tanneries case.

Background of the case

M.C. Mehta v. Union of India and Ors is the 1 st River pollution case to emerge in environmental public interest legal proceeding.

For over a century, Kanpur has been a serious Centre for India’s tannery business and is one among the three necessary industries next to paper and textiles. Most of those tanneries are situated on the southern banks of the Ganga, outside from Kanpur and extremely contaminating. Among all the cities of state (Uttar Pradesh), Kanpur contributes to the vast pollution load into the Holy Ganga.

When this petition came up for preliminary hearing, the court directed the difficulty of notice below Order 1 Rule 8 of the Code of Civil Procedure treating this case as a representative action by publishing the outline of the petition within the newspapers and calling upon all the industrialists and therefore the metropolitan enterprises and the city civil chambers having venue over the zones through that the watercourse Ganga streams to point out up below the steady gaze of the court and to point out cause regarding why headings ought not incline to them as implored by the candidate asking them to not allow the trade effluents and therefore the waste into the watercourse Ganga . Following the said notice various industrialists and native bodies have entered before the court. When the case came up for consideration before the court, it directed that the case against the tanneries at Jajmau space close to Kanpur would be concerned for hearing initial.

Facts of the case

• 1985 in the pilgrimage city of Haridwar, along the Ganga; a matchstick tossed by a smoker resulted in the river catching on fire for more than 30 hours, due to the presence of a toxic layer of chemicals produced by a pharmaceutical firm.
• In response to this incident M.C. Mehta, an environmental lawyer and social activist, filed a Public Interest Litigation (PIL) in the Supreme Court of India against about 89 respondents, wherein Respondent 1, Respondent 7, Respondent 8 and Respondent 9 were Union of India in 1985.
• Mehta filed a petition (PIL) charging that, despite the advances created within the code, government authorities had not taken effective steps to stop environmental pollution of the stream Ganga.
• The scale of the case – the whole 2,500-km stretch of the river – proved to be intractable. So the Court requested Mr. Mehta to narrow down his focus and he chose the city of Kanpur, despite neither being from the city nor living there.
• Exploitation the judicial remedy of writ, he referred to as upon state agencies to stop leather tanneries and also the municipal corporation of Kanpur from taking out industrial and domestic effluent within the stream.
• In some law reports, this can be referred to as the “Ganga Pollution Case. In this petition the petitioner requested the Supreme Court to restrain the respondents from cathartic effluents into the Ganga stream until the time they implements treatment plants for treatment of cyanogenic effluents to arrest pollution.
• Mehta requested the court to order the animal skin (leather) tanneries of the district of Kanpur to prevent discharging their untreated effluent into the stream. He additionally claimed that the Municipal Corporation of Kanpur wasn’t endeavor treatment of domestic biodegradable pollution.

Issues Raised

• Whether the authorities had paid attention to the worsening condition of the sacred watercourse and had initiated probation into the matter?
• Whether any steps, had been taken by the state?
• Whether the smaller industries ought to be funded for fixing effluent treatment plants? If yes, then what should be the standards to determine ‘smaller industries’?

Arguments from the Petitioner

The Petitioner had grieved that neither the authorities nor the individuals, whose lives were intricately connected with the stream and directed laid low with it, perceived to concerning the increasing levels of pollution of the Ganga and necessary steps were needed to stop an equivalent.

The Petitioner, within the capability of a lively public servant, had to sought-after a writ/direction/order within the nature of writ, leading inter alia inhibiting the Respondents from releasing cyanogenic effluents into the Ganga till they integrate applicable treatment plants to treat the effluents to prevent pollution.

Arguments from the Respondents

None of the tanneries controversial the very fact that the effluent discharge from the tanneries grossly pollutes the Ganga.

It was expressed that they discharge the trade effluents into the sewerage, that ends up in the Municipal sewerage Plants before discharge into the stream.

Some tanneries expressed that they already had primary treatment plants, whereas some are presently engaged within the same.

Some of the tanneries were members of the Hindustan Chambers of Commerce and a few of the opposite tanneries bonded that with the approval of Respondent 8 (State Board), they might construct primary treatment plants which might be operational at intervals a amount of six months from the date of hearing and in failing to try and do therefore, can pack up their tanneries.

However, they argued that it might not be potential for them to determine secondary treatment plants to treat the waste aqua because it would involve large expenditure that is on the far side their means that.

 Related Provisions/Sections

The case reminds the voters of the elemental duty to shield the setting as enshrined under Article 51A(g) that instructs the voters to try to shield and improve the natural setting together with forests, lakes, rivers and life, and to possess compassion for living creatures.

Protection of the setting has conjointly been considered the duty of the State under Article 48A of the Directive Principles of State Policy that says that “the State shall endeavour to shield and improve the setting and to safeguard the forests and life of the country”

The court examined the relevant provisions of The Water (Prevention and management of Pollution) Act, 1974 that was enacted in pursuance of a resolution gone many States under Article 252(1) of the Indian Constitution requiring a Parliamentary legislation to manage and forestall pollution in these states.

 The Act was then adopted by province in 1975. Section 24 of the Act prohibits the utilization of any stream or well for disposal of polluting matter. It lays down that “no person shall wittingly cause or allow any toxic, deadly or polluting matter determined following such standards as could also be arranged  down by the State Board to enter (whether directly or indirectly) into any stream or well or sewer or on land; or not a soul shall wittingly cause or allow to enter into any stream the other matter which can tend, either directly or together with similar matters, to impede the correct flow of the water of the stream during a manner leading or seemingly to guide to a considerable aggravation of pollution thanks to different causes or of its consequences.

The term ‘stream’ is outlined in Section 2(j) of the Act in step with that a stream includes “(i) river; (ii) watercourse (whether flowing or for the nonce dry); (iii) midland water (whether natural or artificial); (iv) sub-terranean waters; (v) ocean or periodic event waters to such extent or, to such purpose because the regime might, by notification within the Official Gazette, specify during this behalf institution of Central and State Boards are thought of permissible under the said Act.

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.

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 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.

In addition to the above Act, Parliament has also passed the Environment (Protection) Act, 1986 (29 of 1986) which has been brought into force throughout India with effect from 19-11-1986. Section 3 of this Act confers power on the Central Government to take all such measures as it deems necessary or expedient for the purpose of protecting and improving the quality of the environment and preventing, controlling and abating environmental pollution.

 “Environment” includes water, air and land and the interrelationship which exists among and between water, air and land and human beings, other living creatures, plants, micro-organisms and property. [Vide Section 2(a) of the Environment (Protection) Act, 1986]

Under Section 3(2)(iv) of the said Act the Central Government may lay down standards for emission or discharge of environmental pollutants from various sources whatsoever. Notwithstanding anything contained in any other law but subject to the provisions of the Environment (Protection) Act, 1986, the Central Government may under Section 5 of the Act, in the exercise of its powers and performance of its functions under that Act issue directions in writing to any person, officer or authority and such authority is bound to comply with such directions. The power to issue directions under the said section includes the power to direct the closure, prohibition or regulation of any industry, operation or process or stoppage or regulation of the supply of electricity or water or any other service.

  Section 9 of the above mentioned Act imposes a duty on every person to take steps to prevent or mitigate the environmental pollution.

Section 15 of the said Act contains provisions relating to penalties that may be imposed for the contravention of any of the provisions of the said Act or directions issued thereunder. It is to be noticed that not much has been done even under this Act by the Central Government to stop the grave public nuisance caused by the tanneries at Jajmau, Kanpur.

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.

In this petition the petitioner requested the court to request the Supreme Court to restrain the respondents from cathartic effluents into the Ganga watercourse until the time they incorporate sure treatment plants for treatment of unhealthful effluents to arrest pollution.

At the preliminary hearing the Court directed the problem of notice under Order I Rule 8 of the CPC, The Court highlighted the importance sure provisions in our constitutional framework that enshrine the importance and therefore the would like for shielding our surroundings.

 Article 48-A provides that the State shall endeavor to shield and improve the atmosphere and to safeguard the forests and wild lifetime of the country.

Article 51-A of the Constitution of India, imposes a basic duty on each national citizen to shield and improve the natural atmosphere as well as forests, lakes, rivers and wild life.

The Court declared the importance of the Water (Prevention and management of Pollution) Act, 1974 (‘the Water Act’). This act was passed to forestall and management pollution and maintaining water quality. This act established central and declared boards and bestowed them with power and functions about the management and interference of pollution.

Section 24 of the Act prohibits the employment of the employment of any ‘stream’ for disposal of polluting matter. A ‘stream’ under section 2(j) of the Act includes watercourse, The Act permits the institution of Central Boards and State Boards.

Section 16 and Section 17 of the Act describe the ability of those boards. One amongst the functions of the State Board is to examine waste product or trade effluents, plants for treatment of waste product and trade effluents.

The Court ordered the tanneries to establish primary treatment plants if not Secondary treatment plants. That is the minimum which the tanneries should do in the circumstances of the case.

The Court further held that the financial capacity of the tanneries should be considered as irrelevant while requiring them to establish primary treatment plants.

The Court held the despite the above-stated provisions in the Water (Prevention and Control of Pollution) Act, 1974 Act no effective steps were taken by the State Board to prevent the discharge of effluents into the river Ganga. Also, despite the provisions in the Environment Protection Act, no effective steps were taken by the Central Government to prevent the public nuisance caused by the tanneries at Kanpur.

In addition to this, the Supreme Court further relied on Article 52A (g) of 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.

The entire case was based on the discharge of ‘trade effluents’ into water bodies (Ganga river during this case). Trade Effluents includes any liquid, volatilized or solid substance that is discharged from any premises used for carrying on any trade or business, apart from domestic waste material. The State Board is additionally entrusted with the work of birth down standards of treatment of waste material and trade effluents to be discharged into any specific stream taking under consideration the minimum fair-weather dilution obtainable therein stream and therefore the tolerance limits of pollution permissible within the water of the stream, once the discharge of such effluents.

The case analysed higher than brings out the importance of the surroundings and the way personalities square measure disrupting its natural balance. Numerous ideas associated with nature and therefore the international organisation Conference, 1972 are mentioned at length. The case mandated the industries in India to line up a primary treatment plant mandatorily and taught the authorities involved to require steps within the direction of curb the discharge of harmful effluents into the water-bodies (River Ganga within the instant case). Inconvenience caused to any of the industries by the approach of this specific demand would be thought of impertinent and it’s to be thought of as a primary demand given the prejudicial impact that these effluents will wear the surroundings.

https://lawtimesjournal.in/m-c-mehta-v-union-of-india-ganga-pollution-case/

https://www.cla.auburn.edu/envirolitigators/litigation/ganga-pollution-case-mehta/m-c-mehta-vs-union-of-india/

I am Saumya Patel, a first year law student pursuing BBA.LLB (Hons.) from Amity University Rajasthan. Being a Law student my interest lies in Corporate Law , Public International Law and Commercial law. At last, I am able to work independently and as part of a team and can make valuable contributions to any legal team and looking forward to indulge myself in such opportunities

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Case Study – Ganges/Brahmaputra River Basin

Flooding is a significant problem in the Ganges and Brahmaputra river basin. They cause large scale problems in the low lying country of Bangladesh. There are both human and natural causes of flooding in this area.

Human Causes

Deforestation – Population increase in Nepal means there is a greater demand for food, fuel and building materials. As a result, deforestation has increased significantly. This reduces interception and increases run-off. This leads to soil erosion . River channels fill with soil, the capacity of the River Ganges and Brahmaputra is reduced and flooding occurs.

Natural Causes

• Monsoon Rain
• Melting Snow
• Tectonic Activity – The Indian Plate is moving towards the Eurasian Plate. The land where they meet (Himalayas) is getting higher and steeper every year ( fold mountains ). As a result, the soil becomes loose and is susceptible to erosion. This causes more soil and silt in rivers. This leads to flooding in Bangladesh.

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

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Case Analysis: Ganga Pollution Case

• Did the authorities start an investigation into the deteriorating state of the holy watercourse after becoming aware of it?
• Whether the state had taken any action?
• Should funding be provided to smaller firms to repair wastewater treatment plants? If yes what measures should be used to categorise "minor industries"?
• Constitution of India, 1950 Art. 48-A
• The Water (Prevention and Control of Pollution) Act, 1974

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River Water Pollution - a New Threat to India: a Case Study of River Ganga

River Water Pollution - A New Threat to India : A Case Study of River Ganga

1 © Vivekananda International Foundation 2019

Published in October 2019 by Vivekananda International Foundation 3, San Martin Marg | Chanakyapuri | New Delhi - 110021 Tel: 011-24121764 | Fax: 011-66173415 E-mail: [email protected] Website: www.vifindia.org

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No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form, or by any means electronic, mechanical, photocopying, recording or otherwise without the prior permission of the publisher.

The paper is the author’s individual scholastic articulation. The author certifies that the article/paper is original in content, unpublished and it has not been submitted for publication/web upload elsewhere, and that the facts and figures quoted are duly referenced, as needed, and are believed to be correct. About The Author

Major General (Retd.) Ajay Kumar Chaturvedi, AVSM, VSM has been a Sapper officer, who post his retirement has been working on issues related to Non-traditional threats to National Security and Disaster management. He has written extensively on water security and energy security and disaster management strategies. River Water Pollution - A New Threat to India: A Case Study of River Ganga

Introduction

Water is absolutely essential for the basic sustenance of human being. No wonder most of the civili- sations have come up on the banks of rivers or in the river valleys. India is no exception. In India every city has come up on the bank of a major river. Here it needs to be noted that fresh water is finite. Total water available in the world is 1,400,000 cubic km. However 96.5 percent of it is there in the oceans and only 1.7 percent is ground water, 1.7 percent is in glaciers and .01 percent is in the atmosphere in the form of water vapour.

Only 2.5 percent is fresh water and 98.8 percent of this fresh water is in the form of ice and only 0.3 percent is in the lakes and rivers. Finally, 0.003 percent of the fresh water is within biological bod- ies.1 70 percent of the fresh water is used up for the agriculture and with the changing crop pattern the requirement of the freshwater is on the rise. To make matters worse, the population is also expo- nentially rising. As per the Food and Agriculture Organization (FAO) estimates the water usage has been growing at more than twice the rate of population increase in the last century.2

Water pollution is making matters worse. In case of the Indian Sub-continent, 30 percent of the major Himalayan rivers are biologically dead for fishing and usage for human consumption. Rising population is another factor which is affecting the per capita water availability. In this connection it is pertinent to note that in 1951 water availability in India was 5177 cubic metres per capita per year, which had got reduced to 1342 Cubic metres per person per year by 2000. With rise in popu- lation since year 2000 it must have become worse.3 Shortage of water and its centrality is going to cause major social and geopolitical stresses. Our neighbours like Pakistan, China and Bangladesh are having their own problems due to water scarcity. Since water resources of Indian Sub-continent are monolithic in nature, the shortage has its own international ramifications. Therefore, there is a need to address the water issues on priority.

In India, one of the most important river is Ganga which is the lifeline of a major portion of the population of the Northern India. Decline in its quality of water is affecting the health, agriculture and overall life style of a major portion of the Indian masses. A detailed study to analyse the prob- lems of Ganga will be quite useful and revealing to understand the problems related to the river waters in India.

1. The entire data has been taken from the book, “Water a Source of Future conflicts” by Maj Gen AK Chaturvedi, Pub by Vij Books India Pvt Ltd, New Delhi during 2013. 2. http://www.fao.org 3. Jayshree Nandi, “ Poison in our Paani”, pub in the Times of India , New Delhi addition, dated 02 Dec 2012, page-11. River Water Pollution - A New Threat to India: A Case Study of River Ganga

Case Study of the River Ganga

River Ganga is not a normal river, it is not only the life line of Northern India but also has a very spe- cial place in the spiritual consciousness of every Hindu irrespective of the fact whether that person in based in India or is a Person of Indian Origin (PIO) settled abroad. The 2,525 km river rises in the western Himalayas in the Indian State of Uttarakhand and flows south and east through the Gan- getic plains of India and Bangladesh, eventually emptying into the Bay of Bengal . Enroute a large number of rivers and streams join it to make it a mighty river. The Ganga is a lifeline to 40 percent population of India who live along its course. By discharge, it is the fifth largest river in the World:-

Table-1: Five Largest Rivers of the World by Discharge

Source: Maj gen AK Chaturvedi, “Water a Source of Future Conflicts”, Page 4

River Ganges is considered a sacred river by Hindus , and worshiped as the goddess ‘Ganga’ in Hindu pantheon. It has been important historically: many former provincial or imperial capitals (such as Pataliputra , Kannauj, Kara, Kashi, Prayagraj, Murshidabad , Munger, Bahrampur, Kampilya and Kolkata) have all been located on its banks.

Map-1: Course of Ganges and its Major Tributaries Source: https://www.quora.com

5 River Water Pollution - A New Threat to India: A Case Study of River Ganga

The Course of the River from Sky to Ocean

After originating from the Gangotri Glacier at Goumukh in Garhwal Himalayas in Uttarakhand as the River Bhagirathi, the main stream of the River Ganga begins at the confluence of the Bhagirathi and Alaknanda rivers in the town of Devaprayag of the Indian State of Uttarakhand. The headwa- ters of the River Alaknanda are formed by snowmelt from peaks such as Nanda Devi , Trishul and Kamet.

Although many small streams contribute to the headwaters of the Ganga, six longest and their five confluences are considered sacred and important. The six headstreams are: Alaknanda, Dhauli- ganga, Nandakini, Pindar, Mandakini and Bhagirathi. The five confluences, known as the Panch Prayag , are all along the Alaknanda. From up to downstream order, they are: Vishnuprayag; where the Dhauliganga joins the Alaknanda; Nandprayag, where the Nandakini joins; Karnaprayag, where the Pindar joins, Rudraprayag, where the Mandakini joins; and finally, Devprayag , where the Bha- girathi joins the Alaknanda to form the Ganga.

After flowing 250 km through mountains , Ganga finally emerges in the plains at Rishikesh . At Haridwar , it is dammed at Bheemgoda Dam, from where some of its water gets diverted into the Ganga Canal, whereas the river, whose course has been roughly southwest until this point, now begins to flow southeast through the plains of northern India. The Ganga River follows an 800 km course passing through the cities of Kannauj, Farrukhabad and Kanpur . Along the way it is joined by the River Ramganga near Kannauj. Ganga is joined by the River Yamuna at Prayagraj. At their confluence at Prayagraj, the Yamuna is larger than the Ganga, contributing about 58.5 percent of the combined flow. Now flowing east, Ganga river meets the Tons River (ancient name Tamsa) at Sirsa, about 311 km downstream of Prayagraj. After the Tons, the Gomti River joins Ganga near Saidpur, Kaithi in Varanasi district. Then the Ghaghra River, also flowing south from the Himalayas of Nepal , joins. The largest tributary of the Ganges, it is known as Karnali in Nepal.

Next important river to join is the Son River . It is the principal southern tributary of the Ganga River, which rises in the state of Madhya Pradesh . It flows north past Manpur and then turns north- east. The river joins the Ganga above Patna . The Gandaki River and the Kosi River from Nepal also join Ganga with massive inflow. In fact, Kosi is the third largest tributary of the Ganga, after -Gh aghara and Yamuna. Kosi River merges into Ganga near Kursela in Bihar . Along the way between Prayagraj in Uttar Pradesh and Malda in West Bengal , the Ganga River passes through the towns of Chunar, Mirzapur, Varanasi, Ghazipur, Ballin, Buxar, Chapra, Hajipur , Patna, Bhagalpur and many others. At Bhagalpur, the river begins to flow South-Southeast and at Pakur, it begins its attrition with the branching away of its first distributary, the Bhagirathi-Hooghly which goes on to become the Hooghly River .

Just before the border with Bangladesh, the Farakka Barrage controls the flow of Ganga, diverting some of the water into a feeder canal linked to the Hooghly for the purpose of keeping it relatively silt-free. The Hooghly River is formed by the confluence of the Bhagirathi River and Jalangi River at Nabadwip . River Hooghly also has a number of tributaries of its own. The largest is the Damodar River . Between Malda and the Bay of Bengal , Hooghly River passes the towns and cities of Murshid-

6 River Water Pollution - A New Threat to India: A Case Study of River Ganga abad, Nabadwip, Kolkata and Howrah. Finally; the Hooghly River empties into the Bay of Bengal near the Sagar Island.

What Ails the Quality of Water of River Ganga

The river flows through 100 cities with populations over 100,000, and 97 cities and 48 towns with populations between 50,000 to 100,000. A large proportion of sewage water with higher organic load in the Ganges is from this population through domestic water usage. Because of the establish- ment of a large number of industrial cities on the bank of the Ganges like Kanpur, Prayagraj/Al- lahabad, Varanasi and Patna, countless tanneries, chemical plants, textile mills, distilleries, slaugh- terhouses, and hospitals prosper and grow along this and contribute to the pollution of the Ganges by dumping untreated waste into it. One coal-based power plant on the banks of the Pandu River, a Ganges tributary near the city of Kanpur, burns 600,000 tons of coal each year and produces 210,000 tons of fly ash . The ash is dumped into ponds from which a slurry is filtered, mixed with domestic wastewater, and then released into the Pandu River. Fly ash contains toxic heavy metals such as lead and copper. The amount of parts per million of copper released in the Pandu before it even reaches the Ganges is thousand times higher than what is there in the uncontaminated water.

Industrial effluents are about 12 percent of the total volume of effluent reaching the Ganges. Al- though a relatively low proportion, they are a cause for major concern because they are often toxic and non-biodegradable. Despite being a lifeline of millions of people staying along its course Ganga is steadily getting sick for many reasons; some due to apathy of people and some due to natural phe- nomena. Lifeline of a large number of Indians and a spiritual mooring for a large number of Hindus not only in India but all over the world, Ganga is reckoned as one of the most polluted river in the world today. Some of the important reasons are discussed in succeeding paragraphs.

Sewage from many cities along the river’s course, industrial waste especially from the tanneries and religious offerings wrapped in non-degradable plastics, add large amounts of pollutants to the river as it flows through densely populated areas. During festival season immersion of idles having large amount of plastic and chemicals further add to the pollution of the water. The River is also used for throwing the half burnt dead bodies and animal carcass which add to the pollution of the water. During Monsoon when river water invades the flood plains, the pesticides and chemical manures used in the fields located near the river course; further contaminate the water.

Despite the ongoing campaign against the open defecation, the fact remains that the flood plains are still used by a large number of people as areas for defecating. Feces thus generated find way into the river water. Case in point is the state at Varanasi. The levels of fecal coliform bacteria from hu- man waste in the river near Varanasi, the most ancient living city of the world and a very sacred seat of Hindu faith, today is said to be more than a hundred times that of the Indian government’s stated official limit. It is because Varanasi is a city of over 1.2 million people and is visited by a large number of pilgrims to take holy dip in the Ganges. Such a heavy concentration of human beings re- leases around 200 million litres of untreated human sewage into the river each day, leading to large concentrations of fecal coliform bacteria. Story is same with other cities and human concentrations located on the banks of River.

7 River Water Pollution - A New Threat to India: A Case Study of River Ganga

A large number of small streams join the river during her journey in the mountains, however over a period of time, due to increasing pressure of the population, people have settled next to these small streams and thus the flow of these streams into the main course of the river gets blocked. Such ac- tivities reduce the supply of fresh water into the river rendering its quality getting further degraded.

Large scale deforestation in the catchment areas further reduces soil’s capacity to arrest flow of water and accentuates silt getting carried with the water. Global warming is resulting into faster melting of glacier (22 meters/year) and that will result into increasing instances of floods in the monsoon and increasing reduced flow of water in the main stream of the river in years ahead.

Impact of Pollution

The problem is exacerbated by the fact that many poor people depend on the waters of Ganga on a daily basis for bathing, washing, and cooking. The World Bank estimates that the health costs of water pollution in India equal three percent of the India’s GDP. It has also been suggested that eighty percent of all illnesses in India and one-third of deaths can be attributed to water-borne diseases. The danger Ganga’s polluted water poses is not only to the humans but also to the animals. Some of the important threatened species include, more than 140 fish species, 90 amphibian species, reptiles such as the Gharials , and mammals such as the South Asian River Dolphin. Incidentally, Dolphins and Gharials are also included in the International Union for Conservation of Nature’s (IUCN) critically endangered list and a threat to their survival is of grave consequences.

Impact of Silting of the River

Most of the rivers in North India are glacier fed. Himalayan glaciers are considered dirty glaciers as they are, firstly sun facing, and secondly, their gradient is also quite steep. As such, water from these glaciers is full of silt. Once river reaches plains and the velocity of the flow reduces, the silt starts ac- cumulating. Such accumulation of silt changes the profile of the river cross section from trapezoidal to more saucer shaped. Reduction in depth of the river course makes its water to inundate adjoining areas during monsoon. Also, the river course becomes wider and wider necessitating larger flood plains.

There are two more related issues of need for preservation of flood plains and encroachment of flood plains in the problem areas which need to be addressed. The first one is the need for de-silting and second one is keeping the river plains free from encroachment. Both these are interrelated. Silt accumulation is a common phenomenon in glacier fed Ganga as water comes from a great height and there is massive deforestation in the catchment areas. Both these contribute to increase in the quantity of the silt which comes with the river water. The silt gets accumulated while river travels through plains where slope is reasonably gentler. As stated, such accumulation causes changes in the cross sectional profile of river in plains from a trapezoidal section to more saucer shaped section, and unless river plains are free from encroachment, excess volume of discharge during monsoon causes loss of life and property, besides pollution of the river due to chemical fertilisers and pesti- cides getting mixed with the river water, and degradation of agricultural land and water pollution.

8 River Water Pollution - A New Threat to India: A Case Study of River Ganga

Efforts to Clean Ganga

The Government of India became conscious of cleaning Ganga quite early. Over a period of time the Union government has created an exhaustive structure to cleanse the River. The Central Ganga Authority, an official apex body involved in the cleansing operation, is headed by the Prime Minister and meets once a year to make policy decisions, draw up programmes and assess progress. There is also a steering committee headed by the Secretary to the Ministry of Environment And Forests. In addition to various experts, the committee also includes chief secretaries of the three concerned states - UP, Bihar and West Bengal - and representatives of concerned ministries. Its main task is to financially manage and execute various cleaning projects. The executing agencies include the mu- nicipal bodies of concerned cities. Post formation of Ministry of Jal Shakti in May 2019, the entire effort of River Ganga Rejuvenation has come under this Ministry. Some of the projects taken up by the successive governments are described in the following paragraphs.

The Ganga Action Plan (GAP)

To address the issue of river water pollution, the GAP was launched by Shri Rajeev Gandhi, the then Prime Minister of India on 14 Jan1986 with the main objective of pollution abatement. It was to improve the water quality by interception, diversion and treatment of domestic sewage, toxic industrial chemicals and biological wastes from polluting units entering into the river. Initially the GAP got delayed by two years due to various administrative reasons, which resulted into antici- pated expenditure ballooning to almost double. Between 1985 and 2000, around US $226 million were spent on the GAP. This initiative, which was considered to be “the largest single attempt to clean up a polluted river anywhere in the world”, did not fetch the desired results. A study brought out that much of expenditure was wasted on propaganda. The lethargy on the part of executing agencies kept the cost going up. Release of the urban and the industrial waste was never seriously controlled. Pace of construction of Sewage Treatment Plants (STP) and diversion of sewage through these plants was not seriously taken up and raw sewage kept getting drained into the River. Use of river plains as open defecation area and burning of dead bodies on river banks continued unabated. Use of river for washing of clothes, throwing of half burnt bodies into the River and immersion of idols after religious functions continued, adding to the pollution levels in the river.

In the upper reaches, people continued to check the flow of waters from the springs to the river re- sulting in increase in the quantum of sewage getting discharged into the river. Based on its delivery of results, the scheme is said to be a failure. Some of the reasons according to a study were: rampant corruption; lack of will on the part of people who were entrusted to take the plan to fruition; poor technical expertise and environmental planning; and even more appalling, the pace of execution not being able to keep pace with the mounting pollution levels due to exponentially rising popula- tion. A rigid mindset on the part of religious authorities about certain religious practices which have got warped over a period of time in today’s environment needed to be addressed.

The programme of river cleaning was extended to other major rivers of the country under two sepa- rate schemes of GAP Phase - II and the National River Conservation Plan (NRCP). Yamuna and Gomti Action Plans were approved in April 1993 under Ganga Action Plan Phase - II. Programmes

9 River Water Pollution - A New Threat to India: A Case Study of River Ganga of other major rivers were subsequently approved in 1995 under the NRCP. After launching of NRCP in 1995, it was decided to merge GAP II with NRCP. A notification of this effect was issued on 05 Dec1996. Both, GAP-1 and GAP-II fared poorly as far as implementation is concerned. Quite a few timelines were never adhered to. Finally, the projects included in both GAP-1 and Gap-II have now been made part of the ‘Namami Gange Project’.

The National Ganga River Basin Authority

In November 2008, in another attempt to upgrade the quality and utility of river, River Ganga was declared a ‘National River’, thus facilitating the formation of a National Ganga River Basin Author- ity (NGBRA) with the mandate to plan, implement and monitor measures aimed at protecting the River. The NGRBA was established through a Gazette Notification of the Government of India (Extraordinary) No. 328 dated February 20, 2009, and issued at New Delhi with the following ob- jectives:-

• Ensuring effective abatement of pollution and conservation of the River Ganga by adopting a river basin approach • To promote inter-sectoral co-ordination for comprehensive planning and management; and • Maintaining environmental flows in the River Ganga with the aim of ensuring water quality and environmentally sustainable development. National Ganga River Basin Authority is a financing, planning, implementing, monitoring and co- ordinating authority for the Ganges River, functioning under the Water Resource Ministry. How- ever ,even this failed to improve the quality of water which flagged another issue, that of the lack of ownership of the idea of ‘Clean Ganga’ by the stake holders. People affected never felt that it was in their interest that these schemes should become a success.

Namami Gange

In July 2014, the Government of India announced an integrated Ganges development project titled ‘Namami Gange’. With a budget outlay of Rs 20000 crores, it had to accomplish the twin objectives of effective abatement of pollution and conservation and rejuvenation of the National River Ganga. Some work indeed has been done to address the pollution and improve the navigability of the River and quality of its water by building a number of crematoriums on the banks, dredging the river to improve navigability between Varanasi and Kolkata, building STPs to reduce discharge of raw sew- age into the river, and building of large number of toilets in the habitations along the River to reduce the discharge of fecal matters into the River. The areas in which work is being done are as follows:-

• Creation of sewage treatment capacities. Projects are under implementation and 12 ad- ditional sewerage management projects with the view to create additional facility to treat 1187.33 MLD capacity have been launched. • River front development. 28 river front development projects and 33 entry level projects for construction, modernisation and renovation of 182 ghats and 118 crematoria has been initi- ated. 10 River Water Pollution - A New Threat to India: A Case Study of River Ganga

• River Surface cleaning for collection of floating solid waste from the surface of ghats and the River and its disposal are afoot and pushed into service at 11 locations. • Five bio-diversity centers at Dehradun , Narora, Allahabad , Varanasi and Barrackpore have been developed for regeneration of identified priority species. • Forestry interventions for Ganga are being executed as per the detailed project report pre- pared by Forest Research Institute, Dehradun, for a period of five years (2016-2021) at a cost of Rs.2300 crore. Work on medicinal plants has been commenced in seven districts of Uttarakhand. • Public awareness is an important activity to ensure public participation in the campaign. All possible means of mass media, entailing print, social and digital media is being attempted to generate awareness, besides organising meetings , exhibitions, seminars etc. • Industrial effluent monitoring of 1072number of Grossly Polluting Industries (GPIs) were identified in April, 2019. Regulation and enforcement through regular and surprise inspec- tions of GPIs is carried out for compliance verification against stipulated environmental norms. The GPIs are also inspected on annual basis for compliance verification of the pol- lution norms and process modification, wherever required, through third party technical institutes. • Ganga Gram Project is instituted in 1674 panchayats in five states (Uttarakhand, Uttar Pradesh, Bihar, Jharkhand and West Bengal). A number of Indian Institutes of technology (IIT) and other non-governmental organisations (NGO) have been incorporated in the proj- ect. A concept of adoption of villages by the IITs has also been introduced with the twin ob- jectives of generating awareness and helping to create infrastructure to have a clean villages which do not release polluting waters into the River. However, the pace of implementation, at best, can be described as sluggish. Only 35 of 86 planned STPs have been built in five years. Similarly, instead of 4031.41 km of sewer network, only 1114.75 km could be built in first three years. The incidence of water borne diseases is still quite high at 66 percent of all diseases per year. Recent studies by the Indian Council of Medical Research (ICMR) brings out that the River is so full of killer pollutants that those living along its banks in Uttar Pradesh, Bihar and Bengal are more prone to cancer than anywhere else in the country. Conducted by the National Cancer Registry Programme under the ICMR, the study throws up shocking find- ings, indicating that the River is thick with heavy metals and lethal chemicals that cause cancer. Heavy metals, in particular, cause abiding threat to human health. Exposure to heavy metals has been linked to developmental retardation, kidney damage, various cancers, and even death in in- stances of very high exposure. In fact, in the last five years the pollution level in the river has also gone up. A Comptroller and Auditor General (CAG) report disclosed that the level of pollutants in the River across Uttar Pradesh, Bihar and Bengal was six to 334 times higher than the prescribed levels during the period 2016-17. These findings have been confirmed by an Right to Information (RTI) reply.

11 River Water Pollution - A New Threat to India: A Case Study of River Ganga

As Ganga water cleaning was conceived as a major people’s movement, district level task forces were set up with official and non-official members. Task forces headed by either the divisional com- missioner or the district magistrates act as watch-dogs in addition to suggesting the most effective cleansing and maintenance measures. The Ganga Project Directorate (GPD) emphasises NGO in- volvement in popularising the programme and encourages them to make alternate suggestions. A number of NGOs have also been active, though on a smaller scale, in arousing popular conscious- ness and analysing the official clean-up measures. Some of the important NGOs are as follows:-

• The Indian National Trust for Art and Cultural Heritage (INTACH), having branches in the major cities on the Ganga banks, is associated with the cultural restoration of the river and extends assistance, including financial support, to those active in this area. • The Ganga Maha Samiti has been active for 10 years and has frequently taken up the authori- ties on specific cases of pollution in the Kanpur stretch of the river. About three years ago, it also undertook a small dredging operation with popular participation to divert Ganga water to the Kanpur ghats. • The Central Citizens’ Forum, is another group actively involved in issues like urban sanita- tion, as it affects river pollution. • Sankat Mochan Foundation is a Varanasi based organisation headed by Prof Vir Bhadra Mishra of IIT, Benaras Hindu University (BHU) has done pioneering work in creating mass awareness. It has among its members a large number of scientists and technical experts from various departments of the BHU. Recently, the Foundation also set up its own Swachcha Ganga Laboratory to regularly monitor the river water quality in Varanasi.Ganga Forum has the objective of making Ganga pollution free. Eco-Friends, an NGO, in conjunction with the World Wild Life Fund (WWF) have formed a Ganga Forum to save the river. The Calcutta based All India Institute of Public Health and Hygiene has been commissioned by the GPD to monitor the diseases, particularly water-borne and water-washed ones, among people who live along the Ganga. For this, the institute is focusing on the illness levels of two population groups; those who physically live on the river banks and a control group of those who intensely interact with Ganga water.

The Way Ahead

It goes without saying that if efforts of government are to succeed to rejuvenate River Ganga, a well planned initiative run by a highly committed team, whose members are professionally competent and emotionally attached to the idea of rejuvenation of the River, is a pre-condition for the assured success. It may further be considered that such projects succeed only if they become a mass move- ment. So far the Clean Ganga initiatives have, at best, been only partial success as they were run as Government projects in which people considered themselves only as beneficiaries and not the stake holders. The Government should go ahead with the infrastructure development, afforestation of the catchment area, removal of encroachment from the river plains, enforcement of rules and regulation and implementation of the plans in a time bound manner. The Government also needs to ensure that the laws, rules and regulations to ensure cleaning and environment sustainability are

12 River Water Pollution - A New Threat to India: A Case Study of River Ganga tweaked from time to time to respond to the emerging new ground realities and also their strict compliance.

However, this endeavour would be successful only if it becomes a people’s movement; that currently is far from satisfactory. It can become a mass movement only if people start thinking in terms of being stake holders. Lots of mythical and traditional practices will have to be shunned for people to become part of this movement. Also, migratory tendencies of people need to be reversed. It can happen only if people become conscious of the danger which polluted Ganga and the ever deplet- ing fresh water that is likely to pose to them in near future. Simultaneously, green laws will have to become more stringent to initially put a fear of law in the minds of people in case they continue to pollute the river.

A mass campaign to generate awareness among the people about the disaster that the Ganga water has turned into, which once was considered as Amrut, needs to be taken up with desired emphasis. This campaign needs to be on the lines that not only it is the duty of the current generation to clean River Ganga for their own good, but it has also to be a commitment of the current generation to- wards the future generations - to leave a Ganga which is clean, pristine and is suitable to ensure their good health and to quench their thirst.

The malaise which River Ganga suffers from is not a unique phenomenon; almost every river in India suffers from such malady. While the Government has been taking steps to improve the matter for more than three decades but success has been quite limited. To make matters worse, the demand on rivers is rising on account of rise in population, use of highly water intensive crops and many other reasons. In the series of items demanding higher use of water, the latest is the need for ad- ditional water due to India opting for an Open Defecation Free (ODF) society. This latest demand, while on its own is quite substantial, but unless comprehensively managed it will also add to the contamination of the water table .

Pollution of water bodies is not only a function of higher human load but also on account of tech- nology dependence and lack of awareness amongst the masses about the ill effects of pollution, but the most important factor is the indifference of the society about preservation and conservation of the water bodies. A case in point is that most of the village ponds have been reduced to the state of garbage dumps, and in cities ponds and streams have been vanishing fast due to commercial inter- ests of those who are quite myopic in their attitude. Obviously, the solutions being attempted are not in the right direction.

There is a need to look at the threat on river life more realistically, more comprehensively, and above all, with an eye on future. A scenario buildup for the future will help the decision makers to arrive at a realistic strategy to address the problem. A system of review and the will to do course corrections as and when needed will help the country to save itself for the disaster looming large.

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Real-time assessment of the Ganga river during pandemic COVID-19 and predictive data modeling by machine learning

School of Biochemical Engineering, IIT (BHU) Varanasi, Uttar Pradesh, Varanasi, 221005 India

Associated Data

In this study, four water quality parameters were reviewed at 14 stations of river Ganga in pre-, during and post-lockdown and these parameters were modeled by using different machine learning algorithms. Various mathematical models were used for the computation of water quality parameters in pre-, during and post- lockdown period by using Central Pollution Control Board real-time data. Lockdown resulted in the reduction of Biochemical Oxygen Demand ranging from 55 to 92% with increased concentration of dissolved oxygen at few stations. pH was in range of 6.5–8.5 of during lockdown. Total coliform count declined during lockdown period at some stations. The modeling of oxygen saturation deficit showed supremacy of Thomas Mueller model ( R 2  = 0.75) during lockdown over Streeter Phelps ( R 2  = 0.57). Polynomial regression and Newton’s Divided Difference model predicted possible values of water quality parameters till 30th June, 2020 and 07th August, 2020, respectively. It was found that predicted and real values were close to each other. Genetic algorithm was used to optimize hyperparameters of algorithms like Support Vector Regression and Radical Basis Function Neural Network, which were then employed for prediction of all examined water quality metrics. Computed values from ANN model were found close to the experimental ones ( R 2  = 1). Support Vector Regression-Genetic Algorithm Hybrid proved to be very effective for accurate prediction of pH, Biochemical Oxygen Demand, Dissolved Oxygen and Total coliform count during lockdown.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13762-022-04423-1.

Introduction

With the outbreak of the coronavirus pandemic, the life of people is adversely affected. COVID-19 came into light in December 2019 from Wuhan city in Hubei Province of China (Hasnain et al. 2020 ). It affects the respiratory tract and spreads from person to person through physical contact. As researchers are not sure about its source, having not discovered a vaccine to date, no specific treatment is known yet (Chakraborty and Maity 2020 ). The only options left with the public are social distancing, lockdown and personal hygiene. COVID-19 pandemic has severely affected countries like Italy, the USA, Pakistan, China, Germany and India etc. and their respective Government applied lockdown strictly (Paul et al. 2020 ).

As a consequence, people remained indoors and commercial activities were shut down (Wray 2020 ). India was also under lockdown in the wake of coronavirus pandemic. Restrictions on industrial activities during lockdown significantly lowered air and water pollution. This resulted in the substantial rejuvenation of rivers with a positive impact on stable marine life. During lockdown, the water quality of the Ganga river has improved significantly (Singh 2020 ). Lockdown has caused a reduction in the disposal of hazardous wastes not only in the Ganga but also in other rivers. The Ganga or Ganges is a 1,680 miles long river in India that originates from the Gangotri Glacier of the western Himalayas in Uttarakhand and the river flows from the northwest to the southeast, merges into the Bay of Bengal. In India, it covers states such as Uttarakhand, Uttar Pradesh, Bihar and West Bengal (Chaturvedi 2012 ). The Ganga is the lifeline of millions who live along the way. Approximately 43% of India's population lives in the Ganga basin, which is over 860,000 km 2 and covers 26.3% of the country's total geographical area (Trivedi 2010 ). It is a sacred river, worshipped as the goddess Ganga in the Hinduism, which witnesses high religious and cultural tourism on its banks. In 2008, the Ganga river declared was the ‘National River’ of India (Sati 2021 ). There are over 29 cities, 97 towns and thousands of villages on the banks of the Ganga River (Dutta et al. 2020 ).

It hosts about 140 species of fish and 90 species of amphibians. For most of its course, it is a wide and sluggish stream that flows through one of India's most fertile and densely populated regions. The major contributors of pollution are tanneries in Kanpur, distilleries, paper mills and sugar mills in the Yamuna, Ramganga, Kosi and Kali river catchments (Dutta et al. 2020 ). There has been a decrease in fish population along the river, indicating a lack of supportive habitat and water quality degradation. Fishermen report destructive fishing, overfishing and the construction of Farakka barrage as the significant reasons for the decline in fish population from the river-floodplain in Bihar (Dey et al. 2019 ). In 2017, the river Ganga was considered to be sixth most polluted river in the world (Paul 2017 ). Lots of steps have been taken to clean the river, but the desired results have not been achieved to date. Drew ( 2017 ) mentioned that there are numerous hydropower stations, dams and barrages in the main stem of the Ganga river and its tributaries that are harming and obstructing the flow of the river. Apart from this, construction and widening of roads and tunnels in the upper Ganga region affects the flow of water and leaves the river bed dry. The author termed this as “destructive model of development” and added that the continuous inflow of untreated wastewater in the Ganga, including untreated sewage and hazardous waste from the industry as well as agricultural runoff, is worsening the water quality of the river (Drew 2017 ).

The river Ganga passes through states that serve the various subsistence needs of people living in the surrounding areas, such as drinking, bathing, fishing and agriculture. Despite being one of the most functionally important rivers in the world, serving an estimated 500 million people, the Ganga is contaminated in large amounts by the discharge of untreated wastewater and untreated industrial waste (Postel and Richter 2012 ). High population density at the basin, several festive celebrations at the shore, garbage disposals and dumping of corpses directly into the river Ganga have contributed most to its pollution. The river also serves the agriculture in the surrounding region and therefore ends up with a vast amount of chemical fertilizers, pesticides and insecticides that worsen its quality (Chakraborty 2021 ). A non-point category source of pollution, that is, open defecation, is a significant and worrying cause of the disease-causing microorganisms that dwell in the river Ganga. In the river beyond Kanpur, fecal coliform levels have crossed the acceptable bathing standard (Srinivas et al. 2020 ). High pollution level increases the chances of obstructions, ultimately leads to stagnant water condition which breeds diseases such as dengue, malaria and chikungunya. These deadly diseases take millions of lives and cost the country colossal capital every year. The harmful microorganisms originating from fecal pollution are also suspected of having a pivotal role in antibiotic resistance (Lockwood 2016 ). The government has focused on pollution point source control policies (Srinivas et al. 2020 ), but no significant improvement has not yet been seen so far.

In this study, changes in water quality of the river Ganga have been evaluated during the lockdown phase and compared with pre-lockdown statistics. Bioinspired mathematical models such as Streeter Phelps, Thomas Mueller, Support Vector Regression with Genetic Algorithm (SVR-GA), Lasso regression, Artificial neural network (ANN), Newton’s divided difference (NDD) and Polynomial regression model have been used for the computation of water quality parameters in the river water under both pre-lockdown and during lockdown conditions. Streeter Phelps and Thomas Mueller model were utilized for predicting oxygen saturation deficit in the river Ganga . In addition to this, SVR-GA, Lasso regression and ANN were implemented to model levels of DO, BOD, pH and TC in the Ganga river. Finally, NDD and Polynomial regression models have been used to predict water quality parameters (DO, BOD, pH and TC) in the present condition and future changes in the water quality of the river Ganga such as after unlocking phase-I in India, i.e., 30th June 2020 based on the past trends. SVR-GA is a hybrid algorithm which uses a hyperparameter optimization algorithm (GA) along with a modeling algorithm (SVR) (Jiang et al. 2013 ). The ability of SVR marked by its margin approach is well suited for all kinds of data and has been successfully used for the modeling of pH and DO before. Lasso Regression model, which has a shrink or reject feature is advantageous when dealing with regression data. This model originates from Ridge regression and is a robust regression algorithm which was also used for lockdown data prediction.

ANN is an oversimplified version of the inter-neuron communication process that takes place in the brain. Their architecture depends on the number of hidden layers and the activation functions, thus leaving a room for improvisation and experimentation (Ahmed 2017 ). A highly interconnected neural network is very effective for accurate predictions. Still, it tends to over fit on the training data, that is why smaller and effective neural network models have been developed (Sarkar and Pandey 2015 ). One such model is the Radical Basis Function Neural Network (RBF-NN) is a simple one hidden layer ANN which uses a radical basis as its activation function. In the present study, the RBF-NN model, Levenberg–Marquardt algorithm (LMA) and a two hidden layer Multi-Layer Perceptron (MLP) model for prediction of water quality data have been applied. The RBF-NN model was used with GA as the optimizer of its hyperparameters. GA selects a random population based on the specified constraints and picks out the best possible pair of parameters which have the highest fitness. The GA fitness function has been represented with mean squared error (MSE) in the present work. The present study will be useful in developing technologies for reducing the pollution level in the river Ganga and other rivers, preventing it from returning to the previous state based on the data available from these models. This study is also helpful in formulating/revising the laws dealing with a permissible limit of discharge of industrial effluents in the river Ganga and other natural water resources. The entire analytical study of the Ganga river by using CPCB data was conducted at IIT (BHU) Varanasi (Co-ordinate 25° 15′ 30″ N 82° 59′ 39″ E) Varanasi, India.

Ganga river (literature survey before and during lockdown)

Before lockdown, the river Ganga was not suitable for bathing from Uttar Pradesh to West Bengal with the exception of certain places in Uttarakhand (Webdesk 2020 ). Figure  1 shows the sources of pollution in the river Ganga.

Ganga pollution overview

Over 500 water samples from April to June were analyzed for two consecutive years, i.e., 2017 and 2018 (Haider Naqvi 2020 ). The amount of DO decreased to less than 2 mg/L due to the hypoxic state of the river bed, which made the river unable to sustain aquatic life. The river Ganga has been used for dumping of industrial and domestic waste in industrial towns that contaminated the river. For instance, 400 tanning units contribute 50 MLD (million liters per day) of hazardous waste and 140 MLD of domestic waste in Kanpur (Haider Naqvi 2020 ). The water at Haridwar and Rishikesh was found unfit for drinking and bathing. The river water was in class B ever since the foundation of Uttarakhand was laid (Srivastava 2020 ).

It was reported that only 18 spots were fit while 62 spots were unfit for bathing and the river was almost unfit for drinking with a high level of coliform bacteria in the river. River water from 7 spots out of 86 monitoring stations was drinkable only after disinfection. The spots which were found suitable for drinking purpose after disinfection have been classified as ‘class A’ (Bhagirathi at Gangotri, Rudraprayag, Devprayag, Raiwala-Uttarakhand, Rishikesh, Bijnor and Diamond Harbor in West Bengal). Water at 78 monitoring stations was not suitable for drinking and bathing in Bhusaula in Bihar, Kanpur, Gola Ghat in Varanasi, Dalmau in Raebareli, Sangam in Allahabad, Ghazipur, Buxar, Patna, Bhagalpur, Howrah-Shivpur in West Bengal and many others. Thus, water available in pre-lockdown condition of the river Ganga was not suitable for drinking and bathing.

The industrial and commercial activities almost ceased during the lockdown, allowing the Ganga river to breathe again. In India, a total of four phases of lockdowns were observed for 68 days (Lockdown 1.0 (21 days)—25th March, 2020 to 14th April, 2020, Lockdown 2.0 (19 days)—14th April, 2020 to 3rd May, 2020, Lockdown 3.0 (14 days)—3rd May, 2020 to 17th May, 2020 and Lockdown 4.0 (14 days)—18th May, 2020 to 31st May, 2020).

Amid of lockdown, the CPCB, India reported on April 28, 2020 that the Ganga water has improved significantly for bathing purposes in most of the surveillance centers. Observations recorded during lockdown were as follows:

• Rise in DO level from 22nd March, 2020 to15th April, 2020.
• Level of BOD showed a significant decline. The lower range indicated the better health of the river.
• A gradual rise in BOD level toward downstream stretches of the river Ganga.

Singh ( 2020 ) has made a remarkable observation that the level of DO increased from 25 to 30% at five ghats in Varanasi, while the level of BOD decreased up to 35%. Detailed information on changes in water quality parameters during lockdown is tabulated in Table S2 of supporting material.

Materials and methods

The total length of the Ganga river (measured along the Hooghly) from source to mouth is 2, 525 km. The Ganges originates near the Gangotri and travels about 350 km before entering into the village Balawali (district Bijnor) of Uttar Pradesh. It flows from Balawali approximately 1,150 km in Uttar Pradesh and enters the village Sitab Diara, Bihar. It flows 450 km from Sitab Diara and arrives into the West Bengal in Manikchak village (district Malda town). At the Farraka barrage, the Indian government controls water of the Ganga in distributaries namely Hooghly and Padma in the West Bengal and Bangladesh, respectively. It flows 550 km in West Bengal from village Manikchak to Haldia (near Calcutta) before merging into the Bay of Bengal. The 14 real-time stations from Anoopshahar, Uttar Pradesh to Howrah bridge, West Bengal have been considered in the present study for data modeling.

Water quality data set

The data sets of the pre-lockdown condition were collected from the system software ‘Suitability of river Ganga water’ designed by the Central Pollution Control Board (CPCB), India. This is a real-time water quality monitoring system established by CPCB, which helps in monitoring changes in the river at any given time. In India, CPCB has classified water into five classes (A to E), defining different treatment levels for the various purposes (Table S1 of supporting material shows the classes of water defined by CPCB). This classification helps managers and planners of the water quality monitoring system to set targets for water quality and to design appropriate rehabilitation programs for different water bodies. In India, water quality standards are established by CPCB in terms of the primary water quality criteria.

Water quality parameters

The parameters of water quality considered in the present study were pH, BOD, DO and TC. The pH is a measure of how acidic the water is and about 7.4 is considered as the optimum pH for the river water (Azad 2020 ). Wastewater from sewage treatment plants comprises of organic matter which is decomposed by the microorganisms and in return the dissolved oxygen is consumed. When more oxygen is consumed than produced, the concentration of DO decreases proportionately and possibly the population of a few susceptible organisms may move away, weaken or die. The DO level fluctuates in every 24 h and seasonally. It varies with the temperature of the water and altitude (APHA 1992 ). BOD influences the amount of DO in rivers and streams. Higher is the BOD value, faster is depletion of the oxygen in the stream, which means that there is less oxygen available for higher aquatic life forms. High level of BOD has similar effects as low DO concentration such as suffocation and death of aquatic organisms. A test for TC is the most basic measure for bacterial contamination of a water body. TC counts provide a general indication of a water supply's sanitary conditions. The risk of waterborne infection is increased when coliform bacteria are found in drinking water. Several types of malfunctions can cause TC contamination like seepage through the well casing, faulty well cap and well flooding. In order to cope with bacterial contamination, many long-term solutions are available such as inspection, repair of defective wells and installation of continuous disinfection equipment.

Mathematical models

Streeter phelps model.

Streeter and Phelps in 1925 developed a water quality model based on field data from the Ohio river, which was initially used by the US Public Health Service (Digvijay Kumar 2017 ).

In the present study, the Streeter Phelps model has been used to model DO in 14 real-time stations of the Ganga river.

Considering a mixed system (no in-/out flow) (Fig.  2 ) with the state variables Z and X ,

A mixed system with no inflow/outflow

where Z is degradable organic matter (mg/L) and X is the DO level (mg/L).

• Aerobic decay of organic matter ‘ Z ’ by bacteria suspended in the water column (1st order kinetics)
• Consumption of oxygen ‘ X ’ during mineralization of ‘ Z ’
• Exchange of oxygen between water and atmosphere

Differential equations and parameters involved in the model are

where k d is decay rate (1/Time), k a is aeration rate (1/Time), s is a stoichiometric factor (Mass X /mass Z ) and X sat is O 2 saturation level (mg/L).

These equations are valid only when X  >  > 0.

Re-definition of state variables leads to simplified form at boundary conditions:

where L is BOD (biochemical oxygen demand) and Stoichiometric factor ‘s’ equals 1 → omitted.

Thus, Eqs.  1 and 2 can be rewritten as:

Equation 3 may be expanded by separation of variables for the initial condition L ( t  = 0) =  L o .

Integration of Eq.  3 yields Eq.  5 .

Substituting the value of L from Eq.  5 in Eq.  4 results in Eq.  6

Now, using the method of integrating factor, re-ordering of Eq.  6 yields

Multiplication with the factor “exp ( k a · t )” mimics Eq.  8

Applying the product rule, Eq.  9 was obtained as

Equation  10 was achieved after separation of variables and integration

Equation  10 is O 2 saturation deficit Streeter Phelps model.

Thomas and Mueller model

Thomas ( 1948 ) accounted for settle able BOD in the dissolved oxygen sag equation of Streeter Phelps model. Analytical solutions for simple initial and boundary conditions were developed by Thomann and Mueller ( 1987 ). The model includes changes in DO concentrations due to distributed sources (non-point sources) within the stream. Equation  11 illustrates the model of Thomas and Mueller (TM):

where L d  = non-point source BOD (mg/L).

It is apparent from Eq.  11 that the soluble concentration of the DO generated in range by non-point sources was combined at the entry point with the attenuation phenomenon of the DO entering into the cell.

Polynomial interpolation determines a polynomial of order n that passes through n  + 1 point. The NDD model is of interest due to its clarity and precision. This model shows where a function will go, based on its y -values at respective x -values (Das and Chakrabarty 2016 ). Newton’s polynomial possesses the permanence property, which means that new data values can be represented by ( n  + 1)th degree polynomial and the term can be added to previously obtained n th degree polynomial. Accuracy of the polynomial interpolation depends on how close the interpolated point is to the middle of x -values used. It generates only one polynomial of least possible degree that passes through all the data points. Equation  19 depicts NDD model

Newton’s divided difference interpolation method has been used to generate the function depicting water quality of the Ganga river from pre-lockdown to lockdown period. After obtaining interpolating polynomial, it was extrapolated to predict water quality parameters (BOD, DO, pH and TC) till 7th August, 2020 (200th day from 20th January). In the present study, 20th January, 2020 has been marked as 0th day (pre-lockdown data). Using this model, polynomials were obtained for BOD, DO, pH and TC separately for each of the 14 stations and these were plotted to extrapolate values for upcoming months. This model was trained using python programming language.

Polynomial regression model

Polynomial regression determines nonlinear relationship between the value of ‘ x ’ and the corresponding conditional mean of ‘ y ’ (Ostertagová 2012 ). The expected value of ‘ y ’ can be modeled as n th degree polynomial, yielding a general polynomial regression model (Eq.  13 )

In this study, the polynomial regression model was used to model values of DO, BOD, pH and TC as a function of time to analyze and predict the Ganga water quality till 7th August, 2020. The model was trained to generate polynomials of degree 2, 3 and 4 for DO, BOD, pH and TC at real-time stations. Just to maintain consistency in results, this model was also trained using python programming language.

Radical basis function kernel support vector regression with genetic algorithm (SVR-GA)

Vapnik et al. ( 1997 ) developed an algorithm that used the earlier work of Support Vector Machines to address regression problems, which was then known as Support Vector Regression (SVR). The most powerful aspect of SVR is that it takes into account the error limit of epsilon, which means that an error between the predicted and the true value is allowed to lie within the range of [−  ε , ε ] and that no error greater than that is accepted. Using this rule, a function ‘ f ’ is generated that would be able to fulfill this condition. In linear form, function ‘ f ’ can be estimated as:

where w , x is the dot product of w and x.

Flatness in Eq.  14 would mean to obtain a small value of w by minimizing the norm (Smola and Schölkopf 2004 ).

Usually, it is not always possible to search for a function ‘ f ’ which would produce data pairs which lie in the epsilon margin. Therefore, soft margin like approach is used, where slack variables ξ i , ξ i ∗ representing the distance between the true values and the epsilon tunnel are introduced. This addition helps in making the optimization problem feasible. Thus, a risk function ‘ R ’ is defined by incorporating an epsilon insensitive loss function with a constant ‘ C ’. The regularized convex optimization problem (Smola and Schölkopf 2004 ) can be written as:

where C is a positive constant that plays a role in determining the extent to which a deviation from the error tunnel is tolerated.

This can be seen as a trade-off between the model flatness and empirical risk (Smola and Schölkopf 2004 ). Lagrange construction of the primary function gives a quadratic optimization problem that is solved for α i , α i ∗ (Vapnik and Vapnik 1998 ):

Here, ( α i , α i ∗ ) are Lagrange multipliers.

The vectors x i corresponding to non-zero Lagrange multipliers are then called as support vectors (Vapnik et al. 1997 ). After performing optimization, f ( x ) can be obtained as:

A kernel K x , x i is defined for a nonlinear regression model. The kernel generates an inner product in some feature space and solves the corresponding dual optimization problem (Vapnik et al. 1997 ). Some examples of kernels are Polynomial, Gaussian, Radical basis function. In the present study, Radical basis function (RBF) kernel has been used. The kernel and the nonlinear objective function can then be written as:

The variables C , ε , γ are user-defined while implementing SVR. Since these hyperparameters are crucial for the proper functioning of the algorithm, their right selection is of utmost importance. Genetic Algorithm (GA) was used to meet this requirement. It was first introduced by Holland ( 1992 ) and is a natural evolution-based technique that seeks inspiration from Darwin’s theory of survival of the fittest. The GAs are being applied successfully in a number of areas such as job shop problems (Falkenauer and Bouffouix 1991 ; Nakano and Yamada 1991 ), control system optimization (Krishnakumar and Goldberg 1992 ), pipeline optimization (Goldberg and Kuo 1987 ), molecular geometry optimization (Deaven and Ho 1995 ) and feature subset selection (Yang and Honavar 1998 ).

Goldberg ( 2006 ) has outlined the differences between GAs and other optimization techniques. Some of the advantages include the use of the coding of parameter set and not the parameters themselves, search from a population of points, using payoff information when binding to auxiliary information and the use of probabilistic transition rules over deterministic rules. These four advantages give GAs an edge over other commonly used traditional optimization techniques. GA can be broken down into four steps where the GA selects a population of individuals and computes the fitness function for each individual. Individuals with the highest fitness function are chosen to produce offsprings. The second and third steps involve crossovers and mutations between the selected individuals, which lead to the formation of a new generation. Finally, the fitness function for this new generation is calculated and the process repeats from step one unless the goal of the algorithm is reached.

The combination of SVR with a real-valued GA has been used as the optimization algorithm for SVRs hyperparameters ( C , ε , γ ). Liu et al. 2013 used this hybrid model for water quality estimation (DO and temperature) and compared it with traditional SVR and BP neural network models. Their RGA-SVR model outperformed over the traditional models. Similarly, Wang et al. ( 2011 ) used SVR model with GA automated SVR parameter selection for the prediction of permanganate index (CODMn), ammonia–nitrogen (NH 3 –N) and chemical oxygen demand (COD) and found this superior to MLR algorithm.

Lasso regression

The lasso regression (LR) model was developed by Tibshirani ( 1996 ), which is built upon the robustness of ridge regression. It preserves the quality features of ridge regression and subset selection by shrinking some coefficients and setting others to zero. For data x i , y i , i  = 1, 2, … n . where, x i = x i 1 , … x ik are the predictor variables and y i are the responses.

The lasso optimization problem can be solved by minimizing Eq. ( 20 ).

An assumption is made that x ij are standardized to avoid any dependence on the measurement scale. Here, t ≥ 0 is a prespecified tuning parameter which controls the amount of shrinkage applied (Tibshirani 1996 ). Lasso regression has been previously used as a predictor algorithm for water quality estimates (Ahmed et al. 2019 ; Brooks et al. 2016 ).

Artificial neural network (ANN)

ANN is a very powerful algorithm whose architecture is inspired by the process of communication of neuronal cells. ANN can take many forms and in the present study the LMA, MLP and RBF-NN have been focused. ANN work immensely well with water quality data (El-Shafie et al. 2011 ). Authors compared the ANN model with the linear regression model and found that ANN has high accuracy as compared to the other models. Najah et al. ( 2013 ) performed a comparative study with different ANN models like RBF-NN, MLP-NN and Linear Regression model (LRM) for water quality estimation and found RBF-NN superior to MLP-NN and LRM. Authors showed that RBF-NN could be a reliable water quality predictor model. Both of these studies used a trial and error basis for determining the number of hidden layers and neuron units in the layers.

ANN with LMA

The chosen ANN for the pH, DO, BOD and TC models consisted of one input layer with fourteen input variables, one hidden layer and one output layer. In addition to this, TC consisted of a similar number of hidden and output layers except for 12 input variables. The designed ANN models (pH, DO, BOD and TC) were trained for utilizing LMA as it rapidly solves and tunes the model parameters in comparison with other algorithms (Singh et al. 2009 ). The model simulation has been done by ANN tool in MATLAB 2017a.

The MLP is a neural network with completely connected layers that are stacked against each other. Each layer is activated using a particular activation feature. In order to construct an MLP, two fully connected hidden dense layers were superimposed and activated by the function ‘rectified linear unit’ (RELU) from the python library ‘Keras.’ Data were then iterated over sufficient epochs until it converged to produce the lowest MSE (Gardner and Dorling 1998 ).

The RBF is a feedforward neural network with one hidden layer between the input and output layer. In an RBF-NN, all neurons from a layer are connected to all neurons in the next layer. Harpham et al. ( 2004 ) highlighted the advantages of applying GAs to RBF-NN, thus creating a hybrid. This addition eliminates the test and error approach since GA automatically produces an optimal solution for hyperparameters. In the present study, a GA-based search algorithm has been applied to find optimal hyperparameters for RBF-NN model.

Results and discussion

Statistics of the river ganga: pre-lockdown and during lockdown.

As shown in Table ​ Table1, 1 , the parameters (pH, DO, BOD and TC) of the river Ganga varied in the lockdown period.

Water quality parameters of the river Ganga during pre-lockdown and lockdown period

*Pre-L is Pre-Lockdown and L is Lockdown

In the present study, 14 stations namely Anoopshahar; Farrukabad; Rajghat, Kannauj; Bithoor, Kanpur; Jajmau, Kanpur; Assi ghat, Varanasi; Malviya Bridge, Varanasi; Patna; Bhagalpur; Berhampore; Monipurghat, Nadia; Palta, Barrackpore; Serampore, Hooghly and Howrah bridge, West Bengal were analyzed. The changes in the parameters at these stations have been listed below.

At Anoopshahar, pH increased by 0.1, followed by an increment in BOD and DO with no detectable change in the values of TC. The increment was in the range as delineated by CPCB, India (shown in Table S1 of supporting material). Thus, this water quality at Anoopshahar permitted all the uses of water.

In the Farrukabad and Kannauj, there has been a decrease in pH, TC and DO with the simultaneous increase in BOD level. Though these changes were not positive yet the variation in pH, DO, TC and BOD were in the permissible range of CPCB (Table S1 of supplementary information).

In Bithoor and Jajmau Kanpur, there was a decrease in pH, DO and BOD and water at these stations were considered pollution-free which can be used for drinking, bathing, irrigation and other purposes. Considering TC, its level was increased in Bithoor but declined in Jajmau, Kanpur but it was in the range given by CPCB in Bithoor but not in Jajmau. Thus, the river ganga water can be used for all purpose in Bithoor but not in Jajmau, Kanpur.

In Assi ghat and Malaviya Bridge, Varanasi, a decrease in pH and DO level together with increase in BOD and TC was observed. These changes were not in an acceptable range of CPCB, India.

In Patna, the water quality was found unsuitable owing to a slight decrease in pH and DO and significant augmentation in BOD indicated a high level of pollution. But TC was found to decline here and it was within the acceptable range given by CPCB. At Bhagalpur, Bihar water sample was found unfit for drinking, bathing and irrigation.

In Berhampore, Monipurghat, Nadia; Palta, Barrackpore; Serampore, Hooghly and Howrah bridge, West Bengal a decrease in the pH, DO and BOD was observed with increase in TC and it was much higher than the acceptable range given by CPCB. The decrement in pH, DO and BOD was in the range of permissible limit demarcated by CPCB. Thus, these stations also possessed some positive changes similar to Anoopshahar, Farrukabad, Rajghat and Varanasi. The changes in pH, DO, BOD and TC during lockdown were studied and compared with pre-lockdown data as shown in Table ​ Table1 1 .

As shown in Table ​ Table1, 1 , after lockdown pH in all stations was within an acceptable range of 6.5–8.5. Before lockdown, only two stations, namely Malviya Bridge, Varanasi and Serampore, Hooghly exceeded this range. But during the lockdown, these stations were within the standard range as depicted by CPCB. These changes replenished the Ganga river after a long gap.

It is appropriate to mention that there had been an insignificant change in water quality parameters during lockdown 3.0 and 4.0 as the time difference was of 14 days only.

Specifically, the health indicators of the Ganga's water improved significantly such as increased DO (in Anoopshahar), reduced BOD (in Bithoor, Kanpur; Jajmau, Kanpur; Malviya Bridge, Varanasi; Berhampore; Monipurghat, Nadia; Palta, Barrackpore; Serampore, Hooghly and Howrah bridge) and reduction in TC (Farrukabad, Rajghat, Jajmau, Patna and Palta, Barrackpore) during the lockdown.

Streeter–Phelps model

Streeter Phelps model equation was used to find O 2 saturation deficit ( D ) for 14 real-time stations of the river Ganga (Table ​ (Table2). 2 ). The value of ‘ D ’ was experimentally determined and compared with the theoretical value derived from the model (Fig.  3 ).

Comparison of experimental and theoretical O 2 saturation deficit values with reference to the Streeter Phelps model

Comparison of experimental and theoretical values of D for 14 real-time stations with reference to Streeter Phelps model

It was observed from Table ​ Table2 2 that this model was not accurate for predicting the value of ‘ D ’ as it showed a very high percentage of error for each real-time station of the river Ganga together with a sluggish coefficient of regression ( R 2  = 0.57).

Bhargava ( 1986 ) revealed that Streeter Phelps models could not precisely predict DO sag of a stream instantly after sewage outfalls as model does not take bio-flocculation and sedimentation of the adjustable BOD into account. Jha et al. ( 2007 ) applied Streeter Phelps models for analyzing one of the most polluted rivers in India, i.e., the river Kali and showed the negative outcome with under and over-prediction. Kaushik et al. ( 2012 ) modified Streeter Phelps model by considering the settle able component of BOD and the effect of storage zones on river’s DO. Authors found that the modified model was able to predict parameters of rivers more accurately.

Thomas and Mueller model was used to find ‘ D ’ including non-point sources in the river water for 14 real-time stations. The theoretical results did not show a close agreement with the experimental values (Fig.  4 , Table ​ Table3). 3 ). However, this model had a slightly better fit as compared to Streeter Phelps model based on the value of R 2 (= 0.75).

Comparison of experimental and theoretical of D for 14 real-time stations with reference to the Thomas and Mueller model

Comparison of theoretical and experimental D values with reference to the Thomas and Muller model

The water quality parameters were predicted for 7th August, 2020, i.e., the 200th day starting from 20th January, 2020. Table S3 of supporting material shows the value of predicted parameters on 7th August, 2020.

Assuming that the conditions do not return to original pre-lockdown conditions, this model analyzed the situation from pre-lockdown to lockdown and predicted the possible values for the near future. It also provided incorrect results for 3 stations, i.e., Rajghat, Patna and Bhagalpur, which do not seem to be possible. It was inferred from this model that the actual values were close to predicted values (pH, BOD, DO and TC) for 7th August, 2020.

Water quality parameters were predicted using 2, 3 and 4 degree polynomials on 30th June, 2020 (i.e., on day 162 starting on 20 January 2020) and these values are shown in Tables S4, S5, S6, S7 and S8 of the supporting material. For prediction, 30th June, 2020 was selected as it falls close to 31st May, 2020, and reduces the chance of error that could increase if one moves away from the 31st May, 2020 data values. Considering the range of values from these polynomials, it can be predicted that the water quality parameters (BOD, DO, pH and TC) will fall within the range of values that were predicted for 30th June, 2020.

The actual value of these parameters will depend on how the level of pollution goes back to the previous one. The values will more likely to fall in the ranges stated in Table S4, S5, S6, S7 and S8 of the supporting material.

This model analyses the situation from pre-lockdown to lockdown statistics and predicts somewhat possible values for near future. From the graphs, it was clinched that all values fall in acceptable range except BOD at Patna and Bhagalpur. Also, the DO levels at Rajghat, Patna and Bhagalpur show steep changes. The quality of the Ganga water appeared to be improved from pre-lockdown situation. Since the values and curves for polynomial second degree were the same as for NDD model, this implied that the NDD model was the reliable one.

The polynomial regression model was better than NDD as it provided the range (generated by 2nd- , 3rd- , and 4th-degree polynomial) in which the predicted parameters would lie. The polynomial regression model fitted better than NDD as most of the actual values lie in or near the predicted range. This is due to the fact that NDD is an interpolation method; however, in the present work it predicts the future values by extrapolating the curve. Also, NDD resulted in the second-degree polynomial, which does not correspond to the actual variation in the parameters in due course of the time.

The SVR model, a kernel-based regression model was used and its parameters, i.e., C , ε , γ were optimized for each water quality parameter with the help of a simple GA. Here, GA was employed using a one-point crossover function having mutation with a root mean square as the fitness measure. The algorithm was performed on a population of 50 randomly selected individuals iterated upon 30 generations with a crossover probability of 0.5 and a mutation probability of 0.02. Upon running, the algorithm first randomly selects 50 individuals with their ranges being, C  = [1, 100], γ = 0.1 , 1 , ε = 0.001 , 0.01 . Each of these individuals undergoes crossover and mutation, after which the fitness of an individual is calculated. This process runs over a set of 30 generations with each generation producing a slightly better generation than itself. From the last generation, the individual with the highest fitness function is chosen as the best individual.

The model showed overfitting with zero MSE upon running. To solve this, fivefold cross-validation was used wherein the data were split into test and train set five times. This helped in solving overfitting. The model reported different MSE for pH, DO, BOD and TC in Table ​ Table4 4 .

Mean absolute error using different models

The R 2 value for the pH, DO and TC approached unity signifying a perfect fit. BOD, however, showed a low R 2 value (Table ​ (Table5 5 ).

R 2 value for pH, DO, BOD and TC for different models

These values show that out of the three parameters studied, the SVR—GA model works best for the pH, DO with R 2 value approaching unity (Table ​ (Table6, 6 , Fig.  5 ).

SVR-GA error for 14 real-time stations

SVR-GA predicted values of BOD, pH, DO and TC

For TC analysis, data from January were paired with other parameters (pH, DO, BOD and TC). This was used as the input data set for the prediction of TC during the lockdown. SVR-GA gave an R 2 value of 0.99, pointing toward a high goodness of fit.

In this model, a ‘ t ’ value of 0.01 was used. Trial and error basis were used and alpha values have been modified and tested. The alpha value of 0.01 was finally selected. The model provided R 2 values leaning toward zero for pH, DO, BOD and TC and failed to predict the data correctly (Tables ​ (Tables4, 4 , ​ ,5, 5 , ​ ,7, 7 , Fig.  6 ).

Lasso regression error for 14 real-time stations

Lasso predicted values of BOD, pH, DO and TC

Apart from this, Lasso regression performed robustly for TC prediction and gave R 2 values of 0.93.

In the present study, a nonlinear transfer function (TANSIG) in the hidden layer was used for ANNs. The ANN predicted output and error in pH, DO, BOD and TC model for real-time stations of the river Ganga are shown in Table ​ Table8 8 .

ANN predicted output and error using L–M algorithm for pH, DO and BOD models for 14 stations of the river Ganga

The plots between experimental and theoretical values of pH, DO, BOD and TC values are shown in Fig.  7 .

Comparison of the experimental and theoretical a pH, b DO, c BOD and d TC levels in the river Ganga

The best validation performance in ten neurons was 0.08877, 0.38177, 34.7517 and 16,371,716.42 at epoch 3, 3, 2 and 7 for pH, DO, BOD and TC, respectively, with the lowest MSE (Fig.  8 ).

Performance plot for modeling of a pH, b DO, c BOD and d TC levels in the river Ganga

The linear R 2 values for training, validation and test data sets used for all the models (pH, DO, BOD and TC) are represented in Figure S1 of supporting material. The selected ANN generated the most trustworthy models for all three data sets. The experimental and theoretical values pH, DO, BOD and TC derived through these models were in close agreement ( R 2  = 0.92–1.0). This suggested that the model fitted well with the experimental data sets. ANNs have also been used to estimate and forecast the water quality variables like modeling of DO and BOD in the river water (Singh et al. 2009 ).

Similarly, Shamseldin ( 2010 ) used ANN for forecasting the flow of rivers in the developing countries. The chlorine concentration in the water distribution network has been assessed through ANN by Cordoba et al. ( 2014 ). ANN has been used for the prediction of water quality index (Bansal and Ganesan 2019 ; Gupta et al. 2019 ). The results of ANN-based modeling have shown significant accuracy over other traditional modeling techniques. Shakeri Abdolmaleki et al. ( 2013 ) applied ANN for predicting copper concentration in the drinking water reservoir of Iran. Authors found that predicted values were very close to the real concentration of copper. The BOD, DO and other water quality parameters were forecast by using ANN in the Karoon river (Emamgholizadeh et al. 2014 ). The predicted values were close to the real ones, which proved ANN, an effective modeling technique for predicting water quality variables in the river. Gomolka et al. ( 2018 ) used ANN to estimate the BOD level and for controlling rate of aeration in river.

Two RELU activated hidden layers were used and epochs were performed until full convergence of loss function was observed.

The MLP showed excellent results for pH, DO and BOD with R 2 values very close to one (Tables ​ (Tables4, 4 , ​ ,5, 5 , ​ ,9, 9 , Fig.  9 ) but it's prediction for TC was not at par with its performance for the other indices.

MLP error for 14 real-time stations

MLP predicted values of BOD, pH, DO and TC

An RBF-NN was applied with GA to optimize the hyperparameters like learning rate (lr) and several kernels (k). A multi-feature input algorithm was constructed which picked the hyperparameters using a GA where MSE was chosen as the fitness function. The initial population was picked out where the kernel number and learning rate constrained to a range of [1, 7] and [0.0001, 0.02], respectively. An initial population size of 50 was chosen. The algorithm was run for 30 generations with a crossover and a mutation probability of 0.7 and 0.02, respectively. The model ran for 100 epochs each time. The results of the model showed poor performance for BOD, DO and TC. The model’s goodness of fit for pH is better than Lasso regression but not SVR and MLP (Tables ​ (Tables4, 4 , ​ ,5, 5 , ​ ,10, 10 , Fig.  10 ).

RBF-NN error for 14 real-time stations

RBF-NN predicted values of BOD, pH, DO and TC

Comparative study

Several studies conducted by other researchers on the quality of the Ganga's water during lockdown have been discussed in detail in Table ​ Table11. 11 . The outcomes of their work with the technique involved in the estimation of water quality parameters are included and have been compared with the present study.

Comparative assessment of the present work with that of other researchers to ascertain changes in the Ganga river's water quality characteristics during lockdown

In the present study, the water quality of the river Ganga has been evaluated during the lockdown and predicted for post lockdown conditions. It was found that the pH of all stations was within the standard range 6.5–8.5 in lockdown period. An increment in DO has been observed in Anoopshahar. Apart from that, all stations had DO > 5 mg/L except Patna and Bhagalpur. It was noted that Patna and Bhagalpur stations had very high BOD levels compared to other stations that signified a substantial level of pollution. During the lockdown, Anoopshahar, Farrukabad, Rajghat, Kannauj and Assi ghat, Varanasi had BOD exactly as 3 mg/L. The decrement in TC was observed in Farrukabad, Rajghat, Jajmau, Patna and Palta during the lockdown period. In the present study, bioengineered mathematical models, namely Streeter Phelps, Thomas Mueller, SVR-GA, Lasso Regression, ANN, NDD and Polynomial regression, were attempted to predict the water quality parameters. Polynomial regression and NDD model were able to predict pH, BOD, DO and TC levels from 20th January, 2020 to 30th June, 2020 and 07th August, 2020. Thus, NDD and polynomial regression models were used to predict the near future values of the water quality parameters (BOD, DO, pH and TC) of the river Ganga. But NDD model was not able to predict TC values. However, the NDD model is simply an interpolation method, which can be further extrapolated to predict the values. On the other hand, polynomials of 2, 3 and 4 degrees were generated in polynomial regression model to obtain the range of predicted values. The NDD model is verified by the polynomial degree 2 regression that appeared to be acceptable after comparison. Overall, polynomial regression model was better than NDD model. In ANN models using LMA, the best validation performance was observed with ten neurons as 0.08877, 0.38177, 34.7517 and 16,371,716.42 at epoch 3, 3, 2 and 7 for pH, DO, BOD and TC, respectively. Additionally, SVR-GA hybrid was superior compared to its counterparts such as Lasso Regression and RBF-NN in the prediction of real-time water quality data indices such as pH, DO of the river Ganga. It also produced the best results for TC forecast during the lockdown period. It was unable to predict the lockdown BOD values correctly. MLP was the second-best algorithm after SVR-GA, which showed accurate fits for three (pH, DO, BOD) of the indices but couldn’t accurately predict TC levels. SVR-GA and MLP showed a nearly perfect fit for the pH and TC data with significantly lesser MSE values. The R 2 value for pH modeled by SVR-GA ( R 2  = 0.99) and MLP ( R 2  = 0.99) was near unity, pointing to a perfect fit. Similarly, the R 2 value for TC modeled by SVR-GA is 0.99. The abnormal high deviations in BOD modeling in all the models except MLP ( R 2  = 0.99) can be due to the presence of outliers. It can, therefore, be stated that SVR and MLP are relatively quicker and better choices as the modeling techniques for predicting values of water quality parameters of the river Ganga. Thus, in the present study, SVR-GA, MLP and polynomial regression model were found superior to NDD for the prediction of water quality parameters in the long run. Moreover, as these models are fitted with the least error, there are numerous applications where their use is highly recommended. Like, SVR-GA algorithm can be effectively implemented to estimate parameters of water, MLP is capable of modeling a sequencing batch reactor that will treat municipal wastewater. The comparison of different models showed their applicability in predictive modeling of river flow and wastewater treatment.

Below is the link to the electronic supplementary material.

Acknowledgements

The authors are thankful to the School of Biochemical Engineering, IIT (BHU) Varanasi, Varanasi for financial and technical support of the present research work.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JS, SS and PS. The final draft of the manuscript was reviewed by VM. All authors read and approved the final manuscript.

The authors did not receive support from any organization for the submitted work.

Declarations

The authors have no conflicts of interest to declare that are relevant to the content of this article.

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\(\lambda \) -possibility-center based MCDM technique on the control of Ganga river pollution under non-linear pentagonal fuzzy environment

• Original Research
• Published: 12 June 2024

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• Totan Garai   ORCID: orcid.org/0000-0002-0476-6447 1  

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Indians regard the River Ganga as religious because it sustains the ecosystem and ecology. Over the past few decades, human activities have caused significant changes in the Ganga river system. Ganga pollution’s leading cause is the need for more management of drainage and industrial design in the metropolitan cities of India. Therefore, in this paper, we have developed a \(\lambda \) -possibility-center based multi-criteria decision-making method under a fuzzy environment. With this decision-making technique, we have found which causes one of three leading causes of Ganga river pollution Disposal of industrial waste into the river, Human sewage and animal waste and Increasing population density. \(\lambda \) -possibility MCDM technique can deal the how to control the Ganga river pollution. Finally, we have Ganga river pollution with \(\lambda \) -possibility MCDM technique under a non-linear pentagonal fuzzy environment.

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Garai, T. \(\lambda \) -possibility-center based MCDM technique on the control of Ganga river pollution under non-linear pentagonal fuzzy environment. J Ambient Intell Human Comput (2024). https://doi.org/10.1007/s12652-024-04817-8

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Received : 23 March 2023

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DOI : https://doi.org/10.1007/s12652-024-04817-8

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Analysis of spatial and temporal pattern evolution and decoupling relationships of land use functions based on ecological protection and high-quality development: a case study of the yellow river basin, china, share and cite.

Du, H.; Wang, Z.; Li, H.; Zhang, C. Analysis of Spatial and Temporal Pattern Evolution and Decoupling Relationships of Land Use Functions Based on Ecological Protection and High-Quality Development: A Case Study of the Yellow River Basin, China. Land 2024 , 13 , 862. https://doi.org/10.3390/land13060862

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