presentation on pollution of yamuna river

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• Published: 21 February 2022

Physicochemical and biological analysis of river Yamuna at Palla station from 2009 to 2019

• Pankaj Joshi 1 ,
• Akshansha Chauhan 2 ,
• Piyush Dua 3 ,
• Sudheer Malik 4 &
• Yuei-An Liou 2 , 5  

Scientific Reports volume  12 , Article number:  2870 ( 2022 ) Cite this article

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• Environmental sciences

Yamuna is one of the main tributaries of the river Ganga and passes through Delhi, the national capital of India. In the last few years, it is considered one of the most polluted rivers of India. We carried out the analysis for the physiochemical and biological conditions of the river Yamuna based on measurements acquired at Palla station, Delhi during 2009–19. For our analysis, we considered various physicochemical and biological parameters (Dissolved Oxygen (DO) Saturation, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Alkalinity, Total Dissolved Solids (TDS), and Total Coliform. The water stats of river Yamuna at Palla station were matched with Water Standards of India, United Nations Economic Commission for Europe (UNECE), and World Health Organization (WHO). Maximum changes are observed in DO saturation and total coliform, while BOD and COD values are also seen higher than the upper limits. Total alkalinity rarely meets the minimum standards. TDS is found to be satisfactory as per the standard limit. The river quality falls under Class D or E (IS2296), Class III or IV (UNECE), and fails to fulfill WHO standards for water. After spending more than 130 million USD for the establishment of a large number of effluent treatment plants, sewage treatment plants, and common effluent treatment plants, increasing discharges of untreated sewage, partially treated industrial effluents and reduced discharge of freshwater from Hathnikund are causing deterioration in water quality and no major improvements are seen in water quality of river Yamuna.

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Introduction

Water is the main need of human life. The majority of ancient civilizations were developed on the bank of major rivers across the world. Rivers fulfill the major demands of the freshwater supply from drinking to agriculture. In the northern parts of India, the Yamuna River basin is ranked the second largest basin after the Ganga River. It is the second-largest tributary of the river Ganga (the longest river of India) with a total catchment area of 345,848 km 2 and it originates at Yamunotri Glacier, Uttrakhand, India at a height of 6387 m 1 . It covers a total distance of 1376 km through four major states of India: Uttarakhand, Haryana, Delhi, and Uttar Pradesh, and finally confluences with river Ganga at Triveni Sangam, Prayagraj, Uttar Pradesh. Although it does not flow through Himachal Pradesh but receives water via the river Tons (which originates in Himachal Pradesh). The other tributaries of the river Yamuna are Chambal, Sindh, Betwa, and Ken 2 , 3 . The water abstracted from the river is mostly used for irrigation (about 94%), while 4% for domestic water supply and the remaining 2% for industrial and other uses 4 .

During the last few decades, Yamuna has been considered one of the most polluted rivers of India. Discharge from industries, partially or untreated sewage, and agricultural waste are the main sources for the river Yamuna degradation 5 , 6 , 7 . Almost 85% of the total pollution in the river Yamuna is due to domestic sources mainly from urban cities Sonipat, Panipat, Delhi, Ghaziabad, Mathura, Agra, Etawah, and Prayagraj. Industrial zones at various places like Yamunanagar, Panipat, Ghaziabad, Delhi, Noida, Faridabad, and Baghpat which are in the upper Yamuna basin (Fig.  1 ), comprise industries like Oil refineries, distilleries, pulp, pharmaceutical, chemical, electroplating, weaving, and sugar, and contribute to the degradation of Yamuna water quality significantly 8 , 9 . According to Kumar et al. 10 , Delhi leads the list of cities with 79% pollution load in river Yamuna followed by Agra and Mathura with a contribution of 9% and 4%, respectively, whereas a pollution load of 2% by Sonipat and Baghpat. The annual mixing of sewage from domestic and industrial sources in the Yamuna River basin is about 9.63 km 3 , 11 . In the last few decades, a sudden rise in the built-up and cropland areas is observed in the Yamuna River basin (Fig.  2 ). Kumar et al. 10 suggested a rise of 100% in the urbanization in Haryana and Rajasthan states and significant fall is observed in wetland, grassland, water bodies and forest areas of Yamuna River basin. The green revolution in India helped rise in the productivity of various crops, but the major water supply to the crop depends on the groundwater. The DO level of water in Delhi stretch shows a sudden fall due to high carbon level so that most of the time the river can not sustain fishery.

( Source : HSPCB).

River Yamuna in Haryana.

( Source : WRIS).

Upper river Yamuna basin area.

Parween et al. 12 showed the positive rise in the potassium and nitrate that affected the Yamuna River basin. Domestic waste consists of mainly organic matter and micro-organisms along with detergents, grease and total salts mixed in river Yamuna through various drainages in National Capital region. Lokhande and Tare 3 have shown rise in the flow rate of Yamuna during non-monsoon months due to the sewage water. Industrial effluents are the main source of heavy metal pollution like Cd, As, Cr, Fe and Zn with other inorganic and organic wastes adding to pollutant inventory 10 . According to National Capital Region Planning Board (NCRPB) report, sewage generation in Haryana was 374 MLD in 2001, and 599 MLD in 2011, whereas the sewage treatment capacity was 164 MLD in 2001 and 199 MLD in 2011. State monitoring committee appointed by National Green Tribunal (NGT) 2019 suggested that Haryana discharged 1140 MLD of untreated or partially treated sewage per day into river Yamuna, also 1268 industrial units discharged 138.75 MLD partially treated and another 827 units discharged 48.319 MLD of treated effluents per day in Yamuna River 13 .

Central Pollution Control Board (CPCB) is responsible for controlling the various sources of pollution in India and also monitoring water quality of the rivers with the State Pollution Control Board (SPCB) 4 , 5 , 14 , 15 . CPCB started national water quality monitoring in 1978 under Global Environmental Monitoring System (GEMS), followed by the Monitoring of Indian National Aquatic Resources (MINARS) in 1984, and helped reduce river pollution via National Water Quality Monitoring Programme (NWMP) 16 . Central Water Commission (CWE) is monitoring the water quality of all the major river basins in India through 519 water quality sites and 33 water sampling stations. According to CWC, the water quality of river Yamuna is monitored at 18 different stations of which 12 are manual and 6 are telemetry stations, starting from Naugaon (N-30.78, E-78.13) at Uttarakhand as the first river point station to the last river point station Pratappur (N-25.37, E-81.67) Uttar Pradesh.

Due to degradation in the water quality of the Yamuna River, Yamuna Action Plan (YAP-I) was launched in 1993 by the Ministry of Environment and Forests (MoEF), India to rejuvenate the Yamuna River especially in the Delhi segment having maximum pollution load. Haryana and Uttar Pradesh were also included along with Delhi in YAP-II in 2003. YAP-III, with an estimated cost of Rs.1656 crore, was launched in 2018 as an integrated component of the Namami Gange Mission 17 . The river Yamuna is analyzed monthly, seasonally, and yearly for its physical, chemical, and biological properties in previous research for various states and cities influenced by it or small stretches of river 18 , 19 , 20 , 21 . A sufficient amount of literature is available for the water quality of Yamuna and most of the analysis focused on the Delhi and stations lie after the Delhi’s. Kumar et al. 10 discussed the variability of various water quality parameters from 1999 to 2005 to investigate the relationship between environmental parameters and pollution sources. Kaur et al. 22 discussed the impact of industrial development and land use/land cover changes over the Yamuna water quality in Panipat, which is located between the Hathnikund and Baghpat stretch. The sampling was carried out during February, July, October, and December, 2018 to observe the variations in the quality of water and the impact of pollutants on the river Yamuna. Patel et al. 23 carried out the water quality analysis over the Yamuna River using satellite and remote sensing during the lockdown period and compared it with the water quality parameter before the lockdown. The analysis was carried out from January 2020 to April 2021. The impact of lockdown on the water quality was discussed assuming that the industrial waste and the other pollutants sources were reduced during the time of lockdown. Paliwal et al. 24 carried out the modeling analysis of the Yamuna water quality for the Delhi stretch. They emphasize the inflow of untreated water and effluents from various drains in the Yamuna in the Delhi stretch using QUAL2E-UNCAS before 2007. They also highlighted the need for common treatment plants and a rise in freshwater supply. Krishan et al. 25 conducted the groundwater study near the bank of the Yamuna in the Agra and Mathura districts. The study area is located downstream of Delhi and affected by severe falls in water quality. The change in the water quality at Agra and Mathura was discussed and the assessment of the treated water after the filtration was discussed. Kaur et al. 26 investigated the Yamuna water quality at the Delhi stretch and one site located near the Yamunotri. The analyses were implemented during March and October for 2017 and 2018. They have discussed the impact of the inflow of pollutants from the Delhi and NCR regions in the Yamuna. Jaiswal et al. 21 carried out the multivariate study of the Yamuna river water to study the river water quality across the whole stretch. The samples were collected from July to October and November to June for 2013 and 2014. The analysis suggested that the water quality of Palla was suitable for drinking during the study period. We found that these analyses were mostly carried out for a short period in recent years. Some long-term analyses were carried out before 2007. So, there is a dire need for long-term analysis in recent years. In recent times, Lokhande and Tare 3 performed the first long-term analysis of various water quality parameters of the river Yamuna and discussed the trends of various parameters. Due to classified data, Lokhande and Tare 3 were not able to quantify the monthly variations of various water parameters. Hence, these analyses, lack the long-term variability discussion and quantitative changes. In the current study, we conducted monthly and annual mean analysis of various physical and biological parameters, including Biological Oxygen Demand (BOD), Chemical Oxygen demand (COD), Dissolved Oxygen (DO) Saturation, Total Alkalinity, Total Dissolved Solids (TDS), Total Coliform and average rainfall from 2009 to 2019 at Palla station located at northwestern Delhi. We have shown the quantitative change in the physiochemical and biological parameters of the river Yamuna at the Palla station, which is mostly affected by the pollutants load of the watershed of Haryana. We compared the results with the water quality guidelines of national and international standards given in Table 1 to figure out the changes in water quality of river Yamuna at Palla station in the last 11 years. The current water quality at Palla is not suitable for drinking and sometimes not good for agricultural purposes due to the high influx of water pollutants.

The river Yamuna enters National Capital Territory (NCT) at approximately 1.5 km before village Palla, which is 23 km upstream of the Wazirabad barrage. Palla station (N-28.82 and E-77.22) is a manual type station with zero gauges at 206 m. Before entering NCT at Palla, the river Yamuna traveled about 393 km from its source and about 220 km from the Hatnikund barrage. According to Haryana State Pollution Control Board (HSPCB), the numbers of industries in Yamunanagar, Kernal, Panipat, and Sonipat are 142, 9, 346, and 503, generating effluent of 16,420.90, 26.00, 65,696.97, and 15,668.50 KLD, respectively, up to August 2019. As in Fig.  3 , several drains of Haryana state including 3 major drains at Dhanaura escape, Main Drain No.2, and Drain No. 8 also fallout in river Yamuna before reaching Palla 28 (Figs.  3 , 11 ).

Location of Palla station. The base image is provided by ESRI and projection is done using ArcGIS Pro.

Data retrieval

Water quality data of Yamuna River at Palla station was obtained from Water Resources and Information System (WRIS), India, which is a centralized platform, acting as a database related to all water resources at the national or state level. It was initiated by CWC along with the Ministry of Water Resources, Ministry of Jal Shakti, and Indian Space Research Organization (ISRO) in 2008 to provide a single-window solution to all water resources data and information in a standardized national GIS framework 29 . Depending upon the availability of monthly average data, the parameters BOD, COD, DO saturation, total alkalinity, TDS, and total coliform at Palla station were analyzed during the period from January 2009 to December 2019. The rainfall data was procured from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and available on the website http://www.soda-pro.com/web-services/meteo-data/merra . The spatial resolution of data is 0.50° × 0.625° and the time step ranges from 1 min to 1 month 30 , 31 . We also included the water discharge data in the current study of three locations Hathnikund Barrage, Baghpat, and Delhi Railway Bridge (DRB) stations. The freshwater in river Yamuna reached to the Palla station is controlled at the Hathnikund Barrage. Baghpat station is located just before the Palla station (no data is available at Palla station) and DRB is located after the Palla station. These stations are chosen based on the availability of data and the locations to show the water discharge in the Yamuna. The water discharge data of this location is taken from the Central Pollution Control Board of India ( https://yamuna-revival.nic.in/wp-content/uploads/2020/07/Final-Report-of-YMC-29.06.2020.pdf ).

Ethics approval and consent to participate

The paper did not involve any human participants.

Result and discussion

Analysis of various parameters related to the water quality of river Yamuna at Palla station was carried out.

The annual mean variation in rainfall at the Palla region (Fig.  4 a) suggested that the highest annual mean rainfall (95.8 mm) occurred during the year 2010. After 2010, the yearly average rainfall continuously declined every year till 2014 with a mean value of 17.9 mm. Although from 2014 to 2018, a rise in average rainfall was observed each year and during 2018, the average rainfall was calculated as 53.1 mm, but the linear trend illustrated a sharp decline in average rainfall between 2009 and 2019 as shown in Fig.  4 a.

( a ) Yearly mean rainfall at Palla station and adjacent region from 2009 to 2019. The red line shows a linear trend of rainfall. ( b ) Rainfall monthly distribution at Palla station from 2009 to 2019. ( c ) Monthly mean variation of rainfall from 2009 to 2019.

The box plot in Fig.  4 b shows the rainfall data of each month during 2009–2019. The distribution suggested that the monthly mean rainfall variations were highest during August along with July and September. May was an almost driest month along with April and March as the rainfall was minimum during these months. The highest values of average rainfall for August, September, and July were 515.28, 233.45, 207.99 mm respectively, whereas these months’ minimum average rainfalls were 101.74, 61.95, and 72.76 mm, respectively, during 2009 and 2019. The median values of rainfall during March, April, October, and November were below 10 mm and, during January, February, June, and December, the median values were between 10 and 20 mm, while the median values for July, August, and September were above 60 mm.

The monthly mean variation of rainfall (2009–19) further elaborates the stats of rainfall in the Palla region. From 2009 to 2019 rainfall data showed various ups and downs in monthly rainfall (Fig.  4 c). In India, the onset of monsoon is observed during the start of June each year in the coastal part of India, which lasts approximately for a period of four months (June to September) each year. The northern parts of India receive major rainfall during this period, which is known as the monsoon season. During June, the average rainfall remained lower than the decadal mean during 2014–2019 except for 2017. During July, we observed the same deficit of rainfall during 2014–2019 except in 2018. The major deficit in rainfall was observed for August from 2012 to 2018 whereas, during August 2019, the rainfall remained higher than the decadal mean value. During September, the rainfall deficit was observed from 2012 to 2016. Hence, during 2012–2017, we observed a significant fall in the monsoon rainfall. The winter rainfall is also observed during January and February in northern parts of India. We also observed a significant fall in the winter rainfall for January and February since 2014. Yamuna River before Palla station receives runoff water from cities like Sonipat, Panipat, Karnal, and Yamunanagar. The rainfall statistics of Haryana analyzed by India Meteorological Department (IMD) between 1989 and 2018 showed annual rainfalls of 1053.5, 578.8, 573.9, and 495.3 mm at Yamunanagar, Sonipat, Karnal, and Panipat districts, respectively 32 . During the monsoon season, sudden changes in physicochemical and biological parameters were observed by various researchers. The addition of monsoon runoff in rivers dilutes the industrial effluent and sewage, causing the decline in parameters like BOD, COD, alkalinity, pH, and conductivity 33 , 34 , 35 , 36 . The catchment area of Yamuna is the smallest in Delhi. The size of the catchment area of the river is an important factor for the dilution of anthropogenic waste in river water. The small catchment area increases soil components in the river and makes difficult the dilution process of anthropogenic waste 37 . These conditions can further affect the river water quality. Hence, we further analyzed the variations in the various physical and biological parameters for each month from 2009 to 2019.

Dissolved oxygen (DO) saturation

Dissolved Oxygen (DO) saturation is a vibrant parameter for aquatic system health determination. The pollutants like sewage, soil, agricultural runoff, and other organic pollutants can reduce the DO saturation of water 38 and low DO saturation can impact the life of major aquatic organisms. The water having DO < 5% of saturation lies in an extremely severe pollution region; DO between 5 and 10% of saturation lies in severe pollution; DO in a range of 10–70% represents moderate pollution, whereas DO above 70% indicates slightly or no pollution condition. Heavy pollution load due to untreated sewage and industrial effluents are the main causes of decreasing DO concentration 39 . Value of DO saturation is also affected by the change in water salinity (chlorine), temperature, and air pressure.

Increasing pollution in the Yamuna River caused a decrease in DO saturation concentration along with an increase in temperature and salinity of water 3 . Figure  5 shows the yearly variation of DO saturation, and monthly distribution and mean variation of DO saturation at Palla station from 2009 to 2019. In recent years, the annual mean value of DO saturation during 2017–19 was found to be 4.81%. From 2009 to 2011, the mean DO saturation was found to be 69.49% with a maximum value (81.58%) in 2010. From 2012 to 2015, the yearly mean DO saturation was 86.48%. During this period, a small decrease was seen between 2013 and 2015. In recent years, the DO saturation reached critically low values to the average value before 2015 as demonstrated in Fig.  5 a. These conditions indicate the rise in pollutants in the river Yamuna.

( a ) Yearly variation of DO saturation from 2009 to 2019. The red line shows a linear trend of DO saturation. ( b ) Monthly distribution of DO saturation at Palla station from 2009 to 2019. ( c ) Monthly mean variation of DO (%) saturation from 2009 to 2019.

The monthly distribution of DO saturation during 2009–2019 is shown in Fig.  5 b with boxes ranging from 25 to 75%. During May, the maximum median value was 88%, whereas in January minimum median value was about 25%. Similarly, the maximum monthly mean value was 62.7% during May, while the minimum mean value was 34.1% during January. Similarly, maximum DO saturation of 118% was perceived for May, while a minimum of 2.4% was observed for December. The monthly mean distribution suggested that the mean DO saturation plunged between 50 and 60% with a slight growth in trend from January to December. We further showed the monthly mean values of DO saturation in Fig.  5 c. From 2009 to 2015, the values suggested no major changes with monthly values well above 50, but just after 2017, a sudden fall was observed in the DO saturation each month. We observed 10 times fall in DO saturation just after 2017. During 2009–14, the DO saturation of Yamuna River at Palla station was Class I category of international standards for surface water of UNECE, but during 2016–19 its quality degraded to Class IV due to regular fall in the DO values of the river water. The lack of fresh water and rising carbon concentration have affected the DO concentration significantly.

Biological oxygen demand (BOD)

Biological Oxygen Demand (BOD) is one of the methods to assess the quality of water by calculating the oxygen requirement for decomposition of its organic matter. The yearly variations in BOD at Palla station during 2009–19 are shown in Fig.  6 a. The yearly mean BODs in 2009 and 2010 were estimated to be around 6 mg/l, while, during 2013 and 2012, the least values of BOD (1.4 and 2.7 mg/l, respectively) were found. The year 2015 showed the highest yearly mean BOD value of around 12 mg/l followed by years 2014 and 2016 with a value of about 9.5 mg/l. The decline in yearly mean BOD was observed from 2015 to 2019. In particular, a BOD of 3.5 mg/l was perceived for years 2018 and 2019. The monthly variation of BOD during 2009–19 was shown in Fig.  6 b, where the interquartile range for each month was between 25 and 75 percentiles. The first quartile for all months lay below the range between 0 and 5 mg/l, with outliers for months May, July, October, and December with BOD above 20 mg/l. January and February had the highest median value of BOD at 6.7 mg/l, while September had the least median value of 2.1 mg/l.

( a ) Temporal variation of yearly mean BOD from 2009 to 2019. The red line shows a linear trend of BOD. ( b ) Monthly distribution of BOD at Palla station from 2009 to 2019. ( c ) Monthly mean variation of BOD from 2009 to 2019.

The median value for April, June, July, October, and December lay between 3 and 5 mg/l. The monthly mean BOD during 2009–19 represented that BOD was maximum in December (8.9 mg/l) followed by the second highest mean value of 8 mg/l in May. The minimum mean value of 2.7 mg/l was found in August, while in April, September, October, and November, the mean BOD lay between 3 and 6 mg/l. Mean BOD from January to March and in July was in the range of 6–7.5 mg/l.

The fluctuation in BOD was observed during 2009–19 in Fig.  6 c. In January, February, and March, a decrease in BOD occurred during 2009–13, while in May, June, and July increase in BOD was seen during 2013–16. In August, September, and October, BOD mostly fell below 5 mg/l throughout the study period. The highest BOD values were measured in December 2015, October 2014, and July 2016 (41.6, 26.5, and 24.8 mg/l, respectively). The decreasing trend was observed for January, February, and July, with a rising trend for March, April, May, and November. From 2014 to 2016, the BOD values were found to be several times higher than the acceptable limits. In March and April, BOD values were less than 4, and else BOD was higher than 4 even in monsoon months. In years 2018–19, BOD is observed to be mostly ≤ 4 mg/l. However, the overall BOD values suggest that the water quality of Yamuna mostly lay beyond the C category (BIS) as the BOD values are mostly > 4 mg/l. To attain river quality standard it has to be ≤ 3 mg/l.

Chemical oxygen demand (COD)

Chemical Oxygen Demand (COD) determines the amount of oxygen required for the oxidation of organic matter present in water. We have shown the changes in COD at Palla station in Fig.  7 . Yearly mean COD varied indistinctly during 2009–19 even though the linear trend has moved upward with increasing year as shown in Fig.  7 a. The yearly mean COD increased from 15.9 to 30.8 mg/l during 2010–2012 and from 11.5 to 44.3 mg/l during 2013- 2015. Mean COD for the years 2018 and 2019 was 15.1 and 20.4 mg/l, respectively. The highest yearly mean COD was 44.33 mg/l for the year 2015 followed by the year 2012 with 30.8 mg/l, while the least mean COD value was observed in 2013 as 11.5 mg/l. A sharp decline in yearly mean COD was noted during the years 2012–13 and 2015–16. The monthly data of each month during 11 years period is shown in Fig.  7 b. The width between the first and third quartiles for January, April, and May indicated maximum variation in COD values. The September quartile indicated less variation in COD as compared with the other months. The CODs for January and May had the highest median values of 27 and 24.5 mg/l, respectively, whereas April had the least median value of 8 mg/l. The median CODs for March, August, September, October, and November lay in the range of 11–16 mg/l. We found that the monthly mean CODs of May and November had maximum and minimum values of 33.9 and 14.2 mg/l, respectively.

( a ) Yearly variation of COD from 2009 to 2019. The red line shows a linear trend of COD. ( b ) Monthly distribution of COD saturation at Palla station from 2009 to 2019. ( c ) Monthly mean variation of COD from 2009 to 2019.

The trend of COD followed a decreasing path moving from January to December. The monthly mean CODs of January, February, and May were found to be higher than 30 mg/l whereas for April, June, and October they lay between 20 and 30 mg/l, and for the remaining months mean COD was found to be lesser than 20 mg/l. A decline in the mean COD was observed from May to August, with a sharp increase in mean COD from March to May.

Figure  7 c shows changing COD for each month as the year preceding. From 2009 to 2012, the COD values for January, February, March, and April were higher than 30 mg/l, whereas, since 2017, the COD values were seen well below 30 mg/l during these months. In May, an exceptional high COD of 125 mg/l was observed and for the same month, a continuous increase in COD was noted from the years 2013 to 2016. Also, the rise in COD was seen for June, August, October, and December during 2012–15. Excluding May, February, April, and September had maximum CODs of 85, 78, and 67 mg/l, respectively. During 2009–19, January and June to November showed a growing linear trend, whereas the rest of the months had a declining trend. Compared with the international standards, the annual and monthly mean CODs exceeded the WHO guideline and were classified as Classes III-V of UNECE standards. Also, the values were found well above the threshold value. During the years 2009, 2012, and 2013, the monsoon period showed COD value in Class II, while, during the overall monsoon period, water quality lay in Class III. During the year 2015, Yamuna, in terms of COD, was in the worst condition as it lied in Class V. Monthly variation COD for 2018–19 rarely plunged under WHO standards and represented to be in Classes III and IV.

Total alkalinity

Total alkalinity is mostly due to calcium carbonate (CaCO 3 ) and also important for sustaining aquatic life. The yearly mean variation of total alkalinity during 2009–19 is shown in Fig.  8 a. Total alkalinity was found to be the highest during 2015 followed by 2011 with values of 187.37 and 164.6 mg/l, respectively, while, for the rest of the year, the annual means were in the range of 110–150 mg/l. The linear trend for the yearly mean for the entire period remained constant. With these values of total alkalinity, the quality of river water lay in category II (UNECE 1994).

( a ) Yearly variation of total alkalinity from 2009 to 2019. The red line shows a linear trend of total alkalinity. ( b ) Monthly variation of total alkalinity from 2009 to 2019. ( c ) Monthly mean variation of total alkalinity from 2009 to 2019.

We have shown the monthly distribution of total alkalinity in Fig.  8 b with the third quartile of each month lying below the mark of 200 mg/l. The median values dropped from January to July and then raised from July to December. The monthly mean total alkalinities in February and January showed the first and second-highest median values of 159.8 and 155.5 mg/l, respectively, while July and June showed the lowest median values of 86.5 and 88.9 mg/l, respectively. Median values during May, August, and September lay in the range of 90–100 mg/l, while for March, October, November, and December they were between 100 and 150 mg/l. The linear trend for monthly mean total alkalinity also followed the same pattern as the yearly mean. The total alkalinity declined from January to May from 159.4 to 105.5 mg/l and, then from August to December, it raised from 93.8 to 170.7 mg/l. August and December showed the lowest and highest values of total alkalinity, respectively.

The monthly variation in total alkalinity for each year during 2009–19 is shown in Fig.  8 c. June (2015), November (2011), and December (2015) were the months with the highest total alkalinities of 723.3, 569.9, and 428.1 mg/l, respectively. During 2009–19, total alkalinities for February, March, May, July to September remained within 200 mg/l. The lowest total alkalinity of around 55 mg/l was noticed for June (2010) and July (2012). Although the linear trend for monthly total alkalinity showed almost the same slope except for January, July, and November. A wave pattern in total alkalinity for August, September, and October was noticed with an increasing trend during 2009–19. In 2019, the total alkalinity was below 150 mg/l throughout the year 2019. For maintaining WHO and BIS standards, the minimum total alkalinity must be 200 mg/l, while it was not possible to fulfill the standards for both monthly and yearly aspects in the study areas of concern. There were only a few months when total alkalinity was found to be higher than 200. Comparing with UNECE standard, 11 out of 12 monthly mean alkalinities lay in Class II and the remaining one month in Class III category. March 2018 was the last month since 2018 when Yamuna's total alkalinity was well above-mentioned the water standards.

Total dissolved solids (TDS)

Total Dissolved Solids (TDS) define the presence of inorganic compounds along with organic matter in small concentrations originated by naturally, household, and industrial sources. The data was available from 2013 onwards. The yearly mean TDS was found to be highest in 2015 (447 mg/l) followed by 2017 and 2018 with 421 mg/l (Fig.  9 a). The lowest yearly mean TDS was observed as 256 mg/l in 2010, while the recent value of 272 mg/l was observed in 2019. Although the linear trend for yearly TDS indicated the rise in overall TDS. A maximum drop in yearly TDS was observed with a fall of 36% during 2018–19.

( a ) Yearly variation of TDS from 2013 to 2019. The Red line shows a linear trend of TDS. ( b ) Monthly variation of TDS from 2013 to 2019. ( c ) Monthly mean variation of TDS from 2013 to 2019.

The 25 to 75 percentiles of interquartile range of all twelve months for TDS are shown in Fig.  9 b. Although March had the maximum width of interquartile range, the maximum median value of TDS was 678 mg/l for January. Except for August and October, the median TDS values for the rest of the months lay in the range of 200–400 mg/l. August had the lowest median TDS of 162 mg/l and October had 404 mg/l. A sharp decline in the trend of monthly mean TDS was observed during 2013–19, but mean TDS fluctuated throughout the year. Similarly, with median TDS, the monthly mean TDS for January and August had maximum-minimum mean values of 628.3 and 177.57 mg/l, respectively. Except for August, the monthly mean values of TDS were above the mark of 200 mg/l. From April to July, they lay in the range of 300–400 mg/l, while in February, March, and December, they ranged between 400 and 500 mg/l.

In February and March, TDS had a higher magnitude of rising, while it appeared to be almost constant in August as shown in Fig.  9 c. In January, TDS was measured as 732 mg/l in 2014 and dropped to its lowest point of 310 mg/l in the next year, but then it reached 998 mg/l in 2017. The TDS values during June were well below the mark of 300 mg/l from 2009 to 2019, but during June 2015, the monthly mean TDS was found to be 1333 mg/l. This was the maximum value of TDS during the whole study period. For the same year, the second-highest TDS of 1067 mg/l was also observed in December. TDS remained mostly below 300 mg/l for August and September (mostly during monsoon months) with the lowest TDS of 128 mg/l in August 2017. In 2019, the TDS value mostly ranged between 200 and 300 mg/l. Yearly and monthly mean values of TDS were observed almost under WHO standards and in the Class A category of Indian standards. The TDS value was found below 500 mg/l during each monsoon season. During winter, summer, and post-monsoon months, the TDS of river water never exceeded the Class C category as per BIS standard and during January, the monthly mean average remained higher in comparison to other months.

Total coliform

Human and animal discharges are the main source of fecal coliform bacteria whose excessive presence in water degrades the water quality. During 2009–19, there was an exponential rise in total coliform as shown in Fig.  10 a. The yearly mean in 2009 was 177 MPN/100 ml, which in the decade reached 139,200 MPN/100 ml in 2019. The difference of yearly mean for two periods of 2009–13 and 2016–19 was more than 100 times the value at its starting period. The year 2018 was observed with the highest yearly mean of 490,818 MPN/100 ml, which was reduced in 2019, with the lowest total coliform count of 136.7 MPN/100 ml in 2010. Note that the years 2014–15 were excluded from comparison for this case due to less availability of data for the whole year.

( a ) Yearly variation of total coliform from 2009 to 2019. Redline shows an exponential trend of total coliform. ( b ) Monthly variation of total coliform from 2009 to 2019. ( c ) Monthly mean variation of total coliform from 2009 to 2019.

Figure  10 b shows a large monthly variation in total coliform in each month during 2009–19. The median of most of the months was below 500 MPN/100 ml, whereas median values were 1050, 620, and 16,775 MPN/100 ml for September, October, and November, respectively. The monthly mean for January, February, March, April, June, and October was above 100,000 MPN/100 ml. Maximum and minimum monthly means were observed for October (133,797 MPN/100 ml) and May (12,663 MPN/100 ml), respectively.

Figure  10 c indicates the exponential rise in the trend of total coliform in each month since 2009. Total coliform counts during 2009–14 were well below the 500 MPN/100 ml mark, while some months also showed counts near 800 MPN/100 ml. However, from 2016 onwards, the counts crossed a sustainable mark of 5000 MPN/100 ml for every month of each year. From 2009 to 2013, the total coliform counts fell mostly in Class B but exceeded all limits of Indian water quality standards to a great extent during 2016–19. The monthly mean was not even close to the maximum total coliform limit of 5000 MPN/100 ml, which made water quality in Class D and E categories. WHO standards nullify the presence of fecal coliform in water, whereas the Yamuna River was found to be in its alarming situation for this particular parameter.

Water discharge

The freshwater supply and inflow of wastewater in the river may affect the water quality. To uncover the influence, we analyzed the water discharge in the river Yamuna from 2013 to 2018 (the data is available for this period only). In Fig.  11 , we show the major locations of water abstraction and confluence in the Yamuna river starting from Yamunotri to Faridabad stretch. Hathnikund barrage was constructed to regulate the Yamuna water supply to Haryana, Uttar Pradesh, and Delhi for agricultural and domestic purposes and it was also decided by the Government to maintain 10 cumes of water in the Yamuna downstream to maintain the aqua life in river. In Fig.  12 , we show the water discharge data of Hathnikund, Baghpat, and DRB. From 2013 to 2018, a significant fall in the water discharge is observed especially at Hathnikund, which allows the upstream water to reach Palla. The mean discharge in the river was found to be 123.7 cumes during the whole study period and the minimum was found much lower than 10 cumes (as suggested by the Government of India). Also, a significant fall is observed in recent years. The mean water discharge values at Baghpat are found to be 225 cumes and sometimes they reached far below than 5 cumes.

( Source : https://yamuna-revival.nic.in/wp-content/uploads/2020/07/Final-Report-of-YMC-29.06.2020.pdf ).

Points of water abstraction and additions in Yamuna river.

( a ) Temporal variation of water discharge at Hathnikund, Baghpat, and Delhi Railway Bridge (DRB) stations during Jan 2013 to May 2018. ( b ) The distribution of monthly mean water discharge during Jan 2013 to May 2018 at Baghpat. ( c ) Monthly mean variation of discharge from 2013 to 2018.

At DRB, the mean discharge value is found to be 107 cumes. During monsoon months, the water discharge at Baghpat is higher than that at the Hathnikund so that the watershed of the Yamuna also helps in the rise of the water discharge due to rainfall and also major drains confluence in the Yamuna. We can see that during August, the water discharge is the highest followed by July and September. During January, May, and December, the water discharge values reached far below the prescribed limit, and hence sometimes during these seasons, the Yamuna almost dried up and only the sewage and the industrial wastes water flow during these months (Fig.  12 c). After 2015, a significant fall in water discharge is observed during the summer and winter months. With the abstraction of fresh water at Hathnikund barrage and inflow of the drains, the water quality parameters are affected by a large extent in river Yamuna. Therefore, for further analysis of the impact of water discharge and rainfall, we also analyzed the relationship of monthly mean discharge, rainfall, DO saturation, BOD, and COD as shown by the polar plots (Figs. 13 , 14 ).

The relationship between the water discharge, Rainfall, BOD, and COD.

The monthly relationship between the water discharge, Rainfall and DO saturation.

Variability of DO, BOD, and COD

Water quality can be affected by various factors. Hence, we investigated the variability of the DO, BOD, and COD with water discharge and rainfall. In Fig.  13 , we plot the relationship of monthly mean water discharge at Baghpat, rainfall, BOD, and COD. The rainfall mostly affected the discharge values, but, sometimes, with low rainfall high discharge is observed. COD is mostly higher during the whole study period whereas BOD values have shown a fall in recent years. The impact of rainfall and high discharge is visible and low BOD and COD are observed. When the discharge is more than 525 cumes, the COD and BOD values are lower, and also more than 30 mm of rainfall is observed at that time. Further, we investigated the monthly relationship of the Discharge, rainfall, and DO saturation. Further analysis (Fig.  14 ) clearly shows that most rainfall occurred from June to September and caused a rise in the water discharge. During this time, the DO saturation values are more than 50%. The low DO saturation values are mostly observed during the time of low discharge and low rainfall (< 10%). We also found that during July to September with discharge between 200 to 400 cumes and rainfall more than 40 mm, the DO saturation remains lower than 10%. Further, Drain No. 6 having a catchment area of Samalkha Ganur and Sonipat, carries around 49 MLD of sewage in the year 2017 and rises to 210 MLD in the year 2018. Drain No. 8 crosses Drain No. 6 at Akbarpur, Sonipat, and meets river Yamuna just upstream of Palla village. There is a huge increase in the flow of Drain No. 8 between 2017 and 2018 from 196 to 2590 MLD. Drain No. 6 lined separately flow inside Drain No. 8 for 10 KM. Effluents from both drains mix with each other during the rainy season and due to accidental breach ( https://sandrp.in/2015/04/13/blow-by-blow-how-pollution-kills-the-yamuna-river-a-field-trip-report/ ). Similarly, sewage flow of Drain No. 2, which meets river Yamuna 100 km upstream of Delhi is increased from 62 to 2092 MLD between 2017 and 2018 by Central Pollution Control Board of India. As per Haryana State Pollution Control Board (HSPCB), the capacities of Sewage treatment plants (STP) for Drains No. 2, 6, and 8 were 72, 104.5, 125.3 MLD, respectively, till 2018. Also, the capacities of the common effluent treatment plant (CETP) for Drains No. 2, 6, and 8 were 21, 33.2, and 10 MLD, respectively. Since the total capacity for treating wastewater was far beyond sewage generation during 2017–18 and hence the untreated water mixed with the river water. This rise in wastewater generation from industrial and urban areas has caused a drastic decrease in DO saturation and an increase in Total Coliform. As per CPCB 2018 report ( https://yamuna-revival.nic.in/wp-content/uploads/2020/07/Final-Report-of-YMC-29.06.2020.pdf ), 7-day average discharge of the 10-year return period (7Q10) does not meet the habitat requirements of the indicator fish species. These conditions show the impact of rapid urbanization and industrialization along the river bank with high carbon concentration. With the recent development in industrial regions, change in land use/land cover and rapid urbanization in the Haryana, the watershed of Yamuna suffered a lot and hence the water quality of the river. One of the major causes of the sudden fall in the DO saturation in recent years is the fall in the freshwater discharge at the Hathnikund and also the fall in the rainfall in recent years. Also, the deteriorating water quality of Yamuna is a major concern for the Government and mostly this is affected in the stretch between Hathnikund and Palla due to rise in inflow of untreated drains water supply. In recent years, the National Green Tribunal (NGT) of India also requested the states Government to take necessary actions to combat present situation of river Yamuna by installation of more STEPs, ETPs and maintaining the treatment capacity of present treatment plants and channelizing the sewage network to reach treatment plants properly. However, due to lack of adequate fresh water supply and mixing of untreated sewage through regulated and unregulated drains, the quality of the Yamuna river lies in critical conditions.

Although National Capital Territory (NCT) is held responsible for most polluting river Yamuna, the study reveals that the quality of the river it receives is not admirable. The study of physiochemical and biological parameters shows variation in its monthly and yearly values during 2009–19. The effect of monsoon season can be easily seen on parameters like BOD, COD, total alkalinity, TDS and total coliform as their values declined, while DO saturation % showed a significant rise. DO saturation declined by more than 85% during this period. The BOD values improved during the last two years (2018–2019), but were still slightly higher than the permissible limit, while the COD value always remained quite higher than the permissible limits. In 2015, the worst condition was observed in terms of BOD and COD. Total alkalinity also remained low and below the prescribed standards, but TDS is the only parameter whose value was mostly in desired limits throughout the period. An exponential rise was observed in the total coliform count, which was 100–1000 times the maximum limit of IS:2296. Increasing discharge of partially treated industrial effluent and untreated sewage into the Yamuna in the past decade is considered to be the primary cause of the deterioration of water quality. Even after completion of YAP phases I and II, and ongoing phase III, the river still falls in the category of Class D or E under BIS specification, Class III or IV of UNECE standards, and does not fulfill the WHO guideline for water quality at Palla station.

Data availability

All the data used in the present study is freely available in the public domain and the web addresses are discussed in the manuscript, however, we will provide data to all the interested scientists.

Abbreviations

Bureau of Indian Standards

Biological oxygen demand

Common effluent treatment plants

Chemical oxygen demand

Central Pollution Control Board

Central Water Commission

Dissolved oxygen

Effluent treatment plant

Global Environmental Monitoring System

Haryana State Pollution Control Board

India Meteorological Department

Indian Space Research Organization

Kiloliters per day

Modern-Era Retrospective analysis for Research and Applications, Version 2

Monitoring of Indian National Aquatic Resources

Millions of litres per day

Ministry of Environment and Forests

Most probable number

National Capital Region Planning Board

National Capital Territory

National Green Tribunal

National Water Quality Monitoring Programme

State Pollution Control Board

Total dissolved solid

United Nations Economic Commission for Europe

World Health Organization

Water Resources and Information System

Yamuna Action Plan

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Acknowledgements

We would like to thank the National Water Informatics Centre (NWIC), a unit of the Ministry of Jal Shakti for providing updated data on water resources through a ‘Single Window’ source. We also thank research center O.I.E of Mines Paris Tech and ARMINES for providing meteorological data. The data used in the current study were freely available and their links are mentioned in their respective places. We express a great sense of gratitude towards the Central Pollution Control Board of India and other agencies for making data available.

This research was financially supported by the Ministry of Science and Technology (MOST) of Taiwan under the codes MOST 109-2923-E-008-004-MY2 and MOST 110-2111-M-008-008.

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Joshi, P., Chauhan, A., Dua, P. et al. Physicochemical and biological analysis of river Yamuna at Palla station from 2009 to 2019. Sci Rep 12 , 2870 (2022). https://doi.org/10.1038/s41598-022-06900-6

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Water quality modeling-based assessment for the scope of wastewater treatment of the urban reach of river yamuna at delhi, india.

• Nibedita Verma
• Geeta Singh
• Naved Ahsan

Environmental Monitoring and Assessment (2024)

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Heavy metal contamination in the complete stretch of Yamuna river: A fuzzy logic approach for comprehensive health risk assessment

Maneesh jaiswal.

1 Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, India

Sanjay Kumar Gupta

Mayuri chabukdhara.

2 Department of Environmental Biology and Wildlife Sciences, Cotton University, Guwahati, Assam, India

Mahmoud Nasr

3 Environmental Engineering Department, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt

4 Faculty of Engineering, Sanitary Engineering Department, Alexandria University, Alexandria, Egypt

Arvind Kumar Nema

Jakir hussain.

5 Upper River Yamuna Board, Department of Water Resources, River Development and Ganga Rejuvenation, New Delhi, India

Tabarak Malik

6 Department of Biomedical Sciences, Institute of Health, Jimma University, Jimma, Ethiopia

Associated Data

All relevant data are within the paper and its Supporting Information files.

River Yamuna is one of the most sacred major tributaries of river Ganga. This study aimed to assess the level of heavy metals in monsoon and non-monsoon season in river Yamuna in Uttar Pradesh, India and to assess the possible source of contamination and its associated health risk. Except for iron (Fe), the mean levels of all metals were within drinking water safe limits in both seasons. Except for chromium (Cr), lower values were observed for other metals in the monsoon season could be attributed dilution effect. Multivariate analysis indicated that both geogenic and anthropogenic sources contribute to heavy metals in river Yamuna in monsoon and non-monsoon seasons. The health risk in terms of hazard index (HI) and fuzzy-logic hazard index (FHI) demonstrated that both HI and FHI values among children exceeded the safe limit in most of the sites in non-monsoon seasons and in few in monsoon season. For adults, HI and FHI values were within safe limit.

1. Introduction

River water have been used for various purposes including drinking, irrigation, domestic and industrial applications [ 1 , 2 ]. Unfortunately, the water quality in rivers has recently suffered from dramatic deterioration due to various anthropogenic and natural activities [ 3 ]. Among different pollutants, heavy metals are of serious concern due to their toxic, bioaccumulative, non-biodegradable nature [ 4 ].

Arsenic (As), cadmium (Cd), chromium (Cr) and lead (Pb) rank among the priority metals that are of great public health significance and are also classified as either “known” or “probable” human carcinogens based on epidemiological and experimental studies [ 5 ]. Other complications associated with heavy metals include gastrointestinal and kidney dysfunction, nervous system disorders, skin lesions, vascular damage, immune system dysfunction and birth defects [ 6 ]. The International Agency for Research on Cancer (IARC) has classified nickel as a potentially carcinogenic substance [ 7 ]. Metals such as zinc, copper and iron are essential elements that are required for several chemical or biochemical processes in the body but are toxic above a certain concentration [ 8 ]. In the past few decades the concentration of metals in almost all the Indian rivers has increased due to anthropogenic activities [ 9 – 11 ]. Heavy metals from industrial effluents and surface and agricultural runoff have been considered a major source of water pollution, causing serious human health risks [ 12 , 13 ]. Accordingly, comprehensive investigations should be performed to assess the health risks associated with human exposure to metal-contaminated water, providing sustainable strategies for managing the river systems.

India’s population relies on the River Yamuna as the primary water source for domestic purposes and agricultural applications, fulfilling more than 90% of the total water demand in several districts [ 14 ]. Unfortunately, in recent years the Indian Yamuna river and its major tributaries and catchment area have suffered from severe pollution due to the discharge of untreated or partially treated wastewater containing undesirable levels of toxic metals [ 2 ]. Hence, the study objectives are four fold: (1) to analyze the toxic elements along the entire Yamuna stretch for the monsoon and non-monsoon seasons compared with the national and international standards, (2) to assess their possible sources by using multivariate analysis, (3) estimate potential noncarcinogenic human health risk for adults and children, (4) employ a fuzzy-logic approach to generate a new criterion for health risk forecasting, namely fuzzy-logic hazard index (FHI).

2. Materials and methods

2.1. study area and water sampling.

The Yamuna is one of the largest and longest rivers in India, with a total length of about 1376 km and a catchment area of 366,223 km 2 . It originates from the Yamunotri glacier (38° 59′ N, 78° 27′ E) of the lower Himalayas in the District Uttarkashi (Uttranchal). The river can be classified into five sectors: the Himalayan, Upper, Delhi, Eutrophicated, and Diluted segments, as demonstrated by Sharma and Kansal [ 14 ]. The important geo-environmental conditions of the River Yamuna and the associated catchment basins are listed in the S1 Table . No special permission was needed for collecting the samples from all along the stretch of river Yamuna.

In this study, 13 sampling sites were selected to monitor and assess the distribution of metal pollution along the entire Yamuna stretch. These sites corresponded to 13 districts, namely Poanta, Kalanaur, Mawi, Palla, Delhi, Mohana, Mathura, Agra, Etawah, Auraiya, Hamirpur, Rajapur, and Pratappur ( Fig 1 ). The sampling sites and location details are given in S2 Table . The water sampling procedure was performed twice a year, during the monsoon (July to October) and non-monsoon (November to June) seasons from 2011 to 2018. One liter of water samples was collected from 30 to 50 cm depth using the grab method from the middle of the river. During non-monsoon, there is no or limited rainfall, and river water levels decrease; during monsoon, river water levels increase due to heavy rain. The variation in rainfall during monsoon and non-monsoon seasons can cause the variation of metals concentration in water.

After collection, the samples were filtered, acidified, and preserved at 4°C in an icebox, following Gupta et al. [ 10 ]. Eight metals, i.e., arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), iron (Fe), and zinc (Zn), were selected in this study to assess the human health risk along the river.

2.2 Analytical analysis

The water samples were acid digested for metal analysis by adding 20 mL of concentrated HNO 3 at 100°C until dryness [ 15 ]. The respective digests were cooled to room temperature, diluted, and filtered by Whatman no. 42 filter paper, following Gupta et al. [ 10 ]. Further, the atomic absorption spectrometry (Varian AA240 Zeeman, USA) was used to measure the metal concentrations of each digest. All reagents and chemicals used in this study were of analytical grade and procured from E. Merck Ltd., India. Ultrapure water (18.2 MΩ cm at 25°C; USF Elga, Germany) was used throughout the study to prepare all standards. Certified stock solutions purchased from E. Merck Ltd., Germany, were used for preparing the calibration curves. Quality control of the analytical analysis was guaranteed by the use of standard operating procedures (SOP), reagent blanks, reagent set spike samples, and recovery of spiked control replicate.

2.3 Data analysis

The relationship between various metals was analyzed using Pearson’s correlation coefficient. Correlation analysis was performed to understand the relationship among metal pairs. Principal component analysis (PCA) was carried out to identify the principal sources of variation in the data set due to interrelated variables [ 16 ]. Two multivariate statistical techniques were employed, the PCA and the HCA (Hierarchical Cluster Analysis). The details of PCA and HCA are mentioned elsewhere [ 17 ]. The similarity and variation among various sites were determined by Cluster analysis. Before performing the FA/PCA, the dataset was first standardized to avoid numerical ranges of the original variables. The data were analyzed using a statistical package SPSS ® (Window Version 17.0), and with XlStat, an add-in package of Microsoft Excel 2011.

2.3.1 Health risk assessment

In this study, the magnitude, frequency, and duration of human exposure to metals in the River Yamuna were used to estimate the non-cancer health risk. For this purpose, the average daily dose (ADD) was derived from Eq 1 [ 18 ].

where, ADD is the average daily dose (mg/kg/day), C is the mean concentration of metal (mg/L), IR is the intake rate of metal-contaminated water (3.45 L/day for adults and 2.0 L/day for children), EF is the exposure frequency (365 days/year), ED is the exposure duration (70 years for adults and 10 years for children), BW is the average body weight (60 kg for adults and 25 kg for children), and AT is the average time (25,550 days for adults and 3,650 days for children).

HQ expresses the potential exposure to an element divided by the appropriate chronic or acute dose that has no adverse effects [ 19 ]. HQ of an individual metal in the dose-response assessment was calculated by Eq ( 2 ).

where, RfD is the oral reference dose in mg/kg/day considered as 3.0E−04 (As), 1.0E−03 (Cd), 3.0E−03 (Cr), 3.7E−02 (Cu), 2.0E−02 (Ni), 3.5E−03 (Pb), 7.0E−01 (Fe), and 3.0E−01 (Zn).

HI defines the total hazard of all constituents in a mixture of toxics, affecting a specific route/pathway [ 19 ]. HI was computed from Eq ( 3 ) by the summation of individual HQs of each metal.

For the risk assessment of a mixture of elements, if the value of HQ and/or HI exceeds 1, there could be potential noncarcinogenic effects on human health. The HQ and HI criteria were calculated for two population groups, i.e., adults and children.

2.3.2. Fuzzy logic-based model for health risk assessment

2 . 3 . 2 . 1 Fuzzy model concept . The fuzzy logic approach was initiated by Zadeh [ 20 ] to represent intrinsically vague or linguistic knowledge using a set of If-Then inference rules, i.e., “IF X AND Y THEN Z”. Generally, the number and quality of the rules affect the robustness of the system under study. Models based on fuzzy rules could extract relevant information from uncertain and inaccurate data, depending on the human inference process and knowledge [ 21 ]. Hence, the fuzzy-based classification allows for logical, reliable, and transparent information of data collection by expressing multiple levels within the scale (0–1), i.e., instead of only two levels (0 or 1) in classical clustering.

In this study, fuzzy logic was employed to describe the human health risk associated with exposure to the trace metals in the River Yamuna ( Fig 2 ). The parameters of the fuzzy-based model used to predict the fuzzy-logic hazard index (FHI) were selected based on personal knowledge, experience, and understanding of the metal-health risk relationship. For this purpose, a Mamdani-type fuzzy model was employed to represent concise correlations between eight metals (As, Cd, Cr, Cu, Ni, Pb, Fe, and Zn) and FHI. Each input was classified into three categories (cluster1, cluster2, and cluster3), using the Gaussian curve membership function. The parameters of the Gaussian shape functions, along with the linguistic classification of FHI for adult and children, are given in S3 and S4 Tables. The amount of overlap of membership functions for each input variable was assigned by an expert’s advice and the permissible limit of each metal [ 3 ]. Three fuzzy If-Then rules were considered to be suitable for this study. The ranges of the fuzzy sets for each variable were selected, following the methodologies performed elsewhere [ 12 , 22 , 23 ].

2 . 3 . 2 . 2 Fuzzy-model procedures . The fuzzy logic-based index was developed by conducting three major steps, which can be described as follows ( Fig 3 ):

• (i) Fuzzification (Fuzzify inputs) :

In the Fuzzification process, each non-fuzzy value is transferred into a number between 0 and 1 through a membership function. The Gaussian shape was selected in this study as the appropriate membership function for all variables (input and output). For instance, the first input, “As” was classified into three fuzzy linguistic sets: in1cluster1, in1cluster2, and in1cluster3 ( S3 and S4 Tables).

• (ii) Inference Engines or Fuzzy If-Then Rules :

In the second step, three If-Then inference rules were created to map conceivable relationships between the input and output variables. The first rule can be enunciated simply as follows:

If the inputs to the FIS, i.e., As, Cd, Cr, Cu, Ni, Pb, Fe, and Zn, strongly belong to their respective cluster membership functions, then the output (FHI) must strongly belong to its cluster membership function. In the fuzzy description, this rule can be transformed into an If-Then fuzzy rule:

Rule 1 : IF (As is in1cluster1) AND (Cd is in2cluster1) AND (Cr is in3cluster1) AND (Cu is in4cluster1) AND (Ni is in5cluster1) AND (Pb is in6cluster1) AND (Fe is in7cluster1) AND (Zn is in8cluster1) THEN (FLHI is out1cluster1) (1).

In the same way, the other two rules used to define the behavior of the system are:

Rule 2 : IF (As is in1cluster2) AND (Cd is in2cluster2) AND (Cr is in3cluster2) AND (Cu is in4cluster2) AND (Ni is in5cluster2) AND (Pb is in6cluster2) AND (Fe is in7cluster2) AND (Zn is in8cluster2) THEN (FLHI is out1cluster2) (1).

Rule 3 : IF (As is in1cluster3) AND (Cd is in2cluster3) AND (Cr is in3cluster3) AND (Cu is in4cluster3) AND (Ni is in5cluster3) AND (Pb is in6cluster3) AND (Fe is in7cluster3) AND (Zn is in8cluster3) THEN (FLHI is out1cluster3) (1).

Each rule has a weight, which can take a value between 0 and 1. In this study, the (1) at the end of the rule indicates that the rule has a weight or importance of "1".

The fuzzy operator was employed to give one number that denotes the result of the rule antecedent (i.e., the “If” part of each rule), covering the eight input attributes. The logical “AND” operator (the minimum of the options) was applied to the antecedent, followed by the implication method “MIN” to truncate the output membership function. This output is known as the consequent of the rule (i.e., the “Then” part of each rule).

• (iii) Defuzzification :

The consequents of all rules are combined into a single fuzzy set via the “MAX” (maximum) aggregation method. Other aggregation tools, including “PROBOR” (probabilistic OR) or “SUM” (sum of the rule output sets), could also be selected based on the fuzzy model application. Finally, the result of aggregation was subjected to a defuzzification process to obtain the final decision (a single number). The defuzzification method used was the Centroid calculation, whereas other mathematical techniques, including Bisector, Largest of maximum, Middle of maximum, and Smallest of maximum, could also be adopted. The centroid method was selected for defuzzification since it is the most prevalent and physically appealing to various model structures.

All the computations were processed using the “fuzzy logic toolbox” in MATLAB R2013a ( http://www.mathworks.com/ ).

3. Results and discussion

3.1 trace and toxic elements in water.

In this study, water samples were collected from 13 districts distributed along the River Yamuna stretch and analyzed for metals (As, Cd, Cr, Cu, Ni, Pb, Fe, and Zn). Statistical summary of metal concentrations in river Yamuna in monsoon and non-monsoon seasons are shown in Table 1 .

The mean concentrations of 8 metals in monsoon and non-monsoon seasons in all the districts and its comparison with permissible limits are shown in Fig 4A and 4B . The level of pollution of each element and the associated health concern is given as follows:

a. Mean concentrations of As, Cd, Cr and Cu in different sites of river Yamuna. b. Mean concentrations of Ni, Pb, Fe and Zn in different sites of river Yamuna.

3.1.1. Arsenic

In this study, the As ranges were 0.79–6.21 μg/L for monsoon and 0.89–4.66 μg/L for non-monsoon, with mean values of 2.55 and 3.23 μg/L, respectively ( Table 1 ). The results depicted that the mean As concentrations were within the acceptable limits in both the seasons ( Fig 4A ). The highest As level was reported in Agra during the monsoon season and Hamirpur during the non-monsoon season. Arsenic comes into water from weathering and leaching of rocks, arsenical pesticides, fertilizers, and disposal of industrial and animal wastes [ 24 ]. Arsenic contamination in river Yamuna in Delhi has also been linked to coal-based thermal power plants [ 25 ]. Similarly, in another study in river Yamuna, the maximum As level reported was 6 μg/L [ 26 ].

3.1.2. Cadmium

The mean Cd concentrations was found to be 0.30 μg/L in monsoon and 0.62 μg/L in non-monsoon and the values were within permissible limits ( Fig 4A ). In another study in river Yamuna in Delhi stretch, the mean Cd level reported in monsson, pre-monsoon and post-monsoon season was 26.5 μg/L, 110.1 μg/L and 6.3 μg/L, respectively, indicating higher concentration in all seasons [ 27 ]. Similarly, Kaushik et al. [ 28 ] also reported higher Cd concentration in the range of 10 to 28 μg/L in river Yamuna in Haryana, India. In contrast, in Delhi segment of Yamuna basin, Cd was found to be below detectable limit at all locations [ 26 ]. In the present study, the water samples at the Delhi, Mohana, and Mathura regions contained higher Cd levels during the non-monsoon season. Higher levels in these districts could be attributed to use of Cd-containing fertilizers, combustion emissions, and industrial activities (e.g., mining and metal industry).

3.1.3. Chromium (Cr)

The highest Cr levels during the monsoon and non-monsoon seasons were 18.28 and 10.30 μg/L, respectively. Mean Cr levels in all districts were found to be within permissible limits ( Fig 4A ). High Cr levels in some districts such as Pratappur, Agra, Hamirpur, and Etawah could be linked to dissolution from rain and industrial activities (e.g., electroplating, textile, metal finishing, and leather tanning). In another study in river Yamuna along Delhi stretch, Cr concentration of 60.6, 362.7 and 18.1 μg/L was reported in monsson, pre-monsson and post-monsoon season, respectively [ 27 ]. As high as 52 μg/L and 1374 μg/L of Cr was reported in Yamuna river in Delhi by Asim and Rao [ 29 ] and Sehgal et al. [ 26 ], respectively.

3.1.4. Copper (Cu)

In this study, the Cu ranges were 1.80–19.47 μg/L for monsoon and 2.52–8.71 μg/L for non-monsoon, respectively. The mean values were 5.37 and 6.14 μg/L in monsoon and non-monsoon season, respectively and these values were found to be within the permissible limits ( Fig 4A ). However, increased Cu values in the water at Agra and Etawah may be linked to the excessive application of fungicides, fertilizers, and pesticides in irrigation, in addition to industrial activities (e.g., leather and paint production). A very higher range was reported by Sehgal et al. [ 26 ] (11–595 μg/L), Asim and Rao [ 29 ] (50–120 μg/L) and Bhardwaj et al. [ 27 ] (18.4–17642.4 μg/L) in river Yamuna at different sites.

3.1.5. Nickel (Ni)

In this study, the Ni levels in River Yamuna ranged between 1.97 and 10.98 μg/L for monsoon and 3.08 and 12.70 μg/L for non-monsoon, respectively. As shown in Fig 4B , the mean Ni values (4.79 μg/L in monsoon and 5.41 μg/L in non-monsoon) complied with both Indian Standards [ 30 ] and WHO safe limits [ 31 ]. The high Ni concentration in water samples collected from Delhi could be linked to the existence of several industrial processes such as electroplating, porcelain enameling, and metal finishing. Higher mean level of Ni was reported by Bhardwaj et al. [ 27 ] in river Yamuna in monsoon (232.4 μg/L), pre-monsoon (851.5 μg/L) and post-monsoon (42.8 μg/L). Asim and Rao [ 29 ] (2021) also reported a mean level as high as 164 μg/L in river Yamuna. Higher levels of Ni was also reported in the Godavari river basin [ 32 ], viz ., Bhatpalli (40.25 μg/L), Kumhari (24.26 μg/L), and Hivra (45.26 μg/L).

3.1.6. Lead (Pb)

In this study, Pb ranged between 0.49 and 3.95 μg/L for monsoon and 0.88 and 6.81 μg/L for non-monsoon, with mean values of 1.80 and 3.21 μg/L, respectively. The mean Pb levels along the Yamuna stretch were within BIS and WHO safe limits during both seasons ( Fig 4B ). High Pb concentrations in the Delhi, Mohana, and Agra districts could be ascribed to intense anthropogenic activities (e.g., pigments, electroplating, and battery manufacturing). These industries have also been revealed to discharge effluents containing Pb into the aquatic environment [ 27 ].

3.1.7. Iron (Fe)

In this study, the Fe concentrations reached the highest levels of 412.00 μg/L for monsoon and 303.29 μg/L for non-monsoon, with mean values of 117.35 and 145.35 μg/L, respectively. In the non-monsoon season, the maximum Fe values at several sites was close to the permissible limit. Generally, the elevated Fe concentrations in water samples implied that River Yamuna suffered from discharges of Fe-related industries in several areas such as Agra, in addition to various anthropogenic and geogenic causes.

3.1.8. Zinc (Zn)

Zn level ranged between 4.93 and 279.27 μg/L for monsoon and 13.35 and 459.95 μg/L for non-monsoon, with mean values of 45.27 and 192.02 μg/L, respectively. Although the Zn levels in River Yamuna were higher than other heavy metals in all sites, it was within the safe limit (below 5000 μg/L) during both seasons( Fig 4B ). Some studies reported possible risk to aquatic species and potential environmental risks of zinc in surface waters [ 33 ].

3.2. Correlation analysis for metals in water

Correlation analysis was performed for monsoon and non-monsoon seasons to assess the relationship among metals, as shown in S5 Table . Inter-metal interactions are indicative of metal sources and pathways in the media [ 34 ]. A positive correlation may indicate a common or similar source of these metals. In the present study, significant positive correlations were observed among various metal pairs: As-Cr, Cu-Cr, Pb-Cu, Pb-Ni, Fe-As, Fe-Cr, Fe-Cu, and Zn-Cu in monsoon, indicating a common source of origin. In the non-monsoon season, significant negative correlations were observed among metals pairs: Cr-Cd, Ni-Cr, and positive correlations were observed as Cu-Cd, Pb-Cd, Pb-Ni, Fe-As, Fe-Cr, Fe-Cu, and Zn-Pb. Fe shows a significant positive relationship with As, Cr, and Cu in both seasons. It is a naturally abundant metal and mainly comes from crustal sources. Chromium is used in leather, glass, and pigments industries.

3.3. Principal components analysis and cluster analysis for metals in water

PCA with Varimax normalized rotation was performed separately for monsoon and non-monsoon seasons to understand the relationships among the metals. Based on absolute loading values, the factor loadings were classified as ‘strong(>0.75)’, ‘moderate(0.75–0.50)’, and ‘weak’ (0.50–0.30) [ 35 ]. PCA yielded three PCs for the monsoon season and four PCs for the non-monsoon season with Eigenvalues >1, explaining 71.7% and 69.1% of the cumulative variance, respectively. PCA is depicted by loadings and score plot and is shown in S1 and S2 Figs for monsoon and non-monsoon, respectively. In the monsoon season, PC1, explaining 28.5% of total variance, had strong positive loadings (loadings>0.75)of As, Cr, and Fe ( S1 Fig and S6 Table ).

As is affected by natural factors, such as the parent rock composition [ 36 ] In addition to the natural source, effluents from thermal-based power plants also contribute to As contamination in river Yamuna [ 25 ]. The high As levels reported in this study could be ascribed to the increased industrial processes, automobile emissions, application of As-based pesticides, smelting of metals and usage of fossil fuels [ 37 ] at multiple districts such as Agra, Etawah, and Hamirpur. In general, Fe is present in relatively higher concentrations under natural conditions. Fe is a major component of crustal materials [ 38 ]. Cr is low in concentrations and is within the drinking water guideline limit. This component, therefore, appeared to be primarily associated with the geogenic source. Thus, metals in PC1, therefore, appeared to be primarily associated with the geogenic source. PC2 explained 24.8% of the total variance with strong loadings on Cu and Pb and moderate loading on Ni. These metals may have mainly come from anthropogenic sources such as industrial and urban discharges. Lead battery-based units are a common source of Ni and Pb [ 27 ]. In addition, these sites have high traffic density areas that would release toxic emissions containing Pb to the atmosphere, which are then deposited and accumulated into ecosystems. PC3 explaining 18.4% of total variance, showed strong loadings on Cd and Zn. Since the maximum concentrations of Cd and Zn in the water samples are within the WHO safe limit, it is inferred that this component represents a natural source. A similar result was reported in the surface water of the Lhasa River basin [ 39 ].

In the non-monsoon season, PC1 explained 18.3% showed strong positive loading on Cd and moderate loading on Cu ( S2 Fig and S6 Table ). Cd may have come from very unique anthropogenic sources such as from battery and dye-making industries [ 27 ]. In addition, Delhi and Mohana include large agricultural landscapes, utilizing pesticides and fertilizers that might release Cd into the aquatic environment. Cu may have its origin from both anthropogenic and natural sources. PC2 showed strong loading on Fe and moderate loading on Zn. Although both Fe and Zn are naturally present in the crust, their high concentrations in the river water during the dry season indicate that anthropogenic sources also contribute to them. This may include industrial and urban sewage discharges, electroplating industries, etc. Both As and Pb in PC3 that explained 16.7% of the total variance might have come from mixed sources in the non-monsoon season. Possible anthropogenic sources of As and Pb could be from industries and the use of agrochemicals.

In PC4, Ni showed strong positive loading and may have come from a unique anthropogenic source such as from some particular type of industrial discharges.

Hierarchical agglomerative cluster analysis was performed on the data set of both seasons using Ward’s linkage method using squared Euclidean distance. The result obtained for monsoon and the non-monsoon season is presented in the form of a dendrogram in S3 and S4 Figs. Three clusters are depicted in both seasons. In the first cluster representing monsoon season, As, Cr, Cu, and Ni are very well associated with each other ( S3 Fig ). The second cluster is comprised of Pb and Cd but is also linked to the first cluster. In the third cluster, Fe and Zn are very well linked with each other. Heavy metals such as As, Cr, Cu, and Ni during the monsoon season may be have come mainly from natural sources. Fe and Zn are, although are relatively high concentrations under natural conditions, may have come from anthropogenic sources as well, especially during the non-monsoon season. Pb and Cd may have come from mixed sources. In the non-monsoon season, Cr, As, Pb, Cu, and Ni in the first cluster may have come from mixed sources. Fe and Zn in the first cluster may have mainly natural origin ( S4 Fig ). Cd is separated from other groups indicating a unique source for this metal, and the result is similar to those of PCA. The main anthropogenic sources of various heavy metals in river Yamuna were linked to industrial sources (electroplating, dyeing, paper manufacturing, fertilizer, sugarcane etc) located on the banks of river, agricultural run-off, sewage discharge, agrochemical usage and vehicular sources [ 26 – 29 ].

3.4. Health risk assessment

Table 2A and 2B lists the statistical summary of the noncarcinogenic health risks in monsoon and non-monsoon season, respectively in terms of HQ and HI. In all districts, the mean HQ level of each metal was below 1 among adults in both monsoon and non-monsoon seasons. However, the mean HQ value estimated from As among children exceeded the safe limit of 1, suggesting that As was the main contributing element to non-cancer health risks of the River Yamuna. Similarly, Li and Zhang [ 40 ] demonstrated that As was the main pollutant that caused non carcinogenic risks to children, resulting in HI values above unity.

b. Site-wise calculated values of HQ and HI for adults and children in non-monsoon season.

The data in Table 2A and 2B also depicted that the HI values exceeded the safe limit among children during both seasons, implying that children’s exposure to the water source could potentially trigger adverse non-cancer health effects. Children have higher occasions to interact with environmental contaminants than adults because of their behavioral and physical activities, playing periods, and inattention during eating and drinking food items [ 40 ]. Overall, in the monsoon season, the HI values at different sites followed the order of Agra >Etawah> Hamirpur >Rajapur>Auraiya> Delhi Rly Bridge > Mathura >Pratappur> Mohana >Mawi>Palla>Poanta>Kalanaur. In the non-monsoon season, the HI values at different sites followed the order of Mathura>Hamirpur>Etawah>Auraiya>Rajapur>Mohana>Agra>Delhi Rly Bridge >Mawi>Pratappur>Kalanaur>Palla>Poanta.

Although the HI data provided an appropriate indication of the noncarcinogenic health effects for residents, these values might suffer from a degree of uncertainty [ 3 ]. For instance, the exposure parameters and water consumption rates used to characterize the risks might vary according to regional or individual differences. FHI is presented in the following section based on local and real experience for drinking water contamination to minimize the uncertainty sources and sustain a healthy aquatic environment.

3.5. Fuzzy-based HI classification

3.5.1. fuzzy hazard index (fhi) results.

One of the major problems of the health risk assessment is the uncertainty in the HI values because the employed parameters (e.g., intake rate of water and body weight) can differ among districts and locations. Ali Hosseini et al. [ 41 ] suggested that a new “Quality Index” hypothesis could be established based on the fuzzy logic theory to deal with ambiguous and biased concepts and data. The estimated values of FHI, based on practical experience and understanding of the environmental conditions of the Yamuna River, are shown in the fuzzy inference diagram ( Fig 5 ). The nine plots across the top of this structure represent the antecedent and consequent of the first rule. The first eight columns of plots (the 24 yellow plots) denote the membership functions referenced by the antecedent, or the “If” part of each rule ( S3 and S4 Tables). Rule 1 crisply maps cluster 1 in the input space to cluster 1 in the output space. Similarly, the other two rules map cluster 2 and cluster 3 in the input space to cluster 2 and cluster 3 in the output space, respectively. The ninth column of plots (the three blue plots) demonstrates the membership functions referenced by the consequent or the “Then” part of each rule. The fourth plot in the ninth column represents the aggregate weighted decision for the entire inference system. The defuzzified decision (crisp output) is shown as a bold vertical line on this plot. The metal variables and their instant concentrations are positioned at the top of the columns.

Rule Viewer showing the roadmap of the whole fuzzy inference process for estimating FHI in the Yamuna river for (a) adults, and (b) children.

For the case of As, Cd, Cr, Cu, Ni, Pb, Fe, and Zn, concentrations of 4.23, 0.29, 16.75, 19.47, 4.41, 3.95, 250.33, and 36.47 μg/L, respectively, the estimated FHIs are 0.58 for adults ( Fig 5A ) and 1.82 for children ( Fig 5B ). These defuzzified outputs corresponded to the estimated HIs of 0.61 and 2.00, respectively, equivalent to the aggregation of about 80% out1cluster 1, 10% out1cluster 2, and 10% out1cluster3. The degree of membership allowed for a reliable understanding and practical meaning of the hazard index, especially for public concerns. Hence, the fuzzy theory was employed to overcome the traditional fact that a little shift in metal concentration around its permissible limit would totally change the degree of health risk.

A similar observation has been reported by Tiri et al. [ 22 ], who estimated a water quality index of Oued El-Hai Basin based on fuzzy logic, using ten parameters (e.g., pH, TDS, Ca, Mg, Na, K). The fuzzy water quality index (FWQI) results were compared with the actual WQI, resulting in a correlation coefficient within the 0.88–0.99 range [ 22 ].

3.5.2. Fuzzy model applicability

The fuzzy model results were compared with the actual HI data estimated by Eq 3 , at the 13 districts of the Yamuna stretch ( Fig 6A–6D ). One-way ANOVA was applied to analyze the significant differences among sampling stations for health risks in terms of HI and FHI. Tukey’s t -test was also performed to identify the homogeneous type of the data sets. HI and FHI values showed significant difference (p < 0.05) among sampling locations in both monsoon and non-monsoon seasons. The HI values during the non-monsoon season for children ranged between 0.49 and 2.11, with an average value of 1.52 ± 0.50. These indices correspond to FHIs of 0.82 (minimum) and 1.95 (maximum), with an average value of 1.57 ± 0.32. The adequate correlation between the HI and FHI values ( R 2 : 0.75–0.83) revealed the effectiveness and reliability of the fuzzy logic tool in predicting the noncarcinogenic hazard indices depending on the human expert knowledge and experience. Moreover, the results of both HI and FHI depicted that children were subjected to higher risks due to metals exposure as compared with adults. Also, both HI and FHI retained their maximum values at Agra, Etawah, and Hamirpur ( Fig 6A–6D ). Additionally, the classic HQ and/or HI values were estimated from exposure to a specific source (river); however, this was not the case for the FHI approach that used the human understanding of the multiple pollution pathways in the study area. These benefits also ensure that human expert knowledge is essential in determining the type of water treatment required to meet national and international standards. In addition, the fuzzy logic concept should be incorporated into the national water quality monitoring program to establish a consolidated framework for managing the river systems.

Comparison between new FHI and traditional HI estimated at 13 districts of the river Yamuna: (a) adults during monsoon season, (b) children during monsoon season, (c) adults during non-monsoon season, and (d) children during non-monsoon season. Groups sharing the same letter do not statistically differ from each other at the specified significance level (alpha = 0.05).

Mohanta et al. [ 3 ] proposed an index model using the fuzzy logic approach to investigate the effect of fluoride on human health (adults and children). Fluoride concentration and FHI were the model’s input and output, respectively. The study revealed a high determination coefficient between FHI and HI, reaching up to R 2 = 0.9755. Moreover, the study demonstrated that the FHI approach attained more concise, stringent, and consistent results compared with the conventional HI method. Hence, FHI could include both qualitative and quantitative variables with different values and meanings to estimate the hazard index associated with adults and children during the monsoon and non-monsoon seasons.

Mohanta and Mishra [ 23 ] used the fuzzy theory to develop cancer risk (FCR) and hazard index (FHI) associated with men, women, and children for aniline-enriched groundwater. The model’s input (antecedent) was the aniline concentration described by triangular and trapezoidal membership functions. The results of FCR and FHI were positively correlated with the data estimated from the conventional USEPA method, with R 2 values of 0.97 and 0.99, respectively. The sufficient R 2 for validation implied that fuzzy logic would highly predict risks associated with human health.

Li et al. [ 12 ] proposed a fuzzy water pollution index (FWPI) to assess the quality of Qu River based on 125 fuzzy If-Then rules. The model’s inputs (antecedent sets) were DO, COD, BOD, NH 3 -N, and TP, incorporated as trapezoid and triangular membership functions. Their study demonstrated that the results of FWPI were consistent with those of fuzzy comprehensive evaluation and grey relational model methods. Similar findings were observed by Icaga [ 42 ], who proposed an index model for water quality evaluation using fuzzy logic with eleven water quality parameters (as inputs). Their study demonstrated that environmentalists should be well experienced in giving correct and precise field survey responses, leading to satisfactory fuzzy model accuracy.

4. Conclusion

The study revealed that higher levels of most of the metals in the non-monsoon season in the river Yamuna could be attributed to low flow conditions and less dilution. Overall, natural and anthropogenic sources contributed to the heavy metals in Yamuna river water. Major anthropogenic sources may include industrial and urban discharges and the use of agrochemicals. An earlier study also indicated that the river was affected by pollution coming from untreated household sewage, industrial effluents, and fertilizers used in agriculture [ 2 ]. Health risk assessment indicated possible health threats to the children (HI>1 and As was the main contributing element to non-cancer risk in both the seasons in most sampled sites. Fuzzy logic-based health risk assessment also indicated that children’s exposure to the Yamuna river water might potentially trigger adverse non-cancer health effects. The study further elucidated that incorporating fuzzy logic concept in water quality monitoring may contribute to developing a consolidated agenda for managing the river ecosystems.

Supporting information

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The author(s) received no specific funding for this work.

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Original research article, analysis of water pollution using different physicochemical parameters: a study of yamuna river.

• 1 Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi- NCR Campus, Ghaziabad, India
• 2 Department of Computer Science and Engineering, Gandhi Institute of Engineering and Technology University, Gunupur, India
• 3 School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to Be University, Bhubaneswar, India
• 4 Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden
• 5 Department of Electronics and Communication Engineering, Krishna Institute of Engineering and Technology, Group of Institutions, Ghaziabad, India
• 6 Department of Computer Science, SRM Institute of Science and Technology, NCR Campus, Ghaziabad, India
• 7 Department of Applied Sciences and Humanities, Teerthankar Mahaveer University, Moradabad, India
• 8 Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
• 9 Geotechnical Engineering and Artificial Intelligence Research Group (GEOAI), University of Transport Technology, Hanoi, Vietnam

The Yamuna river has become one of the most polluted rivers in India as well as in the world because of the high-density population growth and speedy industrialization. The Yamuna river is severely polluted and needs urgent revival. The Yamuna river in Dehradun is polluted due to exceptional tourist activity, poor sewage facilities, and insufficient wastewater management amenities. The measurement of the quality can be done by water quality assessment. In this study, the water quality index has been calculated for the Yamuna river at Dehradun using monthly measurements of 12 physicochemical parameters. Trend forecasting for river water pollution has been performed using different parameters for the years 2020–2024 at Dehradun. The study shows that the values of four parameters namely, Temperature, Total Coliform, TDS, and Hardness are increasing yearly, whereas the values of pH and DO are not rising heavily. The considered physicochemical parameters for the study are TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium. As per the results and trend analysis, the value of total coliform, temperature, and hardness are rising year by year, which is a matter of concern. The values of the considered physicochemical parameters have been monitored using various monitoring stations installed by the Central Pollution Control Board (CPCB), India.

Introduction

Due to historical, geographical, religious, political, and sociocultural reasons, India has a unique place in the world Agarwal et al., 2016 . Pollution-causing activities have caused severe changes in aquatic environments over the last few decades. Serious questions have been raised in context to the safe use of river water for drinking and other purposes in recent times. Numerous contaminants are playing a major role in polluting the river water. It is one of the main concerns for most of the metropolitan cities of developing nations. Rivers play a vital role in shaping up the natural, cultural, and economic aspects of any country ( Rafiq, 2016 ). The Yamuna river is one such river. The Yamuna river provides sustenance to ecology and is therefore considered holy by the people of India. It derives from the glacier called Yamunotri in the Himalayan ranges. States through which the Yamuna river flows are the Uttarakhand, Himachal Pradesh, Uttar Pradesh, Haryana, and Delhi. The Yamuna river is also divided into several tributaries such as the Hindon, Tons, Giri, Rishiganga, Hanuman Ganga, Sasur Khaderi, Chambal, Betwa, Ken, Sindh, and Baghain as it is flowing through several cities. These cities are the Yamuna Nagar, Delhi, Faridabad, Mathura, Agra, Etawah, and Prayagraj. It is a tributary of the river Ganges in India. Two of them together have had substantial importance in shaping up the history and geography of our country. The river on which our research primarily focuses is the Yamuna river. It passes through several states such as Uttar Pradesh, Himachal Pradesh, Uttarakhand, Haryana, and Delhi. It has a length of approximately 1,380 km. More than 600 lakh people are dependent on their living and income on this river ( Census Reports of India 2001 , 1971–1991 ). Such is the greatness of this river. Our research is based on the Yamuna river in Dehradun in Uttarakhand.

The process, in which the people from rustic areas shift to the town areas in search of a brighter future, thus resulting in a drastic increment in the population of people living in cities, is called urbanization. As a result, the number of cities and towns increases exponentially. There is an atrocious amount of stress on the weakening natural resources. As it is, the natural resources are facing major deterioration issues considering the unthoughtful plundering by the people. In the last few decades, the rate of spread in various segments of the world has been unprecedented and unimaginable. The proportion of the rate of infrastructure expansion has not been able to match up to the pace of urbanization in most cities. The amplified requirement of water, deficiency of sewage facilities, and scarce wastewater treatment facilities rigorously affect the water resources, and change the environment and ecology. Agricultural lands, rural unpaved areas, and natural wetlands are converted into paved and impervious urban areas, during urbanization. Augmented impervious land surface in urbanized areas leads to severe and radical changes in the natural order of things ( Ahmad et al., 2017 ). There has been a drastic decline in the Yamuna river water quality since the last few years. The water is highly polluted, and it is a joint responsibility of the government and all the citizens to make sure that the Yamuna river is clean again. The primary step toward understanding and deliberating about the sorts of water pollution and developing effective reduction strategies is monitoring ( Marale, 2012 ). Physical, chemical, and biological compositions determine the quality of water ( Allee and Johnson, 1999 ). The substances such as heavy metals, pesticides, detergents, and petroleum form the chemical composition ( Tiwari et al., 2020 ). Turbidity, color, and temperature comprise the physical composition, whereas the biological arrangement includes pigments and planktons. Observation and analysis of these water quality parameters need sampling from extensively distributed locations, which is time consuming and requires a lot of field and lab efforts to come up with statistical results ( Wang et al., 2004 ; Icaga, 2007 ; Kazi et al., 2009 ; Amandeep, 2011 ; Duong, 2012 ; Singh et al., 2013 ; Nazeer and Nichol, 2015 ; Shi et al., 2018 ).

Conventionally, monitoring-based methods are used to find out the water quality parameters. They involve wide-ranging field sampling and expensive lab analysis, which is time inefficient and can only be accomplished for areas that are smaller ( Song et al., 2012 ). Hence, these restraints and drawbacks make the conventional methods challenging for continuous water quality prediction at spatial scales ( Panwar et al., 2015 ; Chabuk et al., 2017 ). For observing and analyzing water quality parameters, such as turbidity, chlorophyll, temperature, and suspended inorganic materials, techniques, such as optical remote sensing, are being used ( Pattiaratchi et al., 1994 ; Fraser, 1998 ; Kondratyev et al., 1998 ). To calculate the measure of solar irradiance at varied wavelength bands reflected by the surface water, remote sensing satellite sensors are used ( Zhang et al., 2003 ; Dwivedi and Pathak, 2007 ; Girgin et al., 2010 ; Ronghang et al., 2019 ). Amplified demand for water, poor sewage facility, and insufficient wastewater management amenities, relentlessly affect the resources of water resources. Models such as hydrological models have been used to evaluate the effect of numerous factors in rain-related procedures of the cosmopolitan areas ( Trombadore et al., 2020 ). Knowledge and information about interconnections between climate, population, and ecology are essential for understanding and promoting sustainable development ( Sharma et al., 2020 ). It also requires better knowledge of equipment and methodical planning. Proper management will reduce the degradation of rivers ( Shukla et al., 2018 ). In this study, we focus on trying to find out contaminants in the river, finding the water pollution index, and subsequently enforcing measures to curb water pollution.

Contribution of the Study:

1. In the present study, water samples were collected every year from the Yamuna river canal in Dehradun, Uttarakhand, India.

2. The samples have been analyzed for 12 different physicochemical attributes like ph, BOD, COD, Total Coliform, Temp, DO, Alkalinity, Chlorides, Calcium, Magnesium, and Hardness as Calcium Carbonate and TDS.

3. The measurement of the water quality index has been taken into consideration for the years 2017, 2018, and 2019.

4. Forecasting the pollution trend for the Yamuna river water from 2016 to 2024.

Materials and Methods

Mathematical model.

In this research paper, the water sample of the Yamuna river is considered for analysis. The 12 physicochemical parameters in the water are studied and analyzed. The water sample of the Holy River called the Yamuna river is considered for a certain period. The ratio of water components mainly Temperature, Total Coliform, TDS, and Hardness are varied irregularly at various locations of India. Due to the abrupt changes in the water component, the water quality is also changed. In this research paper, a sampling distribution-based analytical model called Equipoise Evaluator (EE) is proposed for the discrete parameter value of the water components. The EE model is suitable to analyze random discrete parameters. The EE model can be applied for any kind of sample analysis where the analysis is based on sample molecules. To analyze the discrete sample in the form of the symmetric normal distribution for a particular location, the EE model is applied. In this research paper, the water sample varies based on the molecules of e water components. This EE model is also applicable for the analysis of metallurgy to detect the impurity of the metal. In this research paper, the EE model is deployed for the water sample of the Yamuna river.

Sampling distribution is proposed to transform the variable at different levels.

As per the linear transformation

Now, by applying the Jacobian transformation on a non-singular matrix M,

From Equation (2), the relational equation for all connected differential elements is defined as Equation (3)

As M is considered as an orthogonal matrix, hence Z = MW, which transformed into a quadratic form of preserving from the standard value.

To determine the dissimilarity distance from a standard sample value, the partitioning matrix is deployed.

Assume that matrix M is partitioned into qth numbers, then M i M j ′ = 0    ∀ i ≠ j .

As per the partitioning matrix, all q sub-matrices are orthogonal to each other except orthogonal themselves. Now, Equation (1) is rewritten as

where, M 1 ,…., M q are an exclusive subset of the tested variables.

Applying transformation in Equation (7)

where, B i = ( M i M i ′ ) - 1     a n d   η i = M i μ

Equation (8) determines the transformation of each partition into quadratic form with exclusive subsets of tested parameters. In this analysis, M is considered as fully orthogonal, with each row orthogonal to every other row. The result of transforming all the variables to test bed data variables of D is then,

It is considered that the water molecules of the sample water have symmetric normally distributed for a particular location. The mean of the water molecules is z ¯ = 1 q ∑ i = 1 q w i .

As per orthogonal transformation

where u 1 = q z ̄ and σ = u 1 2 + u 2 2 + . . . . + u q 2 .

u 1 and σ are independently distributed. The sample mean and sample variance of the experimented sample water are independently distributed.

Water Quality Index and Trend Analysis

The primary focus of this study is to measure and analyze the drastic changes in the Yamuna river water quality at Dehradun, Uttarakhand. Standardized and the universally accepted water quality index (WQI) has been adopted to measure the variation in water quality of the Yamuna river at the prime location of the study—Dehradun over 3 years. The standard method has been used to examine and evaluate the water quality for 12 Physicochemical parameters (TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium). In this study, the water quality index has been calculated using the different Physicochemical parameters documented and verified from the monitored locations. The water quality index (WQI) is stated by

where Ii signifies the ith water quality parameters, the weight associated and related to the parameters is denoted by Wi, and p notifies us about the number of water quality parameters. This WQI is based on the index introduced by the NSF (National Sanitation Foundation) ( Bhutiani et al., 2016 ). This index is established by the Central Pollution Control board with different advancements in terms of water quality criteria. The Water quality index is supported and developed by the National Sanitation Foundation (NSF) ( Brown et al., 1970 ). It is also known as NSF-WQI. This water quality index is denoted as

where P denotes the ith parameter measured values, quality rating is denoted by q i , and the relative weight of the ith parameters is denoted by w i .

The water quality index arithmetic index was presented ( Cude, 2001 ). It is a very popular and standard method used by many investors and researchers in their studies ( Ramakrishniah et al., 2009 ; Ahmad et al., 2012 ).

In this study, the quality rating can be calculated using the following equation:

where q i signifies the ith parameter quality rating for n water quality parameters, water.

The quality parameters' actual and definite value is denoted by V actual , the parameters ideal value is symbolized by V ideal , and the standard value of the parameters, which is suggested by the WHO, is denoted by V standard . The ideal values for DO and pH are 14.6 and 7 mg/L, whereas for the other parameters, it is equal to zero. After the calculation of quality rating, (relative weight), Wi has to be calculated by inversing the standard value of the parameter. Finally, the following equation was used to calculate the overall water quality index (WQI):

Here, signifying the relative weight and quality rating is symbolized by Wi and qi .

Trend Analysis

In this study, to forecast the pollution trend analysis, the linear regression model has been used. According to the linear regression model, the relationship between the two variables a and b can be expressed as:

Where x and y are the model parameters, which are known as regression coefficients, and B is the dependent variable. A is known as an independent variable, and € is the error variable. For making a prediction using a linear regression model is

The parameters x and y are calculated using the following equations:

Methodology

The flow chart for the methodology used is shown in Figure 1 . The water quality index is calculated using the weight arithmetic water quality index method, which has been discussed in the Water Quality Index and Trend Analysis section.

Figure 1 . Flow chart for the methodology used.

The value of the water quality index has been compared with the standard values of WQI, which is shown in Table 1 . The water quality rating is divided into five categories. The range from 0 to 25 is coming under (A) grading with excellent water quality, the range from 26 to 50 is for grading (B) with good water quality, and respectively, (C), (D), and (E) gradings are categorized for different WQI values ( Chauhan and Singh, 2010 ).

Table 1 . The standard values of water quality index (WQI) using weight arithmetic water quality index method.

Dataset Collection

The most populous city of Uttarakhand is Dehradun also spelled Dear Doon. It is the capital of Uttrakhand, which is one among the 28 states in India ( Figure 2 ). It is famous for its Doon Basmati Rice. Dehradun city has famous institutions like IMA (Indian Military Academy) regarded as one of the best officer training academies in India, Forest Research Institute, Indian Institute of Petroleum, and the famous ONGC training institute. This city is also famous among the tourists. It has many adventurous activities like rafting, bungee jumping, paragliding, etc. ( Rafiq, 2016 ). The city is located about 255 km from New Delhi and 168 km from Chandigarh. The climate condition of Dehradun is humid, subtropical, and a summer temperature can reach a maximum of 44°C. This city is also located very close to Nainital, which has the famous Jim Corbett National Park attracting many tourists ( Bhutiani et al., 2015 ).

Figure 2 . Map for considering location for Yamuna, Dehradun ( https://www.bcmtouring.com/forums/thread ).

The present study was undertaken for a period of 3 years from 2017 to 2019 to check the water quality analysis for the physicochemical attributes below. In the present study, water samples were collected on a yearly basis from the Yamuna river canal in Dehradun, Uttarakhand, India. The samples were analyzed for 12 different physicochemical attributes like ph, BOD, COD, Total Coliform, Temp, DO, Alkalinity, Chlorides, Calcium, Magnesium, and Hardness as Calcium Carbonate, and TDS ( Tyagi et al., 2020 ). The Yamuna river plays a very crucial role in Dehradun's geography. The Yamuna river is severely polluted and needs urgent revival. The river passes through Uttarakhand. Uttarakhand has always been a tourist spot and experiences heavy tourists perennially, and Dehradun, being the capital city, also bears the brunt. The Yamuna river in Dehradun is polluted due to the exceptional tourist activities. Dehradun is also famous for the Kumaoni Holi, Jhanda Fair, Tapkeshwar Mela, and Bissu Mela. A lot of waste materials are dumped into the Yamuna river, and they contaminate the river. Water might be untreated for long spans of time. Also, a lot of industries–primarily biotechnology and food processing, are set up in Dehradun; they also mindlessly dump their waste in the Yamuna river. Industrial waste is not fully responsible for the pollution, but some poor sewage systems and human activities are also responsible for it ( Bhutiani and Khanna, 2007 ).

Dehradun is a home to many agricultural and horticulture activities such as rice, litchi, and tea plantations. Agricultural waste also plays a major role in polluting the Yamuna river in Dehradun. The pollution is also increased by the excessive usage of insecticides and pesticides ( Tiwari et al., 2020 ). There are also people who wash their clothes, utensils, and defecate in or around the river, thus leading to pollution. The stretch of the Yamuna river in Dehradun thus has a lot of coliform bacteria. Government projects such as road construction might also be responsible for dumping waste, although rules have been drastically upgraded in the last two decades or so. Some cattle washing activities and religious activities also polluted the Yamuna river ( Bhutiani et al., 2018 ).

Results and Discussion

The study aims to examine the alteration in the quality of water of the Yamuna river at Dehradun in the year 2017. Water quality index (WQI) is going to be used in the study so that the changes and variations in the quality of water of the Yamuna river can be measured. The conventional method by which inspection can be done for the water quality has 12 physicochemical parameters (TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium). These parameters will be measured carefully, and their respective value will be found. So, the standard value and observed value will be compared with each other, and the variation is going to be measured between them. By this variation, identification of the quality of water can be done.

Measurement of Physicochemical Parameters at Dehradun for 2017

Water samples have been taken at different months for the year 2017 ( Table 2 ). The mean and standard deviation for the measured values have been also calculated. The mean is the number found by summing every data point and dividing by the number of data points. It is also called average. The standard deviation is defined as the number that is going to tell about the measurements for a group that is spread out from the mean or expected value. A low standard deviation signifies that many numbers are very close to the mean ( Bisht et al., 2017 ). A high standard deviation signifies that the numbers are very much spread out. So, the accurate value for the quality of water can be found out easily using this.

Table 2 . Physicochemical parameters and water quality analysis at Dehradun for 2017.

The maximum value of pH is in the month of January when the water is a little more basic, and the minimum value is in July when it is less basic. The mean pH is 7.735, and the standard deviation is 0.086986589. The maximum value of the biochemical oxygen demand (BOD) is in January, which indicates more polluted water, and the minimum value is in the months of April, July, and October, which indicates less polluted water. The mean of BOD is 1.05, and the standard deviation is 0.1. The maximum value of COD is in January and October, which indicates a large quantity of oxidizable organic materials in the sample, and the minimum value is in April and July which indicates a lesser quantity of oxidizable organic materials in the sample. The mean COD is 5 and the standard deviation is 1.154700538.

The maximum value of Total Coliform is in July, which indicates that the water-borne illness is increased, and the minimum value is in October which indicates that the water-borne illness is decreased. The mean of Total Coliform is 65, and the standard deviation is 17.32050808. The maximum value of Temp is in July, which indicates increased chemical reactions generally, and the minimum value is in January, which indicates decreased chemical reactions. The mean of Temp is 17.75, and the standard deviation is 2.62995564. The maximum value of DO is in October, and the minimum value is in January, April, and July. The mean of DO is 8.7, and the standard deviation is 0.2. The maximum value of Alkalinity/visual titration CaCO 3 is in July, which indicates greater buffering capacity against pH changes, and the minimum value is in April, which indicates lesser buffering capacity against pH changes. The mean of Alkalinity/visual titration CaCO 3 is 64, and the standard deviation is 5.887840578. The maximum value of Chlorides is in July, which indicates body-related diseases, and the minimum value is in April and October. The mean of Chlorides is 5.75, and the standard deviation is 0.9574271078. The maximum value of Calcium as CaCO 3 is in July, which has a positive effect on the body, and the minimum value is in April, which has a lesser positive effect on the body. The mean of CaCO 3 is 41, and the standard deviation is 4.760952286. The maximum value of Magnesium as CaCO 3 is in July, which has a positive effect on the body, and the minimum value is in January, which has a lesser positive effect on the body. The mean of Magnesium as CaCO 3 is 32.5, and the standard deviation is 2.516611478. The maximum value of Hardness as CaCO 3 is in July, which has a good effect on the body, and the minimum value is in April. The mean of Hardness as CaCO 3 is 73.5, and the standard deviation is 6.191391874. The maximum value of TDS is in July, which specifies more toxic minerals, and the minimum value is in April, which specifies less toxic minerals. The mean of TDS is 83.75, and the standard deviation is 14.7507062. Water quality index (WQI) was used for the evaluation of the variation in the water quality of the Yamuna river at Dehradun over 3 years. The standard and prescribed methods have been used to analyze the water quality for 12 physicochemical parameters (TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium). Calculations have been performed using the standardized formula and mathematical models. Detailed calculations and methodology have been used to find the water quality index as accurately as possible. The WQI of the Yamuna river in Dehradun for the year 2017 was 42.87 ( Table 2 ). According to WHO, the WQI should be below 60 for its quality to be at least fair. Here, it can be easily concluded that the Yamuna river is polluted, but it is still revivable. Developmental and maintaining efforts can be adopted to make the Yamuna river clean again and improve the WQI drastically.

Total coliform is positively correlated with CaCO 3 , chlorides, and hardness of CaCO 3 .Temp is positively correlated with the magnesium of CaCO 3 and TDS and negatively correlated with pH, BOD, and COD. DO is positively correlated with COD and negatively correlated with chlorides. Alkalinity is positively correlated with chlorides, TDS, hardness, and the magnesium of CaCO 3 and negatively correlated with pH. Chlorides are positively correlated with calcium and hardness of CaCO 3 and negatively correlated with pH and DO. Magnesium (CaCO 3 ) is positively correlated with hardness and TDS, and negative with pH, BOD, and COD. Hardness (CaCO 3 ) is positive for TDS, Chlorides, Magnesium, and negative for pH. TDS is negative for pH and positive for all. The dendrogram and graphical representation for physicochemical parameters at Dehradun for 2017 are plotted between the months (January, April, July, and October) and the parameters [TDS, Total Coliform (MPN/100 ml), Alkalinity/visual titration CaCO 3 , Hardness as CaCO 3 , Calcium as CaCO 3 , Magnesium as CaCO 3 , Temp, BOD, pH, DO, COD, and Chlorides] ( Figures 3 , 4 ).

Figure 3 . Dendrogram for physicochemical parameters at Dehradun for 2017.

Figure 4 . Graphical representation of physicochemical parameters at Dehradun for 2017.

Cluster 1 (blue) represents lightly polluted, and the parameters include TDS, Total Coliform (MPN/100 ml), Alkalinity/visual titration CaCO 3 , and Hardness as CaCO 3 . Cluster 2 (red) represents moderately polluted, and the parameters include Calcium as CaCO 3 , Magnesium as CaCO 3 , Temp, BOD, pH, DO, COD, and Chlorides. Cluster 3 (black) represents heavily polluted and the parameters include Total Coliform (MPN/100 ml), Alkalinity/visual titration CaCO 3 , Hardness as CaCO 3 , Calcium as CaCO 3 , Magnesium as CaCO 3 , and Temp.

Measurement of Physicochemical Parameters at Dehradun for 2018

Water samples have been taken in different months for the year 2018 ( Table 3 ). Mean and standard deviation for the measured values have been also calculated. The maximum value of pH is in October so the water is a little more basic, and the minimum value is in January, which means the water is less basic. The mean pH is 7.6325, and the standard deviation is 0.420585. The maximum value and minimum value of BOD are equal every month. The mean of BOD is 1, and the standard deviation is 0.

Table 3 . Physicochemical parameters and water quality analysis at Dehradun for 2018.

The maximum value of COD is in April indicating a large quantity of oxidizable organic material in the sample, and the minimum value is in January, July, and October indicating a lesser quantity of oxidizable organic materials in the sample. The mean of COD is 4.5, and the standard deviation is 1. The maximum value of Total Coliform is in July indicating the water-borne illness is increased, and the minimum value is in January, April, and October indicating the water-borne illness is decreased. The mean of Total Coliform is 50, and the standard deviation is 20. The maximum value of Temp is in July indicating increased chemical reactions generally, and the minimum value is in January indicating decreased chemical reactions. The mean of Temp is 18.25, and the standard deviation is 1.707825. The maximum value of DO is in April, and the minimum value is in January and July. The mean of DO is 8.85, and the standard deviation is 0.251661. The maximum value of Alkalinity/visual titration CaCO 3 is in July indicating higher buffering capacity against pH changes, and the minimum value is in April indicating lower buffering capacity against pH changes. The mean of Alkalinity/visual titration CaCO 3 is 64.5, and the standard deviation is 6.608076.

The maximum value of Chlorides is in January, July, and October indicating body-related diseases, and the minimum value is in April. The mean of Chlorides is 5.75, and the standard deviation is 0.5. The maximum value of Calcium as CaCO 3 is in July, which has a positive effect on the body, and the minimum value is in January, April, and October, which has a less positive effect on the body. The mean of CaCO 3 is 41.5, and the standard deviation is 3. The maximum value of Magnesium as CaCO 3 is in July, which has a positive effect on the body, and the minimum value is in April, which has a less positive effect on the body. The mean of Magnesium as CaCO 3 is 33, and the standard deviation is 2.581989. The maximum value of Hardness as CaCO 3 is in July, which has a good effect on the body, and the minimum value is in April. The mean of Hardness as CaCO 3 is 74.5, and the standard deviation is 5.259911. The maximum value of TDS is in July specifying the presence of toxic minerals, and the minimum value is in January specifying the presence of less toxic minerals. The mean of TDS is 87.5, and the standard deviation is 15.60983.

Water quality index (WQI) was used in the evaluation of the variation in water quality of the Yamuna river at Dehradun over 3 years. The standard and prescribed method has been used to analyze the water quality for the 12 physicochemical parameters (TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium). Calculations have been performed using the standardized formula and mathematical models. Detailed calculations and methodology have been used to find the water quality index as accurately as possible. The WQI of the Yamuna river in Dehradun for the year 2018 was 40.47 ( Table 3 ). According to WHO, the WQI should be below 60 for its quality to be at least fair. Here, it can be easily concluded that the Yamuna river is polluted, but it is still revivable. Developmental and maintaining efforts can be adopted to make the Yamuna river clean again and improve the WQI severely. Moreover, it is a positive sign that the WQI of the Yamuna river has improved greatly for the year 2018 compared to the year 2017.

The dendrogram and graphical representation for the physicochemical parameters at Dehradun for 2018 are plotted between the months (January, April, July, and October) and also the parameters [TDS, Total Coliform (MPN/100 ml), Alkalinity/visual titration CaCO 3 , Hardness as CaCO 3 , Calcium as CaCO 3 , Magnesium as CaCO 3 , Temp, BOD, pH, DO, COD, and Chlorides] ( Figures 5 , 6 ). Cluster 1 (blue) represents lightly polluted and the parameters include Temp, BOD, pH, DO, COD, and Chlorides.

Figure 5 . Dendrogram for physicochemical parameters at Dehradun for 2018.

Figure 6 . Graphical representation of physicochemical parameters at Dehradun for 2018.

Cluster 2 (red) represents moderately polluted, and the parameters include Total Coliform (MPN/100 ml), Calcium as CaCO 3 , Magnesium as CaCO 3 , TDS, Alkalinity/visual titration CaCO 3 , Hardness as CaCO 3 . Cluster 3 (black) represents heavily polluted, and the parameters include BOD, pH, DO, COD, Chlorides, Total Coliform (MPN/100 ml), Alkalinity/visual titration CaCO 3 , Hardness as CaCO 3 , Calcium as CaCO 3 , and Magnesium as CaCO 3 .

Measurement of Physicochemical Parameters at Dehradun for 2019

Water samples have been taken in different months for the year 2019 ( Table 4 ). The mean and standard deviation for the measured values have been also calculated. The mean is the number found by summing every data point and dividing by the number of data points. Standard deviation is defined as the number that is going to tell about the measurements for a group that is spread out from the mean or expected value. Comparing the values of this year with those of the previous years leads to the outcomes being observed.

Table 4 . Physicochemical parameters and water quality analysis at Dehradun for 2019.

The maximum value of pH is in the month of January when the, water is a little more basic and the minimum value is in the month of October when the water is less basic. The mean pH is 7.6225, and the standard deviation is 0.411208. The maximum value of BOD is in July when there is a large quantity of polluted water, and the minimum value is in January, April, and October when there is less quantity of polluted water. The mean of BOD is 1.05, and the standard deviation is 0.1. The maximum value of COD is in July and October indicating a greater amount of oxidizable organic materials in the sample, and the minimum value is in January and April indicating a lesser amount of oxidizable organic materials in the sample. The mean of COD is 5, and the standard deviation is 1.154701. The maximum value of Total Coliform is in April indicating that water-borne illness is increased, and the minimum value is in January indicating that water-borne illness is decreased. The mean of Total Coliform is 182.5, and the standard deviation is 93.22911. The maximum value of Temp is in October indicating increased chemical reactions generally, and the minimum value is in January indicating decreased chemical reactions. The mean of Temp is 18.5, and the standard deviation is 1.290994. The maximum value of DO is in April, and the minimum value is in July. The mean of DO is 8.9, and the standard deviation is 0.258199. The maximum value of Alkalinity/visual titration CaCO 3 is in April indicating higher buffering capacity against pH changes, and the minimum value is in October indicating lower buffering capacity against pH changes. The mean of Alkalinity/visual titration CaCO 3 is 68, and the standard deviation is 5.416026.

The maximum value of Chlorides is in October indicating body-related diseases, and the minimum value is in January, July, and April. The mean of Chlorides is 7.5, and the standard deviation is 3. The maximum value of Calcium as CaCO 3 is in October, which has a positive effect on the body, and the minimum value is in April, the month which has a less positive effect on the body. The mean of CaCO 3 is 49.5, and the standard deviation is 8.386497. The maximum value of Magnesium as CaCO 3 is in April, which has a positive effect on the body, and the minimum value is in October, which has a less positive effect on the body. The mean of Magnesium as CaCO 3 is 29, and the standard deviation is 7.745967. The maximum value of Hardness as CaCO 3 is in April and October, which has a good effect on the body, and the minimum value is in July. The mean of Hardness as CaCO 3 is 78.5, and the standard deviation is 1.914854. The maximum value of TDS is in July specifying the presence of toxic minerals, and the minimum value is in October specifying the presence of less toxic minerals. The mean of TDS is 99.25, and the standard deviation is 12.84199.

Water quality index (WQI) was used to evaluate the variation in water quality of the Yamuna river at Dehradun over 3 years. The standard and prescribed method has been used to analyze the water quality for 12 physiochemical parameters (TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium). Calculations have been performed using the standardized formula and mathematical models. Detailed calculations and methodology have been used to find the water quality index as accurately as possible. The WQI of the Yamuna river in Dehradun for the year 2019 was 40.82 ( Table 4 ). According to WHO, the WQI should be below 60 for its quality to be at least fair. Here, it can be easily concluded that the Yamuna river is polluted, but it is still revivable. Developmental and maintaining efforts can be adopted to make the Yamuna river clean again and improve the WQI drastically. Moreover, it is a positive sign that the WQI of the Yamuna river has improved for year 2019 compared to the year 2017, whereas the WQI has increased again in 2019 compared to 2018. It can be documented that the Yamuna river was the cleanest in the year 2018, and its water quality in 2019 has improved in collation to the year 2017.

The correlation coefficients between the inspected parameters of the Yamuna river water at Dehradun in the year 2019 are shown in Table 5 . Ph is positive for TDS, alkalinity, and Magnesium (CaCO 3 ) and negative for COD, Temp, and Calcium (CaCO 3 ). BOD is positive for COD and TDS and negative for DO and hardness (CaCO 3 ). COD is positive for Temp, Chlorides, and Calcium and negative for DO, magnesium (CaCO 3 ), and alkalinity. Temp is positive for calcium (CaCO 3 ) and negative for alkalinity. DO is positive for Magnesium (CaCO 3 ). Alkalinity is positive for TDS and negative for Calcium, Magnesium, and hardness of CaCO 3 . The dendrogram and graphical representation for physicochemical parameters at Dehradun for 2017 are plotted between months (January, April, July, and October) and parameters (TDS, Total Coliform (MPN/100 ml), Alkalinity/visual titration CaCO 3 , Hardness as CaCO 3 , Calcium as CaCO 3 , Magnesium as CaCO 3 , Temp, BOD, pH, DO, COD, and Chlorides) ( Figures 7 , 8 ).

Table 5 . Correlation table for physicochemical parameters at Dehradun for 2019.

Figure 7 . Dendrogram for physicochemical parameters at Dehradun for 2019.

Figure 8 . Graphical representation of physicochemical parameters at Dehradun for 2019.

Cluster 1 (blue) represents lightly polluted, and the parameters include BOD, pH, DO, COD, Chlorides, Temp, and Magnesium as CaCO 3 . Cluster 2 (red) represents moderately polluted, and the parameters include Total Coliform (MPN/100 ml), TDS, Calcium as CaCO 3 , Alkalinity/visual titration CaCO 3 , and Hardness as CaCO 3 . Cluster 3 (black) represents heavily polluted, and the parameters include COD, Chlorides, Temp, Magnesium as CaCO 3 , Total Coliform (MPN/100 ml), and TDS.

In Table 6 , the variations in the 12 physicochemical parameter values for Yamuna water at Dehradun for 2017, 2018, and 2019 are shown.

Table 6 . Water quality index (WQI) for Yamuna river at Dehradun for 2017, 2018, and 2019.

Trend Forecasting

This section is briefing about the Yamuna river water pollution trend in the next 4 years. The study demonstrates the trend of six physicochemical parameters for the years 2020 to 2024. The considered parameters for calculating the trend forecasting are Temp, Total Coliform, TDS, Hardness, pH, and DO. The forecasting for the said parameters are shown in Figures 9 – 11 .

Figure 9 . Trend forecasting of TDS at Dehradun for the years from 2020 to 2024.

Figure 10 . Trend forecasting of total Coliform at Dehradun for the years from 2020 to 2024.

Figure 11 . Trend forecasting of hardness at Dehradun for the years from 2020 to 2024.

According to the trend analysis, the values of four parameters named Temperature, Total Coliform, TDS, and Hardness are increasing yearly, whereas the values of pH and DO are not rising year by year. The trend forecasting is verifying whether the exceptional tourist activity, poor sewage facility, and insufficient wastewater management amenities, is degrading the water of the Yamuna river at Dehradun year by year.

The dendrogram of the mean is plotted between years (2017, 2018, and 2019) and parameters [TDS, Total Coliform (MPN/100 ml), Alkalinity/visual titration CaCO 3 , Hardness as CaCO 3 , Calcium as CaCO 3 , Magnesium as CaCO 3 , Temp, BOD, pH, DO, COD, and Chlorides] ( Figure 12 ). Cluster 1 (blue) represents lightly polluted, and the parameters include Total Coliform (MPN/100 ml), TDS, Alkalinity/visual titration CaCO 3 , and Hardness as CaCO 3 . Cluster 2 (red) represents moderately polluted, and the parameters include Calcium as CaCO 3 and Magnesium as CaCO 3 . Cluster 3 (black) represents heavily polluted, and the parameters include TDS, Alkalinity/visual titration CaCO 3 , Hardness as CaCO 3 , Calcium as CaCO 3 , Magnesium as CaCO 3 , and Temp. Cluster 4 (green) represents equal parameters, and it includes Temp, BOD, pH, DO, COD, and Chlorides.

Figure 12 . Dendrogram for physicochemical parameter mean values for 2017, 2018, and 2019.

The variation in observed values, quality rating, and Wiqi can be analyzed using Tables 2 , 4 , 6 .

According to WHO, the WQI should be below 60 for its quality to be at least fair. If it is more than 60, then the quality of the water is surely poor. If the WQI is <30, then the water quality is good. The WQI of the Yamuna river in Dehradun for the year 2017 was 42.87. It can be easily said that the Yamuna river was quite polluted back then. Developmental and maintaining plans were implemented to make the Yamuna river clean again and improve the WQI drastically. The WQI of the Yamuna river in 2017 was the highest in collation to the subsequent years. This must have set the alarm bells ringing for the government and the citizens. The government has introduced many measures to curb water pollution and revive the Yamuna river as quickly as possible. It is a positive sign that the WQI of the Yamuna river has improved significantly for the years 2018 to 40.47. It was a marked difference in comparison to that of the year 2017. Joint efforts and collaboration by the government and the citizens ensured that the Yamuna river is much cleaner than before, although in 2019, the WQI rose by a small margin to 40.82. It is a sign of relief that it is still much better than the quality of the water in the year 2017. If the measures of the government and corporation by the citizens continue to go hand in hand, the results will be for everyone to see. Even regions in the west would emulate the policies adopted to revive the rivers. Policies included a big budget for the revival project, strict norms for the industries, and appropriate penalties for the defaulters. A common concern for the degrading water quality index of the Yamuna river resulted in some swift actions from the citizens as well as from those who became more aware and conscious. It can be easily and comfortably said that the Yamuna river would be much cleaner and in a much-improved condition by the year 2025.

A comparative analysis is shown in Table 7 . A comparison in description and limitations with previously published approaches are organized in this table.

Table 7 . Comparative analysis with previous work done.

Due to historical, geographical, religious, political, and sociocultural reasons, India has a unique place in the world. Pollution-causing activities have caused severe changes in aquatic environments over the last few decades. This paper aims to calculate the water quality index of the Yamuna river in Uttarakhand using 12 physicochemical parameters for a while for 3 years from 2017 to 2019. The values of the considered physicochemical parameters have been monitored using the various examining stations installed by the Central Pollution Control Board (CPCB), India. According to WHO, the WQI should be below 60 for its quality to be at least fair. If it is more than 60, then the quality of the water is surely poor. If the WQI is <30, then the water quality is good. The WQI of the Yamuna river in Dehradun for the year 2017 was 42.87. It can be easily said that the Yamuna river was quite polluted back then. Developmental and maintaining plans were implemented to make the Yamuna river clean again and improve the WQI drastically. The WQI of the Yamuna river in 2017 was the highest in collation to the subsequent years. This must have set the alarm bells ringing for the government and the citizens.

According to the trend analysis, the values of four parameters named Temperature, Total Coliform, TDS, and Hardness are increasing yearly, whereas the values of pH and DO are not rising year by year. The trend forecasting verifies whether the exceptional tourist activity, poor sewage facilities, and insufficient wastewater management amenities is degrading the water of the Yamuna river at Dehradun year by year. It is a positive sign that the WQI of the Yamuna river has improved significantly for the years 2018 to 40.47. It was a marked difference in comparison to the year 2017. Joint efforts and collaboration by the government and the citizens ensured that the Yamuna river is much cleaner than before, although in 2019, the WQI raised by a small margin to 40.82. It is a sign of relief that it is still much better than the quality of the water in the year 2017. If the measures of the government and corporation by the citizens continue to go hand in hand, the results will be for everyone to see. Even regions in the west would emulate the policies adopted to revive the rivers. A common concern for the degrading water quality index of the Yamuna river resulted in some swift actions from the citizens as well as those who became more aware and conscious.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Author Contributions

Data curation was done by RS and RK. Formal analysis was made by RS, RK, and SS. The investigation was done by RS, RK, and KS. The methodology was done by RS, NA-A, and RM. Project administration was performed by HL and AA. BP was in charge of the resources and software. BP and NA-A supervised the study. Visualization was done by BP and NA-A. RS, AA, RM, and BP wrote the original draft. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: water quality index, Yamuna river, physico-chemical parameters, water pollution, Dehradun city

Citation: Sharma R, Kumar R, Satapathy SC, Al-Ansari N, Singh KK, Mahapatra RP, Agarwal AK, Le HV and Pham BT (2020) Analysis of Water Pollution Using Different Physicochemical Parameters: A Study of Yamuna River. Front. Environ. Sci. 8:581591. doi: 10.3389/fenvs.2020.581591

Received: 09 July 2020; Accepted: 11 November 2020; Published: 11 December 2020.

Reviewed by:

Copyright © 2020 Sharma, Kumar, Satapathy, Al-Ansari, Singh, Mahapatra, Agarwal, Le and Pham. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nadhir Al-Ansari, nadhir.alansari@ltu.se ; Hiep Van Le, levanhiep2@duytan.edu.vn ; Binh Thai Pham, binhpt@utt.edu.vn

This article is part of the Research Topic

The Environmental Hazards of Toxic Metals Pollution

India's Yamuna River is covered in chemical foam yet residents are bathing in it to celebrate Chhath Puja

One of India's most sacred rivers appears to be coated with a thick layer of snow. Except it isn't.

Key points:

• Hundreds stood knee-deep in the toxic river, some even immersed themselves to mark the festival of Chhath Puja
• The toxic white foam is discharged from industrial plants that ring the already polluted capital of New Delhi
• Authorities have tried to disperse the foam and have erected barricades to stop it from washing up on the river banks 

A vast stretch of the Yamuna River is covered with white toxic foam, caused in part by pollutants discharged from industrial plants that ring New Delhi.

Still, on Wednesday hundreds of Hindu devotees stood knee-deep in its frothy, toxic waters, sometimes even immersing themselves for a holy dip to mark the festival of Chhath Puja.

The 1,376-kilometre Yamuna is one of the holiest rivers for Hindus. It is also among the most polluted in the world.

The river provides more than half of New Delhi's water, posing a serious health risk to its residents.

It has become dirtier over the years as most of the capital's sewage, farm pesticides from neighbouring states and industrial effluents from factory towns flow into the waterway, despite laws against polluting.

Yet, devotees flock to it every year during the festival, which is dedicated to the solar deity and is observed with holy bathing.

Rajesh Kumar Verma was among those who offered prayers on Yamuna's banks on Wednesday. He knows the water is harmful but stood in it anyway, unfazed by the health hazard.

"What fear? If we are scared, then how can we pray?" he said.

Authorities deployed motorboats in an attempt to disperse the toxic foam. They also erected barricades of bamboo sticks to keep it away from the river banks.

India's capital, home to more than 20 million people, is one of the world's most polluted cities.

Winters in particular have become a time of health woes, when the city is covered with a toxic haze that obscures the sky and air pollution levels reach catastrophic levels, in a city where the air quality is frequently the world's worst on a daily basis.

Pollution levels soar as farmers in neighbouring agricultural regions set fire to their land after harvests to clear it for the next crop season.

On Wednesday, New Delhi's air quality index was "very poor," according to SAFAR, India's main environmental monitoring agency.

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Mapping pollution in river Yamuna: Untreated effluent a challenge for authorities

Millions of litres of untreated and treated water being pumped into the yamuna river has emerged as a key source of contamination..

Millions of litres of untreated and treated water being pumped into the Yamuna river has emerged as a key source of contamination, polluting the northern Indian river, considered sacred by many, and also considered the lifeline of the national capital.

Even as the Haryana government and Haryana State Pollution Control Board (HSPCB) have spent crores to set up sewage treatment plants (STPs) and common effluent treatment Plants (CETPs) to check the mixing of effluents, a ground report from Hathni Kund Barrage (HKB) in Yamunanagar and Kundli in Sonepat — the stretch through which the river flows — revealed that the Yamuna water continues to be unfit for human consumption.

Since the authorities have not been able to find a solution to utilise the treated water discharged from the STPs and CETPs, that water flows back to the drains.

A primary stumbling block against finding a solution is the high cost of installation of reverse osmosis (RO) systems to purify treated water.

In a bid to find a solution, the HSPCB (what) chairman announced that the government has prepared a detailed plan to utilise the water that’s been treated.

The Yamuna water distribution

At HKB in the Tajewala village of Yamunanagar district, 172 km from its point of origin in Uttarakhand -- from where the Yamuna enters the plains and is sandwiched between Haryana and Uttar Pradesh borders -- the water flow is about 4625 cusecs (a unit of flow equivalent to one cubic foot per second) or about 1,30,965 litre per second.

After the apportionment of 3254 cusecs (about 92,143 litre per second) for Haryana and Delhi through Western Jamuna Canal (WJC) and 1019 cusecs (about 28,854 litre per second) for Uttar Pradesh through Eastern Jamuna Canal (EJC), 352 cusecs (about 9967 litre per second) actually flow in the Yamuna.

And, it is this particular flow of 352 cusecs water, which bears the dirty brunt of untreated and treated pollutants.

The river water is allocated as per the water sharing agreement between Haryana, Uttar Pradesh and Delhi via the Western Jamuna Canal and Eastern Jamuna Canal from the HKB barrage (mentioned earlier.)

Set up by the Centre in 1995, the Upper Yamuna River Board is assigned the task of regulating the supply of water from all storages and barrages until the Okhla barrage in Delhi in accordance with the agreements between the governments of the six basin states of Uttar Pradesh (UP), Himachal Pradesh, Uttarakhand, Haryana, Rajasthan and Delhi.

Contamination indicators turn red as the river flows through Haryana

Data from HSPCB showed that the Yamuna water at HKB was found to be comparatively better at 3.2 milligrams per litre biological oxygen demand (BOD), which represents how much oxygen is required to break down organic matter in water.

A retired HSPCB scientist Rajesh Gharia said that the river water, which has to be made fit for human consumption using the RO process should have a BOD level of below 10mg per litre and a faecal coliform level (a bacteria which is found in polluted waters after being discharged from residential and industrial areas) of less than 100 MPN (most probable number) per 100 ml.

Water with a BOD level of 10 mg per litre or below is good for agriculture and construction activity as well.

As the river flows through five districts of Haryana— Karnal, Panipat, Sonepat, Faridabad and Palwal (besides Yamunanagar) -- the BOD levels increase in an alarming manner.

Data showed that the BOD levels at Karnal increases to 4.2 mg per litre and shoots up to 46 mg per litre at Panipat. At Sonepat, the BOD levels were 28 mg per litre while at Faridabad the levels were 24 mg per litre and 52 mg per litre at Palwal where the river ends its run in Haryana.

Faecal matter count gets worse too.

The levels of faecal coliform present in the river water were found to have deteriorated from 500 counts of MPN per 100 ml of water at Yamunanagar to 3400 count per 100 ml at Panipat.

It further increases to 5800 count per 100 ml of water at Sonepat, pushes further to 8000 count per 100 ml of water at Faridabad and 8500 count per 100 ml at Palwal.

These counts are alarming because board officials said that the permissible faecal coliform count is 100 MPN per 100 ml.

Drains polluting the Yamuna

HSPCB has identified 11 drains between Yamunanagar and Palwal which pour effluents in Yamuna, which are major sources of pollution in the river.

These drains discharged 540 million litres per day (MLD) of untreated effluent into the Yamuna as 25 sewage treatment plants (STPs) located in the Yamuna catchment area remain non-compliant with the prescribed standards.

Yamunanagar’s ‘Ganda Nallah’ (dirty drain) is the biggest source of pollutants.

Even more worryingly, around 50 MLD of industrial waste released from the industrial twin city of Yamunanagar-Jagadhari via the “Ganda Nallah” (dirty drain) of Yamunanagar also flows into the Yamuna.

This toxic water merges into the clean water of the Yamuna river near the Jarauli village of Karnal after travelling in Dhanaura Escape – a canal in which industrial waste of the Yamunanagar city is dumped and it merges with the Yamuna river near Karnal -- for around 80 km.

This is the most visible source of pollution in Yamuna water. The BOD levels of Dhanaura escape stand at 5.8 mg per litre, while the levels of drain two before meeting Yamuna at Khojkipur village of Panipat is 68 mg per litre.

The BOD of drain number six before the water enters Delhi measured at 84 milligrams per litre.

The two Sewage Treatment Plants of 45 MLD set up by the Public Health Engineering department have been found insufficient to treat the over 50 MLD being discharged from the residential areas of these cities. Officials associated with the STP project said that a new plant of 70 MLD will be set up in Yamunanagar to treat this industrial waste.

Treated water going to waste

During a visit to the STPs and CETPs, many of which are complying with the pollution control board norms, it was found that authorities have not been able to find a solution for the utilisation of the treated water being discharged from these STPs and CETPs.

Around 38 MLD treated water coming out from the two STPs located on the outskirts of Yamunanagar mixes into the contaminated water of the Ganda Nallah (the dirty drain mentioned above.)

“This water is treated and it could be utilised for irrigation of crops but due to the lack of required mechanism, we have to dump it into the dirty canal”, said an official associated with the STP of Public Health Engineering Department.

Similar is the story of STPs in Samalkha and Panipat and HSIIDC’s CETPs located in the industrial areas of Panipat, Rai, Barhi, Kundli and Sonepat.

“Even industrial waste is different than domestic waste as it contains harmful chemicals but after treatment, it could be utilised for irrigation. But now it is being utilised to maintain the flow of water in drain number 1, 2, 6 and 8”, said Rahul Kumar, a technical expert at CETP in Panipat’s sector 29.

Representatives of private companies deputed to monitor STP operations have suggested that the water below 10 BOD could be utilised for agriculture and horticulture and can also be purified further to make it fit for human consumption.

But the government has no plans to utilise the treated water and it flows back into the polluted drain water to maintain the flow in Yamuna, said officials at the STPs and CETPs who did not wish to be identified.

Another way to use treated water for agriculture and even human consumption would be by installing RO systems, officials monitoring STPs and CETPs said.

HSPCB Chairman P Raghavendra Rao said that after a thorough study, they have prepared a detailed plan to utilise treated water for different purposes.

Haryana will be among very few states which will utilise treated water released from STPs and CETPs for different purposes, HSPCB said in a statement given to HT.

Technical experts working at the CETPs and STPs said that the cost of setting up RO systems to purify the treated water for human consumption is very high but the move will not only help to deal with the shortage of drinking water but will also cut pollution in the river.

More STPs, and CETPs needed

The estimated sewage generation in the Yamuna catchment area is 1,098 MLD while the total sewage generation in the Ghaggar catchment area is 291.46 MLD.

As per pollution control board officials, a total of 59 STPs with a capacity of 1075.2 MLD have been made operational in the Yamuna belt. As many as 25 STPs, however, have not met the prescribed parameters.

Also, there is a gap of 240 MLD in the installed treatment capacity at present, in comparison to the required amount in some towns in the catchment area of Yamuna, including 144.5 MLD in Faridabad, 86 MLD in Gurugram and 9.3 MLD in Palwal.

Out of the total 379 locations, where untreated or partially-treated effluent is being discharged into the rivers, the action plan to control pollution has been completed at only 129 locations, reveals the HPCB’s February 2021 progress report of Haryana government regarding the Ghaggar and Yamuna Action Plan.

Moreover, the inflow of the sewage at most of the STPs and CETPs is more than the capacity and the excess flow is released directly into the drains.

In Samalkha Public Health Engineering Department’s only STP of 5 MLD, which has been declared as non-compliant, is not sufficient to treat the entire sewage which is around 8 MLD.

The staff operating the STP said that they run the plant round the clock but its capacity is only 5 MLD and they have to discharge the remaining 3 MLD directly into drain number six which joins river Yamuna.

Sonu Kumar, an operator at the plant said that another plant of 5 MLD capacity is being set up but nobody knows when it will be completed.

Manoj Kumar a resident of Samalkha said that the plant was set up in 2002 but expressed concerns at the issue of sewage management.

“Even the number of factories and the number of households in the town got doubled in the past 21 years but nobody has taken the issue of sewage management seriously,” Kumar said.

Similarly, the 16 MLD CETP set up in the Barhi industrial area is not sufficient to treat the entire sewage generated from over 200 industries located in the area. Suresh N Bajpai, regional manager of Gharpure Engineering and Construction company which operates this CETP since November 2017, said, “This plant is automatic and the inlet takes effluent only as per its capacity. Work is on at another plant of 16 MLD capacity but it will take two years to make it operational,” officials said.

Reducing industrial pollution

There is a need to reduce the effluent being released from the industries located in the Yamuna catchment area. A total of 3505 industries located in this area generate 121.11 MLD sewage. The HSPCB’s February 2023 report however revealed that all the industries have effluent treatment plants (ETP) but out of the 3505 industries only 814 are connected to CETPs. Officials said that there is a total of 14 CETPs with a capacity of 161 MLD set up in the Yamuna belt whereas one CETP of 10 MLD is under construction and 7 CETPs with a capacity of 126.5 MLD are proposed.

The HSPCB officials said that they have taken action against the polluters, law violators and officers responsible for the failure of vigorous monitoring.

As per reports, a total of 1644 industries — including 1402 in the Yamuna catchment area, have been inspected in the state and closure notices to 1318 violating units have been issued, 422 violating units prosecuted and an environmental compensation of ₹ 94.6 crore has been imposed on 275 units along with ₹ 262 crore for legacy waste, the waste collected and stored at dumping sites, as per the Monthly Action Taken reports submitted by the HSPCB to the NGT, which didn’t specify the details of these factories.

The NGT had directed the Haryana government to commence setting up STPs and connecting all the drains and other sources of generation of sewage to the STPs by March 31, 2020.

The timeline for completing all steps of this action plan including setting up STPs and their commissioning was set for March 31, 2021.

Approximately 107 lakh MT of legacy waste was lying at the dumping sites in municipalities of the state out of which 43.23 lakh MT (40.4%) of legacy waste has been remediated.

Officials said that the state government has approved the allocation of funds of ₹ 262.67 crore for bioremediation of legacy waste. The first instalment of Rs.44.59 crore has been released by to the Urban Local Bodies department and a further limit of Rs. 115.47 crore has been assigned to various ULBs for bioremediation of legacy waste to implement the necessary measures for this purpose.

The HSPCB chairman said that the state government and HSPCB have taken the required steps to deal with problem of the sewage treatment. He said that most of the non-complying STPs were old and were not meeting the standards. That is why they have been declared non-compliant.

“Our top priority is to tap untreated effluent of industries being discharged into the river and we will achieve it by the end of the next year. Regular monitoring of the STPs, CETPs and industries and action is being taken for non-compliance,” he said.

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Neeraj Mohan is a correspondent, covering Karnal, Kaithal, Kurukshetra, Panipat and Yamunanagar districts of Haryana. ...view detail

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Pollution in Yamuna River

It is the responsibility of States/Union Territories (UTs) and local bodies to ensure required treatment of sewage and industrial effluent, before discharge into recipient water bodies, including rivers, for prevention & control of pollution therein. This Ministry has been supplementing the efforts of the States/UTs by providing financial and technical assistance in abatement of pollution in identified stretches of rivers (river Ganga and its tributaries), through the Namami Gange Programme.

Presently, Government of India, under Namami Gange Programme, has sanctioned 24 projects costing Rs.4355 crore for abatement of pollution in river Yamuna, a major tributary of river Ganga for creation of sewage treatment capacity of 1862 MLD and other associated sewage infrastructure. These projects are in Himachal Pradesh (01 project), Haryana (02 projects), Delhi (13 projects) and Uttar Pradesh (8 projects). Five projects have been completed so far (two projects in Haryana, two projects in Delhi and one project in Uttar Pradesh).

The cleaning of rivers is a continuous process and this Ministry is supplementing the efforts of the States for checking the rising level of pollution of river Yamuna, a tributary of river Ganga, by providing financial assistance to States. The aforementioned projects are in different stages of planning/construction/tendering. Close monitoring is being done to ensure timely completion.

This Information was given by the Minister of State for Jal Shakti and Tribal Affairs, Shri    Bishweswar Tudu in a written  reply in Lok Sabha today.

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Water quality analysis of River Ganga and Yamuna using water quality index (WQI) during Kumbh Mela 2019, Prayagraj, India

• Published: 10 January 2023
• Volume 26 , pages 5451–5472, ( 2024 )

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• Ashok Kumar Kanaujiya 1 &
• Vineet Tiwari 1  

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Rivers Ganga and Yamuna have been subjected to massive degradation and pollution due to the vast amount of wastewater entering the river during the Kumbh Mela. The Kumbh Mela, the largest religious congregation, was organized at Prayagraj, India, from January to March 2019. Despite the efforts, the river quality during Kumbh Mela was not suitable for public use, and even after Kumbh Mela, it was not substantially improved. This study estimated the water quality index (WQI) for the river Ganga and Yamuna at Prayagraj from 01.12.2018 to 30.04.2019. The study focused on the use of WQI to describe the level of pollution in the river during, before, and after the Kumbh Mela. The study also identifies the critical pollutants affecting the river water quality. The properties (pH value, Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and water color) of river water were evaluated at five different locations: (i) Rasulabad (upstream the Ganga river); (ii) Shashtri Bridge (downstream the Ganga river); (iii) Main Sangam ; (iv) Chhatnag Ghat (downstream the Ganga river); and (v) Saraswati Ghat (at the Yamuna river). The study used the water sample data from the Central Pollution Control Board (CPCB), Uttar Pradesh. This study revealed that water quality was within permissible limits except for BOD during the Kumbh Mela. The DO content was within the allowable limit for bathing at all five sampling locations during Kumbh Mela. This high DO content resulted from sanitation management efforts taken by the government and self-cleansing properties of the river Ganga.

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We are grateful to Prayagraj Mela Authority and Central Pollution Control Board for using its data and reports published in various books and documents.

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Kanaujiya, A.K., Tiwari, V. Water quality analysis of River Ganga and Yamuna using water quality index (WQI) during Kumbh Mela 2019, Prayagraj, India. Environ Dev Sustain 26 , 5451–5472 (2024). https://doi.org/10.1007/s10668-023-02907-9

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