Physicochemical analysis of wastewater discharge and impact on Ganges River of major cities of North India Open Access

In many developing countries like India, drinking water resources are scarce and limited. According to the Composite Water Management Index, Ministry of Jal Shakti and Ministry of Rural Development of India, per person water availability is substantially decreasing due to rapid socio-economic growth and inadequate use of water (NITI Aayog 2019; Press India Bureau 2020). In fact, the increasing pollution level and continuous exploitation of groundwater and freshwater resources need some urgent solutions. Although municipal wastewater treatment plants are effectively treating polluted wastewater, most of the natural water resources in India become polluted through the direct discharge of domestic and industrial wastewater effluents that contain clinical pathogens and hazardous elements (Sharma et al. 2014; Sirisha et al. 2017; Kumar et al. 2019). Excessive pollutant discharge into natural water bodies gives rise to the elevated level of total dissolved solids (TDS), nutrients, organic substances and other contaminants, resulting in severe environmental glitches like eutrophication, forfeiture of biodiversity and alteration of aquatic organisms and their behaviours (Scherer & Pfister 2016; Adbarzi et al. 2020). Many studies stated that the use of polluted water for irrigation purposes has impacted with negative effect on agricultural sectors due to the changes in physicochemical properties (Khalid et al. 2017, 2018; Ofori et al. 2021).

The municipal treatment plants are mainly used in the removal/reduction of organic wastes and pollutants to increase dissolved oxygen (DO) level and decrease the excessive richness of nutrients in the downstream receiving watershed and to safeguard human health by deactivating disease-causing pathogens (Sanderson et al. 2019; Zhou et al. 2019). Many treatment technologies are being applied worldwide for wastewater treatment including physical, chemical, biological or combinations of these processes (Soni et al. 2020; Kesari et al. 2021). Physical and chemical technologies are mostly based on evaporation (Aziz et al. 2019), membrane filtration processes like reverse osmosis, nanofiltration (Mulyanti & Susanto 2018; Hafiz et al. 2021), ion-exchange technology (Al-Asheh & Aidan 2020), advanced oxidation process (AOP) (Ghime & Ghosh 2020; Zhou et al. 2020), and electrochemical processes (Muddemann et al. 2019; Marassi et al. 2020) to remove pollutants from wastewater streams. The random discharge of untreated or poorly treated wastewater effluents is the main source of surface water pollution with its associated problems like high biological oxygen demand (BOD) and nutrient contents which may have adverse effects on human health and environmental conservation (Stackpoole et al. 2019).

In many developing countries like India, a drastic increase in wastewater discharge into the environment has been observed during the past few years, due to the consistent increase in population and urbanization (Pirsaheb et al. 2014; Preisner 2020). This comes along with the attendant challenges that include environmental pollution, threats to public health, and increased reliance on rapidly diminishing water resources. In this paper, we report the physicochemical properties of collected water samples of each treatment step of different wastewater treatment plants and nearby Ganges River (downstream environment) water samples in important cities of North India and investigate their removal efficiency and pollution sources. The study presented here is part of a larger project aiming at establishing the best treatment technology for wastewater at each station.

Three wastewater treatment plants (WWTPs) were selected for this study located at the bank of the Ganges River, namely Prayagraj, Varanasi and Kanpur. The treatment plants are identified by their regional names: Bakshi Bandh WWTP, Prayagraj; Bhagwanpur WWTP, Varanasi; and Bingawan WWTP, Kanpur. The Bakshi Bandh WWTP uses a moving bed biofilm reactor (MBBR) while Bhagwanpur WWTP and Bingawan WWTP use activated sludge process (ASP) and upflow anaerobic sludge blanket reactor (UASB) respectively. The biofilm-based treatment technology, MBBR, has the ability to eliminate organic carbon and nutrients viz chemical oxygen demand (COD), BOD, nitrogen and phosphorus from wastewater with plastic carriers which allow microorganisms to grow as biofilms, and their movement in the tank for biofilm formation is attained either by agitation through aeration or mechanically by stirrers (Leyva-Díaz et al. 2017). The activated sludge process, being a suspended growth biological treatment process, involves the utilization of compact microbial culture in the suspension to biodegrade the organic contents under anaerobic conditions and creates a biological floc in the settlement unit for the separation of solids (Stott 2003). The UASB system is the amalgamation of physical and biological processes which involves separation of solids and gases from the liquid while the biological process involves the biodegradation of decomposable organic components under anaerobic conditions. The key feature of this system is granular anaerobic sludge that intrinsically carries good settling properties; therefore, it does not require intensive mechanical agitation (Latif et al. 2011). All the wastewater treatment plants selected for the study receive wastewater from mixed sources viz domestic, municipal, and clinical wastewater, but Kanpur City consists of an industrial network, hence it also receives wastewater from the leather and pharmaceutical industries. Bakshibandh WWTP (MBBR) of Prayagraj has a treatment capacity of 29 million litres per day (MLD) and generally treats the wastewater generated by 2.5 million people of approximately 647 hectares area. Bhagwanpur WWTP of Varanasi mainly consists of three sections: primary clarifier, aeration tank and anaerobic digester tank. It has a treatment capacity of 9.8 MLD out of which 8.0 MLD wastewater is received mainly from Banaras Hindu University (BHU) campus and hostels, whereas the remaining 1.8 MLD is received from the BHU hospital and residential quarters. The Bingawan WWTP of Kanpur (UASB) has a treatment capacity of 210 MLD and serves an area of 130 km² (approximately 25% of the city's area) and has approximately 0.261 million sewerage connections. It receives average wastewater flow ranging from 170 to 180 MLD. The nearby Ganges River sampling points are also indicated by their regional names: Sangam, Prayagraj; Samaneghat, Varanasi; and Bithoor, Kanpur. The map of all the WWTPs is shown in Figure 1 and the details of the WWTPs, geographical locations and working technology are listed in Table 1.

The water samples were collected from each treatment step of the different WWTPs and nearby Ganges River of Prayagraj (Allahabad), Varanasi and Kanpur, India, for physicochemical analysis. All samples were collected in 1 L properly washed and sterile plastic containers during the summer season (June 2019) and winter season (January 2020). The samples were immediately transported to the laboratory under cooling conditions and stored in a refrigerator at 4 °C for further analysis.

Physical parameters of the collected water samples that were measured in this study were temperature, TDS and pH. The pH, temperature, and TDS of the water samples were determined using a multiparameter ion-specific meter (Labtronics, version LT68).

The chemical analysis of the collected water samples measured in this study was of DO, BOD, COD, chloride content and alkalinity. The BOD and COD values indicate the oxygen-level depletion in the water samples due to organic matter degradation. DO and BOD were determined by using Winkler's standard protocol (Aniyikaiye et al. 2019). The chloride content, COD and alkalinity of water samples were analysed through the titration method using the standard methods for the examination of water and wastewater (APHA 1992).

Nutrient concentration is one of the important parameters in determining wastewater characteristics. The high nutrient content of wastewater increases the risk of eutrophication in the water body. The phosphate, nitrate, ammonia and sulphate concentrations were measured in this study. The concentrations of orthophosphate (as phosphate), nitrate, ammonia and sulphate were determined by the standard photometric method. Nitrate and ammonia were measured in mg/L as N.

All the parameters were analysed using standard protocols mentioned in the Central Pollution Control Board (CPCB), India Guide Manual (2016) for water and wastewater analysis, which comply with the standard methods for the examination of water and wastewater (APHA et al. 1998).

The data obtained were analysed using a two-way analysis of variance (ANOVA) to test differences among all possible pairs of treatment means and to analyse the significance between the samples. A correlation analysis was performed in the R software package to measure the strength of the relationship amongst variables and figure out their associations. A multivariate factor analysis was also executed to identify the number of factors depicting the association amongst variables using varimax rotation factor analysis in the R software package.

The temperature profile generally varies significantly (P<0.001) during the summer season and ranged between 31.0 °C and 35.2 °C at all sampling points. In the winter season, temperature variation significantly varies for the river but not for wastewater samples and ranged between 16.8 °C and 22.6 °C. The values obtained from each treatment step are listed in Supplementary Table 1. There was a significant difference (Tables 2 and 3) between the temperatures of Prayagraj and Kanpur in both seasons at a confidence interval of 95%. The wastewater samples collected during the summer season had an elevated level of temperature due to seasonal variation. The data indicates, during the winter and seasons, the temperature level is below the maximum limit defined by USEPA (1977), i.e. 32 °C, which is appropriate for the sustainable growth of aquatic animals. The high temperature was recorded at all the sampling points in June due to the usual atmospheric condition. The temperature also regulates the growth and existence of aquatic biota (Rozen-Rechels et al. 2019).

The pH value observed in this study lies within the permissible limit of wastewater discharge into the downstream river environment. The pH value at all the sample points was obtained within a slight variation towards the alkaline region. The seasonal pH data recorded in Prayagraj and Varanasi WWTPs indicated a significant increase in the summer season whereas Kanpur WWTP samples slightly dropped down. The pH profile significantly (P<0.01) varied at a confidence interval of 95% during the summer and winter seasons between 7.08 and 8.43 at all sampling points (Tables 2 and 3). The variation in pH value (low and high) can have a toxic effect on aquatic life and change the solubility of pollutants in surface water. The CPCB (2017) New Delhi recommended limit for pH in water for domestic use is 6 to 9. Kanpur WWTP's inlet sample was more alkaline than the other two cities, with a pH of 7.95, which could be related to Kanpur's high industrial indulgence in leather, textile, pharmaceutical, agrochemical and plastic manufacturing industries. The Ganges River water of all the cities also recorded an increase in the summer season that indicates the direct impact of temperature on the elevation in pH levels of water. Many anthropogenic activities affect the pH level of water, such as discharge of chemicals, solubilization of rock and salts, and the release of CO₂ due to the decomposition of plants and organic bodies (Kaur 2018). All the collected samples indicate pH within the optimum pH level limits of 5.0–9.0, in which any minor shift, either increase or decrease, could be harmful to the survival of many of the aquatic species (USEPA 2006).

In the winter season, mean values of TDS of Prayagraj and Kanpur wastewater samples were found to be similar at 642.0–713.66 and 647.0–723.66 mg/L, whereas Varanasi wastewater samples had a low TDS content in comparison with Prayagraj and Kanpur of 328.66–393.33 mg/L. TDS content of the river samples was recorded as 213.33, 208.0 and 180.0 mg/L in Prayagraj, Varanasi and Kanpur respectively. In the summer season, wastewater samples of Prayagraj and Kanpur region also showed almost the same TDS content at 687.00–782.66 and 668.00–740.00 mg/L, whereas the Varanasi wastewater samples had TDS of 374.33–459.33 mg/L. Ganges River water samples of all cities had a decrease in TDS content of 235.0, 226.66 and 221.0 mg/L, which is under the WHO standard levels of drinking water (<300 mg/L) (Table 4). There was a significant difference (P<0.001) in Prayagraj, Varanasi and Kanpur samples during both seasons at a confidence interval of 95%, however in the Varanasi WWTP inlet, step 2 and outlet samples did not show a significant difference in the winter season (Tables 2 and 3). Varanasi treatment plant showed a lower TDS value for all steps compared with the other two treatment plants but the TDS value of the nearby Ganges River was recorded as approximately the same as the other river samples. The high TDS values were recorded in river samples due to the direct discharge of the untreated wastewater from illegal sources. High TDS concentration in wastewater generally adversely affects treatment efficiency and causes toxicity through increases in salinity and ionic composition of the water. To remove the TDS content from wastewater, the ASP technology of Varanasi WWTP attained a maximum efficiency of 16.44% in winter and 13.47% in summer whereas of the other two technologies, MBBR technology in Prayagraj WWTP could reach up to 1.78% in winter and 10.91% in summer, and Kanpur WWTP working on UASB technology had an efficiency level of 2.06% in winter and 9.72% in summer (Table 4). Based on TDS value, the removal efficiencies of MBBR and UASB technology were very high in the summer season but ASP-based treatment technology showed high removal efficiency in the winter season (Ahmed 2017).

Total hardness in the winter season samples revealed concentrations of 495.0–513.0 and 336–405 mg/L CaCO₃ in Prayagraj and Varanasi WWTPs respectively, whereas a significantly very low hardness level of 276–296 mg/L CaCO₃ was observed in Kanpur WWTP samples. In the summer season, the wastewater samples of Prayagraj and Varanasi showed a lower concentration of total hardness, i.e. 461–483 and 306–360 mg/L CaCO₃ respectively, whereas Kanpur WWTP technology (UASB) achieved the maximum reduction efficiency of 66% as the Kanpur WWTP samples had total hardness values from 406, 346, 313 and 271 mg/L CaCO₃ significantly decreasing in the inlet, step 1, step 2, and outlet. In a UASB system, under anaerobic conditions, the reduction of sulphate results in an increase in the carbonate alkalinity of wastewater. Further, the biologically generated carbonate alkalinity is used to form calcium carbonate, which eliminates calcium ions from the wastewater (Kim et al. 2003). Therefore the maximum calcium hardness removal can be achieved by UASB technology. The average value of total hardness in the Ganges River water samples was 123–216 mg/L CaCO₃ for the summer season, and in the winter season the average value was 138–328 mg/L for all three cities. In two-way ANOVA, Bonferroni post-tests, the hardness of each sample from all cities exhibited a significant difference (P<0.01) at a confidence interval of 95%, however, in the summer season, a non-significant difference for Varanasi Ganges River water was observed (Tables 2 and 3). Total hardness in all the collected wastewater and Ganges River water samples was found to be within the permissible limit of 500 mg/L (WHO 2008) and the BIS permissible limit of 300 mg/L (Reda 2016). The data analysis explains the significant impact of seasonal variation on the reduction of total hardness in wastewater and river water; as the temperature increases, the decrease in the hardness of the water can be observed. There was an increase in the hardness of wastewater at step 1, step 2 and outlet. Most of the samples from all the treatment plants either showed negligible hardness removal or negative removal efficiency. Apparently, in the summer season, Kanpur WWTP amazingly achieved the removal efficiency of 33.19%, which was −5.42% in the winter.

Dissolved oxygen is an imperative parameter used for the assessment of pollution levels by organic matter and checking the water quality control. The mean value of DO in the winter season samples from Prayagraj and Varanasi was found to be 0.24–3.23 and 1.90–6.6 mg/L, whereas Kanpur wastewater samples had lower DO, i.e. 0.26–2.46 mg/L. Ganges River water samples contained the DO values 7.14, 8.23, 6.73 mg/L in Prayagraj, Varanasi and Kanpur respectively. In the summer season wastewater samples, the DO value was in the range 0.36–4.13, 1.26–6.6 and 0.16–4.06 mg/L, whereas the Ganges River water samples showed the DO amount of 7.26, 8.04 and 8.0 mg/L in Prayagraj, Varanasi and Kanpur respectively. There was a significant difference (P<0.001) in all cities' samples during the winter season at a confidence interval of 95%, however in step 1 and inlet in Prayagraj vs Varanasi and Prayagraj vs Kanpur WWTP samples respectively there was no significant difference shown in the summer season (Tables 2 and 3). Moreover, in the summer season, excluding the Prayagraj vs Varanasi inlet, Prayagraj vs Kanpur inlet, outlet, river and Varanasi vs Kanpur inlet and step 1, the rest of the samples showed a significant difference (P<0.001) at a confidence interval of 95% (Tables 2 and 3). As per the DO levels, the Varanasi WWTP indicates better water quality as compared with the other two treatment plants in both seasons. The lower DO values of the Ganges River water samples of Kanpur region specify an intense pollution level, but all river samples of Prayagraj and Varanasi regions were found to be in the acceptable range, i.e. 4 mg/L, for the sustainable livelihood of biological species (Patel & Vashi 2015). The acceptable range of DO value for drinking purposes is 6 mg/L and for aquatic life is 4–5 mg/L, but a lower DO value in water can disturb aquatic life by reducing the strength of immunity against various infections, affecting reproductive behaviour, hampering swimming behaviour, and making nourishment unstable, leading to the death of aquatic life.

In the winter season, the COD value of Varanasi wastewater samples was very high at 413–420 mg/L whereas COD values of Prayagraj and Kanpur wastewater were 30–125 and 93–160 mg/L respectively. The Ganges River water of Varanasi region also showed a very high COD level of 281 mg/L, however the COD value for the Prayagraj and Kanpur river water samples was recorded as 30 and 60 mg/L. In the summer season, COD levels of Varanasi wastewater samples decreased significantly to 107–171 mg/L whereas Prayagraj and Kanpur wastewater samples showed COD levels of 43–107 and 64–181 mg/L. The Ganges River water of Prayagraj and Kanpur had similar COD levels of 43 mg/L whereas the Varanasi river water sample showed a COD value of 53 mg/L. There was a significant difference (P<0.001) in all three cities' samples during the winter season at a confidence interval of 95%, however in the summer season, apart from Prayagraj vs Varanasi step 2 and river, Prayagraj vs Kanpur outlet, river and Varanasi vs Kanpur step 2, outlet and river, the other samples showed significant difference (P<0.001) at a confidence interval of 95% (Tables 2 and 3). The COD level of all treatment plants significantly decreased in the summer season but compared with the WHO acceptable limit (<3 mg/L) it was beyond the range. However, in the winter season, the COD at Prayagraj and Kanpur WWTPs, which were running on the MBBR and UASB technologies respectively, was efficiently removed in each treatment step. A minor decrease in COD value was observed at Varanasi WWTP, which was based on ASP technology.

The winter season wastewater samples exhibited BOD values in the range 30–43, 80–96 and 115–213 mg/L, whereas the Ganges River water samples showed BOD levels of 7, 6 and 7.66 mg/L in Prayagraj, Varanasi and Kanpur respectively. In the summer season, BOD of wastewater samples was found to be 27.66–42.23, 77.0–98.68 mg/L and 101.57–199.33 mg/L, whereas the river water samples exhibited BOD values from 7.16, 5.3 and 7.01 mg/L in Prayagraj, Varanasi and Kanpur respectively. In the summer season, a significant difference (P<0.001) at a confidence interval of 95% was observed in each parameter of the study excluding the river samples of all cities. Nevertheless, in the winter season, the samples excluding the river of Prayagraj vs Varanasi and step 1, step 2 and the river of Varanasi vs Kanpur showed a significant difference at a confidence interval of 95% (Tables 2 and 3). In the winter season, BOD at Prayagraj and Kanpur WWTPs running on MBBR and UASB technologies respectively was efficiently removed in step 1, step 2 and outlet, whereas there was a minor decrease in BOD value observed at Varanasi WWTP running on ASP technology. However, a significant gradual decrease of BOD was observed in all three cities during the summer season. The data indicated that the maximum BOD removal of 46.08% in winter and 49.04% in summer was achieved by Kanpur WWTP, whereas Prayagraj WWTP achieved BOD removal of 30.00% in winter and 34.50% in the summer season. Moreover, Varanasi WWTP showed the lowest removal efficiency of 16.95% in winter and 21.97% in summer.

The total alkalinity (T alkalinity) in wastewater samples collected from Varanasi during the winter season showed the lowest alkalinity levels of 266–385 mg/L CaCO₃. However, Prayagraj and Kanpur wastewater samples contained 388–406 and 423–460 mg/L CaCO₃ and river samples had an alkalinity of 125, 171 and 405 mg/L in Prayagraj, Varanasi and Kanpur respectively. In the summer season, wastewater samples of Varanasi region had a total alkalinity of 361–371 mg/L CaCO₃ whereas Prayagraj and Kanpur wastewater samples contained 348–421 and 363–436 mg/L total alkalinity. However, the Ganges water samples had alkalinity levels of 108, 163 and 118 mg/L CaCO₃ respectively in Prayagraj, Varanasi and Kanpur. There was a significant difference (P<0.001) in the Prayagraj, Varanasi and Kanpur samples, excluding step 2 of Prayagraj vs Kanpur, during the winter season at a confidence interval of 95%; however in the summer season, step 1 of Prayagraj vs Varanasi, inlet, outlet and river of Prayagraj vs Kanpur, and inlet from Varanasi vs Kanpur samples did not show a significant difference (Tables 2 and 3). Changes in the alkalinity levels occur due to rock, soils, salts, certain plant activities and industrial wastewater discharge, and at the same time, this change moderates the pH level of wastewater, which affects directly the livelihood of aquatic species. The alkalinity levels of wastewater collected from Prayagraj and Varanasi gradually decreased in the series of treatments, i.e. step 1, step 2 and outlet; whereas Kanpur wastewater treatment plant showed a negative removal efficiency of alkalinity, as it was greater in the outlet than in the inlet water. An elevation in the temperature and sulphate reduction also leads to an increase in carbonate alkalinity, due to which, in the summer season, total alkalinity was increased in all treatment plants and complete reduction was not achieved. Along with the wastewater samples of all the three cities, Kanpur Ganges River water also had a high alkalinity level beyond the maximum permissible limit of <200 mg/L (Indian Standard 2012).

In the winter season, total ammonia content in Prayagraj wastewater samples was found to be very high, i.e. 11.35–7.63 mg/L, whereas Varanasi and Kanpur samples had less ammonia content in the range 0.06–4.75 and 2.93–2.76 mg/L, in comparison with Prayagraj wastewater samples. However, the Ganges River water sample of Kanpur region showed high ammonia content of 5.91 mg/L in comparison with Prayagraj and Varanasi river samples of 0.21 and 0.29 mg/L respectively (Figure 2). The summer season wastewater samples collected from Prayagraj and Kanpur had an ammonia content in the range 2.73–2.06 mg/L, whereas Varanasi wastewater samples showed comparatively low ammonia content, i.e. 0.16–0.94 mg/L. The Ganges River water samples of Kanpur and Varanasi showed similar ammonia levels of 0.13 mg/L, whereas ammonia content in Prayagraj Ganges river water was found to be very low at 0.04 mg/L. In the winter season, a significant difference (P<0.001) amongst the ammonia concentrations in all cities was observed at a confidence interval of 95% excluding only the river sample in Prayagraj vs Varanasi, whereas in the summer season, only inlet samples of Prayagraj vs Varanasi, and inlet, step 1, step 2 and outlet samples of Varanasi vs Kanpur showed significant differences (P<0.001) at a confidence interval of 95% (Tables 2 and 3). The study suggests that levels of ammonia obtained from Kanpur WWTP are beyond the safe limits of 1 mg/L and can pose a threat to the survival of several aquatic species (Oregon 2000). The maximum ammonia removal efficiency of 98.73% was achieved by Prayagraj WWTP in winter which was slightly reduced to 82.97% in summer. The data shows that Kanpur WWTP was less efficient as it reached an efficiency level up to 5.80% in winter and 44.77% in summer as compared with the Varanasi WWTP removal efficiency of 32.77% in winter and 58.32% in summer. In the amounts obtained in drinking water, ammonia has no direct health implications, therefore, no health-based guidelines have been described by WHO (WHO 1996a). Efficient ammonia reduction was observed in the series of treatment technologies in all WWTPs during both seasons.

The nitrate content found in the winter season from the wastewater samples of Prayagraj was 0.16–0.21 mg/L, whereas Varanasi wastewater samples showed nitrate levels in the range 0.29–1.37 mg/L. Significantly, Kanpur wastewater samples had a very high nitrate content of 4.91–5.10 mg/L, which may be due to the excessive chemical discharge arising from chemical industries, fertilizer industries or leather industries. Considering the several agrochemical, leather, textile and pharmaceutical industries in the Kanpur region, nitrate and ammonium nitrogen are received by WWTPs and aid in increasing nitrate levels in downstream water bodies. However, nitrate levels in the Ganges water samples were observed to be 0.08 mg/L and 0.04 mg/L in Prayagraj and Varanasi and at a comparatively very high level of 4.85 mg/L in Kanpur. The nitrate levels in summer season wastewater collected from Prayagraj were found to be 0.06–0.15 mg/L whereas Varanasi and Kanpur samples had nitrate levels of 1.02–1.53 and 1.50–1.63 mg/L respectively. The Ganges River nitrate level was found to be 0.06, 0.07 and 0.53 mg/L in the three cities. A significant difference (P<0.001) in the winter season was observed amongst the nitrate levels in all three cities at a confidence interval of 95% excluding only the inlet and river sample in Prayagraj vs Varanasi, whereas in the summer season, apart from the river sample of Prayagraj vs Varanasi, and the outlet sample of Varanasi vs Kanpur, the other samples showed a significant difference (P<0.001) at a confidence interval of 95% (Tables 2 and 3). Nitrate levels in all the treatment plants significantly increased in step 1, step 2 and ultimately decreased in outlet during both seasons. During the winter season, very drastic negative removal of nitrate, i.e. −372.41%, was recorded in Varanasi WWTP, and in the summer season also it showed negative removal efficiency of −50%. However, the nitrate removal efficiency of Prayagraj WWTP was better at 23.80% in winter and 40% in summer. Kanpur WWTP's nitrate removal efficiency was reduced in summer up to 7.97%, which was 30.62% in the winter season. However, all the collected samples had nitrate levels within the maximum permissible limit of 10 mg/L (USEPA 2021). Several research studies, observing 1–20 years of irrigation data, have indicated that the excessive application of treated wastewater for irrigation is responsible for the deposition of a large level of nitrate and ammonia in the soil which is further accumulated in plants and vegetables, and which can pose serious threats to human health (Anjana & Iqbal 2007; Jaramillo & Restrepo 2017). N removal by UASB can be achieved efficiently by incorporating anammox bacteria in granular sludge with a specific retention time. Anammox bacteria utilize nitrite as the electron acceptor to convert nitrate to dinitrogen gas as the final product. In Kanpur WWTP (UASB), integration of anammox bacteria might be responsible for N removal by nitrite reduction with N₂ as an end product. Efficient N removal in an UASB reactor was also observed by Guo et al. (2020) incorporating anammox in a UASB reactor.

Chloride estimation study of the winter season revealed a very high chloride content of 88.80–103.30 mg/L in Prayagraj wastewater samples in comparison with Varanasi and Kanpur in the range 15.82–22.49 and 15.38–22.86 mg/L respectively. The Ganges River water samples showed a chloride content of 20.73, 28.32 and 25.82 mg/L in Prayagraj, Varanasi and Kanpur respectively. The chloride content in the summer season wastewater samples was found to be 109.13–97.46 mg/L in Prayagraj, 38.32–63.31 mg/L in Varanasi, and 152.45–160.78 mg/L in Kanpur; whereas in the Ganges River water, it was found to be very similar at 23.32, 24.15 and 21.65 mg/L chloride in Prayagraj, Varanasi and Kanpur respectively. In the summer season, apart from the river samples of all three cities, the rest of the samples showed a significant difference (P<0.001) amongst the chloride concentrations at a confidence interval of 95%, whereas in the summer season, excluding the river sample of Prayagraj vs Varanasi, the river sample of Prayagraj vs Kanpur and inlet, step 1, step 2 and outlet samples of Varanasi vs Kanpur, the other samples showed significant difference (P<0.001) at a confidence interval of 95% (Tables 2 and 3). The data indicate that the chloride levels were observed in moderately decreasing amounts in step 1 and step 2 in each treatment plant during both seasons, nevertheless, they significantly increased in the outlet water samples. Varanasi WWTP showed a negative removal efficiency in the removal of chloride also viz −42.16% in winter and −65.21% during the summer season. A significant negative removal efficiency of −48.63% was also recorded in Kanpur WWTP during the winter season, which turned to 5.18% in the summer season. Prayagraj WWTP removed chloride content from wastewater with an efficiency level of 14.04% in the winter season and 10.69% in the summer season. The increased chloride content in the outlet water is due to the chlorination treatment for the disinfection of outlet water before the final discharge to the water bodies. However, the chloride levels of all the collected samples during both seasons are under the acceptable levels of <250 mg/L, although there is no health-based guideline issued by WHO (WHO 1996b).

Phosphate content in Kanpur wastewater during the winter season was found to be very high at 30.97–33.52 mg/L whereas Prayagraj and Varanasi wastewater showed low phosphate content of 2.99–8.14 and 2.41–3.29 mg/L in comparison with Kanpur wastewater. However, river samples had phosphate concentrations of 0.19, 0.11 and 0.83 mg/L in Prayagraj, Varanasi and Kanpur. In the summer season the wastewater samples of Prayagraj and Varanasi had phosphate levels in the range 0.4–4.6 mg/L, whereas like in the winter season, Kanpur wastewater samples showed very high phosphate levels of 18.64–25.27 mg/L, and according to WHO, phosphate content is much higher than the maximum limit of 5 mg/L in wastewater before dissemination to water streams (Akan et al. 2008; Oladeji & Saeed 2018). Phosphate removal during the summer season is achieved by a particualar group of microorganisms generally known as phosphate accumulating organisms (PAOs), which absorb and uptake excess of phosphorus and volatile fatty acids (VFAs) for their metabolic activities. It was previously reported that the the maximum PAO activity is achieved at comparatively high temperature, whereas low temperature and low VFA inhibit the efficiency of PAOs (Fanta et al. 2021). A significant difference (P<0.001) between the phosphate concentrations in all cities was observed in the winter season at a confidence interval of 95% excluding only the outlet and river sample in Prayagraj vs Varanasi, whereas in the summer season, excluding only the river samples of Prayagraj vs Varanasi, and Varanasi vs Kanpur, the other samples showed a significant difference (P<0.001) at a confidence interval of 95% (Tables 2 and 3). Through the winter season, negative removal efficiencies of −36.51% and −8.23% of phosphate were observed in Varanasi and Kanpur WWTP respectively, which turned to 43.22% and 25.99% during the summer season, whereas phosphate removal efficiency in Prayagraj WWTP was recorded as 63.26% in the winter season and 58.86% in the summer season. However, the Ganges River water samples of the summer season contained 0.69, 0.58 and 0.41 mg/L phosphate in Prayagraj, Varanasi and Kanpur. Nevertheless, the high levels of phosphate in water bodies lessen the likelihood of algal and other plant growth.

Mechanical aeration and dissolved oxygen in activated sludge provides sustainability to microorganisms in suspension, but the drawback associated with this technology is that over-aeration in the activated sludge tank causes formation of excess dissolved oxygen, which creates operational glitches and affects the working efficiency of microorganisms in uptaking nutrients from the wastewater. In the Varanasi WWTP, the DO content was found to be comparatively high, during both seasons, which might be the major cause of the bacterial inactivation and negative removal of phosphate, sulphate and nitrate from wastewater.

The sulphate concentrations in the winter season wastewater samples of Varanasi was recorded as very high at 60.26–68.87 mg/L in comparison with Prayagraj (29.59–36.23 mg/L), and Kanpur (20.63–25.97 mg/L), whereas river water samples had sulphate content of 24.67 and 31.68 mg/L in Prayagraj and Kanpur and very high content in Varanasi, i.e. 78.57 mg/L. In the summer season, very high sulphate levels were also observed in Varanasi wastewater in the range 54.78–95.79 mg/L whereas Prayagraj and Kanpur wastewater samples had 31.41–85.58 mg/L and 15.97–49.40 mg/L sulphate concentrations. Sulphate levels in the Ganges River water samples were 22.87, 39.43, and 29.16 mg/L in Prayagraj, Varanasi and Kanpur. However, sulphate levels in all the collected samples were found to be in the permissible range of 200 mg/L according to the Indian Standards (2012). There was a significant difference (P<0.001) amongst sulphate levels in samples of all three cities during both seasons at a confidence interval of 95%, however only the inlet sample of Varanasi vs Kanpur WWTP did not show a significant difference in the summer season (Tables 2 and 3). The ASP technology of Varanasi WWTP again failed to remove sulphate from wastewater, showing a negative removal efficiency of −12.50% in winter and −74.86% in the summer season. Prayagraj and Kanpur WWTPs also showed a negative removal efficiency of −22.44% and −25.88% respectively during the winter season. Nevertheless, those WWTPs achieved removal efficiencies of 63.19% and 67.67% respectively in the summer season. The temperature increase due to seasonal variation affects the reduction of sulphate from wastewater, as it is accomplished in the presence of sulphate-reducing bacteria (SRB) (Doshi 2006). The efficacy of SRB is affected at the lower temperatures of the winter season, and low microbial reduction of sulphate is achieved with the MBBR and ASP technologies. Moreover, the intense contamination of sulphate in Varanasi wastewater and river water samples may have arisen from paper mills, textile mills, tanneries, or natural causes like the dissolution of rocks abundant in gypsum or due to the pollution of coal ash (Chen et al. 2020). Moreover, the sulphate levels in Prayagraj and Varanasi wastewater treatment plants increased in step 1, step 2 and outlet during both seasons, whereas in the outlet water of Kanpur, levels significantly increased in the winter season and decreased in outlet during the summer season. Excess sulphate in conjunction with nitrate contamination causes algal blooms in aquatic ecosystems which are responsible for the death of several aquatic animals (Bhateria & Jain 2016).

Data presented in Figures 3–5 depicts the correlation coefficients (r) of all examined parameters for the samples collected during the winter and summer season from each treatment step, with their significance level. The standard correlation coefficient ranges between −1.0 and 1.0, and the parameters close to −1 show a negative correlation whereas the parameters close to +1 have a positive correlation.

On this basis, it was observed that excepting temperature and sulphate, all the parameters showed the most significant correlation either positive or negative with almost all other parameters. The pH and DO had a strong negative correlation with nearly all the other parameters of the Prayagraj WWTP and Ganges River water samples, except for the strong positive correlation (r=0.89; P<0.01) with each other. Nevertheless, temperature showed much less significant correlation with almost all other parameters through the entire treatment steps during both seasons. TDS was positively and significantly correlated with all other parameters. In contrast, DO had a strong negative correlation with BOD (r=−0.97; P<0.01), however BOD was strongly correlated with TDS (r=0.94; P<0.01), hardness (r=0.88; P<0.01), alkalinity (r=0.82; P<0.01), ammonia (r=0.80; P<0.01), nitrate (r=0.71; P<0.01), and chloride (r=0.89; P<0.01), which indicates the direct impact of BOD in regulation of the concentration of these parameters. Moreover, COD had strong negative correlation with DO (r=−0.73; P<0.01), and significant correlation (P<0.01) with other parameters, but it showed non-significant correlation only with temperature (r=0.28; P>0.05). Additionally, hardness of all samples exhibited the most significant positive correlation with all other parameters except temperature (r=−0.09; P>0.05). Total alkalinity had strong positive and significant correlation with TDS (r=0.90; P<0.01), BOD (r=0.82; P<0.01), hardness (r=0.86; P<0.01), and chloride (r=0.85; P<0.01) and most significant correlation with all other parameters except for the non-significant correlation with temperature (r=0.08; P>0.05). Ammonia had strong positive and significant correlation with BOD (r=0.80; P<0.01), hardness (r=0.78; P<0.01), nitrate (r=0.93; P<0.01) and phosphate (r=0.90; P<0.01) and most significant correlation with other parameters apart from temperature (r=−0.39; P>0.05) and sulphate (r=0.12; P>0.05). Nitrate showed strong and significant positive correlation with BOD (r=0.71; P<0.01), ammonia (r=0.93; P<0.01) and phosphate (r=0.85; P<0.01), most significant correlation with other parameters and non-significant correlation with temperature (r=−0.49; P>0.05) and sulphate (r=0.04; P>0.05), whereas chloride showed strong positive correlation with TDS (r=0.93; P<0.01), BOD (r=0.89; P<0.01), alkalinity (r=0.85; P<0.01) and most significant correlation with the rest of the parameters, although it showed non-significant correlation with temperature (r=0.21; P>0.05). Phosphate concentration was only strongly correlated with ammonia (r=0.90; P<0.01) and nitrate (r=0.85; P<0.01) and had non-significant correlation with sulphate (r=−0.20; P>0.05). However, sulphate showed most significant correlation with TDS (r=0.44; P<0.01), DO (r=−0.54; P<0.01), BOD (r=0.46; P<0.01), COD (r=0.43; P<0.01), hardness (r=0.44; P=0.05), and chloride (r=0.53; P<0.01) and non-significant correlation with other physicochemical parameters.

A significant correlation was very scattered in Varanasi wastewater and river water samples during both seasons compared with Prayagraj and Kanpur. In parallel in the Varanasi samples, temperature showed a strong negative correlation with COD (r=−0.81; P<0.01), however, it exhibited a much less significant correlation (P>0.05) with the other parameters of the study (Figure 4). Likewise, in the other two cities, pH showed a negative correlation with all other parameters except DO (r=0.65; P<0.01) in Varanasi, and in addition, it showed a significantly negative correlation with phosphate (r=−0.78; P<0.01). TDS was positively correlated with total alkalinity (r=0.78; P<0.01) and phosphate (r=0.84; P<0.01) and had a strong negative correlation with DO (r=−0.81; P<0.01). DO had an extremely negative correlation with total alkalinity (r=−0.81; P<0.01), a non-significant positive correlation with pH and a negative correlation with the rest of the parameters of the wastewater and river water samples. BOD and COD exhibited strong negative correlation with DO (r=−0.72; P<0.01) and temperature (r=0.81; P<0.01) respectively. Total alkalinity showed a strong positive correlation with TDS (r=0.78; P<0.01) and a non-significant correlation with the other parameters. However, phosphate had only a strong positive correlation with TDS (r=0.84). Nevertheless, hardness, ammonia, nitrate, chloride and sulphate concentrations of all the parameters showed a non-significant correlation with all other parameters.

Wastewater and river water samples collected from Kanpur City showed less positive correlation amongst all the parameters compared with Prayagraj (Figure 5). Temperature showed strong negative correlation with nitrate and strong positive correlation with chloride, and no significant correlation with the other parameters, which indicates chloride concentration can rise and nitrate levels can fall with an increase in the temperature. The pH was not significantly but negatively correlated with nearly all parameters excepting DO (r=0.54; P<0.01), which was also negatively correlated with all parameters and positively correlated with pH, which defines that DO and pH can regulate the concentration of each other in the UASB system. TDS exhibited significant positive correlation with BOD (r=0.91; P<0.01), hardness (r=0.92; P<0.01), ammonia (r=0.91; P<0.01) and phosphate (r=0.87; P<0.01), but significant negative correlation with DO (r=−0.91; P<0.01). BOD was positively correlated with TDS (r=0.91; P<0.01), ammonia (r=0.89; P<0.01), hardness (r=0.86; P<0.01) and phosphate (r=0.82; P<0.01), whereas it had a strong negative correlation with DO (r=−0.91; P<0.01). COD, total alkalinity, nitrate, chloride and sulphate showed moderate or non-significant correlation with all other parameters, however as an exception, nitrate had strong negative correlation (r=−0.87; P<0.01) and chloride showed strong positive correlation (r=0.83; P<0.01) with temperature. Phosphate was found to be positively correlated with TDS (r=0.87; P<0.01), BOD (r=0.82; P<0.01), hardness (r=0.77; P<0.01), and ammonia (r=0.87; P<0.01).

The major findings of the correlation analysis indicated that DO and pH exhibited a significant negative correlation with almost all other parameters, which means elevation in the level of DO and pH can decrease the concentration of the affected parameters. TDS, hardness, ammonia and BOD showed very similar traits in Prayagraj and Kanpur, However, the parameters of the Varanasi samples were much less correlated to each other.

Factor analysis is generally applied as multivariate statistical analysis which depicts the association between calculated variables by decreasing them to reduced numbers of factors. To identify the association between the samples, a varimax rotation factor analysis was executed for the data obtained of all the parameters and seasons of the study (Figure 6).

A scree plot suggested the exact number of factors (viz n=4) to be extracted, therefore, the analysis was done for four factors. The analysis revealed the cumulative variance of 36%, 55%, 67%, and 78% and eigenvalues of 4.84, 2.75, 1.83 and 1.48 for factor 1, factor 2, factor 3 and factor 4 respectively. The parameters TDS, total alkalinity, ammonia and chloride showed high positive loading whereas DO and pH showed high negative loading in factor 1. Dissolved oxygen exhibited negative loading with all other parameters which indicates that all the parameters are inversely proportional to DO. In factor 2, the highest positive loading variables were nitrate (0.84) and BOD (0.70), which means that with an upsurge of nitrate concentration, BOD concentration will also rise. COD showed a high positive load (0.72) in factor 3 whereas temperature, pH, TDS, DO, nitrate and chloride showed significant negative load, which specifies the role of COD in the regulation of increase or decrease of these parameters. However, the temperature was the largest variable, the only one with a high positive load (0.78), the other parameters showing nearly non-significant load in factor 4 (Table 5). Like the correlation analysis, factor analysis also demonstrated the influence of pH and DO on the other parameters of the study. With an increase of pH and DO the concentration of the rest of the parameters can decrease.

Based on the observed data, the present study can conclude that the traditional ASP technology of Varanasi WWTP is inappropriate for the removal of many nutrients from wastewater and can be superseded by modern technologies such as MBBR technology, which efficiently works in Prayagraj WWTP. The UASB technology of Kanpur WWTP was also found to be comparatively better than the ASP technology with respect to nutrient removal. Moreover, the Ganges River water of Kanpur was observed to be very polluted in comparison with Prayagraj and Varanasi, which may be due to excessive industrial applications in the Kanpur region. The findings of the study were upheld by many statistical multivariate variables including ANOVA, Pearson's correlation analysis, and varimax rotation factor analysis which defined pH and DO to be the major variables which can regulate the upsurge or decrease of all the parameters. Nevertheless, the study concludes that the outlet water of all three WWTPs can be reused for irrigation purposes; however, the addition of other advanced treatment technologies like nanofiltration and UV treatment can help to achieve more efficient outlet water to keep the surrounding water less contaminated for a better livelihood of aquatic flora and fauna.

All the authors are highly grateful to the authority of the respective departments and institutions for their support in conducting this research. The authors are thankful to Mr Atul Srivastava for the study area map preparation. The author RS is thankful to the University Grants Commission for the National Fellowship (201819-NFO-2018-19-OBC-UTT-78476) and author AKP would like to thank Science and Engineering Research Board, New Delhi, India (Grant #ECR/2017/001809) for providing Junior Research Fellowship.

This study was funded by the Science and Engineering Research Board, New Delhi, India (Grant #ECR/2017/001809).

The authors declare that there is no conflict of interest regarding the publication of this paper.

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