River Ganga is the primary water source for wider communities in North India. Prayagraj and Varanasi cities are densely populated areas with intense agricultural activities therefore, the Gangetic water stretches in these regions are characterized by high nutrient inputs from the catchment and categorized as eutrophic (Kumar et al. 2020b). The statistics about the river Ganga surface water quality are crucial for various usages and preserving its aquatic life. So far, information about the distribution of MCs producing cyanobacteria is uncertain in Ganga water, which is essential to estimate the health risks associated with the cyanobacterial toxins. Taking recourse to above, the present study was executed to assess the river Ganga's surface water quality at ten different sampling sites in Prayagraj and Varanasi using various physico-chemical and biological characteristics. The monitoring approach for Ganga water further involves the detection of putative MCs producing cyanobacteria and its toxin MC-LR equivalence. Table 1 describes river Ganga's water sampling points and their geographical coordinates (latitude, longitude) at study sites.
Physico-chemical and biological parameters of river Ganga water at Prayagraj and Varanasi
The physico-chemical and biological parameters of river Ganga water at various sites of Prayagraj (sites: S1-S5) and Varanasi (sites: S6-S10) with their mean values were estimated and shown in Table 2. A total of 15 physico-chemical and 7 biological parameters were investigated for two years, i.e. June 2017 and March 2018, respectively. The study suggested both spatial and yearly variations in the physico-chemical and biological parameters.
Physico-chemical Parameters Analysis
It is clearly depicted that (Table 2) the pH level ranges between 7.5 to 7.9 at all sampling sites during the study time and follows the standards provided by Bureau of Indian Standards (BIS), Indian Council of Medical Research (ICMR), CPCB and World Health Organization (WHO). The slight difference in pH value was due to the low annual disparity in free CO2 in the sampling regions for the two years of study. Singh et al. (2016), Siddiqui and Pandey (2019) and Pandey et al. (2015) reported studies of physico-chemical parameters of the Ganga river and observed that the range of pH was 7.1–7.8, 7.6–7.8 and 8.2–8.45, respectively at different sites of Varanasi which is in accordance with the present study. However, Pandey et al. (2014) shown that the Prayagraj region's pH range was 8.1–8.4. Similar results were reported by Dixit et al. (2017), Kumar et al. (2020b) and Satya and Narayan (2018) for pH values that ranged from 7.4–8.1, 7.6–8.8 and 6.0–8.0 in river Ganga at nearby cities Kanpur and Patna.
The dissolved solids including chloride, calcium and magnesium salts in the water samples were responsible for the conductivity level. According to BIS, CPCB, ICMR and WHO, the maximum allowable conductivity level is 300 mho/cm in the water. The measured conductivity values for water samples at sampling sites in the river Ganga varies between 191–386 mho/cm (Table 2). The average conductivity was more (336.2 and 342.4 mho/cm) in June 2017 and slightly lowers (273 and 274 mho/cm) in March 2018 than the permissible limit in both the cities. This change may be due to the seasonal change or Ganga clean drive by government initiatives. Although there is no direct impact of water conductivity on human health but its estimation is required to evaluate the mineralization rate of present mineral salts and determination of the number of chemical reagents implied to treat the water (Kavcar et al. 2009). Higher conductivity leads to the lowering of drinking water's aesthetic value, eliminating habitat-forming plant and corrosion of the metal surface of industrial equipments such as boiler (Rahmanian et al. 2015).
The maximum threshold limit of total suspended solids (TSS) in drinking water and the freshwater stream is not mentioned by any water regulatory bodies (Kumar and Puri, 2012). However, norms used in the guideline are stated and based on indirect effects on human health due to particulate matter. Water regulatory authority of some countries such as National Water Quality Standard (NWQS) Malaysia, provided the maximum threshold limit of TSS 150 mg/L for rivers which support aquatic life (Al-Badaii et al. 2013). The TSS values in water samples of river Ganga at study sites were observed in the range of 62–167 mg/L (Table 2). The average TSS value for June 2017 was observed 107.1 mg/L, whereas the average TSS value during March 2018 was found 113.6 mg/L. This change in the TSS value is not substantial in two different years of study. Pandey et al. (2015) reported the TSS value range 60–117 mg/L in the Ganga water samples at different sites of Varanasi Ghats during 2012 and 2013.
The total dissolved solids (TDS) values in the Prayagraj region at different sites range from 209 to 343 mg/L. The highest value obtained was 343 mg/L recorded at site S3 (Mehdhori Gaon Khachar) during June 2017, and the lowest value obtained was 209 mg/L at site S2 (Daraganj ghat) during March 2018 (Table 2). Additionally, the TDS concentrations in the sampling sites of Varanasi ranged from 227 to 321 mg/L during two years of study. The lowest concentration was 227 mg/L recorded at site S10 (Sheetala Ghat) and the highest concentration was 321 mg/L at site 8. In the present study, the TDS concentration observed was less than reported for the Prayagraj and Varanasi areas in the range of 284 − 707 mg/L and 454–703 mg/L respectively (CPCB 2013). It was noticed that upstream and downstream sites of both cities have TDS values with no significant difference because anthropogenic and land use activities were expanded more in both ends. Moreover, TDS values are within the permissible level (500 mg/L) set by BIS, CPCB and WHO (Kumar and Puri 2012). Pandey et al. (2015) also reported the concentration range of TDS in river Ganga at Varanasi and Prayagraj, the values obtained were 266–344 mg/L and 185–271 mg/L, respectively.
The hardness of water is not a pollution indicator but signifies water quality primarily caused by the presence of Ca2+ and Mg2+, HCO3−, SO42−, Cl−, and NO3− in water. The hardness of water has no known adverse influences on health (WHO 2011). However, some evidence has pointed out its impact on human health, mainly stone formation in kidney and heart diseases, if the water with hardness of 150–300 mg/L or above can be consumed at a rate of 2 liters per day (Mitra et al. 2018). Water has been classified as soft, moderate, hard and very hard based on hardness value > 60, 60–120, 120–180 and < 180 mg/L of CaCO3, respectively (McGowan 2000). The average value of water hardness recorded was 275.2 mg/L and 246.4 mg/L in June 2017 for Prayagraj and Varanasi sampling sites respectively. The hardness values of water samples during March 2018 were more or less similar (287.0 and 244.2 mg/L). Thus, the negligible variation was observed in the water hardness for two years (i.e. June, 2017 and March, 2018) at study sites. The water hardness at study regions observed was within the range of permissible limit (300 mg/L) recommended by BIS. These finding was in agreement with Kumar et al. (2020b), who mentioned similar pattern of the hardness of Ganga water at the middle Ganga plains in different seasons. Alkalinity in water is due to the presence of carbonates, bicarbonates and hydroxides. Alkalinity recorded the highest average value of 161.6 mg/L during the June 2017. The range of alkalinity was observed between 114 to 197 mg/L during two years of study at different sites (Table 2). The average value of alkalinity was 160.2 mg/L in June 2017 and 125.2 mg/L in March 2018 at Prayagraj regions. The average level of alkalinity was observed 161.2 mg/L in June 2017 and 155.8 mg/L in March 2018 at Varanasi regions. The value of alkalinity in the study site was found at the upper side of the permissible limit (120 mg/L) as per BIS records. Pandey et al. (2015) also demonstrated a higher range of total alkalinity (230–279 mg/L) in river Ganga at Varanasi.
Increased nutrients (nitrates and phosphates) are among the primary reasons for low water quality in the freshwater stream (Davie 2003). Agriculture and urban runoff include fertilizer, domestic, sewage and industrial wastewater discharges are major routes of entry into water bodies in terms of nutrient pollution (Dubey et al. 2012). Nitrate and phosphate concentrations in freshwater can cause oxygen depletion that resultant in the deterioration of aquatic life. Consumption of high concentrations of nitrate and phosphate contaminated water causes the blue baby syndrome, muscle damage, breathing problems, and kidney failure (Davie 2003). The maximum permissible limit of NO3− -N level is in the range of 20–45 mg/L set by BIS, ICMR and WHO (Table 2). In the present study, the NO3−-N concentration observed was ranged between 2.52 to 4.92 mg/L. The decrease in overall level in the NO3−-N due to temporal effect was observed ~ 28% in Ganga water. The concentration of nitrates observed in Ganga water was below the level as prescribed by water regulatory authorities. In this study of two years, PO4−-P concentration was observed in the range of 0.92–1.82 mg/L at Prayagraj and Varanasi's sampling sites. The average concentration of phosphate content was 1.46 mg/L and 1.15 mg/L during June 2017 and March 2018, respectively. The trend of phosphate concentration in the particular region was more or less similar and not much effect of time was observed.
Chloride is present naturally in all water types; however, its major contribution is the runoff of inorganic fertilizers from agricultural land and sewage discharge (Lkr et al. 2020). The chloride concentration variation was 65 mg/L (at site 2 and 7) to 104 mg/L (at site 7) in two different times for two years (June 2017 and March 2018). The variation was recorded ranging between 65 to 104 mg/L and 65 to 92 mg/L during June 2017 and March 2018, respectively. In comparison, Singh (2010) and Pandey et al. (2015) were recorded the spatial-temporal variation of chloride concentration in Ganga water in the range of 8.2–81.5 mg/L and 21.4–94.7 mg/L at the Varanasi region. Pandey et al. (2014) reported the variation of chloride concentration in water samples at Prayagraj, ranging between 8.2 to 21.4 mg/L. According to Hem (1985), the major sources of sulfate in the freshwater stream are rocks weathering and human activities such as mining, water discharge and fossil fuel combustion. The sulfate values varied from 32.3 (site S2) to 49.0 mg/L (site S3) at Prayagraj and 35.0 (site S10) to 52.0 mg/L (site S6) at Varanasi during sampling periods. A similar range of results (37.9–54.2 mg/L and 16–36 mg/L) were also obtained in Ganga water samples at Varanasi by Pandey et al. (2015) and Singh et al. (2016), correspondingly. The chloride and sulfate concentration in the Ganga water samples observed were within the permissible limit suggested by BIS, ICMR and CPCB. The iron content in the sampling sites ranges from 0.13 to 0.218 mg/L. Iron content was recorded highest at site S9 (Samane Ghat, Varanasi) during two years of study. The iron level in studied samples fell within the acceptable limit (0.3-1.0 mg/L) of BIS, ICMR and CPCB, except the WHO's water specification (0.1 mg/L).
Chemical oxygen demand (COD), biochemical oxygen demand (BOD) and dissolved oxygen (DO) are important parameters for water quality evaluation. They reflect the physical and biological processes prevailing in the water that indicates the degree of pollution in water bodies. During the study, the spatial-temporal variations in COD, BOD and DO recorded were 30.7–54.3, 3.2–4.9 and 6.7–8.2 mg/L at the ten selected sites. The slight difference (0.3 mg/L) in average DO concentration may be mainly due to the water temperature difference of about 4°C. The dissolved oxygen's desirable limit is more than 4 mg/L as per BIS and CPCB standards. Pandey et al. (2015) observed that the COD, BOD and DO values range between 53.5–79.6, 37.4–58.7 and 3.7–5.8 mg/L, respectively, in the Ganga river of Varanasi region. CPCB (2013) also recorded the range of COD, BOD and DO concentration 46.2-156.2, 3.7–9.6 and 7.0-7.8 mg/L correspondingly at Varanasi region and 17.3–192, 2.6–5.6 and 6.0-9.8 mg/L respectively at Prayagraj region. The DO level recorded at all the sites is sufficient for the planktons to survive and perform various physiological water activities (Al-Badaii et al. 2013).
Biological Parameters Analysis
Transparency is a measure of the water's clarity and is essential for the survival of aquatic plants that require sunlight for photosynthesis. Transparency depth values can be influenced significantly by numerous factors such as day time at which measurement has recorded clearness of the sky (cloudy, partly cloudy or sunny) and suspended solids in water, including plankton (Verma and Saksena 2010; Sarkar et al. 2019). The value of transparency depth recorded was from 0.35–0.62 m for two years in the present study. Changes in values at the study sites are possibly due to the differences in bottom sediments and temperature, dissolved organic matter or entry of municipal and industrial waste material. The higher value of transparency depth noted was during March 2018 (0.51 m) than June 2017 (0.43 m). A similar kind of variation in transparency depth was demonstrated by Matta et al. (2017).
During this study, the spatial-temporal variation in algal cell concentration and algal cell density observed were in the range of 3.1×103-10.7×103 cells/mL and 12.5–28.4 mg/L respectively. The chlorophyll-a (Chl-a) concentration range was found 0.84×10− 2- 2.21×10− 2 mg/L. The variation in three biological parameters (Chl-a, algal cell concentration and algal cell density) are closely related (Table 2). Eutrophication and global warming are the main reasons to promote the excessive growth of algae globally in freshwater bodies (Davis et al. 2009; Sarkar et al. 2019). The toxic cyanobacterial occurrence causes many problems for instance, bad odour, low esthetic value, water quality deterioration and oxygen depletion in water which can impair tourism, transportation and ecosystem health (Son et al. 2015).
The increase in nutrient load in water bodies leads to high productivity that modifies the structure of water communities. Productivity measurement, algal and bacterial concentrations are important environmental change indicators that include acidification, climatic change and eutrophication (Dam et al. 1994). However, these biological parameters varied by numerous factors such as nutrients, temperature and organic matters. The gross productivity (GP) and net primary productivity (NPP) at different Prayagraj and Varanasi sites recorded were between 18.5–30.8 mg C/m2/h and 9.5–16.9 mg C/m2/h for two years of study when the average sunlight per day was 10 h. The increase in NPP to GP value approximately 6.5–11.9 % was found in March 2018, which could be due to higher water transparency in March 2018. Limited studies are available concerning the assessment of productivity and biological indicators and their effect on Ganga water quality (Siddiqui and Pandey 2019; Pandey and Yadav 2015). Pandey and Yadav (2015) reported that the GP at different sites in the Ganga river of Varanasi region ranged from ~ 2-8.5 mg C/m2/h, which are in agreement with our results. Productivity variables such as GP, Chl-a and algal biomass showed synchrony with the concentration of nutrients and indicates that the river Ganga polluted at Varanasi and Prayagraj regions.
Fecal coliform and total coliform concentration in both regions observed were higher than the criteria at most locations, while the highest value always observed were at Varanasi (site-S9, Samne ghat; 11300 MPN/100 mL). CPCB (2013) reported fecal coliform and total coliform level 8000–46000 and 13000–70000 MPN/100 mL respectively in the six-year Ganga survey (2006–2011) for the Varanasi region. These biological parameters observed were also in the range of 3000–5000 and 7000–14000 MPN/100 mL respectively in the Prayagraj region.
Spatial-temporal And Water Quality Index (Wqi) Analysis
The descriptive statistics for spatial-temporal variation of water quality parameters at ten sampling sites of Prayagraj and Varanasi city studied for two years are presented by the box-whisker plot in Fig. 3 (a) and (b). Box and whisker plots represent the full spatio-temporal dynamics of two-year studied physical, nutrient, chemical, ionic and biological parameters investigated for the river Ganga water samples. For most parameters, the higher standard deviation indicates temporal and spatial variations likely caused by polluting sources and/or climatic factors.
The WQI approach applied to assess river Ganga's water quality intending to provide a valid and straightforward method for expressing several parameters rapidly and conveniently. Eight parameters employed by WQI can indicate the water quality at various sampling points of Prayagraj and Varanasi regions. The average WQI score obtained was 46.31 and 50.66 for two different times, June 2017 and March 2018, respectively (Table 3). It was apparent that the water samples of study sites were falls under medium and bad category for drinking or bathing. WQI value observed was mainly due to the high concentration of fecal coliform in the water samples. Based on WQI, water qualities identified at sampling points in the river may indicate that it can be suitable for transportation, irrigation and water supply purposes. Comprehensively, comparable research was conducted in many countries such as surface water of Amazonia Rivers, Brazil (Medeiros et al. 2017), Sarayduzu Dam Lake, Turkey (Kükrer and Mutlu 2019), Kafr El-Sheikh Governorate, Egypt (Jahin et al. 2020) and the Cau river, Vietnam (Son et al. 2020). WQI analysis showed that Ganga water in the sampling regions approaches medium/bad quality conditions but increased human activity warns us about future consequences. Various WQI studies has been recently performed in the various stretches of river Ganga (Kumar et al. 2021; Dimri et al. 2020; Kumar et al. 2020b).
Table 3
Water Quality Index at 10 sampling sites of the river Ganga at Prayagraj and Varanasi using NSFWQI method.
Duration
|
Prayagraj
|
Varanasi
|
Over all
|
S1
|
S2
|
S3
|
S4
|
S5
|
S6
|
S7
|
S8
|
S9
|
S10
|
June 2017
|
49.46
|
46.89
|
45.61
|
46.74
|
47.01
|
43.87
|
45.25
|
48
|
45.8
|
44.49
|
46.31
|
March 2018
|
51.52
|
49.99
|
50.68
|
49.66
|
51.31
|
50.79
|
49.21
|
52.22
|
50.52
|
50.67
|
50.66
|
Sampling sites - PRAYAGRAJ: (S1) Allahabad Sangam,(S2) Daraganj Ghat area, (S3) Mehdhori Gaon Kachar,(S4) Rasoolabad Ghat, (S5) Shastri Bridge area; VARANASI: (S6) Dashaswamegh Ghat, (S7) Ramana Village (S8) Ravidash mandir Ghat,(S9) Samne Ghat, (S10) Sheetla Ghat. Excellent: 90–100, Good: 70–90, Medium: 50–70, Bad: 25–50, and very bad: 0–25. |
Morphological Evaluation Of Cyanobacteria Of River Ganga
Morphological observation of phytoplanktonic water samples by microscopy revealed the diversity of cyanobacterial genera in river Ganga at study sites (Supplementary Fig. 1). It was observed that colonial and buoyant cyanobacteria were abundant preferably the genus Microcystis. Diverse cyanobacterial colonies showed variations in individual cell sizes, cell arrangement, colony morphology and mucilage characteristics. Besides Microcystis, many other species belonging to the genera Leptolyngbya, Planktothrix, Anabaena, Gleocapsa, Phormidium, Hormogonia, Oscillatoria, Aphanizomenon, Aphanocapsa, Scynecoccocus were also observed (Supplementary Fig. 1). Similarly, Rishi and Awasthi (2015) recorded the most dominating genera in river Ganga at Kanpur, U.P, India were Anabaena, Microcystis, Cylindrospermum, Phormidium Aphanizomenon, Chroococcus, Lyngbya, Nostoc, Nodularia, Spirulina and Oscillatoria. Other studies have also demonstrated the widespread occurrence of Oscillatoria, Leptolyngbya, Scynecoccocus, Anabaena in river Ganga (Shukla et al. 2015; Dixit et al. 2017; Sarkar et al. 2019). According to taxonomic keys, the cyanobacteria species were determined based on cell structure, colony morphology and mucilage characteristics. Since the toxic and non-toxic cyanobacterial species can co-exist in an environment and are indistinguishable by microscopy technique (Romanis et al. 2021), therefore, MCs producers were characterized based on the molecular approach (Casero et al. 2019) and MCs detection (Kumar et al. 2020b).
Molecular characterization of uncultured cyanobacteria in water samples of river Ganga
The mcy genes (mcyA to mcyJ) that regulate MCs synthesis in potentially toxic cyanobacteria have been identified and sequenced (Dittmann and Borner 2005). MCs standard structure is cyclo (D-Ala-L-X-D-MeAsp-L-Y-Adda-D-Glu-Mdha), where X and Y are variable L-amino acids. Figure 2 shows the structure of MC-LR, where X is L-leucine and Y is L-arginine. In this study, PCR was conducted with general and genus-specific primers for three genes (mcyA, mcyB and mcyE) designed (Fig. 2) to amplify the generic mcyE (812 bp), Microcystis mcyE (247 bp), Anabaena mcyE (244 bp), Planktothrix mcyE (249 bp), mcyA (291 bp) and mcyB (973 bp) genes from phytoplanktonic uncultured water samples of river Ganga from each of the 10 sampling sites of Varanasi and Prayagraj regions. The PCR amplification with primers for the Anabaena and Planktothrix mcyE failed to produce any amplicons (data not shown). The result indicates that Anabaena and Planktothrix cells if present in the collected phytoplanktonic water samples were non-toxic due to absence of mcy genes. However, the desired length of PCR products was amplified using the oligonucleotide primers sets for generic mcyE, Microcystis mcyE, mcyA and mcyB genes from all the genomic DNA of the uncultured cyanobacteria extracted individually from water samples of 10 different sites as shown in Fig. 4. These findings of PCR amplification were same for both the years of study. Previous studies have also shown the identification and detection of toxic cyanobacteria in environmental samples based on mcy genes using a PCR technique (Baker et al. 2002, Ribeiro et al. 2020) with universal primers targeting conserved sequences (Hisbergues et al. 2003) and species-specific primers designed based on differences within the mcy gene clusters (Rantala et al. 2006), respectively. The mcy genes preferably targeted are mcyA, B, C, D and E however, in several studies a combination of the mcy genes were used (Ouellette et al. 2006). The mcyA gene, because of its conserved sequences, is an appropriate target for PCR based identification of toxic cyanobacteria (Hisbergues et al. 2003; Rantala et al. 2004). The mcyB gene regulates the activation of amino acids as aminoacyl adenylate and it was followed by peptide bond formation by the condensation domain in growing MC molecule (Tillett et al. 2000). The glutamate-activating adenylation domain is encoded by mcyE gene and all known MC variants possess D-glutamate and the carboxyl group of the glutamate side-chain is crucial for the toxicity. Therefore, the mcyE gene used for detection of MCs-producing cyanobacteria (Goldberg et al. 1995). The amplicons of generic mcyE, mcyA and mcyB genes from uncultured cells of Ganga water samples were sequenced and submitted in the NCBI database. Gene bank accession ID numbers are MZ222414, MZ222415 and MZ222414 for mcyE, mcyA and mcyB genes, respectively. The partial gene sequence similarities for mcyA and mcyE genes from uncultured cells of water samples with the genus Microcystis were more than 95 %. The neighbor-joining tree developed using mcyA and mcyE gene sequences showed the grouping with cyanobacterial genus Microcystis (Fig. 4). These results are consistent with the microscopic analysis and advocate the presence of genus Microcystis in river Ganga. However, based on sequenced mcyB amplicons from uncultured cells, the phylogenetic assessment suggested close proximity with filamentous cyanobacterial genus Oscillatoria and Planktothrix. Thus, PCR amplification and phylogenetic analysis propose the presence of various genera of cyanobacteria possessing putative mcy genes in river Ganga.
Quantification of MC-LR equivalence in Ganga water by PPIA and HPLC method
Increasing pollution in river Ganga due to anthropogenic activities triggers MCs producers and MCs concentration in the water (Dixit et al. 2017). A study indicates that MCs that produced intracellularly have released into the extracelluar environments during cell lysis due to stress or age (Wei et al. 2020). MC-LR is one of the most common and toxic MCs (Zhang et al. 2021). In this work, the MC-LR equivalence concentration in phytoplanktonic water samples collected from 10 different sampling points of Varanasi and Prayagraj regions was detected by PPIA and HPLC methods. The colorimetric PPIA technique determined the MC-LR equivalence concentration in river Ganga at study sites attaining low detection (LOD) even below 1 µg/L (Supplementary Fig. 2). PPIA method ensures the presence of MC-LR equivalence in all the collected Ganga water samples (Fig. 5). The HPLC method's calibration curve for MC-LR was built considering the areas of the chromatographic peaks calculated at its six different concentration levels, ranging from 0.1–500 µg/L. The good linearity was obtained for the regression equation of the calibration curve of HPLC analysis and it was Y = 196.12X (R2 = 0.98), where Y is the area of MC-LR peak and X is the concentration of the MC-LR in µg/L. The limit of detection (S/N = 3) and limit of quantification (S/N = 5) was 0.5 µg/L and 1.1 µg/L, respectively. The range of MC-LR equivalence concentration was observed to be 23–172 ng/L by PPIA, whereas, MC-LR equivalence concentration range was obtained to be 13–97 ng/L using HPLC at various sites of Varanasi and Prayagraj. The results of the actual MC-LR equivalence concentration obtained in water samples by the two mentioned methods are summarized in Fig. 5. The MC-LR equivalence concentrations observed by PPIA slightly differ from the HPLC values; however, still being of the same order of magnitude (Fig. 5). This difference may be possible due to the presence of compounds in river Ganga water that interferes on the PPIA, despite the high dilutions used (Garibo et al. 2014; Chen et al. 2005). These analyses further confirm the occurrence of putative MCs producing cyanobacterial strains in the Ganga water at Prayagraj and Varanasi regions. The concentration of MC-LR equivalence (≥ 60 ng/L) was high at Rasoolabad Ghat (S4), Daraganj Ghat (S2) and Mehdori Village (S3) in Prayagraj, where human activities are prevalent. In Varanasi, MC-LR equivalence was higher at Dashashwamegh Ghat (S6) and Ravidash Mandir Ghat (S8). However, between two cities, MC-LR equivalence concentrations were generally higher in Prayagraj. The differences observed may reflect the influence of environmental factors on MCs production. The study demonstrated that MCs production is sensitive to several physico-chemical parameters, including light intensity, temperature, rainfall, pH, iron concentration, nitrogen and phosphorus concentrations (Kaebernick and Neilan 2001, Wagner et al. 2019). This is the first study that reported the quantitative MC-LR equivalence estimation in the water of river Ganga. However, MC-LR equivalence concentration is far less in river Ganga's water than the harmful exposure concentration (1 µg/L) recommended by WHO (WHO, 2011). PPIA is more convenient to perform and could be used as a primary method to detect MCs presence and its concentration in the water reservoirs (Massey et al. 2020).
Isolation and characterization of MC producing cyanobacteria from river Ganga
Three different cyanobacterial strains were isolated and purified by plating and liquid culture methods in BG-11 media. Visualization using phase contrast microscope showed that one of the isolates is the macroscopic, circular, colonial and gas vacuolated form (Ganga isolate 1), the second one is of non-colonial, filamentous structure (Ganga isolate 2) and the third one is the polymorphic colonial form (Ganga isolate 3) (Supplementary Fig. 3). Of the three isolates, only Ganga isolate 1 amplifies the PCR products for mcy genes (Fig. 6). Initially, based on morphological identification, the Ganga isolate 1 was identified as Microcystis. Furthermore, the partial 16S rDNA fragment from Ganga isolate 1 was amplified using cyanobacterial specific universal primer. The amplicon was sequenced and submitted in the NCBI database. Gene bank accession ID number is MZ027619. The BLAST hit of partial 16S rDNA gene sequence of Ganga isolate 1 indicates its maximum phylogenetic identity with Microcystis aeruginosa LMECYA 1 (Fig. 6). Results confirmed that the isolate was Microcystis. Thus the result of morphological identification is supported by phylogenetic analysis based on partial 16S rDNA sequence as shown in Fig. 6. Only one of the three isolated cultures, i.e. Ganga isolate 1 (Microcystis) from river Ganga was a MCs producer based on PPIA and HPLC method (Fig. 6). The majority of the MCs positive water bodies were eutrophic and turbid. The researcher also revealed that physico-chemical factors directly or indirectly affect cell growth and MCs generation in water bodies (Bouaïcha et al. 2019). In this study not much correlation has been drawn between the physico-chemical parameters and MC-LR equivalence concentration at the sampling sites, therefore, it is impossible to reliably determine the cause for putative MCs producers in river Ganga. Though, a weak positive influence of phosphate, ammonium and temperature has been observed. In the future, extensive research with more sampling and a more extended monitoring period is needed to elucidate the relative importance and correlation.