Abstract

This research paper tries to identify and address issues related to efficiency of Sewage Treatment Plants (STPs) and their implications on the Dal Lake ecosystem in Srinagar city, Kashmir, India. Fluidized Aerobic Bioreactor (FAB) and Sequential Batch Reactor (SBR) technologies having been recently installed along the periphery of Dal Lake were evaluated for efficiency for a continuous period of 24 months from December 2016 to November 2018. Apart from chemical quality, total coliform (TC), fecal coliform (FC), and fecal streptococci (FS) analyses were also carried out. Major highlights of our work using one-way ANOVA (Analysis of Variance) revealed significant variations (p < 0.05) in Total Suspended Solids (TSS), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), ammonia (NH3-N), total phosphorus (TP), TC, FC, and FS. Our findings indicate that both technologies are struggling for removal efficiency, which is very low, especially in FAB during three to four months of winter season where a very high drop in the working efficiency of the STP was observed. The treatment facilities did not meet the prescribed standards in respect of TSS, BOD, NH3-N, TP, TC, FC, and FS thereby having the potential to compromise public health and trophic status of Dal Lake.

HIGHLIGHTS

  • Removal efficiency of both STP technologies (FAB and SBR) is not up to the mark.

  • Freezing temperature during winter greatly reduces the efficiency of FAB STP.

  • Important parameters like TSS, BOD, NH3-N, TP, TC, FC and FS were exceeding the standards in both the STPs.

  • Use of inefficiency of STPs has potential to change the Dal lake ecology and endanger public health.

INTRODUCTION

The snapshot of the world's water quality reveals a worrying level of pathogenic and organic pollution as well as salinity in many rivers of the world (UNEP 2016). The world is facing a water quality challenge as economic and demographic changes have triggered an increase in water pollution, posing a risk to public health, food security, biodiversity, and other ecosystem services. Major pollutants like nutrients, pathogens, organic pollutants and emerging contaminants like trace metals, pesticides, nanomaterials, and microplastics directly or indirectly come from waste stream (Williams et al. 2019). There is renewed attention because of unwarranted resource consumption and pressing environmental issues of the sewage treatment facilities (Zhang & Ma 2020). Accordingly, wastewater management has therefore posed a new challenge which requires a response from technologists, researchers, and policymakers at all levels. Water quality is a prerequisite for sustainable water and sanitation in Sustainable Development Goal (SDG) no. 6, and is also equally important for many other SDGs related to health, food security, and biodiversity. Wastewater management is a serious problem throughout the world (EPA 1993). Growing population, urbanization, and change in lifestyle have resulted in increase in the quality as well as volume of sewage generated in cities, thereby having the potential to trigger nutrient and biological hazards in aquatic systems (Gupta et al. 2018). Globally, two million tons of sewage, industrial and agricultural waste is discharged into the world's waterways (UNEP 2010). Around 80% of wastewater generated is disposed off without receiving any treatment (WWAP 2017), leaving low-income countries hardest hit by contaminated water supplies and diseases. Oxygen-demanding wastes present in sewage are considered as serious pollutants with regards to impact on the environment, ecosystem services, freshwater biodiversity, as well as human health (Okoh et al. 1997). There is an increasing recognition regarding cross linkages of wastewater management and water quality and the need for good wastewater management and its contribution to protecting water quality.

In some coastal cities, domestic wastewater is discharged directly into the ocean. This threat can be perceived from the data that about 21 of the world's 33 megacities are on the coast, placing fragile ecosystems at risk (UN-HABITAT 2009). As cities outgrow their sanitation systems, wastewater can go straight into rivers, affecting downstream populations (UN 2010). The true danger of sewage is pathogens; untreated wastewater is brimming with bacteria, viruses, and parasites, many of which cause deadly diseases. Contaminated water, improper sanitation, and improper hygiene practices are responsible for 7% of diseases and 19% of child mortality globally (Cairncross et al. 2010).

Currently, most of cities throughout the world are facing the heat of an urbanization problem which worsens the state of sewage generation and treatment (Kushwah et al. 2011). This, therefore, demands a proportionate increase in the treatment capacity along with the up-gradation of the existing treatment facilities. However, lack of adequate space in cities necessitates improving efficiency so that more wastewater is treated in less time (Nelson et al. 2017). Srinagar city, being no exception to this trend, is also facing a high rate of urbanization and falls among class I cities of India (population more than 10 lakh). Sewage generation of Srinagar city is estimated to be 170 MLD, against which, the installed treatment capacity is only 54.2 MLD, leaving a deficit of about 116 MLD unattended. Previously, a large proportion of wastewater generated within Srinagar city found its way directly into some of the major water bodies of the city, thereby degrading their water quality until 2005 when sewage treatment plants (STPs) based on fluidized aerobic bioreactor (FAB) technology were commissioned at Hazratbal (7.5 MLD), Habbak (3.2 MLD), and Laaam (4.5 MLD) around Dal Lake. In addition, one more STP based on activated sludge process (17.5 MLD) is located at Brari Numbal lagoon of Dal Lake. Later on, two more STPs based on sequential batch reactor (SBR) technology were started at Nallah Ameer Khan (Nigeen basin, 5.4 MLD) and Brari Nambal lagoon (16.1 MLD) of Dal Lake. In the case of Kashmir, the last 50 years have also witnessed a decline in water quality of many water bodies, especially those which are located close to human habitation (Jan et al. 2013). Impact of untreated sewage on the water quality of Dal Lake has also been reported (Parvez & Bhat 2014), which therefore demands immediate attention to trap these pollutants before they are released into the lake. Issues arise where poor removal efficiency of STPs can become the problem rather than the solution for which they were intended. Therefore, wastewater treatment plants (WWTPs) attain extraordinary importance for the protection of aquatic environs from the detrimental effects of sewage. On the other hand, the challenges regarding wastewater treatment are diversified and differ, depending not only on legislation for effluent control but also on regional characteristics and socio-economic conditions (Hosomi 2018), yet operation of a cost-effective and high-performance wastewater treatment system is of paramount importance. Therefore, the main research question in this study was to evaluate the efficiency of these STPs in light of the quantum of sewage generation and treatment capacity and to what extent they are useful in safeguarding the overall ecosystem health of Dal Lake.

MATERIALS AND METHODS

Study area

Dal Lake (34°5′–34°9′N and 74°49′–74°53′E) is an urban lake located north-east of Srinagar with an area of about 24 km2 (Rashid et al. 2017). The lake is one of the rare and unique ecosystems to have a culture of houseboats within the lake. One dimension to the situation is the number of people living on houseboats within the lake and another being that these houseboats act as virtual hotels for all practical purposes such as generation of sewage, solid waste, and providing other services to tourists. Presently, there are almost 1,000 houseboats and Doonga boats on the lake for tourists, and the wastes, especially sewage, from these is neither connected to an STP nor is there any in-situ sewage treatment. This is indeed a big challenge and without addressing it there will be limited success and progress in improving the water quality and health of Dal Lake. The situation is further worsened by the number of families living in hamlets within the lake. In a recent survey conducted by LAWDA (Lakes and Waterways Development Authority) it was reported that out of 60 hamlets in the lake, 53 consisted of 2,908 families. The lake also provides several ecosystem services like vegetables, fish, drinking water, livelihood, recreation, culture, and esthetics (Kawoosa 2017; Nengroo et al. 2017; Khanday et al. 2018; Dar et al. 2020a) to the local population. Despite having great ecological and socio-economic significance, Dal Lake continues to receive large volumes of treated as well as untreated sewage from the entire city and within the lake.

Description of STPs evaluated

The study was conducted at Hazratbal (34°08′06″N–74°50′29″E) and Nallah Amir Khan (NAK) (34°06′49.4″N–074°49′36.4″E) STPs (Figure 1 and Table 1). Five points within the FAB STP were identified for collecting samples. The first was the receiving sump or inlet which receives raw sewage from connected sewerage systems. The second was the outlet of the bioreactor tank or FAB I, where biodegradation of organics takes place. The third was the outlet of the bioreactor tank or FAB II, which is also meant for organic degradation. Both the tanks consist of plastic media for the growth of bacteria. The fourth point was the claritube settler meant for the removal of readily settable solids. The fifth was the outlet of the STP where the treated wastewater leaves the plant and is directed towards Dal Lake. In SBR, wastewater was collected only at two points. The first was the receiving sump or inlet where the raw sewage is collected and afterwards directed towards the SBR where its treatment takes place. The second was the outlet where the treated wastewater leaves the STP and is directed towards the lake.

Table 1

Details related to the design and configuration of the STPs under study

STPType of technologyCapacityCatchment areaHydraulic retention time (HRT) (hr)Design specifications
Hazratbal FAB 7.5 MLD National Institute of Technology, Hazratbal, Naseem Bagh, Kanitar Saderibal and umer colony 3–4 hr Bioreactors capacity (m37.75 m diameter and depth 5 m 
Aeration capacity 650 m3 per hr 
Claritube settler Depth 5 feet 
Nallah Ameer Khan (NAK) SBR 5.4 MLD Lal Bazar, Baghwanpora, Amda Kadal, Mughal Mohalla and Leper Hospital 19 hr Sequential batch reactor capacity (m32,973 
Aeration capacity 600 m3/hr 
STPType of technologyCapacityCatchment areaHydraulic retention time (HRT) (hr)Design specifications
Hazratbal FAB 7.5 MLD National Institute of Technology, Hazratbal, Naseem Bagh, Kanitar Saderibal and umer colony 3–4 hr Bioreactors capacity (m37.75 m diameter and depth 5 m 
Aeration capacity 650 m3 per hr 
Claritube settler Depth 5 feet 
Nallah Ameer Khan (NAK) SBR 5.4 MLD Lal Bazar, Baghwanpora, Amda Kadal, Mughal Mohalla and Leper Hospital 19 hr Sequential batch reactor capacity (m32,973 
Aeration capacity 600 m3/hr 
Figure 1

Location of various STPs around Dal Lake in Srinagar city, Kashmir, India.

Figure 1

Location of various STPs around Dal Lake in Srinagar city, Kashmir, India.

Sampling and analysis

Sampling was carried out for a period of 24 months from December 2016 to November 2018. Water samples at all the selected points were collected in 1-liter polyethylene bottles, which were previously rinsed with ethyl alcohol (70%) followed by distilled water. In order to determine CBOD5, dilutions at a ratio of 5:100 (5%) were prepared and 2-chloro-6-(trichloromethyl) pyridine was used as nitrification inhibitor (APHA 2005). Separate samples were collected for microbiological examination in clean sterile vials which were immediately brought to the lab and kept in a refrigerator at 4 °C prior to analysis. Analysis of various physico-chemical parameters and microbial parameters was carried out as per the standard methods given in APHA (2005) (Table 2). Serial dilution method was used for total colony count (TCC) of bacteria using nutrient agar. The data sets obtained were subjected to various univariate and multivariate statistical techniques such as one-way ANOVA, t test, and PCA (principal component analysis) using statistical software PAST version 3.

Table 2

Description of various physico-chemical and microbial parameters of sewage

S.NoParametersAbbreviationsUnitsMethodology used
Water temperature WT °C Mercury thermometer 
pH pH pH unit Probe (Eutech PCSTEST35-01 × 441506) 
Conductivity EC μScm−1 Probe (Eutech PCSTEST35-01 × 441506) 
Total dissolved solids TDS mg/l Probe (Eutech PCSTEST35-01 × 441506) 
Total suspended solids TSS mg/l Gravimetric after filtration 
Salinity Sal mg/l Probe (Eutech PCSTEST35-01 × 441506) 
7 Turbidity Turb NTU Microprocessor turbidity meter (Labtronics) 
Total alkalinity TA mg/l Phenolphthalein 
Chloride Cl mg/l Argentometric 
10 Free carbon dioxide FCD mg/l Titrimetric 
11 Total hardness TH mg/l EDTA titrimetric 
12 Calcium hardness CaH mg/l EDTA titrimetric 
13 Magnesium hardness MgH mg/l EDTA titrimetric 
14 Dissolved oxygen DO mg/l Winkler azide modification 
15 Ammonical nitrogen NH3-N mg/l Phenate method 
16 Nitrite nitrogen NO2-N mg/l Sulphanilamide 
17 Nitrate nitrogen NO3-N mg/l Salicylate method 
18 Total phosphorus TP mg/l Ascorbic acid method 
19 Ortho phosphate phosphorus PO42-P mg/l Ascorbic acid method 
20 Sulfate  mg/l Turbidimetric method 
21 Silicate Sili mg/l Molybdosilicate 
22 Total iron Fe μg/l Phenanthroline method 
23 Chemical oxygen demand COD mgO2/l Open reflux method 
24 Carbonaceous biochemical oxygen demand CBOD5 mg/l 5-day incubation method 
25 Total coliform TC Log MPN/100 ml Most probable number (MPN) or multiple tube fermentation technique 
26 Fecal coliform FC Log MPN/100 ml Most probable number (MPN) or multiple tube fermentation technique 
27 Fecal streptococci FS Log MPN/100 ml Most probable number (MPN) or multiple tube fermentation technique 
S.NoParametersAbbreviationsUnitsMethodology used
Water temperature WT °C Mercury thermometer 
pH pH pH unit Probe (Eutech PCSTEST35-01 × 441506) 
Conductivity EC μScm−1 Probe (Eutech PCSTEST35-01 × 441506) 
Total dissolved solids TDS mg/l Probe (Eutech PCSTEST35-01 × 441506) 
Total suspended solids TSS mg/l Gravimetric after filtration 
Salinity Sal mg/l Probe (Eutech PCSTEST35-01 × 441506) 
7 Turbidity Turb NTU Microprocessor turbidity meter (Labtronics) 
Total alkalinity TA mg/l Phenolphthalein 
Chloride Cl mg/l Argentometric 
10 Free carbon dioxide FCD mg/l Titrimetric 
11 Total hardness TH mg/l EDTA titrimetric 
12 Calcium hardness CaH mg/l EDTA titrimetric 
13 Magnesium hardness MgH mg/l EDTA titrimetric 
14 Dissolved oxygen DO mg/l Winkler azide modification 
15 Ammonical nitrogen NH3-N mg/l Phenate method 
16 Nitrite nitrogen NO2-N mg/l Sulphanilamide 
17 Nitrate nitrogen NO3-N mg/l Salicylate method 
18 Total phosphorus TP mg/l Ascorbic acid method 
19 Ortho phosphate phosphorus PO42-P mg/l Ascorbic acid method 
20 Sulfate  mg/l Turbidimetric method 
21 Silicate Sili mg/l Molybdosilicate 
22 Total iron Fe μg/l Phenanthroline method 
23 Chemical oxygen demand COD mgO2/l Open reflux method 
24 Carbonaceous biochemical oxygen demand CBOD5 mg/l 5-day incubation method 
25 Total coliform TC Log MPN/100 ml Most probable number (MPN) or multiple tube fermentation technique 
26 Fecal coliform FC Log MPN/100 ml Most probable number (MPN) or multiple tube fermentation technique 
27 Fecal streptococci FS Log MPN/100 ml Most probable number (MPN) or multiple tube fermentation technique 

RESULTS

An examination of the data revealed that certain parameters witnessed significant change during treatment, like pH, DO, and CBOD5 (Table 3 and Figure 2). pH was found to be slightly acidic (FAB, 6.18; SBR, 6.31) in the raw influent, which gradually shifted towards alkalinity (FAB, 7.75; SBR, 7.72) as the wastewater passed through different stages of the treatment facility. DO was mostly absent at the inlet and gradually increased (FAB, 6 mg/l; SBR, 6.8 mg/l) during various stages of the treatment. Its values displayed the minimum concentration during summer (FAB, 2.8 mg/l; SBR 2.9 mg/l) and maximum during winter (FAB, 5.6 mg/l; SBR 6.80 mg/l). Values of CBOD5 also showed a reduction during the treatment. However, in FAB its removal efficiency showed a remarkable decline during winter (30%) as compared to summer (69.64%), but no such seasonal variation in the efficiency was observed in SBR (winter 59.20%; summer 68.75%) (Figure 3). Parameters like TSS, COD, NH3-N, and TP also displayed a substantial reduction during the treatment. In some parameters insignificant decline was recorded, such as WT, Cl, NO2-N, NO3-N (Table 3 and Figure 2). WT remained mostly the same with a slight increase from inlet to outlet. It exhibited seasonal variation at a minimum during winter (FAB, 8 °C; SBR, 11 °C) and maximum during summer (FAB, 20 °C; SBR, 18 °C). Cl displayed irregularity during the treatment. Its concentration decreased as the wastewater moved from inlet to FAB I and FAB II, after which it rose in the claritube settler. Similarly, concentration of NO3-N (FAB, 0.02 to 0.382 mg/l; SBR, 0.112 to 0.306 mg/l) and NO2-N (FAB, 0.007 to 0.21 mg/l; SBR, 0.0104 to 2.895 mg/l) was raised at the outlet of the treatment facility in comparison to that of the inlet. Microbial parameters like TC, FC, and FS also displayed significant decline during the treatment. TCC was observed to be highest at the inlet during summer (FAB, 2.67 × 109; SBR, 2.90 × 109) and least during winter (FAB, 1.46 × 109; SBR 1.10 × 109). However, the removal efficiency was also highest during summer (FAB, 54.68%; SBR, 54.48%) and least during winter (FAB, 11.54%; SBR, 12.73%). t test was performed on microbial load which revealed significant decline in TCC after treatment in FAB (t = 2.83, p = 0.011) and SBR (t = 2.50, p = 0.021) (Table 4).

Table 3

ANOVA of some important physico-chemical and microbial parameters in FAB and SBR STPs

Physico-chemical parameters
ParameterpHTSSDOCBOD5CODNH3-NTPClH Cl NO2-N NO3-N
FAB            
25.86 23.91 30.15 115.04 31.16 26.43 33.90 0.92 0.14 0.65  
P-value 0.000 0.000 0.000 0.000 0.00 0.000 0.000 0.456ns 0.966ns 0.625ns  
SBR 
5.41 27.20 298.31 111.93 41.58 11.26 4.47 1.28 0.31 0.78  
P-value 0.025 0.00 0.000 0.000 0.00 0.000 0.040 0.265ns 0.579ns 0.381ns  
Microbial parameters
TC
FC
FS
FAB       
13.36 14.27 5.78 
P-value 0.001 0.000 0.020 
SBR 
23.49 10.54 15.84 
P-value 0.000 0.002 0.000 
Physico-chemical parameters
ParameterpHTSSDOCBOD5CODNH3-NTPClH Cl NO2-N NO3-N
FAB            
25.86 23.91 30.15 115.04 31.16 26.43 33.90 0.92 0.14 0.65  
P-value 0.000 0.000 0.000 0.000 0.00 0.000 0.000 0.456ns 0.966ns 0.625ns  
SBR 
5.41 27.20 298.31 111.93 41.58 11.26 4.47 1.28 0.31 0.78  
P-value 0.025 0.00 0.000 0.000 0.00 0.000 0.040 0.265ns 0.579ns 0.381ns  
Microbial parameters
TC
FC
FS
FAB       
13.36 14.27 5.78 
P-value 0.001 0.000 0.020 
SBR 
23.49 10.54 15.84 
P-value 0.000 0.002 0.000 

Values in bold indicate significance; ns indicates non-significant.

Table 4

Bacterial load in sewage before and after treatment in FAB and SBR along with statistical analysis

FAB
SBR
SeasonsInletOutletRemoval efficiency (%)InletOutletRemoval efficiency (%)
Winter 1.46 × 109 1.28 × 10 9 12.33 1.10 × 109 9.6 × 108 12.73 
Spring 1.78 × 109 1.18 × 109 33.71 1.84 × 109 1.32 × 109 28.26 
Summer 2.48 × 109 1.22 × 109 50.81 2.90 × 109 1.32 × 109 54.48 
Autumn 1.65 × 109 1.88 × 109 28.48 1.34 × 109 1.16 × 109 13.43 
Winter 7.8 × 10 8 6.9 × 10 8 11.54 1.46 × 109 1.27 × 10 9 13.01 
Spring 1.64 × 109 1.04 × 109 36.59 1.69 × 109 1.29 × 109 23.67 
Summer 2.67 × 109 1.21 × 109 54.68 2.64 × 109 1.28 × 109 51.52 
Autumn 1.42 × 109 1.03 × 109  27.46 1.57 × 109 1.38 × 109 12.10 
t test t = 2.83, p = 0.011  t = 2.50, p = 0.021  
FAB
SBR
SeasonsInletOutletRemoval efficiency (%)InletOutletRemoval efficiency (%)
Winter 1.46 × 109 1.28 × 10 9 12.33 1.10 × 109 9.6 × 108 12.73 
Spring 1.78 × 109 1.18 × 109 33.71 1.84 × 109 1.32 × 109 28.26 
Summer 2.48 × 109 1.22 × 109 50.81 2.90 × 109 1.32 × 109 54.48 
Autumn 1.65 × 109 1.88 × 109 28.48 1.34 × 109 1.16 × 109 13.43 
Winter 7.8 × 10 8 6.9 × 10 8 11.54 1.46 × 109 1.27 × 10 9 13.01 
Spring 1.64 × 109 1.04 × 109 36.59 1.69 × 109 1.29 × 109 23.67 
Summer 2.67 × 109 1.21 × 109 54.68 2.64 × 109 1.28 × 109 51.52 
Autumn 1.42 × 109 1.03 × 109  27.46 1.57 × 109 1.38 × 109 12.10 
t test t = 2.83, p = 0.011  t = 2.50, p = 0.021  
Figure 2

Monthly variation of some important physiochemical and microbial parameters in FAB and SBR during December 2016 to November 2018.

Figure 2

Monthly variation of some important physiochemical and microbial parameters in FAB and SBR during December 2016 to November 2018.

Figure 3

Seasonal variations in the removal efficiency of CBOD5 in FAB and SBR during December 2016 to November 2018.

Figure 3

Seasonal variations in the removal efficiency of CBOD5 in FAB and SBR during December 2016 to November 2018.

PRINCIPAL COMPONENT ANALYSIS (PCA)

In order to carry out PCA, 27 parameters were selected and analyzed for Kaiser Meyer–Olkin (KMO) and Bartlett's sphericity tests (BST). In FAB (Table 5 and Figure 4) and SBR (Table 6 and Figure 5), a total of eight principal components (PC) were identified which contributed to 77.09% and 76.27% of variance, respectively. First PC exhibited about 25.24% (FAB) and 19.45% (SBR) of variance with a strong positive loading from Turb, BOD (FAB) and Turb, BOD, COD (SBR) while a strong negative loading was observed from DO in both the STPs. Second PC displayed 10.64% (FAB) and 10.46% (SBR) of variance with a strong positive loading from TC, FC, FS (FAB) and from TH and MgH (SBR). Third PC accounted for 10.23% (FAB) and 9.43% (SBR) of variance with EC (FAB) and EC, TDS (SBR) as the most influential constituent. Fourth PC showed 7.5% (FAB) and 8.26% (SBR) of total variance with a high score from CH, moderate score from TH (FAB), and high score from TC, FC (SBR). Fifth PC represented 7.1% (FAB) and 7.57% (SBR) of variance with strong positive loading from Cl in FAB and TP in SBR. Sixth PC contributed to 6.11% (FAB) and 7.52% (SBR) of total variance with strong loading from WT (FAB) and a heavy loading from CaH (SBR). Seventh PC constituted 5.31% (FAB) and 7.09% (SBR) of variance with a strong loading from (FAB) and Fe (SBR). Eighth component represented 4.94% (FAB) and 6.45% (SBR) of variance with strong positive loading from nutrient portion of wastewater, i.e., NO2-N (FAB) and (SBR).

Table 5

Factor loading values obtained from PCA of sewage water in FAB STP

PC1PC2PC3PC4PC5PC6PC7PC8
Temp 0.123 0.068 − 0.134 − 0.015 0.156 0.835 − 0.330 − 0.158 
pH 0.821 − 0.087 0.019 − 0.025 0.224 − 0.137 0.016 0.180 
EC 0.157 − 0.097 0.799 0.013 0.064 0.078 − 0.113 0.091 
TDS 0.266 0.050 0.737 − 0.085 0.095 − 0.055 0.026 − 0.215 
TSS 0.757 − 0.075 0.377 − 0.154 − 0.293 − 0.018 − 0.012 0.026 
Turb 0.793 − 0.000 0.112 0.254 − 0.023 0.165 0.232 0.033 
Sal 0.096 − 0.040 0.690 0.101 0.046 − 0.051 0.149 0.354 
DO 0.835 − 0.078 0.002 − 0.115 − 0.051 0.016 − 0.008 − 0.078 
FCD 0.723 0.182 0.107 − 0.228 0.382 − 0.116 − 0.003 0.124 
TA 0.360 0.087 0.064 − 0.223 0.235 0.005 0.468 − 0.118 
Cl 0.002 − 0.019 0.405 0.044 0.789 − 0.065 0.003 0.107 
TH 0.218 0.046 − 0.13 0.714 0.448 0.336 − 0.035 − 0.136 
CaH 0.261 − 0.031 0.125 0.856 − 0.131 − 0.084 − 0.154 0.011 
MgH 0.097 0.078 − 0.265 0.002 0.714 0.460 0.147 − 0.159 
BOD 0.840 0.201 0.154 0.135 0.078 − 0.114 0.044 0.159 
COD 0.717 0.215 0.262 0.279 0.172 − 0.105 0.125 − 0.046 
NH3-N 0.575 0.086 0.207 0.295 0.259 − 0.050 0.133 0.261 
-N − 0.36 0.082 0.229 0.105 − 0.061 0.6405 0.233 0.345 
-N 0.029 0.005 0.048 − 0.055 − 0.002 − 0.021 − 0.039 0.851 
TP 0.638 0.041 0.355 0.346 0.125 − 0.243 0.142 0.0273 
-P 0.631 0.017 0.112 0.157 0.0207 0.044 0.089 − 0.198 
Fe 0.445 0.226 0.536 0.275 − 0.172 − 0.150 0.029 − 0.109 
 0.153 0.006 − 0.021 − 0.042 − 0.004 − 0.109 0.906 0.021 
Sili 0.751 0.208 0.093 0.365 0.154 0.002 0.061 − 0.062 
TC 0.153 0.906 0.094 0.040 0.008 0.089 − 0.077 0.066 
FC 0.155 0.951 − 0.031 − 0.005 − 0.030 0.033 0.010 − 0.036 
FS 0.106 0.927 − 0.096 − 0.022 0.085 − 0.012 0.123 − 0.020 
Eigen value 8.21 2.939 2.287 2.067 1.819 1.34 1.133 1.019 
Total variance (%) 25.245 10.648 10.234 7.5 7.101 6.11 5.311 4.944 
Cumulative variance (%) 25.245 35.893 46.127 53.628 60.729 66.839 72.15 77.094 
PC1PC2PC3PC4PC5PC6PC7PC8
Temp 0.123 0.068 − 0.134 − 0.015 0.156 0.835 − 0.330 − 0.158 
pH 0.821 − 0.087 0.019 − 0.025 0.224 − 0.137 0.016 0.180 
EC 0.157 − 0.097 0.799 0.013 0.064 0.078 − 0.113 0.091 
TDS 0.266 0.050 0.737 − 0.085 0.095 − 0.055 0.026 − 0.215 
TSS 0.757 − 0.075 0.377 − 0.154 − 0.293 − 0.018 − 0.012 0.026 
Turb 0.793 − 0.000 0.112 0.254 − 0.023 0.165 0.232 0.033 
Sal 0.096 − 0.040 0.690 0.101 0.046 − 0.051 0.149 0.354 
DO 0.835 − 0.078 0.002 − 0.115 − 0.051 0.016 − 0.008 − 0.078 
FCD 0.723 0.182 0.107 − 0.228 0.382 − 0.116 − 0.003 0.124 
TA 0.360 0.087 0.064 − 0.223 0.235 0.005 0.468 − 0.118 
Cl 0.002 − 0.019 0.405 0.044 0.789 − 0.065 0.003 0.107 
TH 0.218 0.046 − 0.13 0.714 0.448 0.336 − 0.035 − 0.136 
CaH 0.261 − 0.031 0.125 0.856 − 0.131 − 0.084 − 0.154 0.011 
MgH 0.097 0.078 − 0.265 0.002 0.714 0.460 0.147 − 0.159 
BOD 0.840 0.201 0.154 0.135 0.078 − 0.114 0.044 0.159 
COD 0.717 0.215 0.262 0.279 0.172 − 0.105 0.125 − 0.046 
NH3-N 0.575 0.086 0.207 0.295 0.259 − 0.050 0.133 0.261 
-N − 0.36 0.082 0.229 0.105 − 0.061 0.6405 0.233 0.345 
-N 0.029 0.005 0.048 − 0.055 − 0.002 − 0.021 − 0.039 0.851 
TP 0.638 0.041 0.355 0.346 0.125 − 0.243 0.142 0.0273 
-P 0.631 0.017 0.112 0.157 0.0207 0.044 0.089 − 0.198 
Fe 0.445 0.226 0.536 0.275 − 0.172 − 0.150 0.029 − 0.109 
 0.153 0.006 − 0.021 − 0.042 − 0.004 − 0.109 0.906 0.021 
Sili 0.751 0.208 0.093 0.365 0.154 0.002 0.061 − 0.062 
TC 0.153 0.906 0.094 0.040 0.008 0.089 − 0.077 0.066 
FC 0.155 0.951 − 0.031 − 0.005 − 0.030 0.033 0.010 − 0.036 
FS 0.106 0.927 − 0.096 − 0.022 0.085 − 0.012 0.123 − 0.020 
Eigen value 8.21 2.939 2.287 2.067 1.819 1.34 1.133 1.019 
Total variance (%) 25.245 10.648 10.234 7.5 7.101 6.11 5.311 4.944 
Cumulative variance (%) 25.245 35.893 46.127 53.628 60.729 66.839 72.15 77.094 
Table 6

Factor loading values obtained from PCA of sewage water in SBR STP

PC1PC2PC3PC4PC5PC6PC7PC8
Temp 0.000 0.637 − 0.451 0.311 0.144 − 0.210 − 0.162 − 0.102 
pH − 0.184 − 0.052 − 0.107 0.037 − 0.511 − 0.320 − 0.073 − 0.334 
EC 0.157 0.082 0.828 − 0.131 0.220 0.059 − 0.042 − 0.144 
TDS 0.076 − 0.042 0.827 0.227 − 0.079 − 0.009 0.191 0.205 
TSS 0.592 − 0.256 0.336 0.079 0.353 0.245 0.048 0.025 
Turb 0.898 − 0.066 0.133 0.175 0.109 0.175 0.163 − 0.056 
Sal − 0.127 − 0.469 0.486 0.396 0.132 0.040 0.480 − 0.023 
DO 0.882 − 0.076 − 0.059 − 0.188 − 0.115 − 0.115 − 0.137 0.0246 
FCD 0.458 − 0.150 0.041 − 0.024 − 0.027 0.329 0.561 − 0.181 
TA 0.588 0.179 − 0.140 0.017 0.373 0.048 0.170 0.1334 
Cl 0.138 0.044 0.154 0.093 − 0.024 − 0.066 0.147 − 0.074 
TH 0.096 0.801 0.103 0.106 − 0.068 0.461 0.154 − 0.023 
CaH 0.180 − 0.066 0.138 − 0.019 0.051 0.892 0.021 0.141 
MgH 0.003 0.851 − 0.020 0.150 − 0.033 − 0.260 0.070 − 0.128 
BOD 0.811 0.248 − 0.101 0.362 0.018 0.041 0.147 0.040 
COD 0.849 − 0.122 0.098 0.127 − 0.033 − 0.164 − 0.217 0.165 
NH3-N 0.136 0.318 0.021 0.456 0.440 0.101 0.277 0.198 
-N − 0.371 − 0.209 0.542 − 0.214 − 0.230 0.201 − 0.178 0.345 
-N − 0.006 − 0.390 − 0.068 0.126 − 0.494 0.197 − 0.215 − 0.502 
TP 0.198 − 0.118 0.036 0.078 0.773 0.006 − 0.032 − 0.188 
-P 0.142 − 0.387 − 0.278 0.127 0.193 0.489 0.272 0.394 
Fe 0.185 0.153 0.071 − 0.077 0.041 0.004 0.895 0.104 
 0.118 − 0.190 0.088 0.076 − 0.057 0.172 − 0.001 0.851 
Sili 0.684 0.068 − 0.185 − 0.313 0.128 0.277 0.206 0.280 
TC 0.424 0.190 − 0.068 0.725 0.127 − 0.179 − 0.062 − 0.016 
FC 0.270 0.127 0.023 0.699 − 0.072 0.118 − 0.105 0.074 
FS 0.499 0.162 0.249 0.473 − 0.468 0.267 0.107 − 0.085 
Eigen value 6.999 3.757 2.374 2.11 1.717 1.571 1.406 1.093 
Total variance (%) 19.455 10.466 9.432 8.267 7.579 7.524 7.093 6.456 
Cumulative variance (%) 19.455 29.921 39.354 47.621 55.199 62.723 69.817 76.273 
PC1PC2PC3PC4PC5PC6PC7PC8
Temp 0.000 0.637 − 0.451 0.311 0.144 − 0.210 − 0.162 − 0.102 
pH − 0.184 − 0.052 − 0.107 0.037 − 0.511 − 0.320 − 0.073 − 0.334 
EC 0.157 0.082 0.828 − 0.131 0.220 0.059 − 0.042 − 0.144 
TDS 0.076 − 0.042 0.827 0.227 − 0.079 − 0.009 0.191 0.205 
TSS 0.592 − 0.256 0.336 0.079 0.353 0.245 0.048 0.025 
Turb 0.898 − 0.066 0.133 0.175 0.109 0.175 0.163 − 0.056 
Sal − 0.127 − 0.469 0.486 0.396 0.132 0.040 0.480 − 0.023 
DO 0.882 − 0.076 − 0.059 − 0.188 − 0.115 − 0.115 − 0.137 0.0246 
FCD 0.458 − 0.150 0.041 − 0.024 − 0.027 0.329 0.561 − 0.181 
TA 0.588 0.179 − 0.140 0.017 0.373 0.048 0.170 0.1334 
Cl 0.138 0.044 0.154 0.093 − 0.024 − 0.066 0.147 − 0.074 
TH 0.096 0.801 0.103 0.106 − 0.068 0.461 0.154 − 0.023 
CaH 0.180 − 0.066 0.138 − 0.019 0.051 0.892 0.021 0.141 
MgH 0.003 0.851 − 0.020 0.150 − 0.033 − 0.260 0.070 − 0.128 
BOD 0.811 0.248 − 0.101 0.362 0.018 0.041 0.147 0.040 
COD 0.849 − 0.122 0.098 0.127 − 0.033 − 0.164 − 0.217 0.165 
NH3-N 0.136 0.318 0.021 0.456 0.440 0.101 0.277 0.198 
-N − 0.371 − 0.209 0.542 − 0.214 − 0.230 0.201 − 0.178 0.345 
-N − 0.006 − 0.390 − 0.068 0.126 − 0.494 0.197 − 0.215 − 0.502 
TP 0.198 − 0.118 0.036 0.078 0.773 0.006 − 0.032 − 0.188 
-P 0.142 − 0.387 − 0.278 0.127 0.193 0.489 0.272 0.394 
Fe 0.185 0.153 0.071 − 0.077 0.041 0.004 0.895 0.104 
 0.118 − 0.190 0.088 0.076 − 0.057 0.172 − 0.001 0.851 
Sili 0.684 0.068 − 0.185 − 0.313 0.128 0.277 0.206 0.280 
TC 0.424 0.190 − 0.068 0.725 0.127 − 0.179 − 0.062 − 0.016 
FC 0.270 0.127 0.023 0.699 − 0.072 0.118 − 0.105 0.074 
FS 0.499 0.162 0.249 0.473 − 0.468 0.267 0.107 − 0.085 
Eigen value 6.999 3.757 2.374 2.11 1.717 1.571 1.406 1.093 
Total variance (%) 19.455 10.466 9.432 8.267 7.579 7.524 7.093 6.456 
Cumulative variance (%) 19.455 29.921 39.354 47.621 55.199 62.723 69.817 76.273 
Figure 4

Biplot for PCA displaying different components of wastewater in FAB.

Figure 4

Biplot for PCA displaying different components of wastewater in FAB.

Figure 5

Biplot for PCA displaying different components of wastewater in SBR.

Figure 5

Biplot for PCA displaying different components of wastewater in SBR.

DISCUSSION

Analysis of physico-chemical and microbial parameters for over a period of two years revealed changes in the quality of wastewater during and after treatment (Figure 2). WT is a key parameter affecting microbial community in WWTPs (Griffin & Wells 2017). It was more or less constant within the STPs and the slight increase from inlet to outlet may be due to the fact that these treatment systems are open, due to which, direct insolation results in rise in temperature during treatment. As evident from the seasonal data of temperature there is a decrease in the water temperature during winter season (Figure 2). As Kashmir witnesses three to four months of severe winter this can result in the reduction in efficiency of STPs during this season. pH is also of main concern in WWTPs as it is related to the activity of microbes. There was a slight shift towards alkalinity from inlet to outlet of the treatment facility indicating that it is being regulated by CO2 and (Meenakshirpruya et al. 2008). The optimum range of pH for bacterial growth is between 6.5 and 7.5 and extreme shifts in its values cannot be tolerated by most bacteria (Sincero & Sincero 1996). High EC and TDS are a result of high salinity as well as high mineral content and their reduction might be due to oxidative degradation of dissolved solids during treatment (Singh & Varshney 2013). TSS is the amount of floating particulate content in the wastewater (Johal et al. 2014), imparting turbidity to it apart from organic as well as inorganic matter and microbes which also contribute to turbidity. Reduction in TSS and turbidity may be because of sedimentation process taking place during the treatment (Showkat & Najar 2019). High concentration of salts in the effluent can result in increase in salt content of the receiving water body with harmful effects on aquatic organisms (Fried 1991) and brackish, salty taste to its consumers (WHO 1979). Whereas highly turbid waters upon chlorination result in formation of trihalomethane (THM) (Kushwah et al. 2012) which is genotoxic or carcinogenic to aquatic organisms and humans (Hrudey 2009). Increase in Cl concentration in the effluent was observed in both the STPs due to the addition of PAC (poly aluminum chloride) during treatment. In FAB the increase was clearly observed due to the addition of PAC to wastewater prior to its entry in the claritube settler. There was a reduction in the value of Cl from inlet to FAB I and II, after which, it again increased in the claritube settler (Rao & Shruthi 2002). DO was occasionally absent at the inlet due to prevailing septic conditions along with high organic loading and was added to wastewater due to the aeration process during treatment (Prescott et al. 2002). However, seasonal variation of DO revealed that its maximum concentration was in winter followed by spring, autumn and least in summer (Figure 2). Similar results have been reported by Kushwah et al. (2012). This is attributed to the situation wherein summer microbes utilize more oxygen for breakdown of organic substances and thus oxygen concentration remains low in contrast to winter where biodegradation is less due to inactivity of microbes. BOD is a measure of the amount of organic pollutant or biodegradable substances in wastewater (Hur & Kong 2008). Values of CBOD5 depicted significant decline which could be attributed to the action of microbes in the bioreactor and coagulation and flocculation taking place in the claritube settler (Jan et al. 2013). As is evident from the seasonal efficiency of CBOD5 it was observed that the efficiency was highest in summer, followed by spring, autumn and least in winter in FAB which can be attributed to the inactivity of microbes during winter while no such variation was observed in SBR (Figure 3). Reduction in the value of COD was recorded which may be due to reduction of organic as well as inorganic substances present in the sewage due to various physical and biochemical processes taking place within the STP (Mehdi & Rafiq 2013). Low values of COD in the influent are in agreement with Choi et al. (2017). Ratio of BOD5/COD is referred to as biodegradability index and ranges from 0.4 to 0.8 for domestic wastewaters. Ratio >0.6 implies biodegradable quality of waste which can be treated biologically. Ratio varying from 0.3 to 0.6 implies that seeding has to be done in order to treat it. Ratio < 0.3 indicates that it cannot be treated biologically (Rukeh & Agbozu 2013). In the present study, CBOD5/COD ratio was 0.73 (FAB) and 0.80 (SBR), indicating the biodegradable nature of wastes and can be treated biologically. NH3-N being a main component of raw sewage showed a decline in the effluent mostly due to its conversion to NO3-N (Colt & Armstrong 1981). NO2-N being an intermediate product of nitrification rapidly gets converted to NO3-N and thus its concentration is usually low. NO2-N and NO3-N accumulated in the effluent and their concentration was high as compared to the influent. NO3-N in waste effluents is due to domestic sources and its concentration in treated effluents is high because of oxidation of NH3-N into NO3-N by microbes (Morrison et al. 2001). Phosphate in sewage is contributed by human wastes, detergents, and soaps (Ogunfowokan et al. 2005). It is considered as a growth limiting nutrient in water bodies that is responsible for eutrophication and many other adverse ecological effects (OECD 1982). PO42–P being an inorganic phosphate is also referred to as reactive phosphorus. This form of phosphorus is easily accessible to plants which makes it a most important nutrient for plants as well as algal growth (Wentzel & Ekama 1997). Phosphorus in both the STPs was removed chemically as well as biologically. PAC acts as a coagulant and helps in precipitation of phosphorus with the help of metal salt of aluminum (Hammer 1975). Nitrogen and phosphorus are nutrients, but are also identified as pollutants when accumulated in higher concentrations that can trigger nutrient hazard in lake ecosystems like Dal Lake. Nitrogen and phosphorus when found in excess quantities can stimulate eutrophication which can negatively affect water bodies for their intended use (Igbinosa & Okoh 2009).

Analysis of the data (Supplementary Figure 1) revealed that parameters like EC, salinity, TA, TH, CH, Fe, , and silicate remain unaffected and did not show any improvement while passing through the different stages of STP. The design of the STP is based on the principle that it will take care of the oxidation/stabilization of the organics and also removal of phosphorus and conversion of NH3-N and NO2-N into NO3-N and, therefore, other parameters mostly showed insignificant change.

TC, FC, and FS are being used internationally as the main indicators of fecal pollution (APHA 1998). Their concentration was high in STPs because these receive human sewage (Velusamy & Kannan 2016). Their reduction in the effluent was due to the addition of chlorine as disinfectant during tertiary treatment, yet a significant proportion of the bacteria was retained mostly due to the fact that microbial reduction depends upon bacterial activity, suspended solids’ settlement, inactivity due to sunlight and environment factors (Katsoyiannis & Samara 2004). Turbid water hinders the disinfection process and is often associated with microbial contamination (WHO 2004). Seasonal variation in TCC indicated that the temperature has significant effect on the growth of microbial population (Levantesi et al. 2010) (Table 4). Reduction in TCC was mostly due to sedimentation and flocculation process taking place during the treatment (Gautam et al. 2019) along with the addition of chlorine as disinfectant. Presence of pathogenic bacteria in the effluent of WWTPs is associated with health burden, and its entry in the receiving water body can result in various health issues in the population residing within and around the lake that use the water in several ways. Consumption of water from the lake can cause epidemics in a large proportion of the population utilizing it for drinking, bathing, washing, and agriculture. For the use of treated sewage in agriculture, CPCB has recommended the desirable limit of fecal coliform at 103 MPN/100 ml while its maximum permissible limit is 104MPN/100 ml which was violated by both the STPs under study. Using the waters of the lake for irrigation can be very risky, especially for vegetables which are eaten raw or uncooked. Besides infestation in tourists, loss of biodiversity, weed infestation and degradation of water quality are some of the direct consequences of polluted water. This makes the proper treatment of wastewater indispensable before its subsequent disposal into the lake in order to protect lake ecology and public health.

High loading from turbidity, BOD and COD in the first PC could be attributed to organic as well as inorganic constituents derived from domestic effluents, thereby imparting a negative loading from DO. The second PC signified the dominance of microbial load derived from fecal sources, representing a biological component, while strong loading from TH and MH was as a result of various salts added during the household use of water. High loading from EC and TDS in the third PC was contributed to wastewater from dissolved salts from various municipal pollutants. Ca2+ and Mg2+ ions along with TC and FC in the fourth PC specify their origin from household use of water and human wastes, respectively. Heavy loading of Cl in the fifth PC is also indicative of the role of human excreta in contributing Cl to wastewater apart from the use of PAC during treatment, while heavy loading from nutrient, i.e., TP is contributed from the use of detergents and soaps. The sixth PC displayed heavy loading from WT which is indicative of the influence of sensory property of wastewater. Heavy loading of in the seventh PC depicted its usage during various human activities that involved detergents, cleaners, cosmetics and, subsequently, their addition to wastewater, while Fe input was characterized by natural sources. Heavy load of NO3-N in the eighth PC is a consequence of organic wastes in sewage apart from incomplete nitrification during treatment. In this scenario, wherein a continuous increase in nutrient loading of Dal Lake occurs, it may result in high abundance of macrophytes, especially submerged vegetation and associated community shift of plankton and planktivorous fish. On the other hand, authors have also observed the growth of emergent macrophytes at sewage entry points looking like floating wetlands. This will lead to degradation of the lake and will impair various ecosystem services of the lake apart from shifting dominance patterns of phytoplankton over submerged vegetation and invasive over natives (Janssen et al. 2014; Sinha et al. 2017). Unlike Dal Lake, many lakes of the valley are directly hit by the entry of untreated sewage leading to eutrophication and consequences thereof (Zutshi & Vass 1971; Trisal 1987; Solim & Wanganeo 2008; Mushtaq et al. 2015; Rashid & Aneaus 2019, 2020; Dar et al. 2020b). Now, summing up the findings, it is abundantly visible and clear that these STPs are struggling with efficiency and, as such, merit attention before they create a problem on a long-term basis for Dal Lake instead of offering a solution to sewage management.

CONCLUSION

As is evident from our findings both FAB and SBR are struggling for removal efficiency (Table 7), which is far below that required and recommended by CPCB, especially in FAB during three to four months of winter season where a very high drop in the working of these STPs was observed, whereas as in SBR no such seasonal variations in the removal efficiency were observed. Removal efficiency for parameters like COD, NH3-N, and TP was found to be higher in FAB as compared to SBR while removal efficiency of TSS and BOD was high in the case of SBR. On comparison with discharge standards, it was found that some of the critical parameters like TSS, BOD, NH3-N, TP, TC, FC, and FS exceeded the standards in both STPs which has the potential to change the Dal Lake ecology and endanger public health. Therefore, it is suggested to have continuous monitoring of these STPs for efficiency evaluation purposes and further lines of research in this direction should be initiated and targeted to ameliorate sewage treatment facility in line with the regional features and socio-economic attributes.

Table 7

Mean values of various physico-chemical and microbial parameters along with their discharge standards

S. NoParameterFAB
SBR
Discharge Standards (EPA 2002)
Effluent valuesRemoval efficiency (%)Effluent valuesRemoval efficiency (%)
WT 14.7 – 15.29 – 40 
pH 7.27 – 7.18 – 5–9 
TSS 249.3 46.42 166.0 55.05 100 
Cl- 36.75 8.19 47.21 8.50 750 
CBOD5 52.63 55 49.75 71.17 40 
COD 112.50 29 115.83 23.32 120 
NH3-N 53.36 2.148 31.29 
NO3-N 0.295  0.839  10 
NO2-N 0.06  0.728  
10 TP 44.65 21.82 
11  175.5 25.39 121.91 8.79 750 
12 Fe 1.04 29.22 0.29 52.28 2,000 
13 TC 4.93 44.35 4.95 42.42 <4.00 
14 FC 4.40 41.86 4.58 40.81 <3.00 
15 FS 4.06 41.61 4.03 42.69 <2.70 
S. NoParameterFAB
SBR
Discharge Standards (EPA 2002)
Effluent valuesRemoval efficiency (%)Effluent valuesRemoval efficiency (%)
WT 14.7 – 15.29 – 40 
pH 7.27 – 7.18 – 5–9 
TSS 249.3 46.42 166.0 55.05 100 
Cl- 36.75 8.19 47.21 8.50 750 
CBOD5 52.63 55 49.75 71.17 40 
COD 112.50 29 115.83 23.32 120 
NH3-N 53.36 2.148 31.29 
NO3-N 0.295  0.839  10 
NO2-N 0.06  0.728  
10 TP 44.65 21.82 
11  175.5 25.39 121.91 8.79 750 
12 Fe 1.04 29.22 0.29 52.28 2,000 
13 TC 4.93 44.35 4.95 42.42 <4.00 
14 FC 4.40 41.86 4.58 40.81 <3.00 
15 FS 4.06 41.61 4.03 42.69 <2.70 

ACKNOWLEDGEMENTS

The authors are extremely to the Department of Environmental Science, University of Kashmir for providing the necessary laboratory facilities and financial assistance in favour of the first author in the form of a university scholarship to carry out this research work. The authors also thank Lakes and Waterways Development Authority (LAWDA) for giving permission for sampling from the STPs. The authors declare they have no conflict of interest.

DATA AVAILABILITY STATEMENT

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

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