Abstract
The influence of natural and anthropogenic factors has caused significant impacts on surface water quality worldwide, particularly where monitoring mechanisms are weak or non-existent. Weathering and soil erosion from the lake catchment areas, the inflow of large number of tourists, drainage outlets, etc. pose a serious threat to two urban lakes, namely, Pichola and Fateh Sagar in Udaipur, Rajasthan, India. Subsequently, stringent water quality monitoring programmes are required to assess the water quality of these two lakes in two distinct seasons, i.e., pre-monsoon and post-monsoon. In this research study, the measured physicochemical parameters were computed for the water quality index (WQI), organic pollution index (OPI), and eutrophication index (EI) to evaluate the health of these lakes. The Pichola Lake had shown elevated concentrations for EC, TDS, DO, BOD, COD, TH, Cl−, Ca2+, Mg2+, SO42− as compared to Fateh Sagar. The mean nutrient concentrations were recorded to be higher in Pichola Lake (TN 3.99 mg/L; TP 0.15 mg/L) as compared to Fateh Sagar (TN 2.50 mg/L; TP 0.003 mg/L). The lower values registered in the post-monsoon period were due to the dilution effect caused by the rainfall and influx of runoff water. These two lakes contribute significant ecological and economic value to the area, helping to meet the water requirements for the surrounding population, agricultural practices, and tourists. Therefore, the present study can provide essential information about taking up appropriate measures to protect the water quality of urban lakes and the need to implement better management policies and initiatives for the same.
HIGHLIGHTS
Assessment of anthropogenic and geogenic influences on lake water quality on a spatio-temporal scale.
Geospatial zonation mapping of surface water quality for human consumption using WQI.
Indexing approaches OPI and EI were used for evaluation of lake health.
Application of multivariate statistical analysis to identify potential pollution sources.
The findings from the study will assist in better management of urban lakes.
Graphical Abstract
INTRODUCTION
The lakes are unique and important features on the Earth's surface that have significant social, environmental, and economic contributions, starting from being a drinking water source to controlling floods, supporting biodiversity, and local tourism, thereby providing economic livelihoods to locals (Anand 2014). In many urban as well as rural areas, drinking water is sourced from lakes. But in recent times, significant population growth, exploitative agricultural practices to increase output, and extensive sewage runoff from urban areas have increased the nutrient load on these lakes to a higher level than their natural occurrence, resulting in catalytic eutrophication (Choudhary et al. 2010; Zan et al. 2011). It is to be noted that apart from supporting drinking water concerns, and moderating eco-geological factors such as local climate, urban lakes act as tourist epicentres or as recreational spots featuring water sports activities or other recreational amenities. This greatly contributes to the local economy, thereby helping sustain the social as well as the economic needs of people. But with increased dependency on lake-centric activities, nutrient enrichment has become a major problem. Degradation of urban and rural lakes has vanished under this pressure, with worldwide environmental concerns (Iscen et al. 2008; Prasanna et al. 2011).
Udaipur City is famous for its enchanting lakes and is one of the most favoured tourist destinations all over the world, recognized as the ‘Lake City’ of India. The city is located between 23 °46′ & 25° 05′ N and 73° 09′ & 74° 35′ E at the southern end of the Aravalli range, a frontier that separates the Thar desert from the plains and plateaus of eastern Rajasthan. The city has a total of eight lakes. Among them, Pichola Lake and Fateh Sagar Lake are the most famous for local visitors and tourists. Lakes are more important in a city such as Udaipur, which does not have a perennial river and thus relies on many lakes for its supply of drinking water, irrigation, and recreation. Lake systems are directly or indirectly the life source of this city in terms of surface water resources, tourism, and the ecosystem at large. However, the increase in population density and urban encroachment imposes significant challenges to water quality management. Over the past few decades, massive deforestation and faulty land-use practices have severely degraded the catchments of the lakes of Udaipur, resulting in an increased inflow of sediments into these water bodies. Therefore, the present study was undertaken (i) to explore the influence of anthropogenic stress and temporal variations on the overall quality of lake water, (ii) to assess organic load and trophic state in lake water, and (iii) to identify pollution sources by using geostatistical methods.
Description of the study area and regional geology
The main lithology in these lakes is slaty quartzite, phyltite, and graywacke, often interlayered with massive orthoquartzites with ripple marks, current bedding, and other sedimentary structures, and dolomitic limestone at the west of the lake belonging to the Aravalli supergroup (Heron 1953; Roy 1988). The aquifer properties of the Udaipur district are exposed by the upper weathered strata of the hard-rocks and, under unconfined conditions, the groundwater at low depths (Machiwal et al. 2011). The mean groundwater depth varies from 2-20 m below ground surface in the pre-monsoon season to 2–14 m below ground surface in the post-monsoon season. The aquifers have very little primary porosity (clay and loam), and the groundwater movement is mainly controlled by the secondary porosity in the form of structure features (joints, faults, and fissures) (Machiwal & Singh 2015). The climatic conditions extensive in the area have a strong influence on the type of vegetation cover and control land-use/land-cover types (Figure 1), which are divided into agricultural land with various types of crops and trees, forests, built-up areas, barren land, and water body areas. Increased commercial activities have also contributed considerably to the land-use area along the lake catchment over the past decades.
Pichola Lake
Pichola Lake is situated in Udaipur city in the Udaipur district of Rajasthan at 24 °68′N latitude and 73 °68′E Longitude with a water spread area of 6.96 sq km. Pichola Lake is one of the oldest lakes in the city, whose origin predates the city and can be traced to 1362. Later, Maharana Udai Singh developed the city of Udaipur on its bank and used the lake as a source for drinking and other household needs of the people of Udaipur. This lake connects with Fatah Sagar Lake through a gate. Pichola Lake is about 3.62 km in length from north to south and 2.41 km in width from east to west with a mean depth of 5.6 m. It is estimated that the lake contains 418 million cubic feet of water and covers an area of 9.71 sq km. This lake has a profound influence on the environment by bringing about favourable changes to the micro-climate in a region where people are faced with low humidity, scorching heat, and glare (Samant 2010).
Fateh Sagar Lake
Fateh Sagar Lake, on the other hand, is a major tourist spot, which was originally constructed in 1687. The Lake providing a picturesque view of the Aravalis and also supports recreational boating. It is situated at 24 °36′ N latitude and 73 °40′ E longitude with a 20 sq km catchment area. The lake has four islands, of which one has been converted into a small island called Nehru Park, where a solar observatory has been created. The third rocky outcropping has been developed into a fountain and the fourth one is just near the north-western shore. The runoff emerging from surrounding hills drains into this lake. The lake is pear-shaped and is encircled by the Aravalli hills on three sides with a straight gravity stone masonry dam on the eastern side, which has a spillway to discharge flood flows during the monsoon season.
METHODOLOGY
Sampling strategy and sample collection
The field study of this research work was conducted during the period 2017–2019. To investigate the water quality, 10 sampling sites were chosen in Pichola Lake and 7 sampling sites were chosen in Fateh Sagar Lake. The sampling stations were chosen strategically to provide water samples from the core as well as peripheral region of the lakes. The samples were collected at monthly interval covering two distinct seasons, i.e. pre-monsoon and the post-monsoon season. For collection of lake water samples pre-acid-washed 1 L polyvinylchloride sample bottles were used, first rinsed well with deionized water, followed by sample water before filling up to the capacity. Lake water samples were collected at 0.3 m below the surface level and filled to the brim to prevent any air entrapment.
Analytical methods
Parameters such as pH, electrical conductivity (EC), and total dissolved solids (TDS) were analyzed on field immediately after the collection using portable analyzers (HI 9813-6). For quantification of other physicochemical parameters, water samples were preserved in ice-crested coolers under 4 °C to prevent any kind of chemical/biological reaction within it (Ganiyu et al. 2021). Remaining water quality parameters were analyzed in the laboratory within 48 hours of collection following the standard methods (APHA 2005).
Quality control assurance
Sample collection, preservation, and analysis were performed according to the standard methods (APHA 2005). Utmost care was taken in the entire process of sample collection, preservation, and analysis. All laboratory glassware was cleaned and treated with diluted HNO3 solution (2%), followed by rinsing with deionized water and then stored in the oven overnight under constant temperature of 40 °C to completely sterilize them. Collected water samples were filtered through 0.45 Millipore membrane filter papers (WHA7404002) and then analyzed for quantitative measures. Each water parameter was quantified in replication of three and the mean value was considered for further data computation, tabulation, and interpretation to ensure precision and accuracy in the analytical results. Standard solutions (E-Mark) and high-purity reagents (AR grade) were used in laboratory analysis. Ultrapure water (resistivity=18.2 MΩcm−1) was used for preparation of all standards and intermediate solutions. For calibration of instruments, the standard solution was prepared by diluting stock solutions (Merck AA standard) of 1,000 mg/L using micropipette. To provide accurate instrument readings, reagent blank determinations were used.
Quantification of lake water quality using various indices
Organic pollution index (OPI)
Water quality index (WQI)
Parameters . | WHO standards (2006) . | BIS standards (2012) . | Weight (Wa) . | Relative weight (Wr) . | |
---|---|---|---|---|---|
Desirable . | Permissible . | ||||
pH | 6.5–8.5 | 6.5–8.5 | NX | 4 | 0.111 |
EC | 2,000 | 2,000 | NX | 3 | 0.083 |
TDS | 1,000 | 500 | 2,000 | 4 | 0.111 |
TH | – | 200 | 600 | 3 | 0.083 |
TA | 200 | 200 | 600 | 2 | 0.056 |
DO | 5 | ≥5 | NX | 2 | 0.056 |
Ca2+ | – | 75 | 200 | 1 | 0.028 |
Mg2+ | – | 30 | 100 | 1 | 0.028 |
Cl− | 250 | 250 | 1,000 | 4 | 0.111 |
SO42− | 200 | 200 | 400 | 4 | 0.111 |
NO3− | 45 | 45 | NX | 5 | 0.139 |
PO43− | 0.3 | – | – | 3 | 0.083 |
∑Wa=36 | ∑Wr=1.000 |
Parameters . | WHO standards (2006) . | BIS standards (2012) . | Weight (Wa) . | Relative weight (Wr) . | |
---|---|---|---|---|---|
Desirable . | Permissible . | ||||
pH | 6.5–8.5 | 6.5–8.5 | NX | 4 | 0.111 |
EC | 2,000 | 2,000 | NX | 3 | 0.083 |
TDS | 1,000 | 500 | 2,000 | 4 | 0.111 |
TH | – | 200 | 600 | 3 | 0.083 |
TA | 200 | 200 | 600 | 2 | 0.056 |
DO | 5 | ≥5 | NX | 2 | 0.056 |
Ca2+ | – | 75 | 200 | 1 | 0.028 |
Mg2+ | – | 30 | 100 | 1 | 0.028 |
Cl− | 250 | 250 | 1,000 | 4 | 0.111 |
SO42− | 200 | 200 | 400 | 4 | 0.111 |
NO3− | 45 | 45 | NX | 5 | 0.139 |
PO43− | 0.3 | – | – | 3 | 0.083 |
∑Wa=36 | ∑Wr=1.000 |
*NX=No relaxation, i.e., values/concentrations within the desirable limit are only acceptable; no values/concentrations are permitted/acceptable beyond the recommended standards.
The computed WQI was categorized owing to the suggested categorization of water quality (Singh et al. 2018) as: excellent (WQI < 25), good (WQI: 25–50), moderate (WQI: 51–75), poor (WQI: 76–100), and very poor (WQI > 100).
Eutrophication index (EI)
Statistical methods
In this study, multivariate statistical analysis such as Pearson's correlation and principal component analysis (PCA) were carried out to assess the potential sources, relative behaviour and the interdependency among the water quality parameters in two studied lake waters. The execution of multivariate statistical analysis is divided into two parts, namely, the correlation analysis and the PCA (rotated component matrix that supports the Varimax method with Kaiser normalization) calculation. With the help of correlation matrix table, the interdependence between water quality parameters had been analyzed for two seasons (i.e., pre-monsoon and post-monsoon) of two lakes respectively. The PCA method had been calculated under three components, namely, PC1, PC2, and PC3. After performing the PCA method, the water quality parameters that had come under the respective components mainly portrayed the loadings or weightings associated with them. For both seasons of Fateh Sagar Lake water, the rotated component matrix table showed that the rotation converged in four iterations. On the other hand, for the pre-monsoon season of Pichola Lake water, the rotated component matrix table highlighted that the rotation converged in seven iterations. Similarly, for post-monsoon season of Pichola Lake water, the rotated component matrix table revealed that the rotation converged in seventeen iterations.
RESULTS AND DISCUSSION
Assessment of physicochemical properties of Pichola and Fateh Sagar Lake
The analysis of limnological parameters of the two monitored lakes has provided a considerable insight into the water quality of the lakes. The recorded surface water temperature was found to be between 20.10–32.30 °C and 20.10–29.70 °C in the pre-monsoon season and 18.6–22.4 °C and 18.5–24.7 °C in the post-monsoon season in Pichola Lake and Fateh Sagar Lake respectively. The pH values of collected lake water samples were found to be slightly alkaline in both the seasons. The EC is an indirect measure of TDS and it has showed seasonal fluctuations, with higher values in the post-monsoon season (EC 521–732 μS cm−1, 572–786 μS cm−1; TDS 276–389 mg/L; 283–396 mg/L) as compared to pre-monsoon season (EC 510–699 μS cm−1, 545–645 μS cm−1; TDS 270–344 mg/L, 226–337 mg/L) in Pichola Lake and Fateh Sagar Lake respectively. It can be correlated to influx of surface runoff during the brief monsoon season. The TA of the lake water again revealed higher values (Pichola Lake 117–198 mg/L; Fateh Sagar 137–159 mg/L) in the post-monsoon study period as compared to (Pichola Lake 103–179 mg/L; Fateh Sagar 114–150 mg/L) in the pre-monsoon study period. Higher values in post-monsoon can again be attributed to the surface runoff, which brings organic matter, and with the decomposition of this organic matter, carbon dioxide is released resulting in the addition of carbonate and bicarbonate, which also increases the alkalinity value. TH, Cl−, SO42−, Ca2+, Mg2+ showed slightly lower values in post-monsoon analysis (Table 2) in comparison to pre-monsoon in both the lakes. It is mostly because of the dilution effect caused by infusion of rainwater and higher rates of evaporation during summer (pre-monsoon season). Concentration of chlorides in water is also an indicator of pollution since their main source is discharge of domestic sewage. DO is one of the most important parameters, and it has revealed limited seasonal fluctuations, ranging from 5.1–8.1 mg/L and 4.6–7.2 mg/L in pre-monsoon study period and 5.1–8.7 mg/L and 4.9–7.6 mg/L in the post-monsoon study period in Pichola Lake and Fateh Sagar respectively. BOD too showed a similar trend (Table 2) with limited seasonal variation with slightly higher difference in Fateh Sagar as compared to Pichola Lake but COD was noted to be lower in post-monsoon season (Pichola Lake 17.4–31.5 mg/L; Fateh Sagar- 28.5–50.8 mg/L) as compared to pre-monsoon (Pichola Lake 41.6–76 mg/L; Fateh Sagar- 35.6–62.2 mg/L). Components of nutrients, i.e. total phosphorus (TP) and total nitrogen (TN) are a major contributor for Trophic State evaluation of any water body. The investigation has revealed higher values of TP in the pre-monsoon study period (0.09–0.21 mg/L) of Pichola Lake than in post-monsoon study (0.08–0.19 mg/L). However, Fateh Sagar Lake showed slightly less fluctuation in pre- (0.0026–0.004 mg/L) and post-monsoon (0.002–0.003 mg/L) study. While TP variation was moderate, TN showed remarkable variation in Pichola Lake (pre-monsoon 3.38–5.51 mg/L; post-monsoon 2.79–4.42 mg/L) and Fateh Sagar Lake (pre-monsoon 2.18–2.91 mg/L; post-monsoon 1.87–2.71 mg/L). Such results correspond to a higher rate of evaporation in the pre-monsoon (mostly summer) and high dilution effect during monsoon period.
Water parameters . | Pichola Lake . | Fateh Sagar Lake . | WHO Standards for Drinking water (2006) . | BIS Standards for Drinking water (2012) . | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Pre-monsoon . | Post-monsoon . | Pre-monsoon . | Post-monsoon . | . | . | . | |||||||||||||
Min . | Max . | Mean . | SD . | Min . | Max . | Mean . | SD . | Min . | Max . | Mean . | SD . | Min . | Max . | Mean . | SD . | Desirable . | Permissible . | ||
Temp. (⁰C) | 20.1 | 32.3 | 24.12 | 3.25 | 18.6 | 22.4 | 20.35 | 1.51 | 20.1 | 29.7 | 23.56 | 3.20149 | 18.5 | 24.7 | 21.9714 | 1.90763 | – | – | – |
pH | 7.7 | 8.67 | 8.206 | 0.34 | 7.2 | 8.6 | 8.00 | 0.39 | 8.01 | 8.78 | 8.38 | 0.27 | 7.84 | 8.3 | 8.09 | 0.16 | 6.5–8.5 | 6.5–8.5 | NX |
EC (μS/cm at 25 ⁰C) | 510 | 699 | 627.70 | 63.23 | 521 | 732 | 620 | 64.26 | 545 | 645 | 591 | 40.42 | 572 | 786 | 652.36 | 93.60 | – | 2,000 | NX |
TDS (mg/L) | 270 | 344 | 313.05 | 29.48 | 276 | 389 | 328.37 | 42.92 | 226 | 337 | 288.43 | 47.12 | 283 | 396 | 333.57 | 39.68 | 1,000 | 200 | 600 |
Turbidity (NTU) | 0.8 | 9.3 | 4.58 | 3.30 | 0.69 | 0.91 | 0.78 | 0.08 | 0.27 | 13 | 2.99 | 4.63 | 0.54 | 1.1 | 0.75 | 0.22 | 5 | 1 | 5 |
TH (mg/L) | 90 | 214 | 155.45 | 43.60 | 76 | 141 | 107.45 | 21.14 | 106 | 180 | 147.29 | 29.89 | 92 | 132 | 110.71 | 15.55 | – | 200 | 600 |
TA (mg/L) | 103 | 179 | 146.45 | 27.71 | 117 | 198 | 144.85 | 28.07 | 114 | 150 | 133.29 | 12.80 | 137 | 159 | 145.39 | 7.28 | 200 | 200 | 600 |
DO (mg/L) | 4.2 | 8.1 | 5.87 | 1.23 | 5.1 | 8.7 | 6.53 | 1.07 | 4.6 | 7.2 | 6.17 | 0.91 | 4.9 | 7.6 | 6.54 | 0.94 | 5 | ≥5 | NX |
BOD (mg/L) | 1.9 | 4.1 | 2.71 | 0.67 | 1.8 | 3.9 | 2.70 | 0.72 | 1.9 | 3.4 | 2.72 | 0.51 | 1.34 | 2.8 | 2.02 | 0.50 | – | – | – |
COD (mg/L) | 41.6 | 76 | 51.62 | 10.39 | 17.4 | 31.5 | 22.71 | 4.13 | 35.6 | 62.2 | 45.84 | 11.52 | 28.5 | 50.8 | 38.57 | 7.33 | – | – | – |
Ca2+ (mg/L) | 31 | 49.5 | 39.27 | 6.20 | 26.2 | 38.1 | 30.38 | 3.82 | 28 | 56 | 42.19 | 10.55 | 23 | 44 | 33.04 | 6.38 | – | 75 | 200 |
Mg2+ (mg/L) | 15.4 | 21.5 | 17.89 | 1.78 | 12.8 | 17.9 | 15.97 | 1.51 | 13.2 | 22.4 | 16.76 | 2.77 | 12.2 | 19 | 15.20 | 2.50 | – | 30 | 100 |
Cl− (mg/L) | 69 | 90.6 | 78.67 | 7.36 | 48 | 81 | 61.33 | 12.13 | 57 | 76 | 67.71 | 6.26 | 42.1 | 64 | 58.44 | 7.46 | 250 | 250 | 1,000 |
SO42− (mg/L) | 34.5 | 65 | 42.53 | 8.88 | 29 | 53 | 40.57 | 7.86 | 26 | 33.3 | 29.73 | 2.40 | 30 | 41 | 36.46 | 3.77 | 200 | 200 | 400 |
TN (mg/L) | 3.38 | 5.51 | 4.39 | 0.72 | 2.79 | 4.42 | 3.58 | 0.60 | 2.18 | 2.98 | 2.63 | 0.33 | 1.87 | 2.71 | 2.38 | 0.35 | 45 | 45 | NX |
TP (mg/L) | 0.09 | 0.21 | 0.17 | 0.04 | 0.08 | 0.19 | 0.14 | 0.03 | 0.0026 | 0.0041 | 0.0033 | 0.0006 | 0.0019 | 0.0031 | 0.0025 | 0.0005 | 0.3 | – | – |
Water parameters . | Pichola Lake . | Fateh Sagar Lake . | WHO Standards for Drinking water (2006) . | BIS Standards for Drinking water (2012) . | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Pre-monsoon . | Post-monsoon . | Pre-monsoon . | Post-monsoon . | . | . | . | |||||||||||||
Min . | Max . | Mean . | SD . | Min . | Max . | Mean . | SD . | Min . | Max . | Mean . | SD . | Min . | Max . | Mean . | SD . | Desirable . | Permissible . | ||
Temp. (⁰C) | 20.1 | 32.3 | 24.12 | 3.25 | 18.6 | 22.4 | 20.35 | 1.51 | 20.1 | 29.7 | 23.56 | 3.20149 | 18.5 | 24.7 | 21.9714 | 1.90763 | – | – | – |
pH | 7.7 | 8.67 | 8.206 | 0.34 | 7.2 | 8.6 | 8.00 | 0.39 | 8.01 | 8.78 | 8.38 | 0.27 | 7.84 | 8.3 | 8.09 | 0.16 | 6.5–8.5 | 6.5–8.5 | NX |
EC (μS/cm at 25 ⁰C) | 510 | 699 | 627.70 | 63.23 | 521 | 732 | 620 | 64.26 | 545 | 645 | 591 | 40.42 | 572 | 786 | 652.36 | 93.60 | – | 2,000 | NX |
TDS (mg/L) | 270 | 344 | 313.05 | 29.48 | 276 | 389 | 328.37 | 42.92 | 226 | 337 | 288.43 | 47.12 | 283 | 396 | 333.57 | 39.68 | 1,000 | 200 | 600 |
Turbidity (NTU) | 0.8 | 9.3 | 4.58 | 3.30 | 0.69 | 0.91 | 0.78 | 0.08 | 0.27 | 13 | 2.99 | 4.63 | 0.54 | 1.1 | 0.75 | 0.22 | 5 | 1 | 5 |
TH (mg/L) | 90 | 214 | 155.45 | 43.60 | 76 | 141 | 107.45 | 21.14 | 106 | 180 | 147.29 | 29.89 | 92 | 132 | 110.71 | 15.55 | – | 200 | 600 |
TA (mg/L) | 103 | 179 | 146.45 | 27.71 | 117 | 198 | 144.85 | 28.07 | 114 | 150 | 133.29 | 12.80 | 137 | 159 | 145.39 | 7.28 | 200 | 200 | 600 |
DO (mg/L) | 4.2 | 8.1 | 5.87 | 1.23 | 5.1 | 8.7 | 6.53 | 1.07 | 4.6 | 7.2 | 6.17 | 0.91 | 4.9 | 7.6 | 6.54 | 0.94 | 5 | ≥5 | NX |
BOD (mg/L) | 1.9 | 4.1 | 2.71 | 0.67 | 1.8 | 3.9 | 2.70 | 0.72 | 1.9 | 3.4 | 2.72 | 0.51 | 1.34 | 2.8 | 2.02 | 0.50 | – | – | – |
COD (mg/L) | 41.6 | 76 | 51.62 | 10.39 | 17.4 | 31.5 | 22.71 | 4.13 | 35.6 | 62.2 | 45.84 | 11.52 | 28.5 | 50.8 | 38.57 | 7.33 | – | – | – |
Ca2+ (mg/L) | 31 | 49.5 | 39.27 | 6.20 | 26.2 | 38.1 | 30.38 | 3.82 | 28 | 56 | 42.19 | 10.55 | 23 | 44 | 33.04 | 6.38 | – | 75 | 200 |
Mg2+ (mg/L) | 15.4 | 21.5 | 17.89 | 1.78 | 12.8 | 17.9 | 15.97 | 1.51 | 13.2 | 22.4 | 16.76 | 2.77 | 12.2 | 19 | 15.20 | 2.50 | – | 30 | 100 |
Cl− (mg/L) | 69 | 90.6 | 78.67 | 7.36 | 48 | 81 | 61.33 | 12.13 | 57 | 76 | 67.71 | 6.26 | 42.1 | 64 | 58.44 | 7.46 | 250 | 250 | 1,000 |
SO42− (mg/L) | 34.5 | 65 | 42.53 | 8.88 | 29 | 53 | 40.57 | 7.86 | 26 | 33.3 | 29.73 | 2.40 | 30 | 41 | 36.46 | 3.77 | 200 | 200 | 400 |
TN (mg/L) | 3.38 | 5.51 | 4.39 | 0.72 | 2.79 | 4.42 | 3.58 | 0.60 | 2.18 | 2.98 | 2.63 | 0.33 | 1.87 | 2.71 | 2.38 | 0.35 | 45 | 45 | NX |
TP (mg/L) | 0.09 | 0.21 | 0.17 | 0.04 | 0.08 | 0.19 | 0.14 | 0.03 | 0.0026 | 0.0041 | 0.0033 | 0.0006 | 0.0019 | 0.0031 | 0.0025 | 0.0005 | 0.3 | – | – |
*NX = No relaxation, i.e., values/concentrations within the desirable limit are only acceptable; no values/concentrations are permitted/acceptable beyond the recommended standards.
Evaluation of lake water quality using WQI
WQI is a mathematical expression in the form of a numerical value taking into account all the measured parameters influencing any water body. The resultant mathematical expression is a consolidated standardization of all the assessed parameters. By aggregating various water parameters, the WQI gives a single figure, which expresses the quality of water (Lumb et al. 2011) thereby providing a comprehensive picture of the studied water body. If computed WQI lies between 0 and 25, the water quality can be termed as ‘excellent’; if it lies between 26 and 50; the quality of water is considered to be ‘good’; if the value lies between 51 and 75 the quality is termed as ‘poor’; 76 to 100 signifies ‘very poor’ quality of water; and >100 value is regarded as ‘unsuitable’ for drinking.
The WQI of Fateh Sagar Lake fared slightly better as the pre-monsoon values ranged between 34.76 and 39.16 and post-monsoon values ranged from 24.05 to 27.69. Even though they fall under ‘good’ quality of water (Figure 2), the post-monsoon expressions had a lower value, with one site (S2) revealing ‘excellent’ quality of water having a value of 24.05. By far, when compared, both the lakes have good quality of water throughout the seasons and can be considered safe for drinking purposes.
Development of organic pollution index (OPI)
The study clearly revealed that OPI values in Pichola Lake are higher than Fateh Sagar Lake. Also, mean OPI values in Pichola Lake in pre-monsoon (2.32) and post-monsoon (2.34) seasons are in a similar range. On the other hand, mean OPI values in Fateh Sagar Lake is remarkably less in post-monsoon (1.31) analysis as compared to pre-monsoon (1.80) studies. This observation can be linked with dilution effect due to the additional influx of water during the monsoon season.
The statistical correlation coefficient (r) value of WQI vs OPI showed higher positive correlation during pre-monsoon (r=0.52 and 0.65 for Pichola Lake and Fateh Sagar Lake respectively) than that of post-monsoon (r=0.18 and 0.37 for Pichola Lake and Fateh Sagar Lake respectively) for both the lake. This is attributed to the lake water receiving significant amount of nutrients as well as organic pollutants via discharge of wastewater from highly populated areas, agricultural croplands, and lake catchment forest areas (Liu et al. 2011; Son et al. 2020).
Classifying lake water using eutrophication index (EI)
The eutrophication index considers the combined influence of COD, DIN and DIP to evaluate the water quality in relation to trophic condition. A higher value of EI indicates higher levels of eutrophication.
The calculated EI of Pichola Lake in all the sampling stations was >0, indicating highly eutrophic conditions with distinct seasonal variability presented as supplementary Fig. S1. Elevated EI was registered in the pre-monsoon season (10.25–18.15) as compared to post-monsoon studies (8.71–13.20). The maximum EI values were recorded for sampling stations S6 (18.15, in pre-monsoon) and S2 (13.20, in post-monsoon), which can be linked to direct discharge of sewage and solid waste disposal at these sites.
The results obtained from Fateh Sagar Lake water samples also revealed a similar trend, with all sampling sites having EI >0 in both the seasons; with higher EI values (8.40–14.47) in the pre-monsoon season compared to the post-monsoon season (6.86–11.70) (Fig. S1). During the pre-monsoon, the EI value was found to be maximum at S4 (14.47) and minimum at S6 (8.40), while in post-monsoon, the maximum and minimum values of EI noted at S6 (11.70) and S2 (6.86) respectively. The lowering of EI values during the post-monsoon can be correlated to the dilution effect due to precipitation and the influx of surface runoff.
There is a positive correlation is observed among OPI and EI in post-monsoon (r = 0.63 in Pichola Lake and r=0.75 in Fateh Sagar Lake) because of significant accumulation of sewerage water into the lake ecosystem resulting in enhancement of organic pollutant load as well as eutrophication status during post-monsoon season (Son et al. 2020). While in pre-monsoon, a positive correlation is noted for Pichola Lake between OPI and EI (r=0.17) and negative correlation (OPI vs EI) is found as r=−0.14 for Fateh Sagar Lake. This distinction may be explained that the received amount of sewerage water from densely populated area and agricultural runoff water is quite higher in Fateh Sagar Lake in comparison to Pichola Lake.
Applications of multivariate statistics
The descriptive statistics such as mean and standard deviations (SDs) of water quality parameters in the Fateh Sagar Lake and Pichola Lake were calculated in two different seasons, i.e. pre-monsoon and post-monsoon, respectively. In this study, multivariate statistical analysis such as Pearson's correlation and PCA were applied (with the help of SPSS statistical software, version 21.0) to evaluate the possible sources, relative behaviour and the interdependency among the water quality parameters in the two studied lake waters.
Correlation analysis of Pichola Lake during pre-monsoon and post-monsoon season
The correlation analysis among 16 water quality parameters related to Pichola Lake during pre- and post-monsoon seasons have been revealed in the correlation matrix table (Table 3). Significantly strong positive correlation is observed among TDS-TN, TP; TA-Cl−, COD-SO42−, TN; Ca2+-Mg2+ during pre-monsoon and TDS-TP; turbidity-SO42− during post-monsoon. This is clearly attributed that the Pichola Lake water quality severely regulated by the anthropogenic activities (Panda et al. 2021) as well as geological processes (Gupta et al. 2016). Moderate positive correlation also seen among pH-TP, pH-Cl−, EC-Temp., TDS-TH, TDS-COD, Turbidity-Mg2+, TH-Ca2+, TH-TP, DO-TP; TN-SO42−, TN-TP in pre-monsoon and TDS-TN, turbidity-TA, TA-BOD, TN-BOD, Ca2+-Cl−, TN-TP during post-monsoon. This may suggest the lake water quality is significantly influenced by the sessional and geochemical weathering of rocks such as dissolution of minerals, soil erosion and surface runoff during rainy season (Gupta et al. 2016).
Variables . | Temp . | pH . | EC . | TDS . | Turbidity . | TH . | TA . | DO . | BOD . | COD . | Ca2+ . | Mg2+ . | Cl− . | SO42− . | TN . | TP . |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a. Pre-monsoon | ||||||||||||||||
Temp | 1 | |||||||||||||||
pH | 0.034 | 1 | ||||||||||||||
EC | 0.704 | 0.478 | 1 | |||||||||||||
TDS | −0.116 | 0.564 | 0.443 | 1 | ||||||||||||
Turbidity | 0.574 | −0.129 | −0.438 | 0.06 | 1 | |||||||||||
TH | 0.458 | 0.5 | −0.11 | 0.66 | −0.027 | 1 | ||||||||||
TA | 0.523 | −0.332 | −0.563 | −0.206 | −0.147 | 0.362 | 1 | |||||||||
DO | 0.4 | 0.509 | −0.066 | 0.353 | 0.18 | 0.399 | 0.343 | 1 | ||||||||
BOD | −0.412 | 0.126 | −0.04 | −0.003 | −0.585 | 0.041 | 0.086 | 0.14 | 1 | |||||||
COD | 0.197 | 0.299 | 0.181 | 0.616 | 0.478 | 0.453 | −0.425 | −0.178 | −0.58 | 1 | ||||||
Ca2+ | 0.131 | 0.373 | −0.042 | 0.362 | −0.483 | 0.659 | 0.618 | 0.534 | 0.56 | −0.326 | 1 | |||||
Mg2+ | 0.135 | −0.253 | −0.072 | −0.235 | 0.619 | −0.38 | −0.539 | −0.566 | −0.705 | 0.558 | 0.94 | 1 | ||||
Cl− | −0.5 | 0.572 | 0.685 | 0.142 | −0.097 | −0.298 | 0.911 | −0.18 | 0.008 | 0.248 | −0.407 | 0.327 | 1 | |||
SO42− | −0.138 | 0.306 | 0.453 | 0.565 | 0.107 | 0.342 | −0.539 | −0.426 | −0.448 | 0.906 | −0.333 | 0.492 | 0.423 | 1 | ||
TN | −0.224 | 0.434 | 0.424 | 0.89 | 0.189 | 0.468 | −0.521 | −0.025 | −0.095 | 0.806 | −0.029 | 0.147 | 0.346 | 0.792 | 1 | |
TP | 0.111 | 0.749 | 0.406 | 0.91 | 0.107 | 0.693 | −0.082 | 0.652 | −0.067 | 0.489 | 0.482 | −0.328 | 0.146 | 0.372 | 0.683 | 1 |
b. Post-monsoon | ||||||||||||||||
Temp | 1 | |||||||||||||||
pH | 0.299 | 1 | ||||||||||||||
EC | −0.005 | 0.339 | 1 | |||||||||||||
TDS | 0.153 | 0.252 | −0.054 | 1 | ||||||||||||
Turbidity | 0.294 | 0.16 | −0.611 | −0.202 | 1 | |||||||||||
TH | −0.427 | −0.129 | −0.116 | 0.073 | −0.463 | 1 | ||||||||||
TA | 0.155 | −0.465 | 0.242 | −0.186 | 0.631 | 0.392 | 1 | |||||||||
DO | 0.123 | −0.021 | 0.364 | 0.123 | −0.561 | 0.537 | 0.497 | 1 | ||||||||
BOD | −0.077 | −0.452 | 0.022 | −0.256 | −0.456 | 0.587 | 0.787 | 0.47 | 1 | |||||||
COD | 0.364 | −0.306 | −0.449 | −0.374 | 0.287 | 0.305 | 0.377 | 0.372 | 0.591 | 1 | ||||||
Ca2+ | −0.452 | −0.15 | −0.145 | −0.648 | 0.508 | −0.352 | −0.463 | −0.636 | −0.202 | −0.088 | 1 | |||||
Mg2+ | 0.432 | −0.164 | 0.081 | −0.064 | −0.174 | −0.101 | 0.343 | 0.43 | −0.11 | 0.194 | −0.468 | 1 | ||||
Cl− | −0.585 | 0.108 | 0.496 | −0.206 | −0.067 | −0.301 | −0.42 | −0.344 | −0.382 | −0.68 | 0.64 | −0.427 | 1 | |||
SO42− | 0.219 | −0.06 | −0.626 | −0.417 | 0.886 | −0.358 | −0.348 | −0.629 | −0.334 | 0.318 | 0.53 | −0.038 | −0.101 | 1 | ||
TN | 0.317 | 0.539 | 0.21 | 0.636 | −0.008 | −0.169 | −0.376 | 0.3 | 0.629 | −0.352 | −0.517 | 0.384 | −0.059 | −0.235 | 1 | |
TP | 0.431 | 0.414 | 0.104 | 0.863 | −0.262 | 0.076 | 0.011 | 0.382 | −0.171 | −0.194 | −0.849 | 0.24 | −0.436 | −0.461 | 0.772 | 1 |
Variables . | Temp . | pH . | EC . | TDS . | Turbidity . | TH . | TA . | DO . | BOD . | COD . | Ca2+ . | Mg2+ . | Cl− . | SO42− . | TN . | TP . |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a. Pre-monsoon | ||||||||||||||||
Temp | 1 | |||||||||||||||
pH | 0.034 | 1 | ||||||||||||||
EC | 0.704 | 0.478 | 1 | |||||||||||||
TDS | −0.116 | 0.564 | 0.443 | 1 | ||||||||||||
Turbidity | 0.574 | −0.129 | −0.438 | 0.06 | 1 | |||||||||||
TH | 0.458 | 0.5 | −0.11 | 0.66 | −0.027 | 1 | ||||||||||
TA | 0.523 | −0.332 | −0.563 | −0.206 | −0.147 | 0.362 | 1 | |||||||||
DO | 0.4 | 0.509 | −0.066 | 0.353 | 0.18 | 0.399 | 0.343 | 1 | ||||||||
BOD | −0.412 | 0.126 | −0.04 | −0.003 | −0.585 | 0.041 | 0.086 | 0.14 | 1 | |||||||
COD | 0.197 | 0.299 | 0.181 | 0.616 | 0.478 | 0.453 | −0.425 | −0.178 | −0.58 | 1 | ||||||
Ca2+ | 0.131 | 0.373 | −0.042 | 0.362 | −0.483 | 0.659 | 0.618 | 0.534 | 0.56 | −0.326 | 1 | |||||
Mg2+ | 0.135 | −0.253 | −0.072 | −0.235 | 0.619 | −0.38 | −0.539 | −0.566 | −0.705 | 0.558 | 0.94 | 1 | ||||
Cl− | −0.5 | 0.572 | 0.685 | 0.142 | −0.097 | −0.298 | 0.911 | −0.18 | 0.008 | 0.248 | −0.407 | 0.327 | 1 | |||
SO42− | −0.138 | 0.306 | 0.453 | 0.565 | 0.107 | 0.342 | −0.539 | −0.426 | −0.448 | 0.906 | −0.333 | 0.492 | 0.423 | 1 | ||
TN | −0.224 | 0.434 | 0.424 | 0.89 | 0.189 | 0.468 | −0.521 | −0.025 | −0.095 | 0.806 | −0.029 | 0.147 | 0.346 | 0.792 | 1 | |
TP | 0.111 | 0.749 | 0.406 | 0.91 | 0.107 | 0.693 | −0.082 | 0.652 | −0.067 | 0.489 | 0.482 | −0.328 | 0.146 | 0.372 | 0.683 | 1 |
b. Post-monsoon | ||||||||||||||||
Temp | 1 | |||||||||||||||
pH | 0.299 | 1 | ||||||||||||||
EC | −0.005 | 0.339 | 1 | |||||||||||||
TDS | 0.153 | 0.252 | −0.054 | 1 | ||||||||||||
Turbidity | 0.294 | 0.16 | −0.611 | −0.202 | 1 | |||||||||||
TH | −0.427 | −0.129 | −0.116 | 0.073 | −0.463 | 1 | ||||||||||
TA | 0.155 | −0.465 | 0.242 | −0.186 | 0.631 | 0.392 | 1 | |||||||||
DO | 0.123 | −0.021 | 0.364 | 0.123 | −0.561 | 0.537 | 0.497 | 1 | ||||||||
BOD | −0.077 | −0.452 | 0.022 | −0.256 | −0.456 | 0.587 | 0.787 | 0.47 | 1 | |||||||
COD | 0.364 | −0.306 | −0.449 | −0.374 | 0.287 | 0.305 | 0.377 | 0.372 | 0.591 | 1 | ||||||
Ca2+ | −0.452 | −0.15 | −0.145 | −0.648 | 0.508 | −0.352 | −0.463 | −0.636 | −0.202 | −0.088 | 1 | |||||
Mg2+ | 0.432 | −0.164 | 0.081 | −0.064 | −0.174 | −0.101 | 0.343 | 0.43 | −0.11 | 0.194 | −0.468 | 1 | ||||
Cl− | −0.585 | 0.108 | 0.496 | −0.206 | −0.067 | −0.301 | −0.42 | −0.344 | −0.382 | −0.68 | 0.64 | −0.427 | 1 | |||
SO42− | 0.219 | −0.06 | −0.626 | −0.417 | 0.886 | −0.358 | −0.348 | −0.629 | −0.334 | 0.318 | 0.53 | −0.038 | −0.101 | 1 | ||
TN | 0.317 | 0.539 | 0.21 | 0.636 | −0.008 | −0.169 | −0.376 | 0.3 | 0.629 | −0.352 | −0.517 | 0.384 | −0.059 | −0.235 | 1 | |
TP | 0.431 | 0.414 | 0.104 | 0.863 | −0.262 | 0.076 | 0.011 | 0.382 | −0.171 | −0.194 | −0.849 | 0.24 | −0.436 | −0.461 | 0.772 | 1 |
Values in bold are different from 0 with a significance level alpha=0.05.
Correlation analysis of Fateh Sagar Lake during pre-monsoon and post-monsoon season
The correlation coefficient among the measured variables in Fateh Sagar Lake water during the pre- and post-monsoon season is represented in Table 4. The results display that the strong positive correlation is present among temp.-turbidity; EC-SO42−; TDS-TN, TP; TH-COD, Ca2+; TN-TP in pre-monsoon and pH-COD; TDS-TP; turbidity-Mg2+; TA-Mg2+; TN-TP during post-monsoon. This may explain that the Fateh Sagar Lake quality is highly influenced by the surface influx from lake catchment area, geochemical weathering, and several anthropogenic activities (mainly sewage water discharges) (Gupta et al. 2016). Other significant positive correlation is noticed between pH-DO; EC-turbidity, Mg2+; turbidity-SO42−; TA-Cl−; Mg2+-SO42−; Cl−-TN in pre-monsoon and Temp.-TDS, BOD, SO42−, TN, TP; pH-Cl−; EC-Cl−; TDS-TN; turbidity-TN; TH-Mg2+; SO42−-BOD, COD, Mg2+ in post-monsoon. Generally, Cl− in lake water is derived from non-lithogenic sources. Higher positive correlation coefficient of measured variables in lake water indicates that the lake water quality is severally polluted by the anthropogenic activities such return flows from irrigated land, sewerage discharge and deposition of animal waste and geochemical weathering of rocks (Marghade et al. 2021).
Variables . | Temp . | pH . | EC . | TDS . | Turbidity . | TH . | TA . | DO . | BOD . | COD . | Ca2+ . | Mg2+ . | Cl− . | SO42− . | TN . | TP . |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a. Pre-monsoon | ||||||||||||||||
Temp | 1 | |||||||||||||||
pH | 0.467 | 1 | ||||||||||||||
EC | 0.448 | −0.088 | 1 | |||||||||||||
TDS | −0.005 | 0.21 | 0.424 | 1 | ||||||||||||
Turbidity | 0.829 | 0.548 | 0.698 | 0.474 | 1 | |||||||||||
TH | 0.282 | 0.02 | −0.056 | −0.62 | 0.069 | 1 | ||||||||||
TA | −0.721 | −0.109 | −0.367 | 0.468 | −0.386 | −0.241 | 1 | |||||||||
DO | 0.307 | 0.695 | −0.425 | −0.049 | 0.227 | 0.424 | 0.184 | 1 | ||||||||
BOD | −0.103 | 0.069 | −0.561 | −0.243 | −0.379 | −0.462 | −0.198 | −0.155 | 1 | |||||||
COD | −0.562 | −0.184 | −0.216 | 0.533 | −0.367 | 0.925 | 0.519 | −0.358 | 0.468 | 1 | ||||||
Ca2+ | −0.013 | 0.118 | −0.203 | −0.259 | 0.008 | 0.84 | 0.292 | 0.598 | −0.58 | −0.655 | 1 | |||||
Mg2+ | 0.086 | −0.192 | 0.661 | 0.12 | 0.228 | −0.342 | −0.487 | −0.81 | 0.082 | 0.091 | −0.589 | 1 | ||||
Cl− | 0.578 | 0.031 | −0.087 | −0.683 | 0.047 | 0.336 | 0.799 | 0.13 | 0.297 | −0.455 | −0.148 | −0.045 | 1 | |||
SO42− | 0.325 | 0.024 | 0.898 | 0.55 | 0.601 | −0.415 | −0.301 | −0.459 | −0.34 | 0.114 | −0.52 | 0.706 | −0.106 | 1 | ||
TN | 0.025 | 0.228 | 0.435 | 0.998 | 0.497 | −0.633 | 0.428 | −0.068 | −0.206 | 0.53 | −0.287 | 0.158 | 0.669 | 0.561 | 1 | |
TP | 0.141 | 0.301 | 0.337 | 0.970 | 0.536 | −0.587 | 0.407 | 0.096 | −0.152 | 0.495 | −0.247 | −0.016 | −0.549 | 0.457 | 0.971 | 1 |
b. Post-monsoon | ||||||||||||||||
Temp | 1 | |||||||||||||||
pH | 0.246 | 1 | ||||||||||||||
EC | 0.114 | −0.381 | 1 | |||||||||||||
TDS | 0.72 | 0.337 | 0.229 | 1 | ||||||||||||
Turbidity | 0.474 | −0.279 | 0.035 | 0.458 | 1 | |||||||||||
TH | 0.29 | −0.07 | 0.07 | −0.01 | −0.481 | 1 | ||||||||||
TA | −0.019 | 0.277 | 0.252 | −0.193 | −0.843 | 0.709 | 1 | |||||||||
DO | −0.165 | 0.477 | 0.359 | 0.309 | −0.291 | −0.355 | 0.182 | 1 | ||||||||
BOD | 0.748 | 0.244 | −0.439 | 0.27 | 0.486 | 0.088 | −0.268 | −0.436 | 1 | |||||||
COD | 0.331 | 0.898 | −0.14 | 0.42 | −0.116 | −0.246 | 0.151 | 0.566 | 0.293 | 1 | ||||||
Ca2+ | −0.351 | −0.833 | 0.599 | −0.106 | 0.284 | −0.182 | −0.29 | −0.109 | −0.588 | −0.744 | 1 | |||||
Mg2+ | 0.294 | −0.222 | 0.067 | 0.274 | 0.855 | 0.671 | 0.824 | 0.006 | 0.442 | 0.102 | 0.174 | 1 | ||||
Cl− | 0.029 | 0.771 | 0.623 | −0.034 | −0.542 | 0.335 | 0.479 | −0.003 | 0.155 | 0.436 | −0.767 | −0.656 | 1 | |||
SO42− | 0.683 | 0.338 | 0.181 | 0.449 | 0.401 | −0.221 | −0.134 | 0.288 | 0.637 | 0.657 | −0.416 | 0.618 | −0.191 | 1 | ||
TN | 0.685 | −0.293 | 0.446 | 0.761 | 0.687 | 0.117 | −0.319 | −0.181 | 0.239 | −0.199 | 0.385 | 0.375 | −0.449 | 0.247 | 1 | |
TP | 0.688 | 0.215 | 0.427 | 0.969 | 0.52 | −0.109 | −0.221 | 0.371 | 0.187 | 0.376 | 0.053 | 0.381 | −0.232 | 0.515 | 0.804 | 1 |
Variables . | Temp . | pH . | EC . | TDS . | Turbidity . | TH . | TA . | DO . | BOD . | COD . | Ca2+ . | Mg2+ . | Cl− . | SO42− . | TN . | TP . |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a. Pre-monsoon | ||||||||||||||||
Temp | 1 | |||||||||||||||
pH | 0.467 | 1 | ||||||||||||||
EC | 0.448 | −0.088 | 1 | |||||||||||||
TDS | −0.005 | 0.21 | 0.424 | 1 | ||||||||||||
Turbidity | 0.829 | 0.548 | 0.698 | 0.474 | 1 | |||||||||||
TH | 0.282 | 0.02 | −0.056 | −0.62 | 0.069 | 1 | ||||||||||
TA | −0.721 | −0.109 | −0.367 | 0.468 | −0.386 | −0.241 | 1 | |||||||||
DO | 0.307 | 0.695 | −0.425 | −0.049 | 0.227 | 0.424 | 0.184 | 1 | ||||||||
BOD | −0.103 | 0.069 | −0.561 | −0.243 | −0.379 | −0.462 | −0.198 | −0.155 | 1 | |||||||
COD | −0.562 | −0.184 | −0.216 | 0.533 | −0.367 | 0.925 | 0.519 | −0.358 | 0.468 | 1 | ||||||
Ca2+ | −0.013 | 0.118 | −0.203 | −0.259 | 0.008 | 0.84 | 0.292 | 0.598 | −0.58 | −0.655 | 1 | |||||
Mg2+ | 0.086 | −0.192 | 0.661 | 0.12 | 0.228 | −0.342 | −0.487 | −0.81 | 0.082 | 0.091 | −0.589 | 1 | ||||
Cl− | 0.578 | 0.031 | −0.087 | −0.683 | 0.047 | 0.336 | 0.799 | 0.13 | 0.297 | −0.455 | −0.148 | −0.045 | 1 | |||
SO42− | 0.325 | 0.024 | 0.898 | 0.55 | 0.601 | −0.415 | −0.301 | −0.459 | −0.34 | 0.114 | −0.52 | 0.706 | −0.106 | 1 | ||
TN | 0.025 | 0.228 | 0.435 | 0.998 | 0.497 | −0.633 | 0.428 | −0.068 | −0.206 | 0.53 | −0.287 | 0.158 | 0.669 | 0.561 | 1 | |
TP | 0.141 | 0.301 | 0.337 | 0.970 | 0.536 | −0.587 | 0.407 | 0.096 | −0.152 | 0.495 | −0.247 | −0.016 | −0.549 | 0.457 | 0.971 | 1 |
b. Post-monsoon | ||||||||||||||||
Temp | 1 | |||||||||||||||
pH | 0.246 | 1 | ||||||||||||||
EC | 0.114 | −0.381 | 1 | |||||||||||||
TDS | 0.72 | 0.337 | 0.229 | 1 | ||||||||||||
Turbidity | 0.474 | −0.279 | 0.035 | 0.458 | 1 | |||||||||||
TH | 0.29 | −0.07 | 0.07 | −0.01 | −0.481 | 1 | ||||||||||
TA | −0.019 | 0.277 | 0.252 | −0.193 | −0.843 | 0.709 | 1 | |||||||||
DO | −0.165 | 0.477 | 0.359 | 0.309 | −0.291 | −0.355 | 0.182 | 1 | ||||||||
BOD | 0.748 | 0.244 | −0.439 | 0.27 | 0.486 | 0.088 | −0.268 | −0.436 | 1 | |||||||
COD | 0.331 | 0.898 | −0.14 | 0.42 | −0.116 | −0.246 | 0.151 | 0.566 | 0.293 | 1 | ||||||
Ca2+ | −0.351 | −0.833 | 0.599 | −0.106 | 0.284 | −0.182 | −0.29 | −0.109 | −0.588 | −0.744 | 1 | |||||
Mg2+ | 0.294 | −0.222 | 0.067 | 0.274 | 0.855 | 0.671 | 0.824 | 0.006 | 0.442 | 0.102 | 0.174 | 1 | ||||
Cl− | 0.029 | 0.771 | 0.623 | −0.034 | −0.542 | 0.335 | 0.479 | −0.003 | 0.155 | 0.436 | −0.767 | −0.656 | 1 | |||
SO42− | 0.683 | 0.338 | 0.181 | 0.449 | 0.401 | −0.221 | −0.134 | 0.288 | 0.637 | 0.657 | −0.416 | 0.618 | −0.191 | 1 | ||
TN | 0.685 | −0.293 | 0.446 | 0.761 | 0.687 | 0.117 | −0.319 | −0.181 | 0.239 | −0.199 | 0.385 | 0.375 | −0.449 | 0.247 | 1 | |
TP | 0.688 | 0.215 | 0.427 | 0.969 | 0.52 | −0.109 | −0.221 | 0.371 | 0.187 | 0.376 | 0.053 | 0.381 | −0.232 | 0.515 | 0.804 | 1 |
Values in bold are different from 0 with a significance level alpha=0.05.
PCA
PCA is recognized as a multivariate statistical method, which reproduces the common relationship between the measured variables through implementing factor(s). The geological interpretation of factors provides an insight into the major hydrogeochemical procedure, which controls the distribution of measured variables (Gupta et al. 2016).
PCA calculation of Pichola Lake during pre-monsoon and post-monsoon season
The results of PCA account for 82.60% and 74.56% of the total variation for the measured variables during pre-monsoon and post-monsoon season respectively (Table 5). PC1 indicated 30.86% variance of the total variance and comprises pH (0.751), DO (0.536), TH (0.829), TDS (0.924), TN (0.738), and TP (0.967), which are predominantly influenced by the anthropogenic activities. PC2 (26.19% variance) registered higher loadings for BOD (−0.816), COD (0.774), SO42− (0.599), Ca2+ (−0.745), Mg2+ (0.912), and turbidity (0.775), which indicates that the lake water is significantly influenced by the geological weathering with limited anthropogenic impacts. PC3 (25.55% of total variance) encompasses four water parameters viz. temperature (−0.838), EC (0.815), TA (−0.855), and Cl− (0.860), which can be linked to the seasonal influences on lake water and influx of surface runoff from the lake catchment areas.
Rotated Component Matrix for Pichola Lake . | Rotated Component Matrix for Fateh Sagar Lake . | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Variables . | Component (Pre-monsoon) . | Component (Post-monsoon) . | Component (Pre-monsoon) . | Component (Post-monsoon) . | ||||||||
PC1 . | PC2 . | PC3 . | PC1 . | PC2 . | PC3 . | PC1 . | PC2 . | PC3 . | PC1 . | PC2 . | PC3 . | |
Temp | 0.154 | 0.369 | −0.838 | −0.187 | 0.363 | 0.703 | −0.295 | 0.83 | 0.161 | 0.716 | 0.314 | 0.288 |
pH | 0.751 | −0.155 | 0.278 | −0.219 | 0.636 | −0.136 | 0.156 | 0.377 | 0.452 | 0.271 | 0.859 | −0.284 |
EC | 0.324 | −0.156 | 0.815 | 0.479 | 0.307 | −0.505 | 0.151 | 0.836 | −0.319 | 0.548 | −0.677 | −0.308 |
TDS | 0.924 | 0.056 | 0.191 | 0.147 | 0.768 | 0.085 | 0.943 | 0.315 | −0.078 | 0.897 | 0.107 | 0.116 |
Turbidity | 0.049 | 0.775 | −0.348 | −0.927 | −0.125 | 0.259 | 0.192 | 0.945 | 0.168 | 0.374 | −0.192 | 0.887 |
TH | 0.829 | −0.032 | −0.383 | 0.641 | −0.223 | 0.127 | −0.643 | 0.13 | 0.659 | −0.033 | 0.044 | −0.499 |
TA | −0.051 | −0.366 | −0.855 | 0.749 | −0.33 | 0.347 | 0.717 | −0.566 | 0.325 | −0.021 | 0.166 | −0.887 |
DO | 0.536 | −0.348 | −0.453 | 0.765 | 0.179 | 0.318 | 0.007 | 0.034 | 0.893 | 0.474 | 0.087 | −0.457 |
BOD | −0.012 | −0.816 | 0.112 | 0.685 | −0.584 | 0.271 | −0.167 | −0.441 | −0.441 | 0.19 | 0.596 | 0.62 |
COD | 0.568 | 0.774 | 0.184 | 0.063 | −0.509 | 0.77 | 0.668 | −0.462 | −0.517 | 0.489 | 0.748 | −0.15 |
Ca2+ | 0.521 | −0.745 | −0.393 | −0.59 | −0.584 | −0.521 | −0.17 | −0.066 | 0.885 | −0.037 | −0.983 | 0.097 |
Mg2+ | −0.32 | 0.912 | 0.23 | 0.18 | 0.234 | 0.507 | −0.051 | 0.411 | −0.795 | 0.33 | −0.109 | 0.853 |
Cl− | 0.106 | 0.121 | 0.86 | −0.169 | −0.035 | −0.947 | −0.817 | 0.191 | −0.076 | −0.226 | 0.78 | −0.423 |
SO42− | 0.474 | 0.599 | 0.487 | −0.839 | −0.36 | 0.287 | 0.314 | 0.729 | −0.514 | 0.656 | 0.388 | 0.344 |
TN | 0.738 | 0.348 | 0.416 | −0.056 | 0.928 | 0.092 | 0.93 | 0.337 | −0.105 | 0.7 | −0.391 | 0.341 |
TP | 0.967 | −0.023 | 0.024 | 0.268 | 0.859 | 0.325 | 0.896 | 0.337 | 0.011 | 0.956 | −0.054 | 0.137 |
Eigenvalue | 4.937 | 4.19 | 4.089 | 4.411 | 4.15 | 3.368 | 4.93 | 4.24 | 3.86 | 4.33 | 4.19 | 3.88 |
Variability (%) | 30.859 | 26.186 | 25.554 | 27.57 | 25.939 | 21.051 | 30.80 | 26.48 | 24.13 | 27.05 | 26.17 | 24.26 |
Cumulative % | 30.859 | 57.045 | 82.599 | 27.57 | 53.509 | 74.56 | 30.80 | 57.28 | 81.42 | 27.05 | 53.22 | 77.48 |
Rotated Component Matrix for Pichola Lake . | Rotated Component Matrix for Fateh Sagar Lake . | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Variables . | Component (Pre-monsoon) . | Component (Post-monsoon) . | Component (Pre-monsoon) . | Component (Post-monsoon) . | ||||||||
PC1 . | PC2 . | PC3 . | PC1 . | PC2 . | PC3 . | PC1 . | PC2 . | PC3 . | PC1 . | PC2 . | PC3 . | |
Temp | 0.154 | 0.369 | −0.838 | −0.187 | 0.363 | 0.703 | −0.295 | 0.83 | 0.161 | 0.716 | 0.314 | 0.288 |
pH | 0.751 | −0.155 | 0.278 | −0.219 | 0.636 | −0.136 | 0.156 | 0.377 | 0.452 | 0.271 | 0.859 | −0.284 |
EC | 0.324 | −0.156 | 0.815 | 0.479 | 0.307 | −0.505 | 0.151 | 0.836 | −0.319 | 0.548 | −0.677 | −0.308 |
TDS | 0.924 | 0.056 | 0.191 | 0.147 | 0.768 | 0.085 | 0.943 | 0.315 | −0.078 | 0.897 | 0.107 | 0.116 |
Turbidity | 0.049 | 0.775 | −0.348 | −0.927 | −0.125 | 0.259 | 0.192 | 0.945 | 0.168 | 0.374 | −0.192 | 0.887 |
TH | 0.829 | −0.032 | −0.383 | 0.641 | −0.223 | 0.127 | −0.643 | 0.13 | 0.659 | −0.033 | 0.044 | −0.499 |
TA | −0.051 | −0.366 | −0.855 | 0.749 | −0.33 | 0.347 | 0.717 | −0.566 | 0.325 | −0.021 | 0.166 | −0.887 |
DO | 0.536 | −0.348 | −0.453 | 0.765 | 0.179 | 0.318 | 0.007 | 0.034 | 0.893 | 0.474 | 0.087 | −0.457 |
BOD | −0.012 | −0.816 | 0.112 | 0.685 | −0.584 | 0.271 | −0.167 | −0.441 | −0.441 | 0.19 | 0.596 | 0.62 |
COD | 0.568 | 0.774 | 0.184 | 0.063 | −0.509 | 0.77 | 0.668 | −0.462 | −0.517 | 0.489 | 0.748 | −0.15 |
Ca2+ | 0.521 | −0.745 | −0.393 | −0.59 | −0.584 | −0.521 | −0.17 | −0.066 | 0.885 | −0.037 | −0.983 | 0.097 |
Mg2+ | −0.32 | 0.912 | 0.23 | 0.18 | 0.234 | 0.507 | −0.051 | 0.411 | −0.795 | 0.33 | −0.109 | 0.853 |
Cl− | 0.106 | 0.121 | 0.86 | −0.169 | −0.035 | −0.947 | −0.817 | 0.191 | −0.076 | −0.226 | 0.78 | −0.423 |
SO42− | 0.474 | 0.599 | 0.487 | −0.839 | −0.36 | 0.287 | 0.314 | 0.729 | −0.514 | 0.656 | 0.388 | 0.344 |
TN | 0.738 | 0.348 | 0.416 | −0.056 | 0.928 | 0.092 | 0.93 | 0.337 | −0.105 | 0.7 | −0.391 | 0.341 |
TP | 0.967 | −0.023 | 0.024 | 0.268 | 0.859 | 0.325 | 0.896 | 0.337 | 0.011 | 0.956 | −0.054 | 0.137 |
Eigenvalue | 4.937 | 4.19 | 4.089 | 4.411 | 4.15 | 3.368 | 4.93 | 4.24 | 3.86 | 4.33 | 4.19 | 3.88 |
Variability (%) | 30.859 | 26.186 | 25.554 | 27.57 | 25.939 | 21.051 | 30.80 | 26.48 | 24.13 | 27.05 | 26.17 | 24.26 |
Cumulative % | 30.859 | 57.045 | 82.599 | 27.57 | 53.509 | 74.56 | 30.80 | 57.28 | 81.42 | 27.05 | 53.22 | 77.48 |
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
The PCA results of post-monsoon shows PC1 (27.57 of variance) is strongly associated with TA (0.749), DO (0.765), BOD (0.685), SO42− (−0.839), Ca2+ (−0.590), turbidity (−0.927), and TH (0.641) and corresponded to anthropogenic activities and lithological inputs. The extracted PC2 (25.94% of variance) is strongly associated with pH (0.636), TDS (0.768), TN (0.928), and TP (0.859) which is due to the geogenic contributions. PC3 accounts for 21.05% variance and is dominated by temperature (0.703), conductivity (−0.505), COD (0.770), Cl− (−0.947), and Mg2+ (0.507) and can be linked to seasonal influences such as weathering, erosion and surface influx from near lake catchment area.
PCA calculation of Fateh Sagar Lake during pre-monsoon and post-monsoon season
The extracted PCA result accounts 81.42% and 77.48% of the total variation for the measured variables of Fateh Sagar Lake water during pre- and post-monsoon season, respectively (Table 5). PC1 (30.80% of variance) have higher loadings for TA (0.717), COD (0.668), Cl (−0.817), TDS (0.943), TN (0.930), and TP (0.896) and that mainly attributed due anthropogenic activities. While PC2 (26.48% of variance) consists of Temperature (0.830), EC (0.836), BOD (−0.441), SO42− (0.729), and turbidity (0.945), those are majorly associated with seasonal influences. Finally, PC3 explain 24.13% of total variance and strongly loaded with pH (0.452), DO (0.893), Ca2+ (0.885), Mg2+ (−0.795), and TH (0.659), which may be explained due geogenic contribution and limited anthropogenic inputs.
During the post-monsoon, the variability of PC1, PC2, and PC3 accounts for 27.05%, 26.17%, and 24.26% respectively. PC1 strongly loaded with temperature (0.716), DO (0.474), SO42− (0.656), TDS (0.897), TN (0.700), and TP (0.956) which majorly attributed due to anthropogenic activities and surface influx from nearby agricultural lands. PC2 has shown the higher loading for pH (0.859), EC (−0.677), COD (0.748), Cl− (0.780), and Ca2+ (−0.983), mainly influenced by the geogenic inputs with little anthropogenic sources. PC3 explains for TA (−0.887), BOD (0.620), Mg2+ (0.853), turbidity (0.887), and TH (−0.499) which is majorly influenced by the seasonal impacts. Thus, the results from PCA and correlation studies are well defined for the various pollution sources and consistent with the similar source identification.
Counteractive policy recommendations to prevent degradation water quality for sustainable management of Pichola Lake and Fateh Sagar Lake
Udaipur, the sixth-biggest city in Rajasthan in India, is significantly dependent on its lake system, which is considered the life source of the city in terms of surface water resources, tourism industry growth and ecosystem at large. Hence the water quality analysis of these lakes has become very important not only to examine the pollution level, but also to decide the appropriate measures, policy recommendations, and the type of water/wastewater treatment facilities required to reduce the pollution and better management of urban lakes.
The calculated OPI values of Pichola Lake ranged from 1.62 to 3.08 in the pre-monsoon and 1.83 and 2.87 in post-monsoon seasons respectively. The pre-monsoon analysis revealed the majority of the sampling sites showed the beginning of mild pollution to contaminated levels with the exception of one site, which showed moderate (S4: 3.08) pollution level. However, in the post-monsoon analysis, the majority of the sampling sites showed mild levels of contamination while rest (S5, S8, S9) were beginning to be contaminated. The OPI values of Fateh Sagar Lake had ranged between 1.39 and 2 in the pre-monsoon and between 0.99 and 1.52 in the post-monsoon season. The analyzed results had projected the beginning of contamination levels during both the seasons with just an exception of site S2 in the post-monsoon analysis, which registered slightly better and revealed uncontaminated characteristics.
With this backdrop, while analyzing the possible reasons behind occurrence of such organic pollution, it was found that more than 1,000 toilets were directly linked to the Lake Pichola. Subsequently, a large quantity of human excretion-based organic sewage has been flowing directly from those toilets to the lake over the years. In addition to that, encroachments have posed a major problem to Lake Pichola's water quality condition, as several areas around the lake that were once uninhabited have crowded with residing people during recent times. Further, both solid and liquid waste materials have been mixing into the lake water, mainly outflowing from the large number of hotel buildings and houses located near the lake area. Moreover, erosion of soil from the adjacent areas of the lake has been creating deposit of sediments within the Pichola Lake, thereby degrading its water quality (Dutta 2017). Besides that, the old and damaged manholes, drainage outlets and leaking sewer lines had particularly caused the expose of domestic organic wastes into the lake water. The various points of leakage sewer lines had been creating the groundwater pollution in Udaipur as well. Also, the sewage and wastewater flowing in some open drains were leaking through the perforated old walls of the lake, thereby contaminating the lake water (Pillai 2020).
Apart from organic pollution due to domestic waste and wastewater disposal, Pichola Lake and Fateh Sagar Lake were affected by the discharge of urban wastewater and tourist activities as well. In this regard, the disposal of urban wastewater were not sufficiently monitored and regulated over the years. The small scale industries (SSIs) located particularly in the residential areas had often found disposing their waste materials (mostly untreated) into the sewerage. This had particularly posed problem by affecting the performance of the sewage treatment plants.
So the execution of some of the policy-based action plans may stop the illegal disposal of industrial wastes and organic effluents in these two lakes in Udaipur. In this connection, the administrative authority of Udaipur may implement strict regulation of groundwater pollution under the Water Act, 1974. Such initiative would require adequate stringent guidelines in order to end or significantly reduce the existing water pollution in both Pichola Lake and Fateh Sagar Lake. Additionally, the Corporate Responsibility for Environmental Protection (CREP) programmes are required to be more strengthened compared to the previous ones. The method of corporatization can further attract private investment in such projects and provide competition mechanisms, which may actually benefit to increase efficiency of development and operation of sewage management services. Moreover, all the SSIs located in residential areas near the lakes are required to be shifted to the identified industrial areas. Also, the SSIs should be mandated to use effluent treatment plants through either individual or common effluent treatment plants – for which the industries should pay. Also, such water treatment plants must be regulated under the Water Act, 1974 by the pollution control board. In this regard, the authority should require carrying out strict compliance monitoring for the same.
CONCLUSIONS
In this research study, water quality data for 16 physical and chemical parameters, collected from 10 sampling sites of Pichola Lake and 7 sampling sites of Fateh Sagar Lake in Rajasthan (India) during 2017–19 (pre- and post-monsoon seasons) were analyzed. The present investigation has highlighted the analysis of physiochemical properties of two major lakes in Udaipur city, i.e. Pichola Lake and Fateh Sagar Lake. The study has clearly revealed that the organic pollution load in Pichola Lake is higher than Fateh Sagar Lake. Besides that, the mean OPI values of Pichola Lake were observed as 2.32 in pre-monsoon and 2.34 in post-monsoon seasons respectively, which highlighted that both values were recorded in a similar range. On the other hand, the mean OPI values of Fateh Sagar Lake were remarkably less in post-monsoon (1.31) analysis as compared to pre-monsoon (1.80) studies. Such observations can be associated with the dilution effect due to the additional influx of water during the monsoon season. Hence, during the immediate after the monsoon season, the water quality of all the sample sites of these two lakes has shown improvement. As a result, it can be stated that the monsoon season has played a significant role in the temporary reduction of pollutants in the water of these two lakes.
From an economic standpoint, it can be well assumed that these two lakes are a major lifeline to Udaipur. Therefore, it is highly important to conserve water quality of these lakes. Apart from that, the tourism industry of Udaipur requires promotion of conservation management policies and practices for long-run sustainability. For instance, specific initiatives should be required to reduce the substantial amount of organic pollution prevailing in some of the sampling sites, and simultaneously, curb the eutrophication level in Pichola Lake and Fateh Sagar Lake. As a result, the implementation of appropriate water management policies, the enactment of stringent guidelines, the use of advanced technology, and the adoption of adequate collaborative actions from the government, NGOs, and local civic authorities may help to mitigate the future water pollution crisis in these two lakes. In addition, technologically advanced smart sensors may be installed in the sewer lines to monitor real time sewer leakage and track the excess flow of sewage in the sewer lines. The successful application of all these activities may ensure to keep up standard water quality, control organic pollution, and administer sustainable water management practices in urban lakes in Udaipur.
Further in-depth studies on ion chemistry and trace element composition in water sediments can provide important insight to the governing processes occurring in the lake environment, and toxicological studies of trace elements in aquatic systems are necessary to assess the suitability of lake water for aquatic life as well as human consumption in relation to health effects. The frequent conduct of community engagement programmes and participation in planning and management-related workshops and activities may further contribute to the conservation of tourist spots, including lakes and historical places in Udaipur. In this context, all stakeholders, including the government, state and local authorities, common people, and private entities, must come forward to ensure they execute their respective roles successfully for the conservation purposes of the lake ecosystem in Udaipur.
ACKNOWLEDGEMENTS:
The authors wish to thank Amity University Kolkata and Asansol Engineering College, West Bengal for providing infrastructures and facilities to carry out this research work.
CONSENT FOR PUBLICATION (INCLUDE APPROPRIATE STATEMENTS)
The authors are hereby giving consent for the publication of the manuscript as well as experimental data (represented in tabular, graphical, and image form). Authors also ensure that the manuscript or the experimental results/data has not been submitted elsewhere for the publication.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.