Impact of Land Use Pattern and Heavy Metals on Lake Water Quality in Vidarbha and Marathwada Region, India

Abstract

Lakes are critical resources that support the ecological balance and provide essential services for human and environmental well-being. However, their quality is being increasingly threatened by both natural and anthropogenic processes. This study aimed to assess the water quality and the presence of heavy metals in 15 lakes in the Vidarbha and Marathwada regions of Maharashtra, India. To understand the extent of pollution and its sources, the physico-chemical parameters were analyzed which included pH, turbidity, total hardness, orthophosphate, residual free chlorine, chloride, fluoride, and nitrate, as well as heavy metals such as iron, lead, zinc, copper, arsenic, chromium, manganese, cadmium, and nickel. The results revealed significant pollution in several lakes, with the Lonar Lake showing a pH value of 12, exceeding the Bureau of Indian Standards’ (BIS) limit. The Lonar Lake also showed elevated levels of fluoride having a value of 2 mg/L, nitrate showing a value of 45 mg/L, and orthophosphate showing a concentration up to 2 mg/L. The Rishi Lake had higher concentrations of nickel having a value of 0.2 mg/L and manganese having a value of 0.7 mg/L, crossing permissible BIS limits. The Rishi Lake and the Salim Ali Lake exhibited higher copper levels than other lakes. Cadmium was detected in most of the lakes ranging from values of 0.1 mg/L to 0.4 mg/L, exceeding BIS limits. The highest turbidity levels were observed in Rishi Lake and Salim Ali Lake at 25 NTU. The total hardness value observed in the Kharpudi Lake was 400 mg/L, which is highest among all the lakes under study. The spatial analysis, which utilized remote sensing and GIS techniques, including Sentinel-2 multispectral imagery for land use and land cover mapping and Digital Elevation Model (DEM) for watershed delineation, provided insights into the topography and drainage patterns affecting these lakes. The findings emphasize the urgent need for targeted management strategies to mitigate pollution and protect these vital freshwater ecosystems, with broader implications for public health and ecological sustainability in regions reliant on these water resources.

1. Introduction

Water is essential for all living organisms on Earth, and serves as a fundamental resource for life. Lakes, which constitute a significant portion of the world’s freshwater, have increasingly suffered from nutrient enrichment and bacterial contamination, leading to a decline in water quality globally [1,2,3]. As ecologically and socio-economically valued ecosystems, lakes and wetlands are among the world’s most critical freshwater resources [4,5]. In particular, urban wetlands offer essential ecological services, including flood control, wildlife habitats, fisheries, livelihoods, carbon storage, water purification, and recreation [6,7]. Unfortunately, over the past few decades, many lakes have faced severe pollution stress owing to rapid population growth and intensified anthropogenic activities [8,9,10]. Lakes are valuable indicators of environmental change and provide physical, chemical, and biological data that can be used to measure ecosystem responses, earth surface processes, and human influences [11]. They also serve as vital archives of long-term climatic and environmental variability [11,12]. The surge in population around urban lakes has exacerbated this problem, as the demand for land and water has led to perpetual encroachment in lake areas, often turning these vital water bodies into waste dumping sites, which severely impacts both aquatic ecosystems and human health [13,14,15].

The overuse and pollution of nearby water resources have become an increasing concerns. Water quality is directly linked to human well-being, and environmental pollution, particularly from toxic metals, has become a significant issue in major metropolitan areas. These harmful heavy metals can enter ecosystems, leading to bioaccumulation, geoaccumulation, and biomagnification, all of which pose risks to both the environment and public health [16,17,18]. Heavy metals such as iron, copper, zinc, nickel, and other trace elements are vital for biological systems; however, their excessive accumulation can lead to various disorders [19,20]. These metals can infiltrate the environment through the erosion of parent materials related to regional geology and human activities, such as iron and steel production, mining, road transport, and waste incineration. Heavy metals, such as lead, zinc, and cadmium, are particularly harmful to both human and animal health [21,22]. Dissolved and suspended particulate matter in the water column tends to adsorb heavy metals, which are then rapidly transferred to sediment, leading to prolonged residence times of these metals in the environment. Therefore, the presence of heavy metals in sediments reflects the pollution levels of the entire territory [23,24,25]. In India, the BIS [26] determines the acceptable and permissible limits for water quality, while at the international level, the WHO Guidelines [27] are also used for reference. The major pollutants and their corresponding standard values are listed in

Table 1. Limits according to BIS standards (2012) and WHO (2011) guidelines.

Parameters	BIS Standard (2012)		WHO (2011)
	Acceptable Limit	Permissible Limit
pH	6.5–8.5	No relaxation	6.5–8.5
Turbidity	1 NTU	5 NTU	<5 NTU
Chloride	250 mg/L	1000 mg/L	250 mg/L
Total Hardness	200 mg/L	600 mg/L	100–300 mg/L
Residual Free Chlorine	0.2 mg/L	1 mg/L	0.2–0.5 mg/L
Fluoride	1 mg/L	1.5 mg/L	1.5 mg/L
Nitrate	45 mg/L	No relaxation	50 mg/L
Iron	0.3 mg/L	No relaxation	0.3 mg/L
Lead	0.01 mg/L	No relaxation	0.01 mg/L
Orthophosphate	1 mg/L	No relaxation	-
Zinc	5 mg/L	15 mg/L	5 mg/L
Copper	0.05 mg/L	1.5 mg/L	2 mg/L
Arsenic	0.01 mg/L	0.05 mg/L	0.01 mg/L
Chromium	0.05 mg/L	No relaxation	0.05 mg/L
Manganese	0.1 mg/L	0.3 mg/L	0.4 mg/L
Cadmium	0.003 mg/L	No relaxation	0.003 mg/L
Nickel	0.02 mg/L	No relaxation	0.07 mg/L

.

Mining activities, industrial waste, atmospheric deposition, sewage sludge application, and agricultural fertilizers are significant sources of heavy metal pollutants in water [28,29,30]. In addition to water pollution, land use and land cover (LULC) changes, such as the expansion of built-up areas and agricultural land, are closely linked to human activities and can significantly impact water quality and the aquatic environment. LULC change patterns are influenced by various interconnected factors, including legislation, land development, environmental conditions, socio-economic factors, and demographic trends [31,32,33]. Monitoring spatiotemporal patterns of LULC change is crucial for revealing the role of socio-economic drivers in sustainable landscape management. Digital land use classification and change detection using satellite-based multispectral sensors provide a powerful means of quantifying these temporal phenomena [34,35,36]. By classifying multidate images, researchers can study ecosystem degradation or modification over time, offering critical insights into the complex relationship between human activities and environmental health [37,38,39].

Previous studies by Wagh [40] et al. and Suryawanshi [41] et al. mainly focused on the accumulation of heavy metals in the riverine system of the Marathwada region and source identification. Wagh et al. [40] found that the main sources of pollution are anthropogenic, and the presence of heavy metals, such as chromium and zinc, is higher than the prescribed limit given by the World Health Organization (WHO). Suryawanshi [41] et al. studied the presence of heavy metals in the reservoir of the Marathwada region. They also reported the presence of heavy metals like iron, nickel, manganese, and zinc. A few parameters such as pH, temperature, and turbidity needed to be noted as soon as the samples were collected. For this rapid analysis test, kits were used by many researchers [42,43,44,45,46]. As rapid analysis test kits are convenient in the field, heavy metal analysis test kits were also used to obtain the results instantly.

Previous study carried out on the Lonar Lake by Babar [47] et al. documented the anaerobic condition and presence of Spirulina algae. The study also found that the higher salinity, alkalinity, chloride, total dissolved solids, and pH crossed the permissible limit set by BIS. The sources of pollutants that were found were domestic waste and runoff from the agricultural land. Mawari [48] et al. carried out an assessment of drinking water sources in the Solapur district with respect to heavy metals using ICP-MS techniques. The study found that the total dissolved solids in water were much higher than the permissible limits. The presence of heavy metals such as arsenic and mercury was also detected in urine samples from people in the study area. Mawari [48] et al. reported that the source of pollution could be runoff from agricultural fields. Dabhade [49] and Deshmukh [50] et al. carried out studies regarding eutrophication of the Lonar Lake in which the presence of Spirulina algae was responsible for the low oxygen level in the lake. This study found that the parameters that are indicators of eutrophication were at peak levels, and the source of pollution was domestic sewage. The results of the present study also showed a higher concentration of physico-chemical parameters, such as pH, turbidity, nitrate, copper, and cadmium than the permissible limit. A study on the agricultural soil of the Jalgaon area was carried out by Patil [51] et al., which used the AAS technique for the detection of heavy metals in the samples. The study found the presence of lead, cadmium, nickel, iron, and zinc in the study area. According to the study, the untreated municipal wastewater and anthropogenic activities such as agricultural runoff and industrial wastes are the source of heavy metals. Koshy [52] et al. had carried out a characterization of soil samples from the Lonar Lake with the help of advanced techniques like XRF, FTIR, and ICP-AES. Results obtained showed the higher concentration of sodium and chloride as well as heavy metals like iron and titanium. In the present study, heavy metals like copper and cadmium were detected in the water samples of the Lonar Lake. Patil [53] et al. carried out a study in the Kopargaon area to detect the heavy metals, in which they observed that cadmium is above the permissible limit and the source of it was the burning of fossil fuel. Copper, iron, nickel, and manganese was also detected in the Kopargaon area. The study reported that the source of this heavy metals was industries and the agricultural practices. Concentration of lead in a higher proportion was also detected in the study area and their source might be the burning of fossil fuel and use of poly vinyl chloride (PVC) pipes. In the present study, manganese, copper and cadmium was detected during the analysis in the lake water samples. Wagh [40] et al. carried out a study on the riverine system in the Marathwada region to determine the accumulative level of heavy metals with the help of Atomic Absorption Spectroscopy. During analysis, the presence of heavy metals like zinc, nickel, copper, and chromium was detected. Nickel and zinc exceeded the limit set by the WHO standards for irrigation water. Chromium also exceeded the limit set by the WHO. The sources of pollutant were marked as the municipal wastes, laundry chemicals, paints, leather, etc. In the present study, the presence of nickel was detected in the Rishi Lake while copper was detected in all the lakes in the current study.

This study aims to evaluate the physico-chemical properties of water samples from various lakes in the Marathwada and Vidarbha regions. Specifically, it seeks to analyze the concentrations of key parameters such as pH, turbidity, chloride, and nitrate, along with the presence of heavy metals. The Marathwada and Vidarbha region lacks the study of heavy metals in the lacustrine environment on such a large scale. As well, no large-scale study regarding LULC was previously carried out in the same region. The Vidarbha and Marathwada region of Maharashtra state are developing rapidly in all aspects. Industrialization and its impacts in this region increase concern among researchers. The novelty of this study is that it covers six districts of the Maharashtra state. Such larger research to detect the presence of heavy metals in 15 lakes has not been carried out before in the Vidarbha and Marathwada region. Additionally, the study examines the effects of LULC changes and watershed delineation on water quality in these lakes, providing a comprehensive understanding of the environmental challenges posed by both natural and anthropogenic factors.

2. Materials and Methods

2.1. Study Area

The study encompasses six districts in Maharashtra, with four of them belonging to the Vidarbha region: Amravati, Nagpur, Washim, and Buldhana. The remaining two districts are part of the Marathwada region: Chhatrapati Sambhajinagar and Jalna. The Amravati district is home to the Chhatri Lake, having a mean depth of 4.5 m [54] and the Wadali Lake within the city, having a depth value ranging from 4.5 m to 17 m [55]. According to the 2011 census, Amravati city has a population of more than 600,000 [56]. Asboth lakes are situated in the city, human-induced pollution is dominant in both the lakes. The Shakkar Lake is situated at the Chikhaldara hill station, with a population of 5158 according to the 2011 census [57]. The Shakkar Lake poses a lesser threat of human-induced pollution. The Sawanga Lake is located in the Sawanga Vithoba village of the same district which has a population of 1370 according to the 2011 census [58]. The Sawanga Lake is surrounded by the agricultural land. Agricultural runoff from the surrounding farmland may affect the lake water quality. In Nagpur, the Ambajhari Lake is situated in the middle of city, having a mean depth value of 7.55 m [59] and the Futala Lake is situated in the city center, having an average depth value of 5 m [60]. Both these lakes are surrounded by the built-up land as these are situated in the middle of the city. Both lakes pose a threat of pollution due to human-induced sources. The Surabardi Lake is positioned around 17 km outside the city and surrounded by the agricultural land. It can possibly become polluted by the agricultural runoff. The population of Nagpur city is more than 2 million [61,62,63]. The Khindsi Lake, a significant body of water, is located near the city of Ramtek in Nagpur district, with a population of more than 22,000 as per the 2011 census [64]. The Khindsi lake is surrounded by the hilly terrain and farmlands. Runoff from these areas can directly accumulate in the lake water body which results in the deterioration of lake water. The Rishi Lake is situated in the middle of Karanja Lad tehsil in the Washim district. Karanja Lad has a population size of more than 67,000 according to the 2011 census [65]. As this lake is surrounded by the human settlement and also few industries are situated in the watershed of this lake, it poses a threat of pollution from both sources. The Lonar Lake, a world-renowned hypervelocity natural impact crater lake in Basaltic rock, is situated in Lonar village in the Buldhana district, having an average depth of 135 m [66]. Lonar city has a population of more than 23,000 as per the 2011 census [67]. The Lonar Lake is surrounded by the forest area. Runoff during monsoon season accumulates in the lake which affects the lake water quality. The lakes under study in Vidarbha region are denoted by numbers in the B section of the map and the same lakes are depicted in the C section of Figure 1.

The Salim Ali Lake is located in the central part of Chhatrapati Sambhajinagar city, having an average depth of 4 m [68], while the Harsul Lake is situated in the Harsul area of the same city. The Salim Ali Lake is surrounded by the human settlement and becomes polluted by the human-induced sources of pollution, while the Harsul Lake is surrounded by the hilly terrain and agricultural land. Runoff from the nearby agricultural land deteriorates the lake water quality. The Somthana Lake is approximately 40 km away from Chhatrapati Sambhajinagar city and surrounded by the agricultural land. The population of Chhatrapati Sambhajinagar city is more than 1 million [69]. The Moti Lake is situated in the middle of Jalna city surrounded by the dense population. Human-induced pollution majorly affects the lake water quality. The Kharpudi Lake is located nearly 8 km outside the city and surrounded by the agricultural area. Direct runoff from agricultural land may degrade the lake water quality. According to 2011 census, Jalna city has a population of more than 250,000 [70]. It is important to note that water bodies situated within cities are at a higher risk of pollution due to the direct discharge of domestic wastewater into them without any treatment. This is a concerning issue in the Marathwada region. The lakes under study in the Marathwada region are indicated by numbers and depicted in the B section of Figure 2.

Maharashtra is the third most extensive state in the country. The state features a diverse landscape, including Basaltic rock formations that cover approximately 78% of its area. Geologically, the study area is situated within the Deccan volcanic province, which is characterized by a variety of Basaltic rock layers. The Late Cretaceous–Palaeogene (62 million years ago) Deccan trap comprises lava flows of Basaltic composition [71,72,73]. The Nagpur region is characterized by a felsic volcanic tuff associated with silicified zones, quartz–feldspathic gneiss, schist, and quartzite, along with Kamthi Formation sandstone and clay, Deccan Trap Basalt with inter-trappean sediments, and Lameta Group limestone, sandstone, and Recent alluvium. Basalts are composed primarily of ferromagnesian silicates, such as olivine and pyroxene, plagioclase, and other oxides [74,75].

The Marathwada region is situated in close proximity to the Deccan Volcanic Province of India. Approximately 86% of the Marathwada region is characterized by Basalt rock of the Deccan Trap, while the remaining 14% is composed of Laterite or Bauxite rocks [76,77]. Dyke swarms have been documented in various parts of the Deccan Trap [76,78,79]. The variety of rock types in the region includes gneisses, schists, intrusive, Gondwana sedimentaries, Deccan trap Basalts, and older and newer alluvium [47]. The Marathwada region frequently experiences drought conditions and water scarcity, as evidenced by the mean monsoon rainfall of 636 mm [41,48,80]. The annual precipitation in the region is approximately 903 mm, with the majority of it being received during the monsoon season [81]. The climate of the Vidarbha area is characterized by an arid and semi-arid environment, with well-defined summer months from March to May, a rainy season from June to October, and winter months from November to February. The mean annual temperature in the region is between 33 and 5 °C, with a mean annual precipitation of about 903 mm. The relative humidity is high during the monsoon season, ranging from 75 to 88%, and low during other times, ranging from 30 to 40% [52,53].

2.2. Methods

The study was conducted across 15 lakes located in the Vidarbha and Marathwada regions. In situ and geospatial analysis were carried out during this study. It is crucial to analyze the physico-chemical parameters of water samples as it provides insight into the pollution level of the particular aquatic ecosystem. A detailed overview of the research methodology followed is provided in Figure 3.

2.3. Sample Collection

Fieldwork was carried out in the month of January, employing a random sampling technique to collect both water and sediment samples from each lake during the day. Two samples were collected from each lake. Approximately a 1 L water sample was collected in pre-cleaned, sterile bottle containers. It was ensured that the bottles were tightly sealed to prevent any leakage during transportation. To ensure precise documentation, geotagged photographs were captured at each sampling point and the samples were labeled properly to avoid any misidentification during analysis. Collected samples were stored under controlled conditions at 20 °C and transported to the laboratory for subsequent physico-chemical analysis.

2.4. Physio-Chemical Analysis

A physico-chemical evaluation of the water samples was conducted in the laboratory. Physico-chemical parameters which include pH, turbidity, chloride, total hardness, nitrate, residual free chlorine, fluoride, orthophosphate, and heavy metals like lead, iron, zinc, arsenic, chromium, manganese, cadmium, copper, and nickel were determined. These parameters were selected due to their relevance in assessing water quality and identifying pollution sources. Standard protocols were followed, and calibrated instruments and certified reagents were used to ensure accuracy and consistency in all measurements. Rapid water testing kits manufactured by Himedia Laboratories Pvt. Ltd., Mumbai, India were used for the physico-chemical and heavy metal analysis of the water samples. As rapid testing kits are handy, lightweight, and give precise results in less time, their use is increasing day by day. Many researchers used these kits and they proved their suitability and efficiency in the field [42,43,44,45,82,83,84,85]. The detailed analytical methods for each parameter are summarized in

Table A1. Analytical process for the determination of physico-chemical parameters.

Parameters	Procedure
pH test	1. Cut approximately 2–3 cm length of pH paper strip and dip in water sample to be tested. 2. Observe change in color of pH strip. 3. Match the color obtained on the strip with the color given on front of pH paper strip. 4. Note the corresponding pH value. (Limit of detection of pH—2)
Turbidity test	1. Fill the water sample to be tested in the empty test bottle marked as test sample bottle. 2. Compared the turbidity (Haziness) with the standards of 0 NTU, 5 NTU, 10 NTU, and 25 NTU provided for comparison. 3. Interpret the results in the terms of NTU. (Limit of detection of turbidity—0 NTU)
Chloride test	1. Fill the aqua check test jar with water sample up to 10 mL mark. 2. Add one tiny spoonful of reagent CHL-A and 2 drops of reagent CHL-B. 3. Mix well. 4. Add drop by drop reagent CHL-C counting the number of drops while mixing until the color changes to bluish violate. 5. Now apply the formula given below: Chloride mg/L (ppm) as Cl = 10 × (number of drops of CHL-C). (Limit of detection of chloride—10 mg/L)
Total Hardness test	1. Fill the aqua check test jar with water sample up to 10 mL mark. 2. Add one spoonful of powder reagent TH-A with the tiny spoon provided. 3. Mix well to dissolve the powder completely. 4. Add 4-5 drops of reagent TH-B and mix well. 5. Observe change in color of solution: - Solution turns blue: soft; - Solution turns red: hard. 6. Add drop by drop reagent TH-C counting the number of drops while mixing, until the color changes from red to blue. 7. Now apply the formula given below: Total hardness mg/L (ppm) as CaCO₃ = 25 × (number of drops of TH-C). (Limit of detection of total hardness—25 mg/L)
Fluoride test	1. Fill the aqua check test jar with 10 mL sample. 2. Add 3 drops of reagent FL-A. Mix the contents well. 3. Now add 8 drops of reagent FL-B. Mix the contents and allow to stand for 2–5 min. 4. Match the correct color and read the mg/L (ppm) fluoride from the color chart. (Limit of detection of fluoride—0 mg/L)
Nitrate test	1. Obtain 1.0 mL of water sample in aqua test jar provided. 2. Now add one spoonful of reagent N-A and 5 drops of Reagent N-B. Add 1 spoonful of reagent N-C shake well. Wait for 5 min to allow for maximum color development. 3. Dilute to 10 ml mark with DM water. 4. Match the correct color and read the mg/L (ppm) nitrate from the color chart. (Limit of detection of nitrate—0 mg/L)
Iron test	1. Obtain 5ml water sample in aqua check test jar provided. 2. Add 1 spoonful of reagent Fe-A and 1 spoonful of reagent Fe-B. 3. Mix contents thoroughly by swirling. 4. Match the correct color and read the mg/L (ppm) iron from the color chart. (Limit of detection of iron—0 mg/L)
Residual free chlorine	1. Fill the aqua check test jar with water sample up to the 10 mL mark. 2. Add 4—drops of reagent RCL-A and shake well. 3. Add 2–3 drops of reagent RCL-B. Mix well. 4. Observe change in color of solution: Solution turns blue: free chlorine present; No blue color: chlorine is absent. 5. Add drop by drop reagent RCL-C, counting the number of drops while mixing, until the blue color dis-appears. 6. Now apply the formula given below: Residual free chlorine mg/L (ppm) as chlorine = 0.1 × (No. of drops of reagent RCL-C). (Limit of detection of residual free chlorine—0 mg/L)
Manganese test	1. Fill the aqua check test jar with 10 mL sample. 2. Add 10 drops of reagent 045A and mix the contents well. 3. Now add 5 drops of reagent 045B and mix the content and allow to stand for 5–10 min. 4. Match the developed color with color chart and read the level of manganese. (Limit of detection of manganese—0 mg/L)
Cadmium Test	1. Fill the aqua check test jar with 10 mL sample. 2. Add 1 ml of reagent 070A. Mix the contents well. 3. Now add 2 drops of reagent 070B. Mix the contents. 4. Add 8 drops of reagent 070C. Mix the content well. 5. Now add 10 drops of 070D. Mix the content and allow to stand for 1–2 min. 6. Match the developed color with color chart and read the level of cadmium. (Limit of detection of cadmium—0 mg/L)
Nickel Test	1. Fill the aqua check test jar with 10mL sample. 2. Add 5 drops of reagent 060A. Mix the contents well. 3. Now add 5 drops of reagent 060B. Mix the contents. 4. Add 5 drops of reagent 060C. Mix the contents and allow them to stand for 4–5 mins. 5. Match the developed color with color chart and read the level of Nickel. (Limit of detection of nickel—0 mg/L)
Arsenic Test	1. Use syringe to add 5 mL sample solution into the reaction vessel. 2. Add 1 measuring spoon reagent AS/01 and swirl reaction vessel gently for 1 min. 3. Add 1 measuring spoon reagent AS/2. 4. Insert test strip with test field 2 cm into reaction vessel and clamp it with lid. 5. During the 12-minute reaction time, swirl gently the reaction vessel. The test field should not get in contact with the sample. 6. After 12 min, remove test strip from reaction vessel, dip it for 2 s. with the test field into water, sake off excess liquid. 7. Compare test field to color scale. (Limit of detection of arsenic—0.05 mg/L)
Chromium Test	1. Fill the aqua check test jar with 10 ml sample. 2. Add 2 drops of reagent 050A. Mix the contents well. 3. Now add 2 drops of reagent 050B. Mix the contents and allow them to stand for 4–5 min. 4. Match the developed color with color chart provided and read the level of chromium. (Limit of Detection of Chromium—0 mg/L)
Zinc Test	1. Obtain 1 mL water sample with syringe into test jar. 2. Add 5 drops of reagent 044A, 4 drops of reagent 044B and one spoonful of reagent 044C. Mix the contents well. 3. Add 2 mL of reagent 044D with plastic dropper and shake vigorously till both get mixed thoroughly. 4. Wait for 5 min. to develop pinkish red colored layer at the bottom of the solution (This shows the presence of zinc) otherwise green color indicates absence of zinc. 5. Match color of solution with color chart to find out zinc level in sample. (Limit of detection of zinc—0 mg/L)
Lead Test	1. Fill the aqua check screw tube with 5 mL sample. 2. Add 5 mL of reagent 052A. Mix the contents well. 3. Now add one spoon of reagent 052B. Mix the contents well. 4. Now add 2 mL of reagent 052C. Mix the content, shake for 30–60 s. and allow to stand for 4–5 min 5. Match the developed color with color chart provided and read the level of Lead. (Limit of detection of lead—0 mg/L)
Copper Test	1. Fill the aqua check Test jar with 10 mL sample. 2. Add 2 drops of reagent 0446A. Mix the content well. 3. Now add 1 drop of reagent 046B. Mix the content and allow to stand for 5–10 min. 4. Match the developed color with color chart provided and read the level of copper (Cu), mg/L (ppm). (Limit of detection of copper—0 mg/L)
Orthophosphate Test	1. Fill the aqua check jar with 10 mL sample. 2. Add 2 drops of reagent O59D. Mix the content well. 3. Now add 2 drops of reagent O59E. Mix the content well and allow to stand for 1 min. 4. Match the developed color with the color chart provided and read the level of phosphate. (Limit of detection of orthophosphate—0 mg/L)

of Appendix A. Analysis was conducted to study the overall water quality of the lakes and to detect any pollutants that could impact the aquatic ecosystems.

2.5. Watershed Delineation

Delineation maps of the watersheds for each lake was made using ArcGIS 10.8 software. This process was crucial for understanding the hydrological and geomorphological characteristics of the regions under study. Using SRTM-DEM, the flow of streams from higher to lower elevations was mapped and identified the stream orders within each watershed. This allowed us to evaluate the slope gradients, stream flow dynamics, and the overall topology of the catchment areas. Analysis of these delineation maps helps to assess both the quantity and quality of water available in the watersheds and to identify potential pollution sources that could be affecting the lake water quality. The geomorphological features identified, such as mountains, rivers, and valleys, provided a spatial framework that was essential for interpreting the physico-chemical results [30,34,74].

2.6. Pearson Correlation Index

To better understand the relationships between the various physico-chemical parameters, a Pearson correlation analysis was performed. The Pearson correlation coefficient is a statistical measure that allows us to assess the strength and direction of the linear relationships between pairs of variables. The correlation coefficient ranges from −1 to +1, where values closer to +1 indicate a strong positive correlation, and values closer to −1 indicate a strong negative correlation [23,24]. We can correlate an index using the obtained correlation coefficient: a higher coefficient indicates a stronger correlation, while a lower coefficient indicates a weaker correlation. Mathematically, the correlation coefficient is the ratio of the covariance between two variables to the product of their standard deviations. This measure of correlation also assists in identifying potential pollution sources [86,87,88].

The mathematical formula of Pearson correlation coefficient is as follows:

$$r={\displaystyle \frac{n(\sum xy)-(\sum x\left)\right(\sum y)}{\sqrt{\left[n\sum x^2-{\left(\sum x\right)}^2\right]\left[n\sum y^2-\left(\sum y^2\right)\right]}}}$$

where

r = Pearson correlation coefficient

x = Values in first set of data

y = Values in second set of data

n = Total number of values

This analysis was instrumental in identifying key interactions between different water quality parameters. For instance, the correlation between pH levels and metal ion concentrations, as well as the relationship between turbidity and the presence of suspended particles in the water samples was analyzed. The Pearson correlation analysis provided valuable insights into how these parameters influenced one another and affected overall water quality. pH generally shows negative correlation with heavy metals. Phosphate tends to be more mobile in acidic conditions. Heavy metals like cadmium and nickel show strong correlation with each other as they have the same geochemical behavior and sources. Copper often corelates positively with turbidity [87,88,89,90,91]. The results from this analysis also enabled us to construct predictive models with high accuracy, achieving prediction accuracies exceeding 95% in some cases, for both short- and long-term water quality changes [86,87,92,93]. These models were then used to correlate hydrological conditions with water resource data, ultimately helping to refine the accuracy of the study’s findings.

3. Results

The following text provides a formal discussion of the results obtained from the physico-chemical evaluation of water samples. The graphical representation of the parameters helps to understand the level of pollution in a specific lake. The presence of heavy metals was observed in a trace amount of a few lakes. These heavy metals prove harmful to the living organisms that depends on the lake. Studying the delineation of the catchment can help to understand the slope gradient of the particular watershed. The change in LULC patterns within the watershed can be responsible for the accumulation of pollutants in the nearby aquatic ecosystem. Thus, it is necessary to investigate the land use and land cover patterns of the watershed.

3.1. Physico-Chemical Analysis

The parameters of water quality are regulated by the BIS. Obtained results were compared with the drinking water standards set by the BIS. The range for pH set by BIS is between 6.5 and 8.5, the Lonar Lake exceeded this limit and showed value of 12. The detection limit of the test for pH is 2. The reason behind this is the eutrophication caused due to runoff from surrounding agricultural land and sewage disposal. Many researchers detected the presence of Spirulina algae which is an indicator of eutrophication [47,49,50,94,95]. The permissible limit for turbidity according to BIS standards is 5 NTU, and it was observed that lakes in the Marathwada region exceeded this limit. The detection limit of the test for turbidity is 0 NTU. The Rishi Lake in Karanja and the Salim Ali Lake in Chhatrapati Sambhajinagar showed the highest value of turbidity, which is 25 NTU, significantly higher than the BIS limit. Total hardness indicated the presence of calcium carbonate in the water, and the acceptable range, as per BIS standards, is between 200 and 600 mg/L. The detection limit of the test for total hardness is 25 mg/L. All lakes in the Marathwada and Vidarbha regions fall within this range, with the highest value of total hardness observed in the Kharpudi Lake, which was 400 mg/L. It might be caused due to the accumulation of runoff water from surrounding agricultural land. The lesser value of total hardness was observed in the Shakkar Lake of Amravati and the Khindsi Lake of Nagpur, which was 125 mg/L. The range for chloride, as per BIS standards, is between 250 to 1000 mg/L, and none of the lakes in the Marathwada and Vidarbha regions exceeded this limit. The detection limit of the test for chloride is 10 mg/L. The presence of nitrate was observed in only two lakes, the Lonar Lake and in the Harsul Lake. The Harsul Lake showed a concentration of 10 mg/L, while the Lonar Lake showed a concentration of 45 mg/L, which is the highest acceptable limit set by BIS. The detection limit of test for nitrate is 0 mg/L. Untreated sewage water and runoff from agricultural land becomes accumulated in the lake, which increases the nitrate level in the water. The obtained values after analysis are presented in

Table 2. Obtained values of physico-chemical parameters in ppm (mg/L).

Name of Lakes	pH	Turbidity (NTU)	Chloride (mg/L)	Total Hardness (mg/L)	Fluoride (mg/L)	Nitrate (mg/L)	Iron (mg/L)	Residual Free Chlorine (mg/L)	Lead (mg/L)	Orthophosphate (mg/L)	Zinc (mg/L)	Copper (mg/L)	Arsenic (mg/L)	Chromium (mg/L)	Manganese (mg/L)	Cadmium (mg/L)	Nickel (mg/L)
Somthana Lake	8	10	100	225	0.5	<0.1	<0.1	<0.1	<0.1	<0.1	<0.1	0.5	<0.1	<0.1	0.1	0.1	<0.1
Moti Lake	8	10	150	350	0.5	<0.1	<0.1	<0.1	<0.1	1	<0.1	0.5	<0.1	<0.1	<0.1	0.4	<0.1
Kharpudi Lake	9	10	100	400	0.5	<0.1	<0.1	<0.1	<0.1	<0.1	<0.1	0.5	<0.1	<0.1	0.3	0.4	<0.1
Lonar Lake	12	10	50	175	1	45	<0.1	<0.1	<0.1	2	<0.1	0.5	<0.1	<0.1	<0.1	0.4	<0.1
Shakkar Lake	7	5	30	125	0.5	<0.1	<0.1	<0.1	<0.1	<0.1	<0.1	0.5	<0.1	<0.1	<0.1	0.4	<0.1
Chhatri Lake	9	10	30	225	0.5	<0.1	<0.1	<0.1	<0.1	<0.1	<0.1	0.5	<0.1	<0.1	<0.1	<0.1	<0.1
Wadali Lake	9	5	40	175	0.5	<0.1	<0.1	<0.1	<0.1	<0.1	<0.1	0.5	<0.1	<0.1	<0.1	<0.1	<0.1
Sawanga Lake	8	5	40	250	<0.1	<0.1	<0.1	<0.1	<0.1	1	<0.1	0.5	<0.1	<0.1	<0.1	<0.1	<0.1
Surabardi Lake	9	10	10	150	<0.1	<0.1	<0.1	<0.1	<0.1	<0.1	<0.1	0.5	<0.1	<0.1	<0.1	<0.1	<0.1
Ambajhari Lake	8	10	40	175	<0.1	<0.1	<0.1	<0.1	<0.1	1	<0.1	0.5	<0.1	<0.1	<0.1	<0.1	<0.1
Futala Lake	7	5	20	250	<0.1	<0.1	<0.1	<0.1	<0.1	1	<0.1	0.5	<0.1	<0.1	<0.1	0.1	<0.1
Khindsi Lake	7	5	10	125	<0.1	<0.1	<0.1	<0.1	<0.1	<0.1	<0.1	0.5	<0.1	<0.1	<0.1	0.4	<0.1
Rishi Lake	8	>25	30	175	<0.1	<0.1	<0.1	<0.1	<0.1	1	<0.1	1	<0.1	<0.1	0.7	0.1	0.2
Salim Ali Lake	9	25	100	250	<0.1	<0.1	<0.1	<0.1	<0.1	1	<0.1	1	<0.1	<0.1	<0.1	0.4	<0.1
Harsul Lake	8	5	40	200	<0.1	10	<0.1	<0.1	<0.1	<0.1	<0.1	0.5	<0.1	<0.1	<0.1	0.4	<0.1

Notes: LOD—limit of detection; LOD of pH—2; LOD of turbidity—0 NTU; LOD of chloride—10 mg/L; LOD of total hardness—25 mg/L; LOD of fluoride—0 mg/L; LOD of nitrate—0 mg/L; LOD of iron—0 mg/L; LOD of residual free chlorine—0 mg/L; LOD of lead—0 mg/L; LOD of orthophosphate—0 mg/L; LOD of zinc—0 mg/L; LOD of copper—0 mg/L; LOD of arsenic—0.05 mg/L; LOD of chromium—0 mg/L; LOD of manganese—0 mg/L; LOD of cadmium—0 mg/L; LOD of nickel—0 mg/L.

.

Heavy metal analysis of the Rishi Lake revealed a nickel concentration of 0.2 mg/L exceeding the BIS standard limit of 0.02 mg/L. The detection limit of test for nickel is 0 mg/L. Apart from the Rishi Lake, no other lake in Vidarbha or Marathwada region showed detectable levels of nickel. Cadmium was detected in most of the lakes, except for a few in the Amravati and Nagpur district of Vidarbha. The detection limit of test for cadmium is 0 mg/L. The highest acceptable limit for cadmium set by BIS is 0.003 mg/L, and most lakes exceeded this limit, with some showing concentrations up to 0.4 mg/L. Manganese was detected in the Somthana Lake, the Kharpudi Lake, and in the Rishi Lake. The detection limit of test for manganese is 0 mg/L. The Rishi Lake showed the highest concentration of 0.3 mg/L, which is the highest permissible limit set by BIS. Copper was detected in all lakes of Marathwada and Vidarbha regions, with the Rishi Lake and the Salim Ali Lake having concentrations of 1 mg/L. The detection limit of test for copper is 0 mg/L. Fluoride was detected in a few lakes with the highest concentration observed in the Lonar Lake. The detection limit of test for fluoride is 0 mg/L. The physico-chemical parameter values are depicted in Figure 4.

3.2. Watershed Characteristics

Watershed delineation provides valuable information about elevation, slope gradient, stream flows, and catchment area topology, among other factors. Digital elevation data sourced from the USGS portal, which has a resolution of 30 m, were used to create watershed delineation maps with the help of ArcGIS 10.8 software. It is important to note that water flows from higher elevations to lower elevations, and the geomorphology of the watershed plays a crucial role in determining the quality of water in nearby lakes and water bodies. As shown in Figure 5, the streams depicted flow from higher elevations to lower elevations.

In the state of Maharashtra, specifically in the Vidarbha region, the Ambajhari Lake and the Futala Lake in Nagpur city are located at higher elevations in their respective watersheds. Similarly, the Chhatri Lake and the Wadali Lake in Amravati city are situated at higher elevations. The Shakkar Lake in Chikhaldara is also located at a higher elevation in its catchment area. The Khindsi Lake near Ramtek and the Sawanga Lake in Sawanga Vithoba village are located at medium elevation points. Finally, the Surabardi Lake, located on the outskirts of Nagpur city, is situated at a lower elevation compared to the other lakes mentioned.

Lakes in the Marathwada region of Maharashtra state are situated at lower elevations, as depicted in Figure 6. The Somthana Lake and the Kharpudi Lake are examples of lakes in the watershed that are situated at lower elevations. The Salim Ali Lake, the Harsul Lake in Chhatrapati Sambhajinagar city, and the Moti Lake in Jalna are situated at medium elevations in the watershed. Lakes receive water from various streams within the watershed. Lakes situated at lower elevations receive water from more streams than those situated at higher elevations. Runoff carries various types of sediments that affect the quality of water in the lake. Additionally, many chemicals are present in the dissolved form in the runoff water, which also deteriorate the water quality.

3.3. LULC Pattern and Its Effect on Water Quality

The LULC pattern within a particular watershed is subject to change over time, which may result in increased pollution. The Sentinel-2 data, which were obtained from the Copernicus portal “https://browser.dataspace.copernicus.eu/ (accessed on date 17 May 2024)” were utilized for the purpose of analyzing the LULC pattern within the watershed. ArcGIS software version 10.8 was used to create LULC maps. As the population grows, there may be a reduction in fallow and vegetative land, as buildings are constructed in these areas. The LULC values observed in the study region are presented in

Table 3. LULC percentage in study area.

Watershed	Water Bodies	Built Up	Vegetation	Fallow Land
Ambajhari Lake and Futala Lake	1	25	66	8
Chhatri Lake and Wadali Lake	0.4	16	82	1
Harsul Lake and Salim Ali Lake	2	22	63	12
Kharpudi Lake and Moti Lake	1	20	61	18
Khindsi Lake	2	37	51	10
Lonar Lake	7	5	37	51
Surabardi Lake	2	24	67	8
Rishi Lake	2	8	85	5
Sawanga Lake	2	8	75	15
Shakkar Lake	0.7	17	62	21
Somthana Lake	0.6	7	65	27

.

The predominance of vegetation in most watersheds is evident in Figure 7. Notably, in the Ambajhari Lake, the Futala Lake, and the Surabardi Lake watersheds, which are situated in the city of Nagpur, built-up areas constitute a significant proportion of up to 25% of the watershed. Furthermore, there are numerous smaller water bodies present within the watershed, accounting for 1% of the total area. The city’s expansion in all directions has led to an increase in built-up areas and a corresponding decline in the percentage of fallow land and vegetation. Despite this, the vegetation percent in these watersheds remains substantial at 66%. The Khindsi Lake watershed, located in Ramtek near Nagpur city, exhibits a significant percentage of built-up land, amounting to 37% of the watershed’s total area. Additionally, numerous small water bodies are dispersed throughout the surrounding region, comprising 2% of the overall watershed. The high percentage of built-up land in the Khindsi Lake watershed can be attributed to its proximity to Nagpur city. The Lonar Lake watershed, on the other hand, exhibits a greater proportion of fallow land, amounting to 51% of the total area, while built-up land is relatively less prevalent compared to other watersheds. This disparity may be due to the hilly terrain in the region. The presence of a substantial water body, covering 7% of the total watershed area, is also observed in the Lonar Lake catchment. The Chhatri Lake and the Wadali Lake watersheds, along with the Rishi Lake watersheds, exhibit a higher percentage of vegetation, with over 80% vegetative cover. The Rishi Lake watershed boasts the highest vegetative cover in its catchment area, amounting to 85%. A minimal percentage of fallow land was observed in these watersheds. The Rishi Lake watershed features several water bodies with a considerable surface area, covering 2% of the watershed’s total area. The Shakkar Lake watershed exhibits developed land on one side, vegetation in the center, and fallow land on the other side of the watershed. This may be attributed to the hilly terrain present in the area. The Shakkar Lake watershed possesses a substantial percentage of fallow land, accounting for 21% of the total watershed area. It was also observed that there are a limited number of water bodies in the Shakkar Lake watershed.

In Figure 8, it was observed that the presence of vegetation was dominant in all three watersheds. Additionally, fallow land was also observed in each of the watersheds. The highest percentage of fallow land was observed in the Somthana Lake watershed, while this was observed in a very small proportion in built-up areas. Along with Somthana Lake, there were several small water bodies observed in the given watershed. Both the Salim Ali Lake watershed and the Harsul Lake watershed, which are situated in the Chhatrapati Sambhajinagar city, showed a significant proportion of built-up areas. These watersheds also have a presence of vegetation. Smaller water bodies were observed in these watersheds as well. In the Kharpudi Lake watershed and the Moti Lake watershed, vegetation was more prevalent. The presence of water bodies in these watersheds was minimal. An increase in population leads to an increase in built-up areas, which subsequently leads to a surge in pollution. Additionally, an increase in population puts stress on the surrounding water bodies.

3.4. Statistical Analysis

The Pearson correlation coefficient is a measure of the linear relationship between two sets of data. A positive value of the correlation coefficient indicates a high degree of correlation between the two parameters, while a negative value indicates a low degree of correlation. By analyzing the values presented in

Table 4. Pearson correlational values of physico-chemical parameters.

	pH	Turbidity (NTU)	Chloride	Total Hardness	Fluoride	Nitrate	Orthophosphate	Copper	Manganese	Cadmium	Nickel
pH	1
Turbidity (NTU)	0.22	1
Chloride	0.14	0.28	1
Total Hardness	0.07	0.10	0.75	1
Fluoride	0.61	−0.17	0.35	0.16	1
Nitrate	0.77	−0.04	−0.03	−0.16	0.57	1
Orthophosphate	0.43	0.34	0.16	0.09	0.12	0.57	1
Copper	0.03	0.93	0.12	−0.02	−0.33	−0.12	0.29	1
Manganese	−0.04	0.60	0.02	0.12	−0.10	−0.12	0.06	0.58	1
Cadmium	0.07	0.05	0.40	0.21	0.26	0.34	0.08	0.09	−0.05	1
Nickel	−0.08	0.63	−0.15	−0.14	−0.23	−0.08	0.20	0.68	0.90	−0.15	1

, it is possible to determine the correlation between the various water quality parameters. The Pearson correlation coefficients indicate that there are both positive and negative correlations between certain parameters. It was observed that the pH was correlated with metal ion concentrations and this also affects solubility. Metals such as iron and manganese are more soluble at lower pH levels. Turbidity indicated the presence of suspended particles in water samples. Nitrate affects the presence of lead in water bodies. High levels of nitrate can mobilize both lead and copper. In order to understand the impact of each parameter on water quality and on one another, it is necessary to correlate them with each other. The Pearson correlation method is one of the most effective ways to gain insight into the relationships between parameters.

The following observations were made with regard to the water quality parameters: with the exception of manganese and nickel, all other parameters exhibited a positive correlation with pH. Copper demonstrated a lower affinity, while nitrate showed a strong correlation with pH, resulting in higher values. The positive correlation between nitrate and orthophosphate might be due to fertilizer runoff as both are present in agricultural fertilizers. Fluoride and nitrate displayed a negative correlation with turbidity, while the other parameters exhibited a positive correlation. A positive correlation between pH and fluoride indicates geogenic sources while pH correlating positively with total hardness indicates carbonate weathering. A positive correlation of turbidity with orthophosphate indicates soil erosion from agricultural land. Copper exhibited the highest value of correlation, while cadmium displayed a weak correlation with turbidity. In the case of chloride, nitrate, and nickel, negative correlations were observed, while the other parameters exhibited positive correlations. A positive correlation between copper, cadmium, and nickel indicates the anthropogenic source of pollution. Except for nitrate, copper, and nickel, all other water quality parameters showed positive correlations with total hardness. Copper, manganese, and nickel exhibited negative correlations with fluoride, while nitrates demonstrated a higher positive value with fluoride. Turbidity positively correlating with the heavy metals indicates that metals are bounded to suspended particles, possibly from industrial effluents. Copper, manganese, and nickel exhibited negative correlations with nitrate, while higher positive correlations were observed with orthophosphate. Orthophosphate exhibited positive correlational values with copper, manganese, cadmium, and nickel, indicating the presence of heavy metals. Copper demonstrated the highest positive correlation value, while manganese exhibited a negative association with cadmium and a positive connection with nickel. The strongest correlation was observed between manganese and nickel, while cadmium demonstrated a negative relationship with nickel.

4. Discussion

The analysis of water quality in the study area, encompassing 15 lakes from the Vidarbha and Marathwada regions, provided critical insights into the impact of physico-chemical parameters, heavy metal contamination, and LULC changes on these aquatic ecosystems. The results from this study highlight significant pollution levels, as well as the need for further investigation into the sources of these pollutants and their broader implications for both human and environmental health.

4.1. Water Quality and Heavy Metal Contamination

Water quality, as a broad and multidimensional concept, is affected by various physico-chemical parameters, which provide a clear indication of the pollution levels in aquatic ecosystems [96,97,98,99,100]. The evaluation of parameters such as pH, turbidity, chloride, total hardness, and heavy metal concentrations revealed critical deviations from acceptable standards in several lakes. This is particularly important, as water quality significantly impacts its intended use, whether for drinking, irrigation, recreation, or industrial purposes [99,100,101,102]. The findings from this study demonstrate that most lakes exhibited pH levels outside the acceptable range set by the BIS. Elevated pH levels, particularly in the Lonar Lake, indicate increased alkalinity, which can have detrimental effects on aquatic life by altering the solubility and toxicity of heavy metals such as iron and manganese. Similarly, turbidity levels in many lakes exceeded the BIS permissible limits. Turbidity, which is caused by suspended particulate matter, can reduce light penetration, affecting photosynthetic activity and overall water quality.

Heavy metals such as cadmium, nickel, and manganese were detected in trace amounts across various lakes, with concentrations exceeding the permissible BIS limits in some cases. These findings are concerning, as heavy metals are known to bioaccumulate in aquatic organisms, leading to toxic effects throughout the food chain. For example, cadmium, which was detected in several lakes, can be introduced into water bodies through industrial waste and the weathering of rocks. Given its high toxicity, even in small concentrations, cadmium poses significant risks to both human and environmental health. Similarly, the presence of nickel and manganese, which can also originate from industrial and anthropogenic activities, further emphasizes the potential threat to water quality and aquatic life. The overall presence of heavy metals, even in trace amounts, highlights the importance of continuous monitoring. Heavy metal contamination can have long-term impacts on both the environment and public health, particularly in regions where water bodies are used for drinking and irrigation purposes. The heavy metal load, as indicated by this study, underscores the urgent need for remediation efforts in the affected lakes.

4.2. Impact of LULC Changes on Water Quality

The present study highlights the substantial influence of LULC changes on water quality. The analysis of watershed delineation and LULC revealed substantial transformations in the landscape over time, principally driven by urbanization and agricultural expansion. As human activities persistently alter the natural environment, the transport of pollutants into water bodies via surface runoff has emerged as a critical concern. In this study, watersheds with substantial proportions of built-up areas, such as the Ambajhari Lake and the Futala Lake watersheds, exhibited higher levels of pollution. This observation aligns with previous research [74,75] which has demonstrated that urbanization increases impervious surfaces, thereby leading to increased runoff and the subsequent transport of pollutants into water bodies. Moreover, the conversion of vegetative areas into residential land has diminished the natural filtration capacity of these landscapes, contributing to elevated sediment and chemical loads in lakes.

Usually, watersheds with higher percentages of vegetative cover exhibited lower pollution levels. Vegetation functions as a natural buffer, reducing runoff, filtering pollutants, and stabilizing the soil, thus minimizing the entry of contaminants into water bodies. On the other hand, regions with diminished vegetation, increased built-up areas, and elevated fallow land, such as the Lonar Lake watersheds, were associated with poorer water quality. Most of the lakes are urban lakes and situated in the middle of a city surrounded by the habitats, and they show higher levels of human-induced pollution like the Rishi Lake, the Salim Ali Lake, the Kharpudi Lake, and the Moti Lake. This pattern highlights the substantial role that land management practices play in maintaining or degrading water quality in these ecosystems.

4.3. Application of GIS and Remote Sensing for Water Quality Monitoring

GIS and remote sensing technologies have proven to be effective tools for monitoring water quality over large areas at a lower cost compared to traditional field-based methods [103,104,105]. The utilization of remote sensing technology in this study allowed for the identification of critical areas where urbanization and agricultural activities have intensified, as well as the comprehensive assessment of factors contributing to water pollution [106,107,108,109]. The detected levels of pollution, particularly from heavy metals and human activities, indicate the need for targeted interventions to enhance water quality. Moreover, the employment of Water Quality Indexes (WQI) can offer a simplified representation of the water quality status, making it simpler for policymakers and stakeholders to comprehend and address water quality challenges. This study emphasized the crucial need for continuous monitoring of water quality in the Vidarbha and Marathwada regions, especially considering the ongoing urbanization and industrialization. By utilizing modern technologies such as GIS and remote sensing, and adopting proactive water management strategies, it is possible to alleviate the adverse impacts on water quality and ensure the sustainability of these vital aquatic ecosystems for future generations.

5. Conclusions, Limitations, and Recommendations

The analysis of lake water from the Marathwada and Vidarbha regions of Maharashtra highlights the presence of trace amounts of heavy metals, including cadmium, copper, and nickel, in several lakes. Although most of the parameters fall within the permissible limits set by the Bureau of Indian Standards (BIS), the presence of these heavy metals, even at low concentrations, poses potential risks to ecosystems and communities that depend on these water bodies. Heavy metals can bioaccumulate over time, rendering the water hazardous for both human consumption and aquatic life. The sources of these heavy metals include both natural geological factors and anthropogenic activities, such as domestic waste disposal, leaching of rocks, and industrial effluents. Elevated levels of nitrate in the Lonar Lake were likely caused by agricultural runoff from surrounding areas, and this lake also exceeded acceptable pH limits, indicating additional pollution concerns. Lakes located in urban areas, particularly those in the Marathwada region, exhibited higher turbidity levels, likely due to urban runoff and increased human activity. The Rishi Lake, notably, had concentrations of manganese and nickel that exceeded BIS standards, while cadmium was present at elevated levels across most of the lakes studied. The persistence of cadmium is particularly concerning, given its known health risks, and while copper levels remained within permissible limits, its presence across all lakes suggests potential for future accumulation and toxicity. Watershed characteristics revealed that lakes situated at lower elevations, such as the Khindsi Lake and the Surabardi Lake, receive water recharge from larger catchment areas, which may contribute to the degradation of water quality as pollutants are transported into these lakes. Changes in LULC patterns, particularly in urban areas, also had significant impacts on water quality. Lakes located in urban centres, such as the Ambajhari Lake, the Futala Lake, the Salim Ali Lake, and the Harsul Lake, were especially vulnerable to pollution from domestic waste and urban activities.

The findings of this study suggest that ongoing pollution and the presence of heavy metals pose serious threats to the health of both ecosystems and human populations relying on these water bodies. Immediate action is needed to mitigate pollution, improve water quality, and protect both the environment and public health. However, this study has certain limitations that need to be addressed in future research. First, the study represents a single snapshot in time; long-term monitoring of water quality and heavy metal accumulation would provide a better understanding of seasonal and temporal variations in pollution levels. Additionally, while this study identifies the presence of pollutants, it does not specifically trace their exact sources. Future studies should focus on pinpointing the sources of pollution, whether from geological, industrial, or agricultural activities, to better target mitigation efforts. Another gap in this study is the lack of direct assessment of the health impacts on local populations due to water contamination. Future research should include epidemiological studies linking water quality data with health outcomes to provide critical evidence for policy interventions. Moreover, this study focused on specific heavy metals and physico-chemical parameters, but did not evaluate other potential contaminants such as microplastics or pharmaceuticals. Incorporating a broader range of pollutants in future analyses would offer a more comprehensive assessment of water quality. Finally, the study did not address the social and economic implications of water pollution, which are essential for developing effective water management policies. Understanding the socio-economic impacts of water quality deterioration is crucial for a holistic approach to environmental management.

To address these issues, several recommendations can be made. Public awareness campaigns should be initiated to inform local communities about the importance of lake conservation and pollution prevention. Industries, urban runoff, and agricultural activities near water bodies should be carefully monitored, and measures should be implemented to control and reduce pollution from these sources. Strategies to minimize heavy metal contamination should include proper waste management techniques, reduced use of heavy metal-containing fertilizers, and treatment of industrial effluents before they are discharged into water bodies. Establishing wastewater treatment plants is also essential to ensure the efficient removal of pollutants before they enter lakes and other water bodies. Enforcing stricter regulations to prevent the direct release of harmful chemicals into water bodies is necessary, and sustainable agricultural practices such as precision agriculture, crop rotation, and reduced use of chemical fertilizers should be promoted to minimize agricultural runoff containing pesticides and nutrients. By addressing these gaps and implementing these recommendations, it is possible to improve water quality in the Marathwada and Vidarbha regions and protect the health of the communities that rely on these water bodies.