Spatial pattern analysis of zooplankton and surface water of pit lakes (Raniganj coal field, India)

ABSTRACT Open pit technique is often the simplest and most cost effective mining technology. Coal mining, which began in 1774 in Raniganj, West Bengal, India, led to many abandoned pits, which led to the formation of pit lakes. Mining has a tablvariety of environmental ramifications around the world. On the other hand, the pit lakes of the Raniganj coal field (RCF) are being used for recreational purposes. Knowledge of the spatial pattern in the zooplankton population and surface water quality in pit lakes waters of the Raniganj coal field region is rather limited. Sixteen pit lakes in the RCF in West Bengal, India, were investigated from January 2019 to December 2021. In the present study, we analyzed the spatial pattern and mode of distribution of zooplankton communities based on Coefficient of dispersion methods. To perform a comprehensive analysis of the water quality condition of this study area, we apply water quality index (WQI) methods. A total of 51 taxa were identified and the density was primarily dominated by rotifers. The most dominant species were: Brachionus forficula, Paracyclops sp. and Sida sp. Clumped type of zooplankton distribution pattern was observed in the studied pit lakes. The water quality in most of the studied pit lakes was poor. All the water quality parameters showed significant spatial variations as per Kruskal-Wallis test. The clustering technique produces three groups of pit lakes that are particularly convincing as the pit lakes in these groups showed comparable characteristics.


Introduction
Water resources, both surface and groundwater, will be affected by different stages of coal mining and even after it has ended.This includes the mining operations, mine dewatering, leakages of contaminated leachates and outflows of untreated water (Farhad et al., 2017;Rambabua et al., 2020;Randive & Jawadand, 2020).Due to lack of suitable strategy in India, water is frequently dumped without purification or proper utilization after mining operations.The number of pollutants in mine water can vary significantly.Some sources of mine water are used for industry, agriculture, and even drinking and domestic purposes that can be met with appropriate treatment.In the past several decades, open-cast mining activities have become widespread in India to recover economically viable and valuable ore near the surface (Goswami, 2015).As backfilling is typically tricky or economically unfeasible, an empty pit remains once extraction operations are done.This is known as a mine void (Ghosh, 2012).Later the voids are filled by rainwater, surface and sub-surface water.The once-water-filled mine gap has turned into a pit lake (Palit & Kar, 2019).The water quality in such pit lakes varies greatly depending on the geological catchments.The Raniganj coal field (RCF) has a large concentration of pits lake clusters in West Bengal, India.Mining-induced pit lakes initiated to form in Raniganj in the 20 th century.Most pit lakes are formed due to opencast coal mining, frequently accompanied by clay and sand extraction.The hydro-chemical and hydro-biological characteristics of lakes within the RCF still need to be better understood.The amount of information obtained from the literature from this domain involves either study of a single aspect or just regional-wide studies because the pit lakes are located in inaccessible terrains.As a result, most of them have remained unaltered by anthropogenic activity, retaining their original characteristics.Although some pit lakes are widely accessible and have been utilized for local people for various purposes, including bathing, agriculture, and fish farming, there is currently no systematic monitoring and management strategy for the lakes (Palit & Kar, 2019).For this reason, many pit lakes have faced water quality degradation and rapid water loss.Water quality monitoring helps us to identify water management criteria (Melo et al., 2020).On the other hand, monitoring programs create an enormous dataset that needs interpretation skills.Water quality assessment techniques include the multi-index method, geographical interpolation, fuzzy mathematics, artificial neural network, and multivariate statistical analysis (Chen et al., 2019;Deng & Wang, 2017;Mladenović-Ranisavljević et al., 2018;Rakotondrabe et al., 2018;Singh et al., 2019).The most common is the WQI, which converts a complicated collection of the dataset into a single number water quality indicator and indicates its appropriateness for various applications (Walsh & Wheeler, 2013;Wu et al., 2017).Multivariate statistics, primarily using principal component analysis (PCA) and cluster analysis (CA), is another routinely utilized technique (Dutta et al., 2018) aiding in the development of better knowledge of the spatio-temporal variation of water quality.Various experts have been monitoring RCF's pit lakes for years (Ghosh, 2012;Pal et al., 2013;Palit & Kar, 2019).Several studies have been conducted in this area, including an examination of vegetation, soil quality, the influence of coal extraction on local livelihoods, and water quality (Manna & Maiti, 2018;Mondal et al., 2020;Patra et al., 2022).But the use of multivariate techniques to evaluate water quality and spatial distribution of water quality has not been performed earlier in the pit lakes.The pit lakes of the RCF region have gained national and worldwide interest for a variety of reasons, including biodiversity, plant composition, and current water quality.This study examines several physicochemical characteristics of water for these pit lakes.In addition, a WQI is computed to analyze the optimal quality of pit lakes.WQI is a popular technique for expressing the quality of water and determining its appropriateness for specified usage (Bawoke & Anteneh, 2020;Chaurasia et al., 2018;Finotti et al., 2015;Macwan & Patel, 2019).An essential advantage of WQI is that it significantly reduces the dataset, simplifies the display of water quality status (WQS) and simplifies water quality assessment (Al-Omran et al., 2018).WQIs have been used and are important in measuring the water quality of lakes all over the world, according to a number of studies (Alexakis et al., 2016;Goher et al., 2014;Kukrer & Mutlu, 2019;Trikoilidou et al., 2017;Wang et al., 2019).Therefore, ecosystem monitoring programs can leverage these indicators to assist the decisionmakers with general public awareness about the environmental conditions (Bilgin, 2018;Tiwari et al., 2016;Venkatramanan et al., 2017).It is known as the most successful method of raising public knowledge about the quality of water and its sustainable utilization (Singh et al., 2015).As a consequence, describing the status of water sources using water quality indicators has become standard procedure (Bora & Goswami, 2017;Bouslah et al., 2017;Espejo et al., 2012;Jha et al., 2020).There have been few studies (Pal et al., 2013;Palit & Kar, 2019) on the quality of water from pit lakes, particularly in the RCF area, so this research is useful because it discusses the suitability of surface water sources in this region for human use, and it provides community members and legislators with overall water quality data.In addition to water quality monitoring using WQI method, zooplankton also plays crucial link in the secondary-level energy transfer between autotrophs and heterotrophs .Due to the large density, shorter life span, drifting nature, high taxa/species diversity and different tolerance to the environmental stress, they are being used as indicator organisms for the physical, chemical and biological processes in the aquatic ecosystem (Li & Chen, 2020;Muñoz-Colmenares et al., 2021;Uriarte & Villate, 2004).Despite this enormous role played by zooplankton in water bodies, their distribution has been reported to be affected by factors such as the hydrologic regime and hydrological characteristics of the water body (Alprol et al., 2021;Cem & Bozkurt, 2019;Ismail & Adnan, 2016).The cladocerans, rotifers, ostracods and copepods that makeup zooplankton are thought to be the most significant in terms of population density, biomass production, grazing, and nutrient regeneration in every aquatic environment (Sehgal et al., 2013).In the pit lakes of RCF area, knowledge about zooplankton communities is relatively limited.Nevertheless, knowledge of the zooplankton community is also fundamental in understanding the biogeochemical cycles and energy flows of pit lake ecosystems.The spatial pattern of plants and animals is an important feature of ecological communities (Connell, 1963).There are three distinct patterns: random, clumped, and uniform.Once a pattern has been identified, it is easy to hypothesize the structure of ecological communities.Therefore, to contribute to this discussion, we made an effort to analyze the spatial pattern of surface water quality and zooplankton of some pit lakes in the RCF area.The main objectives of this paper are: 1) spatial pattern analysis of surface water quality of pit lakes and 2) spatial pattern and diversity analysis of zooplankton in the pit lakes.This study can also provide the basis for further scientific investigations into mine lakes formed by mining operations such as coal mining and for decisions on the end use of mine lakes.

Study area
Raniganj Coalfield (RCF) is the country's first coal mining site.RCF was India's primary coal-producing area throughout the nineteenth and early twentieth centuries.The area of the Eastern coalfield limited (ECL) mining leasehold is 753.75 square kilometers, while the area of the surface is 237.18 square kilometers.It spreads between West Bengal and the Jharkhand State of India.The heart of RCF lies north of Ajay and south of the Damodar River of West Bengal.The RCF's terrain is moderately sloping, with elevations ranging from 65 to 75 meters above sea level.The drainage pattern is mostly dendritic to subdendritic and the majority of the mines in this coalfield are located between the Damodar and Ajay Rivers (Srivastava & Mitra, 1995).The Barakar and Raniganj Formations' coal-bearing horizons make up a sizable portion of the coalfield.These two coalbearing layers are separated by the fluvial-lacustrine Iron Stone Shale coal barren sequence (Singh et al., 2010).The principal coal-bearing horizons are the Barakar and Raniganj formations, and the Gondwana Supergroup is represented by the Raniganj coalfield.The center and southern parts are home to the Raniganj formation, which is composed of extensive coal seams, carbonaceous shales, and fine grayish sandstone.Except for the east, where alluvium covers the area, the Raniganj coalfield is surrounded by Archaean rocks on all sides (Singh et al., 2010).The climate is tropical, with substantial temperature swings and is distinguished by moderate winters and hot summers.The typical high temperature in May -June is approximately 38°C, and it decreases to 5-8°C in winter.The rainfall ranges between 1,200 and 1,400 mm annually.In this study, 16 pit lakes of Paschim Bardhaman district of West Bengal, India, were included for water quality analysis (Table 1 and Figure 1).The areas of the pit lakes varied from 3.31 to 92.21 acres and the mean depths varied from 13.72 to 126 m.All the pit lakes were sandy with mud and had permanently waterlogged hydro-periods.Coal mining was the prior mining activity at all sample sites.In terms of shape, most of the pit lakes could be considered irregular.Amdiha, Harabhanga 2, and Searsole Pit Lakes were quite spherical, while Samdiha, Harabhanga 1 Pit Lakes were linear.All the pit lakes had aquatic plants which were submerged and attached to the sediments in the littoral zone.Regarding possible water pollution sources and their directional flow of it, Dalurbandh, Searsole, and Bonbedi Pit Lakes have a possible pollution source from domestic sewage and cattle washing and bathing.

Sampling procedure and analysis
Water samples were collected from January 2019 to December 2021.Due to the regional variance in bathymetry in the pit lakes under study, sampling was limited to the surface layer.Four samples were obtained from each pit lake at four distinct points (n = 144/pit lake) at a depth of 1.5-2 m.The pit lakes were all evaluated using the same set of water quality parameters: pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), phosphate (PO 4 3-), nitrate (NO 3 ), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chloride (Cl − ).pH, EC, TDS and DO were measured in situ.Winkler's titrimetric method was used to determine the DO of the surface water.Mercury thermometers were employed to assess the surface water temperature.The pH of the water was measured using a portable digital pH meter.Total dissolved solids (TDS) were measured using a portable TDS meter, and electrical conductivity (EC) was measured using a conductivity meter.
A one-liter capacity bottle was used to collect the water samples and sent to the laboratory in an icebox for further analysis.All apparatus was calibrated before analysis.Table 2 displays the techniques utilized for each parameter analysis, as well as the equipment and units employed in this investigation.The zooplankton samples were collected fortnightly from the above-mentioned stations (four samples/station) during each visit.Zooplankton samples were collected using a zooplankton net of different mesh size (30 µm, 50 µm and 160 μm) and the sampling times were 6.00 am to 8.00 am.The mesh-size was changed to improve sampling efficiency.Samples were stored in small 100-ml vials and preserved in 4% formalin.Three major zooplankton groups (Cladocerans, Copepods and Rotifers) were counted in a Sedgwick Rafter counter and expressed in numbers/liter.

Water quality index (WQI)
The water quality index measures the overall water quality of a certain source at a given time by utilizing a "single value" specific to different water quality parameters (Kangabam et al., 2017;Mgbenu & Egbueri, 2019;Nazir et al., 2016).In this study, the weighted arithmetic WQI method was applied.The weighted arithmetic WQI was calculated using the standard literature (Akther & Tharani, 2017;Udeshani et al.,

2020
).The WQI is used to calculate the total influence of specific factors on water quality.The following stages were involved in calculating WQI: The equation was: [Wn represented the unit weight; Vs represented the standard value of the i th parameter and represented the proportionality constant.
1 P 1 = Vs ¼ 1; 2; ::::::n (3) Where Vn was the collected value of each parameter selected, Vi is the ideal value of each parameter selected [Vi = 0, but for pH (Vi = 7) and dissolved oxygen (Vi = 14.6)].The standards given by the Bureau of Indian Standards (2015) were taken into consideration to determine the permissible limit of drinking water.WQS was established by classifying the calculated WQI values into five categories.pH, electrical conductivity, total dissolved solids, total hardness, total alkalinity, phosphate, nitrate, dissolved oxygen, biochemical oxygen demand and chloride were among the ten water quality parameters used in the WQI.

Statistical analysis
Descriptive statistical analyses such as mean and standard deviation (SD) were computed to quantify each water quality parameter.The Shapiro test was used to ensure that the data set was normal for all parameters.
Non-parametric statistics were utilized since the majority of the variables had non-normal distributions or unequal variances.
To determine the most important factor influencing the decline of the water quality and to foretell the source of contamination in the pit lake system, PCA was used for each pit lake.Furthermore, basic factor analysis was used to minimize the number of variables.The level of significance of the Kaiser-Meyer-Olkin sphericity test was > 0.004 which indicates the suitability of the dataset to apply PCA.Another PCA was also performed to understand the ordination of the studied pit lakes based on zooplankton abundance.Nonparametric statistics were utilized since the majority of the variables had non-normal distributions or unequal variances.Cluster analysis (CA) focused on physicochemical parameters and WQI was provided in a dendrogram to demonstrate the association among study areas using PAST (version 3) statistical software (Hammer et al., 2001).To assess any significant changes in water quality across stations, the Kruskal-Wallis test was used and P ≤ 0.05 was considered statistically significant.Furthermore, the correlation (Spearman) among the physicochemical parameters was examined in this study to evaluate the degree of reliance on the parameters.The SPSS 22.0 program was employed to analyze the data.For many years, the Coefficient of dispersion was used by plankton ecologists, both as a means of detecting patchiness and as a measure of the degree of patchiness.When it became necessary to quantify different patterns of dispersion in populations, indices based on the variance/mean ratio were inappropriate since they are nearly all influenced by the number of individuals in the sample.A more generally accepted measure of patchiness was based on the index of "mean crowding" (Lloyd, 1967).The formulae were presented below: Pearson's co-efficient of dispersion xi= number of individuals in a sample, N = number of samples.Species of a particular site were considered to be clumped, aggregated or contagious in distribution if the value of mean crowding exceeded the value of X.When X was equal to mean crowding, the distribution pattern was considered to be random or uniform, and when X was less than mean crowding, it was considered to be tending toward regularity.

Spatial variation of hydrological parameters
In this study, drinking water standards from the Bureau of Indian Standards (BIS, 2015) was used as a reference.Table 3 displays descriptive statistics for the 10 physicochemical water parameters for each pit lake examined, together with their determined standard deviations.The pH of water is an essential indicator to assess water quality conditions and the aquatic life is also impacted (Osibanjo et al., 2011).Concerning pH, the present result showed that, the pH tended to be alkaline in all studied pit lakes (7.16 ± 0.20-8.19± 0.18), except for Harabhanga 2 Pit Lake, which was close to neutral (6.97 ± 0.08).The lowest and highest mean values of pH were recorded in the surface water of Samdiha and Khadankali Pit Lakes, respectively.Also the present result showed that pH levels were within appropriate limits (6.5-8.5)similar to the results of Mondal et al. (2015) and Palit et al. (2017) for the pit lakes in RCF region.The EC values ranged from 278.94 ± 51.54 to 635.84 ± 22.45.It is clear that most of the pit lakes crossed the BIS (2015) permissible limit of 300 µS/cm.Agricultural runoff from the surrounding areas might be the cause of the higher EC values.Dissociated ions, their electrical charges, mobility, and temperature all have a big impact on the EC.The TDS ranged from 229.53 ± 10.61 to 394.43 ± 58.28 in the studied pit lakes, which were within the acceptable range (500 mg/l).The weathering processes of certain sedimentary rocks or due to irrigation, household discharge and sewage effluents may control TDS level in the water.Similar results were reported by Mondal et al. (2015) in some pit lakes of the RCF region.Chloride is an essential water quality indicator that may be present in the form of salts of different minerals (sodium, potassium and calcium).The chloride ranged from 25.03 ± 3.37 to 45.52 ± 11.88 and it was within the permissible limit (250 mg/l) in our study.Mondal et al. (2015) also reported a similar type of result in their study about the water quality of coal mine-generated pit lakes in India.A rise in nitrate concentration in lakes improves phytoplankton productivity (Pandit & Yousuf, 2002).An increase in nitrate levels indicates that a water body has been enriched with nutrients.Natural nitrate concentrations in water are typically low, but concentrations rise due to anthropogenic activity such as agricultural operations, the discharge of household wastewater and drainage system effluent (Barakat et al., 2018;Sreenivasulu et al., 2014).The nitrate concentrations in this study ranged from 0.52 ± 0.11 to 7.81 ± 5.72, which is under the permissible limit (45 mg/l).Mondal et al. (2015) also reported a similar type of result in their study about pit lakes in the RCF region.Some manmade activities and the rocks present in the water bodies can also be consider as the source of hardness (Bouslah et al., 2017).The TH values ranged from 144.41 to 304.57mg/l in this study which was under the permissible value (500 mg/l).The TH levels vary significantly among the pit lakes investigated.This might be due to rainwater's solvent effect on the soil and rocks (Vilane & Dlamini, 2016).The most crucial factor in determining the quality of water is DO, which also determines the trophic status and the biological activity of an aquatic environment (Granier et al., 1999).It is the quantity of gaseous oxygen (O 2 ) dissolved in water that reflects the availability of oxygen for aquatic absorption.It is critical for all aquatic life (Ewaid & Abed, 2017).Temperature, salinity, and the presence of organic matter are all important factors that might alter the DO level in the water.The DO values ranged from 4.23 to 6.71 mg/l.Mondal et al. (2015) also observed a similar type of DO level in some pit lakes.It is amongst the most widely used parameter for assessing water quality.The BOD values ranged from 1.92 to 2.74 mg/l.The Phosphate (PO 4 3-) is an important ingredient for all living things and its presence in the water may be due to natural deposition (Tipping et al., 2014).The Phosphate value ranged from 0.98 to 3.91mg/l.Mondal et al. (2015) also observed low PO 4 3-in some pit lakes.The Kruskal-Wallis test findings revealed significant differences (p ≤ 0.05) for all of the variables examined (Table 4).PO 4 3-, DO, TA, and Cl − had the highest variations.
disposal, sanitary landfills, over-application of inorganic nitrate fertilizer, or poor manure management practices (Chapman, 1996).Nitrate concentrations are found to be within permitted limits across the research region, with varying degrees of concentration.The pit lakes of Pandebeswar and Salanpur blocks have a significantly higher DO value.Most pit lakes have BOD levels between 2.08 and 2.25 mg/l.Chloride levels are somewhat elevated notably in the pit lakes of the Raniganj and Andal blocks (Figure 2).

Correlation and cluster analysis
Spearman's correlation matrix is presented in Table 5.
In the present study, EC shows a statistically significant positive correlation with TDS (r = 0.718, p = 0.002), TA (r = 0.565, p = 0.023), and TH (r = 0.547, p = 0.028).TDS shows a statistically positive correlation with TA (r = 0.550, p = 0.027) and a negative correlation with nitrate (r = −0.521,p = 0.039).TH shows a statistically positive correlation with BOD (r = 0.524, p = 0.037).DO shows a significant positive correlation with the BOD (r = −0.570,p = 0.021).The increased contaminants in surface water increase the amount of free ions in the water, which increases EC (Singh et al., 2015), as shown by the positive correlation of TDS, TA, and TH with EC.According to Watkar and Barbate (2015), the negative relationship between EC and pH shows that an increase in the ionic load to water bodies enhances the oxidation of organic matter, resulting in the release of nutrients and a fall in pH.
The values of EC and TDS are correlated as also observed by other studies (Patil et al., 2012;Marandi et al., 2013;Daniels et al., 2016).Cluster analysis exposes a dataset's hidden behavior by categorizing items into groups or clusters based on similar criteria (Dutta et al., 2018.CA was applied to find out geographical similarities among the study locations.It produces a dendrogram applying the Ward linkage and Euclidean distance method (Figure 2), dividing all 16 pit lakes into 3 clusters (Figure 3).The clustering technique produces 3 clusters of pit lakes that are particularly convincing as the pit lakes in these groups showed comparable characteristics.The cluster-1 (Searsole, Joyalbhanga and Samdiha Pit Lakes), cluster 2 (Dhandardihi 2; Khadankali, Nagrakonda, Harabhanga 1; Harabhanga 2; Dhandardihi 1; Dhandardihi 3 and Amdiha Pit Lakes), cluster 3 (Belpahari, Alkushagopalpur, Dalurbandh, Bonbedi and Dalmia Pit Lakes) are the clusters that correspond to contamination of water.This approach is useful in providing dependable categorization of water throughout the region.In the future, the geographical sampling method, instead of monitoring different pit lakes, only four may be chosen without influencing the outcome.The decrease in sample locations will be more cost-effective without compromising the relevance of the outcome in the monitoring program (Bajpai et al., 2013;Sreenivasulu et al., 2014;Vishnu etal., 2014).

Spatial pattern and diversity analysis of zooplankton
A total of 51 zooplankton taxa have been documented.Rotifers were the most abundant group and the most dominant species was Brachionus forficula.Paracyclops sp. was the most dominant copepod and Sida sp. was the most dominant cladoceran species (Table 6).In the present study, a noticeable variations in the zooplankton community structure with regard to different diversity indices were evident among the pit lakes.To evaluate the zooplankton dispersion pattern, coefficient of dispersion and mean crowding were analyzed.In all of the examined pit lakes, spatial pattern analysis revealed a clumped mode of dispersion (Table 7).To determine if a system exhibits a homogeneous or heterogeneous distribution pattern, spatial pattern analysis is used.There have been many studies on a reservoir's temporal dynamics, but research on its spatial dynamics and ecology is scarce in India.As demonstrated by the works of (Bini et al., 1997) at Broa reservoir, Sao Paulo, Brazil, the distribution pattern of zooplankton groups can be regarded as an important tool for such studies.According to comparable research conducted in various eutrophic reservoirs, zooplankton exhibits a heterogeneous or clumped dispersion pattern (Eskinazi-Sant'Anna et al., 2013).The current study's use of Pearson's coefficient of dispersion revealed that zooplankton was distributed in clusters.When the surroundings were consistent, a random pattern of distribution was visible.Utilizing diversity indices is the most practical and straightforward method of examining community features.The outcomes are shown in Table 8.The Shannon-Weiner index for rotifers, copepods and cladocerans were highest in Nagrakonda, Harabhanga 1 Pit Lakes respectively.The dominance for rotifers, copepods and cladocerans were highest in Dhandardihi 2,

Water quality index (WQI)
WQI is one of the most effective approaches to express water quality as it shows overall water quality findings rather than data for each indicator (Adimalla et al., 2018;Toma et al., 2013;Udeshani et al., 2020).Tables 9 and 10 showed the water quality rating based on this WQI, drinking water quality standards and unit weight of individual physico-chemical parameters.The WQI of 16 pit lakes was estimated (Table 11).Spatially, narrow variations in WQI were observed among all 16 sampling sites.The values of WQI ranged from 35.59 to 70.71.These values x indicated the good to the poor class.The WQI map was created and selected quality parameters to decode the many quality classifications for each pit lake, including excellent, good, bad, extremely poor, and unsuitable (Figure 4).The WQI map of the study region showed that the majority of the pit lakes have low water quality, but good water quality (26-50) was found in the Andal block's pit lakes.The map vividly showed that the surface water quality in the pit lakes examined ranged from good to poor.The poor quality of the water samples might be due to the interaction with the different kinds of rock present in the pit lakes or may be due to different anthropogenic activities such as improper disposal of waste and domestic sewage, agricultural runoff, polluted drainage system near the pit lakes, and pollution from the neighboring dumpsite.This study provided us with a vivid overview of water quality on a large scale using the WQI method and established the principal parameters controlling water quality in pit lakes.Further water quality evaluations and local administrative bodies will be benefited from the study's findings.The WQI plays a pivotal role in assessing water quality because it integrates several environmental characteristics into one value; the WQI is crucial in assessing water quality.The local administrative authorities must pay greater attention to evaluating different types of nutrients available throughout the monitoring schedule as well as awareness campaigns to reduce anthropogenic pollution and enhance the overall water quality in the pit lakes.

Principal component analysis
The principal component (PC) is calculated by multiplying the original relational variable by a unique vector.An Eigenvector is a set of coefficients informally called "loading" (Dutta et al., 2018).Kaiser -Meyer-Olkin (Kaiser, 1974) sample adequacy and Bartlett tests of sphericity were used to assess the data's eligibility for PCA.In our investigation, PCA revealed the most important elements influencing water quality.Table 12 summarizes the PCA findings.The number of significant PC was determined based on the Kaiser method with eigenvalues greater than 1 (Kaiser, 1974).PCA of studied water bodies showed that the variables were correlated with four PCs and that 78.33% of the variance was justified.Since the Eigenvalues are greater than 1, the PCs are kept.Four PCs (factors)     components of the observed variance.These were the essential characteristics for determining lake-to-lake differences.
In order to understand the ordination of the studied pit lakes in space, another PCA is applied (Figure 5).Results showed that two principal components were extracted and had defined the stations based on the abundance pattern of three zooplankton groups.The total variance explained by PCA ordination was 98%.A clear spatial ordination of the studied pit lakes was well documented in the PCA bi-plot.It is clear that Amdiha, Samdiha, Dalmia, Bonbedi and Alkusha-Gopalpur Pit Lakes were spatially correlated with a high abundance of copepods and cladocerans.Harabhanga 1, Dhandardihi 2 and khadankali Pit Lakes were mostly represented by Rotifers.Dhandardihi 3 and Nagrakonda Pit Lakes were negatively correlated with rotifer, while the rest of the pit lakes showed a negative correlation with copepods and cladocerans.The highest degree of clumping of cladocerans and copepods was seen in Amdiha, Bonbedi, Alkusa-Gopalpur, Dalmia and Samdiha Pit Lakes.While, rotifers showed the highest degree of clumping in Harabhanga 1, Dhandardihi 2 and khadankali Pit Lakes.In addition, these stations provided a rich nutritional environment for the development of phytoplankton, which in turn promotes the development of zooplankton.Predation and resource availability are the additional factors that contribute to clumping in a particular location, which has previously been established as the main cause (Townsend et al., 2003).Also the fieldwork showed that the aforementioned pit lakes had the greatest fishing success, indicating the presence of resources in this region that contribute to the clumping of zooplankton.It is already known that the presence of planktivorous fish can influence variability in zooplankton distribution (Jeppesen et al., 1997;Romero et al., 2004).

Conclusion
The diagnostic offered by this study is the first step in categorizing water resources based on their intended   usage.In addition to the diagnosis, a spatial map of various water quality parameters was created.There are a few examples in India, but none in the area under investigation.As a result, this research is unique and significant to the management of pit lakes' water resources.This paper outlines a methodological technique that may be used in various environmental science and management studies.We employed an integrated strategy to investigate a study topic, combining field data with data analysis based on WQI, PCA, and CA, all of which have been used in tandem.The current research adds to our knowledge of pit lake surface water  quality and spatial patterns of zooplankton communities.The RCF pit lakes were found to have a variety of physicochemical water conditions, according to the study.The surface water quality in the study area is alkaline in nature.Domestic use would require treatment and disinfection.Most of the pit lake water can be used for different recreational purposes.Furthermore, regional diversity in water chemistry is caused not just by natural processes but also by human activities.In this study, the average quality of the pit lake water is poor.It also has a good zooplankton diversity, which indicates the presence of both phytoplankton and fish in the system.Thus, the reservoir is sustainable, and with this knowledge of reservoir functioning, proper management strategies can be taken up for future concerns.Although there were variances in surface areas, depths, and the presence of aquatic plants, the studied pit lakes had very identical morphological traits, such as hydro-period and structure.

Figure 1 .
Figure 1.Location map of the study area, where 1a and 1b images representing outline of India and Paschim Bardhaman district of West Bengal respectively and 1c depicting the location of the study sites.

Figure 4 .
Figure 4. Spatial distribution of WQI in the selected pit lakes.

Table 1 .
Basic characteristics of the studied pit lakes and their locations.

Table 2 .
Units and methodologies used for water quality analysis during the study period.

Table 3 .
Mean and standard deviation of different physico-chemical parameters of the surface water in different pit lakes.Field survey, 2019-2021.All the parameters are expressed in mg/l except for pH and EC (µS/cm).The table represents averages of the values of the samples examined.Values in each parameter represented as mean with standard

Table 4 .
Kruskal-Wallis test of the physio-chemical parameters for the analyzed pit lakes.

Table 5 .
Spearman correlation matrix between different physico-chemical parameters of the surface water indifferent pit lakes of the RCF.

Table 6 .
List of identified zooplankton in the studied pit lakes.

Table 7 .
Spatial pattern analysis of zooplankton of the studied pit lakes.

Table 8 .
Diversity indices of the studied pit lakes.

Table 9 .
Water quality status of different WQI ranges and their possible use.

Table 10 .
Drinking water quality standards and unit weight of individual physico-chemical parameters studied.

Table 11 .
Calculated WQI and its water quality status in different pit lakes.

Table 12 .
PCA output of the physicochemical parameters in the studied pit lakes of the RCF (Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization).