Physical-chemical analysis of Wetlands with variety in Zooplankton and Phytoplankton

INTRODUCTION

Freshwater resources are essential for life. They occur in minute proportions relative to all water sources on Earth and are inequitably distributed and used. Although Gujarat and Madhya Pradesh are reputed to be adequately endowed with freshwater resources, significant threats to the quality and quantity of those resources are apparent. Surface waters provide about three-quarters of the total volume of water supplied for domestic, industrial/ commercial and irrigation purposes in Gujarat and Madhya Pradesh. The dynamics of phytoplankton are a function of many of the some environmental processes that affect species diversity. For example, the onset of the spring bloom in dimitic lakes is controlled by the relief of light limitation at a time when nutrient concentrations are high and growth abundance is low (Roelke and Buyukates, 2002). The abundance of algae of different kinds is rather closely associated with restricted seasonal periodicity, differing of course in widely separated geographical locations(Smith, 1951). Within reservoirs, the irregular dynamics of inflow and variable flushing rates markedly alter environmental conditions for biotic communities. A reservoir can be viewed as a very dynamic lake in which a significant portion of its volume possesses characteristics of, and functions biologically as,a river (Wetzel, 2001). The zooplanktons are animal plankton and move at the mercy of water currents. They occupy central position between the autotrophs and other heterotrophs in an aquatic ecosystem and form a major link in the entire food chain and main energy source for fishes truly planktonic animals in fresh waters are dominated by Rotifera, Cladocera and · Copepoda. Protozoans also form a significant part of fresh water zooplankton.

REVIEW OF LITERATURE

Most of the Indian lakes are alkaline in nature (Singh and Mathur, 2005; Abd- Ellatif et al., 2016). pH affects the solubility and availability of micronutrients like Fe, Cu, Mn and Zn and also controls the water treatment systems and disinfectants in municipal water supply (Mallampati and Osman, 2015). It also controls the chemical state of DO, nitrate and phosphate (Kumar et al., 2017).Variation of pH was seen between 6.3 and 8.5; lowest in Khanduwa Dam (S4) in winter 2021 and highest Parsada Lake (S1) in post monsoon 2019 (Shrivastava and Kanungo, 2013; Saad et al., 2017; Satyanarayana and Krishna, 2017). But in most of the samples, pH was maintained at an average of 7.5. On an average, the pH was high in winter and pre monsoon (tables 4.1 and 4.2). Higher pH was usually observed in post monsoon and winter in the first sampling period, and increases the amount of carbonates and bicarbonates resulting in higher pH (Manohar et al., 2017). This variation was due to variation in rainfall in the two sampling years (figure 4.2). Increase in pH in pre monsoon was affected by temperature, waste disposal, photosynthesis, respiratory and decomposition activities (Abowei, 2010; Manohar et al., 2017). High pH may increase water temperature and decrease germicidal capacity of Cl. Lower pH was due to hydrolysis of cations, oxidation of sulphur compounds and due to dissolved acidic gases (Ojok et al., 2017).

Hafsa and Gupta (2009) studied the Phytoplankton diversity and reported that growth of phytoplankton is governed by transparency, total suspended solids, calcium and total hardness. These types of studies are prerequisites for evolving fish culture programmes and management of water resources. Diversity, distribution, abundance and variation in the biotic factors provide information of energy turnover in the aquatic systems (Forsberg, 1982). In these systems phytoplankton is of great importance as a major source of organic carbon located at the base (Gaikwad et al., 2004). Their sensitivity and large variations in species composition are often a reflection of significant alteration in ambient condition within an ecosystem (Devassy and Goes, 1988, 1989). Hence for any scientific utilization of water resources plankton study is of primary interest. Several studies on phytoplankton diversity made in India and abroad on the ponds, lakes and reservoirs (Tiwari and Chauhan, 2006; Sridhar et al., 2006; Tas and Gonulol, 2007; Senthilkumar and Sivakumar, 2008) also revealed the importance of this type of study. In this paper an attempt has been made to study the seasonal variation of phytoplankton diversity and dynamics of a part of Chatla floodplain lake and its correlations with the physico-chemical properties of water. This study may be of help to the poor people of Chatla as abundance of phytoplankton is of considerable assistance in evolving fish culture programmes (Bohra and Kumar, 2002). The wetlands of New Raipur are considered as ecological important wetlands. Ecological wetlands are among the most productive ecosystems in the world they also are a source of substantial biodiversity in supporting numerous species. Wetlands provide habitat for assortment of wild life species.

7. Maharajabandh Lake 1.14

Analysis of physico-chemical parameters of water samples help to determine the type of pollution that may be affecting a waterway and can provide some clues as to the sources. Water quality is good if naturally occurring substances are present at levels that support aquatic life. Problems occur when activities alter natural levels or introduced substances that are toxic to aquatic life. In the present investigation, the physico-chemical parameters used to analyze the water quality are pH, temperature, turbidity, electrical conductivity (EC), total hardness (TH), Calcium (Ca), Magnesium (Mg), alkalinity (Al), Chloride (Cl), Fluoride (Fl), Nitrate (NO3), Sulphate (SO4), Phosphate (PO4), Ferrous (Fe), Dissolved oxygen (DO), Biochemical oxygen demand (BOD) and Chemical oxygen demand (COD).

The pH and temperature were recorded on spot. To estimate DO, samples were collected in separate sterilized 300 ml BOD bottles immediately fixed using Winkler‘s reagent. In addition, samples acidified with H2SO4 and nitric acid were collected in three separate bottles of 50 ml. The samples were used for the analysis of BOD and for acidity, alkalinity and inorganic ions in laboratory. examined using Pearson‘s correlation coefficient which ranges from -1 to +1. The obtained data were subjected to PCA, using PAST statistical software program. Water quality assessment using water quality index (wqi) The WQI can be calculated using information software and is compared with water quality ratings.

Bacteriological parameters were analyzed by following methods of AWWA (1995) and APHA (1995). For H2S strip test, updated protocols from Manja et al. (1982) were used. The growth and plating media used in this study were dehydrated media from Hi-media, Mumbai, India. The following bacterial parameters were enumerated and various bacteria were isolated and identified. The presence of H2S producing pathogenic bacteria was identified by filling the H2S strip bottle up to the mark with lake water sample and was incubated for 24- 48 h at Development of black precipitation indicates the presence of H2S producing bacteria. The sample was culture on agar plate and the colonies were subjected to triple sugar iron test for further identification.

The water samples were collected from sampling lakes for microphytoplankton analysis in plastic containers of 1 L. Filamentous algae and other floating debris were avoided. About 25 ml of 4% formaldehyde was added to 1 L water sample (Welch, 1948). Few drops of Lugol‘s iodine were added for preservation until analysis. Sedimentation was done in glass columns; the sediment was finally reduced to 20 ml and was preserved in a glass vial. A drop was mounted on a slide from each vial and a cover slip was carefully placed over it. Five high power fields, one in each corner of the cover slip and one at the centre were performed and the algal populations were calculated. Random observations were made and it was repeated four times for each sample. This procedure was repeated for each sample and the number of each repeated organisms were estimated for organism/L (Rao, 1995). Identification of plankton to species level were made using the monographs of Welch (1948), Desikachary (1962), Philipose (1960), Prescott (1982), Philipose (1967), Sarode and Kamath (1984), Gandhi (1998) and Taylor et al. (2007). The collections, preservation, enumeration of plankton were made as described by Hosmani and Kumar

where, n = total number of phytoplankton counted in 0.1 ml concentrate c = total volume of concentrate in ml v = total volume of water filtered through the net The plankton were expressed as organism per liter and data were subjected to PAST program for calculating diversity indices. The biological data were used for determining the ecological conditions of water and the trophic status was subjected for statistical analysis. Few of the indices like, dominance index, species richness and species evenness index, Simpson index and Shannon and Weaver‘s diversity index have been applied.

A diversity index is a quantitative measure that reflects types (such as species) and evenness (such as individuals) of distribution among plankton. The value of a diversity index increases with increase in types and evenness. For given number of types, the value of a diversity index is maximized when all types are equally abundant (table 3.5). This was calculated by using Paleontological software. The Shannon index is originally an information statistic index, which assumes that all species are present in a sample and the sampling is done in random way. Shannon index can be calculated by, p, = proportion (n/N) of individuals of one particular species found (n) divided by the total number of individuals found (N); ln, = natural log; Σ, = sum of the calculations, and s, = number of species. The Index of saprobity-Eutrophication (IDSE) conception is the diatom index of saprobity (Louis-Leclercq, 2008) with index values from 1 to 5 which indicates the levels of degradation in the lake. It also classifies the lakes based on eutrophication and organic pollution for group E of taxa with saprobic values from 3.0 to 3.5, and group S of taxa with values from 1.0 to 2.9. The surface water samples collected for identifying microzooplankton was preserved immediately with the help of 4% formalin. The samples were analyzed qualitatively under the microscope for different types of zooplankton by wet mounting of technique using safranine dye. Identification of zooplankton was carried out by using the keys in published literature (Dhanapathi, 2000; Altaff, 2003). The result of the study of physico-chemical and microbiological parameters in different seasons during the sampling period were compared to understand the relation between them. Experimental values were compared with the standard values, by traditional approaches to conclude the quality of water and its seasonal variation. Transfer and interpretation of complex data into useful and understandable way to technicians, policy makers and general public can be achieved to some extent by integrating the obtained data set, manual comparison of occurrence and variation in population and by application of statistical methods (Mahadev et al., 2010). Correlations were examined using PCM.

Values of Pearson‘s correlation coefficient, calculated after log10 transformation, were generally used in interpreting the results and it is described in the thesis. All the statistical analyses were carried out by using SPSS for Windows release 16.0 and PAST.

The sampling was done for two years and in each year the sampling was done in four seasons. Sampling and analysis was carried out on the selected lake water for eight times in the whole study period. Physico-chemical parameters analyzed were pH, Temperature, EC, Turbidity, Alkalinity, TH, Ca, Mg, Cl, Fl, Nitrate, Sulphate, Phosphate, Fe, DO, BOD and COD. The physico-chemical result obtained is represented in the following tables 1 and 2.

Figure 1: Seasonal variations in temperature Figure 2: Seasonal variations in pH ml-1 (monsoon) and 29×104 CFU ml-1 (winter) and maintained an average of 38.6×104 CFU ml-1in the sampling period (Krishnan et al., 2007). HPC ranged between 57×104 CFU ml-1 (monsoon) and 24×104 CFU ml-1 (winter) and average of 38.8×104 CFU ml-1 was maintained (Hasan et al., 2006). In Bandha Dam, Fecal streptococci ranged between 270 MPN 100 ml-1 (monsoon) and 33 MPN 100 ml-1 (winter) with an average of 139.5 MPN 100 ml-1. Escherichia coli count ranged from 77 CFU 100 ml-1 (pre monsoon) to 8 CFU 100 ml-1 with an average of 41 CFU 100 ml-1 (winter) (Usha et al., 2008; Negera et al., 2017). In Khuteri Lake I, Fecal streptococci ranged between 350 MPN 100 ml-1 (pre monsoon) and 70 MPN 100 ml-1 (winter), with an average of 191.3 MPN 100 ml-1 (Mishra et al., 2009). Escherichia coli count ranged from 94 CFU 100 ml-1 (pre monsoon) to 4 CFU 100 ml-1 (winter), with an average of 54.6 CFU 100 ml-1 (Omezuruike et al., 2008; Negera et al., 2017). In Khanduwa Dam, APC ranged from 186×104CFU ml-1 (monsoon) to 9 CFU×104 ml-1 (post monsoon), with an average of 88.3×104CFU ml-1 (Abila et al., 2012). HPC ranged from 164×104CFU ml-1 (monsoon) to 73×104CFU ml-1 (winter), with an average of 111.3×104CFU ml-1. In Khuteri Lake II, APC ranged from 62×104 CFU ml-1 (pre monsoon) to 35×104 CFU ml-1 (winter), with an average of 47.9×104 CFU ml-1 (Krishnan et al., 2007). HPC count was from 62×104 CFU ml-1 (pre monsoon) to 29×104 CFU ml-1 (winter), with an average of 44.5×104 CFU ml-1 (Hasan et al., 2006). TC count ranged from 1600 MPN 100 ml-1 (pre monsoon) to 110 MPN 100 ml-1 (winter), with an average of 441.3 MPN 100 ml-1(Shahapure et al., 2014). Fecal streptococci count ranged between 21 MPN 100 ml-1 (pre monsoon) and 4 MPN 100 ml-1 (winter), with an average of 12.9 MPN 100 ml-1 (Krishnan et al., 2007). Escherichia coli count ranged between 71CFU 100 ml-1 (pre monsoon) and 0 CFU 100 ml-1 (winter), with an average of 21.4 CFU 100 ml-1 (Usha et al., 2008; Negera et al., 2017). Tables 1 - 7: Seasonal variations of bacteriological parameters for the study period Table 1: Seasonal variations of bacteriological parameters in Parsada Lake

100 ml-1); Staph: fecal staphylococci (MPN 100 ml-1); Strep: fecal streptococci (MPN 100 ml-1); E. coli: Escherichia coli (CFU 100 ml-1) Table 2: Seasonal variations of bacteriological parameters in Bandha Dam

APC: Aerobic Plate Count (CFU ml-1); HPC: Heterotrophic Plate Count (CFU ml-1); TC: Total Coliform (MPN 100 ml-1); FC: Fecal Coliform (MPN 100 ml-1); Staph: fecal staphylococci (MPN 100 ml-1); Strep: fecal streptococci (MPN 100 ml-1); E. coli: Escherichia coli (CFU 100 ml-1) Table 3: Seasonal variations of bacteriological parameters in Khuteri Lake I

APC: Aerobic Plate Count (CFU ml-1); HPC: Heterotrophic Plate Count (CFU ml-1); TC: Total Coliform (MPN 100 ml-1); FC: Fecal Coliform (MPN 100 ml-1); Staph: fecal staphylococci (MPN 100 ml-1); Strep: fecal streptococci (MPN 100 ml-1); E. coli: Escherichia coli (CFU 100 ml-1) Table 4: Seasonal variations of bacteriological parameters in Khanduwa Dam

APC: Aerobic Plate Count (CFU ml-1); HPC: Heterotrophic Plate Count (CFU ml-1); TC: Total Escherichia coli (CFU 100 ml-1) Table 5: Seasonal variations of bacteriological parameters in Khuteri Lake II

APC: Aerobic Plate Count (CFU ml-1); HPC: Heterotrophic Plate Count (CFU ml-1); TC: Total Coliform (MPN 100 ml-1); FC: Fecal Coliform (MPN 100 ml-1); Staph: fecal staphylococci (MPN 100 ml-1); Strep: fecal streptococci (MPN 100 ml-1); E. coli: Escherichia coli (CFU 100 ml-1) Table 6: Seasonal variations of bacteriological parameters in Chherkapur Lake

APC: Aerobic Plate Count (CFU ml-1); HPC: Heterotrophic Plate Count (CFU ml-1); TC: Total Coliform (MPN 100 ml-1); FC: Fecal Coliform (MPN 100 ml-1); Staph: fecal staphylococci (MPN 100 ml-1); Strep: fecal streptococci (MPN 100 ml-1); E. coli: Escherichia coli (CFU 100 ml-1) Table 7: Seasonal variations of bacteriological parameters in Maharajabandh Lake

100 ml-1); Staph: fecal staphylococci (MPN 100 ml-1); Strep: fecal streptococci (MPN 100 ml-1); E. coli: Escherichia coli (CFU 100 ml-1)

Physico-chemical characters govern the stability of aquatic ecosystem. The planktonic communities are impacted by the changes in abiotic features (Riddhi et al., 2011). Complicated circulation and mixing pattern of lake water, chemical and biological process in lake water column due long retention affect the flora and fauna and the quality of lake water. Parameters like pH, temperature drives many of chemical reaction in living organisms.

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PhD Student, Kalinga University, Raipur (CG)