Analysing Water Quality and Aquatic Vegetation Dynamics in a Proposed Bird Sanctuary: A Case Study of Satajaan Beel, North Lakhimpur, Assam

Analysing Water Quality and Aquatic Vegetation Dynamics in a Proposed Bird Sanctuary: A Case Study of Satajaan Beel, North Lakhimpur, Assam | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysing Water Quality and Aquatic Vegetation Dynamics in a Proposed Bird Sanctuary: A Case Study of Satajaan Beel, North Lakhimpur, Assam Jintu Moni Bhuyan, Pallavi Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4760803/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Water quality assessment is crucial for understanding the environmental status of wetlands, which are among the most significant ecosystems on the planet. Satajaan Beel, a small yet vital wetland located in the Lakhimpur district of Assam on the floodplains of the Ranganadi River, serves as the focus of this study. This research evaluates various water quality parameters from samples collected at ten stations within the study area. The Water Quality Index (WQI) was determined using the weighted arithmetic method. The results revealed WQI values indicating very poor water quality for most samples: Sample 1 (77.93), Sample 2 (92.60), Sample 5 (75.47), Sample 6 (78.27), and Sample 8 (98.275). Samples 3 (117.38), 4 (113.47), 7 (131.79), and 10 (119.23) were deemed unsuitable for use without proper treatment, while Sample 9 (46.02) was the only one indicating good water quality. Additionally, the study assessed the biodiversity status of the area. The Normalized Difference Vegetation Index (NDVI) calculation revealed a significant degradation of aquatic vegetation, with a calculated degradation rate of 2.84 acres or 7.84%. A survey conducted from 2018 to 2019 recorded 262 species of vascular plants within this wetland. The study also identified 42 species of fish belonging to 19 families, highlighting the ecological diversity and the need for conservation efforts in Satajaan Beel. Wetland Water quality index NDVI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Wetlands are among the most significant ecosystems on Earth due to the tremendous number of ecosystem services they provide. They play a crucial role not only as aquatic habitats but also in maintaining natural cycles and supporting a wide range of biodiversity. Wetlands contribute to groundwater recharging, water purification, and replenishment. These water-regulating services have earned wetlands the moniker “kidneys of the ecosystem,” akin to how forests are referred to as the “lungs of the Earth.” Beyond these services, wetlands are highly productive areas for aquatic flora and fauna, as well as for wetland agriculture. They are essential habitats for various water birds and key stopovers for migratory birds (Deka R. M., 2009). Wetlands have also been termed “biological supermarkets” due to their extensive food chains and rich biodiversity (Mitsch and Gosselink, 2000 ). Birds rely on wetlands for drinking water, feeding, resting, shelter, and as sources of materials, particularly during breeding seasons. Despite their importance as highly productive ecosystems, wetlands are also incredibly vulnerable habitats. They face significant threats from various anthropogenic factors, including climate change, population growth, and urbanization. Over the years, approximately 35% of the world's wetlands have been lost from 1970 to 2015, and this loss rate has been accelerating annually since 2000 (Global Wetland Outlook, 2021 ). This trend highlights the urgent need for conservation efforts and sustainable management practices to protect these critical ecosystems and the valuable services they provide to both wildlife and humanity. The remaining wetlands are under threat from pollution, unsustainable use, and invasive species, among other factors. India has reported the largest number of wetlands in South Asia, covering approximately 4.6% of its total geographical area, according to a report by Wetlands International South Asia. Globally, about 50% of the world's wetlands have been lost since 1900, with approximately one-third of the natural wetlands in the Indian subcontinent decreasing over the past forty years, as reported by Wetlands International South Asia (WISA) in 2021. Encroachment and high human interference have significantly impacted India's wetlands, with Mumbai losing the most (71%), followed by Ahmedabad (57%), and Bangalore (56%) according to Chatterjee ( 2020 ). The Ministry of Environment, Forest and Climate Change (MoEFCC) reported that 42 of India's top 100 wetlands are threatened due to these factors. In 1992–1993, the Indian Space Research Organisation conducted the first digital mapping of wetlands in India, revealing an extent of approximately 8.26 million hectares. Satellite data analysis identified 3,513 wetlands in Assam alone, covering approximately 1,012 square kilometers (Boruah & Riba, 2015 ). The Northeast region of India hosts three Ramsar wetlands and numerous smaller wetlands, all facing ecological threats and degradation. For instance, Rudrasager or Twijilikma Lake in Tripura, spanning 2.40 square kilometers, is experiencing issues such as siltation and declining water levels. These changes directly and indirectly affect the ecosystem and the surrounding agricultural activities. Rudrasager received its Ramsar designation on November 8, 2005 (Rawat, 2020 ). The quality of water resources in wetlands is a critical concern and fundamental necessity for all living organisms. Maintaining good water quality is a positive environmental indicator as wetlands provide vast quantities of renewable fresh water, supporting socio-economic development in surrounding areas for human welfare. Water quality serves as a crucial parameter for assessing environmental degradation within wetlands and beyond. Water is indispensable for all living organisms, and any changes in its quality can threaten their survival, affecting drinking, bathing, habitat, and other essential needs. Assessing water quality involves studying both physical and chemical characteristics. Due to increasing population and human negligence, water quality is deteriorating progressively. Therefore, it is essential to rigorously test water before using it for drinking, domestic, agricultural, or industrial purposes. Water quality testing typically includes examining physical parameters such as turbidity and color, and chemical parameters like pH, Electrical Conductivity (E.C), Total Solids (TS), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Total Hardness, Calcium Hardness, among others. High levels of certain elements beyond permissible limits (e.g., temperature, TDS, other chemicals) can harm aquatic organisms inhabiting the water body. Therefore, ensuring water quality within acceptable parameters is crucial for the health and sustainability of both aquatic life and ecosystems. Chakrabarty & Sarma ( 2011 ) conducted a study on drinking water quality in Kamrup district, Assam, focusing on physical and chemical parameters such as Temperature, pH, Electrical Conductivity, Total Solids (TS), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Turbidity, Dissolved Oxygen (DO), Total Hardness (TH), Calcium Hardness (CH), Magnesium Hardness (MH), Chloride (Cl), Sulphate (SO4), Sodium (Na), and Potassium (K). Their statistical analysis revealed patterns in parameter distribution and localization of data, highlighting significant impacts from intensive land use for agriculture, construction, waste dumping, and industrial activities on regional drinking water quality. Kumarasamy et al. (2013) investigated the hydrochemistry of the Tamiraparani River using multivariate cluster analysis (CA) and principal component analysis (PCA) across 20 sampling stations. Their study identified seasonal and spatial variations influencing parameters such as Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, H4SiO4, SO42-, NO2-, and PO43-. They concluded that natural weathering processes predominantly influenced water quality variations more than anthropogenic activities. Patel et al. ( 2009 ) linked wetland degradation to climate change using Resourcesat-1 AWIFS data from 2004-05 to analyze wetland types, distribution changes, and impacts in India. Chopra et al. ( 1999 ) utilized remote sensing techniques to map wetlands in Punjab, focusing on monitoring, management, conservation, and seasonal variations in land use, land cover, vegetation status, and water turbidity using satellite data. Monitoring water quality parameters, Water Quality Index (WQI), and wetland ecosystems through remote sensing and GIS play crucial roles in mitigating wetland degradation rates and facilitating effective ecosystem management and conservation efforts. These approaches are essential for addressing environmental challenges and ensuring sustainable water resource management for current and future generations. 2. Description of the study area 2.1 Location: The district of Lakhimpur, situated on the northern bank of the Brahmaputra River, is one of the most flood-prone districts in Assam. It borders Arunachal Pradesh to the north, Dhemaji District to the east, the Subansiri River, the northern branch of the Brahmaputra, and Majuli Island to the south, and Sonitpur district to the west. The district spans from approximately 26°49' to 27°37' N latitude and 93°42' to 94°38' E longitude, covering an area of about 2,277 square kilometers. Lakhimpur serves as a gateway to several cities in neighboring states, including Itanagar, the capital of Arunachal Pradesh.The present study focuses on Satajaan Wetland, also known as a bird sanctuary, located in the North Lakhimpur subdivision of Lakhimpur District, Assam. This wetland, situated between 27°12'23.7'' to 27°12'40.00'' N latitude and 94°03'08.5'' to 94°03'08.8'' E longitude, lies in the floodplain of the Ranganadi River at an altitude of 101 meters above mean sea level. Satajaan Wetland is known for hosting migratory birds of both national and international significance. It is under severe threat and is adjacent to the Pahumara-Kimin state highway and National Highway 15, approximately 350 kilometers from Guwahati, Assam (Gogoi et al., 2019 ; Sentinel, 2020). 2.2 Climate: The annual rainfall of Lakhimpur district averages around 300 cm, with maximum and minimum temperatures typically reaching up to 31°C and 7°C, respectively (NWAA, 2010). These climatic conditions, characterized by significant rainfall and seasonal temperature variations, play a crucial role in shaping the ecological dynamics of the Satajaan Wetland. The wetland experiences a tropical monsoon climate, similar to the broader district, with four distinct seasons: winter, pre-monsoon, monsoon, and retreating monsoon. Each season brings specific weather patterns that influence the wetland's ecosystem and its biodiversity throughout the year. 2.3 Demography: Lakhimpur district in Assam is located on the northern bank of the Brahmaputra River and is known for its diverse cultural and ethnic composition. The majority of the population is Assamese, but the district is also home to various ethnic groups such as the Mishing tribe, Bodo tribe, Deori, Tiwa, and others. Despite their cultural diversity, these communities coexist harmoniously, sharing strong cultural and traditional bonds. The Satajaan Wetland, also known as a bird sanctuary, is surrounded by three small villages inhabited by the Mishing Tribe. The Mishing Tribe relies heavily on Satajaan Wetland for their livelihood and survival. They gather essential resources like firewood, wild edible plants, and medicinal plants from the wetland to meet their daily needs. This dependence underscores the critical role that Satajaan Wetland plays in supporting local communities, not just ecologically but also socio-economically, by providing direct benefits that sustain their traditional way of life. 3.4 Natural vegetation and Biodiversity: The forested areas of Lakhimpur district are primarily characterized by tropical rainforests, which provide habitats for a variety of wildlife. Among the notable wild animals found in the district are elephants, leopards, wild dogs, monkeys, and langur monkeys (Kalita, 2007 ). According to the India State of Forest Report, 2019, the district has a total forest area of 306.57 sq km, including very dense forest covering 29.00 sq km, moderately dense forest covering 85.88 sq km, and open forest covering 191.69 sq km. Wetlands, often referred to as the "Liver of the Landscape," play crucial roles in regulating various ecological functions and serve as vital habitats for diverse flora and fauna. Satajaan Wetland, densely vegetated with immersed, free-floating, and root-floating plants, supports a rich biodiversity. Numerous surveys conducted by conservationist groups such as the Asian Water Bird Census of India, Bird Conservation Network, and Bombay Natural History Society have recorded significant wildlife diversity in Satajaan Wetland. This includes three types of endangered turtles, 34 species of resident birds, 13 species of migratory birds, and 35 species of fish. Satajaan Wetland serves as a breeding site for Whistling Teal, White Breasted Water Hen, and Bronze Winged Jacana. It also acts as a nesting site for the Indian Purple Moorhen and Coots, while also attracting endangered migratory birds like the White-eyed Pochard from the Pacific Siberia (Ahmed, 2021 ). These diverse habitats and species highlight the ecological importance of Satajaan Wetland as a crucial area for conservation and biodiversity preservation. 3. Materials and methods The samples were collected from 10 sampling points within Satajaan Wetland of Lakhimpur district during the pre-monsoon period in March 2022, using a random sampling method. Water samples were collected from specific depths as required for the study. During the sampling process, a field sheet was maintained to record field parameters, collection date and time, and the coordinates of each sampling location in the study area. Parameters such as pH, temperature, electrical conductivity (EC), salinity, and total dissolved solids (TDS) were measured immediately after sample collection in the field. Further laboratory analysis was conducted for parameters such as calcium, magnesium, sodium, potassium, hardness, phosphate, and sulfate. These laboratory tests provided more detailed insights into the chemical composition of the water samples collected from Satajaan Wetland, supporting a comprehensive assessment of water quality in the area. 4. Results and discussion 4.1 Water Quality Status 4.1.1 Water Quality assessment Table 2 presents descriptive statistics for various water quality parameters collected from Satajaan Wetland. The pH values ranged from 3.26 to 10.56, with an average concentration of 5.2, indicating acidic water conditions. Total dissolved solids (TDS) in the wetland ranged from 114 to 282 mg/L. According to WHO guidelines, the highest desirable limit for TDS is 500 mg/L, while the maximum permissible limit is 1500 mg/L (Buragohain, 2012 ). These findings provide insights into the chemical composition of water in Satajaan Wetland, highlighting areas where water quality standards may be monitored and managed to ensure ecological health and human usability. Table 1 Methods adopted for analysis of different water quality parameters Variables Unit Methods Temperature O C Digital Thermometer pH pH unit Electrometric Electric Conductivity µS Electrometric Total dissolved solid mg/L Electrometric Dissolved oxygen mg/L Winkler method Total Hardness mg/L EDTA method Alkalinity mg/L Titration Method Calcium mg/L EDTA method Magnesium mg/L EDTA method Sodium mg/L Flame photometer Potassium mg/L Flame Photometer Chloride mg/L Titrimetric Sulphate mg/L Spectrophotometric Phosphate mg/L Spectrophotometric Nitrate mg/L Spectrophotometric Table 2 Statistical analysis of different water parameter variation in the study area Parameters Minimum Maximum Mean Std. Deviation pH 3.26 10.56 5.2000 2.00481 Temperature 30.60 31.70 31.0600 .39497 TDS 114.00 282.00 176.800 58.38150 Conductivity 101.00 470.00 250.600 98.99854 Salinity .11 .27 .1600 .04546 Alkalinity 10.00 25.00 14.0000 4.59468 CO2 .02 .02 .0218 .00063 DO 16.11 84.56 34.4295 19.06442 Chloride 11.36 17.04 13.2060 1.77737 Hardness 12.00 36.00 26.4000 7.58947 Calcium 3.21 6.41 4.3286 1.08208 Magnesium 7.19 32.79 22.0714 7.49705 Sulphate 62.65 168.90 105.407 36.65091 Phosphate -.01 5.64 1.7244 1.70649 Nitrate 17.66 32.11 27.8484 4.55657 Sodium .00 1.53 .5540 .45191 Potassium .94 4.10 1.7780 .95708 The total dissolved solids (TDS) values of the samples collected from the study area are within the WHO desirable limit of 500 mg/L. The minimum electrical conductivity (EC) was found to be 101 µS/cm, with a maximum of 470 µS/cm and an average of 250.6 µS/cm. According to WHO standards, the maximum permissible limits for EC are 1000 µS/cm, while BIS sets it at 3000 µS/cm. Dissolved oxygen (DO) levels in the study area ranged from 16.10 mg/L to 40.23 mg/L, averaging 28.38 mg/L. Total hardness (TH) varied from 12 mg/L to 36 mg/L, with an average of 26.4 mg/L. The desirable and maximum permissible limits for TH are 200 mg/L and 600 mg/L, respectively (Garg, 2017 ). These findings suggest that the water quality parameters measured in Satajaan Wetland are generally within acceptable limits according to international and national standards, indicating favorable conditions for aquatic life and potential human use. According to the classification of total hardness by USGS the all samples collected from the study area are soft in nature. The chloride of the study area is being fluctuated from minimum 11.36mg/L to 17.04mg/L with an average value of 12.78mg/L. Calcium is one of the major constituent of the total hardness of any water samples, as hardness is mainly governed by calcium and magnesium. By analyzing the water samples of the study area it has been found that the concentration of Ca varied from minimum 3.20mg/L to 6.41mg/L with an average value of 4.32mg/L. The experimental result of magnesium is presented in the Table 2 . The analysis of the samples collected from the study area showed that the concentration of Mg has fluctuated from 66.84mg/L to 67.04mg/L having an average value of 66.86 mg/L. The observed value of sulfate of the study area ranges from 62.65mg/L to 168.902mg/L with an average value of 105.4976mg/L. From the present investigation it has been observed that the sulfate content of the study area within the standard value set by WHO and BIS (250mg/L & 200mg/L to 400mg/L respectively), which means the water is safe in terms of sulfate content contaminations. The present investigation of the water samples of the satajaan wetlands shows that the phosphate is fluctuated from minimum 0.01mg/L to maximum 5.636mg/L. The present investigations indicated that the average phosphate concentration in the study area is approximately 1.72 mg/L. The relatively low phosphate levels observed in the samples collected from the study area may be attributed to adsorption by soil and uptake by plants (Buragohain, 2012 ). Nitrate concentrations in the study area ranged from 17.662 mg/L to 32.11 mg/L, averaging 27.8 mg/L. These values are below the permissible limit set by WHO (45 mg/L). Elevated nitrate concentrations can lead to excessive algal blooms in water bodies. The main sources of nitrogen in water bodies include fertilizers, animal waste, and decomposing vegetation. Sodium levels in the study area ranged from 0 mg/L to 1.53 mg/L. Table 2 reveals that potassium concentrations varied between 0.94 mg/L and 4.1 mg/L, with an average of 1.778 mg/L. These findings provide insights into the mineral composition of the Satajaan Wetland, highlighting variations in essential nutrients crucial for aquatic and plant life. Correlation coefficient matrix of the different physical and chemical parameters of the samples collected from the study area has been derived in the Table 3. From the Table 3, It has been observed that conductivity of the water samples has a positive significant correlation with the pH and Alkalinity, while pH has a positive correlation with conductivity, salinity, alkalinity and potassium ion. Same way alkalinity of the water samples shows a significant positive correlation with the Potassium and Hardness has a positive correlation with the Magnesium. From the correlation table, it is evident that several significant correlations exist among various water quality parameters in the study area. Strong correlations include Electrical Conductivity (EC) with pH (R²=0.441) (Fig-5.14(a)), EC with Alkalinity (R²=0.354) (Fig-5.14(b)), Alkalinity with Potassium (R²=0.665) (Fig-5.14(d)), Potassium with Salinity (R²=0.855) (Fig-5.14(e)), and Alkalinity with Salinity (R²=0.478) (Fig-5.14(f)). Notably, pH shows no significant correlation with SO₄, NO₃, and PO₄, suggesting minimal influence of anthropogenic activities on pH levels in the study area. 4.1.2 Water Quality Index Among the 16 parameters analyzed during the study area, 14 parameters namely pH, EC, TDS, Chloride, Total Hardness, Calcium, Magnesium, Total Alkalinity, DO, Sodium, Potassium, Sulfate, Nitrate, Free Carbon di oxide were considered for the water quality index study. For the present study WQI was determined using the Weighted Arithmetic Water Quality Index method, which was originally developed by Brown et al. in 1970 (Begum et al., 2022). Table 5 : Water quality standard recommended by BIS, ICMR and WHO Parameters BIS ICMR WHO D P D P D P pH 6.5-8.5 7-8.5 6.5-9.2 7-8.5 6.5-9.2 EC 750 3000 - - - - TDS 500 2000 - - 500 1500 Chloride 250 1000 - - 250 - Total Hardness 300 600 300 600 200 - Calcium 75 200 75 200 75 200 Magnesium 30 100 50 150 50 150 Total Alkalinity 200 600 - - - - DO - - - - - - Sodium - - - - 50 200 Potassium - - - - - - Sulfate 200 400 - - - 250 Nitrate 45 - - - 50 - Free Carbon di oxide - - - - - - Water quality index can be calculated using following formula: WQI = ΣQ n W n / ΣW n Where Q n = quality rating scale of the nth parameter, W n = the unit weight of the nth water quality parameter. The quality rating scale, Q n for each parameter, is calculated by Q n = 100 [(V n -V i )/(V a -V i ) Where V n = the estimated concentration of nth parameter analyzed in the water, V i = the ideal value of this parameter, V i = 0 (except pH = 7.0 and DO = 14.6 mg/l), Vs = standard permissible value for the nth parameter. Unit weight (W n ) is calculated using the formula: W n = k∕V s where k is proportionality constant calculated by k = [1∕Σ1∕ V s = 1, 2,…, n ] The water quality status (WQS) according to WQI is shown in Table 6 . Table 6 : Water Relative weight(Wi) of the parameters used for WQI determination Prameters (unit) Standard value (Sn) Relative weight(Wi) pH 8.5 0.152246696 EC 3000 0.000431366 TDS 2000 0.000647048 Alkalinity 600 0.002156828 Total Hardness 600 0.002156828 Calcium 200 0.006470485 Magnesium 100 0.012940969 Sodium 200 0.006470485 Potassium 2.7 0.479295154 Sulphate 250 0.005176388 Nitrate 45 0.028757709 Chloride 1000 0.001294097 DO 5 0.258819383 Free CO2 30 0.043136564 The primary purpose of identifying the WQI is to protection of public health but also to protect the natural flora and fauna. This chapter will describe the physical and chemical parameter of water of the study area and comparison with national and international water quality guideline. The water quality index of the different samples collected from the study area shows that though water of the study area is safe in terms of different physical and chemical parameter but the water is not healthy in terms of WQI, indicating that the water of the study area is not suitable and assured for the human health. 4.1.3 Principal Component Analysis (PCA) Principal Component Analysis (PCA) was conducted on the Table 5.5(a) to analyze the different chemical and physical parameters of the study area samples, identifying factors contributing to dataset variance. Component 1 explains approximately one-third (30.27%) of the total variance, showing strong positive correlations with pH, salinity, potassium, and moderate positive correlations with alkalinity and nitrate. Potassium and salinity have high positive factor loadings (0.97 and 0.13, respectively), while chloride and TDS show high negative loadings (both > -0.29) on the first principal component (PC1). This component indicates associations with high ion concentrations leading to elevated conductivity. Component 2 explains 20.07% of the total variance, demonstrating strong positive correlations with sodium and sulfate, and moderate positive correlations with nitrate and chloride. Different hydrogeochemical processes leading to increased mineralization are influenced by combinations of Na+, Cl−, HCO3−, and SO4 − ions. PC 2 exhibits high positive factor loadings for sulfate and sodium (0.89 and 0.01, respectively), and negative loadings for free CO2 and TDS (both > -0.71). This suggests sulfate originates from the dissolution of feldspathoid groups and evaporite sediments, while the negative factor for free CO2 indicates biologically mediated sulfate reduction in metamorphosed carbonate rocks. PC 3 demonstrates the highest positive loading for total hardness and magnesium (0.96 and 0.02, respectively), and negative loadings for free CO2 and temperature (both > -0.38). Accounting for 15.77% of the total variance, Component 3 shows strong positive correlations with magnesium and hardness, and a moderate positive correlation with TDS. This component highlights magnesium ions as significant contributors to total hardness. 4.1.3 Cluster Analysis (CA) From the dendrogram analysis, two distinct clusters have been identified. The first cluster includes temperature, free CO2, magnesium, chloride (Cl-), nitrate (NO3-), calcium (Ca+), salinity, pH, alkalinity, potassium (K+), dissolved oxygen (DO), phosphate (PO43-), and potassium (K+). The second cluster consists of total dissolved solids (TDS), sulfate (SO43-), and electrical conductivity (EC). Total dissolved solids (TDS) represent the cumulative concentration of all organic and inorganic substances present in liquid, whether molecular, ionized, or suspended. Elevated TDS levels often indicate increased dissolved substances in water, which can influence its chemical composition and environmental characteristics. From the dendrogram, it has been observed that TDS, SO 4 3− and conductivity are in the same cluster. TDS is measurement of dissolved combined content of all organic and inorganic substances present in liquid in molecular, ionized or suspended form. High TDS is often caused by sodium, chloride and potassium. Sulphate is also a constituent of TDS and may form salts with sodium, potassium, magnesium and other cations. The major sources of sulpahte are the dissolution of gypsum and anhydrite (Strauch, et al., 2001 ). Sulpahate is also a major anion in hard water reservoir. Sulphate can be generated naturally as mentioned or may be result of municipal discharge as well as runoff from the fertilized agricultural land present nearby area also may contributes to the sulphate in the water body (Strauch, et al., 2001 ). The realtion between TDS and EC can be derived as TDS (mg/L) = k e × EC (µS/cm) where k e is a constant of proportionality (Taylor, et al., 2018 ). From the relationship it can be observed that there is a linear positive relationship between TDS and EC, i.e. increasing of TDS will increase the amount of EC, where sulphate is a major constituent of the TDS in case of water samples collected from the Satajaan wetland. The cluster including temperature, free CO2, magnesium, chloride (Cl-), nitrate (NO3-), calcium (Ca+), salinity, pH, alkalinity, potassium (K+), dissolved oxygen (DO), phosphate (PO43-), and potassium (K+) demonstrates interrelated characteristics in water quality parameters. The solubility of carbon dioxide (CO2) in pure water varies with temperature and pressure, with lower temperatures reducing its solubility. When CO2 dissolves in water, it forms carbonic acid, increasing water acidity. Magnesium and calcium significantly contribute to water hardness, with magnesium hardness reflecting total hardness changes. Alkalinity and hardness interact through carbonate and bicarbonate ions, common in aquatic systems, influencing overall water quality. Additionally, phosphate can mitigate water hardness by sequestering metal ions, thereby influencing water mineral composition (Dash et al., 2022 ; Meenaksh & Sriram, 2022). These interactions highlight the interconnectedness of parameters in assessing water quality. 4.2 Biodiversity of Satajaan wetland 4.2.1 Fish diversity of Satajaan wetland Fish being an obligate aquatic fauna, they are very important to evaluate the ecological health of the wetland. The study done by Hazarika ( 2013 ) revealed that there are as many as 42 species of fish belonging to 19 families. Shannon index (H) is derived from a mathematical formula used in communication area by Shannon in 1948 which is applied to biological system (Hazarika, 2013 ). In the present study of the fish diversity show the Shannon diversity index “H” is 3.066, indicates that the Satajaan wetland is in healthy status. Pielou index (J) is one of the significant indexes which are applied to biological system, derived from Shannon index by Pielou in 1966 (Hazarika, 2013 ). The value of Pielou index is 0.920, indicating that the fish of Satajaan wetland is almost equally distributed. Simpson diversity index is a index applied to biological system, which was derived by Simpson in 1949 (Hazarika, 2013 ). According to the Table 5 the “D” and “1-D” values are 0.067 and 0.933 respectively of the Satajaan wetland, indicating satisfactory biodiversity of the habitat still now. Table 7 : WQI and status of water quality Water Quality Index Level Possible usage 0-25 Drinking, irrigation and industrial 25-50 Drinking, irrigation and industrial 50-75 Irrigation and industrial 75-100 Irrigation >100 Proper treatment required before use Table 8 : WQI values of the samples collected from the study area Sample No. WQI Level Water Quality Status Sample no. 1 77.93 Very Poor Water Quality Sample no. 2 92.60 Very Poor Water Quality Sample no. 3 117.38 Unsuitable Sample no. 4 113.47 Unsuitable for drinking Sample no. 5 75.47 Very Poor Water Quality Sample no. 6 78.27 Very Poor Water Quality Sample no. 7 131.79 Unsuitable Sample no. 8 98.275 Very Poor Water Quality Sample no. 9 46.02 Good Water Quality Sample no. 10 119.23 Unsuitable Table 9: Factor loadings on the principal components for various water quality parameters PARAMETERS Component 1 2 3 4 5 6 pH .904 -.309 -.007 .028 .151 .058 Temperature .834 .178 -.283 -.091 -.289 .171 TDS -.143 -.294 .416 -.806 .144 -.025 Conductivity .726 -.086 .096 .072 .503 -.246 Salinity .943 .147 .126 .001 .059 -.174 Alkalinity .870 -.162 -.162 .096 .161 .161 CO2 .143 -.718 -.365 -.150 -.076 .401 DO -.037 .038 -.176 -.016 -.958 -.019 Chloride -.288 .572 .652 -.201 .128 .028 Hardness -.001 .011 .962 .055 .100 .090 Calcium -.068 -.148 .138 .886 .118 -.178 Magnesium .009 .033 .954 -.072 .085 .117 Sulphate -.048 .898 .025 .068 -.220 .038 Phosphate -.136 .050 .255 -.216 -.073 .851 Nitrate .346 .449 .058 .417 .350 .588 Sodium .139 .851 -.093 -.074 .067 .260 Potassium .974 .133 -.030 .019 -.139 -.048 % of variance 30.27 20.07 15.77 11.13 7.92 5.92 Cumulative % 30.27 50.35 66.12 77.25 85.17 91.10 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization., a. Rotation converged in 7 iterations Table 10: Fish diversity indices of Satajan wetland Diversity Indices Index Value Shannon Diversity Index “H” 3.066 Pielou Evenness Index “J” 0.820 Simpson Diversity Index “D” 0.067 Simpson’s index of diversity “1 – D” 0.933 Table 11 : NDVI divided statistics of the wetland of March, 2016 NDVI value based category NDVI value % of category Area of category Non-Vegetation 0.03-0.15 6.87% 2.37 acre Vegetation 0.15-0.50 93.12% 32.14 acre Table 12 : NDVI divided statistics of the wetland of March, 2022 NDVI value based category NDVI value % of category Area of category Non-vegetation -0.009-0.15 53.97% 18.63 acre Vegetation 0.15-0.42 46.02% 15.88 acre 4.2.2 Vascular plants diversity of Satajaan wetland The present study of floristic survey in the Satajaan wetland using secondary data led to record of a total of 262 vascular plant species. It is observed that 262 no. of species of vascular plants have been found in Satajaan wetland. Out which only 7 are Pteridophytes and the remaining 255 are angiosperms that are again represented by 190 species of Magnoliopsida and 65 species of Liliopsida. 4.2.3 Avian diversity of Satajaan wetland The surveys were done on 22 December 2019 from 0600–1400 h and 10 January 2021 from 0700–1200 h to record the diversity of birds visiting the study site in winter. They able to identify total of 71 and 68 birds species in the first and second surveys respectively. According to them they able to record total 87 species commutatively from the both surveys. From the surveys it has been observed that about thirty four were migratory birds, of which eight were specifically waterfowls. In addition to the different species of resident birds found there, some migratory terrestrial birds were also recorded such as Tickell’s Leaf Warbler ( Phylloscopus affinis) , Pallas’s Grasshopper Warbler ( Locustella certhiola) , Black-faced Bunting ( Emberiza spodocephala) , and aquatic birds such as Gadwall ( Mareca strepera) & Ferruginous Duck ( Aythya nyroca). 4.2 Change detection of area of aquatic vegetation The NDVI have been widely used to examine the relation between Spectral variability and the changes in vegetation growth. I t is also used to determine the production of green vegetation and the vegetation changes. Normalized Difference Vegetation Index (NDVI) is calcuated to monitoring the temporal chnages associuated to the vegetation change by using the following formula: NDVI= (NIR-RED)/(NIR + RED) or NDVI = (Band 4- Band 3)/ (Band 4 + Band 3), The value of NDVI varies from − 1 to 1, where soil, rocks have broadly have similar NDVI close to ‘0’, whereas near zero and negative values indicates the non vegetation class such as water, snow, built up areas and barren lands. On the other hand NDVI values little higher than ‘0’ indicates the vegetation cover, moderate and high value of NDVI indicates the stressed and healthy vegetation respectively. Only active vegetation has higher positive NDVI value typically between 0.1 and 0.6 (Nath & Acharjee, 2013 ). From the NDVI calculation it has been observed that the area of the aquatic vegetation in the study area has been degreded drastically. The rate of degradtion of aquatic vegetation in the study area is calculated 2.84 acer or 7.84%. 5. Conclusion The present study investigates the water quality of the Satajaan wetland situated in the Lakhimpur district on the floodplain of Ranganadi. It was found that the quality of the wetland is not in good conditions with WQI value of above 50. The all parameters that has been analyzed within the study area revealed that only sample 9 is within the acceptable limits of WQI. The wetland is dominated by Mg and SO 4 ions. Principle component analysis of the samples collected from the study area indicates the wreathing of feldspathoid groups. There is also indication of decaying of plants and animals matter. The floristic survey carried out in the Satajan Beel (wetland) in the Lakhimpur district of Assam in NE India led to record of a total of 262 Species of vascular plants. The present study revealed that there are as many as 42 species of fish belonging to 19 families. Among the families Cyprinidae having 13 species followed by Channidae (04 Species), Bagridae and Osphronemidae (3 species each). The surveys were done on 22 December 2019 from 0600–1400 h and 10 January 2021 from 0700–1200 h able to record total 87 species commutatively from the both surveys. Declarations Author Contribution Jintu Moni Bhuyan collected samples, conducted analyses, prepared figures, formulated various study methodologies, and drafted the final manuscript. Pallavi Sharma conducted the review process. References Abir S (2014) Seasonal variations in physico-chemical characteristics of rudrasagar wetland - a Ramsar site, Tripura, North East, India. Res J Chem Sci, 31–40 Ahmed F (2021), january North lakhimpur to host Satajaan bird fastival on January 10. 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13:46:53","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":59196,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4760803/v1/a0651fa1ac068e070bc24a9f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysing Water Quality and Aquatic Vegetation Dynamics in a Proposed Bird Sanctuary: A Case Study of Satajaan Beel, North Lakhimpur, Assam","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWetlands are among the most significant ecosystems on Earth due to the tremendous number of ecosystem services they provide. They play a crucial role not only as aquatic habitats but also in maintaining natural cycles and supporting a wide range of biodiversity. Wetlands contribute to groundwater recharging, water purification, and replenishment. These water-regulating services have earned wetlands the moniker \u0026ldquo;kidneys of the ecosystem,\u0026rdquo; akin to how forests are referred to as the \u0026ldquo;lungs of the Earth.\u0026rdquo; Beyond these services, wetlands are highly productive areas for aquatic flora and fauna, as well as for wetland agriculture. They are essential habitats for various water birds and key stopovers for migratory birds (Deka R. M., 2009). Wetlands have also been termed \u0026ldquo;biological supermarkets\u0026rdquo; due to their extensive food chains and rich biodiversity (Mitsch and Gosselink, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Birds rely on wetlands for drinking water, feeding, resting, shelter, and as sources of materials, particularly during breeding seasons.\u003c/p\u003e \u003cp\u003eDespite their importance as highly productive ecosystems, wetlands are also incredibly vulnerable habitats. They face significant threats from various anthropogenic factors, including climate change, population growth, and urbanization. Over the years, approximately 35% of the world's wetlands have been lost from 1970 to 2015, and this loss rate has been accelerating annually since 2000 (Global Wetland Outlook, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This trend highlights the urgent need for conservation efforts and sustainable management practices to protect these critical ecosystems and the valuable services they provide to both wildlife and humanity.\u003c/p\u003e \u003cp\u003eThe remaining wetlands are under threat from pollution, unsustainable use, and invasive species, among other factors. India has reported the largest number of wetlands in South Asia, covering approximately 4.6% of its total geographical area, according to a report by Wetlands International South Asia. Globally, about 50% of the world's wetlands have been lost since 1900, with approximately one-third of the natural wetlands in the Indian subcontinent decreasing over the past forty years, as reported by Wetlands International South Asia (WISA) in 2021. Encroachment and high human interference have significantly impacted India's wetlands, with Mumbai losing the most (71%), followed by Ahmedabad (57%), and Bangalore (56%) according to Chatterjee (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Ministry of Environment, Forest and Climate Change (MoEFCC) reported that 42 of India's top 100 wetlands are threatened due to these factors.\u003c/p\u003e \u003cp\u003eIn 1992\u0026ndash;1993, the Indian Space Research Organisation conducted the first digital mapping of wetlands in India, revealing an extent of approximately 8.26\u0026nbsp;million hectares. Satellite data analysis identified 3,513 wetlands in Assam alone, covering approximately 1,012 square kilometers (Boruah \u0026amp; Riba, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The Northeast region of India hosts three Ramsar wetlands and numerous smaller wetlands, all facing ecological threats and degradation. For instance, Rudrasager or Twijilikma Lake in Tripura, spanning 2.40 square kilometers, is experiencing issues such as siltation and declining water levels. These changes directly and indirectly affect the ecosystem and the surrounding agricultural activities. Rudrasager received its Ramsar designation on November 8, 2005 (Rawat, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe quality of water resources in wetlands is a critical concern and fundamental necessity for all living organisms. Maintaining good water quality is a positive environmental indicator as wetlands provide vast quantities of renewable fresh water, supporting socio-economic development in surrounding areas for human welfare. Water quality serves as a crucial parameter for assessing environmental degradation within wetlands and beyond. Water is indispensable for all living organisms, and any changes in its quality can threaten their survival, affecting drinking, bathing, habitat, and other essential needs. Assessing water quality involves studying both physical and chemical characteristics. Due to increasing population and human negligence, water quality is deteriorating progressively. Therefore, it is essential to rigorously test water before using it for drinking, domestic, agricultural, or industrial purposes. Water quality testing typically includes examining physical parameters such as turbidity and color, and chemical parameters like pH, Electrical Conductivity (E.C), Total Solids (TS), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Total Hardness, Calcium Hardness, among others. High levels of certain elements beyond permissible limits (e.g., temperature, TDS, other chemicals) can harm aquatic organisms inhabiting the water body. Therefore, ensuring water quality within acceptable parameters is crucial for the health and sustainability of both aquatic life and ecosystems.\u003c/p\u003e \u003cp\u003eChakrabarty \u0026amp; Sarma (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) conducted a study on drinking water quality in Kamrup district, Assam, focusing on physical and chemical parameters such as Temperature, pH, Electrical Conductivity, Total Solids (TS), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Turbidity, Dissolved Oxygen (DO), Total Hardness (TH), Calcium Hardness (CH), Magnesium Hardness (MH), Chloride (Cl), Sulphate (SO4), Sodium (Na), and Potassium (K). Their statistical analysis revealed patterns in parameter distribution and localization of data, highlighting significant impacts from intensive land use for agriculture, construction, waste dumping, and industrial activities on regional drinking water quality. Kumarasamy et al. (2013) investigated the hydrochemistry of the Tamiraparani River using multivariate cluster analysis (CA) and principal component analysis (PCA) across 20 sampling stations. Their study identified seasonal and spatial variations influencing parameters such as Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, H4SiO4, SO42-, NO2-, and PO43-. They concluded that natural weathering processes predominantly influenced water quality variations more than anthropogenic activities. Patel et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) linked wetland degradation to climate change using Resourcesat-1 AWIFS data from 2004-05 to analyze wetland types, distribution changes, and impacts in India. Chopra et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) utilized remote sensing techniques to map wetlands in Punjab, focusing on monitoring, management, conservation, and seasonal variations in land use, land cover, vegetation status, and water turbidity using satellite data. Monitoring water quality parameters, Water Quality Index (WQI), and wetland ecosystems through remote sensing and GIS play crucial roles in mitigating wetland degradation rates and facilitating effective ecosystem management and conservation efforts. These approaches are essential for addressing environmental challenges and ensuring sustainable water resource management for current and future generations.\u003c/p\u003e"},{"header":"2. Description of the study area","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Location:\u003c/h2\u003e \u003cp\u003eThe district of Lakhimpur, situated on the northern bank of the Brahmaputra River, is one of the most flood-prone districts in Assam. It borders Arunachal Pradesh to the north, Dhemaji District to the east, the Subansiri River, the northern branch of the Brahmaputra, and Majuli Island to the south, and Sonitpur district to the west. The district spans from approximately 26\u0026deg;49' to 27\u0026deg;37' N latitude and 93\u0026deg;42' to 94\u0026deg;38' E longitude, covering an area of about 2,277 square kilometers. Lakhimpur serves as a gateway to several cities in neighboring states, including Itanagar, the capital of Arunachal Pradesh.The present study focuses on Satajaan Wetland, also known as a bird sanctuary, located in the North Lakhimpur subdivision of Lakhimpur District, Assam. This wetland, situated between 27\u0026deg;12'23.7'' to 27\u0026deg;12'40.00'' N latitude and 94\u0026deg;03'08.5'' to 94\u0026deg;03'08.8'' E longitude, lies in the floodplain of the Ranganadi River at an altitude of 101 meters above mean sea level. Satajaan Wetland is known for hosting migratory birds of both national and international significance. It is under severe threat and is adjacent to the Pahumara-Kimin state highway and National Highway 15, approximately 350 kilometers from Guwahati, Assam (Gogoi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sentinel, 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Climate:\u003c/h2\u003e \u003cp\u003eThe annual rainfall of Lakhimpur district averages around 300 cm, with maximum and minimum temperatures typically reaching up to 31\u0026deg;C and 7\u0026deg;C, respectively (NWAA, 2010). These climatic conditions, characterized by significant rainfall and seasonal temperature variations, play a crucial role in shaping the ecological dynamics of the Satajaan Wetland. The wetland experiences a tropical monsoon climate, similar to the broader district, with four distinct seasons: winter, pre-monsoon, monsoon, and retreating monsoon. Each season brings specific weather patterns that influence the wetland's ecosystem and its biodiversity throughout the year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Demography:\u003c/h2\u003e \u003cp\u003eLakhimpur district in Assam is located on the northern bank of the Brahmaputra River and is known for its diverse cultural and ethnic composition. The majority of the population is Assamese, but the district is also home to various ethnic groups such as the Mishing tribe, Bodo tribe, Deori, Tiwa, and others. Despite their cultural diversity, these communities coexist harmoniously, sharing strong cultural and traditional bonds. The Satajaan Wetland, also known as a bird sanctuary, is surrounded by three small villages inhabited by the Mishing Tribe. The Mishing Tribe relies heavily on Satajaan Wetland for their livelihood and survival. They gather essential resources like firewood, wild edible plants, and medicinal plants from the wetland to meet their daily needs. This dependence underscores the critical role that Satajaan Wetland plays in supporting local communities, not just ecologically but also socio-economically, by providing direct benefits that sustain their traditional way of life.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Natural vegetation and Biodiversity:\u003c/h2\u003e \u003cp\u003eThe forested areas of Lakhimpur district are primarily characterized by tropical rainforests, which provide habitats for a variety of wildlife. Among the notable wild animals found in the district are elephants, leopards, wild dogs, monkeys, and langur monkeys (Kalita, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). According to the India State of Forest Report, 2019, the district has a total forest area of 306.57 sq km, including very dense forest covering 29.00 sq km, moderately dense forest covering 85.88 sq km, and open forest covering 191.69 sq km.\u003c/p\u003e \u003cp\u003eWetlands, often referred to as the \"Liver of the Landscape,\" play crucial roles in regulating various ecological functions and serve as vital habitats for diverse flora and fauna. Satajaan Wetland, densely vegetated with immersed, free-floating, and root-floating plants, supports a rich biodiversity. Numerous surveys conducted by conservationist groups such as the Asian Water Bird Census of India, Bird Conservation Network, and Bombay Natural History Society have recorded significant wildlife diversity in Satajaan Wetland. This includes three types of endangered turtles, 34 species of resident birds, 13 species of migratory birds, and 35 species of fish. Satajaan Wetland serves as a breeding site for Whistling Teal, White Breasted Water Hen, and Bronze Winged Jacana. It also acts as a nesting site for the Indian Purple Moorhen and Coots, while also attracting endangered migratory birds like the White-eyed Pochard from the Pacific Siberia (Ahmed, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These diverse habitats and species highlight the ecological importance of Satajaan Wetland as a crucial area for conservation and biodiversity preservation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Materials and methods","content":"\u003cp\u003eThe samples were collected from 10 sampling points within Satajaan Wetland of Lakhimpur district during the pre-monsoon period in March 2022, using a random sampling method. Water samples were collected from specific depths as required for the study. During the sampling process, a field sheet was maintained to record field parameters, collection date and time, and the coordinates of each sampling location in the study area. Parameters such as pH, temperature, electrical conductivity (EC), salinity, and total dissolved solids (TDS) were measured immediately after sample collection in the field. Further laboratory analysis was conducted for parameters such as calcium, magnesium, sodium, potassium, hardness, phosphate, and sulfate. These laboratory tests provided more detailed insights into the chemical composition of the water samples collected from Satajaan Wetland, supporting a comprehensive assessment of water quality in the area.\u003c/p\u003e"},{"header":"4. Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Water Quality Status\u003c/h2\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.1 Water Quality assessment\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics for various water quality parameters collected from Satajaan Wetland. The pH values ranged from 3.26 to 10.56, with an average concentration of 5.2, indicating acidic water conditions. Total dissolved solids (TDS) in the wetland ranged from 114 to 282 mg/L. According to WHO guidelines, the highest desirable limit for TDS is 500 mg/L, while the maximum permissible limit is 1500 mg/L (Buragohain, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). These findings provide insights into the chemical composition of water in Satajaan Wetland, highlighting areas where water quality standards may be monitored and managed to ensure ecological health and human usability.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMethods adopted for analysis of different water quality parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethods\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003eO\u003c/sup\u003e C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital Thermometer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrometric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectric Conductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026micro;S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrometric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal dissolved solid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrometric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDissolved oxygen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWinkler method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Hardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEDTA method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlkalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTitration Method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEDTA method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEDTA method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlame photometer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlame Photometer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTitrimetric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpectrophotometric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpectrophotometric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpectrophotometric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistical analysis of different water parameter variation in the study area\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e10.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5.2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2.00481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e30.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e31.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e31.0600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e.39497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e114.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e282.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e176.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e58.38150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eConductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e101.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e470.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e250.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e98.99854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.1600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e.04546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eAlkalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e14.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4.59468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.0218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e.00063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e16.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e84.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e34.4295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e19.06442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e17.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e13.2060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.77737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e36.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e26.4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7.58947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.3286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.08208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e32.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e22.0714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7.49705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSulphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e62.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e168.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e105.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e36.65091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.7244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.70649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e17.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e32.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e27.8484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4.55657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.5540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e.45191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.7780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e.95708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eThe total dissolved solids (TDS) values of the samples collected from the study area are within the WHO desirable limit of 500 mg/L. The minimum electrical conductivity (EC) was found to be 101 \u0026micro;S/cm, with a maximum of 470 \u0026micro;S/cm and an average of 250.6 \u0026micro;S/cm. According to WHO standards, the maximum permissible limits for EC are 1000 \u0026micro;S/cm, while BIS sets it at 3000 \u0026micro;S/cm. Dissolved oxygen (DO) levels in the study area ranged from 16.10 mg/L to 40.23 mg/L, averaging 28.38 mg/L. Total hardness (TH) varied from 12 mg/L to 36 mg/L, with an average of 26.4 mg/L. The desirable and maximum permissible limits for TH are 200 mg/L and 600 mg/L, respectively (Garg, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). These findings suggest that the water quality parameters measured in Satajaan Wetland are generally within acceptable limits according to international and national standards, indicating favorable conditions for aquatic life and potential human use.\u003c/p\u003e\n \u003cp\u003eAccording to the classification of total hardness by USGS the all samples collected from the study area are soft in nature. The chloride of the study area is being fluctuated from minimum 11.36mg/L to 17.04mg/L with an average value of 12.78mg/L. Calcium is one of the major constituent of the total hardness of any water samples, as hardness is mainly governed by calcium and magnesium. By analyzing the water samples of the study area it has been found that the concentration of Ca varied from minimum 3.20mg/L to 6.41mg/L with an average value of 4.32mg/L. The experimental result of magnesium is presented in the Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The analysis of the samples collected from the study area showed that the concentration of Mg has fluctuated from 66.84mg/L to 67.04mg/L having an average value of 66.86 mg/L. The observed value of sulfate of the study area ranges from 62.65mg/L to 168.902mg/L with an average value of 105.4976mg/L. From the present investigation it has been observed that the sulfate content of the study area within the standard value set by WHO and BIS (250mg/L \u0026amp; 200mg/L to 400mg/L respectively), which means the water is safe in terms of sulfate content contaminations. The present investigation of the water samples of the satajaan wetlands shows that the phosphate is fluctuated from minimum 0.01mg/L to maximum 5.636mg/L.\u003c/p\u003e\n \u003cp\u003eThe present investigations indicated that the average phosphate concentration in the study area is approximately 1.72 mg/L. The relatively low phosphate levels observed in the samples collected from the study area may be attributed to adsorption by soil and uptake by plants (Buragohain, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eNitrate concentrations in the study area ranged from 17.662 mg/L to 32.11 mg/L, averaging 27.8 mg/L. These values are below the permissible limit set by WHO (45 mg/L). Elevated nitrate concentrations can lead to excessive algal blooms in water bodies. The main sources of nitrogen in water bodies include fertilizers, animal waste, and decomposing vegetation. Sodium levels in the study area ranged from 0 mg/L to 1.53 mg/L. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reveals that potassium concentrations varied between 0.94 mg/L and 4.1 mg/L, with an average of 1.778 mg/L. These findings provide insights into the mineral composition of the Satajaan Wetland, highlighting variations in essential nutrients crucial for aquatic and plant life. Correlation coefficient matrix of the different physical and chemical parameters of the samples collected from the study area has been derived in the Table\u0026nbsp;3. From the Table\u0026nbsp;3, It has been observed that conductivity of the water samples has a positive significant correlation with the pH and Alkalinity, while pH has a positive correlation with conductivity, salinity, alkalinity and potassium ion. Same way alkalinity of the water samples shows a significant positive correlation with the Potassium and Hardness has a positive correlation with the Magnesium.\u003c/p\u003e\n \u003cp\u003eFrom the correlation table, it is evident that several significant correlations exist among various water quality parameters in the study area. Strong correlations include Electrical Conductivity (EC) with pH (R\u0026sup2;=0.441) (Fig-5.14(a)), EC with Alkalinity (R\u0026sup2;=0.354) (Fig-5.14(b)), Alkalinity with Potassium (R\u0026sup2;=0.665) (Fig-5.14(d)), Potassium with Salinity (R\u0026sup2;=0.855) (Fig-5.14(e)), and Alkalinity with Salinity (R\u0026sup2;=0.478) (Fig-5.14(f)). Notably, pH shows no significant correlation with SO₄, NO₃, and PO₄, suggesting minimal influence of anthropogenic activities on pH levels in the study area.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.2 Water Quality Index\u003c/h2\u003e\n \u003cp\u003eAmong the 16 parameters analyzed during the study area, 14 parameters namely pH, EC, TDS, Chloride, Total Hardness, Calcium, Magnesium, Total Alkalinity, DO, Sodium, Potassium, Sulfate, Nitrate, Free Carbon di oxide were considered for the water quality index study. For the present study WQI was determined using the Weighted Arithmetic Water Quality Index method, which was originally developed by Brown et al. in 1970 (Begum et al., 2022).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e: Water quality standard recommended by BIS, ICMR and WHO\u003c/p\u003e\n \u003ctable border=\"1\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 195px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 146px;\"\u003e\n \u003cp\u003eBIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 149px;\"\u003e\n \u003cp\u003eICMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e6.5-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e7-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.5-9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e7-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e6.5-9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eTotal Hardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eTotal Alkalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eSulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eFree Carbon di oxide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWater quality index can be calculated using following formula:\u003c/p\u003e\n \u003cp\u003eWQI\u0026thinsp;=\u0026thinsp;\u0026Sigma;Q\u003csub\u003en\u003c/sub\u003e W\u003csub\u003en\u003c/sub\u003e/ \u0026Sigma;W\u003csub\u003en\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eWhere Q\u003csub\u003en\u003c/sub\u003e = quality rating scale of the nth parameter, W\u003csub\u003en\u003c/sub\u003e = the unit weight of the nth water quality parameter.\u003c/p\u003e\n \u003cp\u003eThe quality rating scale, Q\u003csub\u003en\u003c/sub\u003e for each parameter, is calculated by\u003c/p\u003e\n \u003cp\u003eQ\u003csub\u003en\u003c/sub\u003e= 100 [(V\u003csub\u003en\u003c/sub\u003e-V\u003csub\u003ei\u003c/sub\u003e)/(V\u003csub\u003ea\u003c/sub\u003e-V\u003csub\u003ei\u003c/sub\u003e)\u003c/p\u003e\n \u003cp\u003eWhere V\u003csub\u003en =\u003c/sub\u003e the estimated concentration of nth parameter analyzed in the water, V\u003csub\u003ei\u003c/sub\u003e = the ideal value of this parameter, V\u003csub\u003ei\u003c/sub\u003e = 0 (except pH\u0026thinsp;=\u0026thinsp;7.0 and DO\u0026thinsp;=\u0026thinsp;14.6 mg/l), Vs\u0026thinsp;=\u0026thinsp;standard permissible value for the nth parameter.\u003c/p\u003e\n \u003cp\u003eUnit weight (W\u003csub\u003en\u003c/sub\u003e) is calculated using the formula:\u003c/p\u003e\n \u003cp\u003eW\u003csub\u003en\u003c/sub\u003e = k∕V\u003csub\u003es\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003ewhere \u003cem\u003ek\u003c/em\u003e is proportionality constant calculated by\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ek\u003c/em\u003e = [1∕\u0026Sigma;1∕\u003cem\u003eV\u003c/em\u003e \u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 1, 2,\u0026hellip;, \u003cem\u003en\u003c/em\u003e]\u003c/p\u003e\n \u003cp\u003eThe water quality status (WQS) according to WQI is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e: Water Relative weight(Wi) of the parameters used for WQI determination\u003c/p\u003e\n \u003ctable border=\"1\" width=\"505\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ePrameters (unit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eStandard value (Sn)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eRelative weight(Wi)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.152246696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.000431366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.000647048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eAlkalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.002156828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eTotal Hardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.002156828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.006470485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.012940969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.006470485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.479295154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eSulphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.005176388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.028757709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.001294097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.258819383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eFree CO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e0.043136564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eThe primary purpose of identifying the WQI is to protection of public health but also to protect the natural flora and fauna. This chapter will describe the physical and chemical parameter of water of the study area and comparison with national and international water quality guideline. The water quality index of the different samples collected from the study area shows that though water of the study area is safe in terms of different physical and chemical parameter but the water is not healthy in terms of WQI, indicating that the water of the study area is not suitable and assured for the human health.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.3 Principal Component Analysis (PCA)\u003c/h2\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA) was conducted on the Table\u0026nbsp;5.5(a) to analyze the different chemical and physical parameters of the study area samples, identifying factors contributing to dataset variance. Component 1 explains approximately one-third (30.27%) of the total variance, showing strong positive correlations with pH, salinity, potassium, and moderate positive correlations with alkalinity and nitrate. Potassium and salinity have high positive factor loadings (0.97 and 0.13, respectively), while chloride and TDS show high negative loadings (both \u0026gt; -0.29) on the first principal component (PC1). This component indicates associations with high ion concentrations leading to elevated conductivity. Component 2 explains 20.07% of the total variance, demonstrating strong positive correlations with sodium and sulfate, and moderate positive correlations with nitrate and chloride.\u003c/p\u003e\n \u003cp\u003eDifferent hydrogeochemical processes leading to increased mineralization are influenced by combinations of Na+, Cl\u0026minus;, HCO3\u0026minus;, and SO4\u0026thinsp;\u0026minus;\u0026thinsp;ions. PC 2 exhibits high positive factor loadings for sulfate and sodium (0.89 and 0.01, respectively), and negative loadings for free CO2 and TDS (both \u0026gt; -0.71). This suggests sulfate originates from the dissolution of feldspathoid groups and evaporite sediments, while the negative factor for free CO2 indicates biologically mediated sulfate reduction in metamorphosed carbonate rocks. PC 3 demonstrates the highest positive loading for total hardness and magnesium (0.96 and 0.02, respectively), and negative loadings for free CO2 and temperature (both \u0026gt; -0.38). Accounting for 15.77% of the total variance, Component 3 shows strong positive correlations with magnesium and hardness, and a moderate positive correlation with TDS. This component highlights magnesium ions as significant contributors to total hardness.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.3 Cluster Analysis (CA)\u003c/h2\u003e\n \u003cp\u003eFrom the dendrogram analysis, two distinct clusters have been identified. The first cluster includes temperature, free CO2, magnesium, chloride (Cl-), nitrate (NO3-), calcium (Ca+), salinity, pH, alkalinity, potassium (K+), dissolved oxygen (DO), phosphate (PO43-), and potassium (K+). The second cluster consists of total dissolved solids (TDS), sulfate (SO43-), and electrical conductivity (EC). Total dissolved solids (TDS) represent the cumulative concentration of all organic and inorganic substances present in liquid, whether molecular, ionized, or suspended. Elevated TDS levels often indicate increased dissolved substances in water, which can influence its chemical composition and environmental characteristics.\u003c/p\u003e\n \u003cp\u003eFrom the dendrogram, it has been observed that TDS, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e and conductivity are in the same cluster. TDS is measurement of dissolved combined content of all organic and inorganic substances present in liquid in molecular, ionized or suspended form. High TDS is often caused by sodium, chloride and potassium. Sulphate is also a constituent of TDS and may form salts with sodium, potassium, magnesium and other cations. The major sources of sulpahte are the dissolution of gypsum and anhydrite (Strauch, et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). Sulpahate is also a major anion in hard water reservoir. Sulphate can be generated naturally as mentioned or may be result of municipal discharge as well as runoff from the fertilized agricultural land present nearby area also may contributes to the sulphate in the water body (Strauch, et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). The realtion between TDS and EC can be derived as TDS (mg/L)\u0026thinsp;=\u0026thinsp;k\u003csub\u003ee\u003c/sub\u003e \u0026times; EC (\u0026micro;S/cm) where k\u003csub\u003ee\u003c/sub\u003e is a constant of proportionality (Taylor, et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). From the relationship it can be observed that there is a linear positive relationship between TDS and EC, i.e. increasing of TDS will increase the amount of EC, where sulphate is a major constituent of the TDS in case of water samples collected from the Satajaan wetland.\u003c/p\u003e\n \u003cp\u003eThe cluster including temperature, free CO2, magnesium, chloride (Cl-), nitrate (NO3-), calcium (Ca+), salinity, pH, alkalinity, potassium (K+), dissolved oxygen (DO), phosphate (PO43-), and potassium (K+) demonstrates interrelated characteristics in water quality parameters. The solubility of carbon dioxide (CO2) in pure water varies with temperature and pressure, with lower temperatures reducing its solubility. When CO2 dissolves in water, it forms carbonic acid, increasing water acidity. Magnesium and calcium significantly contribute to water hardness, with magnesium hardness reflecting total hardness changes. Alkalinity and hardness interact through carbonate and bicarbonate ions, common in aquatic systems, influencing overall water quality. Additionally, phosphate can mitigate water hardness by sequestering metal ions, thereby influencing water mineral composition (Dash et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Meenaksh \u0026amp; Sriram, 2022). These interactions highlight the interconnectedness of parameters in assessing water quality.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Biodiversity of Satajaan wetland\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.1 Fish diversity of Satajaan wetland\u003c/h2\u003e\n \u003cp\u003eFish being an obligate aquatic fauna, they are very important to evaluate the ecological health of the wetland. The study done by Hazarika (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) revealed that there are as many as 42 species of fish belonging to 19 families.\u003c/p\u003e\n \u003cp\u003eShannon index (H) is derived from a mathematical formula used in communication area by Shannon in 1948 which is applied to biological system (Hazarika, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the present study of the fish diversity show the Shannon diversity index \u0026ldquo;H\u0026rdquo; is 3.066, indicates that the Satajaan wetland is in healthy status.\u003c/p\u003e\n \u003cp\u003ePielou index (J) is one of the significant indexes which are applied to biological system, derived from Shannon index by Pielou in 1966 (Hazarika, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The value of Pielou index is 0.920, indicating that the fish of Satajaan wetland is almost equally distributed.\u003c/p\u003e\n \u003cp\u003eSimpson diversity index is a index applied to biological system, which was derived by Simpson in 1949 (Hazarika, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to the Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e the \u0026ldquo;D\u0026rdquo; and \u0026ldquo;1-D\u0026rdquo; values are 0.067 and 0.933 respectively of the Satajaan wetland, indicating satisfactory biodiversity of the habitat still now.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e: WQI and status of water quality\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eWater Quality Index Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003ePossible usage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e0-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003eDrinking, irrigation and industrial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e25-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003eDrinking, irrigation and industrial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e50-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003eIrrigation and industrial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e75-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003eIrrigation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u0026gt;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003eProper treatment required before use\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u0026nbsp;\n \u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable 8\u003c/strong\u003e: WQI values of the samples collected from the study area\u003c/p\u003e\n \u003ctable border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample No.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eWQI Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eWater Quality Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample no. 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e77.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eVery Poor Water Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample no. 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e92.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eVery Poor Water Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample no. 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e117.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eUnsuitable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample no. 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e113.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eUnsuitable for drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample no. 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e75.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eVery Poor Water Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample no. 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e78.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eVery Poor Water Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample no. 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e131.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eUnsuitable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample no. 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e98.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eVery Poor Water Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample no. 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e46.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eGood Water Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eSample no. 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e119.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eUnsuitable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 9:\u003c/strong\u003e Factor loadings on the principal components for various water quality parameters\u003c/p\u003e\n \u003ctable border=\"1\" width=\"545\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003ePARAMETERS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 427px;\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eConductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eSalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eAlkalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eCO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eHardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eSulphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e% of variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e30.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e20.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e15.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e11.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eCumulative %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e30.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e50.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e66.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e77.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e85.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e91.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 545px;\"\u003e\n \u003cp\u003eExtraction Method: Principal Component Analysis.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Rotation Method: Varimax with Kaiser Normalization., a. Rotation converged in 7 iterations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable 10:\u0026nbsp;\u003c/strong\u003eFish diversity indices of Satajan wetland\u003c/p\u003e\n \u003ctable border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiversity Indices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003eShannon Diversity Index \u0026ldquo;H\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.066\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003ePielou Evenness Index \u0026ldquo;J\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.820\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003eSimpson Diversity Index \u0026ldquo;D\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.067\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003eSimpson\u0026rsquo;s index of diversity \u0026ldquo;1 \u0026ndash; D\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.933\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 11\u003c/strong\u003e: NDVI divided statistics of the wetland of March, 2016\u003c/p\u003e\n \u003ctable border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eNDVI value based category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eNDVI value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e% of category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eArea of category\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eNon-Vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.03-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e6.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e2.37 acre\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.15-0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e93.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e32.14 acre\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u0026nbsp;\n \u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 12\u003c/strong\u003e: NDVI divided statistics of the wetland of March, 2022\u003c/p\u003e\n \u003ctable border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eNDVI value based category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eNDVI value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e% of category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eArea of category\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eNon-vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.009-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e53.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e18.63 acre\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.15-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e46.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e15.88 acre\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.2 Vascular plants diversity of Satajaan wetland\u003c/h2\u003e\n \u003cp\u003eThe present study of floristic survey in the Satajaan wetland using secondary data led to record of a total of 262 vascular plant species. It is observed that 262 no. of species of vascular plants have been found in Satajaan wetland. Out which only 7 are Pteridophytes and the remaining 255 are angiosperms that are again represented by 190 species of Magnoliopsida and 65 species of Liliopsida.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.3 Avian diversity of Satajaan wetland\u003c/h2\u003e\n \u003cp\u003eThe surveys were done on 22 December 2019 from 0600\u0026ndash;1400 h and 10 January 2021 from 0700\u0026ndash;1200 h to record the diversity of birds visiting the study site in winter. They able to identify total of 71 and 68 birds species in the first and second surveys respectively. According to them they able to record total 87 species commutatively from the both surveys. From the surveys it has been observed that about thirty four were migratory birds, of which eight were specifically waterfowls. In addition to the different species of resident birds found there, some migratory terrestrial birds were also recorded such as Tickell\u0026rsquo;s Leaf Warbler (\u003cem\u003ePhylloscopus affinis)\u003c/em\u003e, Pallas\u0026rsquo;s Grasshopper Warbler (\u003cem\u003eLocustella certhiola)\u003c/em\u003e, Black-faced Bunting (\u003cem\u003eEmberiza spodocephala)\u003c/em\u003e, and aquatic birds such as Gadwall (\u003cem\u003eMareca strepera)\u003c/em\u003e \u0026amp; Ferruginous Duck (\u003cem\u003eAythya nyroca).\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Change detection of area of aquatic vegetation\u003c/h2\u003e\n \u003cp\u003eThe NDVI have been widely used to examine the relation between Spectral variability and the changes in vegetation growth. I t is also used to determine the production of green vegetation and the vegetation changes.\u003c/p\u003e\n \u003cp\u003eNormalized Difference Vegetation Index (NDVI) is calcuated to monitoring the temporal chnages associuated to the vegetation change by using the following formula:\u003c/p\u003e\n \u003cp\u003eNDVI= (NIR-RED)/(NIR\u0026thinsp;+\u0026thinsp;RED) or NDVI = (Band 4- Band 3)/ (Band 4\u0026thinsp;+\u0026thinsp;Band 3),\u003c/p\u003e\n \u003cp\u003eThe value of NDVI varies from \u0026minus;\u0026thinsp;1 to 1, where soil, rocks have broadly have similar NDVI close to \u0026lsquo;0\u0026rsquo;, whereas near zero and negative values indicates the non vegetation class such as water, snow, built up areas and barren lands. On the other hand NDVI values little higher than \u0026lsquo;0\u0026rsquo; indicates the vegetation cover, moderate and high value of NDVI indicates the stressed and healthy vegetation respectively. Only active vegetation has higher positive NDVI value typically between 0.1 and 0.6 (Nath \u0026amp; Acharjee, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFrom the NDVI calculation it has been observed that the area of the aquatic vegetation in the study area has been degreded drastically. The rate of degradtion of aquatic vegetation in the study area is calculated 2.84 acer or 7.84%.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe present study investigates the water quality of the Satajaan wetland situated in the Lakhimpur district on the floodplain of Ranganadi. It was found that the quality of the wetland is not in good conditions with WQI value of above 50. The all parameters that has been analyzed within the study area revealed that only sample 9 is within the acceptable limits of WQI. The wetland is dominated by Mg and SO\u003csub\u003e4\u003c/sub\u003e ions. Principle component analysis of the samples collected from the study area indicates the wreathing of feldspathoid groups. There is also indication of decaying of plants and animals matter.\u003c/p\u003e \u003cp\u003eThe floristic survey carried out in the Satajan Beel (wetland) in the Lakhimpur district of Assam in NE India led to record of a total of 262 Species of vascular plants. The present study revealed that there are as many as 42 species of fish belonging to 19 families. Among the families Cyprinidae having 13 species followed by Channidae (04 Species), Bagridae and Osphronemidae (3 species each). The surveys were done on 22 December 2019 from 0600\u0026ndash;1400 h and 10 January 2021 from 0700\u0026ndash;1200 h able to record total 87 species commutatively from the both surveys.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJintu Moni Bhuyan collected samples, conducted analyses, prepared figures, formulated various study methodologies, and drafted the final manuscript. Pallavi Sharma conducted the review process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbir S (2014) Seasonal variations in physico-chemical characteristics of rudrasagar wetland - a Ramsar site, Tripura, North East, India. Res J Chem Sci, 31\u0026ndash;40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed F (2021), january North lakhimpur to host Satajaan bird fastival on January 10. 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Retrieved from smart water magazine: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://smartwatermagazine.com/blogs/laura-f-zarza/world-wetlands-day-2020-why-biodiversity-important\u003c/span\u003e\u003cspan address=\"https://smartwatermagazine.com/blogs/laura-f-zarza/world-wetlands-day-2020-why-biodiversity-important\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 3 and 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wetland, Water quality index, NDVI","lastPublishedDoi":"10.21203/rs.3.rs-4760803/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4760803/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater quality assessment is crucial for understanding the environmental status of wetlands, which are among the most significant ecosystems on the planet. Satajaan Beel, a small yet vital wetland located in the Lakhimpur district of Assam on the floodplains of the Ranganadi River, serves as the focus of this study. This research evaluates various water quality parameters from samples collected at ten stations within the study area. The Water Quality Index (WQI) was determined using the weighted arithmetic method. The results revealed WQI values indicating very poor water quality for most samples: Sample 1 (77.93), Sample 2 (92.60), Sample 5 (75.47), Sample 6 (78.27), and Sample 8 (98.275). Samples 3 (117.38), 4 (113.47), 7 (131.79), and 10 (119.23) were deemed unsuitable for use without proper treatment, while Sample 9 (46.02) was the only one indicating good water quality. Additionally, the study assessed the biodiversity status of the area. The Normalized Difference Vegetation Index (NDVI) calculation revealed a significant degradation of aquatic vegetation, with a calculated degradation rate of 2.84 acres or 7.84%. A survey conducted from 2018 to 2019 recorded 262 species of vascular plants within this wetland. The study also identified 42 species of fish belonging to 19 families, highlighting the ecological diversity and the need for conservation efforts in Satajaan Beel.\u003c/p\u003e","manuscriptTitle":"Analysing Water Quality and Aquatic Vegetation Dynamics in a Proposed Bird Sanctuary: A Case Study of Satajaan Beel, North Lakhimpur, Assam","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-06 13:38:48","doi":"10.21203/rs.3.rs-4760803/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a062055f-c0da-416a-8078-62c7ce63c587","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T13:38:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-06 13:38:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4760803","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4760803","identity":"rs-4760803","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}