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The present research focuses on addressing various ambiguities in the existing method of integrating information entropy and water quality, thereby presenting a novel approach for an entropy-weighted water quality index. A three-dimensional water quality dataset is considered in the proposed method, the third dimension being the sampling frequency factor. The probability of observed values adhering to desirable limits prescribed by a standard code is estimated, leading to the computation of information entropy and, eventually, entropy weights. These weights are then used for the computation of the Modified Entropy-weight Water Quality Index (MEWQI) values. To verify the proposed method’s applicability, the water quality dataset of Deepor Beel, India, was considered. IS 10500: 2012 was used for estimating MEWQI values. Results showed an excellent correlation with the observed dataset and their uncertainties of occurrence. The reliability and correctness of the proposed methodology were finally confirmed through both cluster analysis and sensitivity analysis. The cluster analysis showed remarkable associations with the computed MEWQI values, while the sensitivity analysis proved that no particular parameter was accountable for the contribution of MEWQI values; instead, all parameters exhibited equal contributions. The proposed methodology was thus found to be the most reasonable and reliable as it considered both factors, i.e., measured values concerning standard limits and the uncertainty, necessary for a consistent water quality monitoring program.

The current study investigates on correlating the heavy metal contamination, its distribution, and the human health risk associated with all three components of an aquatic ecosystem. For this purpose, water, sediment, and fish samples (three species, notably Notopterus notopterus, Clarias batrachus, and Channa striata) from Deepor Beel were considered, and their heavy metal contamination and distribution were determined. The corresponding health risks were then evaluated for six different heavy metals; Cr, Cd, Fe, Mn, Cu, and Pb. Pb and Mn were found to significantly impact the non-carcinogenic human health risks for the water column. Simultaneously, Cd was considered to possess the highest potential for both carcinogenic and non-carcinogenic health effects in the sediment column. Cd also played a critical role in the fish samples' bioaccumulation factor, with the liver showing the maximum bioaccumulation potential. Furthermore, children were found to have considerably higher effects (both carcinogenic and non-carcinogenic) than adults. Finally, the sediment column was found to substantially contribute to the bioaccumulation factor in the fish biota, compared to the water column. The results of this investigation will thus prove consequential in designing, monitoring and restoring aquatic ecosystems.

The present study uses four Environmetrics tools: hierarchical cluster analysis (HCA), discriminant analysis (DA), principal component analysis (PCA), and positive matrix factorization (PMF) for the assessment of water quality and geochemistry of Deepor Beel, Assam, India. The hierarchical clustering classified the 23 sampling locations into three clusters, classifying them as sites of high, low, and moderate contamination respectively. The DA of the water quality dataset resulted in 9 parameters (EC, TDS, TSS, , Na+, Mg, Cd, Pb and OrgN), primarily responsible for the discrimination of the clusters. PCA was then employed on the normalized dataset for the identification of potential pollution sources. PCA yielded two significant principal components, describing anthropogenic and natural factors defining the water contamination. Finally, PMF was employed on the dataset matrix, with four pre-defined factors. Leaching from Boragaon landfill site, surface water runoff, discharge of effluents from the industries in the wetland and discharge from Basistha River were found to be the major contributors. The results of this study provide a comprehensive correlation between water quality parameters and their sources, which would thereby assist in better planning and management of wetland restoration.

Water serves numerous purposes besides drinking, such as irrigation and industrial usage. Most water quality indices developed have primarily focused on drinking water quality. However, assessing other functionalities of water bodies is also equally essential. The present study proposes a novel technique to measure water quality for two highly specific water use, i.e., assessing heavy metal contamination and irrigation suitability. The ambiguities in the current practice of entropy weights were identified, and a novel method was proposed, considering a three-dimensional approach instead of the conventional two-dimensional procedure. Weights to different parameters were assigned based on the probability estimates obtained from the frequency of observed values within acceptable limits. The proposed method's reliability, correctness, and applicability were tested using Deepor Beel's water quality dataset. Results were highly consistent with the experimental values and correlated well with other established methods. The efficacy of the method was determined by employing sensitivity analyses. Both indices showed high reliability and correctness, as no single parameter was found to be highly sensitive compared to others. Therefore, the proposed methodology proved to be the most reasonable, incorporating all the factors required for a reliable water quality monitoring program.

Anthropogenic contamination, including the adaptation of sophisticated agricultural methodologies, has rendered a steep decline in the water and soil quality across the globe. This, in turn, has adversely affected the crop productivity. Hence, to utilize water from a source, a review assessment regarding the water suitability is essential. The present study proposes a new irrigation-based water quality index (IWQI), integrating statistics; hierarchical cluster analysis (HCA) and principal component analysis (PCA), to specific water use. Twenty-three sampling locations were chosen for analyses of 13 water quality parameters from October 2017 to February 2019 from Deepor Beel, Assam, to determine the efficacy of the proposed water quality index. HCA divided the 23 locations into 3 clusters, after which, PCA for each independent cluster provided component scores. These component scores were then used to estimate weights for each parameter for all the 23 locations. The IWQI categorized all the sampling locations as “Very Good”, thereby rendering them fit for irrigation. This was validated through the use of various irrigation parameters, such as sodium adsorption ratio (SAR), Kelly’s ratio (KR), soluble sodium percentage (SSP), residual sodium carbonate (RSC), permeability index (PI), and magnesium adsorption ratio (MgR). USSL and Wilcox plots were also considered for the correlation. IWQI was observed to provide an excellent association with all the parameters. Thus, the study would prove fruitful in establishing long-term and extensive management plans to the conservation of natural water resources.

The aim of this study was application of multivariate statistical techniques – e.g., hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) – to analyse significant sources affecting water quality in Deepor Beel. Laboratory analyses for 20 water quality parameters were carried out on samples collected from 23 monitoring stations. HCA was used on the raw data, categorising the 23 sampling locations into three clusters, i.e., sites of relatively high (HP), moderate (MP) and low pollution (LP), based on water quality similarities at the sampling locations. The HCA results were then used to carry out PCA, yielding different principal components (PCs) and providing information about the respective sites' pollution factors/sources. The PCA for HP sites resulted in the identification of six PCs accounting for more than 84% of the total cumulative variance. Similarly, the PCA for LP and MP sites resulted in two and five PCs, respectively, each accounting for 100% of total cumulative variance. Finally, the raw dataset was subjected to DA. Four parameters, i.e., BOD5, COD, TSS and SO42− were shown to account for large spatial variations in the wetland's water quality and exert the most influence.

In this study, an attempt has been made to monitor the water quality of the Brahmaputra river by analysing two major parameters, viz., DO and BOD by plotting various curves to determine the seasonal as well as the spatially variation of the parameters along the river stretch. The seasonal variation of DO in the Upper Assam region showed similar trends for the pre- and the post-monsoon period with a decreasing trend as the river flows from Kherghat to Dibrugarh and thereafter shows an increase of DO levels near Dhenuka paharh. A decreasing trend was again observed as the river passes through the Nimatighat region. Similarly, for the monsoon season the variation of DO showed an increasing trend as it passes through Dibrugarh and as the river progresses towards the downstream, discharges from various cities along the river stretch blend with the river water, thereby increasing its pollution levels and hence a decreasing trend was observed for the DO in the downstream portion of the Upper Assam region. As far as the variation of BOD along the river stretch is concerned, it was observed that it varies inversely as the DO, which can be seen as an appropriate co-relationship between the two parameters. Similarly, variations of the BOD and DO were obtained for the Lower Assam region. The spatial variation of the BOD and DO showed the map of the depicting the variation of the parameters along the entire river stretch. Proper visualisation of the maps obtained would thereby help in predicting the stretch most polluted so that proper river training works can be done to restore the water quality in the river.

The present study is a first of its kind on the sediment contamination in Deepor Beel, which makes use of source apportionment receptor modelling technique {positive matrix factorization (PMF)} for determining and quantifying the sources’ contribution to the pollution of the sediment column of Deepor Beel, Assam. Sediment samples were collected and analysed for seven different heavy metals from 23 sampling locations for a period from October 2017 to February 2019. Polling the entire dataset to a single matrix and carrying out multiple iterations revealed that four factors were optimum and thus, was applied for the simulation of the model. It was observed that the factors 1, 2, 3 and 4 corresponded to the soil parent material, leaching from the Boragaon landfill, discharge of agricultural and domestic wastes, and effluents from the industries and traffic emissions respectively. The sediment samples were further subjected to elemental analysis; X-ray powder diffraction (XRD) followed by Scanning electron microscope - Energy Dispersive X-Ray Spectroscopy (SEM – EDS), to determine the elemental composition and forms of heavy metals present in the sediment columns from various parts of the wetland. Sediment sample collected from the proximity of the landfill site was observed to be affected the most, probably due to leaching effects, especially during the monsoon. The central zone, however, was found to be devoid of any anthropogenic contaminations, while the sediment column near the industrial complex was found to be contaminated to a moderate extent. The study indicates the quantum of sediment contamination in the wetland and the causative parameters responsible, thus proving to be of immense help to the various governmental bodies in the planning and management of resources for sediment remediation of Deepor Beel.

Eutrophication of lakes and wetlands, resulting from the discharge of excess nutrients, has become a matter of primary concern. The entire ecology of a eutrophic wetland gets severely affected due to the substantial degradation of its water quality. Formulation of ecological models assists in determining the nutrient dynamics of a particular ecosystem, thereby providing an idea of the significant causative parameters aiding eutrophication. The present study aims at providing a suitable foundation for the development of an eutrophic-ecological model for Deepor Beel, India. Water, sediment, and water hyacinth samples were collected from various parts of the wetland and subjected to analyses on a continuous basis from Oct’ 2017 to Feb’ 2019. The collected samples were analysed for various physico-chemical parameters, quintessential for the development of a eutrophication based ecological model. A conceptual diagram was first constructed, and the corresponding differential equations about different functions were formulated. Subsequently, a code was developed in MATLAB based on the logic formulated through the conceptual diagram. Sensitivity analysis was first performed on various state variables, identifying the parameters most sensitive and thus, exhibiting maximum variability in the model. The model was then subjected to calibration for defining the rate constants based on which; it was subjected to further validation. Finally, the model was simulated for two plausible alternatives to verify the reduction of the eutrophication levels in Deepor Beel; (i) Harvesting of water hyacinths and (ii) Setting up of a treatment unit for nitrogen and phosphorus removal. The results obtained for both cases indicated that, harvesting of water hyacinths would not provide a suitable long-term and effective solution. However, setting up of a treatment unit for phosphorus and nitrogen removal can aid in a significant reduction in the nutrient levels in the wetland, thereby assisting in curbing the eutrophication levels. The results obtained through this study would provide significant assistance to the various government as well as private agencies and policymakers for carrying out effective solutions for the increasing eutrophication levels in Deepor Beel, which would thereby help in reviving the wetland.

In the present study, spatial and temporal variability of the heavy metals were investigated for Deepor Beel, India and a modified indexing approach for heavy metal contamination was proposed based on the statistical analyses of the monitored values. Water samples from 23 monitoring stations were collected for a period of one year and subjected to analysis for 7 different heavy metals (Mg, Cr, Cd, Fe, Mn, Cu, and Pb). The observed water quality dataset was first subjected to hierarchical clustering (HCA), which categorized the 23 monitoring locations into 3 statistically independent clusters based on the site similarities i.e. Low pollution (LP), High pollution (HP) and Moderate pollution (MP) respectively. Principal component analysis (PCA) technique was then applied to the three independent clusters to obtain principal components (PCs). These PCs were employed for calculating the weights of each component, from which the proposed heavy metal index (HMI) was estimated. The overall HMI value for Deepor Beel was found to be 123.52, which classified the water in Deepor Beel as “Poor”. Leaching from the contaminated landfill in the proximity to the wetland was found to be a primary source of contamination with respect to heavy metals. The efficacy of HMI was verified by comparing it with the existing heavy metal pollution index (HPI), contamination index (CI) and heavy metal evaluation index (HEI). Results of this study indicate HMI to be a more effective and reliable tool for water quality assessment with respect to heavy metal contamination.