1.2. Background of the research
Continuous monitoring provides a reliable yet sophisticated dataset, which becomes rela- tively ineffective when it comes to interpretation, owing to its complexity (Vega et al. 1998;
Simeonov et al. 2003; Iscen et al. 2008; Chow et al. 2016; Hajigholizadeh & Melesse 2017;
Singh et al. 2019b). However, in recent years, the Environmetrics approach, i.e., application of various statistical techniques, has eased how the datasets are understood, including the classification of spatially distributed monitoring sites and the primary factors or contami- nants responsible for the deterioration of the water body (Jha et al. 2014; Machiwal & Jha 2015; Bodrud-Doza et al. 2016; Chow et al. 2016; Kumar et al. 2017).
Hierarchical clustering of sampling sites through cluster analysis (CA) and the identifica- tion of probable pollution sources through principal component analysis (PCA) have been widely used and accepted. The use of discriminant analysis (DA) as a supervised pattern recognition tool to recognize the most significant water quality variables accountable for spa- tial and temporal variability has also been used more recently than the other two methods (Hajigholizadeh & Melesse 2017). However, these statistical tools cannot solely quantify the contribution of potential pollution sources. For this purpose, various receptor models, such as the positive matrix factorization (PMF) model, are used. The PMF models were initially applied to the dataset pertaining to atmospheric pollution to determine how much is the con- tribution of various pollution sources. Only in recent times, they have been applied to the wa- ter quality (WQ) datasets along with PCA and CA for quantifying the contributions of pollution sources (Zhao et al. 2013; Mustaffa et al. 2014; Chen et al. 2015; Gholizadeh et al. 2016).
However, identifying and apportioning the pollution sources merely reflects the quantifi- cation of pollution entering a water body. To evaluate the status of a water body’s true health with respect to its water quality, it is essential to assess the raw water quality dataset ob- tained from the monitoring programme with regards to a standard recommended for the gen- eral public. Given the vastness and complexity of the raw dataset, it becomes impossible to assess each parameter independently for all the monitoring locations (i.e., spatially) and each frequency (i.e., temporally). Indexing approaches have been used extensively for quite a long time, first coined by Horton (1965). Water quality indices (WQIs) are mathematical tools rep- resenting the water quality status of a particular water body. They consider the desired pa- rameters for estimating a numeric value, thus delivering a much easy interpretation of the water health, which otherwise becomes extremely tough due to the complexity of large da- tasets. These indices are based on the end-use of water and vary from an individual’s percep- tion. Three major categories of indices are usually studied, depending on the water use; they are overall WQI, which takes into account the drinking or domestic use of the water, indies
that assess the heavy metal contamination in a water body; and finally, indices that account for the water body’s irrigation suitability.
Intense industrialization and other natural and anthropogenic activities in the ecosystem have been a major concern regarding safe and potable water (Li et al. 2011; Islam et al.
2015a). Various geological processes, involving weathering of bedrocks and volcanic erup- tion, anthropogenic activities including large-scale use of metal-containing fertilizers and pes- ticides for agricultural practice, metal smelting, mining, and other various metallurgical pro- cesses have resulted in deep scale contribution of heavy metals in the natural aquatic ecosys- tem (Meng et al. 2016; Kumar et al. 2017; Wang et al. 2017). This has not only rendered the water systems unsuitable for drinking but has also degraded the quality of water to an extent unfit for agricultural or industrial purposes. Furthermore, the enrichment of trace heavy metal concentrations in the water bodies causes severe health risks by getting absorbed by various organisms, thereby entering into the food chain (Banerjee et al. 2011; Forti et al.
2011; Yi et al. 2011; Rahman et al. 2013; Ahmed et al. 2015; Bhuyan et al. 2017). Reports have also suggested that these dissolved trace heavy metals prove carcinogenic if consumed in con- siderable amounts persistently. These potential hazards to human health and aquatic ecosys- tems render the heavy metal pollution in water systems a severe environmental issue (Giri &
Singh 2014; Farahat & Linderholm 2015; Wang et al. 2017). Therefore, a systematic study of the heavy metal concentrations, their sources and distribution, and their impact on the qual- ity of water for the abatement of possible future contamination and protection of natural wa- ter resources is inevitable. Likewise, with the increasing food security problems around the globe, it becomes highly essential that the hydro-geochemical analyses of natural waters for both irrigation and drinking purposes be taken care of.
Similar to water contamination, surficial sediment contamination as a consequence of various anthropogenic activities has also been a cause of serious concern in recent times. Pri- mary contaminants possessing critical issues to the global sediment flux constitute various heavy metals, accumulated due to heavy discharge of effluents (majorly industrial, agricul- tural and domestic wastewater) into the aquatic ecosystem (Syvitski et al. 2005; Ouyang et al.
2006; Zhang et al. 2007b; Azhar et al. 2015; Dhamodharan et al. 2019). These heavy metals have typical characteristics of being persistent and thus do not deteriorate or decompose with time, thereby making them toxic when concentrations exceed permissible limits. Further- more, these compounds have less mobility in water columns. Therefore, their continuous ac- cumulation in the natural water systems forces them to precipitate on the sediment column of the waterbody. This makes the sediment columns of the water bodies potential sources of heavy metals, where they can be released back into the water columns or the aquatic flora
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and fauna via natural or anthropogenic ways, thus joining the food-chain system (Yin et al.
2011; Dhamodharan et al. 2019). Lakes and wetlands play pivotal roles in providing nutrients to living organisms. Therefore, their bottom sediments are sensitive indicators to determine the pollution loadings as they act as both sources and sinks for the contaminants in an aquatic environment (Varol 2011; Yin et al. 2011). This necessitates their continuous monitoring and assessment as well.
Heavy metals are naturally occurring elements present in all three spheres of the envi- ronment, i.e., the atmosphere, hydrosphere and lithosphere. Few of these metals (such as Cr, Co, Cu, Mn, Fe, Mo, Se, Ni, and Zn) are usually present in trace quantities, which are beneficial for the existence and sustenance of living organisms as they play pivotal roles in catalytic and enzymatic actions as well as various oxidation-reduction reactions in the body (WHO/FAO/IAEA 1996). However, certain metals (such as Hg, Pb, Cd, and As) also exist in the ecosystem, which is primarily the direct result of various human-made or anthropogenic con- tributions such as industrial, pharmaceutical, agricultural and technological applications (Tchounwou et al. 2012; Grigoratos et al. 2014; Martín et al. 2015; Sobihah et al. 2018). These metals do not possess any biological functions and are rendered non-essential and potentially toxic compounds, even at insignificant concentrations (Tchounwou et al. 2012). When pre- sent in significant concentrations, i.e., more than the desired limits, these compounds fabri- cate cellular and tissue damage in the living bodies, thus resulting in various health risks (Bonsignore et al. 2018). The persistence, long biological half-life and toxicity impact these heavy metals possess render them highly risky for humans to consume (Bortey-Sam et al.
2015). This is primarily because of the negative influences on the human digestive, cardio- vascular and central nervous systems upon accumulating these heavy metals (Crespo-López et al. 2007). In addition to these cancerous impacts, some compounds (such as As, Pb, Cd and Hg) can also contribute to the teratogenic, mutagenic and carcinogenic consequences on the living organisms (Wong 1988). The heavy metals, released from multiple natural and anthro- pogenic events, enter the aquatic ecosystem and get transported through numerous geo- chemical and biological cycles. These phenomena make them bioaccumulated in the natural ecosystem, thus entering the water, sediment and aquatic food chains and eventually getting biomagnified (Atwell et al. 1998; Graci et al. 2017; Rajeshkumar et al. 2018). These processes can be well established and correlated in an aquatic environment by analyzing the heavy metal concentrations in all three components, i.e., sediment, water and the living entities. The heavy metal components discharged into the aquatic ecosystem first come in contact with the water column. These metals get precipitated into the sediment column with time, owing to various physico-chemical and biological metabolisms and their immobile nature in the water column. However, heavy metals’ accumulation is not limited to the sediment columns only, as TH-2896_176104004
they get reverted to the water column via several natural and anthropogenic comportments (Dhamodharan et al. 2019). The aquatic flora and fauna, especially fish, additionally play cru- cial roles in the bioaccumulation and biomagnification processes. Recent years have wit- nessed a significant surge in fish consumption volume, owing to its high nutritional value and lower saturated fat and omega-3 fatty acid content (FAO 2013; Bosch et al. 2016). Fishes are considered major bio-accumulators and bio-magnifiers in the natural aquatic ecosystems, ca- pable of harming individuals exposed to them (Taweel et al. 2013; Ahmed et al. 2015; Saha et al. 2016; Rajeshkumar et al. 2018). There are two principal entrance mechanisms for the heavy metals into the aquatic food chain; first through the direct ingestion, i.e., the digestive tract and second through permeation, i.e., non-dietary routes such as muscles and gills (Ri- beiro et al. 2005). Fishes have become a part of vital nutritional elements, and hence, the as- sessment of their quality and safety has become paramount. Typically, the levels of heavy metal contaminants found in the fish reflect the sediment and water contamination from where it has been sourced and the exposure time (Annabi et al. 2013).
Apart from the heavy metals, nutrient discharges from different anthropogenic sources, such as the release of untreated or partially treated domestic, industrial and nutrient-rich agro-wastewaters, have rendered many wetlands to die due to excessive eutrophication. The entire ecology of a eutrophic wetland gets severely affected due to the substantial degrada- tion of its water quality. The rise in eutrophication levels has been a challenge for environ- mentalists, as this leads to lowering the dissolved oxygen (DO) levels, excessive growth of phytoplankton, and an increased frequency of algal blooms. Effects on the drinking water sup- ply, food security, and public health have also been substantial (Wu et al. 2017). Of all the surface water bodies, wetlands have been a primary victim of the increasing eutrophication levels. Therefore, various ecological monitoring programs, including monitoring water qual- ity on a continuous scale, have become quintessential for assessing the possible factors re- sponsible for the deterioration of the wetlands (Alberto et al. 2001). Although monitoring various parameters in the wetlands provides information about the wetland's current state, it vaguely provides the factors that influence the current state. Hence, in order to have a better insight into the influencing factors, ecological models are formulated. Ecological models are mathematical or physical representations of a particular ecosystem. These models can com- prehend the nutrient/contaminant cycles, identify various characteristics of a concerned pa- rameter or highlight the underlying process mechanism (Hu 2016). In fact, ecological models can also predict the fate of nutrients in the natural ecosystem by revealing the extent and means by which several nutrients such as nitrogen and phosphorus are transformed or re- moved. In some cases, models can also be developed to answer various “what-if” questions that help decision-makers make a correct choice. Thus, ecological models can serve as perfect
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management tools in deciding the right course of action for a particular ecosystem when ap- propriately developed. Ecological models are also helpful to the researchers because well- designed and calibrated models can reveal the missing piece of the puzzle that allows them to understand a particular mechanism or behaviour of the ecosystem (Das et al. 2018). A study of different ecological models developed across the world has provided significant in- sights into the process of ecological modelling along with the extent to which ecological mod- els can be utilized effectively. The need to develop an ecological model for a water body be- comes far more urgent if it is currently endangered.
Keeping in view the above-mentioned problems associated with wetlands, the present research's overarching primary objective is to study their limnology, thereby assessing their responses to different anthropogenic interventions. Accordingly, to achieve the overall aim, different objectives were formulated in the course of this research, described in detail in Chapter 3.