Coastal water has become a primary concern because of its value associated with socioeconomic development and human health. As a result of human population and commercial industry growth, marine water has received significant amounts of pollution from numerous sources such as recreation, fish culture, toilet flushing, and the assimilation and transport of effluents (Zhou et al., 2007). The behavior of estuaries that are isolated from the ocean for variable periods of time is dependent on beach behavior and rainfall. For example, when sandbars are present, tidal flushing is restricted which can create flooding and prevent runoff from the catchment (Baldock et al., 2004). Human and ecological use of instream water requires that both the quantity and the quality of water be considered (Chang, 2008; Masamba and Mazvimavi, 2008). Pollutants entering a river system typically result from many transport pathways including stormwater runoff, discharge from ditches and creeks, vadose zone leaching, groundwater seepage, and atmospheric deposition (Ouyang et al., 2006; Nouri et al., 2011; Jha et al., 2010). And because pathways are seasonal-dependent, seasonal changes in surface water quality must be considered when establishing a water quality management program (Ouyang et al., 2006).
In recent years, multivariate statistical analyses have been successfully used in pollution source identification. Principal component analysis (PCA) and factor analysis are effective tools for reducing data dimensionality and grouping variables according to their common features (Juahir et al., 2010). Some of the recent studies that have successfully applied PCA and factor analysis in evaluating water quality include the work of Kamble and Vijay (2010), Huang et al. (2010), Vialle et al. (2011), Shiliang et al. (2011), Li et al. (2011), and Zhao et al. (2011). These researchers proved that multivariate statistical techniques provide data reduction and the most statistically significant parameters that result in the variation of water quality datasets. In the present study, a large data matrix obtained during one year of monitoring was subjected to different multivariate statistical techniques such as cluster analysis and PCA. The purpose was to alienate information on the similarities or dissimilarities between sampling sites, and to identify water quality parameters responsible for spatial and temporal variations in the water quality of Muttukadu Backwaters. The information on water quality and pollution sources is important for implementation of sustainable water use management strategies (Sarkar et al., 2007; Zhou et al., 2007; Nouri et al., 2008; Bu et al., 2010; Soner Kara and Onut, 2010). Geographic information systems (GIS) is increasingly used in environmental pollution studies because of its ability to conduct spatial analyses and interpolation; spatial interpolation utilizes measured points with known values to estimate an unknown value, and to visualize spatial patterns.
Materials and Methods
Muttukadu Backwater (latitude 128 460 N and longitude 808 180 E) is located in the village of Kovalam, 36 km south of Chennai along the southeast coast of India. Muttukadu Backwater is a bar built estuary, separated from the sea by a sandbar between March and September. From October to December, the sandbar erodes because of inundation by water from the upper reaches, and connection with the sea is restored. The backwater covers an area of 0.87 km2 and is used for fishing and boating activities. The backwater extends to about 15 km in a north-south direction with ranges in width of 800 m to 1050 m, and opens into the Bay of Bengal at its eastern end by a narrow opening varying from a few to 330 m in width (Figure 1).
The estuary is shallow with a maximum depth of 2 m in the center, whereas in most other areas the depth is 1 m or less.
Muttukadu Backwater is a picturesque picnic spot and a center for water sports with widely promoted boating and wind surfing. The Tamil Nadu Tourism Development Corporation (TTDC) has constructed a boat house at Muttukadu to encourage tourism activities. Dumping of oil and grease from engine cleaning, leakage of kerosene into the estuary from boats, and throwing of food waste by tourists are some of the pollution sources. Small-scale artisanal fishing in the backwater is an important source of livelihood for a large portion of an economically disadvantaged population of the fishing village; the area is also surrounded by many aquaculture farms. Fishermen primarily use the traditional catamaran for fishing and wait for hours together to catch fish. Continuous monitoring of water quality in Muttukadu Backwater for one year during 2009 revealed seasonal variations. Based on various environmetal stressors and sandbar formation near the mouth, nine sampling stations were identified as follows (Figure 1):
Station 1: located 1.5 km from the mouth near the TTDC boat house;
Station 2: located 2 km from the mouth;
Station 3: located 3 km from the mouth near seasonal crab culture;
Station 4: located 4 km from the mouth;
Station 5: located 6 km from the mouth near an industrial outlet;
Station 6 and
7: located in Buckingham Canal;
Surface water samples were collected monthly in polyethylene bottles from the above-named stations from January to December 2009 during high tide and brought to the laboratory. Physico-chemical characteristics of water visibility, temperature, pH, salinity, dissolved oxygen, total nitrogen, total phosphorus, Silicate, and Chlorophyll a (Chl-a) were analyzed to understand the role of sandbar formation in water quality. Variables such as dissolved oxygen, water temperature, salinity, and pH were measured in situ with a Cond 330i probe multi-line field kit (WTW, Inc., Weilheim, Germany). Subsamples were filtered in duplicate through 0.45 lm Whatman membrane filter paper (GE Healthcare, Mumbai, India) to determine the Chl-a concentration following Parsons et al. (1984); nutrients were determined following Grasshoff et al. (1983). The northeast monsoon in Chennai brings intense rainfall from October to December. The pattern of rainfall facilitates various seasons of the year: postmonsoon (January to March), summer (April to June), premonsoon (July to September) and monsoon (October to December). The seasonal variations of environmental features in the estuarine system are chiefly controlled by the spectacular regime of the rainfall during monsoon season. The data were
subjected to statistical computations such as PCA using Statistica version 8 (StatSoft, Inc., Tulsa, Oklahoma), and Microsoft Excel statistical tools (Microsoft Corp., Redmond, Washington), and cluster analysis using PRIMER (PRIMER-E Ltd., Ivybridge, United Kingdom).
The physical, chemical, and biological parameters obtained from the laboratory analysis were used as variable inputs for PCA. Before the analysis, the data were normalized because water quality parameters had different magnitudes and scales of measurements. If these differences had not been taken into account, the analysis would have given more weight to certain variables because of their respective variance (Kazi et al., 2009). Because the variance explained by each eigenvector is proportional to its eigenvalue, only those eigenvectors with eigenvalues . 1 are selected as significant independent variables (components). The sum of eigenvalues is equal to the total number of variables. Correlation of the principal components and original variable is referred to as loadings. Each component may then be identified as a source of pollution by determining its most interrelated parameters.
Cluster analysis groups objectives based on similarities within groups, and dissimilarities of other groups. The groups are divided by their unique characteristics, which often helps to interpret the data (Vega et al., 1998). Many studies have shown that cluster analysis reliably classifies surface water quality, and can guide future sampling strategies (Wunderlin et al., 2001; Simeonov et al., 2003; Singh et al., 2004). In this study, hierarchical agglomerative cluster analysis was performed on the normalized dataset by means of the wards method, using single Euclidean distances as a measure similarity (Simeonov et al., 2003; Shrestha and Kazama, 2007). In order to analyze water quality, a GIS software package, Arc GIS version 9.3, was used (Esri, Redlands, California). Inverse distance weighting (IDW) was used, which is the simplest and most practical spatial interpolation method. IDW uses the measured values surrounding the location and assumes that each measured point has a local influence that decreases with distance (Mueller et al., 2004; Wang, 2006).
Results and Discussion
Hydrochemistry of Muttukadu Backwater. The physicochemical parameters such as temperature, pH, salinity, dissolved oxygen, and nutrients showed seasonal variations. The seasonal variations of the environmental features in the estuarine system are chiefly controlled by the spectacular regime of the rainfall during monsoon season. In the present study area, the peak values of rainfall were recorded during the northeast monsoon periods (October to December). The rainfall was sparse during the post-monsoon and summer months (Prema and Subramaniam, 2003). Descriptive statistics of the data is provided in Table 1. The average surface water temperature of study sites varied from 18.9 to 35 8C (Figure 2). The temperature depended upon the climate of the estuary. The lowest temperature was recorded during January and December. Typically, low pH values were recorded during the monsoon period and slightly higher values were recorded during the summer period. A similar seasonal pattern was recorded earlier by Thangaraj (1984), Palpandi (2011), and Santhanam and Perumal (2003) in the Vellar estuary; Murugan and Ayyakkannu (1991) and Soundarapandian et al. (2009) also recorded a similar seasonal pattern in the Uppanar backwaters. Recorded pH levels ranged from 5.8 to 9; the highest levels were recorded at Station 9 during February, and the lowest levels were recorded at Station 1 in January. The extensive buffering capacity of the seawater may be the cause for a change in pH within a very narrow limit in the present study. When the mouth was open, pH was uniform across all estuary sites. There was a slight decrease in the pH level at Stations 1, 2, 3, and 4 when the mouth was fully closed; however, the difference only ranged between 4.8 and 6.9 because of a lack of tidal exchange.
The salinity acts as a limiting factor in the distribution of living organisms; variation of salinity, caused by dilution and evaporation, most likely influences the fauna in the intertidal zone. Typically, changes in salinity in the brackish water habitats, such as estuaries, backwaters, and mangrove waters, are because of the influx of freshwater from land runoff caused by monsoon or tidal variations. Salinity showed a significant positive correlation with temperature. Salinity was lowest at approximately 18 and 21 ppt at Stations 4 and 6, respectively, when the mouth was closed (summer), because there was no interaction of the sea with the estuary. Salinity was at a maximum (34.6 ppt) during monsoon season because of the opening of the mouth. Salinity is greatly influenced by the sandbar formation near the mouth. During summer, because of the formation of the sandbar, salinity had reduced at Stations 4 and 5, as there was no interaction with the sea.
Dissolved oxygen varied from 2.8 to 8.4 mg/L (Figure 2). Dissolved oxygen was found to be greater on the lower reaches when compared to the upper reaches. Dissolved oxygen showed a negative correlation with temperature (0.12). In aquatic systems, oxygenation is the result of an imbalance between the process of photosynthesis, degradation of organic matter, reaeration (Garnier, 2000), and physico-chemical properties of water (Aston, 1980). Dissolved oxygen content was high during monsoon season in the study area, which could be a result of the influx of freshwater during the monsoon season’s higher solubility and low salinity (Prema and Subramaniam, 2003). Dissolved oxygen appeared to be controlled by various factors such as rainfall, temperature, phytoplankton photosynthesis, and salinity (Muthukumaravel et al., 2012). During the present study, salinity was found to be the most important factor that controlled the level of dissolved oxygen in the coastal waters, evident from its insignificant correlation with dissolved oxygen. The above observation was further supported by the significant negative correlation of dissolved oxygen with Chl-a, which confirmed that the contribution of photosynthetic release of dissolved oxygen was negligible (Satpathy et al., 2009).
Nitrite concentrations increased from the mouth region to the upstream region from June to November at all stations except at Station 9. High nitrite concentrations were observed during monsoon season (59.1 lmol/L) at all stations. The highest concentration of 62.5 lmol/L recorded at Station 6 during postmonsoon season was a result of the addition of domestic municipal sewage and agricultural runoff. Nitrite concentrations showed a positive correlation with nitrate and ammonia concentrations (Table 2).
Nitrate concentrations varied from 0.2 lmol/L at Stations 1 and 9, to 40 lmol/L at Station 6. The negative correlation (Table 2) between nitrate concentrations and salinity showed that freshwater influx is considered to be the primary source of this nutrient in coastal waters. Variations in nitrate concentrations and its reduced inorganic compounds are predominantly a result of biologically activated reactions. Quick assimilation by phytoplankton and enhancement by surface runoff results in large scale spatio-temporal variations of nitrates in the coastal water. During monsoon season, the freshwater influx dilutes the coastal water resulting in a decrease in salinity and an increase in solubility of dissolved oxygen in the present study.
Typically, ammonia concentrations (Figure 2) at all stations decreased downstream toward the mouth. Ammonia was low during November and December at all stations. Ammonium values were spatially heterogeneous during these two periods
with high concentrations (24 lmol/L) in interior zones (Stations 4, 6, and 7) and low concentrations in areas near the inlet, revealing that ammonia concentrations within the estuary were often greater than groundwater or marine concentrations (Table 1 and Figure 2 ).
Total nitrogen values, the combination of both organic and inorganic nitrogen species, varied from 0.1 to 36.9 lmol/L (Figure 2). The highest total nitrogen concentration (Table 1) was observed at Station 6, as a result of the discharge of sewage into the Buckingham Canal. Other stations showed a wide range of fluctuations in total nitrogen. A low value of 2 lmol/L was observed during October 2009 as a result of heavy rainfall that added large quantities of freshwater input, increasing the dilution of the nutrients.
Silicate, an important factor for phytoplankton distribution, ranged from 0.01 lmol/L to 5.9 lmol/L (Figure 2). At Station 5, the highest and lowest silicate concentrations were reported during January and October, respectively. Silicate concentrations at Station 4 were very high during pre-monsoon and monsoon seasons, which may be a result of the industrial discharges at this location. Silicate showed a negative correlation with temperature. The spatio-temporal variations of silicate in coastal waters were influenced by several factors; primarily, the proportional physical mixing of seawater with freshwater (Purushothaman and Venugopalan, 1972), adsorption of reactive silicate into suspended sedimentary particles (Lal, 1978), chemical interaction with clay minerals, and biological removal by phytoplankton, especially by diatoms and silicoflagellates. Silicate showed a strong negative correlation with salinity (Chandran and Ramamoorthi, 1984; Thangaraj, 1984) indicating that freshwater could be the primary source of silicate in these coastal waters, because entry of silicate into a coastal zone mainly takes place through land drainage rich with weathered silicate material.
Phosphate values ranged from 0.001 to 38.9 lmol/L (Table 1). Phosphate concentrations were high during monsoon season, ranging from 27.3 to 38.9 lmol/L in October, showing a significant negative correlation with dissolved oxygen and salinity. The highest concentration of phosphate was found primarily in freshwater-receiving areas, and lower at Stations 8 and 9. The highest phosphate concentrations were reported in the upper reaches during monsoon season, and could be a result of runoff from agricultural fields and settlements. Moreover, the release of phosphate from bed sediments as a result of stirring action by strong tidal waves could be a causative factor. The observed variation may be caused by various processes, such as absorption and desorption of phosphate and buffering action of sediments under varying environmental conditions (Pomeroy et al., 1965).
The concentration of total phosphorus showed a marked disparity from 0.01 to 10 lmol/L (Figure 2). The total phosphorus values at Stations 4, 6, and 7 were very high throughout the study period. The discharge of effluents from the nearby industries, and domestic sewage from Buckingham Canal, are the primary reasons for the increase in total phosphorus. Higher total phosphorus concentrations were also observed at Station 3, because of the discharge from aquaculture farms containing large amounts of phosphorus compounds that increase the nutrient load. Lower concentrations are observed during monsoon season. Showing a negative correlation with salanity, the data indicate removal of phosphate at intermediate salinities.
In the estuarine environment, productivity primarily depends on phytoplankton, which alone contributes approximately 90% of the total estuarine primary production. Therefore, Chl-a, constitutes the chief photosynthetic pigment of phytoplankton, and is an index that can provide the primary production potential upon which the biodiversity, biomass, and carrying capacity of the system depends. Chlorophyll-a shows a significant negative correlation with temperature, salinity, and dissolved oxygen, and a positive correlation with nutrients, suggesting that phytoplankton production is nutrient-limited. Relatively higher Chl-a values observed during March to May (1.9 mg/L) could be a result of phytoplankton productivity in summer. A similar observation was made from other coastal waters of India by Madhupratap et al. (2001), Prasanna Kumar et al. (2002), and Sarma et al. (2006).
dissolved oxygen, and a positive correlation with nutrients, suggesting that phytoplankton production is nutrient-limited. Relatively higher Chl-a values observed during March to May (1.9 mg/L) could be a result of phytoplankton productivity in summer. A similar observation was made from other coastal waters of India by Madhupratap et al. (2001), Prasanna Kumar et al. (2002), and Sarma et al. (2006).
Cluster Analysis. Cluster analysis can be an important tool for analyzing water quality data to better understand the relationship between location (stations) and season (months). In this study, month-wise water quality parameters formed three clusters using Euclidean distance (Figure 3) indicating that the coastal water quality for a period of one year exerted three clusters in different seasons. The first cluster was formed by June, July, August, and September, representing a southwest monsoon season where there is complete closure of the mouth and absence of influence of seawater on the estuary. The second cluster was formed by October, November, and December, which corresponds to a northeast monsoon season. The third cluster formed by January, February, March, and April, typically different from the other periods because of the presence of a sandbar near the mouth region.
The station-wise dendrogram showed two clusters (Figure 4). The first cluster was formed by Stations 4 and 5, which receive a continuous freshwater influx in the upper reaches. The second cluster was formed by Stations 1, 2, 3, 6, 7, 8, and 9, located on the lower reaches near the mouth of the backwater. The similarity of tidal influence and low freshwater influence in the area accounts for the cluster formation. These two cluster formations are justified by the prevalence of different environmental conditions at the study sites.
Principal Component Analysis. From the standardized covariance or correlation matrix of the data, the initial factor solution was extracted by the multivariate principal components. A number of principal components were then selected according to their initial eigenvalues and scree diagram. This method aims to transform the observed variables to a new set of variables of principal components which are arranged in decreasing order of importance for simplification. Table 3 represents the determined initial eigenvalues, total, percent of variance, and cumulative percent.
Figure 5 shows the scree plot of the eigenvalue for each component. The first three principal components are the most significant, representing more than 75.9% of the variance in water quality, and eigenvalues of greater than one.
Principal component 1 accounts for 34.3% of the total variance because of the positive load of salinity (0.87), temperature (0.86) and dissolved oxygen (0.33), and a negative load of ammonia (0.62), total phosphate (0.69), and Chl-a (0.58). Principal component 1 exhibited a high correlation with significant physico-chemical variables representing the influence of tidal action and sandbar formation. Principal component 2 accounts for 24.2% of the total variance because of positive loading of nitrate (0.83), nitrite (0.94), and ammonia (0.5), and a negative loading of silicate (0.41) and dissolved oxygen (0.19). Strong loadings on nitrate, nitrite, and ammonia, and a moderate negative loading of dissolved oxygen and silicate indicates a discharge of industrial effluent that is rich in nitrogenous compounds. This factor clearly explains that nutrient enrichment to the estuary maintains productivity through proper phytoplankton growth (Panda et al., 2006). Principal component 3 accounts for 17.3% of the total variance because of positive loading of total nitrogen (0.77) and dissolved oxygen (0.62), and negative loading of Chl-a (0.50) and phosphate (0.52), as shown in Table 4.
Spatial Interpolation of Water Quality Parameters. IDW was used to generate a representative surface sample of Muttukadu Backwater based on the sampling points. Stations 6 and 7 were not included as they were taken from Buckingham Canal. Seasonal effects on various water quality parameters were quite clear from Figures 6 and 7, showing spatial distribution during post-monsoon, summer, southwest monsoon, and northeast monsoon seasons. During the southwest monsoon season, salinity and pH were minimal at Station 5. High nitrate, nitrite, and ammonia concentrations were observed at Station 8 during the post-monsoon and summer seasons because of tourism activity. A significant supply of freshwater from upstream during the monsoon season changed the distribution of nutrients and other physio-chemical parameters. The ecosystem of Muttukadu Backwater was largely dependent on the monsoon season and formation of a sandbar.
The present study summarizes the seasonal fluctuations in various physico-chemical parameters in the waters of the Muttukadu Backwater, and provides exploratory statistical data output. The study showed that physico-chemical properties of the estuarine zone were significantly affected by the presence of the sandbar that formed from March to July, and freshwater input during monsoon season. It was observed that when the sandbar is formed during the summer season, the nutrient concentration increases, and dissolved oxygen decreases at all stations. Tidal action is dominant when the bar is open. As the bar closes, more riverine flow from the Buckingham Canal reaches the estuary and nutrient concentrations increase. It was observed that the estuary acts as a sink when the sandbar is closed, leading to eutrophication. Cluster analysis shows a clear variation in water quality, both station-wise and month-wise. Freshwater inputs and water interchange with the sea force functions that preserve spatial heterogeneity and hydrological gradient variation within the Muttukadu Backwater.
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