Abstract: Investigations on physico-chemical parameters like temperature, pH, salinity, dissolved oxygen (DO), nitrate, nitrite, inorganic phosphate and silicate were carried out in two different zones of Vellar estuary, South east coast of India from Sep. 2006 to Aug. 2008. Nitrate, silicate and DO were relatively high in monsoonal months (Oct-Dec) than the non-monsoonal months. Salinity, pH, atmospheric and water temperatures showed their maximum in middle of summer (May) and onset of pre-monsoon (June), whereas their values were minimum in monsoon. Two way ANOVA indicates, spatial and temporal variation of salinity which showed significant differences, but the other parameters showed significant temporal variations only. Factor analysis revealed that three principal components (PCS) contribute 84.47% total variances in physico-chemical characteristics of Vellar estuary, in which the first component attributed to river run-off from agricultural lands, mangrove leaf litters from Pichavaram mangroves (55.98%), the second to influx of neritic water from Bay of Bengal (17.72%) and the third to anthropogenic activities (10.77%). Maximal influence on first factor in physico-chemical parameters was found during the monsoonal months, thereafter it’s influences were declined when the second factor make dominance into estuary. Third factor influences were found throughout year but their effects depends upon first two factors.
INTRODUCTION
Estuarine ecosystems are environmentally unstable, in opposition to marine and freshwater ecosystems (Mclusky and Elliott, 2004). Freshwater flow tremendously influences and alters the hydrographic nature of estuaries and also affects the biotic communities, particularly the planktonic communities (Kimmerer et al., 2002). Continuous monitoring of an environment is important to analyze the water quality and to predict the level of pollution (Jakher and Rawat, 2003). Nutrients status of an environment is used to assess the health and wealth of an ecosystem (Bragadeeswaran et al., 2007; Robertson and Blabber, 1992). Industrialization and urbanization releases large quantity of pollutants into the estuarine environment through the municipal, industrial discharge and river runoff (Gupta et al., 2009) which can alter the nature of the water.
The assessments of water quality contaminants and securitization on their effects require continuous monitoring to wide ranges of physical, chemical and biological parameters, which yields complex data. Interpretations of that data are more intricate. Recently different kinds of multivariate statistical analysis have been effectively employed to understand the temporal and spatial variations of water quality characteristics and also used to identify the principal source to influence that water system (Astel et al., 2008; Brodnjak-Voncina et al., 2002; Helena et al., 2000; Liu et al., 2003; Simeonov et al., 2003; Simeonova and Simeonov, 2006; Singh et al., 2005; Vega et al., 1998; Wu et al., 2010; Yung et al., 2001; Zhao and Cui, 2009; Zhou et al., 2007). Therefore, the aim of this study was to investigate physico-chemical parameters like temperature, pH, salinity, Dissolved Oxygen (DO), nitrate, nitrite, inorganic phosphate and silicate in two different zones of Vellar-coleroon estuary, South east coast of India.
MATERIALS AND METHODS
Study area: The Vellar estuary is situated at Parangipettai (11°29’ N, 79°46’ E), Southeast coast of India (Fig. 1).Vellar River originates from Sherveraryan hills, Salem district, Tamil Nadu, after meandering for a length of ca 480 km, it forms an estuarine system at Parangipettai, where it enters into the Bay of Bengal. It is classified as semi-diurnal and bar-built estuary but free flow of neritic water into the estuary is always possible due to its incomplete closure of the sand bar. Main Vellar river, four channels (Long Channel, Dog channel, Buckingham channel and Railway canal) and water ways of Pichavaram mangroves along with several drainage canals bring large volumes of freshwater into Vellar estuary.
‘Pichavaram’ mangroves cover an area of about 1100 ha. It consists of 51 islets, ranging from 10 m2 to 2 km2. Fifty percent of the total area is covered by forest, 40% by waterways and remaining 10% by sand and mudflats. Pichavaram mangroves are connecting the Vellar and Coleroon estuaries through the channels and form Vellar Coleroon estuarine complex. The Southern part near the Coleroon estuary is predominant with mangrove vegetation while the northern part near the Vellar estuary is dominated by mud flats.
Tidal influence of Bay of Bengal extends up to 18 km upstream during summer season. Tidal amplitude of Vellar estuary is approximately 1.0 m. Constant wave action from Bay of Bengal and heavy inflow of fresh water from north east monsoon could alter the width, depth and position of mouth in the estuary.
| Fig. 1: | Study area of the vellar estuary |
The average width of estuary is about 200 m and at the mouth it varied between 100 and 600 m depending on seasons. The average depth of the estuary is about 2.5 m and the maximum depth is 5 m during high tide. Upstream area of Vellar estuary is filled by agricultural lands, more than hundreds of agricultural feeder and drainer canals are connected to the Vellar River. Along the banks of Vellar estuary several numbers of shrimp and fish farms are in operation during non-monsoonal months. Annual rainfall of India is largely influenced by two different monsoons viz., Southwest monsoon and northeast monsoon. Rainfall of study area varied from 600 to 1500 mm but it gets 70-80% precipitation during the North east monsoon. It occurs normally Oct. to Dec. In Southwest monsoon times study area gets only very minimal rainfall. Based on the rainfall patterns the months are classified into four seasons, pre monsoon (July-Sep), monsoon (Oct-Dec), post monsoon (Jan-Mar) and summer (April-June). This study emphasis seasonally altered water quality parameters of tropical semi bar built estuary and evaluate their principal sources through factor analysis.
Methods: Two different zones were chosen for the present study. One is the marine zone situated near to estuarine mouth. It has direct influences with adjacent sea, average salinity and depth of the station were about 30 ppt and 2.5 m, respectively. Another one is an estuarine zone situated opposite to marine biological station. It has an average salinity of 25 ppt and 5 m depth. Northern bank of this station cover 10 ha of artificial mangrove plants. Distance between the two stations are about 1.5 km.
Surface water samples were collected in two different tides of full moon days in Sep. 2006 to Aug. 2008. Surface and water temperatures were measured using centigrade thermometer. Salinity was estimated by hand refracto meter (Atago, Japan). pH was measured by digital pH meter (Hanna instruments, EN50081-1). Dissolved oxygen was estimated by the modified Winkler’s method (Strickland and Parson, 1972).
For analysis of nutrients, water samples were collected in 1 L clean polythene bottles and kept in an ice box and transported immediately to the laboratory. The water samples were filtered through Millipore Filtering System (MFS) and analyzed for nitrate, nitrite, dissolved inorganic phosphate and reactive silicate by adopting the standard methods described by Strickland and Parson (1972).
Data analysis: All data of this study are mean data obtained from high and low tide. The standardized skewness and standardized kurtosis were determined to assess whether the data came from a normal distribution or not (Wu et al., 2010). Values of present statistics outside the range of -2 to +2 (skewness and kurtosis were -0.105 to 7.689 and -1.65 to 9.528) indicated that data departures from normality, so the raw data set be transformed into normal data matrix, by z- scale transformation for FA/PCA analysis in order to avoid misclassification due to wide differences in data dimensionality. Standardized data ensures that each variable had the same influence in the analysis (Liu et al., 2003). Furthermore, the standardization procedure eliminates the influence of different units of measurement and renders (Singh et al., 2004). The data were standardized (to the Z score with mean = 0 and S.D = 1) by applying the following equation Kowalkowski et al. (2006):
where, X is the original value of measured parameter, Z the standardized value, X’ the average value of variable and s the standard deviation. Kaiser-Meyer-Olkin (KMO) test was performed to examine the suitability of the data for principal component analysis/factor analysis (Zhao and Cui, 2009).
FA/PCA analysis: Principal component analysis is a technique widely used for reducing the dimensions of multivariate problems. PCA is designed to transform the original variables into new, uncorrelated variables (axes), called the principal components, which are linear combinations of the original variables. The new axes lie along the directions of maximum variance (Shrestha and Kazama, 2007). It reduces the dimensionality of data set by explaining the correlation amongst a large number of variables in terms of a smaller number of underlying factors (principal components or PCS) without losing much information (Helena et al., 2000; Jackson, 1991; Meglen, 1992; Vega et al., 1998; Wunderlin et al., 2001). The principal component (PC) can be expressed as (Kannel et al., 2007; Singh et al., 2004):
zij = ai1x1j + ai2x2j +• • •+aimxmj |
where, z is the component score, a is the component loading, x the measured value of variable, i is the component number, j is the sample number and m is the total number of variables. FA/PCA analysis was carried out in XL Stat software package. All the physico chemical variables were initially tested for normal distribution, if the variables met the normal distribution, two way ANOVA performed directly, whereas the variables didn’t show the normal distribution, that variables were initially square root transformed and performed the Two way ANOVA by SPSS v16.
RESULTS AND DISCUSSION
Variations of physico-chemical parameters were presented in Fig. 2. Results of the two way ANOVA spatial and temporal variations of physico-chemical parameters within and between the stations were presented in Table 2. Environment remains cool in monsoonal months (Nov- Dec) and it get gradually warming in post monsoonal months and reached an extreme hot in month of May. Atmospheric temperature (AT) and Water temperature (WT) were ranged 23.5-29.5 and 24.0-29.8°C, respectively (Fig. 2a and b). Temperature variations within stations were significant (F = 20.5, p<0.001; F = 18.41, p<0.001 in WT and AT, respectively) but between stations were insignificant (F = 1.14, p>0.05; F = 0.54 p>0.05 in WT and AT, respectively). Higher temperature during summer and pre monsoonal months could be due to clear sky and more radiation than the months. Precipitation ranged between 0-440 mm (Fig. 2c). Total average rain fall of study was about 1022 mm in the present study, out of which 74% occurs in north east monsoonal season (Oct-Dec) and very minimal (3%) occurs in summer months (May-July). Post monsoon and premosoonal months have 7 and 16%, respectively.
Salinity variations were more distinct and it ranged 20.3 - 35.56 and 3.65 - 36.41 ppt in Stn.1 and Stn.2, respectively (Fig. 2f). During northeast monsoonal months (Nov-Dec) salinities were low and their values were gradually increased in post monsoonal months and reached as maximum in May. Coastal waters had higher salinity than the estuarine waters except in month of May. Monthly variations of salinity within stations were more significant (F = 11.03, p<0.001) but their variations between stations were less significant (F = 4.91, p<0.05). Dissolved oxygen ranged 3.97-5.15 mL L-1 in station 1 and 4.12-5.65 mL L-1 in station 2 (Fig. 2d).
| Fig. 2(a-j): | Monthly variations of physico-chemical parameters (Mean±SD) in two different stations of Vellar estuary (a) WT (°C), (b) AT (°C), (c) Rainfall (mm), (d) DO (mL L-1), (e) pH, (f) Salinity, (g) Nitrate (μM), (h) Silicate (μM), (I) Nitrate (μM) and (j) Phosphate (μM) |
DO relatively higher in monsoonal months (Sep. to Dec.) but it gradually decreased in later post monsoonal months to summer and reached minimum in May. Maximum salinity and lesser DO noticed in non monsoonal months due to lesser river runoff and neritic water incursions from Bay of Bengal. Do variation between the stations were insignificant (F = 0.4167, p>0.05) but their variation within stations were more significant (F = 13.53, p<0.001) pH varied from 7.46 to 8.52 in Stn.1 and 7.15 to 8.47 in Stn.2 (Fig. 2e).
pH values were lesser in monsoonal months but in post monsoonal months pH values gradually increases and attain in maximum in middle of summer (April). In later summer month and onset of pre monsoonal months have relatively lesser pH then the earlier summer due to lesser dilution of sewages and higher biological oxidation. Station 1 has more pH than the station 2. The variations of pH between the stations (F = 2.04, p>0.05) were insignificant but within stations (F2 = 8.89, p<0.005) were shown significant. Nitrate ranged 3.98 - 6.55 μM in Stn.1 and 4.15 -7.42 μM in Stn.2 (Fig. 2g). Silicate ranged 19.96-50.13 μM, 24.16 - 53.96 μM in Stn.1 and Stn.2, respectively (Fig. 2h). Monsoonal months have relatively higher nitrate and silicate concentrations than the other seasonal months, by reason of nutrient rich water inputs from the adjacent agricultural lands and mangrove litters from the Pichavaram mangroves through irrigational canals and water ways. Nitrite values varied 0.34-0.98 μM in Stn.1 and 0.83-0.29 μM in Stn.2 (Fig. 2i). Inorganic phosphate ranged between 0.56-2.82 μM in Stn.1; 1.96-0.40 μM in Stn.2 (Fig. 2j). Higher nitrite and phosphate concentrations were noticed in the middle of the monsoon, the remaining months showed more or less stable. Phosphate and nitrite concentrations not showed the seasonality patterns. Variations of Nitrate, nitrite and silicate between the stations were insignificant but their variations within stations were highly significant (Table 1).
In FA/PCA analysis KMO must be more than 0.5 (Pradhan et al., 2009; Wu et al., 2010; Zhou et al., 2007), less than 0.5 that data matrix not be considered for PCA/FA analysis. KMO of the present study 0.762 indicated that PCA and FA analysis could achieve a significant reduction in present data matrix. The different units of the physico-chemical parameters indicate the correlation matrix related eigen values and eigen vectors are applicable for the present study (Davis, 1986).
| Table 1: | Two way ANOVA of physico-chemical parameters |
| **Indicates significant at <0.001; * Indicates significant at <0.005 | |
| Table 2: | Factor loading and scores of 3 PCS |
FA/PCA analysis: PCA rendered three PCS (Eigen value >1) contribute the overall distribution of the physicochemical characteristics. The cumulative percentage of three PCS extends to 84.47% of total variance. Factor loading and PCS scores are presented in Table 2. PC1 accounts 55.98% in total variances, with strong positive loaded with rainfall, DO, nitrate and silicate along with negative loadings of salinity, pH, water temperature, atmospheric temperature (Fig. 3a). Additionally PC1 have Strong positive scores with the months of Nov and Dec (Fig. 3b). In Tamil Nadu, rice cultivation patterns mainly depending upon the north east monsoon (Oct-Dec). Before and after crop seedlings, land sowings and seedlings transplantation farmers applied urea and other organic fertilizers in land. Plants utilized some amount of the fertilizers and the remains accumulated in land. Agricultural lands were sub-merged by flood in monsoonal months. Unutilized nutrients of agricultural lands were washed out by flood and finally enter into Vellar estuary through runoff; additionally leaf litters from the Pichavaram mangroves were adjoined into Vellar estuary through water ways. Coastal waters characterized by lesser nutrients, nutrient rich water from the agricultural lands and leaf litters from the mangrove environments (Non-point sources) could be considered as main source to flux the nutrients. River run of waters dilutes the estuarine salinity, increasing the Do and diminishes pH (Pradhan et al., 2009).
| Fig. 3 (a-b): | (a) PCA ordination plot of physico-chemical parameters and (b) PCA ordination plot of months by F1 and F2 using factor analysis |
The negative loadings of salinity, pH and positive loadings of rainfall, DO, nitrate and silicate of the PC1 indicates riverine flow would be the first important factor to influence the water characteristics of Vellar estuary.
PC2 explains 17.72% of total variance (Fig. 3a and b).This factor is positively loaded with salinity, pH and also have negative loadings of rainfall, DO, nitrate and silicate. Monthly patterns PC2 have positively loadings with April, May, June, July and negative loaded with the Nov. and Dec. Factors loading of months and physico-chemical characteristics clearly indicates the neritic water characteristics. During summer month’s river flow of Vellar estuary is less, neritic water dominated into the estuary that reduces the dissolution rate of oxygen and nutrient levels in the water column.
PC3 explains 10.77% of total variances (Table 2). The factor is negatively loaded with the pH and positively loaded with the nitrite. The negative loadings of pH and positive loadings of nitrite indicate anthropogenic activities are the third important factor to influence the physico-chemical characteristics of Vellar estuary. Sewage, municipal, domestic wastages and waste waters from the aqua cultural ponds (point sources) are directly dumped into the Vellar River. They had higher amount of organic materials, ammonia and microbial load which undergoes decomposition and microbial oxidations (Biological oxidation in oxic condition) process leading to formation of organic acids, CO2 and ammonia. Hydrolysis of these acids, dissolution of CO2 in water column and nitrification process cause decrease in the pH and increasing the nitrite (Kim et al., 2003, 2005; Singh et al., 2004; Panda et al., 2006; Vega et al., 1998). Maximal influence on first factor in physico-chemical parameters were found during the monsoonal months, thereafter their influences were declined when the second factor make dominance into estuary. Third factor influences were found throughout year but their effects depends upon first two factors. In the Monsoonal months the third factor influence is diluted by the enormous quantity of river runoff waters, but their influences got dominance together with the second factor during the drought period of summer and premonsoon.
ACKNOWLEDGMENT
We are grateful to the Dean Faculty of Marine science, CAS in Marine Biology, Annamalai University, for providing necessary facilities.