Spatio‐Temporal Variations in Water Quality of Muttukadu Backwaters, Tamilnadu, India

Abstract

Muttukadu Backwater, located on the east coast of Tamilnadu, is one of healthiest estuarine environments in the region. A study pertaining to seasonal variations in physico-chemical properties in water was conducted at nine sites of Muttukadu Backwater for a period of one year (January to December 2009). Multivariate statistical techniques, such as cluster analysis and principal component analysis (PCA), were applied to evaluate the variation in water quality of Muttukadu Backwater and to identify pollution sources. In order to analyze water quality, a geographic information systems (GIS) software package, Arc GIS version 9.3, was used (Esri, Redlands, California). An interpolation technique, inverse distance weighting, was used to produce the spatial distribution of water quality parameters over the backwater. The purpose of the technique was to aid in protection of the environment and ecology of the estuary and aid in effective management. In the present study, observed values of salinity, nitrite, nitrate, ammonia, total nitrogen, phosphate, and silicate were significantly high in the estuarine zone. PCA resulted in three factors explaining 75.9% of the total variance. Principal component 1 exhibited a high correlation with significant physico-chemical variables representing the influence of tidal action and sandbar formation. Principal component 2 represented natural pollution as a result of surface runoff. Principal component 3 represented natural pollution as a result of nutrient pollution.

Spatial and temporal dynamics of water quality in Batticaloa lagoon in Sri Lanka

“…As far as the dissolved oxygen of the Batticaloa Lagoon is concerned, the present study revealed that there was a significant negative correlation found (Table 2) with salinity (r= -0.186**), temperature (r=-0.256**) and TSS (r=-0.594**). Srinivasan and Natesan (2013) also found similar results in Muttukadu Backwaters in Tamilnadu.…”

Variations in the Physicochemical Water Parameters and Phytoplankton Community in Coastal Water of Kudat, Sabah, Malaysia

“…The highest salinity and electrical conductivity values found at station probably due to the shallowest depth of this station and to the low rate of water renewal in this site which is very fare from the inlet (Aknaf et al, 2017). Salinity was at a maximum (34.6 ppt) during monsoon season because of the opening of the mouth (Srinivasan & Natesan, 2013). Salinity was the lowest of 30.31 at ST4 because there was minimum interaction of the sea with the estuary, as the depth observed the lowest of 2.0 m, compare to 5.0 m at ST 2.…”

“…The high pH value (8.46) is due to the algae growth during that period, increasing the consumption of CO 2 as part the photosynthesis process and consequently driving up the pH (Aknaf et al, 2017), but in this study no relationship was observed between the fluctuation of pH with the production of cell density. The extensive buffering capacity of the seawater with the tidal fluctuation may be the cause for a change in pH within a very narrow limit (Srinivasan & Natesan, 2013). Significant differences in monthly fluctuation in salinity were observed in the Kudat Coastal water.…”

“…Salinity mainly caused by dilution and evaporation process, influences the phytoplankton diversity in coastal ecosystem. 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 (Srinivasan & Natesan, 2013). The low salinities were the consequence of higher river discharge from the surface runoff.…”

Combating substance abuse with the potential of geographic information system combining multivariate analysis

“…Then, spatial zones had to be managed. Another study by [59] used PCA and CA to identify the variation of water quality and identify the pollution sources in Muttukadu Backwater and further analyze water quality using the interpolation method in ArcGIS software. In a study by [60], the PCA was used to find the structure in the relationships between variables which were then entered into the logistic model for predictive model fitting.…”