Physico-Chemical Characteristics of the Coastal Water Off Devi Estuary, Orissa and Evaluation of Its Seasonal Changes Using Chemometric Techniques
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RESEARCH ARTICLES Physico-chemical characteristics of the coastal water off Devi estuary, Orissa and evaluation of its seasonal changes using chemometric techniques U. K. Pradhan1, P. V. Shirodkar1,* and B. K. Sahu2 1Chemical Oceanography Division, National Institute of Oceanography, Dona Paula, Goa 403 004, India 2P.G. Department of Marine Sciences, Berhampur University, Berhampur 760 007, India It is for this reason that the river water is mostly enriched Devi estuary is one of the major tributaries of the 1 Mahanadi riverine system in Orissa. Modernization in nutrients compared to other environments . The spatial and industrialization in its neighbourhood in the north heterogeneity within the river, however, is due to existing in the recent past have greatly influenced many tribu- local environmental conditions such as light, temperature, taries of the Mahanadi and the adjacent coastal envi- water discharge and flow velocity that change with time, ronments. To trace the influence of this modernization and differences in the local channel form2. Contrary to activity further down south off Devi estuary and to this, the coastal environments are highly economical, im- understand the quality of the Devi estuarine water portant and are significantly involved in the transport of reaching the coastal region, investigations on physico- terrestrial organic matter and associated nutrient elements chemical parameters (temperature, pH, salinity, dis- to the sea for their biogeochemical cycling. The balance solved oxygen), including dissolved nutrients (PO4-P, in the concentrations of bio-geogenic elements in coastal NO -N, NO -N, SiO -Si) were carried out in the water 3 2 4 water reflects the healthy status of water, while their excess off the mouth of the Devi estuary, during different months of the summer and winter seasons in 2006–07. supply as observed in the continental shelf and upwelling areas has been found to trigger high primary producti- The multivariate statistics and principal component 3,4 analysis applied to the datasets, indicated three vity . The complex dynamism in physico-chemical char- factors each during the summer and winter seasons acteristics of coastal waters is related to riverine flow, influencing the water to the extent of 77 and 80% res- upwelling, atmospheric deposition, vertical mixing and pectively. Principal axis factoring and alpha factoring other anthropogenic sources. have been used to observe the mode of association of The coastal Bay of Bengal is a unique marine environ- parameters and their interrelationships, for evaluat- ment in the tropical belt with marked continental influ- ing water quality during the summer and winter sea- ence due to the drainage by a large number of rivers. One sons. The results indicated the addition of phosphates such riverine system is the Mahanadi river in Orissa, and silicates to the coastal water by the Devi estuary which is the third largest river in India. The three major from natural sources during both the seasons. The anthropogenic nitrogenous species, as a fallout from urban settlements such as Cuttack, Sambalpur and modernization activities in the north, are more clearly Paradeep along the banks of this river have resulted in a observed off the mouth of the Devi estuary during the large amount of untreated domestic waste and effluents winter season. The study indicated that the Devi estuary from fertilizer, paper, textile distilleries and many other adds sufficiently well-oxygenated, nutrient-rich water industries in Orissa5. The Mahanadi river divides into to the coastal region. many branches and one of its main branches is the Devi river, which meets the Bay of Bengal at Nuagarh. Due to Keywords: Coastal environment, multivariate statistics, various anthropogenic influences in the Mahanadi river physico-chemical parameters, principal component analy- basin, large amounts of contaminants arising from nutri- sis, seasonality. ents and other parameters have been observed in many of 1 its tributaries and the adjacent coastal region . Apart from RIVERS are the main inland water resources for domestic, this, the naturally occurring cyclones along the Orissa industrial and irrigation purposes and often carry large coast, which give rise to severe floods, also affect the municipal sewage, industrial wastewater discharges and water quality and productivity of the waters off the Orissa seasonal run-off from agricultural land to the coastal region. coast. There is hardly any information available on the water quality characteristics of the Devi river compared to other estuaries along the east coast. It is important to *For correspondence. (e-mail: [email protected]) know the additions to the coastal water from the Devi CURRENT SCIENCE, VOL. 96, NO. 9, 10 MAY 2009 1203 RESEARCH ARTICLES estuary because it is well-known place for the congrega- The basin geology is characterized by Precambrians tion of Olive ridley turtles during their arribada. In view of the Eastern Ghats. The lithology consists of granite of this, physico-chemical studies were carried out at covering 34% of the basin area, khondalite (7%), char- selected locations in the coastal region off the mouth of nockite (15%), limestone, shale of Lower Gondwana the Devi estuary (Figure 1) in the summer and winter sea- (17%), sandstone, shale of Upper Gondwana (22%) and sons of 2006–07 to assess the water quality of the region coastal alluvium (5%). A part of the richest mineral belt and to understand its input to the coastal water. The pre- of the sub-continent consisting of Fe-ore, coal, limestone, sent study also aims to find out the seasonal variations in dolomite, bauxite, Pb and Cu deposits falls within this physico-chemical parameters of the coastal water off the basin10. The Mahanadi basin is one of the five sedimen- mouth of the Devi estuary and the anthropogenic influ- tary basins occurring along the east coast of India. The ence. morphological structure of this environment is highly dyna- mic, where processes of erosion, accretion and deposition are active. Along its course, the Mahanadi river receives Study area effluents from different industrial and urban centres such as Sambalpur, Cuttack, Choudhar, Jagatpur and Paradeep5. The Devi estuary (86°23′36.5″E, 19°57′43.2″N) belongs It also receives a large amount of agricultural run-off to the Mahanadi river basin in Orissa, along the northeast along its course. The human influences are more pro- Bay of Bengal. The Mahanadi river is a biggest river in nounced at Sambalpur, Cuttack and Paradeep due to the Orissa, which originates in the Baster Hills of Madhya presence of a large number of industrial and sewage dis- Pradesh, flows over different geological formations charges. The different trend of lineament in the northern of the Eastern Ghats and joins the Bay of Bengal, after and southern parts of the Mahanadi river causes growth dividing into different branches in its deltaic region6. The of spit near the Devi river mouth and forms an estuarine- main branches of the Mahanadi river meet the Bay of type of delta, where most of the depositional activities are Bengal at Paradeep and Nuagarh (Devi estuary). The basin within the Devi river11. extends7 over an approximate area of 141,600 sq. km, with a total length of 851 km and a peak discharge of 44,740 m3 s–1. The inland geographical area of the basin Material and methods experiences extremely continental climate. The total amount of renewable water resources in the basin is to the A total of 60 water samples from six selected stations extent of 66.9 km3. The annual average rainfall is 1572 mm, along two transects extending from the mouth of the river of which 70% is during the southwest monsoon (June– towards the offshore region, as shown in Figure 1, were October), with a maximum during July–September8. The drawn from the surface and bottom layers during the mean annual temperature is about 26.2 (± 0.4)°C, with a summer and winter seasons of 2006–07, using Niskin water winter minimum of 20.9 (± 0.7)°C (December–January) sampler. The physico-chemical parameters such as pH, and a summer maximum of about 30.1 (± 0.7)°C (March– dissolved oxygen (DO), phosphate (PO4-P), nitrite (NO2- 9 April) . In all, the three principal seasons, i.e. summer, N), nitrate (NO3-N) and silicate (SiO4-Si) were measured monsoon and winter are experienced in this basin. according to the standard procedures12,13. The data quality was ensured through careful standardization, procedural blank measurements, spike and duplicate samples. Meas- urements of in situ temperature (°C) and salinity (psu) were made using probes, while DO was measured using Winkler’s method14. Multivariate statistics Principal component analysis (PCA) is one of the best statistical techniques for extracting linear relationships among a set of variables15. Principal components are the linear combinations of original variables and are the eigenvectors16. The Varimax rotation distributes the PC loadings such that their dispersion is maximized by minimizing the number of large and small coefficients17. The normalized, promax-rotated principal axis factoring (PFA) eliminates the variance due to unique factors, that are uncorrelated with one another and with the common Figure 1. Map of the study area showing station location points. factor, and is thus excluded from our factor analysis. The 1204 CURRENT SCIENCE, VOL. 96, NO. 9, 10 MAY 2009 RESEARCH ARTICLES reproduced and residual correlation matrix indicates that variable participated more clearly. Liu et al.19 have clas- the factors indeed do capture the relationships between sified the factor loading as strong (>0.75), moderate variables by calculating correlations between them and (0.75–0.50) and weak (0.50–0.40). Residual correlation the correlations between factors and variables. The reli- among the parameters has been determined from descrip- ability (alpha) or alpha factor analysis (AFA) attempts to tive and reproduced correlation matrix that results in create factors which are linear combinations of the vari- low percentage (10) of the non-reductant residuals in the ables, to estimate the ‘latent variables’ or constructs which dataset.