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STUDY OF HEAVY METALS POLLUTION AND PHYSIO-CHEMICAL ASSESSMENT OF WATER QUALITY OF CALABAR RIVER, CROSS RIVER STATE NIGERIA
CHAPTER ONE
INTRODUCTION
1.1 Background of the study
The health of biotic organisms depends on the quality of water available. Water quality is essential and has become an issue of environmental concern because it performs vital roles in biotic health and overall wellbeing. The quality of water is determined by a number of factors. Nouri et al., (2008) and Iwara et al., (2012) noted that natural and anthropogenic factors affect the quality of water. The natural forces include precipitation rate, weathering process, soil erosion, lithology of the aquifer, the quality of recharge water and the type of interaction between water and aquifer, while the anthropogenic forces are urban and agricultural activities such as municipal, industrial and agricultural wastes (Pejman et al., 2009). Among these anthropogenic factors, sewage and industrial wastes discharged into rivers are the most polluting sources. These sources result in the pollution of surface water at different spatial and temporal scales. Also, between the natural and anthropogenic factors, anthropogenic discharge of effluent (human factor) constitutes the primary pollutant (Omole and Longe, 2008; Pejman et al., 2009; Iwara et al., 2012). The composition of effluents from the human factor varies. Effluents from industries contain heavy metals, acids, hydrocarbons and atmospheric deposition, whereas, those from agricultural runoff contain large amounts of nitrogen compound and phosphorus from fertilizers, pesticides, salts, poultry wastes as well as runoff from abattoir (Omole and Longe, 2008).
These effluents modify the chemistry of the water and increase the level of parameters above the natural limits on entering into streams and other surface water. This could have substantial impact on biotic live mostly when bioaccumulation occurs (Iwara et al., 2012). The quality status of surface or subsurface water is determined by examining various conventional parameters in comparison with some globally approved standards. Thus, long-term management of quality of surface water requires an understanding of the hydromorphological, chemical and biological characteristics (Shrestha and Kazama, 2007). Water quality measuring parameters are multivariate in nature, as such, their proportion in a water source can be analysed via multivariate statistical methods. The application of multivariate statistical techniques helps in the interpretation of complex data matrices to better understand the water quality and ecological status of the studied systems, allows the identification of possible factors sources that influence water systems and offers a valuable tool for reliable management of water resources, as well as rapid solution to pollution problems (Reghunath et al., 2002; Pejman et al., 2009). Praus (2007) reported that real hydrological data are mostly not normally distributed and are often autocorrelated. This problem of autocorrelation as well as multicollinearity can only be resolved through the application of multivariate analytical techniques.
Multivariate analytical techniques such principal components analysis (PCA), canonical correlation analysis (CCA), discriminant analysis (DA), factor analysis (FA), cluster analysis (CA) and canonical correspondence analysis (CCA) among others are very useful in the analysis of data corresponding to a large number of variables, because analysis via these techniques produces easily interpretable results (Mazlum et al., 1996; Iwara et al., 2011). Thus, the problem of autocorrelation in hydrological data is resolved through the application of multivariate statistical techniques. These techniques help to make variables uncorrelated through orthogonal rotation as well as make it possible to achieve great efficiency of data compression from the original data, and to gain useful information in the interpretation of the environmental geochemical origin. Most interesting, these techniques, such as PCA is a robust technique which does not require normally distributed and uncorrelated variables (Shihab and Abdul Baqi, 2010).
Several studies have employed multivariate statistics in understanding natural and anthropogenic sources of water pollution (Praus, 2007; Boyacioglu and Boyacioglu, 2008; Pejman et al., 2009; Shihab and Abdul Baqi, 2010; Koklu et al., 2010). However, majority of these studies show ecosystem biases as they were carried out in Europe, Asia and some parts in Africa. In Nigeria and Cross River State in particular, few studies employing multivariate analytical tools have been used to understand the source of pollution of the Calabar River. The few available studies limited their investigation on the Cross River (Ekwere et al., 2011). The Calabar River is the major sink of industrial and municipal wastes, but despite its hydrological importance, not much study has been carried out to understand the interplay of natural and anthropogenic pollutants affecting its natural quality. It is on this premise that this study attempts to study makes attempt to use multivariate statistical techniques notably principal component analysis (PCA), cluster analysis and discriminant analysis to identify main source of pollutants. The outcome of the study will provide information for effective management of water quality through the prevention and control of surface water pollution.
- Statement of the problem
There may have been previous researches in this subject. This work gives further explanations and analysis in study of heavy metals pollution and physio-chemical assessment of water quality of calabar river, cross river state nigeria
- Objectives of the study
1.To understand the impact of heavy metals pollution on physio-chemical assessment of water quality of calabar river, cross river state nigeria
- To understand the relationship between heavy metals pollution and physio-chemical assessment of water quality of calabar river, cross river state Nigeria
- Research questions
- What is the impact of heavy metals pollution on physio-chemical assessment of water quality of calabar river, cross river state nigeria
2.What is the relationship between heavy metals pollution and physio-chemical assessment of water quality of calabar river, cross river state nigeria
- Research hypothesis
H0: There is no relationship between students’ orientation levels and their choice of profession in business education
H1: There is a relationship between students’ orientation levels and their choice of profession in business education
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