Surface water quality assessment of Terengganu River Basin using multivariate techniques.
INTRODUCTIONWater is a gift of nature, the most important for sustaining life and resource in all economic activities from agriculture to industry. Water quality performs significant role in health of human, animals and plants [28, 49]. Rivers constitute the main inland water body for domestic, industrial, and agricultural activities and often carry large municipal sewage, industrial waste water discharges, and seasonal runoff from an agricultural field [12, 29, 9]. Anthropogenic influences and natural processes deteriorate surface water and impair their use for domestic, industrial, agricultural and recreational purposes [32, 34, 35, 36, 37]. Water pollution is harmful not only to aquatic organisms and agricultural activities but also to public health in surrounding areas [3].
Moreover, the major pollution influencing rivers in various countries is untreated sewage and effluent from industries, problem of toxic algal blooms, loss of biodiversity, and loss of oxygen due to high concentration of toxic chemicals and biological nutrients have been observed worldwide [1, 42]. Increasing contamination in river makes it debilitate and in addition undermines human prosperity and the balance of aquatic ecosystems. Therefore, from the environmental, and or social point of view it is vital to identify these sources and their contribution to the total contamination of an area [43], and control water pollution, monitor water quality in the river basin [41].
In recent years, there has been increasing consciousness of, and worry about, surface water contamination everywhere throughout the world, and new approach towards the sources of pollutants furthermore attaining feasible misuse of water assets have been produced. The consolidated utilization of environmetric tools, for example, multivariate statistical techniques empowers the characterization of water samples into different groups, source divisions, relationship, and contrasts in the parameters utilized focused around hydrochemical attributes [40]. They reflect all the more precisely the multivariate nature of the regular environment, which gives an approach to handle expansive datasets with countless by abridging the excess and gives a method for catching and evaluating really multivariate examples of the datasets [23].
The application of different multivariate statistical techniques, such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis(FA), they helps in the interpretation of complex data matrices to better understand about the water quality and lead to the identification of possible sources or factors that influence water system and offers a valuable tool for relievable management of water resources as well as rapid solution to pollution problems [45, 47, 33, 38, 37]. In addition, multivariate techniques have been used to characterize and evaluate surface and fresh water quality since they are useful in variations caused by natural and anthropogenic factors [10, 41, 42, 37, 32].
In the current study a huge data set obtained during an 5 year (2003-2007) monitoring program by DOE is subjected to multivariate statistical analysis techniques to derive information about the similarities between sampling sites, identification of water quality variables liable for spatial variation in river water quality, the unveil factors explaining the structure of the data base and the possible sources (anthropogenic and natural) on the water quality parameters of Terengganu River Basin.
Methodology:
Study Area:
Terengganu river basin is located in Terengganu State; in East Coast Peninsular Malaysia. The river is located (40[degrees]41-50[degrees]20'N, 102[degrees]31'-103[degrees]9'E). Terengganu river basin has a length of 100 km and a total catchment area of approximately 500 [km.sup.2] [26]. Terengganu River basin where included Nerus River, Telemong River, Bereng River, and Pueh River. It originates from Lake Kenyir flows through Kuala Terengganu and flows into South China Sea (Fig. 1).
The climate is tropical wet climate (Koppen Geiger Classification: AF) with no dry or cold season as it is constantly moist (year round rainfall). The annual average temperature is 26.7[degrees]C (80[degrees]F). Average monthly temperature varies by 3[degrees]C (5.4[degrees]F). This indicates that the continentally type is hyper oceanic, subtype truly hyper oceanic. Total annual precipitation averages 2911mm (114.6 inches) which is equivalent to 2911 liters/[m.sup.2] (71.4 Gallons/[ft.sup.2]). On average there are 2412 hours of sunshine per year. Nevertheless, the sea breeze from South China Sea has somehow moderating the humidity in offshore areas while the altitude and lush forest trees and plant as cooled the mountain and rural areas. More so, there are two main types of monsoons the southwest monsoon season is usually established in the latter half of May or early June and ends in September. The prevailing wind flow is generally southwesterly and light, below 15 knots. The northwest monsoon season usually starts in early November and ends in March.
The Terengganu is highly developed in Malaysia with a population of over 1,125,000 as at 2013 [24]. The study area is pristine environment in the upstream catchment area turning urbanized and industrialized downstream with the major settlement of Kuala Terengganu city at the mouth. Establishing and important land uses include forest, commercial plantation (e.g., oil palm, coconut, and rubber, cocoa), agriculture, rural/urban settlements, past mining activities and industry. The construction of a hydroelectric power dam upstream has altered the hydro geochemical compartments consisting of the Kenyir Lake and the main tributary of Terengganu River. The river was the main route and important for foreign traders in those time. A Chinese traveler, "Chao Jukua" mentioned in the notes he wrote in 1225 that 'Teng -Ya- Nong' or Kuala Terengganu was an important trading port along the east coast of the Malay land [16].
Therefore, the river is affected by domestic and municipal waste, agricultural activities, run-off and industrial activities. Collectively, it is affected by point source pollution and non-point source pollution. More so, is necessary to overcome this constraint through determining the changes in water quality.
Monitoring Parameters:
The multivariate statistical analysis were employed in this study consisting secondary data sets of 13 water quality monitoring stations, comprising 30 water quality parameters and 271 observation data sets (data matrix: 30 x 271) monitored over five years (2003-2007), were obtained from the Department of Environment Malaysia (DOE). The parameters monitored are dissolved oxygen (DO), biological oxygen demand (BOD), Chemical oxygen demand (COD), Suspended solid (SS), PH, ammonia nitrate (N[H.sub.3]-NL), temperature, conductivity, salinity, turbidity, dissolved solid (DS), total solid (TS), nitrate (N[O.sub.3]), chlorine (Cl), phosphate (P[O.sub.4]), arsenic (As), mercury (Hg), chromium (Cr), cadmium (Cd), lead (Pb), zinc (Zn), calcium (Ca), iron (Fe), potassium (K), magnesisum (mg), sodium (Na), oil and grease (OG), MBAS, E-coli, and coliform. These data are sorted and arrange station by station following date throughout the years.
Statistical Method:
The statistical technique used was multivariate techniques. Since it aid in lessening, arranging and translation of huge dimensionality of information sets to better comprehend the water quality and biological status of the mulled over frameworks and also the distinguishing proof of conceivable elements that influence water environment frameworks and offers a significant device for dependable administration of water assets [37]. Cluster analysis (CA), discriminate analysis (DA), and PCA/FA were executed on the large data sets. All statistical calculation was done through Microsoft office EXCEL software add-in and XLSTAT version 2014.
Data Treatment:
In statistical analysis data treatment is very important step; it is essentially in determining the right purpose usage of the data and draw conclusion. Preparatory work was tendered on the data sets which include sorting, arranging and transformation. Non numerical variables were subjected to transformation. Data transformation also assists to normalize the whole data set so as fulfill the assumption of cluster and factor analysis [44]. River water quality data sets were subjected to four multivariate techniques: cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA) [47, 38, 41, 42]. All statistical analysis was carried out using XLSTAT version 2014.
Cluster Analysis:
CA is used to construct a significant group or summation of entities which are built on large number of mutuality variables. The purpose is particularly to classify a sample of entities into a smaller number of usually mutually exclusive groups based on the approach which gives correct similarities among entities [23]. CA is common approach which gives correct similarity between sample and the entire data set, represented by dendogram [27]. It divides a large number of objects into smaller number of homogenous on the basis of their correlation structure [5]. According to some objective criteria, within group similarity is maximized and among group similarity is minimized [23].
Therefore, CA was conducted on the treated data through ward method, using squared Euclidean distance as a measure of similarity. Obviously, the result of cluster (dendogram) provides a visual summary of clustering processes, showing an image of the groups and their proximity with a reduction in dimensionality of the initial data [1, 15, 37].
Discriminant Analysis:
Discriminant analysis (DA) provides a statistical classification of the samples that share common properties and is performed with a prior knowledge of the membership of the objects to a particular group. It builds up a discriminant function (DF) for each group using the raw data [14]. This function represents a way of dividing our data into regions occurring groups [12]. Discriminant function is calculated as follows:
F(Gi) = Ki + [[summation].sub.k.sup.n] = 1 WijPij Equation (1)
Where i is the number of groups, G K the constant inherent to each group, n the number of parameters used to classify a set of data into a given group and wj is the weight coefficient assigned by DF analysis (DFA) to a given parameter (Pj) [47, 18, 41].
DA describe the relationships between two or more pre specified groups of sampling entities based on a set of two or more discriminating variables. However, DA was performed on the original data using the standard, forward stepwise and back ward stepwise modes. These were used to develop DFs to assess spatial variations in the river water quality. The stations (spatial) are the grouping (dependent) variables, while all the 30 measured water quality parameters are independent variables. DA constructs a discriminant function (DF) for each group given several quantitative (independent) variables and categorical (dependent) variables [41, 12]
Principal Component Analysis:
PCA and FA are statistical approaches that can be applied to analyze interrelationships among a large number of variables base on their common underlying dimension by providing empirical estimates of the variables [11]. This reduces the relatively large number of variables into a smaller set of variables that still captures the same information. PCA is about identifying a set of independent linear combination of the variables of the study so as to catch the maximum amount of variability of a given data set [31].
The PCA is designed to transform the original variables into new, uncorrelated variables (axes, known as principal components, which are linear combination of the original variables. It provides an objective way of finding indices of this type so that the variation in the data set can be accounted for as concisely as possible ([4]. PCA gives information on the most significant parameters that describe the majority of the data set; affording data reduction with minimum loss of original information [10]. The principal component (PC) can be expressed as:
[z.sub.ij] = [a.sub.i1][x.sub.1k] + [a.sub.i2][x.sub.2j] + [a.sub.i3][x.sub.3j] + -- + [a.sub.im][x.sub.mj] Equation (2)
Where z is the component score, ais the component loading, x the measured value of variable, I is the component number, j the sample number of variables.
FA follows principal component analysis. The main purpose of FA is to decrease the contribution of less significant variables to clarify even more of the data structure coming from the PCA. This can be achieved through rotating the axis defined by PCA according to well established rules to construct new variables, also known as varifactors (VF). A principal component is a linear combination of observed water quality variables, while a VF can include unobservable, hypothetical latent variables [45, 10].
Principal component analysis used normalized variables to remove significant PCs to further decrease the contribution of variables with minor significance; these PCs were subjected to varimax rotation (raw) generating VFs [38, 6, 4, 41, 42, 20, 2, 48]. In addition, a small number of variables would normally account for approximately the same amount of information as do the much larger set of the original variables. The FA can be shown as:
[z.sub.ji] = [a.sub.f1][f.sub.1i] + [a.sub.f2][f.sub.2i] + [a.sub.f3] [f.sub.3i] + -- + [a.sub.fm] + [f.sub.mi] Equation (3)
where z is the measured variable, a is the factor loading, f is the factor score, e the residual term accounting for errors or source of variation, I the sample number and m the total number of factors.
Result and Discussion:
Department of Environment Malaysia (DOE) was consistently observed 13 stations of Terengganu river basin for the time of 5 years (2003-2007). The entire show of the information (30 x 271) was analyzed for 30 water quality parameters. Descriptive statistics for the river water quality information are underneath in Table 1.
Grouping and Spatial Similarities:
The grouping of the sampling sites was achieved through the use of cluster analysis and helps to identify the similarity groups between the sampling sites. It grouped 13 monitoring stations of the water quality into two statistically significant clusters at (Dlink/Dmax) x100 based on similar water quality characteristics. The product of cluster analysis is presented in dendogram (Fig. 2). In cluster analysis dendogram provides a useful picture dictating the number of clusters which explain principal process that resulted to spatial variation [5].
The cluster 1 (Sg. Terengganu (4TE01, 4TE04, 4TE10), Sg. Bereng (4TE02, 4TE03, 4TE12), Sg. Nerus (4TE05, 4TE06, 4TE07), Sg. Pueh (4TE08, 4TE09), and Sg. Telemong (4TE13)) correspond to the relatively low polluted (LP) sites. In cluster 1 eleven stations are situated at upstream sites, only station 4TE01 is situated at downstream site of the river and shows similar characteristics. The land use activities practiced not fully been explored hence has a moderate development in the area. Never the less, human activities at this cluster are very low which make the water quality under preserves. Emulating the Table 2 of clear detail of stations shows the mean estimation of all stations of group 1 which is more prominent than 80% for WQI which is clean water quality. This is focused around the National Water Quality Standard Malaysia (NWQS) (Table 2). Cluster 2 (Sg. Nerus (4TE11)) correspond to moderate polluted sites (MP). This station has high population growth coupled with settlements, agricultural, industrial and commercial activities, which contribute to the contamination. Therefore, this station receives pollution mostly from industries, domestic wastewater and agriculture (rubber plantation, palm oil). The result of cluster analysis proposed for enthusiastic appraisal of a water quality just a station in each one bunch is obliged to speak to remedy spatial evaluation of the water quality for the whole system. Obviously cluster analysis is relevant in the entire area and can be used to build future spatial testing procedures in a perfect structure, which can decrease the quantity of inspecting stations and related expenses [13].
Discriminant Analysis:
Spatial variation in water quality was assessed with the help of DA and clusters obtained from cluster analysis. The clusters represent dependent variables whereas all 30 water quality parameters were independent variables both standard mode, backward stepwise mode and forward stepwise mode were performed Table 3.
Through DA, in forward stepwise mode variables is incorporated one by one beginning with the more critical until no huge progressions are obtained, though, in backward stepwise mode, variables are uprooted one by one beginning with less noteworthy until no noteworthy progressions are acquired [41]. Emulating the DA, standard mode where 30 variables were incorporated and give 83.03%, using the stepwise forward mode 4 discriminant variables (BOD, conductivity, NO3 and Zn) with 80.81% right correct assignation were acquired and the backward stepwise mode having 8 discriminant variables (BOD, SS, DS, N[O.sub.3], Cd, Zn, Fe and salinity) with right meeting of 81.55%.
Therefore, stepwise forward mode gives correct assignation of 80.81% with only 4 significant discriminant variables (BOD, conductivity, N[O.sub.3] and Zn). This shows that these parameters have high variation in terms of spatial variation in the river. Box and whisker plots (Fig. 3) show the most significant parameters (BOD, N[O.sub.3], Zn, and conductivity) over the years (2003-2007) for the two clusters of these water quality parameters.
Data Structure Determination and Source Identification:
To discover and identify the significant water quality parameters resulted in the variation of river, PCA was used. Thirty water quality parameters from 13 sampling stations over the period of 5 years (2003-2007) were monitored and subjected to PCA. An Eigenvalue greater than >1 was considered as bench mark for the removal of principal components needed to spell out the origin of variation within the data. It gives eight PCs (Fig. 4) describing the overall variance as 73.62%. An Eigenvalue grant a measure of the magnitude for the factor, one with highest is the most significant [17]. However, the factor loading have being grouped as 'strong', 'moderate', and 'weak', correlate with absolute loading values of >0.75, 0.75 - 0.50 and 0.50 - 0.30 respectively [19].
The first factor with 28.57% of the total variance have a strong loading on DS, conductivity, salinity, Fe, Ca, Mg, and moderate loading factor on Pb, Na, TS and Zn. This is shown in Table 4. The strong loading between the parameters shows correlation in altering the river water quality. This factor signifies minerals component of the river water. They resulted from rock features, likely from dissolved solids of gypsium soil and limestone [45]. Erosion rise the concentration of DS, salinity and conductivity through discharging of minerals ions into the river. High loading of DS in a river may result it to be salty, and the overall dissolved solid is considered salinity. In addition, the moderate loading on TS and Zn stand as a pollution associated with anthropogenic activities since agricultural activities are taking place along the river. Pb originates from ships maintenance and equipment repair in the river. This fact bears the study of The VF2 shows 11.56% of the total variance with a strong loading on BOD, COD and Oil and Gas. This factor suggests sign of organic pollution which take it source from domestic waste and industrial waste. These parameters could be described by the chemical components of various anthropogenic activities that constitute point source pollution especially from domestic, industrial and agricultural runoff areas [12]. Strong loading on organic compounds in the water signifies the water is altered with inorganic and organic pollutants [27].
VF3 has K, P[O.sub.4], CL, Cr and [NH.sub.3] - NL having strong loading with 9.88% of the total variance. It shows sign of deposit of dust, pebbles and rock (geological deposits) and natural organic matter decomposition [5]. P[O.sub.4] and K resulted due to agricultural activities from fertilizer. However, P[O.sub.4] originate from the soil as a result of phosphate fertilizer usage in the area. [NH.sub.3] - NL is caused by domestic waste whereas chromium which originate from industries. This factor distinguishes the importance of anthropogenic inputs mainly from non-point pollution sources (such as agricultural runoff) and point pollution sources (such as domestic and industrial wastewater effluents) [7]. The VF4 consist of SS, TS, and turbidity, these parameters have strong loading describing 7.64% of the total variance. This factor appears to be associated to soil erosion, runoff and agricultural activities from which fertilizer are leach to river. Suspended particles such as clay silt and fine organic matter could have generated total solids and turbidity. The state resulting from suspended solids in water is turbidity. This factor comes up with donation of non-point source pollution from agricultural activities in the area. The strong loading on these parameters could have been due to anthropogenic activities through road construction, clearing of lands, runoff, and erosive processes taking place near the study area [25]. However, this factor resulted from anthropogenic activities.
A total variance of 4.75% of VF5 have strong loading on coliform and E-coli. This factor signifies that Ecoli and coliform is considered from domestic, municipal waste and treatment plants wastewater [8]. This will definitely increase the percentage of virus and bacteria in the river due to discharge of domestic waste; it could be supported by strong loading of BOD in VF 2. The VF6 describe 4.02% of the total variance having strong loading on Cd. This factor was believed to have evolved from industries. However it is associated with mining industries. Along Bereng and Telemong rivers mining actives are carried out this could lead to the contribution of this factor. [46] Stated the donation of Cd presents industrial waste and agricultural activity (fertilizer) as well as leach ate from dumpsite.
VF7 described moderate loading factor on temperature and arsenic with 3.73% of the total variance. Arsenic is one of the carcinogenic agents, attributed to industrial waste. Waste water from leather processing and
dyeing industries contain high arsenic. The concentration in river water was related to electroplating and metal mining activities. The moderate loading of temperature is related to seasonal changes.
VF8 account for 3.49% of the total variance, and has a strong factor loading on Hg. This factor has little percentage of the total variance among the factors. Hg takes it source from industrial activities. The factor has chemical parameters which show contamination from industrial waste [30]. Plastic and pharmaceutical industries produce wastewater with Hg. This may happen as a result of industries around Gong Badak which discharge their waste into Nerus River.
Conclusion:
The results this study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for Effective River water quality management.
The findings of the surface water quality assessment using multivariate statistical techniques has obviously provides the basis for the spatial variations and prospective pollution sources in the Terengganu river basin. Cluster analysis (CA) categorized 13 monitoring station into 2 clusters (LPS and MPS) in terms of their similar water quality features. It gives important groups, since stations are clustered as low pollution source and moderate pollution source, this can be used to enhance a future spatial monitoring with low costs. Hence monitoring stations are reduced. Discriminant analysis (DA) announced a meaningful reduction of data with only four significant variables (BOD, NO3, Zn and conductivity) having correct assignation of 80.81%. PCA/FA assist in the identification of the prospective pollution sources resulted in the variation of the water quality in the river. The result of principal component analysis (PCA) suggested that the major sources of pollution is related to domestic waste, agricultural activities and industrial(anthropogenic activities) and natural processes (erosion and runoff), which lead to the variation in the water quality. However, PCA tendered eight components with total variance of 73.62%.
ARTICLE INFO
Article history:
Received 11 October 2014
Received in revised form 21 November 2014
Accepted 25 December 2014
Available online 16 January 2015
ACKNOWLEDGEMENT
The author will like to thank the Director and staff of East Coast Environmental Research Institute, Faculty of Agriculture and Biotechnology, University Sultan Zainal Abidin Terengganu, Malaysia, whose guidance yielded in a much improved paper.
REFERENCES
[1] Alkarkhi, A.F.M, A. Ahmad and A.M. Easa, 2008. Assessment of surface water quality selected estuaries of Malaysia: multivariate statistical techniques, The Environmentalist, 29: 255- 262.
[2] Abdul-Wahab, S.A., C.S. Bakheit and S.M. Al-Alawi, 2005. Principal component and multiple regression analysis in modeling of ground-level ozone and factors affecting its concentrations, Environmental Modeling and Software, 20(10): 1263-1271.
[3] Adebola, A., M.A. Oketola and O. Oladele, 2013. Water Quality Assessment of River Ogun using Multivariate statistical Techniques, Journal of Environmental protection, 4: 466-479.
[4] Brumelis, G., L. Lapina, O. Nikodemus and G. Tabors, 2000. Use of an artificial model of monitoring data to aid interpretation of principal component analysis, Environmental modeling and software, 15(8): 755763.
[5] Boyaciaglu, H. and H. Boyaciaglu, 2007. Water pollution sources assessment by multivariate statistical methods in the Tahtali Basin, Turkey. Environ. Geol, 54: 275-282.
[6] Bu, H., X. Tan, S. Li and Q. Zhang, 2010. Water quality assessment of the Jinshui River (China) using multivariate statistical techniques, Environment Earth Science, 60(8): 1631- 1639.
[7] Camargo, J.A. and A. Alonso, 2006. Ecological and toxicological effects of inorganic nitrogen pollution in aquatic ecosystem: A global assessment, Environment International, 32: 831-849.
[8] Frenzel, S.A. and E.S. Couvillion, 2002. Feca1 indicator Bacteria in streams along a gradient of residential development, Journal of the American Water Resources Association, 28(1): 265- 273.
[9] Hu, J., Y. Qiao, L. Zhou and S. Li, 2011. Spatiotemporal distributions of nutrients in the downstream from Gezhouba Dam in Yangtze River, China, Environmental Science and Pollution Research, 19: 2849-2859.
[10] Helena, B., R. Pardo, M. Vega, E. Barrado, J.M. Fernandex and L. Fernendex, 2000. Temporal evolution of ground water composition in an alluvial aquifer (Pisuega River, Spain) by principal component analysis, Water Research, 34: 807-816.
[11] Hair, J.F., R.F. Anderson and C. William, 1995. Multivariate data analysis with readings, Prentise Hall, Eagle wood cliffs.
[12] Juahir, H., M. Zain, M.K. Yosoff, T.T.H. Ismail, A.M.A. Samah, M.E. Toriman and M. Moktar, 2010b. Spatial assessment of Langtan River Basin (Malaysia) using environmentric techniques, Environmental Monitoring Assessment, 173: 625-641.
[13] Juahir, H., S.M. Zain, R.A. Khan, M.K. Yusoff, M. Mokhtar and M. Toriman, 2009. Chemometric in Assessing Langtan River water Quality and designing a Cost-effective water sampling strategy, Science and Technology, 3(01): 26-42.
[14] Jornson, R.A. and D.W. Wichern, 1992. Applied Multivariate Statistical Analysis, 3rdedn, Prentice--Hall lut, New Jersey.
[15] Jornson, R.A. and D.W. Wichern, 2002. Applied Multivariate Statistical Analysis, 5: 1-13, Prentice Hall Upper Saddle River, NJ, USA.
[16] Kuala Terengganu city Council (MBKT), 2014.
[17] Kim, J.O. and C.W. Mueller, 1987. Introduction to factor analysis: what it is and how to do it. Quantitative Applications in the Social Sciences Series. Sage University Press, Newbury Park.
[18] Lattin, J., D. Carroll and P. Green, 2000. Analyzing Multivariate Data, New York: Duxbury.
[19] Liu, C., K.H. Lin and Y.M. Kuo, 2003. Application of factor analysis in the assessment of groundwater quality in a Blackfoot disease area in The Taiwan, Science of the Total Environment, 313(1-3): 77-89.
[20] Love, D., Hallbauer, A. Amos and R. Hranova, 2004. Factor analysis as a tool in groundwater quality management: two southern African case studies, Physics and Chemistry of the Earth, 29: 1135-1143.
[21] Mohammad, A.H.B., M.A. Rakib, S.B. Dampare, S. Ganyaglo, S. Suzuki, 2011. Surface water Quality assessment in the central part of Bangladesh using multivariate analysis, KSCE Journal of Civil Engineering, 15(6): 995-1003.
[22] McKenna Jr, J.E., 2003. An enhanced cluster analysis program with boot strap significance testing for ecological community analysis, Environmental Modeling and Software, 18(3): 205- 220.
[23] McGarial, K., S. Cushman, and S. Stafford, 2000. Multivariate statistics for wildlife and ecology research, Springer, New York.
[24] Malaysia population census Department of Statistic, Population of Terengganu, 2014.
[25] Mustapha, A., A.Z. Aris, M.F. Ramli and H. Juahir, 2012b. Temporal aspects of surface water quality variation using robust statistical tools. The Scientific World Journal, 20: 5630-5644.
[26] Noor, A.S. and S. Khawar, 2010. Geochemical baselines of major, minor and trace elements in the tropical sediments of the Terengganu River Basin Malaysia, International Journal of Sediment Research, 25: 340-354.
[27] Otokunfor, T.V. and C. Obiukwu, 2005. Impact of refinery effluent on the physicochemical properties of a water body in the Niger delta, ApplEcol Environ Res, 3(1): 61-72.
[28] Pejman, A.H., G.R. Nabi Bidhendi, A. Karbassi, R.N. Mehrdadiand M. Esmaeili Bidhendi, 2009. Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques, Int. J. Environ. Sci. Tech., 6 (3): 467-476.
[29] Pradhan, U.K., P.V. Shirodkarand B.K. Sahu, 2009. Physico-chemical characteristics of the coastal water off Devi estuary, Orissa and evaluation of its seasonal changes using chemometric techniques, Curriculum Science, 96(9): 1203-1209.
[30] Papaioannou, A., A. Mavridau, C. Hadjichristodoulou, P. Papastergiou, O. Pappa, E. Dovriki and I. Rigas, 2010. Application of multivariate statistical methods for groundwater physiochemical and biological quality assessment in the context of public health, Environment Monitoring Assess, 170(1-4): 87-97.
[31] Panigrahi, S., B.C. Acharya, R.C. Panigrahy, B.K. Nayak, K. Banayee and S.K. Sarkad, 2007. Anthropogenic impact of water quality of Chilika lagoon RAMSAR site a statistical approach, Wet lands, Ecology Management, 15(2): 11-126.
[32] Qadir., R.N. Mali and S.Z. Husain, 2007. Non-conventional water resources and opportunities for water augmentation to achieve food security in water scarce countries, Environmental Monitoring Assessment, 140(1-3): 43-59.
[33] Reghunath, R., T.R.S. Murthy and B.R. Raghavan, 2002. The utility of multivariate statistical techniques in hydro geochemical studies: an example from Karnataka, India, Water Research, 36: 2437-2442.
[34] Sayadi, M.H. and M.R.G. Sayyed, 2010. Short-term accumulative signatures' of heavy metal in river bed sediments, Tehran Iran, Environment Monitoring and Assess, 162: 465-473.
[35] Sayyed, M.R.G. and M.H. Sayadi, 2011. Variation in the heavy metal accumulations within the surface soils from the Chitgar Industrial Area of Tehran (Iran), Proceedings of the International Academy of Ecology and Environmental Sciences, 1: 27-37.
[36] Sayadi, M.H. and M.R.G. Sayyed, 2011. Comparative assessment of baseline concentration of the heavy metals in the soils of Chitgar Industrial Area Tehran (Iran) with the comprisable reference data, Environmental Earth Science, 63: 1179-1188.
[37] Sayadi, M.H. and M.R. Rezaei, 2014. Impact of land use on the distribution of toxic metals in surface soils in Bujandcity, Iran, Proceedings of the International Academy of Ecology and Environmental Sciences, 4: 18-29.
[38] Simeonova, P., V. Simeonova and G. Andreev, 2003. Environmetric analysis of the Struma River water quality central, European journal of Chemistry, 2: 121-126.
[39] Shrestha, S. and F. Kazama, 2007. Assessment of surface water quality using multivariate statistical techniques: Fuji river basin Japan, Environmental Modeling and Software, 22: 464-475.
[40] Shretha, S., F. Kazama and T. Nakamura, 2008. Use of principal component analysis, factor analysis and discriminant analysis to evaluate spatial and temporal variations in water quality of the Mekong River, Journal of Hydroinformation, 10: 43-56.
[41] Singh, K.P., A. Malik, D. Mohan and S. Sinha, 2004. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India): a case study, Water Research, 38: 3980-992.
[42] Singh, K.P., A Malik and S. Sinha, 2005. Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques: a case study, Analytica Chemical Acta, 538: 355-374.
[43] Tobiszewski, M., S. Tsakovski, V. Simeonov and J. Namiesnik, 2010. Surface water quality assessment by the use of combination of multivariate statistical classification and expert information, Chemosphere, 80: 740-746.
[44] Voudouris, K., A. Panagopoulos and J. Koumantakis, 2000. Multivariate statistical analysis in the assessment of hydrochemistry of the Northern Korinthia Prefecture Alluvial Aquifer System (Paloponnese, Greece),Natural Resources Research, 9: 135-146.
[45] Vega, M., R. Pardo, E. Barrado and L. Deban, 1998. Assessment seasonal and polluting effects on the quality of river water by exploratory data analysis, Water Research, 32: 3581- 3592.
[46] World Health Organization (WHO), 1983. Guidelines for drinking water quality, (2nd ed.), 1, Recommendations, Geneva, pp: 188.
[47] Wunderlin, D.A., M.P. Diaz, M.V. Ame, S.F. Pesce, A.C. Hued and M.A. Bistoni, 2001. Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquia river basin (Cordoba, Argentina), Water Research, 35: 2881-2894.
[48] Zhang, Y., F. Guo, W. Meng and X. Wang, 2009. Water quality assessment and source identification of Daliao river basin using multivariate statistical methods, Environmental Monitoring and Assessment, 152: 105-121.
[49] Zhao, Y., X.H. Xia, Z.F. Yang and F. Wang, 2012. Assessment of Water Quality in Baiyangdian Lake using Multivariate Statistical Techniques, Proceedia Environmental Science, 13: 1213-1226.
Aminu Ibrahim, Hafizan Juahir, Mohd Ekhwan Toriman, Mohd Khairul Amri Kamarudin and Hamza A. Isiyaka
East Coast Environmental Research Institute, Faculty of Agriculture and Biotechnology University Sultan Zainal Abidin Terengganu, Malaysia
Corresponding Author: Aminu Ibrahim, East Coast Environmental Research Institute, Faculty of Agriculture and Biotechnology University Sultan Zainal Abidin Terengganu, Malaysia.
Tel: +60146456439. E-mail: aminu479@gmail.com
Table 1: Descriptive Statistic
Variables Minimum Maximum Mean Std. deviation
DO (mg/l) 1.760 8.330 6.374 1.230
BOD (mg/l) 1.000 142.000 2.768 9.849
COD (mg/l) 15.000 329.000 24.339 22.937
SS (mg/l) 0.500 1040.000 44.057 84.754
PH (unit) 3.170 8.840 6.683 0.787
NH3-NL (mg/l) 0.005 10.210 0.306 0.900
TEMP (Deg) 23.050 82.200 27.560 3.753
COND (uS) 0.000 979.000 67.100 112.155
SAL (ppt) 0.010 0.470 0.031 0.054
TUR (NUT) 0.000 626.600 53.502 82.405
DS (mg/l) 6.000 569.000 31.554 69.966
TS (mg/l) 0.000 1054.000 74.598 107.535
N[O.sub.3](mg/l) 0.005 1.290 0.183 0.145
Cl (mg/l) 0.500 92.000 3.592 7.132
P[O.sub.4](mg/) 0.005 1.250 0.037 0.100
As (mg/l) 0.001 0.005 0.001 0.001
Hg (mg/l) 0.000 0.128 0.001 0.008
Cd (mg/l) 0.001 0.003 0.001 0.000
Cr (mg/l) 0.001 0.018 0.001 0.002
Pb (mg/l) 0.005 0.015 0.005 0.001
Zn (mg/l) 0.005 0.600 0.035 0.048
Ca (mg/l) 0.050 20.700 1.627 2.373
Fe (mg/l) 0.005 14.500 0.767 1.310
K (mg/l) 0.050 121.000 2.562 8.030
Mg (mg/l) 0.050 22.000 0.987 2.434
Na (mg/l) 0.050 29.100 4.131 3.382
OG(mg/l) 0.500 51.000 0.749 3.087
MBAS 0.025 0.025 0.025 0.000
E-coli 0.000 41000.000 3411.255 6216.669
(cfu/100ml)
Coliform 0.000 970000.000 40102.590 99760.988
(cfu/100ml)
Table 2: Summary statistics of stations.
Variable Obs Obs. with Obs. without Mean S/d
missing data missing data
4TE01 25 0 25 87.6 4.082
4TE02 25 0 25 89.56 2.945
4TE03 25 0 25 88.84 3.923
4TE04 25 0 25 81.44 5.37
4TE05 25 0 25 86.08 4.281
4TE06 25 0 25 86.04 8.044
4TE07 25 0 25 88.52 3.906
4TE08 25 0 25 83.8 9.925
4TE09 25 0 25 79.32 13.02
4TE10 25 0 25 88.36 2.97
4TE11 25 0 25 66.16 13.28
4TE12 25 0 25 92.08 2.886
4TE13 25 0 25 86.8 5.605
Table 3: Classification matrixes by DA for spatial
variation in Terengganu river basin.
Regions assigned by DA
Sampling regions %Correct LPS MPS Total
Standard DA mode
LPS 98.06 202 4 206
MPS 35.38 42 23 65
Total 83.03 244 27 271
Stepwise Forward Mode
LPS 99.03 204 2 206
MPS 23.08 50 15 65
Total 80.81 254 17 271
Stepwise Backward Mode
LPS 98.54 203 3 206
MPS 27.69 47 18 65
Total 81.55 250 21 271
Table 4: Factor loadings after varimax rotation.
F1 F2 F3 F4
DO (mg/l) -0.365 -0.112 -0.299 0.206
BOD (mg/l) 0.040 0.972 0.108 0.041
COD (mg/l) 0.042 0.944 0.165 0.122
SS (mg/l) -0.032 0.077 -0.017 0.915
PH (units) -0.534 0.029 0.164 -0.135
N[H.sub.3]-NLmg/l 0.393 0.148 0.751 -0.053
TEMP ([degrees]C) 0.090 -0.015 -0.062 -0.248
COND(uS) 0.889 0.043 0.301 -0.005
SAL (%) 0.888 0.045 0.299 0.003
TUR (NUT) -0.077 0.150 -0.014 0.772
DS (mg/l) 0.902 0.017 0.264 0.021
TS (mg/l) 0.557 0.075 0.152 0.734
N[O.sub.3] (mg/l) -0.049 -0.084 -0.041 0.443
Cl (mg/l) 0.396 0.048 0.801 -0.026
P[O.sub.4] (mg/l) 0.052 0.040 0.831 0.007
As (mg/l) -0.046 -0.043 0.481 0.139
Hg (mg/l) -0.017 0.009 0.006 -0.018
Cd (mg/l) 0.108 0.021 -0.026 -0.052
Cr (mg/l) 0.066 0.026 0.764 0.001
Pb (mg/l) 0.674 -0.023 -0.170 -0.079
Zn (mg/l) 0.504 0.011 -0.081 0.028
Ca (mg/l) 0.742 0.006 0.314 0.000
Fe (mg/l) 0.861 0.030 -0.020 0.094
K (mg/l) 0.198 0.045 0.911 0.014
Mg (mg/l) 0.702 0.003 0.459 -0.019
Na (mg/l) 0.634 -0.021 0.288 -0.122
OG (mg/l) -0.004 0.922 -0.080 0.059
MBAS 0.000 0.000 0.000 0.000
E-coli(cfu100 -0.054 0.253 0.062 0.049
Coliform -0.035 -0.046 -0.010 0.189
Eigenvalue 8.284 3.351 2.864 2.214
Variability (%) 28.565 11.555 9.876 7.636
Cumulative % 28.565 40.119 49.995 57.631
F5 F6 F7 F8
DO (mg/l) -0.015 -0.024 -0.556 0.057
BOD (mg/l) 0.074 0.024 0.005 -0.004
COD (mg/l) 0.094 0.027 0.007 0.016
SS (mg/l) 0.045 -0.012 0.010 -0.023
PH (units) -0.014 -0.260 -0.473 0.070
N[H.sub.3]-NLmg/l 0.018 -0.052 0.081 -0.036
TEMP ([degrees]C) -0.046 -0.069 0.567 0.058
COND(uS) 0.014 0.044 0.091 -0.001
SAL (%) 0.015 0.035 0.099 -0.008
TUR (NUT) 0.102 -0.059 -0.131 -0.031
DS (mg/l) -0.046 0.113 0.141 -0.007
TS (mg/l) 0.007 0.060 0.115 -0.026
N[O.sub.3] (mg/l) 0.225 -0.010 -0.333 0.096
Cl (mg/l) 0.057 0.155 -0.035 -0.002
P[O.sub.4] (mg/l) -0.126 -0.074 0.080 -0.079
As (mg/l) 0.044 0.024 0.509 0.053
Hg (mg/l) -0.020 -0.011 0.049 0.981
Cd (mg/l) -0.003 0.867 0.063 -0.020
Cr (mg/l) 0.122 -0.017 0.052 0.116
Pb (mg/l) 0.025 -0.282 0.031 -0.006
Zn (mg/l) -0.209 0.326 -0.052 0.056
Ca (mg/l) 0.007 0.281 0.096 -0.042
Fe (mg/l) -0.047 -0.179 0.157 0.003
K (mg/l) -0.017 -0.001 0.030 -0.038
Mg (mg/l) -0.002 0.009 0.020 -0.030
Na (mg/l) -0.046 0.294 -0.124 0.030
OG (mg/l) 0.019 -0.026 -0.036 -0.005
MBAS 0.000 0.000 0.000 0.000
E-coli(cfu100 0.831 -0.014 0.046 -0.011
Coliform 0.868 -0.016 -0.042 -0.012
Eigenvalue 1.378 1.166 1.082 1.011
Variability (%) 4.750 4.020 3.732 3.486
Cumulative % 62.381 66.401 70.132 73.618
Bold indicate strong and moderate loading factor
 Printer friendly
 Cite/link
 Email
 Feedback
| |
| Author: | Ibrahim, Aminu; Juahir, Hafizan; Toriman, Mohd Ekhwan; Kamarudin, Mohd Khairul Amri; Isiyaka, Hamza |
|---|---|
| Publication: | Advances in Environmental Biology |
| Article Type: | Report |
| Geographic Code: | 7IRAN |
| Date: | Dec 1, 2014 |
| Words: | 6760 |
| Previous Article: | Comparative analysis of chemical composition of three ecotypes of Chenopodium botrys L. in Iran. |
| Next Article: | Fe(III) reduction by Halomonas sp. TBZ9 and Maribobacter sp. TBZ23, isolated from Urmia Lake in Iran. |
| Topics: | |