Spatial Water Quality Assessment of Juru, Kuantan and Johor River Basins Using Environmetric Techniques

Journal of Survey in Fisheries Sciences 7(2) 19-40 2021

Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and Johor River Basins using environmetric techniques

Masthurah A.1; Juahir H.2; Mohd Zanuri N.B.1*

Received: June 2020 Accepted: October 2020

Abstract This study investigates spatial water quality assessment of selected river basins in the three different states in Malaysia. Environmetric techniques namely, cluster analysis (CA), principal component analysis (PCA), and discriminant analysis (DA), were applied to study the spatial variations of the most significant water quality variables in order to determine the origin of pollution sources on water quality data of Juru River Basin, Kuantan River Basin and Johor River Basin. 13 water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on CA, and these clusters were designated as high pollution source (HPS), medium pollution source (MPS), and low pollution source (LPS) at the three river basins, respectively. Forward and backward stepwise DA managed to discriminate water quality variables, respectively from the original 13 variables. The result of this spatial analysis assessment is supported by PCA (varimax functionality,) that was used to investigate the origin of each water quality variable due to land use activities. Thus, this analysis makes it possible to observe the significance of the pollutant sources which contribute to river pollution. Five principal components (PCs) were obtained for all HPS, MPS and LPS regions of all the three river basins, respectively. Pollution sources for the three river basins were mainly originated from industrial waste, municipal waste, domestics waste and also from agricultural runoffs. Finally, the environmetric techniques analysis manage to provide convincing result on the spatial variation of water quality in all the three studied river basins and this eventually will allow more effective and efficient river quality management activities.

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] Keywords: Cluster analysis, Principal component analysis, Discriminant analysis

1- Centre for Marine and Coastal Studies (CEMACS), Universiti Sains Malaysia, 11800 Gelugor Pulau Pinang. 2- East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Kampung Gong Badak, 21300 Terengganu *Corresponding author's Email: [email protected]

20 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

Introduction concerned due to the land use activities Water is an important resource that is that affect river ecosystem (Razali, necessary for all aspect of human and Syed Ismail, Awang, Praveena, & ecosystem survival (Najah, El-Shafie, Zainal Abidin, 2018) This land use Karim, & El-Shafie, 2013). Water is activities will gradually alter the types widely used for irrigation, culturing of of pollutant loadings into the river fishes and drinking. However in recent system (Juahir, Zain, Aris, Yusoff, & years, the flow of river contains heavy Mokhtar, 2010). . Spatial analysis can industrial effluent and municipal be conducted by using environmetric sewage effluent with the advent of technique. Environmetric or also known urbanization (Varunprasath & A. Daniel, as chemometric is one of the 2010). The general term water quality environmental analytical chemistry is used to describe the condition of fields that utilize multivariate statistical water characteristic including physical, approach for the data analysis (Einax, chemical and biological characteristic Zwanziger, & Geiss, 1997; Simeonova, of the water (Dogan, Sengorur, & Simeonov, & Andreev, 2003). It can be Koklu, 2009). . Many rivers are considered to be the most appropriate experiencing from deterioration quality analysis performance in order to of its characteristic condition, which in prevent misinterpretation upon turn affects people’s health, economy analyzing a large environmental data set and as well as the environment (Simeonov, Einax, Stanimirova, & (Department of Environment (DOE), Kraft, 2002; Varmuza & Filzmoser, 2003). Surface water is one of the 2009) . Three common environmetric environmental components that are analysis that usually perform in order to most vulnerable to pollution impact classify wide range of data into groups because this surface river water is the are the hierarchical agglomorative place that received all of the waste that cluster analysis (HACA) and principal are being disposed into the river by component analysis (PCA) which are anthropogenic activities (Hamirdin, then furthered by pattern recognition 2000) In Malaysia, river is the main analysis namely, discriminant analysis source of drinking water supplies. The (DA) (Adam, 1998) . The objectives of contaminated river will result into a this study are (i) to evaluate spatial limited quantity of clean water and thus variations in the river water quality data Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] will eventually increase the water of Juru, Kuantan and Johor river basins treatment cost. using environmetric techniques and (ii) Spatial analysis is one of the to identify the pollution loadings methods that usually performed for the variations due to land use and purpose of evaluating and identifying anthropogenic activities in the three the most significant water quality studied river basins. parameters that supposed to be Journal of Survey in Fisheries Sciences 7(2) 2021 21

Study areas and methods discharged into this river. The sources Site description of pollutant which are mainly from the Three river basins namely, Juru River sewerage network of Johor Bahru, Pasir Basin, Kuantan River Basin and Johor Gudang, Ulu Tiram and Kota Tinggi River Basin have been selected in this cities, the industrial wastewater from study. many industries in the surrounding and agricultural wastewater that contain Johor River Basin fertilizers and pesticides are discharges Johor which is the second largest state into Johor River (Ismail, 2009). in Peninsula Malaysia drains a catchment about 2636 km2 and the Juru River Basin Johor River Basin become the main The Juru River Basin is about 75 km2 in river in Johor that located at coordinate area which is originated from Bukit 1°27′00″N 104°01′00″E. This river Mertajam Hill (Lim & Kiu, 1995). This flows through a north-south direction river basin is made of 2 main upstream and empties into the Straits of Johor. which are western and eastern upstream. Water quality of this river basin is The western upstream are Permatang gradually decline due to the increasing Rawa River and Rambai River while levels of various pollutants. The the eastern upstream are made of contaminants eventually flow into Johor Kilang Ubi River and Pasir River. River estuaries which are rich in Meanwhile, Juru River forms the habitats that provide spawning and middle and downstream (Zali, Retnam, feeding areas for fish and poultry & Juahir, 2011). Juru River Basin had (Najah et al., 2013). The Johor River been identified as one of the polluted Basin originates from Gunung Belumut river in Malaysia ('Department of and Bukit Gemuruh in the north and Environmental' (DOE), 2008). This flows to the south eastern part of Johor river has been undergoing extensive and finally into the Straits of Johor. The monitoring on its water quality as this maximum length and breadth of this river experience industrial activities catchment are 80km and 40km, from nearby areas where by this river is respectively. About 60% of the continuously become the strategic main catchment area is undulating highland effluent discharges location from rising to a height of 366m while the manufacturing industrial at Prai Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] remainders are lowland and swamps. Industrial Estate (Zali et al., 2011). The highland in the north is mainly Types of industries that are operated jungle. In the south, a major portion had along Juru River Basin are electronics, been cleared and planted with oil palm textiles, food processing, metal and rubber. A great amount of products, rubber, chemical plants and pollutants from various sources are transport equipment industries

22 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

(Alkarkhi, Ismail, Ahmed, & Easa, the period of 1990-2002. Since 1995, 2009) . There is also a vast harbour there was a degradation of forest areas operation which involves petroleum due to logging activities that had been based activities located at the estuary carried out nearby the basin and there which may influence the river water was also an increasing of agricultural quality during intertidal phase changes area especially in the area of Kenau (Zali et al., 2011). Juru River Basin and River. Land use activities in the forest its tributaries flow through urbanized of Kuantan River Basin affect the river areas and are heavily polluted by water quality due to soil erosion and domestic waste and discharges from pig surface runoff (Fig. 1). farms. According to the DOE report,

water quality of this river basin was The Data poor and it seems to be no improvement For the purpose of this study, data of over the years (Department of river water quality from three river Environmental (DOE), 1977; DOE- basins, namely Juru River Basin, USM, 1992). Kuantan River Basin and Johor River

Basin which consist of a number of Kuantan River Basin monitoring stations were obtained from On the other hand, Kuantan River Basin Department of Environment (DOE), is about 52,903 ha and located mainly Ministry of Natural Resource and in the forest reserve area. Kuantan Environment of Malaysia. All the water River Basin is an important water quality data from each selected station catchment area which is surrounded by in this study were based on the dipterocarp forest area that store variety available data that had been recorded of flora and fauna of tropical moist from 2003-2007. Referring to the forest. However, over the last decade, sample site, 5 sites represent the Juru the development of land for agricultural sub-basin, namely, Juru, Kilang Ubi, purposes and the production of natural Pasir, Rambai, and Ara while 8 sites resources such as timber had changed represent Kuantan sub-basin, namely, the entity coverage of this Kuantan Belat, Kuantan, Galing Besar, Galing River basin and indirectly affect the Kecil, Pinang, Charu, Riau and Kenau. water river quality of the basin. There is On the other hand, 21 sites represent the

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] a clear land use change, particularly in Johor subbasin namely, Layang, the area of the origin forest reserve area Serai(Hilir), Tiram, Tiram(Hulu), Bukit of Kuantan River Basin. It is reported Besar, Semanger, Johor, Telor, that more than 9000 ha of forest land Berangan, Temon, Layau Kiri, around the basin has been transformed Semenchu, Chemangar, Lebam, Sening, into bushes or agricultural land during

Journal of Survey in Fisheries Sciences 7(2) 2021 23

Santi, Anak Sg. Sayong, Sayong, Penggeli, Sebol and Linggiu.

Figure 1: Locations of sampling stations at the three river basins.

Due to the fact that some monitoring biological oxygen demand (BOD), stations in these three studied river chemical oxygen demand (COD), basins have missing data, only 13 suspended solid (SS), pH, ammoniacal

consistent parameters were analyzed nitrogen (NH3-N), dissolved solid (DS), - and examined among all the 30 river total solid (TS), nitrate (NO3 ), chloride - 3- water quality data available. A total of (Cl ), phosphate (PO4 ), Escherichia 205 samples in Juru River Basins, 275 coli, and coliform were subjected for samples in Kuantan River Basins and the environmetric techniques analysis Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] 865 samples in Johor River Basins were by using XLSTAT2012 software. The used for the analyses. For this study, all descriptive statistics of the 5 years the data obtained with 13 water quality measured data set of each river basin parameters; dissolved oxygen (DO), are summarized in Table 1.

24 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

Table 1: Mean values of water quality measurement in Juru, Kuantan and Johor River Basins (2003-2007).

Juru River Basin

Kuantan River Basin

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ]

Journal of Survey in Fisheries Sciences 7(2) 2021 25

Johor River Basin

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ]

26 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

Methods of analysis cluster analysis groups based on the multivariate CA operates on data sets and forms similarities among entities (McGarigal, well-defined groups that actually do not Stafford, & Cushman, 2000) . In this exist, but are assign to be clustered paper, hierarchical agglomerative Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] together due to the similar level (HACA) was employed in order to characteristic that occupied by them. identify the group of river region site The objective of CA is to classify a (spatial). Ward’s method, using sample of entities into a smaller Euclidean distances to measure numbers usually mutually exclusive similarity in HACA is the common

Journal of Survey in Fisheries Sciences 7(2) 2021 27

analysis used which aimed at Discriminant analysis (DA) refers to identifying variables that have a high a couple of closely related procedures rate of homogeneity level into a group that have similar objective in based on the selection criteria set. discriminating among well-defined HACA result is illustrated by a group of sampling entities based on a dendrogram, presenting the clusters and suite of characteristic. This is contrast their proximity (Juahir et al., 2010). to cluster analysis (CA), which attempt Agglomerative techniques begin with to organize entities into classes or each entity in a class of its own, then groups. Cluster analysis often serves as fuse (agglomerate) the classes into a precursor to DA when prespecified larger classes. These procedures are groups do not exist where artificial well known and they are used widely in groups are created by CA, and then ecological research (McGarigal et al., ecological differences among the newly 2000). In this paper, HACA was created groups are described using DA conducted in order to determine the (McGarigal et al., 2000). In this paper, classification of sampling sites (spatial) discriminant analysis was performed in in the three river basins; Juru River order to examine whether group differ Basin, Kuantan River Basin and Johor with regards to the mean of variable in River Basin into groups. predicting the group membership. In order to conduct this analysis, Discriminant Analysis data from the three assigned region Discriminant analysis specifies and groups in each of the three river basins examines variables that are dominant or which had been obtained from CA were well-discriminated among certain data selected. The discriminant analysis was groups. Discriminant function (DF) for performed by using standard, forward each group is created through this DA stepwise and backward stepwise modes. technique (Johnson & Wichern, 2008) These three modes were performed in by using below equation, Eq, 1: order to determine water quality n variables that have high variations F (Gi) = ki + ∑ wij Pij (1) according to their spatial distribution j=1 among the three studied river basins. In Where i is the number of groups (G) , ki the forward stepwise mode, variables is the constant inherent to each group, n are included gradually starting from the Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] is the number of parameters used for the most significant variables until no classification a set of data into a given significant changes are obtained. In group and wj is the weight coefficient backward stepwise mode, the variables assigned by discriminant function are removed gradually starting with the analysis (DFA) to a given parameter less significant variables until no (pj). significant changes are obtained.

28 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

Principal component analysis this study, only the VF coefficient that Principal component analysis gives have strong loadings (greater than 0.7) information upon the most significant were being considered. Source variables according to the spatial and identification of different pollutants was temporal variations which distinguish made on the basis of different activities the whole data set by excluding the less in the catchment area in light of significant parameters with minimum previous literatures. In this paper, PCA loss of original information (Kannel, was applied to the data set consist of 13 Lee, Kanel, & Khan, 2007; Singh, parameters for each region (HPS, MPS Malik, Mohan, & Sinha, 2004; Singh, and LPS) of the three studied river Malik, & Sinha, 2005) The principal basins. Calculations of input data component (PCs) can be expressed as matrices (variables × cases) for this (Eq 2): PCA were 13 × 224 for HPS region, 13

× 312 for MPS region and 13 × 807 for zij=ai1x1j+aai2x2j+...+...aimxmj LPS. (2)

Where z is the component score, a is the Results and discussion Determination of the sampling station component loading, x is the measured groups Cluster analysis value of variable, i is the component Under this section, water quality number, j is the sample number, and m parameters in each data set were is the total number of variables. examined by using cluster analysis. The PCs generated by PCA are Cluster analysis was performed towards sometimes not readily interpreted; all of the data sets in the three river therefore, it is advisable to rotate the basins in order to specify each PCs by varimax rotation. Kim and monitoring stations according to the Mueller (1987) states that, the result of level of their homogenous the PCs after varimax rotation with the characteristics. This analysis was amount of eigenvalues more than 1 are performed towards all of the water considered significant for the purpose to quality data in order to assess spatial acquire new groups of variables called variation among the sampling stations varimax factors (VFs). The VF on each of the three river basins. The

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] coefficients which have correlation cluster analysis resulted into three greater than 0.7 are considered as cluster of sampling stations in Juru, ―strong‖;0.5–0.69, as ―moderate‖; and Kuantan and Johor River Basins, 0.30–0.49, as ―weak‖ significant factor respectively (Fig. 2). loadings (Liu, Lin, & Kuo, 2003). In

Journal of Survey in Fisheries Sciences 7(2) 2021 29

Juru River Basin

Kuantan River Basin Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ]

Johor River Basin

Figure: 2 Dendrogram shows sampling stations that had been classified in each three river basins.

The cluster procedure formed three similar characteristics and natural clusters or groups in a very convincing backgrounds. For example, in Juru way as the sites in each group have River Basin, cluster 1 (2JR01, 2JR02, 30 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

2JR03, 2JR04, 2JR05, 2JR06, 2JR07) three clusters with three different represent the high pollution sources regions also generated in the other two (HPS) region, cluster 2 (2JR10, 2JR11, river basins, Kuantan River Basin and 2JR12) represent the moderate pollution Johor River Basin. Table 2 represents sources (MPS) region and cluster 3 the sampling stations that have been (2JR08, 2JR09) represent the low grouped by HACA. pollution sources (LPS) region. The

Table 2: Sampling stations that have been grouped into regions. Regions River basins /sampling stations Juru River Basin Kuantan River Basin Johor River Basin HPS 2JR01,2JR02,2JR03, 4KN03,4KN04 3JH10,3JH46 2JR04,2JR05,2JR06, 2JR07 MPS 2JR10,2JR11,2JR12 4KN01,4KN02,4KN075, 3JH05,3JH06,3JH09,3JH18,3JH32, 4KN07 3JH35 LPS 2JR08,2JR09 4KN06,4KN08,4KN09, 3JH03,3JH07,3JH08,3JH1,3JH12, 4KN10,4KN11 3JH13,3JH15,3JH16,3JH19,3JH20, 3JH22,3JH25,3JH27,3JH28,3JH30, 3JH33,3JH36,3JH37,3JH40,3JH42, 3JH43,3JH44,3JH45,3JH47

The result of HACA technique implies (CA) technique is functional upon the that rapid assessment of water quality classification of river water quality data for the whole stations can be done by for optimum future sampling and monitoring only one station in each monitoring strategies. cluster that had been assigned by CA. Among the three river basins, Juru This is because only one monitoring River Basin is proved to be the most station is already enough to represent polluted river basin as 7 of its the water quality data for the whole monitoring stations are clustered under group members in each cluster group as high pollution sources (HPS) group. every group have their own similar This is due to the fact this river basin level of data quality characteristics. For receives heavy pollution loadings from example, in Kuantan River Basin, only nearby urbanized area that densely station 4KN03 in cluster 1 (HPS), populated by humans and multiple

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] station 4KN01 (MPS) in cluster 2 and types of industrial located along the station 4KN06 (MPS) in cluster 3 need basin. In Kuantan River Basin, 2 to be monitored in order to represent the monitoring stations; 4KN03 at Galing water quality assessment of the whole Besar River and 4KN04 at Galing Kecil Kuantan River Basins. The result of this River are clustered under high HPS analysis proved that cluster analysis group. Galing Besar River is mainly

Journal of Survey in Fisheries Sciences 7(2) 2021 31

being polluted by sediment deposition Discriminant Analysis and siltation that resulted from DA was performed in order to anthropogenic activities. This river is determine water quality variables that also overwhelmed with various have high variation according to their contaminants entering it. The other spatial distribution which had been monitoring stations are not heavily classified by CA into three main polluted as Kuantan River Basin is clusters for each river basin. The mainly located in the forested area. The combination of HPS region for natural vegetation within this river Juru,Kuantan and Johor River Basin basin act as filters that prevent the represent as the dependent variables sediment deposition and uptake nitrate while the water quality parameters were and phosphorus which is mainly the independent variables. The same originate from fertilizer usage. There is goes for the combination of MPS and also a little disturbance along the river LPS region for Juru, Kuantan and Johor water within this basin which is not River Basin. DA was carried out via resulted into a serious negative impact standard, forward stepwise and to the river water quality such as Belat backward stepwise modes. In the River where by it support mainly forward stepwise mode, variables are residential areas with light industries included gradually beginning from the with low water demand and the existing most significant variable until no houses are served by water closets and significant changes are obtained. In septic tanks, but there is no evidence of backward stepwise mode, variables are serious water pollution problem removed gradually beginning with the although the sullage water is discharged less significant variable until no into surface drain (Hill, 1981). For significant changes are obtained. The Johor River, station 3JH10 at Bukit accuracy of spatial classification all the Besar River and station 3JH46 at three regions of the studied river basins Sayong River are clustered under HPS were 79.33% (13 discriminant variables) group. The major land use at the Johor for standard mode, 79.33.74% (9 River Basin is oil palm and other crops discriminant variables) for forward plantations. There are many oil palm stepwise mode and 70.73% plantation and FELDA land (10discriminant variables) for backward development located in the surrounding stepwise mode. In forward stepwise

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] - area of Bukit Besar River and Sayong mode, DO, NH3N, CI, pH, NO3 , BOD, 3- River which may influence to the river COD and PO4 were determined to be water quality of the two rivers (Hamza, the significant variables which indicate 2009). that all these nine parameters have high variation upon their spatial distribution Spatial variation of river water quality in the region of all the three studied

32 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

river basins. For backward stepwise, E. shown in Table 3. Box and whiskers coli exist as the tenth parameter that plot of some water quality parameters have high distribution upon the spatial in five years periods (2003-2007) is variation. The result of DA which had shown in Fig. 3. been illustrated in a classification matrix for each clustered region is

Table 3: Classification matrix for DA of spatial variations in the three studied river basins. Sampling regions of 3% Regions assigned HPS LPS MPS studied river basins Correct by DA Standard DA mode (13 variables) HPS 75.34% 32 2 20 LPS 95.91% 0 635 13 MPS 39.23% 6 59 97 Total 79.55% 38 696 130 Forward stepwise mode ( 9 variables) HPS 77.58% 32 2 20 LPS 95.91% 0 634 14 MPS 39.23% 5 59 98 Total 79.70% 37 695 132 Backward stepwise mode (10 variables) HPS 75.34% 32 2 20 LPS 95.91% 0 634 14 MPS 39.23% 6 60 96 Total 79.33% 38 696 130

The Wilk’s Lambda test for standard computed p-value is lower than the mode gave a Lambda value of 0.281 significance level of alpha=0.05, the and p<0.0001. The null hypothesis null hypothesis should be rejected and states that the means of vector of the the alternative hypothesis should be three clusters region (HPS, MPS and accepted. The risk of rejecting null LPS) are equal. The alternatives hypothesis while it is true is lower than hypothesis, on the other hand, states 0.01%. Thus, the clusters are indeed that at least one of the means of vectors different from each other. is different from another. Since the

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ]

Journal of Survey in Fisheries Sciences 7(2) 2021 33

Figure 3: Box and whisker plots of some parameters separated by spatial DA associated with the three river basins. The crosses are mean values, top and bottom of whiskers indicate maximum and minimum values, respectively while horizontal lines of the boxes from top to bottom indicate the third quartile, median, and first quartile, respectively.

Principal Component Analysis (PCA) of data set almost 81.9%, 78.8% and Principal component analysis (PCA) 74.1% respectively. Varimax rotation was performed on the data set for the that had been performed through this purpose of identifying the source of PCA technique managed to obtain five pollutant loading in each clustered varimax function (VF) in each HPS, Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] region among the three river basins. MPS and LPS region. Table 4 shows Five PCs were obtained for each HPS, the details of five VFs obtained together MPS and LPS regions, with the with the amount of variable loading and concerned amount of eigenvalues variance explained for every region (larger than 1) sum up the total variance groups in the three river basins.

34 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

Table 4: Result of VFs that consist of variables loading after varimax rotation for water quality data in HPS, MPS and LPS regions of the three river basins

High Pollution Source (HPS) can be assumed to be originated from For HPS region, among five VFs, VF1 point sources (PS) and non-point accounts for 25.6% of the total variance, sources (NPS) (Ha & Bae, 2001)) as which have strong positive loadings on these river basins receive a lot of BOD, COD and SS. In this region, changes in the land development that loading of BOD and COD are assumed also depends on the seasonal variation to be contributed by the direct in studied area. Cl- on the other hand, is discharges from nearby pig farm which identified to be originated from the are not equipped with proper sanitary mineral salt content in the river. VF3 treatment system (Lim & Kiu, 1995). It explaining15.4% of the total variance is reported that Juru River flow through has strong loading on E. coli and largely urbanized areas where it had coliform which indicate the been polluted by domestic waste and microorganism parameters in the HPS discharges from pig farms (Lim & Kiu, region of these three rivers. In Juru 1995). The present of COD in the river River for example, source of E. coli and basins was assumed to come from the coliform are possibly originated from anthropogenic activities that arise from Juru sewage pond located near the river the nearby industrial areas that and also from nearby residential areas discharge their industrial waste into as human settlements including these three rivers. The strong loading on squatters along the river banks at Juru Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] SS is possibly originated from the high River are not equipped with proper load of soil runoff and also from wood sanitary systems.VF4, explaining 10.8% industry (Zali et al., 2011) nearby these of the total variance and has high 3- three river basins. VF2, account 20.4% loading of NH3-N and PO4 . The NH3- total variance with the positive loading N indicates that the HPS region of these of three variables which are DS, TS three river basins experienced from AND Cl. The two variables DS and TS pollution that caused by livestock waste Journal of Survey in Fisheries Sciences 7(2) 2021 35

and as well as the agricultural and loading of DS and TS in this MPS domestic sewage waste. For example, region are possibly due to extreme river 3- PO4 loads were mainly originated bank erosion that usually occur during from agricultural runoff such as the storm flow which eventually cause fertilizers at Juru River flow nearby the the bedload sediment enter the river Prai Industrial Estate. VF5, explaining region (Bolstad & Swank, 1997; Hart, 9.5% of the total variance and has 2006). This assumption is reasonable - strong loading on NO3 . The loading of especially to the river water in Kuantan - NO3 is possibly due to the runoff from River Basin which is mainly polluted agricultural land along the HPS region due to land development through - of these three river basins. This NO3 is agricultural, timber logging and forest mainly originated from commonly used clearing activities. Strong positive - nitrogen and potassium fertilizers at the loading on NO3 is expected to originate crop planted area of this HPS region. from the cultivation area (Vega, Pardo, Barrado, & Debán, 1998) , where crops Medium Pollution Source (MPS) are planted and the use of inorganic For MPS, among five VFs, VF1 fertilizers such as ammonium nitrate is accounts for 25% of the total variance rather frequent (Juahir et al., 2010) . - which include BOD, COD, NH3-N and NO3 may also arise from 3- PO4 . BOD and COD are among decomposition and degradation of organic factors that assumed to be organic matters containing nitrogen attributed from anthropogenic activities (USGS, 2007). The organic matters such as farming and timber logging contained in the municipal waste activities that take place along the river include urea and protein from the

basin. The presence of NH3-N in the wastewater discharges which enters this river is due to the excessive runoff from MPS region of the three river basins. the agricultural area nearby the basin VF3, explaining 14% of the total regions (Sharip, Zaki, Shapai, Suratman, variance, has strong loading on E. coli 3- & Shaaban, 2014). The PO4 loading is and coliform which are related to assumed to be originated from domestic waste and treatment plant phosphate fertilizer that contain in soils from paper manufacturing industry, from the agricultural farm area located rubber and palm oil refineries that nearby the MPS region of these three located near the river (Qadir, Malik, & Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] river basins. VF2, explaining 23% of Husain, 2008). the total variance, has strong loadings Normally, faecal contamination from - on DS, TS AND NO3 . Farming and human occurred when structural and construction were more frequent near technical flaws in the sewerage system these river basins area and had resulted that causing the sewage to be flowed into sediment deposited. Thus, the into the river which then leads to the

36 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

present of E. coli and coliform.VF4, operation that is operated at Johor River accounts the total variance 8.6%, area. The loading of Cl- is probably showing loading on SS that can be comes from the mineral constituent in attributed from high loads of soil and the water of this LPS region. VF2 waste disposal runoff. The last one is represent the total variance of 18.1% VF5 which accounts 7.9% of the total and show the strong positive loading of - variance and include NO3 as the BOD and COD. The presence of these - positive strong loading variable. NO3 is BOD and COD in this LPS region of expected to arise from vegetables farm, the three river basins is believed to be oil palm and rubber plantation that are attributed from the influence of point located along the MPS region of these source organic pollutants from three river basins. The nitrate content in sewerage network of the cities located river water is caused by agricultural nearby the river. VF3, explain 12.5% of activity that is commonly associated the total variance and has strong with the use of chemical fertilizer to loadings on E. coli and coliform that facilitate the growth of trees. Thus, signify the contribution of domestic when surface runoff occurs during rainy waste to this LPS region. VF4, season, waste chemical fertilizer will explaining 9% of the total variance and - flow into these basins and caused has strong loading on pH and NO3 . The - increasing of NO3 content in the river. strong loading of pH is expected to arise from several causes such as Low Pollution Source (LPS) industrial effluent discharges and other For LPS region, among five VFs, VF1 environmental factors. The decrease of represent 26.7% of the total variance, pH range into acidic condition are explaining strong loadings on DO, DS, mainly caused by the industrial effluent TS and Cl-. The strong negative loading that release acidic discharges into the on DO is cause by the presence of E. river while the significant increase in coli in this LPS region of the three river pH level into alkaline condition are basins which consumed large amount of possibly resulted from environmental oxygen in order to undergo anaerobic factors such as the rapid algae growth fermentation. The negative loading of which remove carbon dioxide from the DO explained that the LPS region in water during the process of - these three river basins had been photosynthesis. The NO3 loading may Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] polluted by municipal waste, oxidation additionally derived from agricultural ponds and animal husbandry. DS and area where inorganic nitrogen fertilizer TS can be assumed as the sediment are in common use such as at vegetable accumulation result that happened due farm near the river. VF5, accounts for to anthropogenic activities at these three 7.7% of total variance, showing strong 3- river basins such as sand mining loadings on NH3NL and PO4 . NH3-N

Journal of Survey in Fisheries Sciences 7(2) 2021 37

indicates that the LPS region of the basins. Generally, this study had three river basins experienced from showed the ability of environmetric pollution that caused by livestock waste techniques for conducting the analysis and as well as the agricultural and and interpretation of a large complex domestic sewage waste while a large data set for water quality assessment 3- amount of PO4 loading is possibly and as well as the identification of originated from the contamination of pollution sources. This analysis is also fertilizer and pesticide discharges from useful upon investigating spatial vegetables farm located nearby the variations of water quality as an effort river basin. toward a more effective river basin Environmetric analysis techniques management. Overall results obtained managed to determine spatial variation from this study indicate that among the three studied river basins anthropogenic activities have namely, Juru River Basin, Kuantan significantly influence river water River Basin and Johor River Basin. quality variations. If these activities are Cluster analysis has successfully not controlled, they will consequently classified the cluster region namely, generate a great pressure on the river HPS, MPS and LPS in each of the three ecosystem and finally become severely river basins. This classification enables polluted river with the loss of critical the designation of sampling strategy habitat and overall decrease in the which can reduce the number of quality of life that inhabit this river sampling stations and the monitoring ecosystem. cost as on one station in every cluster is enough to represent the accurate rapid Acknowledgements assessment of spatial water quality for The authors would like to express the whole region among the group. whole gratitude and are grateful to Discriminant analysis on the other hand thanks the Department of Environment also gives encouraging results upon (DOE) Malaysia for providing the discriminating the data of every secondary the data for this research monitoring stations with discriminant project upon the process of completing variables assigning high correctly this manuscript. percentage of correlation matrix using forward and backward stepwise modes. References Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] Principal component analysis that Adam, M.J., 1998. The Principles of applied on the data set for each Multivariate Data Analysis. In P. R. classified region had managed to Ashurst & M. J. Dennis (Eds.), Analytical Methods of Food identify the pollutant loading variation Authentication. (pp. 350). London, due to land use and anthropogenic UK: Blackie Academic & activities in the three studied river Professional.

38 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

Alkarkhi, A.F., Ismail, N., Ahmed, A. Ha, S. R., and Bae, M. S., 2001. Effects and Easa, A., 2009. Analysis of of land use and municipal wastewater heavy metal concentrations in treatment changes on stream water sediments of selected estuaries of quality. Environ Monit Assess, 70(1- Malaysia--a statistical assessment. 2), 135-151. Environ Monit Assess, 153(1-4), 179- doi:10.1023/a:1010649705723 185. doi:10.1007/s10661-008-0347-x Hamirdin, I., 2000. Pengaruh aktiviti Bolstad, P.V. and Swank, W.T., 1997. manusia terhadap kualiti air Cumulative Impacts of landuse on permukaan di Lembangan Langat- water quality in Southern Semenyih dan Linggi. In H. Mohd Appalachian Waterhed 1. JAWRA Yusof, I. Nor Azizan, & Z. M. Journal of the American Water Lukman (Eds.), Isu-isu pembangunan Resources Association, 33(3), 519- di awal abad ke-21. Bangi, Selangor 533. doi:10.1111/j.1752- (Malaysia): Fakulti Sains 1688.1997.tb03529.x Pembangunan, Universiti Kebangsaan Department of Environment (DOE). Malaysia. 1977. Report on Water Pollution Hamza, H., 2009. Water quality trend at Control: Juru River Basin, the upper part of Johor river in Department of Environment, Ministry relation to raindall and runoff pattern. of Science, Technology and (Masters), Universiti Teknologi Environment, Malaysia. Malaysia, Johor, Malaysia. Department of Environment (DOE). Hart, H.M., 2006. Effect of land use on 2003. Water quality management in the total suspended solids and Malaysia. DOE Documents, Kuala turbidity in the little river watershed, Lumpur. Blount County, Tennesse. (Masters), Department of Environment (DOE). University of Tennessee, Knoxville. 2008. River Water Quality Report Hill, A.R., 1981. Stream phosphorus 2008. exports from watersheds with Department of Enviroment (DOE)- contrasting land uses in Southern USM. 1992. Development of Water Antario. JAWRA Journal of the Quality Criteria and Standards American Water Resources Classification-Juru River, Vol VIII. Association, 17(4), 627-634. Draft Final Report, Departmental of doi:10.1111/j.1752- Environment, Malaysia, Innovation 1688.1981.tb01269.x and Consultancy Centre, Universiti Ismail, M., 2009. Application of Sains Malaysia. hydrodynamic water quality model in Dogan, E., Sengorur, B., & Koklu, R. Sungai johor Estuary. (Masters), 2009. Modeling biological oxygen Universiti Teknologi Malaysia, Johor, demand of the Melen River in Turkey Malaysia.

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] using an artificial neural network Johnson, R.A., & Wichern, D.W., 2008. technique. Journal of Environmental Applied Multivariate Statistical Management, 90(2),1229-1235. Analysis. United States: Pearson. doi:10.1016/j.jenvman.2008.06.004 Juahir, H., Zain, S. M., Aris, A. Z., Einax, J. W., Zwanziger, H.W. and Yusoff, M. K., & Mokhtar, M. B. Geiss, S. 1997. Chemometrics in 2010. Spatial assessment of Langat environmental analysis. Germany. river water quality using

Journal of Survey in Fisheries Sciences 7(2) 2021 39

chemometrics. Journal of Razali, A., Syed Ismail, S. N., Awang, Environmental Monitoring, 12(1), S., Praveena, S. M., & Zainal 287-295. doi:10.1039/B907306J Abidin, E. 2018. Land use change in Kannel, P. R., Lee, S., Kanel, S. R., & highland area and its impact on river Khan, S.P. 2007. Chemometric water quality: a review of case studies application in classification and in Malaysia. Ecological Processes, assessment of monitoring locations of 7(1), 19. doi:10.1186/s13717-018- an urban river system. Analytica 0126-8 Chimica Acta, 582(2), 390-399. Sharip, Z., Zaki, A. T. A., Shapai, M. doi:10.1016/j.aca.2006.09.006 A. H. M., Suratman, S., & Shaaban, Kim, J. O., & Mueller, C. W. 1987. A. J. 2014. Lakes of Malaysia: Water Introduction to factor analysis: What quality, eutrophication and it is and How to do it (Quantitative management. Lakes & Reservoirs: application in sosial sciences). United Science, Policy and Management for State: SAGE UNIVERSITY PAPER. Sustainable Use, 19(2), 130-141. Lim, P.E., & Kiu, M.Y. 1995. doi:10.1111/lre.12059 Determination and speciation of Simeonov, V., Einax, J., Stanimirova, heavy metals in sediments of the Juru I., & Kraft, J. 2002. Environmetric River, Penang, Malaysia. modeling and interpretation of river Environmental Monitoring and water monitoring data. Analytical and Assessment, 35(2), 85-95. Bioanalytical Chemistry, 374(5), 898- doi:10.1007/BF00633708 905. doi:10.1007/s00216-002-1559-5 Liu, C. W., Lin, K. H., & Kuo, Y. M. Simeonova, P., Simeonov, V., & 2003. Application of factor analysis Andreev, G. 2003. Water quality in the assessment of groundwater study of the Struma river basin, quality in a blackfoot disease area in Bulgaria (1989–1998). Central Taiwan. Sci Total Environ, 313(1-3), European Journal of Chemistry, 1(2), 77-89. doi:10.1016/s0048- 121-136. doi:10.2478/BF02479264 9697(02)00683-6 Singh, K. P., Malik, A., Mohan, D., & McGarigal, K., Stafford, S., & Sinha, S. 2004. Multivariate Cushman, S. 2000. Multivariate statistical techniques for the Statistics for Wildlife and Ecology evaluation of spatial and temporal Research New York, NY: Springer variations in water quality of Gomti New York. River (India)—a case study. Water Najah, A., El-Shafie, A., Karim, O. A., Research, 38(18), 3980-3992. & El-Shafie, A. H. 2013. Application doi:10.1016/j.watres.2004.06.011 of artificial neural networks for water Singh, K. P., Malik, A., & Sinha, S. quality prediction. Neural Computing 2005. Water quality assessment and and Applications, 22(1), 187-201. apportionment of pollution sources of

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ] doi:10.1007/s00521-012-0940-3 Gomti river (India) using multivariate Qadir, A., Malik, R. N., & Husain, S. statistical techniques—a case study. Z. 2008. Spatio-temporal variations in Analytica Chimica Acta, 538(1), 355- water quality of Nullah Aik-tributary 374. doi:10.1016/j.aca.2005.02.006 of the river Chenab, Pakistan. United States Geological Survey Environ Monit Assess, 140(1-3), 43- (USGS). 2007. Water quality in the 59. doi:10.1007/s10661-007-9846-4 upper Anacostia River, Maryland:

40 Masthurah et al., Case study Malaysia: Spatial water quality assessment of Juru, Kuantan and …

Continous and discreate monitoring Vega, M., Pardo, R., Barrado, E., & with simulation to estimate Debán, L. 1998. Assessment of concentrations and yields. Retrieved seasonal and polluting effects on the from Virginia, United States: quality of river water by exploratory Varmuza, K., & Filzmoser, P. 2009. data analysis. Water Research, 32(12), Introduction to Multivariate 3581-3592. doi:10.1016/S0043- Statistical Analysis in Chemometrics. 1354(98)00138-9 Varunprasath, K., & A. Daniel, N. Zali, M. A., Retnam, A., & Juahir, H. 2010. Physico-Chemical Parameters 2011. Spatial Characterization of of River Bhavani in Three Stations, Water Quality Using Principal Tamilnadu, India. Iranian (Iranica) Component Analysis Approach at Journal of Energy & Environment, Juru River Basin, Malaysia. 1(4).

Downloaded from sifisheriessciences.com at 16:55 +0330 on Monday October 4th 2021 [ DOI: 10.18331/SFS2021.7.2.2 ]