Trends Of Water Quality Data At Perlis River Biology Essay

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Malaysia is enriched by abundance of natural resources and one of them is the rivers or also known as surface water. River or surface water is precious and vital natural resource for all life on Earth especially human being as it provides water supply for human domestic use, irrigation for agriculture, as a means of transportation, industries use and so on. Even though there always has been plenty of fresh water in Malaysia, clean water supply has not always been available due to the rising water pollution problem.

Nowadays, Malaysia has become an industrialized country and no longer left behind the other developed countries but at the cost of the environment. Effluent from the industrial area which is then discharged directly into the rivers without prior treatment is the main sources of water pollution. Besides, human activities such as the uses of agricultural chemicals, soil erosion due to improper development project, land use and so on are also contributed significantly to water pollution (Niemi et al., 1990). Pollution of river with high concentration of toxic chemicals and excess nutrients, which are resulting from surface water runoff, leaching from landfill site and ground water discharges has been aroused the public concern towards the water pollution issue. Thus, there is a need to assess river or surface water quality due to the increased understanding of the importance of water quality towards public health and aquatic life (Ying, 2005).

Peninsular Malaysia consists of 11 states and two federal territories whereas Perlis is the smallest state among them. Perlis state is situated at the northwest of peninsular Malaysia. There are more than ten major rivers within the Perlis area while Perlis River is one of the most important rivers in Perlis. The length of the Perlis River is approximately 11 km through Kangar city to Kuala Perlis while the size of the river basin is approximately 310 km² (www.1s1rcommunity.net). Perlis River has provided a medium for public to socialize and for recreational activities particularly at Denai Larian Sungai Perlis. The number of visitors who are visiting to the river and surrounding area for rest and recreation purpose is estimated to be achieved 10,000 peoples per month. However, the quality of Perlis River does not reach the desired level and it is much difference if compared to the major rivers at developed countries.

There are some problems which occurred at Perlis River at present. According to the Interim National Water Quality Standards for Malaysia, the status of water quality at Sungai Perlis is classified as Class III. There is heavy erosion occurred at Perlis River and leading to the river banks become very shallow. Residents which located surrounding the river have thrown rubbish or solid waste into the river therefore causing to unaesthetic scene. Besides, there is a landfill located in Kuala Perlis. When the leachate is leaching out and flow into water body, the water quality of the river will be get influence. Squatters located around the river area are also contributed to the river pollution problems and there is a need to regular patrols or hut-to-hut checks. In addition, shrimp livestock ponds, Kangar wet market, esplanade at Perlis River, food stalls and the Kuala Perlis fisherman jetty are the main sources which contributed significantly to the water pollution at Perlis River too. (www.1s1rcommunity.net)

On the other hand, the sources of water pollution can be categorized into point and non-point sources. Point sources of pollution refer to those easily identifiable pollutants which enter the water resource though a direct route, for example, effluent from wastewater treatment plants. Whereas for non-point source pollution, it refer to those pollutants which enter from diffuse sources and they are difficult to control, such as stormwater runoff (P.Jamwal et al., 2008).

In this study, Mann-Kendall trend test and principal component analysis (PCA) have been applied to detect the trends of water quality data and to obtain the most significant parameters in order to trace the sources of pollutants. This kind of non-parametric test had been widely used to detect significant trends in time series. Besides, it has the advantage that their power and significance are not affected by the actual distribution of the data. Thus, this method is very suitable to apply in detecting trends in hydrological time series which contained the outliers and usually skewed (Hamed, 2009).

Mann-Kendall trend test has been widely used in assessing the variability on hydrological time series (Hamed, 2008). The examples of earlier studies which applied Mann-Kendall technique are trend study and assessment of surface water quality in the Ebro River (Bouza-Deano et al., 2008), hydrological trend analysis due to landuse changes at Langat River basin (Juahir et al., 2010), identification of hydrological trends at Canadian Rivers (Khaliq, 2009) and so on.

Principal component analysis (PCA), one of the multivariate statistical techniques, is helps in reducing redundant parameters with minimum loss of original information (Helena et al., 2000). A better understanding of water quality can be achieved through the interpretation of complex water quality data matrices. Besides, it allows the identification of possible factors or sources that affect water systems and therefore a swift solution to pollution problems can be found out (Vega et al., 1998; Wunderlin et al., 2001; Reghunath et al., 2002; Simeonova et al., 2003; Simeonov et al., 2004).

In recent years, principal component analysis (PCA) has been applied in various aspect of field including environmental issues. It has been used to characterize and assess water quality data, and it is efficacious in verifying temporal and spatial variations which is caused by natural and anthropogenic factors (Helena et al., 2000; Singh et al., 2004, 2005). The examples of application of PCA in environmental issues are interpretation of ground water hydrographs (Winter et al., 2000), examination of spatial and temporal patterns of heavy metal contamination (Shine et al., 1995) identification of herbicide species related to hydrological conditions (Tauler et al., 2000) and so on.

The aims of this study are to detect the trends of water quality data at Perlis River and determine the significant parameters that contributed to water pollution by using Mann-Kendall and principal component analysis techniques. A better understanding of the evolution in water quality from the year 2003 until year 2007 can be achieved via the Mann-Kendall trend test. Besides, by using the principal component analysis (PCA), the sources of pollutants can be traced and thus some mitigation measures can be carried out.

Problem statement

River is vital natural resource for all life on the Earth as it supplies water for domestic use, means of transportation, industrial use and so on. However, the available water sources are getting scarce and scarce due to water pollution. Meanwhile, industrialization process in Malaysia have brought to economic growth but at a cost of the environment. Water pollution is a by-product of industrialization, due to the toxic and hazardous wastes which generated by various industries and have discharged into water body (Abdullah, 1995). There are several factors that contributed to the water pollution at Perlis River such as human behavior, landfill leachate and so on. Hence a proper monitoring and controlling of water quality needs to be carry out in order to mitigate the water pollution problem at Perlis River. On the other hand, there is a huge amount of water quality data which provided by Department of Environment (DOE) resulting from regular monitoring, however, it is quite challenging and time-consuming especially in interpreting them. Thus, a correct method needs to be use in drawing meaningful data from huge and complex data matrices. Besides, the changes of particular types of land use and pollutant discharged will influence the water quality at Perlis River. It is quite a challenge to identify the sources of pollutants and the most significant parameters which contribute to pollution problem.

1.2 Significance of Study

This study was carried out with the purpose to detect the trends of water quality and determine the significant parameters which contributed to water pollution at Perlis River. Mann-Kendall technique is used in this study to analyze and determine the water quality trends at Perlis River. Meanwhile, principal component analysis (PCA) is also used to figure out the most significant parameters which contributed to the water pollution problem and help in identifying the superfluous stations. It can reduce the cost and eliminate unnecessary wastage in manpower and effort. As a result, a more effective and efficient river quality management activities can be implemented (Juahir et al., 2010). Besides, government can play their important role in enforcing rules and regulations in more strictly way towards the industries which discharged pollutants into the water body in order to improve the river water quality and to regain its clean status. It is also important to the surrounding residents since the water pollution problem will influence their livelihood as well.

1.3 Objectives

To assess spatial and temporal variations of water quality data at Perlis River.

To identify the most significant parameters which contribute to the water quality trends at Perlis River.

To identify and trace the sources of pollutants at Perlis River.

CHAPTER 2

LITERATURE REVIEW

2.0 River

2.1 Water Pollution

According to Schaffner (2009), most of the rivers in developing countries are experiencing severe deterioration of water quality. Because of the population growth, economic development, and the associated intensification of human activities around the river area, the pressure on the water bodies rises, especially at urban areas. Besides, based on Milovanovic (2007), rivers are served as the recipients of huge amounts of waste discharged by human beings through their daily agricultural, industrial and domestic activities. Agriculture sector is one of the most significant sources of pollution due to the surface runoff from fertilized land. Effluent which discharged from industrial area and disposal of wastes are considered as the main anthropogenic sources of pollution. Human activities which lead to river water pollution may engender freshwater shortage throughout the world.

Other than that, Jonnalagadda and Mhere (2001) had stated that wastewater from urbanization process, increasing land development and industrial activities which discharged into water body without adequate wastewater treatment, has contributed to the poor water quality. More and more water pollution problems nowadays have also led to grave ecological and environmental consequences (Ma et al., 2009). Therefore, most of the developing countries have putting efforts on water resources management, in order to deal with the water pollution problems in an effective way.

2.2 Water Quality Parameters

2.3 Mann-Kendall Trend Test

2.4 Multivariate Analysis

CHAPTER 3

MATERIAL AND METHOD

3.0 Study area

The location of the study is Perlis River. Perlis is the smallest state if compared with the other states and it is situated in the northwest of the Peninsular Malaysia. There are more than ten major rivers within the Perlis area while Perlis River is one of the most important rivers in Perlis. The length of the Perlis River is approximately 11 km through Kangar city to Kuala Perlis while the size of the river basin is approximately 310 km². The status of water quality at Sungai Perlis is classified as Class III. There is heavy erosion occurred at Perlis River and leading to the river banks become very shallow. Figure 3.1 below showed the map of landuse around the Perlis River.

Figure 3.1: Map of landuse around Perlis River. (Source: Jabatan Pengairan dan Saliran Negeri Perlis. http://www.rrperlis.blogspot.com/)

There are a total of 10 water quality monitoring stations located along the Perlis River Basin in order to monitor the river quality changes at the particular area. The monitoring stations had been installed at Perlis River (2PS01), Arau River (2PS02), Ngulang River (2PS03), Serai River (2PS04), Jernih River (2PS05 and 2PS06), Tasoh River (2PS07), Jarum River (2PS08), Kok Mak River (2PS09) and Pelarit River (2PS10). Figure 3.3 below showed the location of 10 water quality monitoring stations along the Perlis River basin whereas Table 3.1 showed the longitude and latitude of water quality monitoring stations at Perlis River.

Figure 3.2: Map of Perlis River with 10 water quality monitoring stations along the main river. (Source: Alam Sekitar Sdn Bhd, Water Quality Monitoring Station, Perlis River Basin. http://www.enviromalaysia.com.my/wqm_perlis.asp)

Table 3.1: Longitude and latitude of water quality monitoring stations at Perlis River Basin. (Source: Alam Sekitar Sdn Bhd)

Station ID

Station Name

Longitude

Latitude

2PS01

Perlis River , Perlis

E 102° 35.504'

N 02° 36.520'

2PS02

Arau River , Perlis

E 102° 35.490'

N 02° 38.743'

2PS03

Ngulang River , Perlis

E 102° 32.356'

N 02° 38.335'

2PS04

Serai River , Perlis

E 102° 30.121'

N 02° 42.308'

2PS05

Jernih River , Perlis

E 102° 24.758'

N 02° 43.621'

2PS06

Jernih River , Perlis

E 102° 27.453'

N 02° 53.414'

2PS07

Tasoh River , Perlis

E 102° 28.508'

N 02° 59.397'

2PS08

Jarum River , Perlis

E 102°27.560'

N 02° 48.778'

2PS09

Kok Mak River , Perlis

E 102° 27.618'

N 02° 49.332'

2PS10

Pelarit River , Perlis

E 102° 24.582'

N 02° 49.560'

3.1 Data

The water quality data at Perlis River was obtained from 10 monitoring stations along the Perlis River as shown in Figure 2 which manned by Department of Environment, Ministry of Natural Resource and Environment of Malaysia. The data contained a total number of 30 parameters collected from year 2003 until year 2007. The 30 parameters are dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), Suspended Solid (SS), pH, NH3-NL, Temperature (TEMP), Conductivity (COND), Salinity (SAL), turbidity (TUR), dissolved solid (DS), total solid (TS), nitrate (NO3), chlorine (Cl), phosphate (PO4), arsenic (As), mercury (Hg), cadmium (Cd), cromium (Cr), plumbum (Pb), zinc (Zn), calcium (Ca), ferum (Fe), kalium (K), magnesium (Mg), sodium (Na), oil and grease (OG), MBAS, E-Coli, and also coliform.

3.2 Method

3.2.1 Data pretreatment

All the data has to undergo data pretreatment before proceed to further interpretation. The data which showed the sign below or above the detection limits shall be treated by replacing it with mean value because the system is unable to detect these limits. Besides, for the missing values in the data set, one should delete the whole row of data for that particular parameter.

3.2.2 Mann-Kendall trend test

After done the data pretreatment, all these data will undergo further analysis by using Mann-Kendall trend test. As we know, the Mann-Kendall trend test is widely used to detect and assess the significance of a trend. In this study, Mann-Kendall trend test was used to detect the trends of water quality data at Perlis River. The test is based on the correlation between the observations and their time series. The formula for Mann-Kendall trend test is defined as follow,

S = ij ---------(1)

where

aij = sign (xj - xi) = sign (Rj - Ri) = --------(2)

From the equation above, the Ri and Rj are the ranks of observations xi and xj of the time series, respectively. Besides, it can be observed that the test statistic relies only on the ranks of the observations, instead of their actual values. It is so-called distribution-free test statistic. Distribution-free tests are well-known with the ability that their power and significance are not influenced by the actual distribution of the data. In contrast, for the parametric trend test such as the regression coefficient test, we can assume that the data obey to the normal distribution and its power can be greatly influenced by skewed data (Yue et al., 2002).

The mean and variance of the S statistic in Equation (1) above are given by the equations as shown below based on the assumption that the data set are independent and distributed,

E(S) = 0 --------(3)

Vâ‚’(S) = n (n-1) (2n+5) /18 --------(4)

where n is the number of observations. A reduction of the variance of S will be computed when the tied ranks are existed in the data. The equation is given as below

σs = -------(5)

where m is the number of groups of tied ranks and tj is the tied observations. When the number of observations has become larger, the statistic S will be normally distributed as the equation below.

Z = -------(6)

3.2.3 Principal component analysis (PCA)

The water quality data will undergo analysis again by using principal component analysis (PCA) technique with XLSTAT 2010 software. Principal component analysis is known as one of the multivariate analytical methods. It was used in this study to provide information on the most significant parameters which contributed to river pollution problem from the whole data set and help in reducing the redundant parameters with minimum loss of original information (Vega et al., 1998). Besides, the original inter-correlated variables can be transformed into new, uncorrelated variables which called the principal components through the principal component analysis. The principal component can be described as,

zij = pci1 x1j + pci2 x2j + … + pcim xmj

where z is the component score, pc is the component loading, x is the calculated value of the variable, i is the component number, j is the number of sample and m is the total number of variables. Figure 3.3 below showed the summarized methods when analyzing the water quality data at Perlis River.

Figure 3.3 Summarized methods in analyzing water quality data at Perlis River.

CHAPTER 4

RESULT AND DISCUSSION

4.0 Result

4.1 Mann-Kendall trends test

Mann-Kendall trends test was used in this study to detect the trends of water quality at Perlis River. It is a non-parametric statistical method which considered more suitable for water quality data analysis, especially for trends detection and estimation with long term data sets. There were 30 water quality parameters had been analyzed such as dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), Suspended Solid (SS), pH, NH3-NL, Temperature (TEMP), Conductivity (COND), Salinity (SAL), turbidity (TUR), dissolved solid (DS), total solid (TS), nitrate (NO3), chlorine (Cl), phosphate (PO4), arsenic (As), mercury (Hg), cadmium (Cd), cromium (Cr), plumbum (Pb), zinc (Zn), calcium (Ca), ferum (Fe), kalium (K), magnesium (Mg), sodium (Na), oil and grease (OG), MBAS, E-Coli, and coliform according to spatial and temporal trends analysis. On the other hand, 95% confidence level was used to determine the trends and assumptions had been made as below:

H0 : There is no trend present in the data.

Ha : There is an increasing or decreasing trend present in the data.

If the computed p-value was less than alpha = 0.05, the null hypothesis was rejected and the alternative hypothesis was accepted. If the computed p-value was greater than alpha = 0.05, the null hypothesis was accepted and the alternative hypothesis was rejected. Table 4.1 and Table 4.2 below showed the result of the trends analysis in term of spatial and temporal variations.

(a)Spatial trends analysis,

Table 4.1: Trends of 30 water quality parameters for 11 monitoring stations at Perlis River.

Parameter

Station No.

2PS01

2PS02

2PS03

2PS04

2PS05

2PS06

2PS07

2PS08

2PS09

2PS10

DO

NT

NT

NT

NT

NT

NT

NT

NT

NT

BOD

NT

NT

NT

NT

NT

NT

NT

NT

NT

NT

COD

NT

NT

NT

NT

NT

NT

SS

NT

NT

NT

NT

NT

NT

NT

NT

pH

NT

NT

NT

NT

NT

NT

NT

NT

NT

NT

NH3-NL

NT

NT

NT

NT

NT

NT

NT

NT

Temp

NT

NT

NT

NT

NT

NT

NT

NT

NT

NT

Cond

NT

NT

NT

NT

NT

NT

NT

NT

NT

Sal

NT

NT

NT

NT

NT

NT

NT

NT

NT

Tur

NT

NT

NT

NT

NT

NT

NT

NT

NT

DS

NT

NT

NT

NT

NT

NT

NT

NT

NT

TS

NT

NT

NT

NT

NT

NT

NT

NT

NT

NO3

NT

NT

NT

NT

NT

NT

NT

NT

Cl

NT

NT

NT

NT

NT

NT

NT

NT

NT

NT

PO4

NT

NT

NT

NT

NT

NT

NT

As

NT

NT

NT

NT

NT

NT

NT

Hg

NT

NT

NT

NT

NT

NT

NT

NT

NT

NT

Cd

NT

NT

NT

NT

NT

NT

NT

Cr

NT

Pb

NT

NT

NT

NT

NT

NT

NT

NT

NT

NT

Zn

NT

NT

NT

NT

NT

NT

NT

NT

NT

Ca

NT

NT

NT

NT

NT

NT

NT

Fe

NT

NT

NT

NT

NT

NT

K

NT

NT

NT

NT

NT

NT

NT

NT

Mg

NT

NT

NT

NT

NT

NT

NT

NT

NT

NT

Na

NT

NT

NT

NT

NT

NT

NT

NT

NT

NT

OG

NT

NT

NT

NT

NT

NT

NT

NT

NT

NT

MBAS

NT

NT

NT

NT

NT

NT

NT

NT

NT

NT

E-coli

NT

NT

NT

NT

Coliform

NT

NT

NT

NT

NT

*Note: ↑ indicates an upward trend, ↓ indicates a downward trend, NT indicates there is no trend.

(b)Temporal trends analysis,

Table 4.2: Trends of 30 water quality parameters from year 2003 until year 2007 at Perlis River.

Parameter

Year

2003

2004

2005

2006

DO

NT

NT

BOD

NT

NT

NT

COD

NT

NT

NT

NT

SS

NT

NT

NT

pH

NT

NT

NT

NH3-NL

NT

NT

NT

Temp

NT

NT

NT

Cond

NT

NT

Sal

NT

Tur

NT

NT

NT

DS

NT

NT

TS

NT

NT

NT

NO3

NT

NT

Cl

NT

NT

PO4

NT

As

NT

NT

Hg

NT

NT

NT

NT

Cd

NT

Cr

NT

NT

NT

NT

Pb

NT

NT

NT

NT

Zn

NT

NT

NT

Ca

NT

NT

Fe

NT

NT

K

NT

NT

Mg

NT

NT

NT

Na

NT

NT

NT

OG

NT

NT

NT

NT

MBAS

NT

NT

NT

E-coli

NT

NT

Coliform

*Note: ↑ indicates an upward trend, ↓ indicates a downward trend, NT indicates there is no trend.

4.2 Principal Component Analysis (PCA)

Principal Component Analysis (PCA) was used to provide information on the most significant parameters and thus reducing the cost and time which spent on the contamination monitoring. Besides, it allowed the identification of the possible sources that influence water system. The original inter-correlated variables can be transformed into new, uncorrelated variables which called the principal components. PCs with eigenvalues that were greater than 1 will undergo further analysis by using Varimax rotation and it was significant in obtaining Varimax Factors (VFs). Meanwhile, VF coefficients with correlation greater than 0.75 are classified as 'strong', while between 0.75 - 0.50 as 'moderate' and between 0.50 - 0.30 as 'weak'. The VF coefficients that showed strong significant factor loadings will be discussed in this study. Table 4.3 below showed the eigenvalues which have the value greater than 1 while Table 4.4 below showed the factor loadings after Varimax rotation.

Table 4.3: Principal components (PCs) with eigenvalues that were greater than 1.

 

F1

F2

F3

F4

F5

F6

F7

F8

F9

Eigenvalue

8.966

3.659

2.341

2.105

1.460

1.275

1.183

1.126

1.014

Variability %

29.886

12.198

7.804

7.015

4.866

4.249

3.944

3.753

3.380

Cumulative %

29.886

42.084

49.888

56.903

61.769

66.018

69.962

73.715

77.095

Table 4.4: Factor loadings after Varimax rotation.

 

D1

D2

D3

D4

D5

D6

D7

D8

D9

DO

-0.129

-0.059

-0.260

-0.183

-0.202

-0.704

0.110

0.084

-0.124

BOD

-0.009

0.006

0.958

-0.009

-0.018

0.050

-0.023

-0.039

0.028

COD

-0.002

0.002

0.948

-0.001

0.013

0.021

-0.026

-0.001

-0.014

SS

0.027

0.924

-0.042

0.115

0.050

0.008

0.020

-0.015

-0.048

pH

-0.076

-0.237

-0.439

-0.057

0.047

-0.232

0.518

0.245

-0.207

NH3-NL

0.446

0.036

-0.122

0.150

0.083

0.386

-0.214

0.064

0.159

TEMP

0.085

0.081

-0.008

0.038

-0.103

0.815

0.144

-0.005

-0.178

COND

0.995

-0.010

-0.004

0.005

0.003

0.027

-0.004

-0.005

0.000

SAL

0.995

-0.006

-0.003

0.004

-0.006

0.008

-0.008

-0.002

-0.006

TUR

-0.027

0.930

-0.028

0.066

0.083

0.003

0.004

0.042

-0.027

DS

0.996

-0.009

-0.003

0.003

0.001

0.020

-0.002

-0.003

-0.002

TS

0.995

0.027

-0.005

0.007

0.002

0.022

-0.001

-0.005

-0.002

NO3

-0.086

0.161

-0.058

0.016

0.016

-0.019

-0.161

0.741

0.112

Cl

0.990

0.002

-0.012

0.004

0.013

0.021

-0.028

-0.006

0.005

PO4

-0.008

0.411

0.046

-0.062

-0.283

0.240

0.003

0.250

0.501

As

0.444

-0.045

-0.044

-0.103

-0.211

0.185

0.341

0.248

0.146

Hg

0.140

-0.015

0.032

-0.069

0.087

0.088

0.550

0.115

0.111

Cd

-0.034

-0.053

0.023

-0.074

0.738

0.052

-0.014

0.268

0.040

Cr

-0.024

-0.097

-0.068

0.192

-0.106

0.077

0.749

-0.269

0.047

Pb

0.034

0.219

-0.042

-0.082

0.730

-0.015

-0.056

-0.202

0.045

Zn

0.065

0.628

0.207

-0.179

-0.146

-0.011

-0.225

-0.039

0.214

Ca

0.735

-0.148

0.218

0.053

-0.022

0.072

0.335

-0.097

0.067

Fe

-0.097

0.766

0.084

-0.022

-0.003

0.163

-0.175

0.063

0.139

K

0.982

0.021

-0.014

-0.001

-0.009

0.076

0.016

0.029

0.044

Mg

0.982

-0.023

0.010

-0.005

0.002

0.026

0.040

-0.027

-0.014

Na

0.993

0.006

-0.012

0.007

-0.001

0.009

-0.037

-0.011

0.001

OG

-0.033

0.018

-0.003

-0.133

-0.157

0.193

-0.061

-0.020

-0.756

MBAS

-0.020

0.163

0.212

-0.121

-0.099

0.205

-0.089

-0.488

0.418

E-coli

0.059

0.078

0.011

0.930

-0.044

0.065

0.026

0.011

0.013

Coliform

-0.034

0.026

-0.013

0.938

-0.029

0.043

0.043

0.005

0.035

Note: The highlight and bold numbers indicates strong significant factor loadings.

Discussion

From the table 2 above, the results showed that there were 22 out of 30 parameters exhibited an increasing or decreasing trends and the other parameters showed no trend in the spatial trends analysis. Parameters such as DO, SS, NH3-NL, Conductivity, Sal, Turbidity, DS, TS, NO3, PO4, As, Cd, Cr, Ca, K, Mg, E-coli and Coliform had shown an increasing trend at the particular stations but only the Cr, E-coli and coliform showed the most significant trends in this study. On the other hand, parameters such as COD, Zn, Fe and Na had shown a decreasing trend yet there is only COD and Fe showed significant trends. Meanwhile, parameters such as BOD, pH, temperature, Cl, Hg, Pb, OG and MBAS had shown no trend throughout the monitoring stations.

Cr showed a significant upward trend at Station 1 and also from Station 3 until Station 10. It showed no trend at Station 2 and Station 11. This maybe due to the dumping of industrial waste materials significantly increases chromium concentration at the Perlis River. Besides, E-coli showed an upward trend at Station 1, Station 4 to Station 5, and also Station 8 to Station 10. There were no trend shown at Station 2 to Station 3, Station 6 to Station 7 and also Station 11. Whereas Coliform showed an upward trend at Station 1, Station 5 to Station 6, Station 8 to Station 9 and also Station 11. Yet it showed no trend at Station 2 to Station 4, Station 7 and Station 10. Leachate which was leaching out from landfill contained high concentration of nutrient and thus induce the growths of E-coli and coliform.

COD showed a downward trend at Station 3, Station 5, Station 8 and Station10. There were no trend shown at Station 1 to Station 2, Station 4, Station 6 to Station 7, Station 9 and last but not least Station 11. It showed a good sign since lower the COD, lower the amount of pollution at the river. Meanwhile Fe showed a downward trend at Station 5 to Station 6, and Station 9 to Station 10. It showed no trend from Station 1 until Station 4, Station 7 to Station 8 and also Station 11. As we know, human activities especially industries have contributed significantly to the concentration of Fe. Maybe there were a decreasing of production near the downstream area and thus less effluent that contained Fe had been discharged into the river.

Conversely, for the temporal variations, the results showed that there were 27 out of 30 parameters exhibited an increasing or decreasing trend or showed two opposite trend whereas the other parameters showed no trend. Parameters such as DO, SS, Tur, Fe, MBAS and E-coli showed an upward trend. Meanwhile parameters such as BOD, pH, NH3, Conductivity, Sal, DS, TS, Cl, As, Hg, Cr, Ca, K, Mg and Na showed a downward trend. There were parameters which showed two opposite trend such as Temp, NO3, PO4, Cd, Zn and Coliform. The remaining parameters such as COD, Pb, OG had shown no trend from the year 2003 until year 2007.

DO showed an increasing trend at year 2003 and year 2004. DO measurement determines the available molecular oxygen. The higher the DO level indicates that the lower the pollution loadings of high organic or oxidisable matter. Besides, SS showed an upward trend at year 2003 and year 2007. It was believed that the river was experiencing heavy erosion at its river banks and thus contributed to the high suspended solid at Perlis River. Fe showed an upward trend at year 2003 and year 2005. This may due to the effluent which contained high concentration of Fe from industrial area during the year mentioned above. In addition, Sal showed a downward trend from year 2003 until year 2007 except for the year 2005. There were high volume of precipitation during the years and thus the salt water had diluted by continued precipitation. Whereas conductivity showed a downward trend at year 2003, year 2006 and year 2007 due to the decrease of dissolved salts or ions and inorganic dissolved solids which present in the water body.

As overall, the data that sorted by year contained high variation in parameters if compared with the data that sorted by station. For the data that sorted by station, results showed that parameters such as BOD, pH, Temp, Cl, Hg, Pb, OG and MBAS exhibited no trend. While for the data that sorted by year, results showed that only parameters such as COD, Pb and OG exhibited no trend. This may due to the data which displayed by year was less consistent and thus less parameters showed no trend. The location of each station contributed significantly to the consistency of the data. The value of parameter was greatly influenced by the environmental factors at surrounding.

On the other hand, for the result that obtained via principal component analysis (PCA), D1 showed strong positive loadings on conductivity, Sal, DS, TS, Cl, K, Mg and Na. Strong positive loadings on conductivity, Sal, Cl, K, Mg, Na can be attributed to the chemical components of various anthropogenic activities such as industrial, domestic and agricultural runoff whereas strong positive loadings on DS and TS was due to the heavy erosion at Perlis River. Meanwhile D2 showed strong positive loadings on SS, turbidity and Fe. The presence of SS and turbidity can be attributed to the surface runoff and erosion at Perlis River. For the strong positive loadings on Fe, it was due to the effluents from industrial area. D3 showed strong positive loadings on BOD and COD which can be explained as the anthropogenic pollution from industrial, domestic and agricultural sources. Besides, D4 showed strong positive loadings on E-coli and coliform which can be attributed to the leachate from landfill that near the Perlis River. D7 and D9 showed strong positive loadings on Cr and OG respectively. Strong positive loadings on Cr and OG may due to industrial wastes around the area.

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