Water Quality Status For Selected Rivers Biology Essay

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The water chemistry of selected rivers at Kota Marudu, Sabah, were studied based on its major ion chemistry and its suitability for drinking and irrigation propose. This study was conducted for four months included August and November 2009 and also February and March 2010. Ten sampling stations were chosen around Kota Marudu and water samples were collected from each station respectively to assess the water quality. The physical and chemical variable selected were temperature, conductivity, total dissolved solids (TDS), salinity, dissolved oxygen (DO), pH, turbidity, ammoniacal-nitrogen (NH3-N), biological oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solid (TSS). Beside, major elements, Zn, Cu, Mn, Al, Fe, K, Mg, Ca, Cd, Pb and Na were also analysed by ICP-MS in this study. The results show most of rivers in Kota Marudu are still in clean condition and suitable for drinking propose except for Sg. Ragaroh which is considered as slightly polluted. The calculated values of sodium adsorption ratio (SAR) and salinity hazard indicate that the river water is of high quality and suitable for agricultural and irrigation purposes. Based on analyses of heavy metal, the concentration of heavy metals are varies at the different sampling station. The guideline verifies that most of heavy metals are below the permissible limit recommended by World Health Organization (WHO) and Ministry of Health (MOH) except for Pb, Cd, Fe and Al. However, these concentrations are still in acceptable limit. This preliminary study on river water of Kota Marudu was undertaken to provide guidance and baseline data for future references. It also set up recommendations for relevant ministry and government bodies in managing the river water quality.

INTRODUCTION

Water has been known to be an essential for all forms of growth and development based on human, plant and animals. About 97% of the world's water found in the oceans and only 2.5% of the world's water is non-saline freshwater. However, 75% of all freshwater is bound in glaciers and ice caps, 24% are present as groundwater and there is only 1% of freshwater are found in lakes, rivers, and soils (Radojevic and Bashkin, 2006). River is always remarked with biological productivity and also high accessibility by which there are numerous interactions occurs in between the whole ecosystem. There is why river is considered one of the important assets based on their immense biological diversity. These regions are highly. Coastal ecosystems are not only considerable sources of food for both human and animal consumption, as breeding ground and sanctuary for aquatic organism but also sink of contaminant. Nonetheless, the high economical value poses by this ecosystem makes its suitability for aquaculture activity, source of food for sustaining food security, recreation, nature tourism and genetic resources.

A parallel increase between human population and water demand are one of many concerns related to water. As the human population increases, the tendency on freshwater demand based on quality and quantity increases. The major water demand are comes from agriculture, industry as well as domestic sector (DOE, 2007). These sectors generated economic beneficial while led a profound effect on water resources as its produces some unwanted residue that may harm to the environmental resources. In turn, most of these waste will discharged into water such as drainage and river and finally end up in ocean. The quality and quantity of water had deteriorated with such increasing water demands and this directly affects the socio-economic condition of society. Hence, a holistic approach for water quality monitoring and resources management is crucial in order to find adequate supplies and maintaining water quality to maintain a high quality of freshwater in the required quantity at selected placed (Radojevic & Bashkin, 2006).

Due to the scarcity of freshwater such as river, water pollution has become one of the global concerns in many years. Malaysia is one of the renowned ongoing developing countries in south East Asia. As a result of the rapid economic growth in Malaysia over the past two decade, water pollution is becoming more challenging. 97% of water resources in Malaysia are come from river. However, discharging of domestic sewage, manufacturing, pig farming, agriculture production, land clearing and earthworks led the problem of river pollution becoming serious according to Malaysia Quality of Life (2004). These sources of pollution include biological hazards and chemical hazards that will cause serious health hazard to all life of forms. According to World Health Organization (WHO), in developing countries, waterborne disease such as cholera, tyhpoid fever and hepatitis A are among the leading causes to illness and death killing over 3.4 million people every year. This is because, around 1.1 billion people globally do not have access to improved water supply sources whereas 2.4 billion people do not have access to any type of improved sanitation facility. Once the water quality had depleted, the natural resources and overall environment will become unhealthy (Ujang et al, 2008). Consequently, an access to clean river water becomes a critical issue to be overcome by government. In other word, to protect the continuous of freshwater resources, a water quality monitoring program is necessary (Fulazzaky et al., 2010; Pesce and Wunderlin, 2000).

In this study, Kota Marudu, Sabah which is near to estuary is chosen as study area due to polution issue posed by human interference to the natural environment. The Malaysian state of Sabah is situated between latitudes 4°8' and 7°22' north of the equator on the northeastern tip of Borneo with total area of 76,000 km². In Sabah, the main sources of freshwater are estuary and river water, local people are more relies on rivers for domestic and industrial use. River also plays an important role for agricultural practice, for obtained food and for transport that can lend to economic development of Sabah. Like most parts of Malaysia, human activities such as excessive forest cutting, crop cultivation and waste dumping have had a considerable impact on Sabah's rivers in recent decades. There is no exception for Kota Marudu, one example is, the Marudu Bay is polluted by the discharge of nutrient and pollutants from the population of Kota Marudu. Since the majority of the community depend on the water resources to support their livelihood, there is a need to understand both the natural evolution of water chemistry under natural water circular processes and background information of the study area in order to help to improve the condition of water resources and minimizing threats to its ecological balance (Mokhtar et al., 2008). Thus, the water quality was a concerning issues since river water is an important source need to equip to citizens in Kota Marudu.

This study tends to focuses on the assessment the status of river water quality of selected rivers at Kota Marudu. It attempts to identify the critical parameters affecting the quality of water. It also attempt to the determination of the spatial distribution of toxic metals (Zn, Cu, Mn, Al, Fe, K, Mg, Ca, Cd, Pb and Na) concentration in the river water. Finally, the data presented in this study are important to provide baseline data of selected parameters in the Kota Marudu River water for future references.

MATERIAL AND METHODS

Study area

Kota Marudu is located at the southern end of Marudu Bay in the north of Sabah and lie within latitude of 6° 15" to 6° 45" N and the longitude of 116° to 117° E (Figure 1). It is situated in the Kudat Division with an area of 1,917km2 and is one of the main towns other than Kudat and Pitas. In 2009, the population at Kota Marudu is approximately 72,900 with the average population of five per household (Department of Statistics Malaysia, 2010). Kota Marudu is made up by three distinct areas that is valley, coastal area and mountainous terrain and majority of the area is situated in the mountainous area. The study area located within the Crocker Range with two mountains known as Mount Tambayukum (8,462m) and Mount Tendok Sirong (3,315m). In fact, Kota Marudu District is in sedimentary formation made up by sandstone and mudstone.

Sabah has an equatorial climate with uniform temperature, high humidity and copious rainfall due to its proximity to the equator (Malaysian Meteorological Department, 2011). On average, Sabah receives about 2500-3500 mm of rainfall annually in most part of the state. The chosen study area is influenced by two monsoon periods; northeast monsoon blows approximately from November to April and southwest monsoon that usually occurs between May to September where it is much more influenced by northeast monsoon and bring heavy rainfall to its particular area.

Figure 1: Map of sampling stations in Kota Marudu

Field Sampling and preservation

Ten sampling stations were chosen within the study area (Figure 1) and the exact longitude and latitude of the sampling locations are recorded by Global Positioning System (GPS) techniques (Table 1). The selection of these ten sampling stations were based upon the observed possibility of contamination from domestic waste discharge and on the practicability of collecting samples The sampling were carried out on August and November 2009 and continued on February and March 2010 which include both northeast and southwest monsoon.

Before sampling, all the laboratory apparatus and polyethylene bottle are pre-cleaned with acid washed by soaked overnight in 5 % (v/v) nitric acid and rinsed thoroughly first with distilled water. This procedure is very crucial in order to ensure any contaminants and traces of cleaning reagent were removed before the analysis (APHA, 2005). It is performed in clean laboratory to minimize the potential risk of contamination. Polyethylene bottles were used for collecting water sample in order to avoid and minimize interference for heavy metal analysis. For BOD analysis, water sample was store in the BOD bottle that warped by aluminium foil. Afterward, the collected samples were stored in the ice box with approximately 4áµ’C to minimize the microbial activity in the water (APHA, 2005). During sampling, the polyethylene bottle were normalized with river water and then filled up with water running in the direction of flow. Triplicate samples were collected and homogenized from each sampling station in order to obtain an average value for the analysis. Each bottle was labelled with its corresponding sampling station and time of sampling was recorded.

Table 1: The longitude and latitude of the sampling stations

STATION

LOCATION

LATITUDE

LONGITUDE

M1

Sg. Manggaris

06°31.842'N

116°41.252'E

M2

Sg. Langkon

06°31.703'N

116°42.869'E

M3

Sg. Sungoi

06°30.479'N

116°42.744'E

M4

Sg. Ragaroh

06°29.886'N

116°43.314'E

M5

Unknown

06°29.770'N

116°45.359'E

M6

Sg. Taritipan

06°33.345'N

116°57.290'E

M7

Sg. Rasak

06°31.409'N

116°50.837'E

M8

Sg. Tandek

06°31.693'N

116°50.584'E

M9

Sg. Rakit

06°29.649'N

116°48.373'E

M10

Sg. Sumbilingan

06°29.704'N

116°47.201'E

Water Analysis

Certain basic water quality parameters included in-situ parameters (temperature, conductivity, total dissolved solids (TDS), salinity, dissolved oxygen (DO) and pH), turbidity, ammoniacal-nitrogen (NH3-N), biological oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solid (TSS) were taken into account for measurement. The measurement of in-situ parameters was done immediately during each field work by using multiparameters probe (Orion Star Series Portable Meter) except for turbidity measurement which was carried out by potable turbidity meter. Besides, BOD (5 days incubation at 20°C), COD and TSS were analyzed on site using unfiltered samples accordance with the standard method procedure (APHA, 2005; HACH, 2003). The NH3-N was determined using spectrophotometer at a specified wavelength (Hach Method 8038) while COD with APHA 5220B open refluxed techniques and TSS with APHA 2540D method (APHA, 2005; HACH, 2003).

For heavy metal analysis, about 500mL water samples were filtered through 0.45µm cellulose nitrate membrane filter paper and acidified with nitric acid (HNO3) to pH <2 and stored in HDPE containers (APHA, 2005). This process is crucial to obtain dissolved metal which is always smaller than 0.45 µm and also avoiding the occurrence of clogging during analysis with spectrometry instrument. The samples were analyzed for Zinc (Zn), Copper (Cu), Manganese (Mn), Aluminum (Al), Iron (Fe), Potassium (K), Magnesium (Mg), Calcium (Ca), Cadmium (Cd), Lead (Pb), and Sodium (Na) using Inductive Couple Plasma Mass Spectrometry (ICP-MS) (Perkin Elmer ELAN DRC-e). Average values of three replicates were taken for each analysis. Standard calibration solutions and blank sample were prepared with MilliQ water. These toxic metals in the water will be expressed as microgram per liter (μg/L).

Data analysis

Further data analyses were conducted using the raw data obtained from the sample analysis. The obtained data were analyzed using software SPSS Statistic ver. 17 in order to describe the descriptive statistic and also verify the relationship between various environmental matrices. It provides useful generalization about water quality for both either physical parameter or heavy metal analysis such as the mean and average concentration of the water sample. Correlation coefficient and One-Way ANOVA were applied in order to indicate the sufficiency of one variable to predict to other and also to split the selected variables and sampling points into finite number of groups with similar hydrogeochemical composition (Davis, 1986). All obtained results were compared with the permissible limit recommended by Ministry of Health (MOH, 2004) and World Health organization (WHO, 2004) for drinking water quality.

Results and discussion

The descriptive analysis for raw data obtained from different sampling period and stations are shown in Table 2. Table 3 interpret the results for selected physico-chemical parameters and also heavy metals concentrations in water samples by compared with Drinking Water Quality by World Health organization (WHO, 2004) and the Malaysian National Standard for Drinking Water Quality (NSDWQ) by the MOH (2004) while Table 4 presents the correlation coefficient between various water matrices. There is no significant difference (p>0.05) observed in pH among the stations. The result revealed that the most of river water in different stations were slightly acidic to slightly alkaline condition with pH mean value ranging from 6.87 to 7.66. The observed values were under the permissible limit for drinking water standard recommended by WHO (2004). This alkaline value normally indicates the presence of carbonate magnesium and calcium in water (Reza and Singh, 2010; Begum et al., 2009; Connell and Miller, 1984). These two elements are abundance in earth's crust and can be found that their concentration is indeed higher with other element such as Pb and Mn (Table 3).

The temperatures of the water vary between different sampling stations. The lowest temperature, 27.6 °C was recorded at station M6 while highest of 30.3°C obtained from station M5. The temperature recorded among all sampling stations were slightly high since the sampling were conducted during the shinning day and the weather is hot. Dissolved oxygen fluctuated between 4.24 to 6.34 mg/L was noticed the DO concentration was decreasing with increasing of temperature (Figure 2). This can be concluding that the temperature is inversely control the solubility of oxygen in water and support by significant relationship between these two variables (r= 0.776, p<0.05). Except temperature factor, microorganism' activity will also consumed oxygen in order to undergone metabolism or decay organic matter (Yang et al., 2007). Domestic wastes such as organic pollutant from community discharged were also depleted DO in water with increase the COD (Clark, 1996). This study showed that DO in rivers of Kota Marudu was significantly negative correlated with both AN (r=-0.719, p<0.05) and also COD (r= -0.643, p<0.05). These results indicated that the main contributing factors for dissolved oxygen (DO) are the present of decomposition rate of organic matter (COD and ammoniacal-nitrogen) and also weather (temperature). The concentration of COD in this study are ranged between 2.00 to 25.25 mg/L with the mean concentration was 11.45mg/L. Two ways ANOVA was indicated that there are significant different value of COD between the station (p<0.05). The high COD value in station M 10 could have occurred due to high rate of organic decomposition resulting from human activities also. Turbidity indicates value ranged from 8.40 NTU to 80.00 NTU. Correlation analysis showed that turbidity has a perfect positive linear relationship with TSS (r= 1.000, p<0.01). The highest value of turbidity (80.00 NTU) and TSS (64.01 mg/L) were recorded at station M10 which indicated the increase of turbidity with increased of TSS. BOD, COD, TSS and turbidity with high value were always marked as pollution indicator. From this study, it was found that there is a direct relationship between BOD, COD, TSS and turbidity which these four parameters posed a similar trend (Figure 3) among stations as each parameter increase with other increase. This was also supported by significant positive relationship between BOD and COD (r = 0.754; p< 0.05), BOD and TSS (r = 0.813; p< 0.01), BOD and turbidity (r = 0.815; p< 0.01), COD and TSS (r = 0.823; p< 0.01), COD and turbidity (r = 0.818; p< 0.01) and also TSS and turbidity (r = 1.000; p< 0.01).

Electrical conductivity is measurement that indicate the the ability of water sample to allow electric current to flow and TDS is a good indicator on amount of dissolved ions in all the sampling station (Reza and Singh, 2010). The relative difference in water ion constituent was represented by different value of EC from each station. Subsequently, the higher the dissolved salt content present in water, the higher the EC value obtained. The mean variations in electrical conductivity, salinity and total dissolved solids followed a similar trend From Figure 4; it showed that there is a direct relationship between EC, TDS and also salinity. These three variables showed significant differences in their concentrations among the stations. This was supported by strong significant value between EC and TDS (r = 0.915; p< 0.01), TDS and salinity (r = 0.996; p< 0.01), and also EC and salinity (r = 0.927; p< 0.01). The mean variations in EC, TDS and salinity followed similar trend. The EC mean values ranged from 0.14 to 1.08 mS/cm at ten different stations, salinity mean value range from 0.07 to 0.51o/oo while TDS mean values ranged from 0.10 to 0.65 mg/L. The low electrical conductivity might due to the nature characteristic of the river itself. High value of conductivity might responsible for the heavy rainfall during monsoon season which increased the concentration of Na and Mg. This was supported by both strong relationship of EC with Na (r = 0.982; p< 0.01) and also Mg (r= 0.899; p<0.01). However, high value of EC might be also responsible for the contribution of high turbidity, BOD, COD and TSS at related sampling station with. This was indicated by significant relationship between EC and turbidity (r= 0.765; p< 0.05), BOD (r = 0.860; p< 0.01), COD (r=0.643; p<0.05) and TSS (r= 0.753; p<0.05) (Table 4; Figure 5). It means that the related station (M10) might be exposed to human-induced activities which produce domestic waste and agricultural run-offs into the river.

Figure 2: Temperature (°C) and DO (mg/L) of rivers at Kota Marudu accordingly to each sampling station

Figure 3: BOD (mg/L), COD (mg/L), TSS (mg/L) and turbidity (NTU) of rivers at Kota Marudu accordingly to each sampling station

Figure 4: Electrical conductivity (mS/c), total dissolved solid (mg/L) and salinity (o/oo) of rivers at Kota Marudu accordingly to each sampling station

Figure 5: EC (mS/c), BOD (mg/L), COD (mg/L) and TSS (mg/L) of rivers at Kota Marudu accordingly to each sampling station

Table 2: Statistical variation (minimum, maximum, range, mean and standard deviation,) among various water matrices according to different sampling period

Parameter

Unit

M1

M2

M3

M4

M5

M6

M7

M8

M9

M10

Temp

°C

Min

26.29

27.53

23.26

26.26

27.19

26.25

26.86

27.45

28.14

27.69

Max

33.01

31.56

32.90

29.71

34.69

28.94

30.19

28.60

29.79

30.75

Mean ± SD

30.25±

2.830

29.51±

2.086

28.24±

3.952

27.84±

1.647

30.29±

3.225

27.55±

1.136

28.35±

1.548

27.93±

0.496

28.85±

0.818

28.98±

1.285

EC

mS/cm

Min

0.19

0.26

0.18

0.17

0.00

0.22

0.15

0.24

0.15

0.16

Max

0.32

0.44

0.23

0.21

0.23

0.28

0.19

0.66

0.17

3.32

Mean ± SD

0.24±

0.055

0.33±

0.084

0.21±

0.024

0.18±

0.020

0.14±

0.101

0.25±

0.025

0.17±

0.018

0.37±

0.200

0.16±

0.007

1.08±

1.509

TDS

mg/L

Min

0.11

0.16

0.11

0.10

0.09

0.14

0.09

0.15

0.10

0.10

Max

0.18

0.28

0.14

0.31

0.13

0.17

0.11

0.40

0.97

2.02

Mean±SD

0.14±

0.028

0.20±

0.055

0.12±

0.014

0.16±

0.104

0.11±

0.016

0.16±

0.014

0.10±

0.010

0.22±

0.120

0.32±

0.435

0.65±

0.920

Salinity

o/oo

Min

0.08

0.11

0.08

0.07

0.06

0.10

0.06

0.11

0.07

0.07

Max

0.13

0.20

0.10

0.10

0.09

0.12

0.08

0.30

0.70

1.61

Mean±SD

0.10±

0.021

0.14±

0.042

0.09±

0.010

0.08±

0.014

0.08±

0.014

0.11±

0.010

0.07±

0.010

0.17±

0.090

0.23±

0.315

0.51±

0.739

Dissolved oxygen

mg/L

Min

3.80

3.96

3.91

3.39

5.57

3.47

3.72

3.14

3.62

2.47

Max

8.39

6.09

6.37

5.09

7.16

5.77

6.56

6.80

6.78

7.26

Mean±SD

6.34±

1.956

5.10±

1.149

5.34±

1.167

4.24±

0.697

6.00±

0.776

4.43±

0.973

5.25±

1.452

4.55±

1.570

4.99±

1.334

4.24±

2.133

pH

-

Min

7.15

7.12

7.09

6.73

6.86

6.93

6.83

6.60

7.23

6.58

Max

8.39

7.79

7.79

7.79

7.55

7.37

7.16

7.12

7.80

7.45

Mean±SD

7.66±

0.539

7.38±

0.293

7.35±

0.319

7.22±

0.436

7.19±

0.326

7.15±

0.196

7.04±

0.147

6.87±

0.263

7.43±

0.264

7.08±

0.394

Turbidity

NTU

Min

5.78

5.11

5.35

11.40

4.57

4.65

21.20

9.11

19.70

18.80

Max

16.40

12.50

16.10

17.10

24.50

59.20

69.90

52.70

61.20

219.00

Mean±SD

9.92±

4.581

8.40±

3.208

10.74±

4.412

14.25±

2.649

10.20±

9.607

19.81±

26.299

39.25±

21.174

28.05±

19.080

46.53±

18.959

80.00±

93.273

AN

mg/L

Min

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

Max

0.04

0.08

0.03

0.36

0.04

0.14

0.07

0.23

0.08

0.12

Mean±SD

0.02±

0.023

0.03±

0.034

0.01±

0.015

0.13±

0.168

0.01±

0.020

0.04±

0.067

0.02±

0.034

0.07±

0.109

0.03±

0.038

0.06±

0.049

COD

mg/L

Min

0.00

1.00

0.00

6.00

1.00

2.00

2.00

0.00

7.00

7.00

Max

13.00

9.00

5.00

20.00

25.00

25.00

24.00

31.00

17.00

42.00

Mean±SD

3.50±

6.351

4.00±

3.559

2.00±

2.449

12.00±

5.831

10.75±

11.087

16.50±

10.214

15.00±

9.309

13.50±

14.617

12.00±

4.761

25.25±

14.408

BOD

mg/L

Min

0.15

0.03

0.69

0.16

0.14

0.09

0.01

0.16

0.13

0.79

Max

1.12

0.80

0.90

2.34

1.07

2.28

1.92

0.95

1.65

3.36

Mean±SD

0.69±

0.493

0.48±

0.403

0.77±

0.112

1.01±

1.169

0.57±

0.468

0.90±

1.199

0.83±

0.982

0.58±

0.397

0.78±

0.784

2.09±

1.285

TSS

mg/L

Min

4.62

4.09

4.28

9.12

3.66

3.72

16.96

7.29

15.76

15.08

Max

13.12

10.00

12.88

13.68

19.60

47.36

55.92

42.16

48.96

175.20

Mean±SD

8.44±

4.314

6.72±

2.567

8.59±

3.529

11.40±

2.119

8.16±

7.685

15.85±

21.040

31.40±

16.939

22.44±

15.264

37.22±

15.167

64.01±

74.609

(Temp, temperature; EC, electrical conductivity; TDS, total dissolved solid; DO, dissolved oxygen; AN, ammoniacal-nitrogen; COD, chemical oxygen demand; BOD, biological oxygen demand; TSS, total suspended solid)

Table 3: Average value for water matrices according to sampling stations

Parameter

Temp

EC

TDS

Salinity

DO

pH

Turbidity

AN

BOD

COD

TSS

Na

Unit  

°C

mS/cm

mg/L

o/oo

mg/L

-

NTU

mg/L

mg/L

mg/L

mg/L

µg/L

Min

27.55

0.14

0.10

0.07

4.24

6.87

8.40

0.01

0.48

2.00

6.33

5882.50

Max

30.29

1.08

0.65

0.51

6.34

7.66

80.00

0.13

2.09

25.25

64.01

63250.00

Mean ± SD

28.78 ± 14.619

0.31 ± 0.227

0.22 ± 0.149

0.16 ± 34.184

5.05 ± 1.235

7.23 ± 18.690

26.71 ± 20.896

0.04 ± 0.529

0.87 ± 7.267

11.45 ± 14.505

21.21± 18.497

14353.0± 17906.690

WHO (2004)

NA

1.5

1000

NA

NA

6.5-9.2

NA

NA

NA

NA

NA

200 000

MOH (2004)

NA

NA

1000

NA

NA

6.5-9.0

5

NA

NA

NA

NA

200 000

Parameter

Pb

Cd

Ca

Mg

K

Fe

Al

Mn

Cu

Zn

Unit  

µg/L

µg/L

µg/L

µg/L

µg/L

µg/L

µg/L

µg/L

µg/L

µg/L

Min

20.80

2467.50

2220.00

4095.00

1510.00

755.00

4360.00

8.08

0.15

35.00

Max

688.25

7581.33

16626.00

23480.00

10901.50

3693.75

49167.25

305.00

635.00

1614.50

Mean ± SD

139.00 ± 224.178

4575.00 ± 1844.779

5453.00 ± 4112.963

9038.00 ± 5744.046

3232.00 ± 2812.122

1788.00 ± 863.469

9919.00 ± 13851.884

77.53 ± 97.988

109.00 ± 224.872

231.00 ± 488.178

WHO (2004)

10

3

200 000

150 000

200 000

NA

200

400

2000

3000

MOH (2004)

10

3

NA

150 000

NA

300

200

100

1000

3000

(NA, no available standard; temp, temperature; EC, electrical conductivity; TDS, total dissolved solid; DO, dissolved oxygen; AN, ammoniacal-nitrogen BOD, biological oxygen demand; COD, chemical oxygen demand; TSS, total suspended solid)

Table 4: Pearson correlation coefficient (r) between water quality parameters and heavy metals

Temp

EC

TDS

Sal

DO

pH

Tur

AN

BOD

COD

TSS

Na

Pb

Cd

Ca

Mg

K

Fe

Al

Mn

Cu

Zn

Temp

1.000

0.949

0.948

0.892

0.008**

0.098

0.696

0.135

0.674

0.354

0.669

0.966

0.941

0.617

0.316

0.810

0.355

0.174

0.384

0.602

0.056

0.555

EC

0.023

1.000

0.000**

0.000**

0.193

0.410

0.011*

0.565

0.000**

0.045*

0.012*

0.000**

0.910

0.549

0.932

0.000**

0.465

0.742

0.965

0.953

0.569

0.922

TDS

0.024

0.915

1.000

0.000**

0.149

0.600

0.001**

0.525

0.002**

0.031*

0.001**

0.000**

0.730

0.936

0.653

0.000**

0.651

0.399

0.870

0.864

0.508

0.906

Sal

0.050

0.927

0.996

1.000

0.191

0.583

0.001**

0.659

0.002**

0.033*

0.001**

0.000**

0.734

0.835

0.666

0.000**

0.644

0.427

0.875

0.723

0.536

0.906

DO

0.776

-0.449

-0.491

-0.451

1.000

0.069

0.161

0.019*

0.122

0.045*

0.148

0.174

0.749

0.575

0.630

0.123

0.937

0.321

0.807

0.239

0.171

0.972

pH

0.552

-0.294

-0.190

-0.198

0.596

1.000

0.286

0.333

0.493

0.037*

0.265

0.261

0.645

0.912

0.347

0.447

0.749

0.354

0.425

0.973

0.983

0.569

Tur

-0.142

0.756

0.873

0.877

-0.479

-0.374

1.000

0.709

0.004**

0.004**

0.000**

0.006**

0.337

0.867

0.191

0.001**

0.769

0.079

0.393

0.456

0.327

0.430

AN

-0.507

0.207

0.229

0.160

-0.719

-0.342

0.136

1.000

0.357

0.298

0.699

0.469

0.758

0.693

0.578

0.775

0.897

0.356

0.671

0.021*

0.338

0.754

BOD

-0.153

0.860

0.846

0.838

-0.521

-0.246

0.815

0.327

1.000

0.012*

0.004**

0.001**

0.443

0.993

0.340

0.003**

0.756

0.297

0.380

0.950

0.361

0.436

COD

-0.329

0.643

0.677

0.673

-0.643

-0.663

0.818

0.366

0.754

1.000

0.003**

0.021*

0.504

0.752

0.151

0.011*

0.544

0.152

0.222

0.760

0.650

0.314

TSS

-0.155

0.753

0.871

0.874

-0.493

-0.390

1.000

0.140

0.813

0.823

1.000

0.006**

0.347

0.834

0.194

0.001**

0.776

0.080

0.398

0.471

0.336

0.439

Na

-0.016

0.982

0.914

0.926

-0.466

-0.393

0.799

0.260

0.866

0.713

0.797

1.000

0.592

0.527

0.608

0.001**

0.711

0.475

0.719

0.839

0.583

0.758

Pb

0.027

-0.041

-0.125

-0.123

-0.116

0.167

-0.339

-0.112

-0.274

-0.240

-0.333

-0.194

1.000

0.920

0.001**

0.934

0.010*

0.007**

0.002**

0.301

0.703

0.000**

Cd

0.181

0.216

0.029

0.076

0.203

-0.040

-0.061

-0.143

-0.003

0.115

-0.076

0.228

0.037

1.000

0.900

0.574

0.837

0.829

0.716

0.203

0.928

0.683

Ca

0.354

-0.031

-0.163

-0.156

0.174

0.333

-0.451

-0.201

-0.338

-0.490

-0.448

-0.185

0.874

-0.046

1.000

0.715

0.000**

0.000**

0.000**

0.212

0.369

0.000**

Mg

-0.088

0.899

0.930

0.943

-0.520

-0.272

0.874

0.104

0.825

0.759

0.872

0.880

0.030

0.203

-0.133

1.000

0.679

0.491

0.901

0.578

0.381

0.989

K

0.328

0.262

0.164

0.168

-0.029

0.116

-0.107

-0.047

-0.113

-0.219

-0.104

0.135

0.763

-0.075

0.904

0.150

1.000

0.019*

0.000**

0.204

0.538

0.000**

Fe

0.467

-0.120

-0.301

-0.283

0.350

0.329

-0.580

-0.327

-0.367

-0.488

-0.577

-0.256

0.783

0.079

0.916

-0.247

0.718

1.000

0.007**

0.386

0.085

0.007**

Al

0.310

0.016

-0.059

-0.057

0.089

0.285

-0.304

-0.154

-0.312

-0.424

-0.301

-0.130

0.847

-0.132

0.964

-0.045

0.957

0.788

1.000

0.213

0.576

0.000**

Mn

-0.188

-0.021

-0.062

-0.129

-0.410

0.012

-0.267

0.713

0.023

-0.111

-0.259

-0.074

0.364

-0.440

0.433

-0.201

0.440

0.309

0.432

1.000

0.993

0.184

Cu

0.621

-0.205

-0.238

-0.223

0.469

0.008

-0.346

-0.339

-0.324

-0.164

-0.340

-0.198

0.138

-0.033

0.319

-0.312

0.222

0.570

0.202

-0.003

1.000

0.599

Zn

0.213

0.036

-0.043

-0.043

-0.013

0.205

-0.282

-0.114

-0.278

-0.355

-0.276

-0.112

0.896

-0.148

0.960

-0.005

0.950

0.788

0.991

0.457

0.190

1.000

(Temp, temperature; EC, electrical conductivity; TDS, total dissolved solid; sal, salinity; DO, dissolved oxygen; tur, turbidity; AN, ammoniacal-nitrogen; BOD, biological oxygen demand; COD, chemical oxygen demand; TSS, total suspended solid)

Ion concentration and distribution

The location of Kota Marudu is more indentified as rural area and lack of development, the concentration of heavy metal may come from natural factors other than anthropogenic input Metal concentrations in water is a good indicator of the degree of river contamination. However, as part of the natural biogeochemical cycle, metals are released from rocks by weathering processes, are cycled through various environmental compartments by biotic and abiotic process. Background levels of trace metal in rocks are controlled by the abundance of the common rock-forming minerals (Garrett, 2000). The water chemistry may influence by interactions of water with rocks and their weathering products.

The major ion chemistry of the rivers at Kota Marudu were dominated by Na+K > Mg > Ca based on ternary diagram (Figure 6). Ca, Mg, Na and K are known as major cations and its' constitute more than 30% of total element content of the earth's crust (Alloway, 1995). These four elements are considered as relatively soluble cations which are easily leached by water due to rock weathering processes. Hence, they were found to be significantly higher in the rivers at Kota Marudu. The ions concentration of Na, Mg and K collected from different sampling stations shows no significant differences (p> 0.05) among the sampling station. This suggests that their concentration where found within the natural levels and may be origin from weathering of rocks. For Ca, the concentration of Ca in this study was much higher compared with baseline data from Maliau basin which is pristine area (Mokhtar et al., 2008). There is a significant different (p< 0.05) between the Ca concentration with different sampling time. This suggest that the Ca may not only origin from dissolution or weathering of calcite rock as hydrogen ion will react with calcium carbonate (CaCO3) and produce Ca+ and HCO3- (Bradl, 2005) but also another anthropogenic input such as fertilizer that been used on palm oil plantation which located at near with sampling station. This can be supported by a studied done by Garret (2000) stated that particularly calcium ions that form certain sedimentary rock phosphate mined is from the production of agricultural fertilizers such as monocalcium phosphate (Ca(H2PO4)2), calcium cyanamide (CaCN2), calcium nitrate (Ca(NO3)2). However, the average concentration of for these four elements at each station shows that the concentrations are still under the permitted level recommended from WHO drinking water standard and also MOH respectively.

Statistical analysis showed that the metal concentrations were significantly different between sampling stations. It was observed that there were trend of decreasing concentrations from highest value of Na followed by other ten metals (Al, Mg, Ca, Cd, K, Fe, Zn, Pb, Cu and Mn) analysed (Figure 7). Except for Na, Mg, Ca and K, elements such as Al and Fe are also considered as abundant elements in the earth crust (Alloway, 1995). Al is the third most abundant element making up about 8% of the earth' surface and the natural process far outweigh the contribution of anthropogenic sources (WHO, 2004). Thus, these element found to be higher concentration in rivers at Kota Marudu. Overall, the heavy metals concentration from river waters at Kota Marudu were low except Al, Fe, Pb and Cd. These four elements found to be exceeded the permissible limits set by both WHO and MOH within the overall result analysed. The concentration of Al in this study had exceeded the permitted limit for drinking water standard by WHO (2004) and MOH (2004) regarding to the average value that ranged from 4360.00 to 49167.25 µg/L with the mean is 9919µg/L. This high concentration may cause a bitter taste and has a little indication that orally ingested Al is acutely toxic to humans. The daily uptake of Al at higher concentration has been hypothesized as a risk factor for the development or acceleration of onset of alzheimer disease in human (WHO, 2004). The variation of Fe concentrations in this study were ranged between 755.00 to 3693.75µg/L which had exceeded the permissible limit (300µg/L) set by MOH. There was a significant different for all the recorded value with (p<0.05). The present of iron in this study may not only due to the geological weathering (Dai and Martin, 1995) but also anthropogenic source. The high concentration of Fe can be observed through the colour of water sample collected which is more reddish because when ferrous iron exposed to atmosphere, it will oxidizes to ferric iron and giving an objectionable reddish-brown colour to the water (WHO, 2004). The concentration of Cd in this study were ranged from 2467.50 to 7581.33 µg/L. The possible source of Cd in station M1 are from the human activities and natural environment based on the geological charactheristic of this station that have many distribution of rock. According to GESAMP, (1987) weathering and erosion result in the transport by river of large quantities of cadmium to the world's ocean and this represents a major flux of the global cadmium cycle. Used of of fertilizer on the palm oil plantation and banana plantation that near to the sampling point may also contribute Cd due to Cd will remains in the product of fertilizer manufacture, and may be added to agricultural lands with the P required to foster plant growth (Garret, 2007). The concentrations of lead (Pb) in this study were ranged from 20.80 to 688.30 µg/L. Pb is used as the metal in alloys and in compounds such as in paint, glazes and petrol. Possible contributor of Pb concentration in this study may come from the usage of petrol from boating activities (Ebrahimpour and Mushrifah, 2008) since people in Kota Marudu tend to using boat as their transport and also fishing activity.

Figure 6: Ternary diagram of the water samples from rivers at Kota Marudu

Figure 7: Metal distribution accordingly to its sampling stations

Based on the selected parameters

Suitability on irrigation

The suitability of water for use in agricultural irrigation was largely depending on water quantity and quality. For determination of suitability for irrigation purpose, salinity hazard and sodium absorption ratio (SAR) were always considered as prior the determining factors. Normally, the parameters used for measure water salinity are total dissolved solids (TDS) or electrical conductivity (EC). For salinity hazard, the water was categorized as low-salinity water (<250µS/cm), medium-salinity water (250-750 µS/cm), high-salinity water (750-2250 µS/cm) and very high-salinity water (>2250 µS/cm) based on EC values (Mokhtar et al, 2008; Nishanthiny et al., 2010). SAR is a ratio of the sodium (detrimental element) to the combination of calcium and magnesium (beneficial elements) in relation to known effects on soil dispersibility. It was used to characterize the relative sodium status of irrigation water. The SAR value is calculated using the equation as follow:

where [Na+], [Ca2+], and [Mg2+] are the concentrations in milliequivalents per liter (meq/L) of sodium, calcium, and magnesium ions in the water. The concentration of these three cations are determined by analysed using atomic adsorption spectrometry (AAS) after filtered through a 0.45µm membrane filter paper and preserved with concentrated acid nitric (HNO3) as described by APHA (2005). In general, the SAR of irrigation water is greater if the water has higher concentration of salt and poses a salinity hazard. Table 5 list the general guideline and explanation for assessment of SAR of irrigation water. It is important to note that irrigation water hazard levels and soil salinity hazard levels are not equivalent.

Table 5: General guideline and explanation for assessment of SAR of irrigation water

Class

SAR

Explanation

Low

<10

Can be used for irrigation on almost all soils with little danger of developing harmful levels of sodium

Medium

10-18

May cause alkalinity problem in fine-textured soils under low-leaching conditions. It can be used on coarse-textured soils with good permeability

High

18-26

May produce alkalinity problem. This water requires special soil management such as good drainage, heavy leaching and possibly the use of chemical amendments such as gypsum.

Very high

>26

Usually unsatisfactory for irrigation purposes

Based on EC values out of the ten selected sampling stations at Kota Marudu, 60% of the sampling stations have low salinity of water, 30% of medium and 10% of the sampling stations have high salinity water (Figure 8). The low salinity of water type can be used for irrigation on most crops in most soils while high salinity water were not suitable be used on soils with restricted drainage. However, the SAR values for all sampling stations were classified as low which the water is suitable for irrigation. A Wilcox diagram (Figure 8) was constructed based on both salinity hazard and SAR values from ten sampling station respectively. From the Wilcox diagram, it was clearly showed that the suitability of water for use in agricultural irrigation was grouped into two major groups. This was supported with cluster analysis with two main clusters which are identified from 10 sampling stations- Group 1 and Group 2. From the CA result, Group 1 includes sampling stations M1 to M9, representing the water quality having similar condition. Group 1 was further divided into two groups - Group 1:i and Group 1:ii. Sampling stations in Group 1:i have similar hydrochemistry condition but Group 1:ii poses slightly higher of salinity hazard and SAR values from other eight stations. Group 2 contain only station M10 which pose high salinity hazard compared with the rest of sampling station revealing the river water having major impact from pollution loading discussed previously.

Figure 8: Wilcox diagram of irrigation water accordingly to its sampling stations

Figure 9: Dendrogram groups of sampling stations determined in Cluster Analysis

Conclusion

In this study, the water quality assessment of Kota Marudu was identified. From the analyses of heavy metal (Na, Pb, Cd, Mg, K, Fe, Al, Mn, Cu, Zn, and Ca), it is found that the concentration values of those heavy metal are varies at the different sampling station. The concentrations are not consistent between the different times of sampling even at the same sampling station. Compare to drinking water standard, the result of heavy metal concentration in this study indicate that most of the heavy metal were below the limit set by WHO (2004) and MOH (2004) except for Pb, Cd, Fe and Al. However, the concentration levels of the heavy metals are still in acceptable limit. The possible causes of the increasing the heavy metal concentration are because of the anthropogenic activity by the people surrounded the sampling location especially the use of fertilizer and petrol for boating purpose beside the natural process. Hence, the water quality of river is not only control by the natural environment process but also influenced with the anthropogenic activities. The chemical assessment of water shows that for irrigation use the water is suitable except M10 which pose different hydrochemical characteristic with other stations.

The data presented in this study are important because they give baseline data of selected parameters in the Kota Marudu River water for future references. In general, the river water of the Kota Marudu is suitable for drinking, domestic use and also irrigation purpose. However, the chemical treatments are recommended for drinking water uses. In order to create a proper management and better understanding to the river water at Kota Marudu, continuing to collect and re-evaluate water chemistry data also possible in order to observed long term pattern and for better understanding the process causing the change. This type of practical study may be particularly very useful for Kota Marudu due to the population increasing from year to year.

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