Bahan Kualiti Air

Water Resources Publication No.
21
STUDY ON THE RIVER WATER QUALITY TRENDS

AND INDEXES IN PENINSULAR MALAYSIA
2009
WATER RESOURCES MANAGEMENT AND HYDROLOGY DIVISION

DEPARTMENT OF IRRIGATION AND DRAINAGE
MINISTRY OF NATURAL RESOURCES AND ENVIRONMENT
MALAYSIA
Water Resources Publication No. 21
STUDY ON THE RIVER WATER QUALITY TRENDS

AND INDEXES IN PENINSULAR MALAYSIA
Price: RM 20.00
WATER RESOURCES MANAGEMENT AND HYDROLOGY DIVISION
DEPARTMENT OF IRRIGATION AND DRAINAGE
MINISTRY OF NATURAL RESOURCES AND ENVIRONMENT
MALAYSIA
DISCLAIMER
Although every effort and care has been taken in selecting the methods and
proposing the recommendations that are appropriate to Malaysian conditions, the
user is wholly responsible to make use of this water resources publication. The use
of this Manual requires professional interpretation and judgment to suit the
particular circumstances under consideration.
The department or government shall have no liability or responsibility to the user or

any other person or entity with respect to any liability, loss or damage caused or
alleged to be caused, directly or indirectly, by the adoption and use of the methods
and recommendations of this publication, including but not limited to, any
interruption of service, loss of business or anticipatory profits, or consequential
damages resulting from the use of this publication.
i
ACKNOWLEDGEMENTS
The Water Resources Management and Hydrology Division of the Department of

Irrigation and Drainage (DID), Ministry of Natural Resources And Environment
Malaysia would like to express sincere appreciation to Abad Consultants Sdn.
Bhd. and Dr. Abdullah Al-Mamun in preparing this report. Valuable contributions
and feedbacks from DID, the Department of Chemistry and the Department of
Environment Malaysia are greatly acknowledged.
ii
iii
TABLE OF CONTENTS
Page
Disclaimer i
Acknowledgement ii
Table Of Contents iv
List of Tables vi
List of Figures vii
CHAPTER 1: INTRODUCTION
1.1 Background 1
1.2 Objectives 1
1.3 Scope of Work 1
CHAPTER 2: STUDY AREA AND DATA AVAILABILITY 2
2.1 Study Area 2

2.2 Data Availability 2
2.3 Review of JPS Water Quality Data 2
CHAPTER 3: LITERATURE REVIEW

3.1 Water Quality Parameters 9
3.1.1 Physical Parameters 9
3.1.2 Chemical Parameters 10
3.1.3 Biological Parameters 11
3.2 Water Quality Indexes 12
3.2.1 National Sanitation Water Quality Index 13
3.2.2 Review of Malaysia Water Quality Index 15
3.3 Nonpoint Source Pollution 17
3.3.1 Nonpoint Source Pollutants 17
3.3.2 Source of Nonpoint Source Pollutants 18
3.3.3 First Flush Phenomenon 19
3.3.4 NPS Pollution Load Calculation 20
3.3.5 Site Selection Criteria 24
3.3.6 Runoff Sampling Procedure 24
CHAPTER 4: METHODOLOGY OF RIVER INDEX DEVELOPMENT
iv
4.1 Selection of the Parameters 26
4.2 Development of the Rating Curves 27
4.3 Selection of the Weighing Factors 27
4.4 Selection of Limits for Classes and Parameters 28
4.5 Classification of River Status 29
4.6 Flowchart of the JRI Methodology 29
CHAPTER 5: RESULTS AND DISCUSSION 30

5.1 Evaluation of Water Quality data 30
5.2 Evaluation of Water Quality Trends 47
5-3 Evaluation of River Flow Data 47
5-4 Evaluation for Nonpoint Source Pollution Loading 47
5-5 Development of JPS River Index (JRI) 56
5.5.1 Selection of Parameters 56
5.5.2 Rating Curves 56
5.5.3 Weighing Factors 60
5.5.4 Proposed JRI 61
5.5.5 Limits Selected for Each Class and Parameter 61
5.6 Usefulness and Application of JRI 62
5.7 How to Apply JRI 63
5.8 Sample Calculation of JRI 63
5.9 Evaluation of JPS Data by JRI 78
5.10 JPS Water Quality Monitoring Program 78
5.10.1 Monitoring Parameters 78
5.10.2 Quality Control and Quality Assurance Procedure 78
5.10.3 TIDEDA Program 79
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 82

6.1 Conclusions 82
6.2 Recommendations 83
REFERENCES 86
Appendix A: WQI Rating Curves A-1

Appendix B: Statistical Summary of the JRI B-1
Water Resources Publication Previously Published
v
LIST OF TABLES
Table Title Page

1 List and Particulars of the Stations 4
2 Status of Water Quality Data Availability for Various Stations 7
3 NSF WQI Analytes and Weights 14
4 NSF WQI Quality Scale 15
5 Classes in Malaysia Water Quality Index 16
6 Coefficients of the Empirical Equation for Pollution Load 23
7 Summary of Weighing Factor from Three Existing Indexes 28
8 Topography and Landcover of the Catchments at Water
Quality Monitoring Stations 52
9 List of Parameters Considered in Various Water Quality
Indexes in the World 57
10 The Rating Equations for Each Parameter Considered for JRI 59
11 Determination of Parameter Weighing Factor for JRI 61
12 Determination of Weighing Factor for JRI 62
13 Classes Various Water Quality Indexes Worldwide 66
14 Classes for JPS River Index 67
15 Calculated Percentile JRI Values of the Stations 68
16 Selection of JPS Water Quality Parameters for Various
Applications 79
17 Proposed Parameters for JPS Water Quality Monitoring Program 85
vi
LIST OF FIGURES
Figure Title Page
1 The Study Area and Locations of the River Water Quality Stations 3
2 Water Quality Datasheet used by JPS 6
3 Types of First Flush Phenomenon of Storm Runoff 20
4 Effect of First Flush on Shapes of Pollutograph and Loadograph 21
5 Possible Inter-storm Variation of Pollutograph and EMCs 22
6 Flowchart for the Development of JRI 29
7 Percentile Values of Turbidity at All Stations 31
8 Percentile Values of TSS at All Stations 33
9 Percentile Values of TDS at All Stations 35
10 Percentile Values of COD at All Stations 37
11 Percentile Values of AN at All Stations 39
12 Percentile Values of Nitrate at All Stations 41
13 Percentile Values of PO4 at All Stations 43
14 Percentile Values of Fe (Iron) at All Stations 45
15 Annual Percentile Values of Turbidity and TSS at Station 2224632 48
16 Annual Percentile Values of TDS and COD at Station 2224632 49
17 Annual Percentile Values of Nitrate and Ammoniacal Nitrogen at
Station 2224632 50
18 Annual Percentile Values of Phosphate and Iron at Station 2224632 51
19 Rating Curves of Specific Flow for JRI 59
20 Rating Curves of Water Quality Parameters for JRI 60
21 Display Screen of TIDEDA Output for all Water Quality
Data at Any Station 80
22 Display Screen of TIDEDA Output for Daily Water Quality
Data for Any Parameter at Any Station 81
23 Display Screen of TIDEDA Output for Any Water Quality
Data at Any Station 81
vii
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
Jabatan Pengairan dan Saliran (JPS) is monitoring about 25 parameters at 28 river

gauging stations located in the Peninsular Malaysia to study the water quality trends in
the rivers. Data has been collected from 1996 but no study was conducted to evaluate
the data. Therefore, it was long due to carry out a study to evaluate the data and
determine the water quality trends in the rivers.
1.2 OBJECTIVES
The main objective of this water resources publication (WRP) is to document the findings
of a study funded by JPS. The specific objectives were to:
o Review the available water quality data in terms of practicality and

requirement to suit the need of local environment;
o Develop a tool to examine the trend or pattern of each pollutant; and
o Develop a tool to establish the river index relating the quantity and quality of
the river flow.
1.3 SCOPE OF WORK
Within the above framework, the major scopes of work included but not limited to the
followings:
o To examine the nature and quality of the existing water quality data and
parameters for the development of a river index for JPS to suit local
environment.
o To develop the relationship of each (groups) parameters and river index

based on appropriate mathematical formulation.
o To assign the river index scores of selected important parameters to various

percentiles for rating curve development.
o To review the method of monitoring and quantification of non point source

pollution loading.
o To comment on the existing parameters monitored by JPS.
1
CHAPTER 2
STUDY AREA AND DATA AVAILABILITY
2.1 STUDY AREA
Locations of the 28 stations are shown in Figure 1. It was observed that most of the
stations are located in the States of Johor (5), Selangor (7), Kelantan (8) and Kuala
Lumpur (5). Few are located in Melaka (2) and Kedah (1), as listed in Table 1.
2.2 DATA AVAILABILITY
Although 28 water quality parameters and 12 other information (Figure 2) were supposed
to be recorded, according to the usual monitoring scheme/plan of JPS, a few parameters
were not recorded in the filed data sheet. Among those, DO, pH, river flow, stage, etc.
are the most important ones. Few other data were also sometimes missing for certain
stations. The status of water quality data availability against each parameter is reported
in Table 2.
All of the stations are manual, from where grab samples are collected periodically
(usually monthly or when gauging exercise is conducted). Then the samples are sent to
the nearest laboratory of the Department of Chemistry, Malaysia for tests. Standard
procedures (MIHP, 2007 and DID, 1981) are followed during the sampling and testing of
the water samples.
2.3 REVIEW OF JPS WATER QUALITY DATA
The following observations were noted during review of the water quality data recorded
by JPS and Department of Chemistry, Malaysia:
Many stations did not have data for certain years (without any certain pattern).
Although the information on the rainfall (during sampling) should be recorded in
the data sheet (item 14 in Figure 2) but it was not available. As such, the flow
data was estimated based on the hourly water flow data recorded by the JPS.
pH should be measured at site and at laboratory. However, only one pH value
was available in the report furnished by the Department of Chemistry.
Few water quality data are not reliable, either exceptionally low or high. It was
also not realistic to consider those values as outliers.
Detection limits for certain parameters (e.g. Ammonia, F-, Cl-, NO3-, Mn, PO4-,
Turbidity, etc.) were not consistent.
2
Table 1: List and Particulars of the Stations
Station Year Year & No. Hourly Latitude Longitude Catch. Area
No Station Name State District Flow Data Active
Number Start of Records (xxoxxxx) (xxoxxxx) 2
(km )
1 1737651 Sg. Johor at Rantau Panjang Johor Kota Tinggi 09/05 3 & 16 Yes Yes 01 46 50 103 44 45 1130
Sg. Bekok di Batu 77, Jalan Yong Yes

2 2130622 Johor Segamat 05/06 2 & 16 Yes 02 07 15 103 02 30 350
Peng/Labis
Sg. Lenggor di Bt 22, Yes
3 2237671 Johor Mersing 07/05 3&6 Yes 02 15 30 103 44 10 207
Kluang/Mersing
4 2527611 Sg. Muar di Buloh Kasap Johor Segamat 01/05 3 & 46 Yes Yes 02 33 20 102 45 50 3130
5 2528614 Sg. Segamat di Segamat Johor Kota Tinggi 01/05 3 & 46 Yes Yes 02 30 25 102 49 05 658
6 5606610 Sg. Muda di Jam Syed Omar Kedah Kuala Muda 01/97 6 & 96 Yes Yes 05 36 35 100 37 35 3330
7 5120601 Sg. Nenggiri di Jambatan Bertam Kelantan Gua Musang 11/98 8 & 50 Yes Yes 05 08 55 102 02 45 2130
8 5222652 Sg. Lebir di kampong Tualang Kelantan Kuala Krai 02/98 8 & 46 Yes Yes 05 16 30 102 16 00 2430
9 5320643 Sg. Galas di dabong Kelantan Kuala Krai 05/97 4 & 27 Yes Yes 05 22 55 102 00 55 7770
10 5419601 Sg. Pergau di Batu Lembu Kelantan Kuala Krai 11/98 8 & 80 Yes Yes 05 25 05 101 53 30 1290
11 5718601 Sg. Lanas di Air Lanas Kelantan Jeli 04/97 9 & 74 Yes Yes 05 47 10 102 09 00 80
12 5721642 Sg. Kelantan di Guillmard Kelantan Tanah Merah 06/97 4 & 38 Yes Yes 05 45 45 101 53 30 11900
13 5818601 Sg. Golok di Kg. Jenob Kelantan Tanah Merah 04/97 9 & 79 No Yes 05 50 25 101 58 40 216
14 6019611 Sg. Golok di Rantau Panjang Kelantan Pasir Mas 08/00 4 & 24 Yes Yes 06 01 30 102 29 35 761
15 2224632 Sg. Kesang di Chin Chin Melaka Selatan 07/97 11 & 226 Yes Yes 02 17 25 102 15 10 161
4
Table 1: List and Particulars of the Stations (Continued)
Station Year Year & No. of Hourly Latitude Longitude Catch. Area
No Station Name State District Flow Data Active
Number Start Records (xxoxxxx) (xxoxxxx) 2
(km )
16 2322613 Sg. Melaka di Pantai Belimbing Melaka Utara 07/97 7 & 132 Yes Yes 02 20 35 101 47 10 350
17 2917601 Sg. Langat Di Kajang Selangor Ulu Langat 01/93 10 & 180 Yes Yes 02 59 40 101 52 20 380
18 3118645 Sg. Lui di Kg. Lui Selangor Ulu Langat 01/93 10 & 169 Yes Yes 03 10 25 101 26 35 68
19 3414621 Sg. Selangor di Rantau Panjang Selangor Kuala Selangor 01/93 10 & 116 Yes Yes 03 24 10 101 35 05 1450
20 3516622 Sg. Selangor di Rasa Selangor Hulu Selangor 01/93 9 & 140 Yes No 03 30 25 101 20 40 321
Sg. Selangor di Ulu Ibu No
21 3613601 Selangor Hulu Selangor 01/93 10 & 154 Yes 03 41 35 101 31 20 1290
Empangan
22 3615612 Sg. Bernam di Tanjung Malim Selangor Hulu Selangor 01/93 10 & 179 No Yes 03 40 45 101 21 50 186
23 3813611 Sg. Bernam di Jambatan SKC Selangor Sabak Bernam 01/93 10 & 201 No Yes 03 48 15 101 41 50 1090
24 3116630 Sg. Klang di Jambatan Sulaiman WP, KL Kuala Lumpur 07/05 0.4 & 7 No Yes 03 08 20 101 41 50 468
25 3116633 Sg. Gombak di Jalan Tun Razak WP, KL Kuala Lumpur 07/05 0.4 & 7 Yes Yes 03 10 25 101 41 50 122
26 3116634 Sg. Batu di Sentul WP, KL Sentul 07/05 0.4 & 7 No Yes 03 10 35 101 41 50 145
Sg. Klang Di Lorong Yap Kuan No

27 3117602 WP, KL Kg. Baru 07/05 0.4 & 7 No 03 09 55 101 43 10 160
Seng
Sg. Gombak Ibu Bekalan KM 11 No
28 3217601 WP, KL Gombak 07/05 0.4 & 7 No 03 14 10 101 42 50 85
Gombak
5
(a) Sample of Field Data Sheet
(a) Sample of Lab Data Sheet

Figure 2: Water Quality Data Sheets used by JPS
6
Table 2: Status of Water Quality Data Availability for Various Stations
Station pH Colour Cond. Turbidity Alkalinity Hardness Ca Mg TS DS SS AN Si K Na COD BOD Cl- F- NO3 PO4 SO4 Fe Mn
No Station Name State River DO
Number (unit) (Hazen) (S/cm) (NTU) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)
(mg/L)
Sg. Johor at
1 1737651 Rantau Johor Sg. Johor Y N Y Y Y Y Y Y Y Y Y Y Y Y I I I I Y I Y Y Y Y Y
Panjang
Sg. Bekok di
Batuu 77, Jalan N
2 2130622 Johor Sg. Labis Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y I Y Y Y I Y
Yong
Peng/Labis
Sg. Kesang di Sg.
3 2224632 Melaka Y Y Y Y Y Y Y Y Y Y Y Y Y I I Y I Y Y I Y Y Y Y
Chin Chin Kesang N
Sg. Lenggor di
Sg.
4 2237671 Bt 22, Johor Y N Y Y Y Y Y Y Y Y Y I I Y Y Y Y Y I I I Y Y Y Y
Jemaluang
Kluang/Mersing
Sg. Melaka di
Sg.
5 2322613 Pantai Melaka Y N Y Y Y Y Y Y Y Y Y Y Y Y I I Y Y Y Y Y Y Y Y Y
Melaka
Belimbing
Sg. Muar di
6 2527611 Johor Sg. Muar Y Y Y Y Y Y Y Y Y Y Y Y Y I I Y I Y I Y Y I I Y
Buloh Kasap N
Sg. Segamat di
7 2528614 Johor Sg. Sebol Y Y Y Y Y Y Y Y Y Y Y Y Y I I Y Y Y I Y Y Y I I
Segamat N
Sg. Langat Di
8 2917601 Selangor Sg. Langat Y Y Y Y Y Y Y Y Y Y Y Y I I I I I I Y I I I I I
Kajang N
Sg. Klang di
9 3116630 Jambatan WP, KL Sg. Klang Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Sulaiman
Sg. Gombak di
Sg.
10 3116633 Jalan Tun WP, KL Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Gombak
Razak
Sg. Batu di
11 3116634 WP, KL Sg. Batu Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Sentul N
Sg. Klang Di
12 3117602 Lorong Yap WP, KL Sg. Klang Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Kuan Seng
Sg. Lui di Kg.
13 3118645 Selangor Sg. Lui Y Y Y Y I I I I I I I I I I I I I Y Y I I I I I
Lui N
Ibu Bekalan KM Sg.
14 3217601 WP, KL Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
11 Gombak Gombak N
Sg. Selangor di
Sg.
15 3414621 Rantau Selangor Y N Y Y Y Y Y Y Y I I I I I I I I I Y Y I I I I I
Selangor
Panjang
7
Table 2: Status of Water Quality Data Availability for Various Stations (Continued)
K Cl-
Station pH Colour Cond. Turbidity Alkalinity Hardness Ca Mg TS DS SS AN Si Na COD BOD F- NO3 PO4 SO4 Fe Mn
No Station Name State River DO (mg/ (mg/
Number (unit) (Hazen) (S/cm) (NTU) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)
(mg/L) L) L)
Sg. Selangor Sg.
16 3516622 Selangor I I I I I I I I I I Y Y Y Y Y I I Y Y Y Y Y Y Y
di Rasa Selangor N
Sg. Selangor
Sg.
17 3613601 di Ulu Ibu Selangor I N I I I I I I I I I Y Y I I I I I Y Y Y Y Y I I
Bernam
Empangan
Sg. Bernam di
Sg.
18 3615612 Tanjung Selangor I N I I I I I I I I I Y Y I I I I I Y I Y Y I I I
Bernam
Malim
Sg. Bernam di
Sg.
19 3813611 Jambatan Selangor I N I I I Y I I I I I I Y I I I I I I Y Y I I I I
Bernam
SKC
Sg. Nenggiri
Sg.
20 5120601 di Jambatan Kelantan Y N Y Y I Y Y Y Y Y Y Y I I I I N N Y N Y I I I N
Nenggiri
Bertam
Sg. Lebir di
21 5222652 kampong Kelantan Sg. Lebir Y N Y Y I Y Y Y Y Y Y Y I Y I I N N Y N Y I Y I N
Tualang
Sg. Galas di Sg.
22 5320643 Kelantan Y Y I I Y Y Y Y Y Y I I I I I N N Y N Y I I I N
dabong Galas N
Sg. Pergau di Sg.
23 5419601 Kelantan Y Y Y I Y Y Y Y Y Y Y I Y I I N N Y N Y I I I N
Batu Lembu Pergau N
Sg. Muda di
Sg.
24 5606610 Jam Syed Kedah Y N N I N I N Y Y N N I I I I N I I Y I I Y Y I I
Muda
Omar
Sg. Lanas di Sg.
25 5718601 Kelantan Y Y I I Y Y Y Y Y Y I I I I I N N Y N Y I I I N
Air Lanas Lanas N
Sg. Kelantan Sg.
26 5721642 Kelantan I I I I Y Y I I I I I I I I I I I I I I I I I I
di Guillmard Kelantan N
Sg. Golok di Sg.
27 5818601 Kelantan Y Y Y I Y Y Y Y Y Y Y I I I I N N Y N Y I I I N
Kg. Jenob Golok N
Sg. Golok di
Sg.
28 6019611 Rantau Kelantan Y N Y Y I Y Y Y Y Y Y Y I I I I N N Y N Y I I I N
Golok
Panjang
Legend,
Y- Data complete
I - Data incomplete
N - No data available
8
CHAPTER 3
LITERATURE REVIEW
The literature review was necessary to evaluate the existing river or water quality indexes
(local and international) and to determine their suitability to asses the water quality data
collected from JPS stations. The literature review also helped propose a new unique index
with the main intention to assess the river status based on the quantity (flow) and quality
data collected by JPS.
3.1 WATER QUALITY PARAMETERS
Having good water quality is important for a healthy river and ecosystem. Several basic
conditions must be met for aquatic life to thrive in the water. When these conditions are not
optimal, species populations become stressed. When conditions are poor, organisms may
die. Thus, various water quality parameters need to be measured in order to determine the
health of the river water so that it is safe to use for any purpose. In order to develop a water
quality or river index, there are several parameters that need to be considered. These
parameters can be divided into four groups, which are physical, chemical, biological and
radioactive.
3.1.1 Physical Parameters
There are many types of physical parameters such as temperature, turbidity, total dissolved
solids, total suspended solids, etc. used for the evaluation of water quality. Each of the
parameters has significant impact on the water quality.
The water temperature is a measure of the heat content of the water mass and influences
the growth rate and survivability of aquatic life. Different species of fish have different needs
for an optimum temperature and tolerances of extreme temperatures (Davis and McCuen,
2005). Many of the physical, biological, and chemical characteristics of a river are directly
affected by temperature. Most waterborne animal and plant life survives within a certain
range of water temperatures, and few of them can tolerate extreme changes in temperature
(WSDE, 2002).
Turbidity indicates the amount of fine particles suspended in water. High concentrations of
particles can damage the habitats for fish and other aquatic organisms (Said et al., 2004).
Turbidity is more concern with aesthetic point of view. High turbid water shortens the filter
runs. Many pathogenic organisms may be encased in the particles and protected from the
disinfectant (Avvannavar and Shrihari, 2007).
9
Total suspended solids (TSS) is usually referred to the particles in water which is usually
larger than 0.45 m. Many pollutants (e.g. toxic heavy metals) can be attached to TSS,
which is not good for the aquatic habitat and lives. High suspended solids also prevent
sunlight to penetrate into water. Total dissolved solid (TDS) consists of dissolved minerals
and indicates the presence of dissolved materials that cannot be removed by conventional
filtration. The presence of synthetic organic chemicals (fuels, detergents, paints, solvents
etc) imparts objectionable and offensive tastes, odors and colors to fish and aquatic plants
even when they are present in low concentrations (Avvannavar and Shrihari, 2007).
3.1.2 Chemical Parameters
pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand
(COD), nitrates, total phosphate, metals, oil and grease are the examples of chemical
parameters used to determine the water quality. The pH value of water is a measure of the
acid strength in the water.
The pH directly measures the activity (approximately the concentration) of the hydrogen ion,
H+. The lower the pH, the higher the H+ activity and the more acidic is the water (Davis and
McCuen, 2005). The neutral pH is considered as 7.0. DO is a measure of the amount of
oxygen freely available in water. It is commonly expressed as a concentration in terms of
milligrams per liter, or as a percent saturation, which is temperature dependent. The colder
the water, the more oxygen it can hold (Said et al., 2004).
Biochemical Oxygen Demand (BOD) determines the strength of pollutants in terms of

oxygen required to stabilize domestic and industrial wastes. For the degradation of
oxidizable organic matter to take place minimum of 2 to 7 mg/L of DO level is to be
maintained at laboratory experimentation or should be available in the natural waters
(Avvannavar and Shrihari, 2007) BOD also measures the amount of food (mainly organic)
for bacteria found in water. The BOD test provides a rough idea of how much biodegradable
waste is present in the water (WSDE, 2002). COD test is commonly used to measure the
amount of organic and inorganic oxydizable compounds in water. Most applications of COD
determine the amount of total oxidizable pollutants found in surface water, making COD a
useful measure of water quality. It is expressed in milligrams per liter (mg/L), which indicates
the mass of oxygen consumed per liter of solution.
Nitrates are a measure of the oxidized form of nitrogen and are an essential macronutrient in
aquatic environments. Nitrates can be harmful to humans, because our intestines can break
nitrates down into nitrites, which affect the ability of red blood cells to carry oxygen. Nitrites
can also cause serious illnesses in fish (Davis and McCuen, 2005). Phosphorus is important
to all living organisms. However, excessive phosphorus causes algae blooms, which are
harmful to most aquatic organisms. They may cause a decrease in the DO levels of the
10
water, and in some cases temperature rise. This can result in a fish kill and the death of
many organisms (Said et al., 2004).
Metals occur naturally and become integrated into aquatic organisms through food and
water. Trace metals such as copper, selenium, and zinc are essential metabolic components
at low concentrations. However, metals tend to bioaccumulate in tissues and prolonged
exposure or exposure at higher concentrations can lead to illness. Elevated concentrations
of trace metals can have negative consequences for both wildlife and humans. Human
activities such as mining and heavy industry can result in higher concentrations than those
that would be found naturally (Carr and Neary, 2006).
Oil in water can be present in four basic forms which are free oil, mechanically emulsified oil,
chemically emulsified oil, and dissolved oil. Free oil will rise to the surface of the water in
which it is contained. Mechanically emulsified oil is caused by agitating a free oil and water
mixture to the point where it breaks the oil up into very small droplets (10-20 microns). High
water temperatures and use of liquid vegetable oils promote mechanically emulsified oil. Oil
and grease may also become chemically emulsified, primarily through the use of detergents
and other alkalis. Chemically emulsified oil particles are very small (<1 micron) and do not
rise to the surface of the water regardless of how much time is allowed. Oil may also be
present as dissolved oil in which case it is no longer present as discrete particles. Oil
generally becomes dissolved in water through the use of degreasing compounds which are
soluble in both oil and water.
3.1.3 Biological Parameters
In order to assess the quality of water, biological parameters should also be considered.
Fecal coliform and groups of microorganism are the examples of biological parameters.
Fecal coliform is a form of bacteria found in human and animal waste. Fecal coliform are
bacteria whose presence indicates that the water may have been contaminated with human
or animal fecal material. If fecal coliform counts are high in a site, it is very likely that
pathogenic organisms are also present, and this site is not recommended for swimming and
other contact recreation (Said et al., 2004).
A few microorganisms are an important cause of the corrosion of steel pipes. Water for the
purpose of drinking that contained microorganisms can cause sensory defects in odor,
color and taste. Various health related problems due to contaminated waters are diarrhea,
abdominal cramps and vomiting due to salmonella, cholera is due to vibro cholera, infection
of lungs due to mycobacterium (Avvannavar and Shrihari, 2007).
3.2 WATER QUALITY INDEXES
11
Extensive literature review was conducted to evaluate, compare and find a method suitable
to develop an index for JPS, Malaysia. Most of the countries practices Water Quality Index
(WQI) method which is similar to the existing DOE index (DOE, 1994) that expresses quality
of water via a single number by combining measurements of selected physical, chemical,
biological and radioactive parameters (Cude et al., 1997). Generally, WQI is a unitless
number varies between 0 and 100. A higher index value represents good water quality.
Therefore, a numerical index is used as a management tool in water quality assessment
(Avvannavar and Shrihari, 2007).
WQI basically acts as a mathematical tool to convert the bulk of water quality data into a
single digit, cumulatively derived, numerical expression indicating the level of water quality.
This, consecutively, is essential for evaluating the water quality of different sources and in
observing the changes in the water quality of a given source as a function of time and other
influencing factors (Sarkar and Abbasi, 2006). WQI has been developed to assess the
suitability of water for a variety of uses. The index reflects the status of water quality in lakes,
streams, rivers, and reservoirs. The concept of WQI is based on the comparison of the water
quality parameter with respective regulatory standards (Khan et al., 2003).
Water quality index combines several important water quality parameters that give an overall
index of the water quality for a specific use. Different pollutants and factors are required for
the development of an index. The simplest WQI reflect on several simple water quality
parameters such as dissolved oxygen, total suspended solid, pH, and possibly some
nutrients. Measurements of each of these parameters are taken and compared to a
classification table, where the water is identified as excellent, good, fair, poor or very poor
(Davis and McCuen, 2005).
There are numerous water quality indexes that have been developed to help water quality
divisions in some U.S. states, Canada, and Malaysia. However, most of these indexes are
based on the WQI developed by the U.S. National Sanitation Foundation (NSF) (Said et al.,
2004). The present method used in Malaysia to calculate the WQI is based on opinion poll
(Khuan et al., 2002).
Although WQI has the potential to summarize complex scientific information on water quality
into a simpler form for assessment, communication and reporting purposes; there are merits
and demerits of using WQI approach (UNEP GEMS, 2005).
Some of the advantages of indexes are:

WQIs can be used to show water quality variation both spatially and temporally;
Provide a simple, concise and valid method for expressing the significance of regularly
generated laboratory data;
Aid in the assessment of water quality for general uses;
Allow users to easily interpret data with respect to certain parameters;
12
Can identify water quality trends and problem areas based on selected variables;
Provide a screening tool for further evaluation;
Improve communication with the public and increases public awareness of water quality
conditions;
Assist in establishing priorities for management purposes.
Some of the limitations are:

Provide only a summary of the selected parameters;
Cannot provide complete information on water quality;
Cannot evaluate all water quality risks;
Can be subjective and biased in their formulation;
Because of differing climates and conditions they are not universally applicable;
Are based on conceptual generalisations that are not universally applicable;
Have the prerequisite of requiring groups/sets of indicators in their formulation;
Perfectionist scientists and statisticians tend to disapprove of, and criticise,
methodology, thereby eroding credibility as a screening management tool.
The most widely used water quality index developed by National Sanitation Foundation
(NSF) of the USA and the Malaysian WQI are briefly discussed in the following section.
Literature on the other WQI can be obtained from Said et al., 2004; Rocchini and Swain,
2001; Cude, 2001; Sarkar and Abbasi, 2005; CCME, 2001 and Boyacioglu, 2007.
3.2.1 National Sanitation Foundation Water Quality Index
One of the earliest efforts to develop a WQI was done in association with the National
Sanitation Foundation (NSF). A panel of 142 persons was assembled throughout the U.S.A
with known expertise in water quality management. Three questionnaires were mailed to
each panelist to solicit expert opinion regarding the WQI and the procedure incorporated
many aspects of the Delphi method, an opinion research technique first developed by Rand
Corporation. In the first questionnaire, the panelists were asked to consider 35 analytes for
possible inclusion in a WQI and to add any other analytes they felt should be included. The
panelists also were asked to rate the analytes that they would include on a scale from 1
(highest significance) to 5 (lowest significance).
The results from the first survey were included with the second questionnaire and the
panelists were asked to review their original response. The purpose of the second
questionnaire was to obtain a closer consensus on the significance of each analyte. Also
included was a list of nine new analytes that had been added by some respondents in the
first questionnaire. For the second questionnaire, the panelists were asked to list no more
than 15 most important analytes for inclusion from the new total of 44.
13
From these first two responses, nine analytes had been derived for inclusion in the WQI. In
the third questionnaire, the panelists were asked to draw a rating curve for each of the nine
analytes on blank graphs provided. Levels of water quality (WQ) from 0 to 100 were
indicated on the y-axis of each graph while increasing levels of the particular analyte were
indicated on the x-axis. Each panelist drew a curve which they felt best represented the
variation in WQ produced by the various levels of each parameter. Then, all the curves had
been averaged to produce a single line for each analyte. Statistical analysis of the ratings
was used to assign weights to each analyte, where the sum of the weights is equal to 1. The
nine parameters and their corresponding weights are listed in Table 3. The water quality
value for each analyte then was calculated as the product of the rating curve value (also
known as the Q-value) and the WQI weight (WSDE, 2002).
Table 3: NSF WQI Analytes and Weights
Parameter/Analyte WQI Weights
Dissolved oxygen 0.17

Fecal coliform (or E. coli) 0.15
pH 0.12
BOD 0.10
5
Nitrates 0.10
Phosphates 0.10
o
t C from equilibrium 0.10
Turbidity 0.08
Total solids 0.08
Once the overall WQI score is known, it can be compared against a scale given in Table 4 to
determine how good the water is on a given day.
Table 4: NSF WQI Quality Scale (WSDE, 2002)
WQI Quality of water

91-100 Excellent
71-90 Good
51-70 Medium or average
26-50 Fair
0-25 Poor
3.2.2 Review of Malaysian Water Quality Index
The water quality index introduced by the Department of Environment (DOE) is being
practiced in Malaysia for about 25 years. The index considers six parameters. The
Malaysian WQI is an opinion-poll formula. A panel of experts was consulted on the choice of
14
the parameters and the weightage was assigned to each parameter. The parameters which
have been chosen are dissolved dissolved oxygen (DO), biological oxygen demand (BOD),
chemical oxygen demand (COD), suspended solid (SS), pH value (pH), and ammonical
nitrogen (AN) (Khuan et. al, 2002).
The WQI approved by the DOE (Equation 1) is calculated based on the above six
parameters. Among them DO carries maximum weightage of 0.22 and pH carries the
minimum of 0.12 in the WQI equation. The WQI equation eventually consists of the sub-
indexes, which are calculated according to the best-fit relations given in Equations 2 - 7.
These equations are graphically presented in Appendix A. The formulas used in the
calculation of WQI are:
WQI = 0.22 SIDO+0.19 SIBOD+0.16 SICOD+0.16 SISS+0.15 SIAN+0.12 SIpH (1)
Where,
WQI = Water quality index; SIDO = Sub-index of DO; SIBOD = Sub-index of BOD; SICOD = Sub-
index of COD; SIAN = Sub-index of AN; SISS = Sub-index of TSS; SIpH = Sub-index of pH.
Sub-index for DO (in % saturation):
SIDO = 0 for DO < 8 (2a)
= 100 for DO > 92 (2b)
= -0.395 + 0.030DO2 0.00020DO3 for 8 < DO < 92 (2c)
Sub-index for BOD:
SIBOD = 100.4 4.23BOD for BOD < 5 (3a)
= 108e-0.055BOD 0.1BOD for BOD > 5 (3b)
Sub-index for COD:
SICOD = -1.33COD + 99.1 for COD < 20 (4a)
= 103e-0.0157COD 0.04COD for COD > 20 (4b)
Sub-index for AN:
SIAN = 100.5 105AN for AN < 0.3 (5a)
= 94e-0.573AN 5 AN 2 for 0.3 < AN < 4 (5b)
=0 for AN > 4 (5c)
Sub-index for SS:
SISS = 97.5e-0.00676SS + 0.05SS for SS < 100 (6a)
= 71e-0.0016SS 0.015SS for 100 < SS < 1000 (6b)
=0 for SS > 1000 (6c)
15
Sub-index for pH:
SIpH = 17.2 17.2pH + 5.02pH2 for pH < 5.5 (7a)
= -242 + 95.5pH 6.67pH2 for 5.5 < pH < 7 (7b)
= -181 +82.4pH 6.05pH2 for 7 < pH < 8.75 (7c)
= 536 77.0pH + 2.76pH2 for pH > 8.75 (7d)
Based on the Malaysian WQI, water quality is classified according to one of the following
categories shown in the Table 5.
Table 5: Classes in Malaysian Water Quality Index (DOE, 2005)
Class
Parameter
I II III IV V
AN < 0.1 0.1-0.3 0.3 0.9 0.9 2.7 > 2.7
BOD <1 13 36 6 12 > 12
COD < 10 10 25 25 50 50 100 > 100
DO >7 57 35 1-3 <1
pH >7 67 56 <5 <5
TSS < 2.5 25 50 50 - 150 50 - 30 > 300
WQI > 92.7 76.5 92.7 51.9 76.5 31.0 51.9 < 31.0
However, a few limitations were identified while reviewing Malaysian water quality index
procedure and the long term data recorded in various river basins in Malaysia. These are
given below (Mamun et al., 2007):
a. pH is not a problem for most of the Malaysian rivers and thus can be eliminated from
the existing WQI equations. However, pH should be monitored to assess the
suitability of water for other usages as required by the National Water Quality
Standards NWQS;
b. No nutrient (phosphorus, nitrogen, etc.) is considered in the existing WQI equation;
c. Aesthetically the river water should be attractive to the citizen. There are suspended
solids (SS) in the existing WQI procedure but SS do not always represent the clarity
of the water. Thus, one parameter (Turbidity) could be included to indicate the
transparency of water;
d. The distribution of WQI values are not uniform for five Classes set for the
assessment of water quality in Malaysia.
The existing WQI was assessed for its suitability for the JPS data and discussed in the
following section. Other international WQI procedures was studied too and were evaluated to
fit in this study. This activity was conducted based on the published literature accessible
16
through printed and electronic sources. The JPS water quality data could not be fitted to the
existing DOE WQI equations due to lack of dissolved oxygen (DO) data. Similarly, WQI
equations practiced in overseas countries were not suitable due to lack of certain data
required for the specific WQI procedures.
3.3 NONPOINT SOURCE POLLUTION
The easiest way to define nonpoint source pollution is to term as storm generated pollution.
The rainwater washes away the pollutants accumulated on the land surfaces, rooftops and
vegetation, and ultimately drains into the water bodies. Most of the pollutants are generated
due to human activities, while the rests are due to natural degradation of soil and other
components of the urban environment. The broad category of NPS pollutant is sediment,
nutrient, organic, inorganic and toxic substance originating from landuse activities and/or
from the atmosphere, which are carried to surface water bodies by storm runoff. NPS
pollution is said to occur when the rate at which these materials enter water bodies exceed
natural levels.
3.3.1 Nonpoint Source Pollutants
The most common nonpoint source pollutants from urban areas are stated according to their
groups.
Chemo-physical Pollutants: The chemo-physical pollutants that may be significant in the

case of NPS pollution are pH, Total Dissolved Solids (TDS), Conductivity, Turbidity and Total
Suspended Solids (TSS). pH may be a problem in the highly industrial regions due to the
potential of generating acid rain and runoff. The most common problems are encountered
due to high turbidity and high TSS.
Organic Pollutants: These pollutants are composed of organic matters, which are
degraded fast and have the potential to cause oxygen depletion in the receiving water
bodies. These pollutants are expressed in terms of biochemical oxygen demand (BOD),
chemical oxygen demand (COD), Total Organic Carbon (TOC), Oil and Grease (O&G), etc.
However, BOD and COD are the most common parameters studied for the NPS pollution
monitoring and control (US EPA, 1983; Pitt et al., 1993).
Inorganic Pollutants: Inorganic pollutants are mainly the metals and others in organic
compounds. A few of the metals are toxic at high concentration and have the tendency to
accumulate into the tissue of aquatic flora and fauna. The most common heavy metals
observed (US EPA, 1983) in the urban storm runoff due to urban activities are Zinc (Zn),
Lead (Pb), Copper (Cu), Chromium (Cr), Cadmium (Cd), Nickel (Ni), etc.
17
Toxic Pollutants: Besides heavy metals, toxic pollutants in urban runoff are mainly referred
as herbicides, pesticides, PAHs, PCBs and other carcinogenic elements including the most
common heavy metals (Pitt et al. 1993; Lee and Lee, 1993).
Microbial Pollutants: The most common microbial pollutant in the urban runoff is coliform
bacteria. Total and faecal coliforms are of special interest due to their easy access into the
storm runoff either through anthropogenic sources or sewer overflows. Spread of
waterborne diseases in the developing countries due to NPS pollution is identified as one of
the main issues, which is more detrimental than the sedimentation problem (Field et al.,
1993; Wanielista and Yousef, 1993).
3.3.2 Sources of NPS Pollutants
In general, the most predominant source of nonpoint pollution are the agricultural, urban and
urbanising areas. These activities include plantation, construction or renovation activities,
transportation, gardening, solid waste handling, accidental spills, etc.
According to DID (2000) the typical sources of urban NPS pollutants include:
air emissions (chemicals, nutrients and metals);

household gardens, public open spaces, sporting facilities (TSS, pesticides,
fertilisers, etc.);
street litter and garbage (leaves, cans, bottles, papers, plastics, etc.);
domestic and wild animals (faeces, BOD, bacteria, etc.);
automobiles (COD, motor oil, heavy metals, tyre, brake materials, etc.);
wastewater discharges and sewer overflows (nutrients, BOD, bacteria, zinc, copper,
etc.);
industry and industrial processes (chemicals, COD, metals, etc.);
commercial activities e.g. stock yards, vehicle repair workshops in open spaces, etc.
(TSS, COD, oil & grease, etc.);
construction sites (litter, soils, building products, rubble, etc.);
accidents and spills (petrol, oil, chemicals, etc.) and
landfills (nutrients, BOD, COD, metals, etc.)
3.3.3 First Flush Phenomenon
The term First Flush is frequently used in NPS or diffuse pollution control. A first flush is
defined as the initial high pollutant loadings that may occur in the initial period of a storm
event. Depending on rainfall pattern and catchment properties the initial part of the storm is
sometimes referred to as either the first hour of rainfall or a specific amount of runoff in the
first hour (Harrison and Wilson 1985; Kuo and Zhu, 1989). Vorreiter and Hickey (1994)
18
100
Cumulative Pollutant Mass (%)

considered the
80 occurrence of
first flush if the
60 concentration
of pollutants
40 Advanced
increased from
Delayed
dry levels on the
20 Uniform
incline of the
Dilution
hydrograph.
0
Loading 0 20 40 60 80 100 characteristics
of urban Cumulative Runoff Volume (%) runoff can be
advanced,
lagging, mixed or uniform, as shown in Figure 3 (Griffin et al., 1980). The accepted and
easiest way to determine the existence of first flush is to plot the cumulative flow versus
cumulative load. If the pollutant loadings result in a curve that lies above a diagonal line
extended from the origin (the first flush divider), then a flushing action occurred because the
amount of pollutant mass at certain time is higher than the amount of runoff.
First flush has been regarded as one of the important issues in the management of water
quality due to the shock loadings of pollutants into water ways, either in terms of the
pollutant mass or the pollutant concentration. However, the extent of shock load is relative
to the size of the receiving water bodies. The result of these shock loadings of pollutants
may be an acute toxicity towards the aquatic environment. Studies on the impact of shock
pollutant loadings with a high pollutant concentration have shown that these shock loads are
acutely toxic to aquatic invertebrate (Hall and Anderson 1988).
Figure 3: Types of First Flush Phenomenon of Storm Runoff (Griffin et al., 1980)
19
A few studies have shown that the pollutant concentrations are highest in the early stages of
the runoff process (Ellis and Sutherland, 1979; Griffin et al., 1980; Lee et al., 2002 and
Gupta and Saul, 1996; Harrison and Wilson 1985; Vorreiter and Hickey, 1994). In some
studies pollutant loading, instead of pollutant concentration, was considered as the main
criteria to define first flush. However, in the NURP data, the first flush was not clearly
evident (US EPA, 1983). There are several factors that affect the first flush; these include:
storm intensity and depth;

catchment characteristic (slope, imperviousness, shape and size);
landuses;
drainage network; and
nature of the pollutant.
3.3.4 NPS Pollution Load Calculation
For the design of any structural facility to abate NPS pollution, it is important to know how
much pollution load is expected to be generated from the area concerned. According to the
present global practices, pollution from the NPS sources are calculated four ways:
1. Event mean concentration (EMC) method;

2. Pollution loading rate method;
3. Export equation method; and
4. Modelling through build-up and wash-off data.
Event Mean Concentration (EMC) Method: This is the most common method to estimate
pollution loading due to storm runoff. In this method, stormwater samples are taken at
various intervals to study the quality of storm runoff during the whole rain event. The
collected samples are analysed for the quality and Equation 8 is used to calculate the event
mean concentration. It is considered that EMC of any particular parameter represents the
average concentration over the storm event. In order to calculate annual or any other
pollution load due to diffuse pollution, the EMC value is multiplied with the corresponding
runoff amount.
QwwfCwwf - QdwfCdwf
EMCstormwater = (8)
Qwwf - Qdwf
where, the subscripts wwf and dwf denote the wet weather flow (combined wastewater &
stormwater) and dry weather flow (wastewater only) from the study area. If there is no
discharge of wastewater from the point sources of the area then the components of flow (Q)
and concentration (C) for the dwf in Equation 8 should be ignored in calculating EMC of
storm runoff.
It is important to note that the EMC results from a flow-weighted average, not simply a time
average of the concentration (DID, 2000). When the EMC is multiplied by the runoff volume,
20
an estimate of the event loading to the receiving water is obtained. As is evident from
Figure 4, the instantaneous concentration during a storm can be higher or lower than the
EMC, but the use of the EMC as an event characterisation replaces the actual time variation
of concentration. This ensures that mass loadings from storms will be better represented.
Load=
Q C QxC
t t t
(c) Loadograph with
(a) Hydrograph (b) Pollutograph with constant C
constant concentration
Load=
C QxC
t t
(d) Pollutograph with (e) Loadograph with
first flush first flush
FFigure 4: Effect of First Flush on Shapes of Pollutograph and Loadograph (DID, 2000)
Just as instantaneous concentrations vary within a storm, EMCs vary from storm to storm
(Figure 5) and from site to site as well (DID, 2000). The median or 50th percentile EMC at a
site, estimated from a time series of the type illustrated in Figure 5, is called the site median
EMC. When site median EMCs from different locations are aggregated, their variability can
be quantified by their median and coefficient of variation to achieve an overall description of
the runoff characteristics of a constituent across various sites.
Pollutograph
Concentration, C
EMC(3)
EMC(1)
EMC(2)
Event 1 Event 2 Event 3

Time
Figure 5: Possible Inter-storm Variation of Pollutographs and EMCs (DID, 2000)
21
Pollution Loading Rate Method: In this method, EMC values are determined for various
ranges of storm event and then multiplied with the runoff generated during the corresponding
storm event to calculate the loading in terms of kg. pollutant/mm runoff. Sometimes the
calculated load is again divided by the catchment areas studied to express the loading rate
in terms of kg./ha/mm runoff. However, long-term data is required to apply this method with
reasonable accuracy and higher confidence level. A typical equation for the estimation of
pollution load for a certain amount of rainfall is given in Equation 9.
L = Lr . RO . C . A (9)
where,
L = Pollution load of any parameter (kg.);
Lr = Loading rate of particular pollutant (kg./mm/ha);
RO = Runoff (mm); and
A = Watershed area (ha).
Export Equation Method: The pollutant export equations are determined based on the
statistical analysis of long-term runoff quality data. The most common parameters
considered in the equations are rainfall, runoff, catchment size, landuse type, etc. If the
equations are developed based on the pollution load generated by each unit of the
catchment area then the effect of the catchment area is ignored in the equation (DID, 2000).
Format of NPS pollutant export relations used in MSMA is given in Equation 10.
Lr = a.ROb (10)
where,
Lr = Loading rate of particular pollutant (kg./km2);
a = Coefficient;
b = Exponent; and
RO = Runoff (mm per storm event).
Besides statistical regression equation, empirical equations are also used to estimate
pollution loading from the NPS sources (Chin, 2000). The most widely used pollutant
equations are the USGS model and EPA model. Based on 2,813 storms at 173 urban
stations in 13 metropolitan cities in the USA, Driver and Tasker (1990) developed empirical
NPS export formula for ten pollutants (Equation 11). Dependent and independent variables
of the equation for various pollutants are given in Table 6.
Y = 0.454( N )( BCF )10[ a + b ( DA) + c ( IA) + d ( MAR) + e ( MJT ) + f ( X 2 )]

(11)
where, Y is the pollutant load (kg.) for the pollutants listed in Table 6, N is the average
number of storms in a year, BCF is the biased correction factor, DA is the total contributing
22
drainage area (ha), IA is the impervious area as a percentage of the total catchment area
(%), MAR is the mean annual rainfall (cm), MJT is the mean minimum temperature in
January (oC) and X2 is an indicator variable that is equal to 1.0 if commercial plus industrial
landuse exceeds 75% of the total catchment area and is zero otherwise.
Table 6: Coefficients of the USGS Empirical Equation for Pollution Load
Coefficients of the Empirical Equation

Pollutants
(Y) a b c d e f BCF
COD 1.1174 0.1427 0.0051 - - - 1.298

TSS 0.5926 0.0988 - 0.0104 -0.0535 - 1.521
TDS 1.1025 0.1583 - - -0.0418 - 1.251
TN -0.2433 0.1018 0.0061 - - -0.4442 1.345
AN -1.4022 0.1002 0.0064 0.0089 -0.0378 -0.4345 1.277
TP -2.0700 0.1294 - 0.00921 -0.0383 - 1.314
DP -1.3661 0.0867 - - - - 1.469
Cu -1.9336 0.1136 - - -0.0254 - 1.403
Pb -1.9679 0.1183 0.0070 0.00504 - - 1.365
Zn -1.6302 0.1267 0.0072 - - - 1.322
The USEPA (Heany et al., 1977) also developed a set of empirical formulae that is used to
estimate the mean annual pollutant loads in the urban storm runoff. The Equation 12 - 14
are valid for the urban areas in the USA having separate sewer systems. The average
pollutant concentration can be calculated from the annual pollutant load by dividing the load
by annual runoff amount following the formula given in Equation 15 and 16.
Ms = 0.0442Pfs (12)
f = 0.142 + 0.134D0.54 (13)
s = Ns/20 (14)
R = [0.15 + 0.75(I/100)]P 3.004d0.5957 (15)
d = 0.64 0.476(I/100) (16)
where, Ms is the amount of pollutant (kg./ha/yr), is a pollutant loading factor (e.g. for TSS of
residential area = 16.3), P is annual rainfall (cm/yr), f is a population density function
(Equation 13) depends on the population density in person per hectare (D), s is the street
sweeping factor which depends on sweeping interval, if Ns > 20 days then s = 1.0 and if Ns
20 days then s should be determined from Equation 14. R is the annual runoff (cm), I is the
average imperviousness of the catchment area (%) and d is the depression storage that can
be determined from Equation 16.
23
3.3.5 Site Selection Criteria
Site or catchment selection for NPS or diffuse pollution study is very important. It is also
important to make sure that runoff or discharges from other areas do not enter into the
drainage system of the selected study area. Once the study catchment is selected, its
important parameters such as total area, slope, imperviousness, directly connected
impervious area (DCIA), road coverage and drainage length should be determined.
3.3.6 Runoff Sampling Procedure
In the case of runoff sampling, the automatic sampler should be used, which can be
programmed with condition (based on the rainfall amount, water level in the river or drain,
runoff volume, etc.) to activate pump to take grab samples at various intervals. For each
storm event, a maximum of 24 samples can collected from the drainage outlet to cover the
whole runoff hydrograph. Non-uniform sampling intervals can be chosen to cover the whole
runoff hydrographs and also to suit the requirements of studying the first flush phenomenon.
For example, the first 10 samples can be collected at 1-minute interval, the next 9 samples
at 3-minute and the rest 5 samples at 5-minute intervals. However, the intervals will depend
on the size of the catchment or study area. Unless first flush determination is one of the
objectives of the NPS pollution study, composite sample should be prepared to determine
the EMC from one testing only. The procedure to determine amount of aliquots (sample
volumes) required from individual bottle can be followed by the method mentioned in Section
30.2.4 of Chapter 30 in MSMA (DID, 2000).
24
CHAPTER 4
METHODOLOGY OF RIVER INDEX DEVELOPMENT
Based on the extensive literature review, it is understood that none of the indexes are
developed based on historical data and the existing methods are also not suitable for the
development of WQI based on the JPS data only. It is also realised that an index can be
developed for overall protection of the water environment considering physical, biochemical,
microbial, biodiversity, toxicology, etc. or it can be developed to serve the activity or purpose
of a single organization. As such, due to absence of certain important parameters (such as,
DO, total nitrogen, certain heavy metals, bacteria, etc.) it is recommended that the proposed
river index for JPS should be named as JRI and include specific flow (m3/s/km2), TSS
(mg/L), TDS (mg/L) and Turbidity (NTU). The naming of the index as JRI and selection of
parameters eliminates any chance of conflict with the existing WQI developed by the DOE,
Malaysia. The uniqueness of the JRI is that this is the only index that considers flow as one
of the variables. Based on the literature review none of the indexes practiced through out the
world considers flow data as a variable.
4.1 SELECTION OF THE PARAMETERS
Depending on the data availability and to suit JPS main activity, the following parameters
are selected for the JPS River Index (JRI):
1. Specific Flow, which is instantaneous flow divided by the catchment area at the
station (m3/s/km2). This parameter would indicate the decrease in dry day baseflow
and increase in rainy day runoff rate. The baseflow rate in a natural catchment would
be about 0.05 m3/s/km2 as recommended by JICA and practiced by JPS. Any
reduction from this value during dry days would indicate lowering in baseflow due to
development activity, which is not good for a healthy river environment. On the other
hand the frequent (e.g. annual) specific runoff or flood flow for the natural catchments
in the Peninsular Malaysia is close to 1. Therefore, any specific flow value greater
than 1 would indicate increased specific flow due to land developments (agricultural,
urban, etc.). As such, inclusion of specific flow in the JRI would be very useful and
represent the river status in a better and holistic way (considering water quantity and
quality).
2. Total Suspended Solids, which represents the sediments that adsorbs many
pollutants on the surfaces (mg/L);
3. Total Dissolve Solids, which represents salts and minerals that indicates the
dissolved minerals in the water (mg/L); and
4. Turbidity, which represents the clarity and aesthetic property of water that is very
important to make the river and water appealing to the people (NTU).
25
As, the existing WQI (DOE, 1994) already considers other pollution parameters (pH, DO,
BOD5, COD and AN), scope of the JRI is set to four parameters only, which are more
relevant to JPSs nature of responsibility. JRI developed based on these parameters will also
help achieve the objective of evaluating the past data collected by JPS. This is due to the
fact that most of the stations, generally, got those data required for the proposed JRI.
4.2 DEVELOPMENT OF THE RATING CURVES
The rating curves for the JRI sub-indexes were developed based on the following
considerations.
1. It is understood from the literature review that most of the rating curves for the
indexes are developed based on expert peoples perception, understanding and
understanding on the effect of the selected parameter on the environment and target
usage.
2. Sub-indexes of four JRI parameters are also developed based on that concept.
3. The rating curves for JRI are proposed based on local and international practices.
4. National water quality standards (NWQS), MASMA (DID, 2000) and other materials
were also referred in selecting the parameters and rating curves for JRI.
5. Wherever possible, the proposed JRI rating curves are compared with the similar
curves practiced worldwide.
6. Two rating curves for specific flow are proposed to represent the flow condition for
rainy and non-rainy day flow. Rating curve for the flow was not compared as the
rating curve of flow is not considered in any of the indexes practiced worldwide.
4.3 SELECTION OF THE WEIGHING FACTORS
1. The existing WQI weighing factor for each parameter was used as a guide to develop
the new weighing factor in this study (for JRI). The existing WQI used for the
selection of weighing factor would be Malaysian WQI, Universal WQI and NSF WQI
(Table 7).
2. Weighing factor for each parameter was determined based on the importance of the
parameter with respect to the over all index and its importance on the river status and
morphology. The weighing factor was calculated based on the weightage (based on
a scale of 1 to 5) assigned to each parameter selected for the JRI.
Table 7: Summary of Weighing Factor from Three Existing Indexes
Weighing Factor
Parameters
Malaysia WQI Universal WQI NSF WQI
DO 0.22 - 0.17
26
BOD 0.19 - 0.10
COD 0.16 - -
AN 0.15 - -
SS 0.16 - -
pH 0.12 - 0.12
Total coliform - 0.114 0.15
Cadmium - 0.086 -
Cyanide - 0.086 -
Mercury - 0.086 -
Selenium - 0.086 -
Arsenic - 0.113 -
Nitrate-nitrogen - 0.086 -
DO - 0.114 -
pH - 0.029 -
BOD - 0.057 -
Total
- 0.057 -
phosphorus
Nitrates - - 0.1
Phosphates - - 0.1
o
T C from - - 0.10
equilibrium
Turbidity - - 0.08
Total solids - - 0.08
References (DOE, 2005) (Boyacioglu, 2007) (Irvine et al., 2003)
4.4 SELECTION OF LIMITS FOR CLASSES AND PARAMETERS
The National water quality standard was used (wherever possible) as the guide to select
the limits and classes for each parameter.
4.5 CLASSIFICATION OF RIVER STATUS
1. The river status can be classified into five main classes from I to V.
2. Class II, III, and IV was further sub-divided into three classes (A, B and C), where
each class will have the range of 10 values. This is proposed to control and monitor
the river water quality in a more protective manner. A wide range of the class will
result in loose monitoring and control of the river water quality. Most of the time the
27
polluters may like to satisfy the minimum quality or standard to belong to any target
class.
4.6 FLOWCHART OF THE JRI METHODOLOGY
Procedure of the formulation of new JRI is shown, as a flowchart, in Figure 6.
Literature
Literaturereview
review
Collect and analyze water quality data from JPS Malaysia
Selection of important parameters for JRI
Development of sub-index rating curve
Determination of weighing factor for each sub-index
Selection of threshold values (limits) for each class and parameter
Development of JRI equation for JPS
End
Figure 6: Flowchart for the Development of JRI
28
CHAPTER 5
RESULTS AND DISCUSSION
5.1 EVALUATION OF WATER QUALITY DATA
Various annual percentiles of the water quality parameters were calculated to assess the
violation of water quality with respect to the existing National Water Quality Standards
NWQS developed by the Department of Environment Malaysia (DOE). In general, it was
observed that median value of iron (Fe), chemical oxygen demand (COD) and in few cases
suspended solids (SS) exceeded the Class III limit, which is the minimum class of water
expected in the river that can be treated with conventional treatment facilities. Statistical
summary of the water quality data is presented in Appendix B. The water quality of the
important parameters for each station (for all available data) were analysed and plotted for
comparison purpose (Figure 7 to 14).
29
aqqq 700 qq q 651
600 95 %tile 50 %tile 5 %tile
Concentation (mg/L)
500
400 330
300 244
200 142
83 59 70
100 48 34 51 53
7 15 15 6 21 20 0 0 0 12
0
1737651 2130622 2237671 2527611 2528614 5606610 5120601
Station ID
700
495
Concentation (mg/L)
500 442
400
290
300
196
200
110 112
100 26 26 36 51
2 4 20 2 10 3 4 11 2 13 2
0
5222652 5320643 5419601 5718601 5721642 5818601 6019611
Station ID
National Water Quality Standards (NWQS) and Ministry of Health (MOH) Malaysia Guidelines
Parameter Unit Class I Class IIA Class IIB Class III Class IV Class V MOH
Turbidity NTU 5 50 50 - - - 1000
Figure 7: Percentile Values of Turbidity at Various Stations

700
612
Concentation (mg/L)
500 465
399
400
304
300 259
200 136 155
70 72 71 90
100 58
11 6 5 14 3 16 14 3 8
0
2224632 2322613 2917601 3118645 3414621 3516622 3613601
Station ID
700
95 %tile 50 %tile 5 %tile
600
Concentation (mg/L)
500
400
294 269
300 229
200 146 161
114 127
100 64 43 61 115 52
16 3 7 16 27 22 17 18 10
0
3615612 3813611 3116630 3116633 3116634 3117602 3217601
Station ID
Turbidity NTU 5 50 50 - - - 1000
Figure 7: Percentile Values of Turbidity at Various Stations (Continued)

aqqq 1200 qq q
1000
Concentation (mg/L)
766
800
600
400 342
237 242
166 138
200 66 101 52 69 70 64
13 41 15 29 10 18 23 22 38
0
1737651 2130622 2237671 2527611 2528614 5606610 5120601
Station ID
1200
95 %tile 50 %tile 5 %tile 1008
1000 893
Concentation (mg/L)
800 676
600
400 366
206 245
149 190 155
200 96 138
16 34 16 43 14 46 56 15
8 10
0
5222652 5320643 5419601 5718601 5721642 5818601 6019611
Station ID
TSS mg/L 25 50 50 150 300 >300
Figure 8: Percentile Values of TSS at Various Stations

1200
95 %tile 50 %tile 5 %tile 1075
1000
Concentation (mg/L)
800 672
578
600
410
400 276
220 240
176 131
200 72 91 84
16 19 18 34 7 48 31 6 30
0
2224632 2322613 2917601 3118645 3414621 3516622 3613601
Station ID
1200
1000
Concentation (mg/L)
800
600
400
400 341 314
201 213
200 94 130 93
34 33 53 75 34 49
10 22 120 28 15 25 15
0
3615612 3813611 3116630 3116633 3116634 3117602 3217601
Station ID
TSS mg/L 25 50 50 150 300 >300
Figure 8: Percentile Values of TSS at Various Stations (Continued)

aqqq 500 qq q
450
400
Concentation (mg/L)
350 309 297

300 235
250 95 %tile
200 168
150 113 106 120 50 %tile
73 92
100 63 48 51 5 %tile
38 23 27 22 26
50 11 0 0 0
0
1737651 2130622 2237671 2527611 2528614 5606610 5120601
Station ID
500
432
400
Concentation (mg/L)
350
300
239 228
250
200
143
150 111 103 105 96
100 64 51 57 50 64 55 51
50 19 26 24 31 20 23
0
5222652 5320643 5419601 5718601 5721642 5818601 6019611
Station ID
TDS mg/L 500 1000 - - 4000 - 1500
Figure 9: Percentile Values of TDS at Various Stations

500
400
Concentation (mg/L)
350
300
250
200 178 169
138
150 94 156 89
87 78 85 71
100 45 45 47 50
30 25 27 34 37 18
50 15
0
2224632 2322613 2917601 3118645 3414621 3516622 3613601
Station ID
500
400
Concentation (mg/L)
350
300
250
200 169 153 169 160
139 191 147
150 129
89 108 101 86 101 90
100 77 70
41 51
32 18 17
50
0
3615612 3813611 3116630 3116633 3116634 3117602 3217601
Station ID
TDS mg/L 500 1000 - - 4000 - 1500
Figure 9: Percentile Values of TDS at Various Stations (Continued)

aqqq 90 qq q
80 95 %tile 50 %tile 5 %tile 76
Concentation (mg/L)
70
60 55
50
37 36
40 31
30 24 23
17 20
20 16
12 9 12 9
8 7 6
10 3 0 0 0
0
1737651 2130622 2237671 2527611 2528614 5606610 5120601
Station ID
90
70
Concentation (mg/L)
60
50
40
29
30
20
9
10 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0
0
0
5222652 5320643 5419601 5718601 5721642 5818601 6019611
Station ID
COD mg/L 10 25 25 50 100 >100 10
Figure 10: Percentile Values of COD at Various Stations

90 85
80
70
Concentation (mg/L)
60
47 48
50 41 38 37 37 95 %tile
40 32
30 50 %tile
18 15 15
20 14
8 7 10 7 10 7 5 %tile
10 5 3 4
0
2224632 2322613 2917601 3118645 3414621 3516622 3613601
Station ID
90
70
Concentation (mg/L)
60
50 41 42
39 37 44
40 30 30 30
30
30 22 24 26
21 18
17
20 13 11 10
9 7
10 5
0
3615612 3813611 3116630 3116633 3116634 3117602 3217601
Station ID
COD mg/L 10 25 25 50 100 >100 10
Figure 10: Percentile Values of COD at Various Stations (Continued)

aqqq 10.00 qq q
9.00 95 %tile 50 %tile 5 %tile
8.00
Concentation (mg/L)
7.00
6.00
5.00
4.00
3.00 1.84
2.00 1.13 0.77
1.00 0.56 0.22 0.32 0.26 0.20 0.58 0.39 0.05 0.03 0.20 0.06
0.17 0.08 0.13 0.16 0.05 0.13 0.05
0.00
1737651 2130622 2237671 2527611 2528614 5606610 5120601
Station ID
10.00
8.00
Concentation (mg/L)
7.00
6.00
5.00
4.00
3.00
2.00 1.58 1.76
0.62 0.95
1.00 0.33 0.05 0.55 0.55 0.55 0.54 0.31 0.21 0.17 0.17 0.17
0.09 0.02 0.09 0.04 0.11 0.05
0.00
5222652 5320643 5419601 5718601 5721642 5818601 6019611
Station ID
AN mg/L 10 25 25 50 100 >100 10
Figure 11: Percentile Values of Ammoniacal Nitrogen at Various Stations

10.00
8.00
Concentation (mg/L)
7.00
6.00
5.00
4.00 2.84
3.00
2.00 1.12 1.02 1.03 0.97 0.81
1.00 0.21 0.05 0.23 0.06 0.37 0.05 0.18 0.05 0.52 0.11 0.11 0.04
0.10 0.03 0.04
0.00
2224632 2322613 2917601 3118645 3414621 3516622 3613601
Station ID
10.00
9.00 95 %tile 50 %tile 5 %tile 8.24 9.20
7.80
8.00
Concentation (mg/L)
6.80
7.00
6.00
4.70 4.48
5.00 4.00
4.00
3.00 2.20
1.64
2.00 1.10
0.74 0.47 0.10 0.69 0.58 0.58
1.00 0.10 0.05 0.05 0.28 0.20
0.00
3615612 3813611 3116630 3116633 3116634 3117602 3217601
Station ID
AN mg/L 10 25 25 50 100 >100 10
Figure 11: Percentile Values of Ammoniacal Nitrogen at Various Stations (Continued)

aqqq 35.0 qq q

Concentation (mg/L)
25.0
20.0
15.0
10.0
5.0 2.58 0.57 0.20 0.02
0.09 0.04 0.28 0.12 0.03 0.19 0.12 0.05 0.43 0.11 0.04 0.44 0.11 0.04 0.33 0.10 0.05
0.0
1737651 2130622 2237671 2527611 2528614 5606610 5120601
Station ID
35.0
Concentation (mg/L)
25.0
20.0
15.0
10.0
5.0 1.11 0.18 0.03
0.64 0.20 0.03 0.56 0.24 0.04 0.54 0.19 0.03 0.50 0.16 0.04 0.50 0.23 0.07 0.47 0.14 0.04
0.0
5222652 5320643 5419601 5718601 5721642 5818601 6019611
Station ID
NO3 mg/L - - - - - - -
Figure 12: Percentile Values of Nitrate at Various Stations

35.0
25.00
Concentation (mg/L)
25.0 22.00
20.0
15.0
13.00
10.0 6.10 5.80
4.60 3.89 3.21
5.0 2.60 2.22 2.63 1.52 0.10
0.75 1.04 0.06 1.11 0.05 0.13 0.70 0.09
0.0
2224632 2322613 2917601 3118645 3414621 3516622 3613601
Station ID
35.0
95 %tile 50 %tile 5 %tile 31.25
30.0
25.75
Concentation (mg/L)
25.0 22.25
20.70
18.95
20.0
15.0
10.95
8.70 9.55
10.0
5.40
4.00 3.02 2.90
5.0 1.65 1.60 1.75
0.09 0.11 0.70 0.33 0.76 0.98
0.0
3615612 3813611 3116630 3116633 3116634 3117602 3217601
Station ID
NO3 mg/L - - - - - - -
Figure 12: Percentile Values of Nitrate at Various Stations (Continued)

aqqq 3.0 qq q
2.5
Concentation (mg/L)
2.0
1.5
1.0
0.5 0.18 0.08 0.23 0.27 0.20 0.10 0.11
0.01 0.05 0.01 0.01 0.05 0.03 0.02 0.08 0.04 0.05 0.02 0.00 0.02 0.01
0.0
1737651 2130622 2237671 2527611 2528614 5606610 5120601
Station ID
3.0
2.5
Concentation (mg/L)
2.0
1.5
1.0
0.5
0.09 0.03 0.01 0.03 0.02 0.00 0.19 0.04 0.01 0.03 0.01 0.01 0.09 0.05 0.02 0.04 0.02 0.01 0.07 0.04 0.01
0.0
5222652 5320643 5419601 5718601 5721642 5818601 6019611
Station ID
PO4 mg/L - 0.10 - 0.10 - - -
Figure 13: Percentile Values of PO4 at Various Stations

3.0
2.5
Concentation (mg/L)
1.97 2.01
2.0 1.95 1.66
1.5
1.0 0.72 0.65

0.58 0.50
0.43
0.5 0.24 0.12 0.20 0.11 0.20 0.09 0.14 0.04 0.16 0.10 0.07 0.04
0.0
2224632 2322613 2917601 3118645 3414621 3516622 3613601
Station ID
3.0
2.5 2.46
Concentation (mg/L)
1.99 1.93
2.0
1.50 1.45
1.5 1.31
1.00 1.00 0.93
1.0 0.81 0.70
0.49 0.56
0.30 0.35 0.40 0.40
0.5 0.20
0.07 0.05 0.15
0.0
3615612 3813611 3116630 3116633 3116634 3117602 3217601
Station ID
PO4 mg/L - 0.10 - 0.10 - - -
Figure 13: Percentile Values of PO4 at Various Stations (Continued)

aqqq 25.0 qq q
20.0
Concentation (mg/L)
15.0 13.80
11.00
10.0
5.49 5.49
4.00 4.30 3.90
5.0 2.20 2.54 1.99 2.22
1.02 1.51 1.13 1.72 1.62 1.20 0.30
0.43 0.60 0.14
0.0
1737651 2130622 2237671 2527611 2528614 5606610 5120601
Station ID
25.0
20.0
Concentation (mg/L)
15.0
10.0
5.0 2.74
2.24 1.15 0.50 1.64 0.90 1.00 0.30 0.10 0.88 0.50 0.20
0.60 0.10 0.10 0.10 0.10 0.30 0.09 0.05 0.38
0.0
5222652 5320643 5419601 5718601 5721642 5818601 6019611
Station ID
Iron mg/L - 0.3 - 1 1 - 1
Figure 14: Percentile Values of Fe (Iron) at Various Stations

25.0
20.60 20.25
20.0
Concentation (mg/L)
15.0 13.68
11.16
10.0 7.68
5.96 6.72
4.00 3.00 3.50
5.0 2.90 2.20
1.12 1.20 0.30 1.10 0.30 0.70 1.10 0.20 0.60
0.0
2224632 2322613 2917601 3118645 3414621 3516622 3613601
Station ID
25.0
20.0
Concentation (mg/L)
15.0
10.0 9.02
5.34 4.89
5.0 2.53 3.42
2.30 1.96
1.10 0.20 0.50 0.60 0.13 0.50 0.10 1.30
0.30 0.13 0.30 0.16 1.97 0.31
0.0
3615612 3813611 3116630 3116633 3116634 3117602 3217601
Station ID
Iron mg/L - 0.3 - 1 1 - 1
Figure 14: Percentile Values of Fe (Iron) at Various Stations (Continued)

5.2 EVALUATION OF WATER QUALITY TRENDS
The presence or absence of trends over time is a good indication of the degree to which
water quality is responding to changes in the catchment and season. Trend analyses of the
water quality data was done graphically and with the help of statistical tools. Annual median,
95 percentile and 5 percentile values for each station was calculated and plotted to see the
annual trends. However, the plots could not reveal any specific trend due to missing data.
Sample plots of water quality trend at a station are shown in Figure 15 and 18.
5.3 EVALUATION OF RIVER FLOW DATA
One of the good things of JPS water quality monitoring scheme is that flow values can be
estimated (except a few missing cases) at the sampling locations which are eventually
happen to be the JPS river gauging stations. Flow data is very important to evaluate the
status of river condition. Therefore, quartile analysis was also conducted to study the
variation of historical daily average flow data (minimum, mean and maximum) at the station
and during sampling (Appendix B). The specific flow was used to compensate the effect of
catchment size on the flow data and to make the data comparable with that of other stations.
5.4 EVALUATION FOR NONPOINT SOURCE POLLUTION LOADING
Monitoring of non-point source pollution loading is a necessary but costly element in water
quality monitoring study, as it requires capture of event rainfall and runoff data which
includes collection of runoff sample at various intervals for the whole event. The existing
monitoring system is not suitable for the reliable calculation of pollution loading due to NPS
sources. Confirmed information is not available if the sampling was done during rainfall
events. It is most likely that most of the water quality data collected by JPS was during the
non-rainy periods, if not all. If that is the case the data mainly represents the dry-weather
flow water quality. Therefore, it is very unlikely to use the existing data to estimate pollution
loading from the non-point pollution sources.
47
700
Turbidity Class 95 %tile 50 %tile 5 %tile
600
- V
500 - IV
- III
Turbidity (NTU)
400 50 II
5 I
300 1000 MOH
200
100
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year
700
600 TSS Class
> 300 V
500 150 - 300 IV
50 - 150 III
TSS (mg/L)
400 25 - 50 II
25 < I
300 - MOH
200
100
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year
Figure 15: Annual Percentile Values of Turbidity and TSS of Sg. Kesang at Chin Chin (Station 2224632)
48
300
95 %tile 50 %tile 5 %tile TDS Class
250 - V
4000 IV
200 - III
1000 II
TDS (mg/L)
500 < I
150
- MOH
100
50
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year
80
95 %tile 50 %tile 5 %tile COD Class
70
> 100 V
60 50 - 100 IV
25 - 50 III
50 10 - 25 II
COD (mg/L)
< 10 I
40
10 MOH
30
20
10
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year
Figure 16: Annual Percentile Values of TDS and COD of Sg. Kesang at Chin Chin (Station 2224632)
49
40
30
Nitrate (mg/L)
25
20
15
10
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year
2.5
AN Class 95 %tile 50 %tile 5 %tile
Ammoniacal Nitrogen (mg/L)
2.0 > 2.7 V

0.9 - 2.7 IV
0.3 - 0.9 III
1.5 0.1 - 0.3 II
0.1 < I
0.5 MOH
1.0
0.5
0.0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year
Figure 17: Annual Percentile Values of Nitrate and Ammoniacal Nitrogen of Sg. Kesang at Chin Chin (Station 2224632)
50
2.0
1.6
PO4 Class
1.4
Phosphate (mg/L)
- V
1.2 - IV
1.0 - III
0.1 II
0.8
- I
0.6 - MOH
0.4
0.2
0.0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year
30
25
Iron Class
20 - V
Iron (mg/L)
- IV
1 III
15
0.3 II
- I
10 1 MOH
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year
Figure 18: Annual Percentile Values of Phosphate and Iron of Sg. Kesang at Chin Chin (Station 2224632)
51
Landuse, landcover and topographical information for the catchment up to the
gauging/sampling stations provided in Table 8 would be useful for the calculation and
verification of pollution loading from point and non-point sources. However, information in
provided in the table might not be up to dates. Therefore, a proper reconnaissance survey
would be required to evaluate the present landuse pattern of the catchments.
Table 8: Topography and Landcover of the Catchments at Water Quality Monitoring Stations
Statn Area Topography Vegetation

No Station Name 2
No (km )
About 60% of the catchment is undulating The highland in the north is
highland rising to heights of 366 m while mainly under jungle while in the
Sg. Johor at
1 1737651 1130 the remainder is lowland and swampy. south a major portion had been
Rantau Panjang
cleared and planted with oil palm
and rubber.
About 30% of the area is fairly Towards the north-east of the

mountainous country covered by forest station is a small stretch of both
rising to a maximum height of cover 610m. virgin and logged over forest and
Sg. Bekok di Batuu
The remainder is hilly lands with a small towards the east, inland swamps
2 2130622 77, Jalan Yong 350
area and swampy low lying land along Sg.. and virgin forest.
Peng/Labis
Bekok towards the lower part of the
catchment. The central part is rubber
estates.
About 30% of the catchment is Totally forested, but many areas

Sg. Lenggor di Bt mountainous country rising to heights of have been logged over.
3 2237671 207
22, Kluang/Mersing 549m, while the remainder is undulating to Generally low-lying with some
flat lands. hilly areas.
About 30% of the catchment area mainly In the upper hill area, patches of
on the eastern side is mountainous rising forest are found most of which
to a maximum height of over 610 m. The has been logged over. Part of the
Sg. Muar di Buloh
4 2527611 3130 rest consists of hilly undulating land and at area has been developed and
Kasap
the western border is a small patch of fresh water swamps are found in
swampy land around the river station, 60% the north-east direction of the
of the area is covered by primary forest. station.
About 70% of the catchment is hilly to The mountainous areas are

mountainous country rising to heights of under jungle, while undulating
Sg. Segamat di
5 2528614 658 915 m and the remainder is hilly land is mainly under rubber with
Segamat
undulating land with swamps. some padi cultivation in the
lowlands.
The catchment area is generally of fairy About 60% of the catchment is

undulating land from the central towards under forest cover which is
the southwestern region, but very managed mainly under forest
Sg. Muda di Jam mountainous and steep on the reserves namely. Ulu Muda F.R,
6 5606610 3330
Syed Omar northeastern side. The mountainous Rimba Teloi F.R, Bukit Perak
region has heights reaching to 2700 F.R. and Gunung Inas F.R.
meters above mean sea level, and it The lower part is covered mainly
makes 70% of the catchment. with under rubber and paddy.
52
No Station Name 2
No (km )
Sg. Nenggiri di Data Not Available Data Not Available
7 5120601 2130
Jambatan Bertam
Almost the whole area is mountainous and Whole area is under forest, most
steep with heights of over 914 m. above of which is jungle and a few
mean sea level especially in the eastern patches had been harvested.
border. There is a small area of low lying
land for cultivation along Sg.. Lebir and
Sg. Lebir di
8 5222652 2430 Sg.. Aring and a very small area of
kampong Tualang
swampy land and limestone hills on the
western side of the catchment area. The
highest peak is Gunong Badong of 1326
m. The whole catchment is under the Lebir
Relai Forest Reserve.
Situated on the eastern side of the Main Almost 80% of the area is under
Range, the majority of the catchment area forest which is virgin except for
is steep mountainous and hilly country patches cleared for development.
rising to a maximum height of over 1830
Sg. Galas di
9 5320643 7770 m, above mean sea level. On the southern
Dabong
side of the catchment there is a small area
of limestone hills and also a small area of
low lying land for cultivation along the river
valley.
Sg. Pergau di Batu Data Not Available Data Not Available

10 5419601 1290
Lembu
Sg. Lanas di Air Data Not Available Data Not Available

11 5718601 80
Lanas
About 95% of the catchment is steep The mountainous areas are

Sg. Kelantan di mountainous country rising to heights of under virgin jungle while rubber
12 5721642 11900
Guillmard 2135 m while the remainder is undulating and some rice are planted in the
lands. lowlands.
Sg. Golok di Kg. Data Not Available Data Not Available

13 5818601 216
Jenob
The main river, Sg.. Golok, with its two The majority of this catchment is
major tributaries, Sg.. Jedok and Sg.. undeveloped and covered with
Golok, with its two major tributaries. Sg.. virgin jungle, lalang and swamp.
Jedok and Sg.. Lanas, drains this basin of A very small portion is cultivated
Sg. Golok di
14 6019611 761 lowlying to undulating country. The sourse for rubber. Padi is cultivated
Rantau Panjang
of these rivers is in the southern part of the along the rivers on a small scale.
catchment where the terrain lies within the
76 m to 763 m contour lines. These flow in
a northerly direction.
About 10% of the catchment is hilly More than half of the catchment
country rising to heights of 305 m, and the is developed for rubber with padi
Sg. Kesang di Chin
15 2224632 161 bulk of the southern catchment is low-lying cultivation along the banks of the
Chin
undulating land. river. The rest of the catchment is
under belukar and jungle.
53
No Station Name 2
No (km )
This catchment consists of low-lying and This catchment is developed for
undulating hills in the south and rubber to a limited extent. Padi is
mountainous country in the north border. A cultivated on a small scale along
small area, extending from Kg.. Dalong the rivers. Hilly and mountainous
down stream is below the 15m contour areas are covered with lalang
Sg. Melaka At line. and virgin jungle.
16 2322613 350
Pantai Belimbing The main river, Sg.. Malacca, and its
major tributary, the Sg.. Batang Melaka,
rise in hilly to mountainous terrain in the
north. These two rivers meander through
low-lying and undulating land on their way
to the sea.
The major part of the catchment area is The low lying areas are under
fairly mountainous country rising to rubber with a small portion of
Sg. Langat Di maximum height over 305m, in the north. forest towards the north of the
17 2917601 380
Kajang The remainder is hilly undulating land with station
about 10% of the lowland above 15m,
along the Sg. Langat
The area is fairly undulating with hills The mountainous are under virgin
rising to about 275m at the edge of the jungle while rubber is cultivated in
catchment. The low lying area are found the lesser hilly area and foothills
18 3118645 Sg. Lui di Kg. Lui 68 along the flood plains of Sg. Mantau and along Sg. Lui and its tributaries. A
its tributaries little wet rice is cultivated in
certain areas of the flood plains
of Sg. Lui
About 30% of the catchment is steep About two-third of the catchment

mountainous country above 610m and is under jungle and the remainder
Sg. Selangor di
19 3414621 1450 rising to heights of 1678m, 38% is hilly mostly under rubber. There is
Rantau Panjang
country and the remainder undulating low some tin mining within the
terrain catchment
The majority of the catchment is At the southern end of the

mountainous; only a very small area long catchment rubber is cultivated in
Sg. Selangor di the Sg. Selangor, at Rasa, is below the the hilly areas. The remainder
20 3516622 321
Rasa 76m contour line. The source of the Sg. areas are covered with virgin
Selangor is in rugged mountainous jungle.
country, above the 915m contour line
Sg. Selangor di Ulu Data Not Available Data Not Available

21 3613601 1290
Ibu Empangan
About 78% of this catchment is steep The mountainous areas are

Sg. Bernam di mountainous country rising to height of under jungle, while the hilly
22 3615612 186
Tanjung Malim 1830m. while the remainder is hilly country undulating areas are mainly
under rubber

mountainous country rising to height of under virgin jungle, while the hilly
Sg. Bernam di
23 3813611 1090 1830m. The remainder is hilly land with areas are mostly under rubber.
Jambatan SKC
swamps. Tin mining is being carried out
within this catchment
54
No Station Name 2
No (km )
Situated on the western side of the Main Hilly areas are mostly under
Sg. Klang di Range, about half of the entire catchment rubber and small low-lying areas
24 3116630 Jambatan 468 is steep mountainous country rising to are under tin mining.
Sulaiman heights of 1433 m, the remainder is hilly
land.

mountainous country rising to heights of under virgin jungle, while the hilly
Sg. Gombak di
25 3116633 122 1220 m. the remainder is hilly undulating areas are mostly under rubber.
Jalan Tun Razak
land. Small low-lying areas are under
padi cultivation and tin mining.

mountainous country rising to heights of under jungle, while the hilly areas
26 3116634 Sg. Batu di Sentul 145 1220 m. remainder is hilly undulating with are mostly under rubber. Some
some swamps along its lower reaches. tin mining is being carried out
within this catchment.
An urbanized catchment area very little Areas other than residential and
cultivation being done. Tin mining is still rubber plantations are covered by
being carried out on the eastern part of the forests located within the
Sg. Klang Di catchment, and muddy soils along the Gombak F.R. and Ampang F.R.
27 3117602 Lorong Yap Kuan 160 main river (Sungai Kelang) is obvious. The About 45% of the forest cover is
Seng eastern region, which is part of the main still undisturbed comprising
range, is a mountainous and steep area lowland and Hill Dipterocarp
with heights rising up to 1700 metres Forests with patches of good
above mean sea level. Seraya Forests.
Ibu Bekalan KM 11 Data Not Available Data Not Available

28 3217601 85
Gombak
Note: Adopted from Hydrological Data stream flow and river suspended sediment records 1986-
1990, produced by Department of Irrigation and Drainage, Ministry of Agriculture, Malaysia, 1995.
For a reliable NPS pollution loading estimation, baseline dry weather water quality hourly
data at each location should be collected for at least three days (one working, one Saturday
and one Sunday). Then, runoff events of various return periods should be sampled for runoff
quality and development of event mean concentration (EMC) values which can be used to
estimate the pollution loading due to NPS.
It is also recommended that rainfall data (using data logging rain gauge) should be collected
for the whole event duration during the water sampling. Sampling program for EMC and
NPS pollution loading calculation needs to be planned properly to cover the whole
hydrograph. Depending on the size of the catchment sampling intervals should be estimated
to cover the whole hydrograph. One grab sample, same as what is done for dry-weather
water quality monitoring program, is not suitable to calculate the NPS pollution load at any
river station. A brief description on the NPS pollution together with standard procedure is
recommended in the following section.
55
5.5 DEVELOPMENT OF JPS RIVER INDEX (JRI)
The parameters for the JRI were selected based on extensive literature review (Table 9),
comparison with the NWQS and statistical analysis of the available data. The JRI was
developed to evaluate the river status based on Quality (pollution) and Quantity (specific
flow) data.
5.5.1 Selection of Parameters
The parameters considered for JRI are Specific Flow, which is instantaneous flow divided by
the catchment area at the station (m3/s/km2) and indicates the changes of flow through the
river; Total Suspended Solids (TSS), which represents the sediments that adsorbs many
pollutants on the surfaces (mg/L); Total Dissolve Solids (TDS), which represents salts and
minerals that indicates the dissolved minerals in the water (mg/L); and Turbidity (TURB) in
NTU, which represents the clarity and aesthetic property of water that is very important to
make the river and water appealing to the people.
5.5.2 Rating Curves
Rating curves for the specific (normalized) flow indexes were developed to match the local
climate and weather conditions (Figure 19). The cut off point of specific flow for dry and rainy
day was considered as 0.05 m3/s/km2, which is recommended by JICA and commonly used
by the professionals as a typical value of baseflow in Malaysian rivers. The rating curves for
the JRI parameters were developed based on the Malaysian WQI, NWQS and comparing
with the overseas water quality indexes. Comparative rating graphs are shown in Figure 20.
Two rating curves are given for turbidity as the values will be very different during rainy and
non-rainy days. Naturally high turbidity is observed during the storm events due to high flow
velocity. The regression equations of the rating curves are given in Table 10.
56
Table 9: List of Parameters Considered in Various Water Quality Indexes in the World
No Parameter NSF Oregon Washi UWQI Argent Chile Turkey Spain Zimba Nigeria Korea China Thai Indon Mala This
WQI WQI ngton Europe ina bwe land esia ysia Study
Physical
1 Turbidity - - - - - - - -
2 TSS - - - - - - - -
3 TDS - - - - - - - - - - - -
4 Conductivity - - - - - - - - - -
5 TS - - - - - - - - - - - - -
6 Temperature - - - - *
Biochemical
7 pH - *
8 DO - *
9 BOD - -
10 COD - - - - - - -
11 Ammonia-N - - - - - - -
12 Chloride - - - - - - - - - - - - -
13 Fluoride - - - - - - - - - - - - - - -
14 Cyanide - - - - - - - - - - - - - - -
15 Oil & Grease - - - - - - - - - - - - - - -
16 Hardness - - - - - - - - - - - - -
17 Surfactants - - - - - - - - - - - - - - -
(MBAS)
Nutrient
18 TN - - - - - - - - - -
19 TP - - - - - -
20 SO4 - - - - - - - - - - - - -
21 NO3 - - - - - - - -
22 NO2 - - - - - - - - - - - -
23 PO4 - - - - - - - - - - - -
57
Table 9: List of Parameters Considered in Various Water Quality Indexes in the World (Continued)
No Parameter NSF Oregon Washi UWQI Argent Chile Turkey Spain Zimba Nigeria Korea China Thai Indon Mala This
WQI WQI ngton Europe ina bwe land esia ysia Study
Metals
22 Iron - - - - - - - - - - - - - - -
23 Mercury - - - - - - - - - - - - - -
24 Selenium - - - - - - - - - - - - - - -
25 Arsenic - - - - - - - - - - - - - -
26 Cadmium - - - - - - - - - - - - - - -
27 Nickel - - - - - - - - - - - - - - - -
28 Chromium - - - - - - - - - - - - - - - -
(IV)
29 Lead - - - - - - - - - - - - - - - -
30 Copper - - - - - - - - - - - - - - - -
31 Calcium - - - - - - - - - - - - - -
32 Magnesium - - - - - - - - - - - - - -
Microbial
33 Faecal - - - - - - - - -
Coliform
34 Total - - - - - - - - - - - - - -

Coliform
58
Table 10: The Rating Equations for each Parameter Considered for JRI
Parameter Equations Conditions

Non-rainy Day Sampling for
y = -71429x2 + 5851.4x - 19.446
Specific Point Source (< 0.05 m3/s/km2)
Flow (SF) Rainy Day Sampling for Non-
y = 3.2167x2 - 32.989x + 101.29
point Source (> 0.05 m3/s/km2)
Turbidity Non-rainy Day Sampling for
y = 0.0003x2 - 1.1978x + 112.04
(Turb) Point Source (< 150 NTU)
Turbidity Rainy Day Sampling for Non-
y = 0.0005x2 - 0.4634x + 113.97
(Turb) point Source (< 500 NTU
y = 0.003x2 - 0.7969x + 105.52 TSS 100
TSS
y = 0.0001x2 - 0.1785x + 71.431 TSS > 100
TDS y = 7E-05x2 - 0.1666x + 100.04 -
100
80
Sub-index
60
40
y = -71429x 2 + 5851.4x - 19.446
20
0
0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05
3 2
Non-rainy Specific Flow (m /s.km )
100
80 y = 3.2167x 2 - 32.989x + 101.29

Sub-index
60
40
20
0
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
3 2
Rainy Specific Flow (m /s.km )
Figure 19: Ratings Curves of Specific Flow for JRI
59
100
JAS
y = 0.003x 2 - 0.7969x + 105.52
80 Turkey
Spain
Sub-index
60
y = 0.0001x 2 - 0.1785x + 71.431 JPS-RI
40
20
0
0 100 200 300 400 500 600 700 800 900 1000
TSS (mg/L)
100
NSF
80
JPS-RI
Sub-index
60
40 y = 7E-05x 2 - 0.1666x + 100.04
20
0
0 100 200 300 400 500 600 700 800 900 1000
TDS (mg/L)
100
Turkey
80 y = 0.003x 2 - 1.1978x + 112.04
NSF
Sub-index
60 JPS-RI (Non-rainy Day)
40 JPS-RI (Rainy Day Flow)
20 y = 0.0005x 2 - 0.4634x + 113.97
0
0 50 100 150 200 250 300 350 400 450 500
Turbidity (NTU)
Figure 20: Ratings Curves of Water Quality Parameters for JRI
5.5.3 Weighing Factors
Effect of each parameter on the river/aquatic environment was rated or taken care of by
means of the rating curve. Therefore, relative importance of the selected parameters on the
river status was evaluated by assigning weighing factor for each parameter. In this exercise,
a highest value of 5 could be given to the critical parameter, while the least important
60
parameter could be assigned the value of 1 or less. Then, the calculated fraction for each
group of parameters was considered as the weighing factor of each parameter selected for
the JRI (Table 11). Various weighing factors practiced worldwide are listed in Table 12, for
the purpose of comparison only.
Table 11: Determination of Parameter Weighing Factor for JRI
Parameter Priority Index (out of 5) Weighing Factor

Sp. Flow 3.5 0.30
Turb 1.5 0.13
TSS 4.0 0.35
TDS 2.5 0.22
Total 11.5 1.00
5.5.4 Proposed JRI
The tool/equation obtained to determine the quality of the rivers in Malaysia based on JPS
River Index (JRI) is;
JRI = 0.30*(SISF) + 0.13*(SITurb) + 0.35*(SITSS) + 0.22*(SITDS) (17)
where,
SISF = Sub-index for specific flow
SITurb = Sub-index for Turbidity
SITSS = Sub-index for TSS
SITDS = Sub-index for TDS
5.5.5 Limits Selected for each Class and Parameter
A thorough review of the available literature was conducted to compare the ranges of quality
indexes used in various countries (as given in Table 13). In this study, the class of JRI was
divided into five main categories that are from Class I to V. Class II, Class III, and Class IV
were then further divided into three sub-sections to make the classifications become more
target oriented. Each section was assigned certain range of JRI values, varied from 0 to
100. The threshold values for parameters were determined using the equation of rating
curve obtained. The summary of selected limits for each class and parameter is given in the
Table 14.
61
Table 12: Determination of Weighing Factor for JRI
NSF UWQI Korea Argentina Chile Turkey Spain Malaysia

Parameter
Factor Factor Factor Factor Relative Factor Factor Relative Factor Relative Factor
DO (% saturation) 0.17 - - - - - - - - - 0.22
Faecal coliform (or

0.15 - - - - - - - - - -
E. coli)
pH 0.12 0.029 - 0.0233 1 0.1 0.0323 1 0.0385 1 -
BOD5 0.1 0.057 0.34 0.0698 3 0.17 0.0968 3 0.1154 3 -
Nitrates 0.1 - - 0.0465 2 0.07 0.0645 2 0.0769 2 -
Phosphates 0.1 - - - - 0.12 0.0323 1 - - -
t oC from
0.1 - - 0.0233 1 0.1 0.0323 1 0.0385 1 -
equilibrium
Turbidity 0.08 - - 0.0465 2 - 0.0645 2 - - -
Total solids 0.08 - - 0.0930 4 - - - - - -
Total Phosphorus - 0.057 0.33 0.0233 1 - - - 0.0385 1 -
Total Nitrogen - - 0.33 - - - - - - - -
Total coliform - 0.114 - 0.0698 3 - - - - - -
Cadmium - 0.086 - - - - - - - - -
Cyanide - 0.086 - - - - - - - - -
Mercury - 0.086 - - - - - - - - -
Selenium - 0.086 - - - - - - - - -
Arsenic - 0.113 - - - - - - - - -
Fluoride - 0.086 - - - - - - - - -
Nitrate-nitrogen - 0.086 - - - - - - - - -
DO (conc.) - 0.114 - 0.0930 4 0.18 0.1290 4 0.1538 4 -
Ammonia nitrogen - - - 0.0698 3 0.13 0.0968 3 0.1154 3 0.15
Calcium - - - 0.0233 1 - 0.0323 1 - - -
Chloride - - - 0.0233 1 - 0.0323 1 - - -
Conductivity - - - - - 0.06 0.0645 2 0.0769 2 -
COD - - - 0.0698 3 0.17 0.0968 3 0.1154 3 0.16
Hardness - - - 0.0233 1 - 0.0323 1 - - -
Magnesium - - - 0.0233 1 - 0.0323 1 - - -
Nitrites - - - 0.0465 2 0.07 0.0645 2 0.0769 2 -
Oil and grease - - - 0.0465 2 - - - - - -
Dissolved solids - - - 0.0465 2 - - - - - -
Sulfates - - - 0.0465 2 - 0.0645 2 - - -
Surfactants as
- - - 0.0930 4 - - - - - -
MBAS
Total suspended
- - - - - - 0.0323 1 0.1538 4 0.16
solids
5.6 USEFULNESS AND APPLICATION OF JRI
The proposed JRI can be considered useful and unique in the sense that it considered river
water quantity and quality together. No index can be found in the literature which considered
both quantity and quality aspects of river water together with considerations of dry and rainy
62
day conditions. There should be no doubt that flow is a very important component of a river
index. The JRI is kept simple by considering 4 important parameters that should be
considered to identify a healthy river. Therefore, it is expected that the tool would assist DID
in evaluating the status of the rivers and set target to improve the river status.
5.7 HOW TO APPLY JRI
Public, practitioners, and authority personals can easily assess the river water status by
using the JRI equations and by following the steps given below:
1. Collect data on river flow (m3/s), catchment area (km2), TSS (mg/L), TDS (mg/L) and
Turbidity (NTU).
2. Calculate specific flow by dividing the river flow by the catchment area at the
sampling point.
3. The calculate sub-index of each parameter using the rating curve equations given in
Table 10.
4. Multiply the sub-index value with the weighing factor (Table 11) to get the weighted
value of sub-index.
5. Compare the value of the JRI with the classification of given in Table 14 and
determine the class or status of the river in terms of the selected parameters.
5.8 SAMPLE CALCULATION OF JRI
Sample calculations of JRI for Station 3414621 (Sg.. Selangor at Rantau Panjang) are given
below. The examples show how to apply JRI for dry day flow and rainy day flow conditions.
The following data (for JRI) are available for the sampling station at which the catchment
area is 1450 km2. The procedure is given step by step in the following section:
Step 1: Collect Relevant Data (In this case actual data of Station 3414621, Sg.. Selangor at
Rantau Panjang is used).
Flow at
Sample Sampling Sampling Sp. Flow Turb. TSS TDS
Sampling
ID Date Time (m3/s.km2) (NTU) (mg/L) (mg/L)
(m3/s)
(a) 14/03/2006 4:06 p.m 31.0 0.021 88 59 72
(b) 22/03/2006 4:00 p.m 132.7 0.092 279 261 64
(a) Flow during sampling is 31.0 m3/s.
63
Step 2: Calculate Specific Flow,
SF = Flow/Catchment Area
= 31.0/1450 = 0.021 m3/s.km2
As the specific flow is less than 0.05 m3/s.km2 it is considered Non-rainy Day Flow
Sample
Step 3: Calculate Subindexes for four parameters using the equations from Table 10,
SISF = -71429x2 + 5851.4x - 19.446
= -71429*0.0212 + 5851.4*0.021 - 19.446
= 73.0
SITurb = 0.0003x2 - 1.1978x + 112.04
= 0.0003*882 - 1.1978*88 + 112.04
= 29.9
SITSS = 0.003x2 - 0.7969x + 105.52
= 0.003*592 - 0.7969*59 + 105.52
= 68.9
SITDS = 7E-05x2 - 0.1666x + 100.04
= 7E-05*722 - 0.1666*72 + 100.04
= 88.4
Step 4: Calculate JRI for non-rainy day flow by using Equation 17,
JRI = 0.30*(SISF) + 0.13*(SITurb) + 0.35*(SITSS) + 0.22*(SITDS)

= 0.30*73.0 + 0.13*29.9 + 0.35*68.9 + 0.22*88.4
= 69.3
69 (to be rounded up to nearest full number)
Step 5: Compare the value of JRI with the values given in Table 14 and determine Class
and status of the river.
For this instance, the river belonged to Class III-A with a Status of Fair.
(b) Flow during sampling is 137.0 m3/s.
Step 2: Calculate Specific Flow,
SF = Flow/Catchment Area
= 137.0/1450 = 0.092 m3/s.km2
64
As the specific flow is higher than 0.05 m3/s.km2 it is considered Rainy Day Flow Sample
Step 3: Calculate Subindexes for four parameters using the equations from Table 10,
SISF = -71429x2 + 5851.4x - 19.446
= -71429*0.0922 + 5851.4*0.092 - 19.446
= 98.3
SITurb = 0.0005x2 - 0.4634x + 113.97
= 0.0005*2792 - 0.4634*279 + 113.97
= 23.6
SITSS = 0.0001x2 - 0.1785x + 71.431
= 0.0001*2612 - 0.1785*261 + 71.431
= 31.7
SITDS = 7E-05x2 - 0.1666x + 100.04
= 7E-05*642 - 0.1666*64 + 100.04
= 89.7
Step 4: Calculate JRI for rainy day flow by using Equation 17,
JRI = 0.30*(SISF) + 0.13*(SITurb) + 0.35*(SITSS) + 0.22*(SITDS)

= 0.30*98.3 + 0.13*23.6 + 0.35*31.7 + 0.22*89.7
= 60.8
61 (to be rounded up to nearest full number)
Step 5: Compare the value of JRI with the values given in Table 14 and determine Class
and status of the river.
For this data, the river belonged to Class III-B with a Status of Fair.
65
Table 13: Classes Various Water Quality Indexes Worldwide
Range of WQI Value
British
Class USA Diff. Class Oregon Diff. Class Diff. Class UWQI Diff. Class Korea Diff.
Columbia
95- 91-
Excellent 91-100 9 Excellent 90-100 10 Excellent 0-3 3 Excellent 5 Very low 9
100 100
Good 71-90 9 Good 85-89 4 Good 4 - 17 13 Good 75-94 19 Low 71-90 9
Medium
or 51-70 19 Fair 80-84 4 Fair 18 - 43 25 Fair 50-74 24 Medium 51-70 19
Average
Fair 26-50 24 Poor 60-79 19 Borderline 44 - 59 15 Marginal 25-49 24 High 26-50 24
Poor 0-25 25 Very poor 10 - 59 49 Poor 60 - 100 40 Poor 0-24 24 Very high 0-25 25
66
Table 14: Classes for JPS River Index
Class and Status of the River
Very
Parameter Unit Clean Good Fair Poor
Poor
I II-A II-B II-C III-A III-B III-C IV-A IV-B IV-C V
JRI - > 90 90-85 84-78 77-71 70-65 64-58 57-51 50-45 44-38 37-31 <30
Specific Flow, 0.0261 0.0231

3 2 0.0201 0.0181 0.0161 0.0146 0.0131 0.0111 0.009
Non-rainy Day (m /s.km ) > 0.029 < 0.009
0.0290 0.0260 0.0230 0.020 0.0180 0.0160 0.0145 0.0130 0.0110
(SF)
Specific Flow, 0.370 0.535 0.785 1.035 1.275 1.565 1.875 2.185 2.585 > 3.050
3 2
Rainy Day (m /s.km ) < 0.37 0.534 0.784 1.034 1.274 1.564 1.874 2.184 2.584 3.050
(SF)
Turbidity, Non-
NTU < 20 20 26 27 33 33 38 39 46 47 53 54 60 61 69 70 78 79 87 > 87
rainy Day
Turbidity, 108 150 170 219
NTU < 55 55 71 72 188 189 107 129 149 195 218 > 228
Rainy Day 128 169 194 243
127 217
TSS mg/L < 21 21 29 30 41 42 54 55 69 70 92 93 126 167 216 > 270
166 270
193 291 346 467
TDS mg/L < 66 66 95 96 144 145 192 231 290 397 466 > 539
230 346 396 539
67
Table 15: Calculated Percentile JRI Values of the Stations
Flow at Individual Sub-index Group Sub-index

Statistical Sp. Flow Turb. TSS TDS Sample River
Sampling Sp. Sp. JRI Class
Parameter 3 (m3/s.km2) (NTU) (mg/L) (mg/L) Represents Turb. TSS TDS Turb TSS TDS Status
(m /s) Flow Flow
Statistical Values for the Station 1737651 (Sg. Johor di Rantau Panjang)
95 Percentile Value 158.06 0.140 142 166 168 Rainy Day 99 90 96 94 30 12 33 20 88 II -A Good
75 Percentile Value 74.65 0.066 87 91 115 Rainy Day 97 80 74 92 30 10 26 20 81 II -B Good
50 Percentile Value 30.89 0.027 48 66 73 Dry Day 87 76 66 88 26 10 23 19 75 II -C Good
25 Percentile Value 18.07 0.016 24 49 49 Dry Day 56 52 58 82 17 7 20 18 68 III -A Fair
5 Percentile Value 10.48 0.009 7 13 38 Dry Day 27 21 45 74 8 3 16 16 45 IV -A Poor
Number of Data 12 12 16 16 15 - - - - - - - - - - - - -
Mean Value 54.33 0.048 59 76 87 Dry Day 75 65 67 86 23 8 23 19 72 II -C Good
Standard Deviation 54.03 0.048 45 46 47 Dry Day 29 27 16 7 9 3 5 2 15 - - -
Minimum Value 5.05 0.004 3 9 30 Dry Day 5 3 44 71 2 0 15 15 43 IV -B Poor
Maximum Value 168.98 0.150 149 171 190 Rainy Day 99 92 99 95 30 12 34 21 90 II -A Good
Statistical Values for the Station 2130622 (Sg. Bekok di Batu 77 Jalan Yong Peng Labis)
75 Percentile Value 12.43 0.036 45 81 95 Dry Day 97 94 89 94 29 12 31 20 87 II -A Good
50 Percentile Value 9.94 0.028 34 41 63 Dry Day 89 87 78 90 27 11 27 20 81 II -B Good
Number of Data 14 14 16 16 16 - - - - - - - - - - - - -
Mean Value 13.93 0.040 40 52 96 Dry Day 86 79 75 85 26 10 26 19 81 II -B Good
Minimum Value 6.45 0.018 15 13 15 Dry Day 64 51 52 42 20 7 18 9 60 III -B Fair
Maximum Value 47.97 0.137 165 114 421 Rainy Day 99 95 96 98 30 12 33 21 92 I Clean
Statistical Values for the Station 2237671 (Sg. Lenggor di Batu 42 Kluang Mersing)
95 Percentile Value 108.39 0.524 51 52 92 Rainy Day 99 99 96 98 30 13 33 21 91 I Clean
Number of Data 51 51 51 52 92 - - - - - - - - - - - - -
Mean Value 108.39 0.524 51 52 92 Rainy Day 99 99 96 98 30 13 33 21 91 I Clean
Standard Deviation 56.92 0.275 20 19 36 Rainy Day 28 5 11 6 8 1 4 1 8 - - -
Minimum Value 2.23 0.011 3 10 9 Dry Day 35 88 70 83 11 11 24 18 73 II -C Good

(m /s) Flow Flow
Statistical Values for the Station 2527611 (Sg. Muar di Buloh Kasap)
75 Percentile Value 33.59 0.011 118 119 154 Dry Day 35 69 85 87 11 9 30 19 57 III -C Fair
5 Percentile Value 2.02 0.001 21 16 22 Dry Day 0 0 35 57 0 0 12 12 40 IV -B Poor
Number of Data 40 40 46 46 46 - - - - - - - - - - - - -
Mean Value 28.60 0.009 91 93 129 Dry Day 24 42 66 80 7 6 23 17 53 III -C Fair
Minimum Value 1.74 0.001 19 5 11 Dry Day 0 0 0 47 0 0 0 10 18 V Very Poor
Maximum Value 171.65 0.055 280 663 377 Rainy Day 100 98 100 98 30 13 35 21 84 II -B Good
Statistical Values for the Station 2528614 (Sg. Segamat di Segamat)
50 Percentile Value 11.48 0.018 70 70 106 Dry Day 61 60 65 83 19 8 22 18 62 III -B Fair
Number of Data 40 40 46 46 46 - - - - - - - - - - - - -
Mean Value 12.96 0.026 96 100 123 Dry Day 59 55 62 81 18 7 21 18 65 III -A Fair
Statistical Values for the Station 5606610 (Sg. Muda di Jam Syed Omar)
95 Percentile Value 213.14 0.064 - 342 - - 100 - 89 - 30 - 31 - - - - -
Number of Data 84 84 - 94 - - - - - - - - - - - - - -
Mean Value 78.36 0.024 - 108 - - 52 - 62 - 16 - 22 - - - - -
Standard Deviation 78.54 0.024 - 118 - - 37 - 21 - 11 - 7 - - - - -
Minimum Value 11.73 0.004 - 7 - - 0 - 0 - 0 - 0 - - - - -
Maximum Value 434.24 0.130 - 697 - - 100 - 100 - 30 - 35 - - - - -

(m /s) Flow Flow
Statistical Values for the Station 5120601 (Sg. Nenggiri di Jam Bertam)
50 Percentile Value 123.14 0.058 53 138 51 Rainy Day 99 86 49 92 30 11 17 20 76 II -C Good
Number of Data 46 46 46 50 49 - - - - - - - - - - - - -
Mean Value 165.18 0.078 160 358 60 Rainy Day 96 71 40 90 29 9 15 20 73 II -C Good
Minimum Value 44.27 0.021 5 23 20 Dry Day 71 3 0 75 22 0 0 16 51 III -C Fair
Statistical Values for the Station 5222652 (Sg. Lebir di Kg Tualang)
Number of Data 41.00 41.000 44 46 46 - - - - - - - - - - - - -
Mean Value 513.95 0.212 109 232 63 Rainy Day 84 68 54 90 26 9 19 20 73 II -C Good
Minimum Value 15.60 0.006 1 9 10 Dry Day 15 0 0 79 5 0 0 17 47 IV -A Poor
Statistical Values for the Station 5320643 (Sg. Galas di Dabong)
Number of Data 25 25 25 26 25 - - - - - - - - - - - - -
Mean Value 411.61 0.053 75 307 152 Rainy Day 97 73 39 78 30 10 14 17 69 III -A Fair

(m /s) Flow Flow
Statistical Values for the Station 5419601 (Sg. Pergau di Batu Lembu)
Number of Data 60 60 73 80 79 - - - - - - - - - - - - -
Mean Value 97.83 0.076 47 140 68 Rainy Day 94 89 58 89 28 12 20 19 81 II -B Good
Standard Deviation 94.55 0.073 83 145 47 Rainy Day 9 20 24 7 3 3 8 2 10 V Very Poor
Statistical Values for the Station 5718601 (Sg. Lanas di Air Lanas)
Number of Data 68 68 70 71 73 - - - - - - - - - - - - -
Standard Deviation 0.17 0.002 2 7 15 Dry Day 0 0 35 34 0 1 12 7 49 IV -A Poor
Statistical Values for the Station 5721642 (Sg. Kelantan di Jam Guillemard)
Number of Data 36 36 25 37 27 - - - - - - - - - - - - -
Standard Deviation 84.35 0.007 3 7 26 Dry Day 18 0 0 61 6 0 0 13 56 III -C Fair

(m /s) Flow Flow
Statistical Values for the Station 5818601 (Sg. Golok di Kg Jenob)
95 Percentile Value - - 112 245 105 - - - 98 97 - - 34 21 - - - -
Number of Data - - 73 79 79 - - - - - - - - - - - - -
Mean Value - - 25 75 58 - - - 72 91 - - 25 20 - - - -
Standard Deviation - - 1 5 17 - - - 21 62 - - 7 13 - - - -
Minimum Value - - 1 5 17 - - - 21 62 - - 7 13 - - - -
Maximum Value - - 247 350 256 - - - 100 97 - - 35 21 - - - -
Statistical Values for the Station 6019611(Sg. Golok di Rantau Panjang)
Number of Data 23 23 22 24 24 - - - - - - - - - - - - -
Standard Deviation 5.70 0.007 2 4 17 Dry Day 20 76 39 83 6 10 14 18 60 III -B Fair
Minimum Value 5.70 0.007 2 4 17 Dry Day 20 76 39 83 6 10 14 18 60 III -B Fair
Statistical Values for the Station 2224632 (Sg. Kesang di Chin Chin)
Number of Data 166 166 226 225 226 - - - - - - - - - - - - -
Mean Value 32.83 0.204 118 118 95 Rainy Day 69 69 62 85 21 9 22 18 70 III -A Fair
Standard Deviation 0.15 0.001 2 5 29 Dry Day 0 0 0 0 0 0 0 0 27 V Very Poor

(m /s) Flow Flow
Statistical Values for the Station 2322613 (Sg. Melaka di Pantai Belimbing)
Number of Data 23 23 132 132 131 - - - - - - - - - - - - -
Mean Value 3.26 0.009 145 185 102 Dry Day 24 43 57 84 7 6 20 18 51 III -C Fair
Maximum Value 12.71 0.036 2150 1701 424 Dry Day 99 99 100 98 30 13 35 21 63 III -B Fair
Statistical Values for the Station 2917601 (Sg. Langat di Kajang)
Number of Data 145 145 180 179 180 - - - - - - - - - - - - -
Mean Value 8.40 0.022 152 291 83 Dry Day 55 53 43 87 17 7 15 19 58 III -B Fair
Statistical Values for the Station 3118645 (Sg. Lui di Kg. Lui)
Number of Data 153 153 166 167 168 - - - - - - - - - - - - -

(m /s) Flow Flow
Statistical Values for the Station 3414621 (Sg. Selangor di Rantau Panjang)
Number of Data 92 92 116 115 114 - - - - - - - - - - - - -
Statistical Values for the Station 3516622 (Sg. Selangor di Rasa)
Number of Data 83 83 137 135 139 - - - - - - - - - - - - -
Statistical Values for the Station 3613601 (Sg. Bernam di Ulu Ibu Ampangan)
Number of Data - - 150 153 152 - - - - - - - - - - - - -
Mean Value - - 86 114 41 - - - 59 93 - - 21 20 - - - -
Minimum Value - - 3 5 8 - - - 1 77 - - 3 17 - - - -
Maximum Value - - 530 595 145 - - - 100 99 - - 35 21 - - - -

(m /s) Flow Flow
Statistical Values for the Station 3615612 (Sg. Bernam di Tanjung Malim)
Number of Data - - 173 179 178 - - - - - - - - - - - - -
Mean Value - - 33 56 39 - - - 78 94 - - 27 20 - - - -
Minimum Value - - 0 4 6 - - - 0 50 - - 0 11 - - - -
Maximum Value - - 370 673 353 - - - 100 99 - - 35 22 - - - -
Statistical Values for the Station 3813611 (Sg. Bernam di Jam S.K.C)
Number of Data - - 200 199 197 - - - - - - - - - - - - -
Mean Value - - 100 133 44 - - - 56 93 - - 20 20 - - - -
Standered Deviation - - 4 11 4 - - - 0 77 - - 0 17 - - - -
Minimum Value - - 4 11 4 - - - 0 77 - - 0 17 - - - -
Maximum Value - - 839 1375 149 - - - 100 99 - - 35 22 - - - -
Statistical Values for the Station 3116630 (Sg. Klang di Jam Sulaiman)
Number of Data - - 7 7 7 - - - - - - - - - - - - -
Mean Value - - 59 80 143 - - - 69 78 - - 24 17 - - - -
Minimum Value - - 15 19 100 - - - 31 74 - - 11 16 - - - -
Maximum Value - - 204 270 170 - - - 91 84 - - 32 18 - - - -

(m /s) Flow Flow
Statistical Values for the Station 3116633 (Sg. Gombak di Jalan Tun Razak)
Number of Data 5 5 7 7 7 - - - - - - - - - - - - -
Maximum Value 5.76 0.047 275 320 159 Dry Day 100 83 87 87 30 11 30 19 89 II -A Good
Statistical Values for the Station 3116634 (Sg. Batu di Sentul)
Number of Data - - 7 7 7 - - - - - - - - - - - - -
Mean Value - - 48 48 170 - - - 79 74 - - 27 16 - - - -
Standered Diavation - - 16 15 141 - - - 48 71 - - 17 15 - - - -
Minimum Value - - 16 15 141 - - - 48 71 - - 17 15 - - - -
Maximum Value - - 117 141 192 - - - 94 78 - - 33 17 - - - -
Statistical Values for the Station 3117602 (Sg. Klang At Lorong Yap Kwan Seng)
Number of Data - - 7 7 7 - - - - - - - - - - - - -
Mean Value - - 37 41 130 - - - 84 80 - - 29 17 - - - -
Standered Diavation - - 9 5 93 - - - 46 74 - - 16 16 - - - -
Minimum Value - - 9 5 93 - - - 46 74 - - 16 16 - - - -
Maximum Value - - 151 159 169 - - - 100 85 - - 35 19 - - - -

(m /s) Flow Flow
Statistical Values for the Station 3217601 (Sg. Gombak Ibu Bekalan Km 11 Gombak. This station shifted from Sg. Gombak at Damsite)
Number of Data - - 8 8 8 - - - - - - - - - - - - -
Mean Value - - 134 162 70 - - - 45 89 - - 16 19 - - - -
Minimum Value - - 50 40 47 - - - 78 92 - - 27 20 - - - -
Maximum Value - - 307 492 93 - - - 8 85 - - 3 19 - - - -
5.9 EVALUATION OF JPS DATA BY JRI
Many of the JPS stations have the four parameters required to calculate JRI and classify the
rivers according to the JRI. As such, the JRI of the stations (with complete sets of data) was
calculated for the quantile values and given in Table 4.12. It was observed that, based on the
median value (50 percentile), most of the rivers belongs to the category of fair (Class III, 7
stations) to good (Class II, 11 stations). The median value of no station was found to be clean
and one station (2322613 at Pantai Belimbing, Sg.. Melaka) was found to be in poor status
mainly due to low flow and high turbidity. The JRI value of this station was less due to low
specific flow and high turbidity. Nine other stations did not have complete sets of data to
calculate the JRI values.
5.10 JPS WATER QUALITY MONITORING PROGRAM
The existing water quality parameters analysed statistically and compared to the national water
quality standards (NWQS) of Malaysia. The parameters monitored by the JPS were also
compared to those of the DOE Malaysia. Duplication of water quality parameters were observed
in JPS monitoring program. It is strongly recommended that JPS and DOE should come into
agreement on the locations of the stations to minimise redundancy. If any JPS and DOE station
is nearby, only one station can be maintained for the agreed parameters.
5.10.1 Monitoring Parameters
Twenty four water quality parameters are monitored by JPS water quality monitoring program
(Figure 2). The JPS has justified the selection of parameters in the HP No 22, which is given in
Table 16. A few parameters, such as total nitrogen, total kjeldhal nitrogen and ammoniacal
nitrogen can be added in the list, as these parameters indicates nitrogenous compounds which
are often required for most of the water quality simulation softwares.
5.10.2 Quality Control and Quality Assurance Procedure
The consultants have looked into the existing water quality sampling, preservation, transport
and laboratory testing procedure for quality control (QC) and the quality assurance (QA). The
following points are identified based on the standard practices approved by SIRIM, JPS and
DOE Malaysia. It is of utmost importance that whatever procedure is mentioned in the Guide to
Water Quality Monitoring Practices in Malaysia - Practices and Techniques of Sampling and
Application of Water Quality Data by Various Government Agencies in Malaysia, should be
followed in full. Negligence in any of the elements of the whole water quality monitoring exercise
would jeopardize the objectives of this expensive activity which require significant amount of
human labour, monitory input, chemical and costly equipments.
78
Table 16: Selection of JPS Water Quality Parameters for Various Applications
No Water Fisheries Experimental Pollution Representative Irrigation

supply Basin Study Basin Study
1 Colour Colour Colour Colour Colour Colour
2 Turbidity Turbidity Turbidity Turbidity Turbidity Turbidity
3 Conductivity Conductivity Conductivity Conductivity Conductivity Conductivity
4 Hardness Hardness Hardness Hardness Hardness Hardness
5 Total solids Total solids Total solids Total solids Total solids Total solids
6 Suspended Suspended Suspended Suspended Suspended Suspended
solids solids solids solids solids solids
7 Dissolved Dissolved Dissolved Dissolved Dissolved solids Dissolved
solids solids solids solids solids
8 pH pH pH pH pH pH
9 Calcium Calcium Calcium Calcium Calcium Calcium
10 Chloride Chloride Chloride Chloride Chloride Chloride
11 Potassium Potassium Potassium Potassium Potassium Potassium
12 Magnesium Magnesium Magnesium Magnesium Magnesium Magnesium
13 Sodium Sodium Sodium Sodium Sodium Sodium
14 Silica Silica Silica Silica Silica Silica
15 - Nitrate Nitrate Nitrate - Nitrate
16 Ammonia Ammonia - - - Ammonia
17 - Dissolved - Dissolved - -
oxygen Oxygen
18 - - - Phosphate - -
19 - - - BOD - -
20 - - - COD - -
21 Iron - - - - -
22 Sulphate - - - - -
23 Fluoride - - - - -
5.10.3 TIDEDA Program
The TIDEDA software is used by JPS to store and analyse the water quality data. The software
has many good features to archive and make use of the data. However, this program is not
79
accessible to the public. Therefore, the clients of the JPS downlads the data in CSV or TEXT
format and use their own programs to analyse the data.
Besides storing data, the common features that the TIDEDA program can offer are:
Tabulate and display data of all water quality parameters according to the stations (Figure
21)
Generation of daily data according the required parameter (Figure 22).
The actual values of a few water quality parameters (pH, Turbiidity, Alkalinity, Calcium, etc.)
are multiplied by factors varying from 10 to 100. It is strongly recommended that the
TIDEDA should be customized to accept and reproduce the water quality values exactly
same as reported from the site and laboratory test results.
The turbidity is measured as NTU but in the TIDEDA program it appears to be as Fullers.
The unit of turbidity should be changed in the TIDEDA program as NTU.
The program is also able to conduct statistical analyses and produce various graphs (Figure
23).
The program can produce annual time series data.
It is also recommended that the order and arrangement of the parameters in TIDEDA.
program should match the data sheet used for the site and laboratory data.
Figure 21: Display Screen of TIDEDA Output for all Water Quality Data at any Station
80
Figure 22: Display Screen of TIDEDA Output for Daily Water Quality Data for any Parameter
Figure 23: Display Screen of TIDEDA Output for any Water Quality Data at any Station
81
CONCLUSIONS AND RECOMMENDATIONS
6.1 CONCLUSIONS
The advantages of JPS water quality monitroing program is that it includes the river flow data
wihich is very important for the calculation of pollution loading and necessary for water quality
modelling exercise. However, the study output could have been of better quality if all the data
were available in regular interval, for all parameters and at all 28 stations monitored by JPS.
Missing data and irregularity of the sampling posed a great challenge in achieving the objectives
of the study.
Although 24 water quality parameters are being monitored under the existing scheme, a few
important parameters (e.g. DO, Nutrients, Toxic Heavy Metals, E.coli Bacteria, etc.) were not
monitored. As a result the exsiting data was not suitable for the development of comprehensive
river index to covering all aspects of the water quality. Adequacy of the parameters were
evaluated and appropriate recommendataions are made to improve and optimise the monitoring
exercise done by the JPS and Chemistry Departmetn of Malaysia.
Pollution loadings for the parameters are calculated for each stations having the water flow
data. In order to compare the contribution of pollution from each catchment, the loads are
expressed in terms of kg./km2/hr. It was observed that most of the stations are relatively located
in less developed areas. As a result the stations, generally, indicate the nature of pollution from
less developes areas. However, due to irregualarity of data collection, pollution loading for
various ARI was not calculated.
Suitability of the exsiting sampling and monitoring scheme was evaluated to quantify the
contribution of pollution load from the non-point sources (NPS). Sampling procedure for NPS
pollution monitoirng is described in the report. It is also realised that a nationwide NPS pollution
study for various landuses would be the first step to develop the EMC database, which is a
fundamental requirement for the calculation of NPS pollution loading at any location.
JPS archives all data with the aid of a computer software called TIDEDA, which is found to be
very usefull in properly handling huge amount of data. The capability of the customised TIDEDA
module for water quality data is reviewd and improvements are recommended to avoid
confusion on the format of the values and to ease the data transfer from laboratory data sheet to
the TIDEDA program.
A comprehensive literature review was conducted to study the the existing water quaity indexes
used in various parts of the worls. Based on the extensive literature review the best possible
index is propsed to make use of the JPS Data.
82
Due to unavailability of a few important parameters (as the original JPS water quality monitoring
program was not intended for any index), a simplified river index (JRI) is proposed consisting of
data on Specific Flow, which is instantaneous flow divided by the catchment area at the station
(m3/s/km2); Total Suspended Solids (TSS), which represents the sediments that adsorbs many
pollutants on the surfaces (mg/L); Total Dissolve Solids (TDS), which represents salts and
minerals that indicates the dissolved minerals in the water (mg/L); and Turbidity (TURB) in NTU,
which represents the clarity and aesthetic property of water that is very important to make the
river and water appealing to the people.
Rating curves are proposed for the JRI parameters (Specific Flow, Turbidity, TSS, and TDS).
Weighing factors for each of the 4 parameters are calculated based on the relative index and
the overall JRI is developed to evaluate the river status based on the past data collected by
JPS. Due to unavailability of other important data required to develop a comprehensive river
index, the proposed JRI is kept simple but very relevent to JPS main activites and line of
actions.
6.2 RECOMMENDATIONS
JPS is recommended to go for ISO for the water quality monitoring system and services. For the
time being, the existing guidelines (HP No. 22 and others) should be followed in full. The data
sheets used in the sites and laboratory should be completed properly. Proper care should be
taken in transporting the data from the site to the laboratory. It is highly recommended to send
the sample to the laboratory within 24 hours of sampling.
Two data forms should be used for the data collection one for field information (Bacaan Luar)
and other for laboratory data (Laporan Makmal) in Appendix D of HP NO. 22. However, no data
was available for Items 4, 5, 6, 8, 9, 13, 14, 15 and 16 of the Bacaan Luar data sheet. It is
recommended that these data is important and should be recorded and made available to the
customers.
In-situ quality monitoring instruments (e.g. DO, pH, TDS, conductivity, turbidity meter, etc.
should be calibrated and operated according to the guideline (operation manual) provided by
the supplier (Pengukuran In-situ Water Quality Menggunakan Portable Multiparameter by
Lizawati Duri and Azmi Jafri).
Chemicals required for the calibration of the equipments should be stored properly as required
and checked for the expiry dates.
Monitoring of in-situ parameters and collection of samples should be done from the running
water not from the stagnant water near the banks. All personnel involved in the whole exercise
(from sampling, storing, in situ monitoring, transporting, laboratory testing, etc.) must realise that
every components are very important to produce reliable data.
83
Collected samples should be preserved according to the standard procedure (2.7.7.4 Sample
Preservation, page 5 of the Guide To Water Quality Monitoring Practices In Malaysia - Practices
And Techniques Of Sampling And Application Of Water Quality Data By Various Government
Agencies In Malaysia) to preserve the quality of water from any unwanted decay. For certain
parameter (COD and Ammonia) pH of the samples should be reduced less than 2.0 to
discourage decay of the pollutants.
Due to advancement of the monitoring devices and precision of the laboratory equipments,
metals and other parameters should be detected and reported to more decimal points.
Although the information on the rainfall (during sampling) should be recorded in the data sheet
(item 14 in Figure 2) but it was not available. As such, the consultant team had to depend on the
available flow data to anticipate if the samples represented the flow due to storm events.
pH should be measured at site and at laboratory. However, only one pH value was available in
the report furnished by the Department of Chemistry. Detection limits for certain parameters
(e.g. Ammonia, F-, Cl-, NO3-, Mn, PO4-, Turbidity, etc.) were not consistent.
If JPS is interested to develop a comprehensive JRI for the classification of rivers in Malaysia,
the revised monitoring program should include groups of several parameters namely, River
Flow, Physical (TSS, Turbidity, TDS, etc.), Chemical (COD, Ammonia, Heavy Metals, Toxic
Elements, etc.), Biological (Coliform Bacteria), etc. In order to make the data useful the
frequency of sampling should be properly planned and regular without any missing schedule.
One of the main objectives of the study was to develop a tool to calculate the JPS River Index
(JRI). After reviewing many references it was realized that certain important parameters (e.g.
dissolved oxygen, toxic metals, faecal coliform E. coli, etc.) are important for any water quality
index but not monitored by the JPS. If JPS wish to revise the monitoring program to enhance
data acquisition for better assessment and to aid water quality modeling exercises additional
parameters would be necessary to be included. Therefore, a revised list of JPS water quality
parameters is proposed in Table 17, which indicates few important parameters should be
monitored monthly and others could be monitored quarterly. Different monitoring frequencies
are proposed to reduce the operation cost of the Jabatan Kimia and JPS. The parameters
included in the TIDEDA database should be same as that shown in Table 17 and the software
should be customized to receive the data from the Jabatan Kimia Malaysia without any error.
The bold items in Table 17 should be monitored monthly and the other parameters are
recommended to be monitored quarterly. The proposed list of parameters includes all the
parameters important for point and non-point pollution sources covering, physical, chemical,
nutrients and microbial pollutants. However, the toxic chemicals are not included as those
elements are more suited for the DOEs monitoring activity.
.
84
Table 17: Proposed Parameters for JPS Water Quality Monitoring Program
Sampling
Event Description Depth Flow
Sampling Sampling Sample Weather Temp
Rainfall of Sample from Rate pH (unit) o
Date Time ID Condition 3 ( C)
(mm) Colour Water (m /s)
Surface)
PS, PS,
PS, NPS PS, NPS PS, NPS NPS PS, NPS PS PS PS
NPS NPS
PS,
PS, NPS PS, NPS PS, NPS Hydro Physical Hydro Hydro Chemi Physical
NPS
At Site &
At Site At Site At Site At Site At Site At Site At Site At Site At Site
At Lab
Turb. DO DO (% TDS Ca TSS Na COD BOD

Mg (mg/L)
(NTU) (mg/L) Sat.) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)
PS, PS,
NPS PS PS PS PS, NPS NPS PS, NPS PS
NPS NPS
Physical Chemi Chemi Physical Chemi Chemi Phys Chemi Chemi Biochem
At Site At Site At Site At Site At Lab At Lab At Lab At Lab At Lab At Lab
Fecal
Fecal
Streptoco
TKN AN Nitrate TP As Coliform
TN (mg/L) SO4 (mg/L) Fe (mg/L) cci
(mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (CFU/100
(CFU/100
mL)
mL)
PS, PS, PS,
PS, NPS PS PS, NPS PS PS, NPS PS, NPS PS, NPS
NPS NPS NPS
Chemi Chemi Chemi Chemi Chemi Chemi Chemi Chemi Bacteria Bacteria
At Lab At Lab At Lab At Lab At Lab At Lab At Lab At Lab At Lab At Lab
Note: PS - Point Source; NPS Non-point Source; Hydro Hydrological; Chemi - Chemical.
85
REFERENCES:
1. APHA (1998). Standard Methods for the Examination of Water and Wastewater. 20th Ed.,
American Public Health Association (APHA), American Water Works Association (AWWA) &
Water Environment Federation (WEF), the USA.
2. Avvannavar, S. M., and Shrihari, S. (2007). Evaluation of water quality index for drinking
purposes for river Netravathi, Mangalore, South India. Environmental Monitoring and
Assessment.
3. Boyacioglu, H. (2007). Development of a water quality index based on a European
classification scheme. Water SA. 33(1), 101-106.
4. Canadian Council of Ministers of the Environment. (2001). Canadian water quality
guidelines for the protection of aquatic life: CCME Water Quality Index 1.0, Technical
Report. In: Canadian environmental quality guidelines (1999) Canadian Council of Ministers
of the Environment, Winnipeg.
5. Canadian Council of Ministers of the Environment. (2005). CCME Water quality index FAQs.
Retrieved on January 17, 2008, from http://www.ccme.ca/initiatives/waterfaqs.html
6. Carr, G. M., & Neary, J. P. (2006). Water quality for ecosystem and human health. Canada:
United Nations Environment Programme Global Environment Monitoring System (GEMS).
7. Chin, D.A. (2000). Water resources engineering. Prentice Hall, New Jersey, the USA, pp.
396 400.
8. Cude, C. G. (2001). Oregon water quality index: A tool for evaluating water quality
management effectiveness. Journal of the American Water Resources Association. 37(1).
9. Cude, C., Dunnette, D., Avent, C., Franklin, A., Gross, G., Hartmann, J., Hayteas, D.,
Jenkins, T., Leben, K., Lyngdal, J., Marks, D., Morganti, C., and Quin, T. (1997). Exploring
possibilities for an international water quality index applied to river streams. In Best, G. A.,
Bogacka, T., & Niemirycz, E. (Eds). International river water quality. London: E & FN Spon.
10. Davis, A. P. & McCuen, R. H. (2005). Storm water management for smart growth. 1st edition.
Springer Science and Business Media.
11. Debels, P., Figueroa, R., Urrutia, R., Barra, R. and Niell, X. (2005). Evaluation of water
quality in the Chillan River (central Chile) using physicochemical parameters and a modified
water quality index. Environmental Monitoring and Assessment. 110: 301322
12. Department of Environment - DOE (1994). Classification of Malaysian rivers. Final report
on development of water quality criteria and standards for Malaysia (Phase IV River
Classification). Department of Environment Malaysia, Ministry of science, technology and
the environment.
13. Department of Environment Malaysia. (2005). Interim National Water Quality Standards For
Malaysia. Retrieved on January 17, 2008 from http://www.doe.gov.my/index.php?
option=com content&task=view&id=244&Itemid=615&lang=en
14. Department of Irrigation and Drainage DID (1981). River water quality sampling.
Hydrological Procedure No. 22. Jabatan Pengairan dan Saliran, Kementerian Pertanian
Malaysia.
15. Department of Irrigation and Drainage - DID (2000). Urban stormwater management
manual for Malaysia. Department of Irrigation and Drainage, Ministry of Agriculture,
Malaysia.
86
16. Driver N.E. and Tasker G.D. (1990). Techniques for estimation of storm-runoff loads,
volumes and selected constituent concentrations in urban watersheds in the United States.
Water-supply paper 2363, U.S. Geological Survey, Washington D.C.
17. Ellis J.B. and Sutherland R.C. (1979). An app.roach to urban pollutant washoff modelling.
International symposium of urban storm runoff, University of Kentucky, Lexington, pp. 325
340.
18. Field R., Oshea M.L. and Brown M.P. (1993). The detection and disinfection of pathogens
in storm-generated flows. Wat. Sci. Tech. Vol. 28, No. 3-5, pp. 311-315.
19. Griffin D.M. Grizzard T.J. Helsel D.R. and Hartigan J.P. (1980). Analysis of nonpoint
pollution export from small catchments. Journal of Water Pollution Control Federation, Vol.
52, No. 4, pp. 780791.
20. Gupta K. and Saul A.J. (1996). Specific relationships for the first flush load in combined
sewer flows. Wat. Res., Vol. 30, pp. 1244-1255.
21. Hall K.J. and Anderson B.C. (1988). The toxicity and chemical composition of urban
stormwater runoff, Canadian Journal of Civil Engineering, Vol. 15, pp. 98-106.
22. Harrison R.M. and Wilson S.J. (1985). The chemical composition of highway drainage
waters 1: Major ions and selected trace metals. Science of Total Environment, Vol. 43,
Elsevier Science Publisher, Amsterdam, pp. 6377.
23. Heany J.F., Huber W.C., Sheikh S., Medina M.A., Doyel J.R., Peltz W.A. and Darling J.E.
(1977). Nationwide evaluation of combined sewer overflows and urban stormwater
discharges, Vol. 2, cost assessment and impacts. Report EPA-600/2-77-064, USEPA,
Washington D.C.
24. Irvine, K.N., Snyder, R.J., and Diggins, T.P. (2003). Chapter 7: Site evaluation matrix.
Retrieved on January 17, 2008, from http://www.fbnr.org/programs/tributary/
buffalo_river/aha/CHAPTER%207.pdf
25. Karakaya, N., and Evrendilek, F. (2009). Water quality time series for Big Melen stream
(Turkey): its decomposition analysis and comparison to upstream. Environmental Monitoring
Assessment. DOI 10.1007/s10661-009-0932-7
26. Khan, F., Husaini, T., and Lumb, A. (2003). Water quality evaluation and trend analysis in
selected watersheds of the Atlantic region of Canada. Environmental Monitoring and
Assessment. 88, 221242.
27. Khuan, L. Y., Noraliza Hamzah, and Rozita Jailani. (2002). Prediction of water quality index
(WQI) based on artificial neutral network. Student Conference on Research and
Development Proceedings, Shah Alam, Malaysia.
28. Kuo C.Y. and Zhu J. (1989). Design of a diversion system to manage the first flush. Water
Resources Bulletin, Vol. 25, No. 3, pp. 517525.
29. Lee G.F. and Lee A.J. (1993). Water quality impacts of stormwater-associated
contaminants: Focus on real problems. Wat. Sci. Tech. Vol. 28, No. 3-5, pp. 231-240.
30. Lee J.H., Bang K.W., Choe J.S., Yu M.J. and Ketchum L.H. (2002). First flush analysis of
urban storm runoff. The Science of the Total Environment Vol. 293, pp. 163-175.
31. Lumb, A., Halliwell, D., and Sharma, T. (2006). Application of CCME water quality index to
monitor water quality: A case of the Mackenzie River basin, Canada. Environmental
Monitoring and Assessment. 113, 411429.
32. Malaysian International Hydrological Program MIHP (2007). Guide to water quality
monitoring practices in Malaysia. Practices and techniques of sampling and application of
water quality data by various government agencies in Malaysia.
87
33. Mamun, A. A., Idris., A., Sulaiman, W. N. A., and Muyibi, S. A. (2007). A revised water
quality index proposed for the assessment of surface water quality in Malaysia. Pollution
Research. 26(4), 523-529.
34. Pescem, S. F. and Wunderlin D. A. (2000). Use of water quality indices to verify the impact
of Cordoba City (Argentina).
35. Pitt R., Lalor M., Field R. and Brown M. (1993). The investigation of sources area controls
for the treatment of urban stormwater toxicants. Wat. Sci. Tech. Vol. 28, No. 3-5, pp. 271-
282.
36. Sanchez, E., Colmenarejo, M. F., Vicente, J., Rubio, A., Garcia M. G., Travieso, L., and
Borja, R. (2007). Use of the water quality index and dissolved oxygen deficit as simple
indicators of watersheds pollution. Ecological Indicators. 7:315328.
37. Said, A., Stevens, D. K., and Sehlke, G. (2004). An innovative index for evaluating water
quality in streams. Environmental Management. 34(3), 406414.
38. Sarkar, C., and Abbasi, S. A. (2006). Qualidex A new software for generating water quality
indice. Environmental Monitoring and Assessment. 119, 201231.
39. Tao Song and Kyehyun Kim (2009). Development of a water quality loading index based on
water quality modeling. Journal of Environmental Management. 90: 15341543.
40. US EPA (1983). Final report of the nation-wide urban runoff program. U.S. Environmental
Protection Agency, Water Planning Division, Washington DC.
41. Vorreiter L. and Hickey C. (1994). Incidence of the first flush phenomenon in catchment of
the sydney region. Proceedings of hydrology and water resources symposium water down
under 94, 21-25 November, Adelaide, Australia, pp. 359364.
42. Wanielista M. and Yousef Y.A. (1993). Stormwater Management. John Wiley & Sons Inc.,
New York.
43. Washington State Department of Ecology (2002). Introduction to water quality index.
Retrieved on January 20, 2008, from http://www.fotsch.org/WQI.htm
88
APPENDIX - A
WQI RATING CURVES

Figure A1: Graphs for each Analyte of NSF WQI
(a) BOD Test Results (b) Dissolved Oxygen Results (c) Fecal Coliform Results
(d) Temperature Results (e) Phosphate Results (f) Nitrate Results

Figure A1: Graphs for each Analyte of NSF WQI (Continued)
(g) Total Dissolved Solids Results (h) pH Results (i)Turbidity Results

Figure A2: Sub-indices to Determine WQI in Malaysia
100 100 100

90 90 90
80 80 80
70 70 70
60
Sub-index
60 60
Sub-index
Sub-index
50 50 50
40 40 40
30 30
30
20 20
20
10 10
10
0 0
0
0 10 20 30 40 50 60 0 50 100 150 200
0 20 40 60 80 100
Concentration (mg/l) Concentration (mg/l)
Saturation (%)
(a) Dissolved Oxygen (DO) (b) Biochemical Oxygen Demand (BOD) (c) Chemical Oxygen Demand (COD)
100 100 100
90 90 90
80 80 80
70 70 70
60
Sub-index
60
Sub-index
60
Sub-index
50 50 50
40 40 40
30 30 30
20 20 20
10 10 10
0 0 0
0 1 2 3 4 5 0 200 400 600 800 1000 0 2 4 6 8 10 12
Concentration (mg/l) Concentration (mg/l) Concentration (mg/l)
(d) Ammoniacal Nitrogen (AN) (e) Suspended Solids (SS) (f) pH

APPENDIX - B
STATISTICAL SUMMARY OF THE JRI

Table B1: Statistical Summary of Pollutant Concentration Data at JPS Stations (from 1995 to 2007)
Sp.
Flow Flow pH Colour Cond. Turb. Alka. Hard. Ca Mg TS DS SS NH3-N Si K Na COD BOD Cl- F- NO3-N PO4 SO4 Mn
Parameter Fe (mg/L)
(m3/s) (m3/s.k (unit) (Hazen) (uS/cm) (NTU) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)
m 2)
Statistical Values for the Station 1737651 (Sg. Johor di Rantau Panjang)
95 Percentile 158.1 0.140 6.70 463 79 142.3 16.3 32.5 7.7 4.4 260 168 166 1.13 25.8 6.5 5.16 37 2 10.50 0.10 2.58 0.18 9.78 5.49 0.08
75 Percentile 74.6 0.066 6.40 263 64 86.8 10.3 20.0 4.7 2.9 190 115 91 0.31 19.5 5.1 3.95 23 2 9.78 0.09 0.25 0.10 7.00 3.90 0.03
50 Percentile 30.9 0.027 6.20 150 57 48.0 9.4 15.0 3.4 1.9 159 73 66 0.17 11.5 4.3 3.30 12 2 8.00 0.07 0.09 0.08 4.01 2.20 0.03
25 Percentile 18.1 0.016 5.73 80 46 23.8 5.6 10.8 2.8 1.1 124 49 49 0.11 8.2 2.3 2.30 9 2 5.75 0.05 0.06 0.04 1.92 1.80 0.03
5 Percentile 10.5 0.009 4.88 58 35 7.0 2.8 7.5 2.3 0.7 51 38 13 0.08 3.4 1.6 1.26 8 2 2.52 0.02 0.04 0.01 1.23 1.02 0.02
No. of Data 12.0 12 16 16 16 16 16 16 16 12 16 15 16 12 16 15 15 15 1 16 4 11 8 16 15 10
Mean 54.3 0.048 5.98 203 56 58.9 9.0 16.9 4.3 2.2 157 87 76 0.35 13.9 4.0 3.17 17 2 7.61 0.06 0.56 0.08 4.53 2.84 0.04
Std. Deviation 54.0 0.048 0.64 165 17 45.3 5.3 8.7 3.0 1.3 74 47 46 0.56 8.3 1.8 1.38 11 - 2.76 0.04 1.29 0.06 3.05 1.60 0.03
Minimum 5.1 0.004 4.80 20 24 2.9 1.2 5.5 1.8 0.7 39 30 9 0.07 2.0 1.2 0.22 7 2 1.99 0.01 0.03 0.00 1.00 0.60 0.02
Maximum 169.0 0.150 7.00 650 98 149.0 23.0 37.0 15.0 4.7 330 190 171 2.11 34.0 7.7 5.30 47 2 12.00 0.10 4.40 0.21 10.60 6.40 0.11
Statistical Values for the Station 2130622 (Sg. Bekok di Batu 77 Jalan Yong Peng Labis)
95 Percentile 35.3 0.101 7.28 263 97 82.5 20.3 34.3 6.8 4.3 350 309 101 0.56 15.3 10.0 6.75 36 4 9.18 0.25 0.28 0.05 13.25 5.49 0.19
75 Percentile 12.4 0.036 6.70 143 72 45.0 14.5 27.5 5.3 3.8 161 95 81 0.25 12.0 6.8 3.15 19 2 8.25 0.19 0.18 0.02 7.33 2.15 0.06
50 Percentile 9.9 0.028 6.40 120 62 33.5 13.0 17.0 4.9 1.4 122 63 41 0.22 11.0 6.2 2.35 17 2 6.00 0.11 0.12 0.01 4.00 1.51 0.06
25 Percentile 8.4 0.024 6.03 80 57 16.8 10.0 15.0 4.3 1.1 80 37 23 0.18 10.0 5.0 2.18 15 2 5.40 0.06 0.07 0.01 2.68 0.90 0.04
5 Percentile 6.7 0.019 5.60 60 50 15.0 6.1 12.0 3.8 0.9 53 23 15 0.13 9.4 3.6 1.55 9 2 4.75 0.06 0.03 0.01 0.94 0.43 0.03
No. of Data 14.0 14 16 16 15 16 15 16 16 16 16 16 16 14 16 16 16 16 5 16 4 11 12 16 15 11
Mean 13.9 0.040 6.42 133 67 39.6 12.3 21.4 5.0 2.2 148 96 52 0.26 11.4 6.4 3.08 19 2 6.59 0.14 0.13 0.02 5.26 2.05 0.07
Std. Deviation 11.3 0.032 0.62 93 17 36.3 4.5 8.4 1.1 1.5 114 106 34 0.19 2.5 3.0 1.82 15 1 1.69 0.10 0.09 0.02 4.03 1.84 0.07
Minimum 6.5 0.018 5.30 60 49 15.0 5.5 12.0 3.1 0.6 44 15 13 0.12 8.6 1.2 1.40 7 2 4.00 0.06 0.02 0.01 0.22 0.30 0.02
Maximum 48.0 0.137 7.80 450 113 165.0 21.0 38.0 6.9 5.2 502 421 114 0.88 19.0 16.0 8.40 72 4 9.70 0.27 0.29 0.06 14.00 7.10 0.27
Statistical Values for the Station 2237671 (Sg. Lenggor di Batu 42 Kluang Mersing.)
95 Percentile 108.4 0.524 6.95 210 134 50.8 5.2 10.3 2.5 1.9 107 92 52 0.32 18.3 3.4 2.70 31 - 2.36 - 0.19 0.05 1.95 2.54 0.05
75 Percentile 9.9 0.048 6.25 88 100 19.0 3.8 8.0 1.2 1.3 83 51 33 0.30 9.8 1.1 2.18 23 - 1.98 - 0.16 0.04 1.75 2.09 0.03
50 Percentile 8.3 0.040 5.40 80 20 15.0 2.2 7.0 1.0 1.2 61 27 29 0.26 8.6 0.8 1.45 16 - 1.73 - 0.12 0.03 1.50 1.99 0.02
25 Percentile 3.3 0.016 4.85 80 17 13.3 2.2 5.3 1.0 1.1 43 16 11 0.23 7.9 0.7 1.21 10 - 1.12 - 0.08 0.02 1.30 1.38 0.02
5 Percentile 2.4 0.012 4.63 32 9 5.7 2.1 2.0 0.8 0.7 15 11 10 0.20 5.3 0.5 0.96 7 - 0.62 - 0.05 0.02 1.13 1.13 0.02
No. of Data 5.0 5 6 6 6 6 5 6 5 6 6 6 5 2 6 5 6 6 - 6 - 2 4 3 6 4
Mean 31.4 0.151 5.62 99 54 21.2 3.2 6.5 1.3 1.2 62 40 28 0.26 10.1 1.4 1.69 17 - 1.57 - 0.12 0.03 1.53 1.84 0.03
Std. Deviation 56.9 0.275 1.00 79 60 20.3 1.5 3.4 0.8 0.5 38 36 19 0.10 5.7 1.4 0.75 10 - 0.73 - 0.11 0.01 0.46 0.60 0.02
Minimum 2.2 0.011 4.60 16 6 3.3 2.1 1.0 0.7 0.6 6 9 10 0.19 4.4 0.5 0.90 6 - 0.49 - 0.04 0.02 1.09 1.10 0.02
Maximum 133.0 0.643 7.10 250 136 61.0 5.5 11.0 2.8 2.1 114 104 57 0.33 21.0 3.9 2.80 33 - 2.48 - 0.20 0.05 2.00 2.68 0.05
Sp.
Parameter Fe (mg/L)
m 2)
Statistical Values for the Station 2527611 (Sg. Muar di Buloh Kasap)
95 Percentile 102.3 0.033 7.18 688 151 243.8 33.0 57.8 11.8 6.9 414 297 237 0.58 26.8 20.0 13.85 76 2 16.75 0.16 0.43 0.23 14.25 13.80 0.56
75 Percentile 33.6 0.011 6.79 400 117 117.5 24.0 36.8 9.7 3.9 269 154 124 0.34 21.0 9.9 7.05 32 2 11.75 0.11 0.30 0.13 11.00 6.80 0.18
50 Percentile 17.5 0.006 6.56 200 108 58.5 19.0 31.0 8.0 2.8 194 113 69 0.16 18.0 6.8 5.19 24 2 9.90 0.08 0.11 0.08 5.84 4.00 0.09
25 Percentile 4.9 0.002 6.30 171 91 40.0 16.0 28.0 6.5 2.4 149 84 29 0.08 14.0 5.0 3.80 15 2 8.08 0.05 0.06 0.06 4.12 2.90 0.06
5 Percentile 2.0 0.001 5.65 120 61 21.3 9.3 19.3 3.7 1.6 104 22 18 0.05 8.6 2.1 2.60 12 2 5.50 0.03 0.04 0.04 1.40 1.72 0.03
No. of Data 40.0 40 46 46 46 46 46 46 46 45 46 46 45 38 46 42 44 46 7 46 4 30 31 36 45 43
Mean 28.6 0.009 6.46 296 108 91.1 20.5 33.6 8.0 3.4 226 129 95 0.22 17.4 8.7 6.63 29 2 10.12 0.09 0.22 0.10 7.32 5.71 0.17
Std. Deviation 35.8 0.011 0.66 177 34 72.6 7.6 10.5 2.5 1.7 127 77 109 0.18 6.2 6.9 5.34 23 0 3.36 0.06 0.33 0.07 4.83 4.23 0.21
Minimum 1.7 0.001 3.20 80 49 19.0 8.1 14.0 1.6 1.6 92 11 9 0.04 0.9 1.1 2.20 7 2 4.43 0.03 0.02 0.02 0.39 0.40 0.02
Maximum 171.7 0.055 7.70 700 271 280.0 45.0 65.0 14.0 10.0 807 377 663 0.63 29.0 35.0 33.00 130 2 19.00 0.17 1.83 0.32 22.00 19.00 0.92
Statistical Values for the Station 2528614 (Sg. Segamat di Segamat)
95 Percentile 27.2 0.043 6.92 600 127 330.0 32.0 43.0 9.5 4.5 449 235 242 0.77 27.0 10.0 14.00 55 3 11.43 0.10 0.44 0.27 9.12 11.00 0.19
75 Percentile 13.3 0.020 6.53 148 57 37.4 14.5 15.8 3.8 1.8 230 104 126 0.23 18.8 4.3 6.20 18 - 4.28 - - 0.04 2.76 9.20 0.18
50 Percentile 10.6 0.016 6.40 140 51 27.3 10.6 14.5 2.9 1.6 144 81 78 0.13 16.0 3.8 4.70 15 - 3.90 - - 0.04 2.38 7.70 0.17
25 Percentile 8.5 0.013 6.20 150 57 30.0 12.3 16.0 3.7 1.5 147 75 50 0.07 13.3 3.8 3.65 15 2 4.92 0.07 0.05 0.03 2.46 2.68 0.04
5 Percentile 3.9 0.006 5.83 80 46 20.0 8.0 13.0 2.8 0.6 92 48 23 0.05 3.4 2.1 2.46 6 2 3.90 0.05 0.04 0.02 0.65 1.62 0.02
No. of Data 40.0 41 46 46 46 46 46 46 46 43 46 46 46 37 46 41 39 46 5 46 3 34 27 34 44 42
Mean 13.0 0.026 6.43 262 75 96.2 16.8 22.0 5.2 2.4 223 123 100 0.22 17.2 5.9 6.60 25 2 6.90 0.08 0.16 0.09 4.34 5.21 0.09
Std. Deviation 8.8 0.044 0.38 166 28 113.4 7.4 9.0 2.1 1.5 112 64 76 0.24 8.2 3.3 5.54 17 1 2.67 0.03 0.14 0.09 2.72 3.44 0.06
Minimum 2.9 0.004 5.50 65 42 10.0 5.8 12.0 2.4 0.5 87 42 11 0.04 0.6 0.5 0.28 6 2 2.40 0.05 0.02 0.02 0.35 0.30 0.02
Maximum 53.7 0.291 7.50 750 179 570.0 39.0 53.0 12.0 9.1 564 311 363 1.15 51.0 18.0 34.00 89 3 15.00 0.10 0.53 0.37 12.00 16.00 0.29
Statistical Values for the Station 5606610 (Sg. Muda di Jam Syed Omar.)
95 Percentile 213.1 0.064 7.83 - 66 - 26.0 - 7.7 2.4 - - 342 0.39 20.0 2.1 - 23 1 9.65 0.20 0.33 0.20 16.00 3.90 -
75 Percentile 104.7 0.031 7.35 - 58 - 21.0 - 6.0 2.0 - - 110 0.05 16.0 2.1 - 13 1 5.00 0.10 0.14 0.10 6.93 1.80 -
50 Percentile 45.1 0.014 7.00 - 45 - 14.0 - 5.0 1.2 - - 64 0.05 10.0 2.1 - 9 1 3.00 0.07 0.10 0.10 3.00 1.20 -
25 Percentile 22.6 0.007 6.80 - 36 - 9.0 - 4.0 1.0 - - 45 0.04 8.0 2.1 - 7 0 1.00 0.00 0.05 0.00 2.00 0.80 -
5 Percentile 13.9 0.004 6.40 - 28 - 5.0 - 2.3 0.0 - - 22 0.03 6.0 2.1 - 3 0 0.00 0.00 0.05 0.00 0.00 0.30 -
No. of Data 84.0 84 95 - 94 - 90 - 96 96 - - 94 35 94 1 - 86 60 96 93 34 80 96 93 -
Mean 78.4 0.024 7.08 - 47 - 15.6 - 4.9 1.4 - - 108 0.30 12.9 2.1 - 12 1 3.56 0.07 0.12 0.08 4.94 1.62 -
Std. Deviation 78.5 0.024 0.46 - 14 - 8.5 - 1.8 0.8 - - 118 1.34 6.4 - - 13 1 3.32 0.07 0.09 0.09 5.47 1.50 -
Minimum 11.7 0.004 6.20 - 15 - 3.8 - 0.4 0.0 - - 7 0.02 4.0 2.1 - 1 0 0.00 0.00 0.03 0.00 0.00 0.00 -
Maximum 434.2 0.130 8.50 - 117 - 62.0 - 10.0 4.0 - - 697 8.00 50.0 2.1 - 120 3 18.50 0.30 0.46 0.41 36.00 8.60 -
Sp.
Parameter Fe (mg/L)
m 2)
Statistical Values for the Station 5120601 (Sg. Nenggiri di Jam Bertam)
95 Percentile 313.0 0.147 7.60 390 62 650.8 29.1 28.1 5.6 3.5 814 120 766 1.84 16.0 2.6 7.19 - - 3.50 - 0.57 0.11 16.68 2.22 -
75 Percentile 179.7 0.084 7.40 175 50 126.0 24.0 20.0 4.4 2.8 602 79 549 0.53 12.0 1.9 5.21 - - 2.00 - 0.26 0.05 10.00 1.51 -
50 Percentile 123.1 0.058 7.00 85 46 52.7 20.0 18.0 3.6 2.2 226 51 138 0.20 10.0 1.7 3.45 - - 1.00 - 0.20 0.02 8.80 0.60 -
25 Percentile 96.6 0.045 6.63 40 39 23.7 16.9 15.0 2.5 1.5 126 35 73 0.15 8.0 1.6 2.48 - - 1.00 - 0.10 0.01 5.80 0.23 -
5 Percentile 52.7 0.025 6.34 15 29 12.1 13.7 11.0 2.0 0.6 72 26 38 0.06 2.0 0.8 1.91 - - 1.00 - 0.02 0.01 1.25 0.14 -
No. of Data 46.0 46 50 45 50 46 50 50 50 50 49 49 50 8 49 8 8 - - 44 - 42 7 46 10 -
Mean 165.2 0.078 6.99 127 46 159.6 20.7 18.4 3.7 2.2 420 60 358 0.54 10.1 1.7 4.04 - - 2.14 - 0.23 0.04 8.07 0.91 -
Std. Deviation 160.5 0.075 0.43 120 16 238.1 5.3 6.0 1.4 1.2 509 32 502 0.82 4.1 0.7 2.14 - - 3.72 - 0.23 0.04 4.30 0.84 -
Minimum 44.3 0.021 5.90 10 21 4.6 8.3 9.5 1.6 0.5 21 20 23 0.03 2.0 0.6 1.80 - - 1.00 - 0.01 0.00 1.00 0.09 -
Maximum 1110.2 0.521 7.62 500 131 909.9 35.0 38.0 10.0 6.3 3164 161 3130 2.50 20.0 2.9 7.40 - - 23.00 - 1.33 0.13 19.00 2.40 -
Statistical Values for the Station 5222652 (Sg. Lebir di Kg Tualang)
95 Percentile 3007.7 1.238 7.80 294 83 442.2 35.8 31.0 9.4 3.9 706 111 676 1.58 14.0 3.7 7.78 - - 4.00 - 0.64 0.09 16.41 2.74 -
75 Percentile 197.4 0.081 7.60 181 68 158.5 29.0 25.8 7.4 2.8 332 77 260 0.45 12.0 2.1 4.10 - - 2.00 - 0.27 0.04 10.70 1.55 -
50 Percentile 114.3 0.047 7.35 85 52 26.3 21.9 20.5 4.4 1.8 190 64 149 0.33 10.0 1.7 3.10 - - 1.00 - 0.20 0.03 9.20 0.60 -
25 Percentile 67.8 0.028 6.80 30 40 9.6 17.3 17.0 3.6 1.5 120 48 50 0.20 8.0 1.3 1.90 - - 1.00 - 0.14 0.03 3.63 0.25 -
5 Percentile 30.4 0.013 6.40 10 23 2.0 9.2 8.9 2.0 0.7 83 19 16 0.05 4.0 0.9 1.12 - - 1.00 - 0.03 0.01 1.00 0.10 -
No. of Data 41.0 41 46 44 46 42 46 46 46 44 46 46 46 8 46 43 44 - - 42 - 44 6 42 39 -
Mean 514.0 0.212 7.24 118 54 113.8 22.8 21.6 5.2 2.2 294 63 232 0.51 9.8 1.8 3.40 - - 1.77 - 0.25 0.04 8.45 1.32 -
Std. Deviation 931.0 0.383 0.48 125 20 187.2 8.5 9.1 2.5 1.6 386 27 383 0.69 3.9 0.8 1.96 - - 1.13 - 0.19 0.03 5.29 2.26 -
Minimum 15.6 0.006 6.38 5 13 1.0 3.8 5.0 1.2 0.5 55 10 9 0.02 2.0 0.7 0.70 - - 0.50 - 0.02 0.01 0.30 0.05 -
Maximum 3254.1 1.339 8.10 700 96 837.0 37.0 62.0 10.8 10.2 2589 134 2480 2.17 20.0 4.3 9.00 - - 5.00 - 0.96 0.10 22.20 14.00 -
Statistical Values for the Station 5320643 (Sg. Galas di Dabong)
95 Percentile 848.7 0.109 7.90 200 69 290.4 27.7 34.2 7.6 4.6 928 432 893 0.55 20.0 2.1 7.82 - - 3.00 - 0.56 0.03 14.25 0.10 -
75 Percentile 437.6 0.056 7.75 156 61 88.5 25.0 24.5 6.6 1.8 515 148 381 0.55 14.5 1.9 6.70 - - 2.00 - 0.34 0.02 13.13 0.10 -
50 Percentile 377.0 0.049 7.60 113 52 25.6 22.0 21.0 6.0 1.3 362 103 206 0.55 12.0 1.6 5.30 - - 1.00 - 0.24 0.02 4.50 0.10 -
25 Percentile 276.6 0.036 7.10 30 45 10.8 20.0 17.0 5.1 1.0 182 72 81 0.55 10.0 1.4 3.90 - - 1.00 - 0.19 0.01 2.70 0.10 -
5 Percentile 232.6 0.030 6.56 11 40 3.7 16.3 13.3 3.7 0.7 138 51 34 0.55 4.6 1.2 2.78 - - 1.00 - 0.04 0.00 1.00 0.10 -
No. of Data 25.0 25 27 24 26 25 27 27 27 27 25 25 26 1 24 2 2 - - 23 - 24 2 16 2 -
Mean 411.6 0.053 7.41 107 53 75.1 22.2 21.4 5.8 1.7 411 152 307 0.55 12.2 1.6 5.30 - - 1.52 - 0.26 0.02 6.93 0.10 -
Std. Deviation 212.8 0.027 0.44 79 11 99.4 3.4 6.3 1.3 1.3 281 144 301 - 4.6 0.7 3.96 - - 0.85 - 0.15 0.02 5.39 0.00 -
Minimum 209.8 0.027 6.40 10 32 2.0 15.0 13.0 2.4 0.5 99 47 21 0.55 2.0 1.1 2.50 - - 1.00 - 0.03 0.00 1.00 0.10 -
Maximum 1132.4 0.146 7.90 300 80 300.0 29.0 38.0 8.0 5.6 1085 645 1013 0.55 20.0 2.1 8.10 - - 4.00 - 0.65 0.03 15.00 0.10 -
Sp.
Parameter Fe (mg/L)
m 2)
Statistical Values for the Station 5419601 (Sg. Pergau di Batu Lembu.)
95 Percentile 240.1 0.186 7.80 183 65 195.6 26.1 24.1 6.0 4.1 443 143 366 1.76 16.0 2.2 7.20 - - 4.00 - 0.54 0.19 13.64 2.24 -
75 Percentile 116.0 0.090 7.50 70 43 38.6 19.0 18.3 4.0 2.4 294 78 211 0.30 13.0 1.9 3.93 - - 2.00 - 0.25 0.10 10.00 0.98 -
50 Percentile 69.9 0.054 7.30 40 37 20.2 16.0 15.0 3.2 1.7 153 57 96 0.09 11.0 1.3 3.25 - - 1.00 - 0.19 0.04 8.75 0.30 -
25 Percentile 42.1 0.033 6.90 20 33 9.2 14.0 11.8 2.0 1.0 91 38 42 0.04 8.0 1.2 2.33 - - 1.00 - 0.10 0.02 6.50 0.13 -
5 Percentile 28.4 0.022 6.34 9 25 2.0 10.0 7.0 1.2 0.5 57 26 16 0.02 3.8 1.1 2.19 - - 1.00 - 0.03 0.01 1.00 0.09 -
No. of Data 60.0 60 80 75 79 73 80 80 80 78 79 79 80 8 79 10 10 - - 78 - 64 5 69 10 -
Mean 97.8 0.076 7.18 60 39 46.8 16.8 15.4 3.3 1.8 205 68 140 0.43 10.5 1.5 3.76 - - 1.63 - 0.21 0.08 8.16 0.72 -
Std. Deviation 94.5 0.073 0.46 57 13 83.2 4.6 6.1 1.5 1.1 162 47 145 0.80 3.9 0.5 1.91 - - 1.02 - 0.16 0.08 3.62 0.86 -
24.1 0.019 5.95 5 23 1.2 8.0 5.0 1.2 0.2 49 21 4 0.02 2.0 1.1 2.10 - - 1.00 - 0.02 0.01 0.80 0.09 -
Maximum 499.3 0.387 8.00 250 100 477.9 32.0 40.0 10.0 6.1 1027 311 949 2.32 20.0 2.2 7.60 - - 6.00 - 0.74 0.21 16.20 2.60 -
Statistical Values for the Station 5718601 (Sg. Lanas di Air Lanas)
95 Percentile 27.6 0.344 7.70 139 55 110.2 21.9 24.1 4.9 3.5 297 239 190 0.62 18.0 2.2 7.00 - - 3.60 - 0.50 0.03 13.00 1.15 -
75 Percentile 5.6 0.070 7.50 70 44 20.8 18.0 19.0 4.0 2.2 163 76 88 0.28 14.0 1.7 2.85 - - 2.00 - 0.24 0.02 10.08 0.75 -
50 Percentile 2.8 0.035 7.20 30 40 10.3 17.0 15.0 3.6 1.7 102 50 43 0.09 11.0 1.5 2.60 - - 2.00 - 0.16 0.01 7.50 0.50 -
25 Percentile 1.7 0.021 6.80 25 35 5.9 14.0 13.0 3.2 1.2 67 34 16 0.04 8.0 1.2 2.20 - - 1.00 - 0.10 0.01 4.00 0.11 -
5 Percentile 0.5 0.007 6.20 7 22 3.2 10.0 10.0 1.6 0.7 49 24 8 0.04 2.0 1.1 1.00 - - 1.00 - 0.04 0.01 1.00 0.05 -
No. of Data 68.0 68 74 70 72 70 74 74 74 73 73 73 71 4 73 10 12 - - 69 - 61 4 64 11 -
Mean 6.2 0.078 7.13 51 40 22.9 16.2 16.2 3.5 1.8 133 74 60 0.23 10.9 1.5 3.05 - - 1.91 - 0.19 0.02 7.15 0.49 -
Std. Deviation 9.8 0.122 0.47 42 9 32.9 3.9 5.0 1.0 0.9 94 78 56 0.32 4.2 0.4 2.14 - - 1.11 - 0.13 0.01 3.94 0.43 -
Minimum 0.2 0.002 6.00 5 20 2.2 5.0 8.0 0.8 0.2 41 15 7 0.04 2.0 1.1 1.00 - - 1.00 - 0.04 0.01 1.00 0.04 -
Maximum 58.7 0.734 7.90 175 63 153.0 26.0 35.0 6.0 5.3 523 506 232 0.71 20.0 2.4 8.80 - - 7.50 - 0.67 0.03 17.00 1.30 -
Statistical Values for the Station 5721642 (Sg. Kelantan di Jam Guillemard)
95 Percentile 806.5 0.068 8.02 196 71 495.0 30.5 30.2 8.0 3.3 1107 228 1008 0.95 18.5 2.7 7.54 29 3 4.00 0.51 0.50 0.09 15.55 1.64 -
75 Percentile 407.1 0.034 7.70 131 62 137.7 25.0 23.8 6.4 2.4 332 88 283 0.33 14.0 2.3 3.20 20 2 3.00 0.30 0.34 0.08 11.10 1.20 -
50 Percentile 266.6 0.022 7.50 60 56 36.4 23.0 20.0 5.6 1.6 238 64 138 0.11 12.0 1.9 2.60 9 1 2.00 0.30 0.23 0.05 7.70 0.90 -
25 Percentile 186.8 0.016 7.00 38 46 11.8 20.0 18.0 4.8 1.0 111 38 54 0.08 8.0 1.6 2.00 5 1 1.00 0.20 0.14 0.03 3.00 0.60 -
5 Percentile 93.9 0.008 6.20 11 35 4.2 17.0 15.3 3.6 0.7 73 31 14 0.05 4.0 1.3 1.40 3 1 1.00 0.13 0.07 0.02 1.45 0.38 -
No. of Data 36.0 36 37 24 36 25 38 38 37 36 27 27 37 15 36 17 17 12 11 31 7 37 6 30 17 0
Mean 336.9 0.028 7.33 84 54 113.0 23.4 21.2 5.6 1.8 329 84 249 0.31 10.8 1.9 3.15 13 1 1.97 0.29 0.24 0.05 7.60 0.93 -
Std. Deviation 241.8 0.020 0.55 66 12 164.7 4.8 4.9 1.3 1.0 358 65 332 0.49 4.6 0.5 1.92 10 1 1.05 0.16 0.14 0.03 4.73 0.48 -
Minimum 84.4 0.007 6.00 5 33 3.0 15.0 10.0 3.6 0.5 53 26 7 0.05 2.0 1.1 1.00 2 1 1.00 0.10 0.04 0.02 1.00 0.30 -
Maximum 1185.6 0.100 8.10 225 83 576.0 39.8 36.0 8.0 4.6 1575 264 1534 1.98 20.0 2.9 8.10 37 4 4.00 0.60 0.69 0.09 17.00 2.20 -
Sp.
Parameter Fe (mg/L)
m 2)
Statistical Values for the Station 5818601 (Sg. Golok di Kg Jenob)
95 Percentile - - 7.80 150 60 111.7 20.1 25.2 4.8 4.2 305 105 245 0.54 18.0 2.8 5.04 - - 5.00 - 0.47 0.04 13.00 1.00 -
75 Percentile - - 7.50 70 43 25.0 17.0 18.5 3.6 2.7 163 68 91 0.44 14.0 2.1 3.90 - - 3.00 - 0.20 0.02 10.30 0.50 -
50 Percentile - - 7.10 30 38 10.7 15.0 14.0 3.2 1.5 101 55 46 0.31 12.0 1.7 3.30 - - 2.00 - 0.14 0.02 8.50 0.30 -
25 Percentile - - 6.80 15 33 5.5 12.0 11.0 2.4 1.2 76 39 23 0.26 10.0 1.2 2.70 - - 1.00 - 0.09 0.01 5.50 0.20 -
5 Percentile - - 6.23 5 24 1.9 8.5 9.0 2.0 0.7 57 20 10 0.21 4.0 0.7 1.60 - - 1.00 - 0.04 0.01 1.00 0.10 -
No. of Data - - 79 78 79 73 79 79 79 77 79 79 79 3 77 69 69 - - 77 0 68 5 65 59 0
Mean - - 7.11 49 40 25.3 14.8 15.4 3.1 1.9 133 58 75 0.36 11.4 1.7 3.32 - - 2.25 - 0.18 0.02 7.85 0.47 -
Std. Deviation - - 0.49 45 17 43.5 3.8 5.3 1.0 1.1 83 33 77 0.18 4.2 0.7 1.16 - - 1.00 - 0.16 0.01 3.61 0.63 -
Minimum - - 5.90 5 15 1.4 6.0 7.0 1.2 0.5 39 17 5 0.20 2.0 0.5 1.20 - - 1.00 - 0.02 0.01 1.00 0.05 -
Maximum - - 8.00 200 150 247.0 26.0 36.0 6.4 6.6 460 256 350 0.56 20.0 3.5 8.80 - - 6.00 - 0.97 0.04 17.00 4.70 -
Statistical Values for the Station 6019611 (Sg. Golok di Rantau Panjang)
95 Percentile 208.8 0.274 7.59 150 44 50.6 23.4 29.4 4.8 3.6 214 96 155 0.17 15.7 2.9 3.79 - - 4.85 - 1.11 0.07 11.50 0.88 -
75 Percentile 56.0 0.074 7.03 100 39 27.1 16.0 20.5 3.6 3.0 142 65 80 0.17 12.0 1.9 3.08 - - 3.00 - 0.35 0.06 10.53 0.55 -
50 Percentile 19.5 0.026 6.90 60 36 12.6 13.0 16.0 2.8 2.2 107 51 56 0.17 10.0 1.8 2.70 - - 2.00 - 0.18 0.04 7.85 0.50 -
25 Percentile 12.1 0.016 6.55 30 31 7.2 9.2 14.8 2.4 1.7 66 29 21 0.17 10.0 1.5 2.03 - - 2.00 - 0.09 0.02 6.50 0.30 -
5 Percentile 7.0 0.009 6.17 20 23 2.5 7.1 12.2 2.0 1.2 49 23 15 0.17 8.0 1.1 1.61 - - 1.15 - 0.03 0.01 4.64 0.20 -
No. of Data 23.0 23 24 24 24 22 24 24 24 24 24 24 24 1 24 22 22 - - 24 - 23 2 22 23 -
Mean 53.0 0.070 6.85 69 35 20.0 13.5 18.5 3.4 2.4 112 50 61 0.17 12.2 1.9 2.62 - - 2.67 - 0.37 0.04 8.40 0.47 -
Std. Deviation 76.9 0.101 0.45 49 7 20.6 5.6 6.63 2.2 1.1 56 25 48 - 7.29 0.8 0.75 - - 1.17 - 0.59 0.05 3.04 0.22 -
Minimum 5.7 0.007 6.10 5 23 1.7 6.0 10.0 2.0 1.1 44 17 4 0.17 8.0 0.6 1.50 - - 1.00 - 0.03 0.01 3.50 0.20 -
Maximum 330.3 0.434 7.70 200 48 90.2 29.0 41.0 13.0 6.1 268 106 206 0.17 45.0 4.8 4.40 - - 6.00 - 2.80 0.08 16.90 1.00 -
Statistical Values for the Station 2224632 (Sg. Kesang di Chin Chin)
95 Percentile 136.3 0.847 7.00 348 137 398.5 26.0 29.0 8.3 2.9 578 178 410 1.12 25.4 6.3 10.36 47 6 12.52 0.26 22.00 0.72 15.15 13.68 0.26
75 Percentile 41.0 0.255 6.60 150 95 121.8 21.0 22.0 6.4 1.9 234 104 130 0.35 16.1 4.8 6.80 26 4 9.00 0.15 7.70 0.42 8.50 4.60 0.11
50 Percentile 13.6 0.084 6.30 83 80 70.0 17.0 19.0 5.5 1.4 161 87 72 0.21 12.0 4.0 5.70 18 3 7.70 0.07 4.60 0.24 5.55 2.90 0.05
25 Percentile 2.0 0.012 6.00 60 71 33.0 13.0 16.0 4.6 1.0 122 69 44 0.11 9.5 3.5 4.60 12 2 6.80 0.06 3.60 0.18 3.93 2.00 0.04
5 Percentile 0.5 0.003 5.40 26 56 11.0 8.2 13.0 3.5 0.6 90 45 16 0.05 4.0 2.8 2.66 8 2 5.51 0.02 0.75 0.12 2.73 1.12 0.03
No. of Data 166.0 166 225 226 224 226 225 226 226 212 226 226 225 163 224 207 209 213 35 223 118 171 121 206 224 109
Mean 32.8 0.204 6.28 124 87 118.0 17.1 20.0 5.7 1.6 213 95 118 0.32 14.7 4.2 6.11 22 3 8.05 0.11 6.76 0.34 6.85 4.33 0.10
Std. Deviation 47.8 0.297 0.47 114 28 191.3 6.2 6.0 1.8 1.0 59 51 141 0.37 10.1 1.1 2.61 15 1 2.47 0.11 6.70 0.28 4.50 5.07 0.11
Minimum 0.2 0.001 4.80 10 49 2.4 1.5 5.0 2.0 0.5 59 29 5 0.02 1.4 2.1 0.90 2 2 0.10 0.01 0.10 0.03 0.12 0.10 0.01
Maximum 317.2 1.970 7.70 700 311 2100.0 46.0 59.0 20.0 8.5 1143 554 1025 2.50 80.0 8.5 19.00 99 8 21.70 0.64 40.00 2.04 37.40 45.00 0.82
Sp.
Parameter Fe (mg/L)
m 2)
Statistical Values for the Station 2322613 (Sg. Melaka di Pantai Belimbing)
95 Percentile 11.7 0.034 6.80 350 170 464.9 26.3 29.8 8.8 3.0 887 169 672 1.02 35.7 7.1 13.65 41 3 13.65 0.28 25.00 0.58 18.60 20.60 0.35
75 Percentile 4.9 0.014 6.60 140 119 145.5 19.0 24.0 7.3 1.7 318 116 223 0.41 16.3 5.8 8.85 22 3 10.95 0.21 14.00 0.29 11.60 7.80 0.15
50 Percentile 1.8 0.005 6.38 80 100 71.9 16.0 21.0 6.6 1.3 186 94 91 0.23 12.8 5.3 6.95 15 2 9.10 0.16 6.10 0.20 8.90 4.00 0.10
25 Percentile 1.2 0.004 5.90 50 87 31.5 12.8 18.0 5.6 1.0 135 74 44 0.11 10.5 4.6 5.60 9 2 7.70 0.07 4.30 0.14 6.60 2.25 0.07
5 Percentile 0.7 0.002 5.36 30 71 6.0 9.2 16.0 4.3 0.5 98 45 19 0.06 7.8 3.8 3.94 5 2 5.55 0.02 1.04 0.11 3.48 1.20 0.02
No. of Data 23.0 23 132 131 131 132 132 132 132 116 131 131 132 89 131 127 128 126 34 131 71 89 106 129 131 57
Mean 3.3 0.009 6.21 114 106 145.1 16.7 21.6 6.5 1.5 288 102 185 0.35 15.6 5.3 7.67 18 3 9.47 0.16 9.74 0.27 9.66 6.74 0.14
Std. Deviation 3.4 0.010 0.49 102 32 259.8 6.3 5.2 1.5 1.0 286 58 270 0.37 9.4 1.1 3.70 12 4 3.04 0.10 7.98 0.24 4.64 7.56 0.13
Minimum 0.6 0.002 4.40 7 53 0.6 3.5 11.0 2.7 0.5 79 15 7 0.05 4.0 1.0 2.20 1 2 3.80 0.01 0.15 0.04 1.50 0.40 0.01
Maximum 12.7 0.036 7.00 700 233 2150.0 47.3 52.0 12.3 8.5 1774 424 1701 2.40 63.1 8.4 26.00 68 27 25.00 0.55 39.00 1.90 25.50 44.00 0.70
Statistical Values for the Station 2917601 (Sg. Langat di Kajang)
95 Percentile 29.0 0.076 7.30 265 218 612.0 57.2 55.0 19.0 2.7 1123 156 1075 2.84 24.0 7.9 17.25 85 19 14.39 0.40 13.00 1.95 19.00 20.25 0.34
75 Percentile 9.8 0.026 6.70 50 131 182.0 34.6 35.6 12.4 1.3 437 100 371 0.89 16.0 5.6 9.43 52 7 8.30 0.25 6.73 0.25 11.07 5.68 0.17
50 Percentile 5.3 0.014 6.40 21 99 71.1 26.2 25.0 8.8 1.0 251 78 176 0.37 16.0 4.4 6.95 32 3 6.00 0.20 2.60 0.20 7.20 3.00 0.10
25 Percentile 4.1 0.011 6.20 10 69 33.8 17.1 18.0 6.0 0.7 183 51 95 0.18 12.0 3.4 4.50 16 2 4.00 0.12 0.24 0.10 3.70 1.30 0.05
5 Percentile 2.2 0.006 6.00 5 39 5.3 10.0 10.0 2.8 0.5 60 30 18 0.05 8.0 2.1 2.30 7 1 1.49 0.07 0.06 0.09 1.17 0.30 0.02
No. of Data 16.0 16 179 16 16 16 15 16 16 14 16 16 16 16 16 16 16 16 14 16 16 16 2 16 15 13
Mean 8.4 0.022 6.53 59 107 151.6 29.2 28.4 9.6 1.1 373 83 291 0.80 15.3 4.8 7.71 38 6 6.68 0.21 4.37 0.40 8.19 4.99 0.13
Std. Deviation 54.0 0.048 0.64 165 17 45.3 5.3 8.7 3.0 1.3 74 47 46 0.56 8.3 1.8 1.38 11 - 2.76 0.04 1.29 0.06 3.05 1.60 0.03
Minimum 0.3 0.001 5.63 5 15 0.0 3.0 8.0 1.0 0.0 43 10 4 0.02 2.0 1.4 0.90 2 1 0.52 0.03 0.03 0.00 0.10 0.10 0.01
Maximum 54.6 0.144 8.83 600 295 1400.0 86.0 85.0 25.0 6.1 1844 261 1834 8.40 51.0 22.0 25.00 192 41 34.00 1.04 36.00 1.95 26.00 40.70 0.68
Statistical Values for the Station 3118645 (Sg. Lui di Kg. Lui)
95 Percentile 0.106 7.40 113 94 136.3 24.7 22.4 7.7 1.7 291 85 220 1.03 32.0 4.6 5.81 38 6 7.00 0.30 3.89 0.43 5.79 5.96 0.17
75 Percentile 0.036 6.95 40 51 30.0 17.4 13.9 3.8 1.1 124 61 68 0.18 20.0 3.2 4.00 14 1 3.00 0.14 1.80 0.18 2.11 1.90 0.05
50 Percentile 0.024 6.76 20 40 14.0 15.5 11.0 3.2 0.9 78 47 34 0.10 18.0 2.8 3.50 10 1 1.40 0.10 1.11 0.14 1.60 1.10 0.03
25 Percentile 0.017 6.58 15 36 9.2 13.9 10.0 2.8 0.7 64 35 18 0.06 16.0 2.3 3.10 6 1 0.90 0.08 0.30 0.13 1.30 0.60 0.02
5 Percentile 0.007 6.28 5 33 2.9 12.0 8.6 2.4 0.5 43 25 7 0.03 8.0 1.9 2.50 3 1 0.60 0.03 0.05 0.04 0.44 0.30 0.01
No. of Data 153 168 168 169 166 167 166 166 150 168 168 167 99 168 166 167 142 45 166 148 150 7 162 164 107
Mean 0.034 6.80 35 50 38.4 16.6 12.7 3.7 0.9 126 51 72 0.29 18.9 3.0 3.94 14 2 2.39 0.13 1.36 0.18 2.18 1.94 0.05
Std. Deviation 54.0 0.048 0.64 165 17 45.3 5.3 8.7 3.0 1.3 74 47 46 0.56 8.3 1.8 1.38 11 - 2.76 0.04 1.29 0.06 3.05 1.60 0.03
Minimum 0.005 5.74 0 30 0.0 3.4 4.0 1.6 0.0 28 6 4 0.02 0.1 1.5 1.80 1 1 0.08 0.01 0.03 0.00 0.20 0.10 0.01
Maximum 0.288 8.50 400 383 1170.0 63.0 48.5 15.0 2.7 1960 243 1850 7.20 64.0 14.0 22.00 184 31 17.90 1.04 8.84 0.53 16.30 30.90 0.32
Sp.
Parameter Fe (mg/L)
m 2)
Statistical Values for the Station 3414621 (Sg. Selangor di Rantau Panjang)
95 Percentile 159.5 0.110 7.23 210 71 304.0 21.0 23.3 7.2 1.5 712 138 578 0.97 20.0 4.1 5.61 37 3 5.37 0.30 5.80 1.66 7.10 11.16 0.17
75 Percentile 78.0 0.054 6.80 120 55 130.0 16.0 16.0 5.2 1.0 272 69 205 0.42 16.0 3.2 3.88 20 2 4.00 0.12 3.90 0.40 5.30 5.70 0.11
50 Percentile 52.5 0.036 6.51 70 49 90.0 13.0 14.0 4.5 0.7 185 50 131 0.18 12.0 2.9 2.75 15 2 2.80 0.10 2.22 0.16 4.10 3.50 0.08
25 Percentile 32.5 0.022 6.30 30 42 54.6 10.0 12.0 3.6 0.6 139 36 87 0.09 10.0 2.5 2.20 12 1 2.19 0.09 0.52 0.12 3.00 2.00 0.06
5 Percentile 13.2 0.009 6.06 14 33 15.5 7.1 8.8 2.2 0.5 99 27 48 0.05 4.0 2.0 1.59 7 1 1.44 0.06 0.13 0.10 0.58 0.70 0.03
No. of Data 92.0 92 116 116 116 116 116 115 115 100 115 114 115 90 110 110 110 107 48 115 104 112 11 109 109 83
Mean 62.7 0.043 6.60 86 50 114.5 13.4 15.7 4.5 0.9 245 61 190 0.33 12.4 2.9 3.29 18 3 3.14 0.14 2.42 0.46 4.10 4.35 0.09
Std. Deviation 54.0 0.048 0.64 165 17 45.3 5.3 8.7 3.0 1.3 74 47 46 0.56 8.3 1.8 1.38 11 - 2.76 0.04 1.29 0.06 3.05 1.60 0.03
Minimum 6.8 0.005 5.85 5 27 6.4 3.0 4.7 1.3 0.3 1 8 14 0.02 2.0 0.1 1.00 3 1 0.37 0.01 0.06 0.10 0.20 0.10 0.01
Maximum 247.5 0.171 8.60 420 92 693.0 25.0 115.0 11.0 3.7 1172 414 1103 2.69 41.0 6.6 20.00 76 38 10.00 1.10 7.20 2.40 12.00 17.30 0.30
Statistical Values for the Station 3516622 (Sg. Selangor di Rasa)
95 Percentile 35.1 0.109 7.61 120 47 155.2 16.2 13.0 4.1 1.4 322 71 276 0.52 24.0 3.3 4.01 37 4 5.05 0.34 2.63 1.97 4.55 6.72 0.16
75 Percentile 15.7 0.049 7.00 60 37 32.0 12.4 10.0 2.8 0.9 112 42 80 0.18 16.0 2.8 3.40 16 1 2.29 0.20 1.40 0.88 1.70 2.15 0.05
50 Percentile 11.0 0.034 6.73 20 30 14.0 10.4 7.5 2.0 0.6 65 34 31 0.11 16.0 2.4 3.00 10 1 1.10 0.19 0.70 0.50 1.20 1.10 0.03
25 Percentile 8.8 0.027 6.51 10 26 7.7 9.1 6.0 1.6 0.5 47 25 16 0.07 12.0 2.2 2.50 6 1 0.70 0.11 0.25 0.20 0.90 0.60 0.01
5 Percentile 5.4 0.017 6.14 5 24 2.5 5.8 4.4 1.0 0.2 29 15 6 0.04 8.0 1.8 2.00 4 1 0.40 0.07 0.09 0.07 0.30 0.20 0.01
No. of Data 83.0 83 135 136 137 137 138 137 136 111 139 139 135 86 140 140 140 116 36 140 132 126 11 131 139 82
Mean 14.1 0.044 6.75 46 33 34.5 10.7 8.0 2.3 0.7 106 37 72 0.24 15.8 2.5 3.04 14 1 1.91 0.19 1.03 0.71 1.63 1.84 0.04
Std. Deviation 9.7 0.030 0.48 72 11 56.1 3.1 2.8 1.4 0.7 122 19 114 0.52 5.6 0.6 0.82 12 1 2.44 0.14 1.14 0.73 1.47 2.30 0.05
Minimum 3.0 0.009 4.80 5 17 0.3 1.6 0.0 0.0 0.0 18 5 5 0.02 8.0 1.3 1.40 2 1 0.10 0.02 0.03 0.00 0.10 0.10 0.01
Maximum 56.1 0.175 8.00 700 103 380.0 20.0 16.0 14.1 7.1 860 160 830 4.07 48.0 7.9 7.50 77 8 21.66 1.30 8.61 2.50 10.30 16.10 0.23
Statistical Values for the Station 3613601 (Sg. Bernam di Ulu Ibu Ampangan)
95 Percentile - - 7.18 300 46 258.9 17.2 13.0 3.4 1.6 356 89 240 0.81 24.0 3.5 3.48 48 13 5.00 0.29 3.21 2.01 3.49 7.68 0.11
75 Percentile - - 6.80 90 36 110.0 12.5 10.0 2.8 1.0 193 49 150 0.17 16.0 2.7 2.50 19 4 2.40 0.12 2.00 0.85 2.00 4.18 0.06
50 Percentile - - 6.50 70 29 58.0 9.2 8.1 2.3 0.7 123 37 84 0.11 14.0 2.4 2.10 14 1 1.87 0.10 1.52 0.65 1.55 2.20 0.05
25 Percentile - - 6.27 30 27 30.3 7.7 6.2 1.8 0.5 90 26 51 0.07 12.0 2.1 1.70 10 1 1.30 0.07 0.88 0.22 1.10 1.40 0.03
5 Percentile - - 5.78 10 24 8.2 5.8 5.0 1.3 0.3 57 18 30 0.04 5.0 1.8 1.10 7 1 0.80 0.04 0.10 0.04 0.66 0.60 0.01
No. of Data - - 151 151 151 150 151 148 146 106 152 152 153 110 147 146 145 136 25 152 114 146 8 142 146 100
Mean - - 6.52 80 33 86.3 10.3 8.5 2.4 0.8 156 41 114 0.22 14.5 2.5 2.21 17 3 2.24 0.12 1.59 0.75 1.75 3.30 0.05
Std. Deviation 54.0 0.048 0.64 165 17 45.3 5.3 8.7 3.0 1.3 74 47 46 0.56 8.3 1.8 1.38 11 - 2.76 0.04 1.29 0.06 3.05 1.60 0.03
Minimum - - 5.29 5 18 2.9 3.1 3.1 0.6 0.0 35 8 5 0.02 1.6 1.2 0.50 1 1 0.26 0.01 0.05 0.00 0.20 0.04 0.01
Maximum - - 8.80 400 258 530.0 28.0 28.0 9.7 2.3 708 145 595 3.35 51.0 7.0 6.10 74 15 13.50 0.48 9.50 2.56 8.70 25.90 0.27
Sp.
Parameter Fe (mg/L)
m 2)
Statistical Values for the Station 3615612 (Sg. Bernam di Tanjung Malim)
95 Percentile - - 7.27 80 46 145.8 16.8 13.0 4.0 1.5 268 77 201 0.74 22.5 3.3 3.20 39 3 5.00 0.28 4.00 1.31 4.26 5.34 0.11
75 Percentile - - 6.81 40 36 31.0 12.0 11.0 3.2 0.9 98 42 57 0.18 16.0 2.4 2.50 14 1 2.10 0.12 2.30 0.51 2.70 1.60 0.05
50 Percentile - - 6.59 20 32 16.0 10.0 9.1 2.6 0.6 67 32 34 0.10 12.0 2.2 2.10 9 1 1.30 0.10 1.65 0.30 2.10 1.10 0.04
25 Percentile - - 6.40 10 28 9.0 8.5 7.1 2.1 0.5 50 26 17 0.07 11.0 2.0 1.80 6 1 1.00 0.09 0.98 0.15 1.60 0.60 0.02
5 Percentile - - 5.98 5 24 3.4 6.2 5.2 1.5 0.3 38 18 10 0.05 7.9 1.7 1.50 5 1 0.60 0.05 0.09 0.07 0.30 0.20 0.01
No. of Data - - 113 112 114 110 114 114 114 66 115 115 116 96 113 113 112 93 2 114 101 115 12 113 113 53
Mean - - 6.60 32 33 32.8 10.5 9.1 2.7 0.7 97 39 56 0.25 13.9 2.3 2.23 13 1 1.93 0.12 1.72 0.46 2.38 1.62 0.05
Std. Deviation - - 0.52 36 7 50.4 3.0 2.6 0.8 0.4 100 31 82 0.81 7.6 0.9 0.69 12 2 1.85 0.08 1.30 0.61 2.31 2.10 0.04
Minimum - - 3.40 3 19 0.4 2.3 3.6 1.0 0.1 28 6 4 0.03 2.0 1.1 1.20 1 1 0.00 0.01 0.02 0.00 0.10 0.01 0.01
Maximum - - 8.68 300 69 370.0 20.0 17.0 5.6 2.3 785 353 673 8.92 84.0 12.0 8.10 88 11 17.00 0.74 6.60 2.50 26.00 16.00 0.28
Statistical Values for the Station 3813611 (Sg. Bernam di Jam S.K.C)
95 Percentile - - 7.20 213 44 294.1 16.0 12.0 3.5 1.3 424 89 341 0.47 20.0 3.5 3.30 37 2 6.00 0.27 3.02 0.81 3.42 9.02 0.11
75 Percentile - - 6.80 83 34 123.5 12.0 9.6 2.8 0.9 217 52 170 0.15 16.0 2.7 2.60 19 1 2.11 0.10 2.00 0.30 2.00 4.90 0.07
50 Percentile - - 6.50 60 29 63.5 9.4 8.0 2.2 0.6 137 41 94 0.10 13.0 2.3 2.20 13 1 1.50 0.10 1.60 0.20 1.50 2.30 0.05
25 Percentile - - 6.30 30 26 29.0 7.9 6.4 1.8 0.5 97 29 57 0.06 12.0 2.1 1.80 9 1 1.10 0.08 0.82 0.13 1.10 1.30 0.03
5 Percentile - - 5.90 5 23 7.1 5.4 5.1 1.3 0.4 65 17 33 0.05 8.0 1.8 1.20 7 1 0.77 0.03 0.11 0.05 0.40 0.50 0.02
No. of Data - - 200 200 200 200 201 196 194 149 197 197 199 132 194 194 194 180 30 198 152 189 11 190 193 125
Mean - - 6.52 82 31 100.1 10.2 8.1 2.3 0.7 179 44 133 0.16 14.2 2.6 2.31 17 1 2.09 0.11 1.53 0.28 1.94 3.46 0.05
Std. Deviation - - 0.41 90 9 110.9 4.7 2.6 1.1 0.3 149 23 137 0.27 5.9 2.1 1.22 20 1 2.11 0.07 1.00 0.29 2.56 3.15 0.03
Minimum - - 5.23 5 18 3.8 2.3 1.6 0.8 0.1 8 4 11 0.01 2.0 1.2 0.60 4 1 0.27 0.01 0.04 0.00 0.10 0.10 0.01
Maximum - - 7.86 700 103 839.0 56.0 28.0 12.0 2.1 1487 149 1375 2.73 54.0 24.0 16.00 255 4 22.50 0.49 6.20 1.01 26.00 20.70 0.18
Statistical Values for the Station 3116630 (Sg. Klang di Jam Sulaiman)
95 Percentile - - 6.97 88 364 161.1 104.3 74.1 25.7 2.7 331 169 213 6.80 15.7 7.0 19.70 41 11 17.90 0.40 25.75 1.99 19.00 2.53 0.24
75 Percentile - - 6.90 55 293 53.5 90.5 72.0 24.5 2.4 230 164 73 6.80 13.5 6.4 19.00 36 10 12.50 0.35 22.25 1.40 18.50 0.50 0.24
50 Percentile - - 6.80 40 270 43.0 81.0 65.0 22.0 1.7 197 139 53 4.70 11.0 5.8 18.00 30 8 12.00 0.30 8.70 1.00 18.00 0.30 0.24
25 Percentile - - 6.75 35 262 20.5 68.0 63.5 22.0 1.7 186 132 36 3.20 11.0 5.5 16.00 23 8 11.50 0.30 1.60 0.70 14.00 0.25 0.24
5 Percentile - - 6.70 30 196 15.9 61.5 58.8 20.6 1.6 165 108 22 0.69 9.7 4.7 10.01 22 7 8.69 0.23 0.70 0.49 12.00 0.13 0.24
No. of Data - - 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
Mean - - 6.83 50 276 58.6 81.1 66.9 23.0 2.0 222 143 80 4.31 12.2 5.8 16.61 30 9 12.53 0.31 11.67 1.11 16.29 0.76 0.24
Std. Deviation - - 0.11 24 65 15.0 17.41 57.0 20.0 1.6 70 25 86 2.82 9.2 4.3 4.14 8 2 3.71 0.07 11.78 0.61 3.09 1.17 -
Minimum - - 6.70 30 168 15.0 60.0 57.0 20.0 1.6 158 100 19 0.06 9.2 4.3 8.30 22 6 7.70 0.20 0.60 0.40 12.00 0.10 0.24
Maximum - - 7.00 100 387 204.0 110.0 75.0 26.0 2.8 370 170 270 6.80 16.0 7.0 20.00 42 11 20.00 0.40 26.00 2.20 19.00 3.40 0.24
Sp.
Parameter Fe (mg/L)
m 2)
Statistical Values for the Station 3116633 (Sg. Gombak di Jalan Tun Razak)
95 Percentile 5.5 0.045 6.97 176 310 228.5 87.4 62.0 21.5 2.2 403 153 314 8.24 15.7 6.0 16.20 42 13 9.97 0.27 22.25 1.93 14.40 3.42 0.21
75 Percentile 4.7 0.038 6.85 100 174 116.0 63.5 52.5 17.5 2.0 344 133 248 4.80 14.5 5.1 12.00 34 10 9.25 0.20 19.00 1.15 12.50 0.75 0.21
50 Percentile 4.5 0.037 6.80 60 155 61.0 58.0 46.0 16.0 1.7 234 101 75 4.00 13.0 4.4 10.00 24 9 8.30 0.20 10.95 1.00 9.70 0.30 0.21
25 Percentile 3.7 0.031 6.75 50 144 39.0 43.5 44.5 14.5 1.5 190 96 58 2.60 12.0 4.2 9.70 20 7 5.45 0.20 3.13 0.80 9.15 0.30 0.21
5 Percentile 3.7 0.030 6.70 33 129 26.6 33.3 39.8 13.3 1.2 146 86 34 0.58 11.3 3.5 7.40 17 6 3.35 0.20 1.75 0.56 8.48 0.16 0.21
No. of Data 5.0 5 7 7 7 7 7 7 7 7 7 7 7 5 7 7 7 7 6 7 7 6 7 7 7 1
Mean 4.5 0.037 6.81 84 183 96.0 57.0 49.0 16.6 1.7 262 114 147 4.11 13.3 4.6 11.13 27 9 7.19 0.21 11.45 1.09 10.90 1.00 0.21
Std. Deviation 0.9 0.007 0.11 59 82 87.3 21.6 8.8 3.3 0.4 108 28 26 3 1.8 1.0 3.55 10 3 2.76 0.04 9.39 0.55 2.48 1.57 -
Minimum 3.7 0.030 6.70 30 125 26.0 30.0 38.0 13.0 1.2 127 84 26 0.07 11.0 3.2 6.50 16 5 2.60 0.20 1.60 0.50 8.30 0.10 0.21
Maximum 5.8 0.047 7.00 200 365 275.0 97.0 65.0 23.0 2.2 404 159 320 9.10 16.0 6.3 18.00 45 14 10.00 0.30 23.00 2.20 15.00 4.50 0.21
Statistical Values for the Station 3116634 (Sg. Batu di Sentul)
95 Percentile - - 7.20 91 416 114.9 134.0 81.7 29.7 2.7 266 191 120 9.20 14.1 8.0 22.70 44 9 17.00 0.50 31.25 2.46 22.50 1.97 0.29
75 Percentile - - 7.15 65 359 71.5 118.0 81.0 29.0 2.5 230 183 56 8.88 12.0 7.7 21.50 36 9 17.00 0.45 29.00 1.80 18.00 1.30 0.21
50 Percentile - - 7.00 60 341 22.0 100.0 80.0 28.0 2.2 220 169 28 7.80 10.0 7.1 20.00 30 8 15.00 0.40 18.95 1.50 15.00 0.60 0.10
25 Percentile - - 6.75 35 320 19.0 94.5 77.0 27.0 2.0 189 162 21 2.02 9.6 6.6 16.50 28 6 14.00 0.40 2.98 1.10 13.50 0.25 0.10
5 Percentile - - 6.56 30 289 16.6 91.6 72.9 24.6 1.9 186 147 15 0.28 8.9 6.1 14.00 26 4 9.99 0.33 0.33 0.93 7.33 0.13 0.09
No. of Data - - 7 7 7 7 7 7 7 7 7 7 7 6 7 7 7 7 7 7 7 6 7 7 7 3
Mean - - 6.93 56 344 48.0 108.0 78.6 27.7 2.2 218 170 48 5.74 11.0 7.1 19.00 33 7 14.67 0.41 16.67 1.56 15.27 0.83 0.17
Std. Deviation - - 0.27 25 49 45.1 18.0 3.7 2.1 0.3 35 18 45 4.28 2.2 0.8 3.65 8 2 3.03 0.07 14.96 0.62 5.88 0.80 0.12
Minimum - - 6.50 30 280 16.0 91.0 72.0 24.0 1.8 185 141 15 0.23 8.8 6.0 14.00 25 4 8.70 0.30 0.10 0.90 4.90 0.10 0.09
Maximum - - 7.20 100 433 117.0 140.0 82.0 30.0 2.7 282 192 141 9.30 15.0 8.0 23.00 46 9 17.00 0.50 32.00 2.70 24.00 2.00 0.31
Statistical Values for the Station 3117602 (Sg. Klang At Lorong Yap Kwan Seng)
95 Percentile - - 6.97 102 312 114.1 87.2 65.7 23.0 2.2 228 160 130 4.48 16.7 5.4 19.00 30 7 13.80 0.40 20.70 1.45 22.80 1.96 0.15
75 Percentile - - 6.85 55 262 26.5 76.0 64.0 22.5 2.1 170 136 39 3.20 15.0 4.9 18.00 26 7 9.95 0.40 14.35 0.95 20.00 0.60 0.15
50 Percentile - - 6.80 40 190 18.0 71.0 59.0 20.0 1.8 161 129 25 2.20 13.0 4.7 17.00 21 5 8.50 0.30 2.90 0.70 18.00 0.50 0.15
25 Percentile - - 6.70 30 185 14.0 68.0 58.0 20.0 1.7 148 122 17 0.78 12.0 4.2 13.50 20 5 7.75 0.30 1.75 0.53 14.00 0.10 0.15
5 Percentile - - 6.63 23 163 9.7 60.0 55.6 19.3 1.5 142 101 15 0.20 10.0 3.6 8.41 18 4 6.05 0.30 0.76 0.35 13.30 0.10 0.15
No. of Data - - 7 7 7 7 7 7 7 7 7 7 6 5 7 7 7 7 7 7 7 7 6 7 7 1
Mean - - 6.79 50 224 37.0 72.6 60.6 21.0 1.8 170 130 47 2.21 13.3 4.5 15.19 23 6 9.21 0.34 8.07 0.80 17.57 0.64 0.15
Std. Deviation - - 0.13 34 62 50.7 10.7 4.2 1.6 0.3 140 23 56 2 2.6 0.7 4.40 5 1.13 3.04 0.05 8.90 0.46 4.08 0.85 -
Minimum - - 6.60 20 155 8.7 57.0 55.0 19.0 1.5 140 93 14 0.05 9.2 3.4 7.30 17 4 5.60 0.30 0.40 0.30 13.00 0.10 0.15
Maximum - - 7.00 120 331 151.0 92.0 66.0 23.0 2.2 252 169 159 4.80 17.0 5.5 19.00 30 7 15.00 0.40 21.00 1.60 24.00 2.50 0.15
Sp.
Parameter Fe (mg/L)
m 2)
Statistical Values for the Station 3217601 (Sg. Gombak Ibu Bekalan Km 11 Gombak. This station is shifted from Sg. Gombak at Damsite)
95 Percentile - - 7.07 246 129 268.9 50.4 33.7 10.3 2.1 458 90 400 1.64 18.0 4.0 8.86 30 6 6.74 0.28 9.55 0.40 7.57 4.89 0.16
75 Percentile - - 6.95 120 83 170.5 27.0 26.0 8.4 2.1 253 78 177 1.40 18.0 3.6 6.65 17 4 2.95 0.20 8.40 0.40 5.60 2.25 0.16
50 Percentile - - 6.90 80 75 127.0 24.0 25.0 7.4 1.4 175 70 93 1.10 15.0 3.2 5.60 11 2 2.50 0.20 5.40 0.40 4.20 1.30 0.16
25 Percentile - - 6.80 80 71 56.5 22.5 23.0 7.0 1.3 147 64 77 0.89 15.0 2.8 5.15 10 2 2.35 0.13 3.15 0.33 3.90 0.80 0.15
5 Percentile - - 6.80 45 66 51.5 19.9 21.3 6.1 1.1 121 51 49 0.58 14.3 2.5 4.13 10 2 0.76 0.10 0.98 0.15 1.66 0.31 0.15
No. of Data - - 7 7 7 7 7 7 7 7 7 7 7 5 7 7 7 7 7 7 6 6 4 7 7 2
Mean - - 6.90 116 85 134.0 28.9 25.9 7.8 1.6 232 70 162 1.12 16.1 3.2 6.10 16 3 3.07 0.18 5.45 0.33 4.59 1.89 0.16
Std. Deviation - - 0.12 87 29 93.0 14.0 5.3 1.7 0.4 146 15 155 0.46 1.8 0.6 1.88 9 2 2.50 0.08 3.72 0.15 2.30 1.91 0.01
Minimum - - 6.80 30 65 50.0 19.0 21.0 5.7 1.1 113 47 40 0.50 14.0 2.4 3.80 10 2 0.10 0.10 0.30 0.10 0.70 0.10 0.15
Maximum - - 7.10 300 149 307.0 60.0 37.0 11.0 2.1 539 93 492 1.70 18.0 4.1 9.70 35 6 8.30 0.30 9.80 0.40 8.20 5.70 0.16
National Water Quality Standard (DOE Malaysia)
pH Colour Cond. Turb. Alka. Hard. Ca Mg TS DS SS NH3-N Si K Na COD BOD Cl- F- NO3-N PO4 SO4 Mn
NWQS Classes Fe (mg/L)
(unit) (Hazen) (uS/cm) (NTU) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)
Class I 6.8 - 8.5 - 1000 5.0 - - - - 525 500 25 0.10 - - - 10 1 - - - - - - -

Class IIA 6.5 - 9.0 - 1000 50.0 - - - 0.1 1050 1000 50 0.30 - - - 25 3 200 - - 0.10 - 0.30 -
Class IIB 6.5 - 9.0 - - 50.0 - - - - 50 - 50 0.30 - - - 25 3 - - - - - -
Class III 5.0 - 9.0 - - - - - - - 150 - 150 0.90 - - - 50 6 - - - 0.10 - 1.00 -
Class IV 5.0 - 9.0 - 6000 - - - - - 4300 4000 300 2.70 - - 3 SAR 100 12 79 - - - - 1(leaf) -
Class V - - - - - - - - - - >300 >2.7 - - - >100 >12 - - - - - -
MOH Limit 5.5 - 9.0 - - 1000.0 - - - 150.0 1500 1500 - 0.50 - - 200.00 10 6 250 - - - - 1.00 -
WATER RESOURCES PUBLICATIONS PREVIOUSLY PUBLISHED
WRP No. Title Price (RM)
1 Surface Water Resources Map (provisional) of Peninsular Malaysia 1974 5.00

2 Hydrology Regions of Peninsular Malaysia 6.00
3 Sungai Tekam Experimental Basin Annual Report No.1 for 1973-1974 5.00
(1975)
4 Notes on Some Hydrological Effects of Land Use Changes in Peninsular 5.00
Malaysia
5 Evaporation in Peninsular Malaysia (1975) 5.00
6 Average Annual Surface Water Resources of Peninsular Malaysia (1975) 5.00
7 Sungai Lui Representative Basin Report No. 1 for 1971/72 to 1973/74 5.00
(1977)
8 Water Resources for Irrigation of Upland Crops in South Kelantan 5.00
9 Sungai Lui Representative Basin Report No. 2 for 1974/75 to 1975/76 5.00
10 Sungai Tekam Experimental Basin Annual Report No. 2 for September 5.00
1974 to March 1977 (1978)
11 Comparison of Performance of U.S. Class A Evaporation Galvanised 5.00
Iron Pan and Aluminium Pan
12 Average Annual and Monthly Surface Water Resources of Peninsular 10.00
Malaysia, 1982
13 Sungai Tekam Experimental Basin Calibration Report from July 1977 to 5.00
June 1980 (1982)
14 Comparison of Raingauge Performance under Tropical Climate 5.00
Conditions (1984)
15 Average Annual Surface Water Resources of Sabah and Sarawak (1984) 20.00
16 Sungai Tekam Experimental Basin Transition Report from July 1980 to 8.00
June 1983 (1986)
17 Variation of Rainfall with Area in Peninsular Malaysia (1986) 6.00
18 Tanjung Karang Evapotranspiration Study (1978) 5.00
19 Mean Monthly, Mean Seasonal and Mean Annual Rainfall Maps for 5.00
Peninsular Malaysia (1988)
20 Sungai Tekam Experimental Basin Final Report July 1977 to June 1986 10.0

Bahan Kualiti Air

Uploaded by

Copyright:

Available Formats

Bahan Kualiti Air

Uploaded by

Document Information

Original Description:

Copyright

Available Formats

Share this document

Share or Embed Document

Sharing Options

Did you find this document useful?

Is this content inappropriate?

Copyright:

Available Formats

Bahan Kualiti Air

Uploaded by

Copyright:

Available Formats

Water Resources Publication No.

STUDY ON THE RIVER WATER QUALITY TRENDS

WATER RESOURCES MANAGEMENT AND HYDROLOGY DIVISION

STUDY ON THE RIVER WATER QUALITY TRENDS

The department or government shall have no liability or responsibility to the user or

The Water Resources Management and Hydrology Division of the Department of

CHAPTER 2: STUDY AREA AND DATA AVAILABILITY 2

2.1 Study Area 2

CHAPTER 3: LITERATURE REVIEW

CHAPTER 4: METHODOLOGY OF RIVER INDEX DEVELOPMENT

CHAPTER 5: RESULTS AND DISCUSSION 30

CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 82

Appendix A: WQI Rating Curves A-1

Water Resources Publication Previously Published

Table Title Page

Figure Title Page

Jabatan Pengairan dan Saliran (JPS) is monitoring about 25 parameters at 28 river

o Review the available water quality data in terms of practicality and

o Develop a tool to examine the trend or pattern of each pollutant; and

1.3 SCOPE OF WORK

o To develop the relationship of each (groups) parameters and river index

o To assign the river index scores of selected important parameters to various

o To review the method of monitoring and quantification of non point source

o To comment on the existing parameters monitored by JPS.

STUDY AREA AND DATA AVAILABILITY

2.1 STUDY AREA

2.2 DATA AVAILABILITY

2.3 REVIEW OF JPS WATER QUALITY DATA

Sg. Bekok di Batu 77, Jalan Yong Yes

Sg. Klang Di Lorong Yap Kuan No

(a) Sample of Lab Data Sheet

3.1 WATER QUALITY PARAMETERS

3.1.1 Physical Parameters

3.1.2 Chemical Parameters

Biochemical Oxygen Demand (BOD) determines the strength of pollutants in terms of

3.1.3 Biological Parameters

3.2 WATER QUALITY INDEXES

Some of the advantages of indexes are:

Some of the limitations are:

3.2.1 National Sanitation Foundation Water Quality Index

Table 3: NSF WQI Analytes and Weights

Parameter/Analyte WQI Weights

Dissolved oxygen 0.17

Table 4: NSF WQI Quality Scale (WSDE, 2002)

WQI Quality of water

3.2.2 Review of Malaysian Water Quality Index

WQI = 0.22 SIDO+0.19 SIBOD+0.16 SICOD+0.16 SISS+0.15 SIAN+0.12 SIpH (1)

Sub-index for DO (in % saturation):

SIDO = 0 for DO < 8 (2a)

= 100 for DO > 92 (2b)

= -0.395 + 0.030DO2 0.00020DO3 for 8 < DO < 92 (2c)

Sub-index for BOD:

SIBOD = 100.4 4.23BOD for BOD < 5 (3a)

= 108e-0.055BOD 0.1BOD for BOD > 5 (3b)

Sub-index for COD:

SICOD = -1.33COD + 99.1 for COD < 20 (4a)

= 103e-0.0157COD 0.04COD for COD > 20 (4b)

Sub-index for AN:

SIAN = 100.5 105AN for AN < 0.3 (5a)