i
APPLICATION OF GEOGRAPHIC INFORMATION SYSTEM (GIS) IN WATER
QUALITY STUDY ON A ROAD CONSTRUCTION PROJECT
NOORAZLINDA BINTI ABDUL RAZAK
UNIVERSITI TEKOLOGI MALAYSIA
iii
APPLICATION OF GEOGRAPHIC INFORMATION SYSTEM IN WATER
QUALITY STUDY ON A ROAD CONSTRUCTION PROJECT
NOORAZLINDA BINTI ABDUL RAZAK
A project report submitted in fulfillment of the requirements for the award of the degree of
the Master of Engineering (Civil-Environmental Management)
Faculty of Civil Engineering
Universiti Teknologi Malaysia
NOVEMBER, 2009
v
To my beloveds
vi
ACKNOWLEDGEMENT
Alhamdullilah, a very special thanks and heaps of appreciation to my supervisor,
Dr Mohd Badruddin Mohd Yusof for being the most understanding, helpful and
patient in guiding me throughout the project. His encouragement and moral support
really helps me in completing my project successfully. I would also like to express
my heartiest gratitude to Mr. Mohd Samsudin Bin Ahmad, senior lecturer in FKSG for
his valuable time, guidance and encouragement throughout the course of this research.
Not forgetting my lovely family that is always by my side to support me all
the way. I also wish to extend my heartfelt thanks to all environmental laboratories
technicians for their continuous support during my projects.
Last but not least, I also owes special thanks to my mates, who always
been there for me and extended every possible support.
I am grateful indeed
vii
ABSTACT
According to the Malaysian Department of Environment’s (DOE) Water Quality
Index for 2005, 52 river basins were polluted. Non point source pollution is a major
pollution that has been affecting the quality of these river basins. It has been
considered as the major pollution for river as the source can come from anywhere.
But one of the most significantly of nonpoint sources is from the combined effects of
land disturbances to construct new developments during construction activity. A
study is conducted on a road construction project in Labis, Segamat to assess the
non-point source pollution level through GIS. This study is done by generating the
river network for the area in order to estimate the pollution loads. Rainfall and runoff
are the components that are able to transport the pollution from one place to another.
As the non-point source pollution is a diffused matter transported from land to river,
the determining factors include the land use and soil type. A GIS software, ArcGIS
9.2 was used to produce the Digital Elevation Model (DEM) to generate a river
network. With the aid of the 3D extension, visualization for the terrain produced was
enhanced. The generated river was overlaid with the land use and geological map
and interpolates with the WQI data from the sampling to estimate the loads of
pollution. Expected Mean Concentration (EMC), which is associated with the land
use, were used in this study to produce the pollutant load. Based on the final
Pollutant Accumulated Loading Map, the total annual loading through the outlet of
the river are 1940, 471, 595 and 1396 kg/year for total BOD, total TSS and NH3N,
respectively. This project also demonstrates that Geographical Information System
(GIS) fits in the role with its analytical operations of spatial and non-spatial
information to identify and solve the pollutant loading in this construction area.
viii
ABSTRAK
Merujuk kepada data Jabatan Alam Sekitar, bagi Indeks Kualiti Air tahun 2005,
terdapat 52 lembangan sungai di Malaysia yang telah tercemar . Pencemaran bukan poin
(rawak) adalah punca pencemaran utama yang telah menjejaskan kualiti lembangan
sungai di Malaysia. Ia dianggap sebagai punca pencemaran yang utama kerana
sumbernya berasal dari pelbagai tempat dan aspek. Tetapi salah satu sumber utama bagi
pencemaran bukan titik (rawak) berpunca dari kesan kerosakan muka bumi yang terjadi
semasa aktiviti pembinaan dijalankan di tapak pembinaan. Kajian ini dijalankan di
sebuah projek pembinaan jalan raya di Labis, Segamat untuk mentafsir tahap
pencemaran bukan poin (rawak) menggunakan aplikasi Geographic Information System
(GIS). Kajian ini dilakukan dengan menghasilkan rangkaian sungai secara digital
menggunakan perisian ArcGIS 9.2. Hujan dan aliran permukaan adalah komponen
utama yang membawa bahan pencemaran dari satu lokasi ke lokasi yang lain. Oleh
kerana pencemaran bukan titik(rawak) adalah bahan rawak yang dibawa dari tanah ke
sungai, maka faktor yang perlu diambil kira adalah jenis guna tanah. Model permukaan
digital (DEM) dihasilkan dan seterusnya menghasilkan rangkaian sungai secara digital .
Rangkaian sungai kemudiannya ditindihkan bersama dengan peta guna tanah dan
disisipkan dengan data Indeks Kualiti Air yang diperolehi dari tapak projek untuk
menganggarkan jumlah kuantitaif pencemaran. Konsentrasi Purata Anggaran (EMC)
dimana ia berkaitan dengan jenis guna tanah, digunakan untuk menghasilkan jumlah
kuantitatif pencemaran. Berdasarkan hasil akhir kajian ini, jumlah kuantitatif
pencemaran bagi kawasan kajian ialah 1940, 471 595 dan 1396 kg/tahun bagi BOD, TSS
dan NH3N, masing-masing. Kajian ini dapat menbuktikan kemampuan GIS bagi
memenuhi fungsi dalam menganalisis maklumat spatial dan bukan spatial untuk
mengenalpasti maklumat kuantitatif
pencemaran bagi kawasan pembinaan ini.
ix
TABLE OF CONTENTS
CHAPTER
TITLE
PAGE
DECLARATION
ii
DEDICATION
v
ACKNOWLEDGEMENT
vi
ABSTRAK
v
ABSTRACT
vii
CONTENT
viii
LIST OF TABLE
xiv
LIST OF FIGURE
xvi
LIST OF ABBREVIATIONS
xix
I
INTRODUCTION
1.0
Introduction
1
1.1
Definition of Nonpoint Source Pollution
2
1.2
Definition of Geographic Information System (GIS)
3
1.3
Background of Study
5
1.4
Statement of Problem
5
1.5
Objective of Study
7
1.6
Significance of Study
7
1.7
Scope of Study
8
x
1.7.1
Site Description
9
1.7.2
Catchment Area
11
1.7.3
Frequency and Parameter of Sampling
12
II
LITERATURE REVIEW
2.0
Introduction
13
2.1
Project Description
14
2.2
River
15
2.3
Water Quality
16
2.3.1
Physical Parameter
18
2.3.1.1 Suspended Solid
19
2.3.1.2 Turbidity
19
2.3.1.3Temperature
20
2.3.1.4 Odor
21
Chemical Parameter
22
2.3.2.1 Biological Oxygen Demand
22
2.3.2.2 Chemical Oxygen Demand
21
2.3.2.3Dissolved Oxygen
23
2.3.2.4 Ammoniac Nitrogen
24
2.3.2.5 pH
24
2.3.2
2.4
Water Quality Assessment: Water Quality Index (WQI)
27
2.5
Water Pollution and Sources
29
2.5.1
29
Sources of Water Pollution
2.5.1.1 Point Source
29
2.5.1.2 Non-point Source
30
2.6
Assessment for Water Quality
31
2.7
Water Quality Monitoring
32
2.8
Geographic Information System (GIS)
34
xi
2.8.1
2.9
ArcGIS 9.1
36
2.8.1.1 ArcMap
36
2.8.1.2 ArcGIS 3D Analyst
37
Nonpoint Source Pollution Models
39
2.9.1
Agriculture Nonpoint Source (AGNPS)
40
2.9.2
Areal Nonpoint Source Watershed Environment
Response Simulation (ANSWERS)
41
2.9.3
Storm Water Management Model (SWMM)
41
2.9.4
Universal Soil Loss Equation (USLE)
42
GIS-Based Nonpoint sources Pollution Model
43
2.10.1 Expected Mean Concentration
44
2.10.2 Rainfall-Runoff Analysis
46
2.11
Related Works
47
2.12
Previous Study
49
2.13
Summary
50
2.10
III
METHODOLOGY
3.1
Introduction
52
3.2
Identification of Study Area
53
3.3
Data Acquisition
54
3.4
3.5
3.3.1 Primary data
54
3.3.2 Secondary Data
55
Laboratory Work
55
3.4.1
Biochemical Oxygen Demand (BOD)
55
3.4.2
Chemical oxygen demand (COD)
58
3.4.3
Total Suspended Solid Test
59
3.4.4
Ammonical nitrogen
61
3.4.5
Oil and grease (O&G)
62
GIS Integration
64
xii
3.5
3.5.1 Nonpoint Source Pollution Modeling Description
66
3.5.2 Establishing a River Basin GIS Database
67
3.5.3 Generation of DTM
71
3.5.4 Rainfall-runoff analysis
73
3.5.5 Expected Mean Concentration Values
74
3.5.6 Pollutant Loading Assessment
77
3.5.7 Accumulative Pollutant Loading Assessment
78
3.5.7.1
Flow Direction
78
3.5.7.2
Flow Accumulation
78
Summary
79
IV
RESULT AND ANALYSIS
4.1
Introduction
81
4.2
Water Quality Index (WQI)
81
4.3
GIS Modeling result and analysis
85
4.3.1
Digital Elevation Model
85
4.3.2
Rainfall-Runoff Analysis
86
4.3.3
Expected Mean Concentration value (EMC)
88
4.3.4
Accumulative Pollutant Loading Assessment
88
4.3.4.1
Flow Direction
89
4.3.4.2
Flow Accumulation
90
4.4
Assessment Approach
90
4.5
Results Assessment
94
4.5.1
Biochemical Oxygen Demand (BOD)
94
4.5.2
Total Suspended Solid (TSS)
97
4.5.3
Ammonical Nitrogen (NH3-N)
98
4.6
Corrective Measure
100
4.7
Summary
102
xiii
V
CONCLUSION AND RECOMMENDATION
5.1
Introduction
103
5.2
Major Finding
103
5.3
Future Research
105
REFERENCES
106
Appendices A-D
111
xiv
LIST OF TABLE
TABLE NO.
TITLE
PAGE
1.1
Land Use Area of the Project Site
9
1.2
The Area of Coverage for the Water Catchment
11
2.0
Physical and Chemical Parameters
19
2.1
Water Quality Index (WQI) Criteria
27
2.2
DOE Water Quality Index Classes
29
2.3
The Possible Land-Water Transform Coefficients
44
3.0
Measurable BOD using Various Dilutions of Samples.
56
3.1
Monthly Average of Generated Discharge for 2009
73
3.2
Land Use Description and Codes
76
3.3
EMC Values of Pollutants Based on Land Use.
75
4.2 (a)
Water Quality Index for First Sampling, 13 February 2009
81
4.2 (b)
Water Quality Index for Second Sampling 21 April 2009
82
4.2 (c)
Water Quality Index for Third Sampling 1 September l 2009
83
xv
4.3
Annual Runoff
87
4.4
Expected Mean Concentration Table for Study Area.
88
4.5
Pollutant Load Calculation
92
4.6
The Annual Accumulative Pollutant Load (kg/year)
94
xvi
LIST OF FIGURE
FIGURE NO.
TITLE
PAGE
1.0
Oil Palm Plantation and Reserved Forest the Proposed Road
10
1.1
Sampling Points
10
1.2
The Catchment of the Study Area
12
2.1
Status of River Basin Water Quality 2005
18
2.2
Network for Water Quality Monitoring
34
2.3
Perform Modeling and Analysis Using ArcGIS
38
2.4
The 3D Extension for ArcGIS Server
39
2.5
Hydrograph, Pollutograph and Loadgraph
46
2.6
Comparison of Pollutographs and EMC for Storm Events
46
3.1
Methodology Flow Chart
53
3.2
Apparatus Used to Measure Dissolved Oxygen in BOD Test.
58
3.3
Apparatus Used to Measure COD
59
3.4
Apparatus Used to Measure TSS
61
xvii
3.5
Apparatus for Oil and Grease Test
64
3.6
The GIS Integration Overview
65
3.7
The Nonpoint Source Pollutant Modeling
67
3.8
The Digitized River Network
69
3.9
Construction of the Runoff Grid, Expected Mean
Concentration (EMC) Grid and Assessment of Pollutant Loading
70
3.10
Contour Line Digitizing Using ArcGIS
72
3.11
Chart for Discharge Value for Each Station
74
3.12
Joining of Table land use and EMC
76
4.0
Elevation Surface of Study Area
85
4.1
Stream network overlaid with elevation surface
86
4.2
The Yearly Runoff Grid
87
4.3
The Flow Analysis using GIS
89
4.4
Flow Direction Grid of the study area
89
4.5
Flow Accumulation Grid of the study area
90
4.6
Pollutant Accumulated Loading Map
91
4.7 (a)
Annual Accumulative Pollutant Load for BOD and NH3N
93
4.7 (b)
Annual Accumulative Pollutant Load for TSS
93
4.8
Differences in BOD Loading
95
xviii
4.9
Daily Rainfall for the Study Area
95
4.10
BOD Pollutant Laoding for W4
96
4.11
Differences in TSS Loading
97
4.12
BOD Pollutant Laoding During Construction Stage
98
4.13
NH3N Loading for Each Sampling Station
99
xix
LIST OF ABBREVIATIONS
µg
-
micro gram
AN
-
Ammoniacal-nitrogen
BMP
BOD
Best Management Practice
-
Biochemical Oxygen Demand
C
Runoff Coefficient
CAD
Computer-aided Design
COD
-
Chemical Oxygen Demand
CN
Curve Number
DEM
Digital Elevation Model
DFX
AutoCAD Exchange Format
DO
-
Dissolved Oxygen
DOE
-
Department of Environment
EMC
Expected Mean Concentration
ESRI
Environmental System Research Institute
g
-
gram
GIS
-
Geographic Coordinate System
GPS
-
Global Positioning System
GRASS
ha
Geographical Analysis Support System
-
Hectares
IDW
Inverse Distance Weighted
JKR
Jabatan Kerja Raya
JUPEM
-
Jabatan Ukur dan Pemetaan Malaysia
m
-
meter
xx
mg
-
NPS
milligram
Non-Point Source
ppm
-
part per million
RSO
-
Rectified Skew Orthomorphic
TIN
USGS
Triangulated Irrigular Network
-
United State Geological Survey
USLE
Universal Soil Loss Equation
UV
Ultraviolet
WQI
3D
-
Water Quality Index
Three Dimension
xxi
LIST OF APPENDIX
APPENDICES
TITLE
PAGE
A
Excel Sheet of WQI Calculation
103
B
The Modeler for the NPS Modeling
104
C1
Johor Land Use Map
105
C2
Rainfall Data
106
D1
Tables of Attributes
107
D2
Metadata of GIS Output Grid (FDGC ESRI Xml)
108
1
CHAPTER 1
INTRODUCTION
1.0
Introduction
River play an important role as the natural drainage arteries of a country, meeting
the needs of an expanding population and supporting agricultural, industrial and socioeconomic development besides being the most important freshwater resource for man.
Social, economic and political development has, in the past, been largely related to the
availability and distribution of fresh waters contained in reverie system.
As development intensifies, the natural river systems are subjected to increasing
stresses resulting in a host of problems, among which include natural runoff, dissolved
chemicals in water that percolates through the soil and human sources, such as
agriculture, mining, construction, industry, homes and businesses (Prabhakar, V.K.
2000). The introduction of pollutants from human activity has seriously degraded water
quality even to the extent of turning pristine trout streams into foul open sewers with few
life forms and less beneficial use (Hill, K.M. 2004). Actually there are many sources
where pollutants can enter the water body. In general the sources of pollutions divided
into two categories know as point sources and non point sources.
River basin is an intricate natural resource which demands varied practices,
complex management decisions and manifold research efforts in order to ensure its
2
efficient utilization. In Malaysia, there are about 150 river basins, which are made up of
more than 1800 rivers (DOE, 2005). Hence, one of the greatest challenges in river basin
management is achieving an appropriate balance between the developments of natural
resources and maintaining an optimal natural environment. For example, since nonpoint
source pollution control cannot begin until the location and severity are identified.
Geographical Information System (GIS) and nonpoint source pollution models are
becoming an integral part of national and state efforts to control the degradation of water
bodies. The emergence of GIS will provide database management, tools, visualization,
simulation and assessment models that supply open and unclouded information that
generate dependence and a common purpose between all parties involved in river basin
management.
1.1
Definition of Nonpoint Source Pollution
Nonpoint source pollution (NPS) is the introduction of impurities into a surfacewater body, usually through a non-direct route and from sources that are diffuse in
nature. Discharge from nonpoint sources are usually intermittent, associated with a
rainfall runoff event and occur less frequently and for shorter periods of time than do
point source discharges. Nonpoint source of pollution are often difficult to identify,
isolate and control. In many cases, nonpoint source pollution such as runoff from
agriculture land, runoff from construction site, urban storm water and strip mining are
becoming major water quality problem (Alexander 1976). According to United States
Environmental Protection Agency (EPA), nonpoint source pollution is the nation’s
largest water quality problem. NPS pollution is caused by rainfall moving over and
through the ground. As the runoff moves, it picks up and carries away natural and
human made pollutants, finally depositing them into lakes, rivers, wetlands, coastal
waters and even or groundwater resources (United States Environmental Protection
Agency 1997). These pollutants include:
3
•
Excess fertilizer, herbicides and insecticides from agricultural lands and
residential areas;
•
Sediment from improperly managed construction sites, crop and forest lands and
eroding stream banks;
1.2
•
Oil, grease and toxic chemicals from urban runoff and energy production;
•
Salt from irrigation practices and acid drainage from abandoned mines;
•
Bacteria and nutrients from livestock, pet wastes and faulty septic systems.
Definition of Geographic Information System (GIS)
According to Taher (1989), GIS is a computer-based system that supports
functions for managing, analyzing and displaying geographical data. Another definition
states that GIS allows users to effectively organize, update and query mapped data
(Berry 1993). Although many definitions of GIS have been discussed, they all have a
common feature, namely GIS systems deal with geographic information (Maguire
1991). This technology has developed so rapidly over the past two decades that it is now
accepted as essential tools for the effective use of geographic information.
GIS is sometime considered a subset of other information systems like computeraided design (CAD), computer carthography, database management and remote sensing.
Many feel that GIS is simply a catch-all for almost any type of automated geographic
data processing. These systems all predate GIS which, because they have evolved from
them, have many features in common. GIS, however, has a number of other features not
available in other system. The major characteristic of GIS is the emphasis placed on
analytical operation, thus distinguishes GIS from systems whose primary objective is
map production.
4
However, GIS is not an entity that stands by itself solely. It covers and embraces
disciplines that deal with the concept of space like geography, practical tools that gather
and utilize spatial data like remote sensing, land surveying, geodetic science, theory and
concept that make GIS functional like computer science, statistics, software,
engineering, artificial intelligence, with countless application of a GIS in forestry, urban
and infrastructure planning and engineering.
In this study, Geography Information System (GIS) will be implemented to
monitor water quality changes within a water body in the road construction area and
calculate loads to a surface water body. A load is the product of flow and concentration,
and it refers to how much mass of a contaminant or chemical enters a system in a
specified amount of time.
The GIS software that will be used in this study is ArcGIS Version 9.2. ArcGIS
provides a scalable framework for implementing GIS for varieties of environmental
application. The hydrologic modeling function in ArcGIS provides methods for
describing the hydrologic characteristic of a surface. For example in this study, by using
an elevation raster data set as a input, it is possible to model where water will flow,
create watersheds and stream networks and at last, by using map algebra function which
is one of the common analysis language in ArcGIS, the accumulated loading of the
pollutants can be determine.
Undoubtedly, the term GIS is always associated with computer hardware and
software. Together these will provide the means for data input, storage, manipulation,
analysis and output. The use of GIS for water quality study also can be useful for
creating digital visual interpretation of water quality characteristic. Therefore, GIS can
be described as best tools to address environmental problems.
5
1.3
Background of the study
The Malaysian Government intends to make a new 22 km-road with bridges
linking an existing one from Felda Maokil in Segamat to Bukit Kepong in Muar, Johor
Darul Takzim. The purpose of this new road and bridge project is to shorten the trip
from Chaah to Bukit Kepong by a distance of 24 km. It will pass through the existing
Felda Maokil oil palm estates along with open fields, forest and farms. There are
settlements at both ends of the roads, namely Felda Maokil 1 in the east, and Kg. Raja
and Bukit Kepong in the west. The rest of the road is mainly of plantations and part of
forested areas (Maokil Forest Reserve) in the east of Kg. Raja.
Earthworks will involve the cutting of existing slopes and hills to fill low lying areas
to minimize the importation of outside soils. Imported soils, if involved will be taken
from a nearby hill. Thus, minimal truck movements will occur outside the project area
and therefore reducing the safety and health impacts on nearby residents and plantation
workers. All roadside drain along the proposed road will be earth drains. The water from
the drains will be channelled to the existing rivers and swamps.
1.4
Statement of Problem
There is a concern that deficiencies in the project development may adversely
effect the long-term health of the existing environment especially water quality. The
potential problems regarding water quality include transport of potential pollutants into
water bodies, standing water and flooding of nearby estates, unregulated storm water
runoff surges and accumulation of sediment. The runoff from the construction site will
flow to Sungai Gatom which located downstream of the area. Sungai Gatom is
tributaries of Sungai Muar. Downstream, there are water intakes, including the Gresik
intake point, from which Malacca buys water from Johor. Therefore the runoff from the
upstream to the water supply in downstream of Sungai Muar area is such a big concern.
6
The road construction sites can result in the release of significant amounts of
pollutants into the storm drain system, Sungai Gatom without proper management.
Construction work involves the removal of vegetation and the excavation of soils
causing erosion and the subsequent discharge of sediment. Activities conducted at the
construction sites can also result in the release of other pollutants to Sungai Gatom.
Therefore, GIS is applied in this study as it has been used in the area of environmental
modeling, by providing ease and accuracy in surface terrain representation, watershed
delineation, precipitation, data compilation, non-point source pollutant loading
calculation and other concepts related to environmental processes.
The national government is fully aware of the need to protect and enhance the
environment. And, the Government is committed to take the appropriate action to
ensure that the development is sustainable and balanced. Towards this end, environment
and conservations considerations will increasingly be integrated with development
planning. In line with the Government's interest in protecting and enhancing the
environment, focus on the potentially significant adverse impacts that its projects could
have on the environment should be emphasized especially on water quality monitoring.
Mostly all research and studies that been conducted on the Malaysian context
had been on point source pollution as this pollution is giving acute impacts on the
ecosystem. Controlling of point source pollution is also much easier. As for non point
source pollution, this area is still new in term of number of study that been conducted
and due to much difficulty in acquiring information regarding the pollution as this kind
of impacts is long term. With its characteristic such as diffuse, arise over an extensive
area of land and are in transit over land before entering the navigable waters, technology
such as Geographic Information System (GIS) can be applicable in this area.
Furthermore, managing data by using conventional method is laborious task. In
the conventional methods, technicians are confronted with restrictions and limitation
when there are too much water quality data. There is no systematic system that helps to
reduce their work loads as well as updating the data. Therefore, applying GIS in water
7
quality management may improve the traditional method in the preparation of
geographical information.
1.5
Objective of Study
A set of objectives has been drawn up to archieve the purpose of this study:
i.
To analyze the effect of the Segamat road project to the water quality;
ii.
To integrate GIS (ArcGIS) in the analysis of water quality and estimation of
loading for the site;
iii.
To illustrate the proficiency of GIS technology as an application tool on data
management, analysis and visualization of nonpoint source pollution models
1.6
Significant of Study
Generally, Water Quality Index (WQI) and INWQS are used to determine the
classification and pollutant status of particular water bodies. Since the road construction
will cause several adverse effects to the environment, therefore the water quality of the
water body within the project area should be study to level of construction impact
towards the environment with focus to quality of the water bodies. Therefore this study
is conducted to determine the existing quality of the water bodies within the project area.
The importance of monitoring the water quality from the road construction site is to
ensure the quality and sanitation of the water supply sources as the runoff from the site
will flow to Sungai Gatom which is the of Sungai Muar. More over, downstream of
Sungai Muar are water intakes, including the Gresik intake point, from which Malacca
buys water from Johor.
It is also important to ensure that water quality management during the
construction phase be closely related to management of soil erosion and sedimentation,
8
as excessive erosion and siltation will contribute to water quality deterioration. Other
related aspects of water quality management include management of waste disposal
from site clearing works, solid waste and sewage as well as potential pollution of oil and
grease from waste oils, fuels and lubricants from machinery.
The integration of Geographical Information System to monitor the water quality
of the construction area will give the better information access regarding to the quality
of the water bodies. GIS helps identify and map critical areas of land use and reveal
trends that affect water quality during the construction period. ArcGIS 3D Analyst can
map multiple contamination layers at multiple depths.
1.7
Scope of the study
The scope of this study cover the modeling of nonpoint source pollution for the
selected study area by determining and utilizes appropriate spatial analysis data
classification
technique in ArcGIS and its extention, ArcGIS Spatial Analyst and 3D Analyst.
1.7.1
Site Description
The study area is situated in Daerah Labis, about 4 km from Chaah in the east.
The proposed road will connect the existing State Road J32 from Bukit Kepong to Felda
Maokil and continue to the State Road. It will pass through the existing Felda Maokil oil
palm estates along with open fields, forest and farms. Figure 1.0 show the land use of the
sampling point location. Some of the land uses surrounding the project site include:
9
Table 1.1: Landuse Area of the Project Site
Land use
Area Covered
Residential Areas
Bukit Kepong, Kg. Raja, Kg Jawa, Kg. Sg. Muda Luar,
Kg. Lenga, Felda Maokil, Kg. Jawa Baru, Kg.Batu Tanum,
Kg.Baharu, Tmn. Chaah Baru, Kg. Kuala Sabar, Kg.Btg.
Merbau
Commercial Areas
Pekan Bukit Keopng, Chaah
Institutions
Primary School (S.K. Bukit Kepong), Sk. Felda Maokil
2, Labis, Segamat 85300 Labis
Plantations/Orchards
Felda Maokil Satu, Felda Maokil Dua, Ladang Song near
Kg. Raja, Ladang Lim Sim Eng
The boundary of this study is from the upstream of the construction area (20 17’
06.6”) until the downstream of the water bodies at (20 18’ 15”). Figure 1.0 and 1.1
shows the sampling point location. Detailed description of the location will be discussed
in Chapter III.
Figure 1.0 (a)
(b)
Figure 1.0(a): Oil Palm Plantation North-west (b) Reserved forest at West of the
Proposed Road
10
Figure 1.1: Sampling Points
1.7.2
Catchment area
They are 12 small catchment areas along the proposed locations ranging from 0.3
to 4.5 km2. The biggest catchment is located south of the Felda Maokil Dua. The
sampling points represent the major catchment area in the proposed location. The area of
coverage for the major water catchments are described in table 1.2 and illustrated in
figure 1.2:
11
Table 1.2: The Area of Coverage for the Water Catchment
Water Catchment
Area of coverage
Water Catchment NO 4
3.15 km2
Water Catchment NO 6
2.4 km2
Water Catchment NO 10
3.1 km2
Water Catchment NO 12
4.5 km2
Source : Pengiraan Hydrologi dan kapasiti hidraulik, Ogos 2007 with reference to
Urban Stormwater Management Manual For Malaysia (MASMA)-Volume 4: Design
Foundamentals
Figure 1.2: The Catchment of the Study Area
1.7.3
Frequency and Parameters of Sampling
The sampling frequency of water quality is taken at eight stations with three
times of frequency for during both dry and rainy days. Two samples will be taken for
each major catchment area. There are two types of parameters considered in order to
determine the water quality which are physical parameters and chemical parameters.
12
Water quality is determined by assessing three classes of attributes: physical, chemical,
and biological. The detail of the parameter will be discussed in Chapter 2.
13
CHAPTER II
LITERATURE REVIEWS
2.0
Introduction
There are several topics regarding the rivers and water quality has been reviewed
before this study is carried out. The first parts of this section will discuss the origins of
the river and its usage. This discussion is followed by description of the project area
provided by the construction engineering company related. All the characteristics of
water catchment area and its importance will be discussed in the following parts. This
information is very important as the road construction project is located within Sungai
Gatom Catchment area. Information on water quality of rivers in Malaysia is also
provided along with the source of the pollutants of rives in Malaysia. The study
regarding the water quality of rivers in Malaysia was carried out by the Department of
environment. The water quality of the rivers are described several existence of several
pollutants in the river such as BOD and Suspended Solids. These pollutants can be
classified as point source pollutants and non-point source pollutants.
At the end of this chapter, several issues regarding degradation of rivers in Johor
Bahru have been discussed. This information is important to imply the human activities
that definitely have cause river degradation Johor Baharu district. The previous study
regarding this topic are also been review.
14
the waste treatment processes for industrial wastes for which BOD5 test is not
applicable or for which more rapid data is needed. For the test, the usage of strong
oxidizers under standards such as permanganate, dichromate, or periodate oxidizes the
organic compound.
Dissolved 2.1 Project Description
The proposed road, categorized as rural, is situated in Daerah Muar, about 4 km
from Chaah in the east. It will connect the existing State Road J32 from Bukit Kepong
to Felda Maokil and continue to the State Road J34. It will pass through the existing
Felda Maokil oil palm estates along with open fields, forest and farms.
Currently there is no traffic data recorded, however, an estimated of 500 vehicles
per day is used as basis of projection with an annual increment of 5 percent. Design of
the road has been finalized by the JKR according to the topographical features of the
terrain. Other public utilities to be set out include water pipelines, cable, and land
takeover plan which has been made with relevant body, namely Felda of the related
areas.
Construction and upgrading works will include road works, bridges, drainage
work, soil improvement works to swampy area involving some cut and fills. The final
road will be flat, rolling (50 km/hr), and mountainous (40 km.hr), considering the natural
existing environment and topographical nature of the area.
Earthworks will involve the cutting of existing slopes and hills to fill low lying
areas to minimize the importation of outside soils. Imported soils, if involved will be
taken from a nearby hill. Thus, minimal truck movements will occur outside the project
area and therefore reducing the safety and health impacts on nearby residents and
plantation workers. All roadside drain along the proposed road will be earth drains. The
15
water from the drains will be channelled to the existing rivers and swamps. All existing
culvert along the proposed road will be demolished and replaced with new box culverts.
The Project involves a land acquisition along the alignment with a ROW of 20
meters involving Felda Maokil 1 and 2. Jabatan Kerja Raya has appointed a consultant,
KFI Engineers Sdn. Bhd. to together monitor the project.
Current progress right now is in site clearing stage, where the clearing of
anything inside the right-of-way will be done. It will be followed by the earthworks.
There are settlements at both ends of the roads, namely Felda Maokil 1 in the east, and
Kg. Raja and Bukit Kepong in the west. The rest of the road is mainly of plantations and
part of forested areas (Maokil Forest Reserve) in the east of Kg. Raja.
2.2
River
River is a natural stream of water, usually fresh water, flowing toward an ocean,
a lake or another stream. Sometimes, river flows into the ground or dries up completely
before reaching another water body. Larger streams are generally called rivers while
smaller streams are called creeks, brooks and many other terms.
A river is a component of the water cycle where the water within a river is
generally collected from precipitation through surface runoff, and from groundwater
recharge Almost all rivers are joined by other rivers and streams termed tributaries and
the highest of which are known as headwaters. From their sources, rivers flow downhill
eventually ends up in a sea or lake. The term upriver of upstream refers to the beginning
or source of the river flow regardless of the direction of the flow. Therefore the term
downriver or downstream refers to the direction of flow that the river continues in.
16
River has very importance roles to human. In Malaysia, river may be channeled
in to the lake or dam for human usage. The water from the dam can be used for
supplying water to residential and industrial areas. The water in the dam can also be
used to generate hidro-electric energy like Kenyir Dam and Temenggor Dam. Besides
that, the water from river can also be used for irrigation purpose like the Rancangan
Pengairan Muda which irrigates water to paddy fields in Kedah. Rivers can also play
their natural habitat for flora and fauna and place fro recreational activities. River
conservation will generally encourage tourism industry. Therefore it is very important
for us to protect and preserve rivers.
River management plan must be implemented in order to protect and preserve
our rivers. One of the ways to protect the river is by controlling pollutants that flows into
the rivers. Rehabilitation programs should be carried out in order to remedy the degrade
river and turn it back to almost predisturbance condition. The following sections in the
report will discuss about river rehabilitation and river management plan.
2.3
Water Quality
Water quality is the physical, chemical and biological characteristics of water in
relationship to a set of standards. Water Quality Standards are created by state agencies
for different type of water bodies and water body per desired uses. The primary uses
considered for such characterization are parameters which relate to drinking water,
safety of human contact and health of ecosystem. Another general perception of water
quality is that of a simple property that tells whether water is polluted or not. Industrial
pollution is a major cause of water pollution, as well as runoff from agricultural areas,
urban storm water runoff and discharge of untreated sewage especially in developing
countries. The water pollution in Malaysia is originated from point sources and nonpoint sources. Point sources generally waste collected by a network of pipes or channel
and conveyed to a single point of discharging into receiving water. Point sources that
17
have been identified include sewage treatment plants, manufacturing and agro-based
industries and animal farms. Non-point sources are mainly diffused ones such as
agricultural activities and surface runoffs. Non-point sources often the flow of the
polluted water flows over the surface of the land or along natural drainage channels to
the nearest water body.
According to Malaysia Environment Quality Report 2005, the Department of
Environment has recorded 17,991 water pollution point sources in 2005 comprising
mainly earthwork and land clearing (44%), animal farms (28%) and manufacturing
industries (15%). The distribution of the water pollution sources is shown in Figure 2.11
below.
Figure 2.1: Status of River Basin Water Quality 2005
Source: Department of Environmental Malaysia 2005
The river water quality in Malaysia has been monitored by the Malaysian
Department of Environment (DOE). The Water Quality Index (WQI) used to appraise
the river water quality is based on 6 parameters that are Dissolved oxygen, Biochemical
Oxygen Demand, Chemical Oxygen Demand, Ammoniacal Nitrogen (NH3N),
18
Suspended solids (SS) and pH. Dissolved is very important that can support aquatic life
such as fishes and aquatic plants. Water pollution generally will decrease the amount of
dissolved oxygen in the water.
Water quality is determined by assessing three classes of attributes: physical,
chemical, and biological. Table 1.2 below shows physical and chemical parameters.
Table 2.0: Physical and Chemical Parameters
PHYSICAL PARAMETERS
CHEMICAL PARAMETERS
Suspended Solid
BOD
Salinity
COD
Turbidity
pH
Odor and Taste
DO
Temperature
AN
Oil and Grease
2.3.1
Physical Parameter
Physical attributes of a waterway can be important indicators of water quality.
Commonly measured physical characteristics of a stream include temperature, odor,
turbidity and suspended solid
2.3.1.1 Suspended Solid
Suspended matter consists of material originating from the surface of the
catchment area, eroded from river banks and resuspended from the bed of water body.
The type and concentration of suspended matter also controls the turbidity and
transparency of the water. Suspended matter consists of silt, clay, fine particles of
19
organic and inorganic matter, soluble organic compounds, plankton and other
microscopic organism.
Measurement of suspended matter transport is particularly important where it is
responsible for pollutant transport. Usually sediment concentration and load increase
exponentially with discharge. Particles may settle or resuspended under different
discharge condition. Particles vary in sizes from approximately 10nm in diameter to
0.1mm in diameter, although it is usually accepted that suspended matter is the fraction
that will not pass through a 0.45µm pore diameter film.
2.3.1.2 Turbidity
Turbidity is the cloudiness or haziness of a fluid, or of air, caused by individual
particles (suspended solids) that are generally invisible to the naked eye, similar to
smoke in air. The measurement of turbidity is a key test of water quality. Fluids can
contain suspended solid matter consisting of particles of many different sizes. While
some suspended material will be large enough and heavy enough to settle rapidly to the
bottom container if a liquid sample is left to stand (the settle able solids), very small
particles will settle only very slowly or not at all if the sample is regularly agitated or the
particles are colloidal. These small solid particles cause the liquid to appear turbid.
Turbidity in open water may be caused by growth of phytoplankton. Human
activities that disturb land, such as construction, can lead to high sediment levels
entering water bodies during rainstorms, due to storm water runoff, and create turbid
conditions. Urbanized areas contribute large amounts of turbidity to nearby waters,
through storm water pollution from paved surfaces such as roads, bridges and parking
lots.
20
The higher the turbidity level, the higher the risk of that people may develop
gastrointestinal diseases. This is especially problematic for immune-compromised
people, because contaminants like viruses or bacteria can become attached to the
suspended solid. The suspended solids interfere with water disinfection with chlorine
because the particles act as shields for the virus and bacteria. Similarly, suspended solids
can protect bacteria from ultraviolet (UV) sterilization of water.
High turbidity levels can block light from reaching lower depths of water bodies,
which can inhibit growth of submerged aquatic plants and consequently affect other
species dependent on those plants, such as fish and shellfish.
2.3.1.3 Temperature
Temperature is one of the environmental factors that determine which organisms
will thrive and which will diminish in numbers and size. Heat input into aquatic systems
and the resultant temperature significantly affects the biological community and the
beneficial use of the system waters. Temperature conditions may be either unnatural
highs or lows or they may be because of natural seasonal and daily temperature
fluctuations.
Temperature problems may also develop in industrialized nations as a result of
the use of the water resource for cooling purposes. Changes in temperature can also
affect both fish and lower forms of aquatic life as each organism has it range of
temperature for optimum growth as well as lethal level. It also affects the usability of
water for beneficial purposes.
21
2.3.1.4 Odor
An odor is a volatilized chemical compound, generally at a very low
concentration, which humans and other animals perceive by the sense of olfaction.
Odors are also called smells, which can refer to both pleasant and unpleasant odors.
Assessment of odor is usually not included in the water quality assessment. If a change
in odor is detected, it might indicate a water quality problem that requires further
investigation.
2.3.2
Chemical Parameter
Chemical attributes of a waterway can be important indicators of water quality.
Chemical attributes of water can affect aesthetic qualities such as how water looks,
smells, and tastes. Chemical attributes of water can also affect its toxicity and whether or
not it is safe to use. Since the chemical quality of water is important to the health of
humans as well as the plants and animals that live in and around streams, it is necessary
to assess the chemical attributes of water. Commonly measured chemical parameters
include BOD, COD, AN, pH and DO.
2.3.2.1 BOD
When
biodegradable
organic
matter
is released
into
a
watercourse,
microorganisms feed on them and break them down into simpler substances. With
oxygen present when the decomposition, the process is said to be in an aerobic
environment and this process produces non-objectionable, stable end products such as
carbon dioxide (CO ), sulphate (SO ) and nitrate (NO ). Processes without the presence
2
4
of oxygen are said to be in an anaerobic environment.
3
22
Therefore, the biological oxygen demand defined as the amount of oxygen
required by microorganisms to oxide organic wastes aerobically –that is, in the presence
of oxygen. It is often expressed in milligrams of oxygen required per litre of wastewater
(mg/L) although it may have various units. Most pristine rivers will have a 5-day BOD
below 1 mg/l. Moderately polluted rivers may have a BOD value in the range of 2 to 8
mg/l.
The total amount of oxygen that will be required for biodegradation an important
measure of the impact that a given effluent will have on the receiving watercourse. A
standard practice to measure and report the depletion of oxygen demand had been
o
restricted to a five-day period and the test is run at a fixed temperature of 20 .The fiveday BOD, also known as BOD , is the total amount of oxygen consumes by
5
microorganisms during the first five days of the biodegradation process.
2.3.2.2 Chemical Oxygen Demand (COD)
The chemical oxygen demand (COD) test is commonly used to indirectly
measure the amount of organic compounds in water. Most applications of COD
determine the amount of organic pollutants found in surface water (e.g. lakes and rivers),
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. Older
references may express the units as parts per million (ppm).
2.3.2.3 Dissolve Oxygen
The COD test is more sophisticated and has more advantages than the BOD5 test
because the results are available within three hours instead of five days. The test also has
the capability of measuring organic material which is resistant to biological decay. The
COD is used comprehensively in evaluating Dissolve Oxygen (DO).
23
The amount of dissolved oxygen present in a watercourse is one of the most
important measures of the water quality. It is also commonly used as indicators of a
river’s health. The number of life forms that survive begins to decrease as the level of
DO drops below 4mg/L. In extreme cases, when anaerobic condition exists, most high
forms of life are either killed or driven off. Eventually conditions like floating sludges,
bubbling, odorous gasses and slimy fungal growths will subsist.
Concentrations in unpolluted waters are usually close to or less than 10 µmg/L.
Concentrations below 5mg/L may adversely affect the functioning and survival of
biological communities and below 2mg/L may lead to the death of most fish. There are
various factors that affect the amount of DO available in a river. Oxygen demanding
wastes reduces the DO level while photosynthesis process further add the DO during the
day but removes oxygen in the night. The respiration of the life forms in the water body
also reduces the DO level. Besides that, tributaries also bring in their own oxygen supply
that mixes with the main river.
2.3.2.4 Ammoniac Nitrogen (AN)
Water containing nitrogen in gaseous nitrogen, organic nitrogen, ammonia, and
nitrite or nitrate forms is used to test on the chemical quality of a water source.
However, from the aspect of water quality, water sampling is usually focused on
ammonical nitrogen (NH -N). This is because the existence of NH -N is usually the
3
3
result of microbiological activities that are present in the sources of earth’s surface water
and groundwater. The basic principle is that when a nitrogen ion combines with certain
compound and the reaction results in a colour. Water with high contents of ammonical
nitrogen can cause algal bloom and this parameter can provide a rough approximation to
the level of pollution in the water body.
24
2.3.2.5 pH
pH is an important variable in water quality assessment as it influences many
biological and chemical processes within a water body and all processes associated with
water supply and treatment. pH is a measure of the hydrogen ion concentration or
activity, [H+]. The pH value is the negative normal logarithm of the hydrogen ion
activity (mol/L) and has a value of 7 at 25oC in pure water (neutral point). The pH value
can increase or decrease because of the presence of acids (decrease) or alkali (increase)
and the hydrolysis of dissolved salts.
The determination of the pH value is usually performed in field. Water samples
require no preparation for measurements. The pH values of neutral waters usually lie
between 6.5 and 7.5 and the lower values are a result of free CO2. In the case of
unpolluted waters, pH is principally controlled by the balance between the carbon
dioxide, carbonate and bicarbonate ions as well as other natural compounds such as
humic and fulvic acids. Sometimes, the biogenic decalcification in surface waters can
cause the pH value to reach 9.5. Lower values of pH can occur in diluted waters that are
high in organic content. Changes in pH can indicate the presence of certain effluents,
particularly when continuously measured and recorded, together with the values of the
conductivity of a water body.
The following Table: 2.1 show the details regarding Water Quality Index (WQI)
criteria. The table shows the class of the river and the corresponding index and the river
classification. This river classification from I-V would determine the type of treatment
and water uses either for water supply, fishes and etc.
25
Table 2.1: Water Quality Index (WQI) criteria
CLASS
WQI
CLASSIFICATION
Natural condition
I
>92.7
Water supply-no treatment required
Aqua culture- supports sensitive areas
Water supply- basic treatment required
II
76.5- 92.7
Aquaculture - support most river species
Recreation
III
51.9-76.5
- used for recreation purposes
Water supply– extensive treatment required
Aqua culture – supports hardened river species
Source of drinking for animals
IV
31.0-51.9
Irrigation only
V
< 31.0
None of the above
(Source: Department of Environmental Malaysia, 2004)
Provided below is the equation used to calculate WQI. The six parameters used
in the index are determined by the concentration of the parameters in mg/L except for
pH and DO. Equation 2.1 show the calculation of sub index for each parameter.
WQI= 0.22 x SIDO + 0.19 x SIBOD + 0.16 x SICOD + 0.15 x SIAN + 0.16 x SISS +
0.12 x SIpH
(eq. 2.1)
Where
SIDO = Sub-Index DO (in % saturation)
SIBOD= Sub- Index BOD
SICOD= Sub- Index COD
SIAN = Sub- Index NH3N
26
SISS = Sub-Index SS
SIpH = Sub-Index Ph
According to Malaysia Environment Quality Report 2005, a total of 926 water
quality monitoring stations located within 120 river basins were monitored. The overall
results in terms of river basin water quality showed 58 river basins (48.3%) were found
clean, 53 river basins (44.1%) slightly polluted and 9 river basins (7.5%) polluted and
the major pollution sources in Malaysia are Biochemical Oxygen Demand (BOD),
ammoniacal-nitrogen and suspended solids. High BOD is contributed by untreated or
partially treated sewage and discharges from agro-based and manufacturing industries.
The main source of ammoniacal nitrogen was sewage which include livestock farming
and domestic sewage while the sources of suspended solids were earthwork and landclearing.
2.4
Water Quality Assessment: Water Quality Index
A water quality index provides a single number (like a grade) that expresses
overall water quality at a certain location and time based on several water quality
parameters. The objective of an index is to turn complex water quality data into
information that is understandable and useable by the public. A water index is based on
some very important parameters can provide a simple indicator of water quality. It gives
the public a general idea the possible problems with the water in the region.
Water Quality Index (WQI) is computed based on 6 main parameters:■
Biochemical Oxygen Demand (BOD)
■
Chemical Oxygen Demand (COD)
■
Ammoniacal Nitrogen (NH3N)
■
pH
■
Dissolved Oxygen (DO)
27
■
Suspended Solids (SS)
Table 2.2: DOE Water Quality Index Classes
Parameter
Unit
Classes
I
II
III
IV
V
Ammoniacal Nitrogen
mg/l < 0.1 0.1 - 0.3 0.3 - 0.9
0.9 - 2.7
> 2.7
Biochemical Oxygen
mg/l < 1
1-3
3-6
6 - 12
> 12
Chemical Oxygen Demand
mg/l < 10
10 - 25
25 - 50
50 - 100
> 100
Dissolved Oxygen
mg/l > 7
5-7
3-5
1-3
<1
pH
mg/l > 7.0 6.0 - 7.0
5.0 - 6.0
< 5.0
> 5.0
Total Suspended Solids
mg/l < 25
50 - 150
150 -
> 300
Demand
25 - 50
300
Water Quality Index
> 92. 76.5 -
51.9 -
31.0 -
7
76.5
51.9
92.7
< 31.0
Source: Department of Environmental, 1994
2.5
Water Pollution and Sources
When the discharge of wastes disturbs the natural ecological balance of a water
body, water pollution occurs. The wide range of water pollutants can be classified
principal sources of each category. Point sources include domestic sewage and industrial
wastes because they are collected and discharged into receiving surface and groundwater
from a single point. The pollutant sources are non-point sources if the pollutants are
discharged to water from multiple points. The major non-point sources can be classified
as agricultural return flows and urban runoffs.
28
Proper treatment processes can implement reduction or elimination of point
sources of pollution before discharging to receiving waters, but treatment of non-point
effluents usually is not economically feasible.
2.5.1
Sources of Water Pollution
Municipal, industrial and agricultural are classified as the major sources of water
pollution in Malaysia. It occurs when the accumulation of large amounts of materials to
the water affects adversely to the water body. While it is unfit for its intended use, the
water is considered polluted. There are two types of water pollutants exist; point source
and non-point source. Point-source pollution is pollutants that enter to the stream at
known location and can be measured quantitatively. Meanwhile, non-point source
pollution is unidentified pollutant that goes into river via surface runoff from many
uncontrollable places (DOE 2005).
2.5.1.1 Point Source
Point sources usually come from discharges of sewage, manufacturing industries,
agro-based industries and animal farms (DOE 2006). It contains grist, chemical
contamination, nutrients, bacteria and others. The sources of pollutant are easy to detect
to ensure the rehabilitations of river can be doing immediately. The point source
pollution can be discarding or reduced by proper treat the wastewater rather than
discharge to river like the current practice
2.5.1.2 Non-point Source
Based on the river study of Langat, Segget and Tebrau, more than 50% of the
pollution comes from non-point source (DOE 2006). To determined and monitor the
29
non-point source needs skill, time consuming and costly since it is difficult to control.
Thereby is a challenge to remove the contributions remained and their solution is not
direct as controlling point source. Most of the non point source pollution occurs during
the rain event where it directly delivers pollutant through the runoff. The runoff form the
construction site is the major non point spurce of the nearby water catchment area.
According to Novotny and Chesters (1981), there are several general characteristics that
describe nonpoint source pollution:
•
Nonpoint sources discharge enter surface waters in a diffuse manner and at
intermittent intervals that are related mostly to the occurrence of meteorological
events.
•
Pollution arises over an extensive area of land and its in transit overland before it
reaches surface waters
•
Nonpoint sources generally cannot be monitored at their point of origin, and their
exact source is difficult or impossible to trace.
•
Elimination or control of pollutants must be directly at specific sites.
•
In general, the most effective and economical controls are land management
technique and conservation practices in rural zones and architectural control in
urban zones.
•
Compliance monitoring for nonpoint sources is carried out on land rather than in
water.
•
Nonpoint source pollutants cannot be measures in term of effluent limitation.
•
The extent of nonpoint pollutant is related, at least in part, to certain
uncontrollable climatic events, as well as geographical and geologic conditions
and may differ greatly from place to place and year to year.
•
Nonpoint source are derived from consecutive operations on extensive units of
land, as opposed to industrial activities that typically use repetitive operations in
intensive (small) units of land.
30
2.6
Assessment for Water Quality
Water quality is assessed by its physical, biological and chemical characteristics.
Contamination can alter one or all of these characteristics and may originate from point
or from ambient sources. The investigation and management of water resources systems
for water quality must include consideration and evaluation of:
•
The physical, chemical and biological composition of headwaters and significant
groundwater discharges.
•
Water quantity and quality requirements for all existing and potential water uses.
•
The means of water withdrawal and their effect on water quality and quantity.
•
The existing and future water and wastewater treatment technology used to alter
water quality.
•
The wastewater outfall configuration and effluent mixing.
•
The eutrophication status of the receiving waters.
•
The waste assimilative capacity of the receiving waters.
•
The ecological changes that might be caused by wastewater discharges.
•
The potential effects of discharged waters.
2.7
Water Quality Monitoring
Monitoring is defined by the International Organization for Standardization
(ISO) as “the programmed process of sampling, measurement and subsequent recording
or signaling, or both, of various water characteristics, often with the aim of assessing
conformity to specified objectives”.
Before the planning of water sampling and analysis can be started, it is necessary
to define clearly what information is needed and what is already available and to
identify; as a major objective of the monitoring programme, the gaps that need to be
31
filled. It is useful toprepare a “monitoring programme document” or “study plan”, which
describes in detail the objectives and possible limitations of the monitoring programme.
Monitoring provides the information that permits rational decisions to be made
on the following:
•
Describing water resources and identifying actual and emerging problems of
water pollution.
•
Formulating plans and setting priorities for water quality management.
•
Developing and implementing water quality management programmes.
•
Evaluating the effectiveness of management actions.
To fulfil these functions some preliminary survey work must first be done to
provide basic, background knowledge of existing water quality conditions. Subsequent
monitoring efforts will identify problems and problem areas, short- and long-term trends
and the probable cause of the problems. Once sufficient data have been gathered, it is
possible to describe the average conditions, the variations from average and the extremes
of water quality, expressed in terms of measurable physical, chemical and biological
variables. In the meantime, priorities may have been set, plans may have been made and
management programmes may have been implemented. Ultimately, information from
the monitoring programme is fed back into the management system so that any
necessary changes to priorities and plans can also be made.
Specifications for the collection of data should be uniform so as to ensure
compatibility and make it possible to apply to any particular location the experience
gained in another. Networks for water quality monitoring should be developed in close
co-operation with other agencies actively collecting water data. This not only minimizes
the cost of establishing and operating the network but also facilitates the interpretation of
water quality data. The particular hydrological measurements and water characteristics
required will differ from one water body to another. For example, in rivers and streams it
is necessary to measure the velocity of flow and intensity of longitudinal mixing, while
32
thermal regimes are important considerations when monitoring lakes. Measurement of
wastewater discharges containing nutrients may not be necessary for many rivers but
may be important in lakes because the additional input of nutrients may accelerate the
eutrophication process.
Figure 2.2: Network for Water Quality Monitoring (Source: Downs, 1991)
Networks for water quality monitoring must conform to programme objectives
(Figure 2.1). A clear statement of objectives is necessary to ensure collection of all
necessary data and to avoid needless and wasteful expenditure of time, effort and
money. Furthermore, evaluation of the data collected will provide a basis for judging the
extent to which programme objectives were achieved and thus justify the undertaking.
Before observations begin, it is also essential to specify the location of sampling
stations, the frequency of sampling and the water quality variables to be determined.
Monitoring programmes should be periodically reviewed to ensure that
information needs are being met. As greater knowledge of conditions in the aquatic
system is gained, a need for additional information may become apparent. Alternatively,
it may be concluded that some of the information being collected is unnecessary. In
either case, an updated monitoring programme document must be prepared and
distributed to the information users. If such users are not kept fully informed of the exact
33
scope of the programme they may expect more than it can deliver and may not support
its continuation
2.8
Geographic Information System (GIS)
Geography has traditionally provided an important framework and language for
organizing and communicating key concepts about the world. Knowledge is shared
through many abstract forms. Attempts to articulate and explain human experience and
understanding use these abstractions summaries of a larger body of knowledge.
Abstractions, such as text, hieroglyphics, language, mathematics and statistics, music
and art, drawings, images, and maps, are used to record and communicate experiences,
culture, and history from generation to generation.
Digital computing allows the capture and sharing of knowledge across networks
such as the Internet. Simultaneously, geographic information system (GIS) technology is
evolving and provides a critical methodology to understand, represent, manage, and
communicate the many aspects of physical and human landscapes and to better
understand the earth as a system. A GIS is a system for the management, analysis, and
display of geographic knowledge, which is represented using a series of information
sets. GIS abstracts geographic knowledge into five basic elements which are Geographic
datasets and data models, maps and globes, geoprocessing models and script, GIS
methods and workflows, metadata. These five information sets are the primary elements
of geographic information.
The technology has provided and exciting potential for geographic information to
be used more systematically and by greater diversity of disciplines than ever before. The
applications are diverse, for example:
34
•
Finding the coincidence of factors, such as the areas with a certain
combination of soil type and vegetation, or the areas in a city with a high
crime rate and low income level.
•
Updating the geographical information, such as forest cover maps to show
recent logging, or updating land use maps to show recent conversion of
agriculture land to residential development.
•
Managing municipal services, such as scheduling maintenance activity,
notifying local residents of re-zoning applications or assigning police patrol
areas.
•
Land use planning with respect to the availability, cost and characteristic of
land, including spatial criteria such as size and proximity can be combined
with priority weightings in models that can identify the rank suitable areas for
various land use options
(Aronoff 1989)
GIS clearly has enormous commercial importance and more significantly, it is
already being used to make valuable contributions to the understanding and solution of
key socio-economic and environmental problems.
2.8.1
ArcGIS 9.2
ArcGIS 9.2 is an integrated family of GIS software products for building a
complete GIS ArcGIS provides a scalable framework for implementing GIS for a single
user or many users on desktops, in servers, over the Web, and in the field. It is the
primary platform for GIS professionals to manage their complex GIS workflows and
projects and to build data, maps, models, and applications. It’s the starting point and the
foundation to perform and deploy GIS across organizations. ArcGIS includes a suite of
applications including ArcCatalog, ArcMap, ArcGlobe, ArcToolbox, and Model
Builder. Using these applications and interfaces in unison, users can perform any GIS
35
task, from simple to advance. ArcGIS is scalable and can address the needs of many
types of users.
2.8.1.1 ArcMap
The main application in ArcGIS is ArcMap, which is used for all mapping and
editing tasks as well as for map-based query and analysis. It is the primary application
for all map based tasks including cartography, map analysis, and editing. ArcMap is a
comprehensive map authoring application for ArcGIS. There are two primary map
display panels in ArcMap—the data frame and the layout view. The data frame provides
a geographic “window” or map frame in which you can display and work with
geographic information as a series of map layers. The layout view provides a page view
where map elements (such as one or more data frames, a scale bar, and a map title) are
arranged on a page. ArcMap is the application used to compose maps on pages for
printing and publishing. The example of the modeling and analysis performed by
ArcGIS is illustrated in figure 2.3.
Figure 2.3: Perform modeling and analysis using ArcGIS
36
2.8.1.2 ArcGIS 3D Analyst
ArcGIS 3D Analyst enables effective visualization and analysis of surface data.
With ArcGIS 3D Analyst, users can view a surface from multiple viewpoints, query a
surface, determine what is visible from a chosen location on a surface, and create a
realistic perspective image by draping raster and vector data over a surface. The core of
the ArcGIS 3D Analyst extension is the ArcGlobe application. ArcGlobe provides the
interface for viewing multiple layers of GIS data and for creating and analyzing
surfaces. ArcGIS 3D Analyst also provides advanced GIS tools for three-dimensional
modeling such as cut/fill, line of sight, and terrain modeling. User can also create and
manage terrain datasets using ArcGIS 3D Analyst. A terrain dataset is a multiresolution,
TIN-based surface built from z measurements stored as features in the geodatabase.
They’re typically made from LIDAR, SONAR, and photogrammetric sources and can
easily support billions of x, y, z points as part of a multiresolution triangulated surface.
The 3D Analyst extension offered a wide range of application such as in figure 2.4.
37
Figure 2.4: The 3D extension for ArcGIS Server provides a powerful set of tools that
allows users to create, query, and analyze surface data.
2.9
Nonpoint Source Pollution Models
Nonpoint Source Pollution Models are part of a category of “loading models”
which represent the input and movement of materials from the points of origin to water
bodies.
Generally, the structures of models are based on hydraulic rainfall-runoff
transformation process with attached quality components. Most of the models have the
following components:
i
Surface Runoff generation describe the hydrologic process of
transformation rainfall to runoff and its role in transporting pollutants
38
based on factor such as terrain slope, soil type, infiltration rates and land
use.
ii
Absorption and movement of water through soil and groundwater
component from unsaturated zones to groundwater zones
iii
Particle accumulation and wash-off that carries non-point pollutants
aggregate at the outlet point of the entire river basin.
There are two approachs to modeling nonpoint source pollutant. The lumped
models treat the river basin as a unit. The coefficients and system parameters for each
unit are determined mostly by calibrating the response of the model against extensive
field data. The distributed model involved dividing the river basin into smaller
homogeneous units with uniform characteristics. Each areal unit is modeled separately,
and the total output is obtained by summing all individual output from homogeneous
units. Distributed parameter models require large computer storage and extensive
description of system parameter, which must be provided for each unit.
The following describe some of the more established nonpoint source pollution
models.
2.9.1
AGNPS
Agriculture Nonpoint Source (AGNPS) is a distributed parameter model that
simulates runoff, sediment and nutrient transport from agricultural watershed to analyze
nonpoint source pollution (Young et al.1987). The model incorporates separate modules
to route water, sediments and contaminants through cell from catchment area to the
outlet in the stepwise fashion. The Universal Soil Loss Equation (USLE) is used for the
prediction soil erosion. Its hydrology module is based on the Soil Conservation Service
(SCS) Curve Number Technique. AGNPS uses another developed model Chemical,
Runoff and Erosion from Agricultural Management System (CREAMS) to predict
nutrient/pesticide and soil particle size generation and interaction. To use AGNPS, the
39
watershed of interest is subdivided into a grid of square element. Each element, typically
about 100m square, requires 22 parameters (coefficient) to describe its antecedent
conditions, physical characteristic, management practices and rainfall. To predict NPS
pollution, the USLE, SCS Curve Number and CREAMS relationship are computed for
each element as a function of time.
Srinivasan et al. (1993) found that by integrating AGNPS model and the raster
based Geographical Analysis Support System (GRASS) can reduce the time required to
obtain the data needed by AGNPS, simplify the operation of AGNPS and most
important, identify problem areas very quickly compared with manual methods.
2.9.2
ANSWERS
The Areal Nonpoint Source Watershed Environment Response Simulation
(ANSWERS) model which was developed by Purdue University and United States
Environment Protection Agency is a single-event, distributed parameter model to
simulate hydrology, sediment transport and routing through a basin on agricultural land.
It predicts the erosion caused by the specific land use and management practices and
also provides a water quality analysis associated with sediment associated chemical.
Engel (1996) evaluated ANSWER, AGNPS, Soil and Water Assessment Tool
(SWAT) and Water Erosion Prediction Project (WEPP) for their relative ability to
predict the hydrology, sedimentation and pesticide and nutrient loading of runoff from
storm events. Although these models are characterized as distributed watershed scale
models that do not require calibration, they require extensive amounts of information on
watershed characteristics which may not be readily available (Dilaha 1997).
40
2.9.3
SWMM
Storm Water Management Model (SWMM) is a comprehensive computer model
for analysis of quantity and quality problem associated with urban runoff (DeVries and
Hromadka, 1992). Both single event and continuous simulation can be performed on the
catchment having storm sewers, or combined sewers and natural drainage for the
prediction of flows and pollutant concentrations. Modeler can simulate all aspects of the
urban hydrologic and quality cycles, including rainfall, surface and subsurface runoff,
flow routing through drainage network, storage and treatment. SWMM can be used for
both planning and design. The planning model is used for an overall assessment of the
urban runoff problem and proposed abatement options.
There have been numerous efforts to integrate SWMM and GIS. Barber et al.
(1994) used EPA’s SWMM Version 4.0 to generate ASCII header file containing
SWMM job control and rainfall data for storm water management. The file will be read
by Intergraph GIS software to display the SWMM model results graphically. Another
similar approach for determination of flood extent in Volusia Country, Florida was
carried out using EPA’s SWMM Arc/Info GRID module (Cera et.al. 1996). Both
examples above utilized GIS as a pre-processor for data input needed by EPA’SWMM
and also a visualization tool for results output.
2.9.4
USLE
The Universal Soil Loss Equation (USLE) developed by the United States
Agricultural Research Service scientist W.Wischmeier and D.Smith, has been the most
widely accepted and utilized soil loss equation for over 30 years. Designed as a method
to predict average annual soil loss caused by sheet and rill erosion, the USLE is often
criticized for its unsatisfactory estimation due to its poor relation to mechanics of soil
erosion (Morgan, 1986). While it can estimate long term annual soil loss and guide
conservations on proper cropping, management and conservation practices, its cannot be
41
applied to a specific year or a specific storm. The USLE is a mature technology and
enhancement to it is limited by the simple equation structure.
An erosion risk map for peninsular Malaysia was created using Arc/Info software
by Malaysia Department of Agriculture based on the USLE model (Mustafa and Guire,
1997). In addition to this, most of the Environmental Impact Assessment (EIA) reports
in Malaysia are utilizing the USLE model for assessment of erosion (Department of
Environmental, 1998).
2.10
GIS-Based Nonpoint sources Pollution Model
Over the years, nonpoint source pollution has become more eminent and
government departments, research and academic institutions and consulting firms have
developed methods of assessing this form of pollution. Many of these methods require
complex and repetitive calculation which lead to the development of computer based
program. Simulation models of water resources systems, whether a small watershed or
an entire region require and produce spatial and temporal data. As these methods
contains spatial content, GIS technology has been incorporated or utilized in data
calculations and better visualization compared with tabular numeric and statistical
results are obtained.
Generally, there are two levels of integrating GIS and hydrology models (Tim
and Jolly 1994) as shown in figure 2.1. The first level is called Ad-hoc integration where
GIS and model processing are done separately and independently. These are then
combined to give the desirable results.
Level 2 integration is solely modeling within GIS. This is to state that the
hydrology model will have to be suited to the condition of GIS in term of data structure
42
and modeling. It is possible to achieve this level of integration as long as time variability
or temporal aspect is not needed (Maidment 1993).
2.10.1 Expected Mean Concentration Values
The relationships between land and water resources can be characterized by
indicator numbers or coefficients, called here transform coefficients because they
describe the way a land system transforms the quantity and quality of water as it flows
through a landscape. Water quality is characterized by the Expected Mean
Concentration, (EMC), which is the ratio of pollution load to flow during a runoff
period. The difference between these coefficients can be shown in table 2.3.
Table 2.3: The Possible Land-Water Transform Coefficients
Land-Water Connection
Transform Coefficient
Water Yield
Runoff Coefficient, C
Flood Runoff
SCS Curve Number, CN
Groundwater
Recharge Rate (mm/year)
Water Quality
Expected
Mean
Concentration,
EMC
(mg/l)
Sediment yield
Erosion Rate (tons/ha-year)
(Source: David R. Maidment, 1998)
Pollutant loads are found by taking the product of the EMC and the streamflow
rate. Typically, nonpoint source flows originate from rainfall events and follow the
temporal and spatial characteristic of rainfall to a large extent. Pollutograph is a plot of
load (concentration X flow rate) versus time. If the concentration is uniform throughout
the storm, the shape of the loadgraph is similar to the hydrograpgh. Due to the difficulty
43
of obtaining accurate data in required amount to generate pollutographs and loadgraphs,
nonpoint source pollution can be represented by Expected Mean Concentration (EMC).
Just as instantaneous concentrations vary within a storm, EMC vary from storm
to storm. The median or 50th percentile MEC at a site, estimated from a time series is
shown in figure 2.5. When site median EMC from different locations are aggregated,
their variability can be quantified by their median. Figure 2.6 show the comparison of
pollutographs and EMC for storm events.
Figure 2.5: Hydrograph, Pollutograph and Loadgraph (Source: Huber, 1993)
44
Figure 2.6: Comparison of Pollutographs and EMC for Storm Events
2.10.2 Rainfall-runoff analysis
Runoff is that portion of precipitation that flows over land surfaces toward larger
bodies of water. Before runoff can occur, rainfall must satisfy the immediate demands of
infiltration, evaporation, interception, surface storage, surface detention and channel
detention.
There are two broad categories of factors that affect runoff; rainfall
characteristics and watershed physical conditions. Important rainfall characteristics
include duration, amount, intensity and distribution. Key watershed factor are size,
shape, topography (slope and roughness) and soils.
Although there are elaborate methods in determining runoff, the lack of data and
complexities hinder their continued widespread use and popularity (Yevjevich, 1992).
For purpose of this study, runoff is estimated based on Tank Model which was used to
generate long term discharge data from daily rainfall data.
45
2.11
Related Works
The following present some review of work done on nonpoint source pollution
models and GIS.
The main focus with GIS usage with existing hydrologic models has been one–
directional data extraction to the model rather than direct data exchange to and from the
model. Wan Mokhtar and Kamarul (1993) have used digital geographic information
system technology for integrated catchment-river management in Sintok river basin. In
their study, a hydrologic computer model is linked to GIS to simulate the impact of land
use change to stream flow. Their system uses an ad-hoc integration which makes
interrogation and communication between the hydrologic computer model and the GIS
very difficult to implement, thus making it not suitable to be an integrated river basin
information system.
Lam and Swayne (1993) formulated a recent example where integration draws
together GIS, models, spreadsheet and expert systems in Regional Analysis by
Intelligent Systems ON microcomputers (RAISON). Any integration at this level
requires open GIS architecture to provide the necessary procedure calls for tight
coupling. Evidently, this most optimum form of integration is also the most costly in
term of development undertaking. Another example of direct linkage is where
Agriculture Nonpoint Source Model (AGNPS) and a vector-based GIS,Geo/SQL
through the effective use of Oracle relational database management system contribute to
better hydrologic modeling as preparation of inputs to model and procedures to analyze
and display corresponding results are wholly within the GIS environment. This study
will model the nonpoint source pollution model through complete integration within the
Arc/View software. While there are still inherent problems associated with this
approaches such as non compatibility between GIS and nonpoint source pollution model
structures and high development cost, this approach seems to be the most accurate and
optimum methods.
46
Hughes et.al (1994) developed a hydrological modeling system called
Hydrological Model Application System (HYMAS). Recognizing that hydrological
models share information requirements, they develop 7 hydrological models which fully
optimize data in multiple models utilization. This study will take this impetus as
secondary data like slope, aspect, flow direction and flow accumulation which can be
derived solely from elevation data with the usage of GIS analytical functions.
Kim et.al (1993) analyzed the potential of using GIS to obtain precise estimates
of pollutant loading for 11 sewer sheds based on the SLAMM model. It demonstrates
that GIS technology is effective for urban nonpoint source control, using its digitaloverlay analysis and database management capabilities. For this study, ArcView Spatial
Analyst extension will be used to perform GIS functions like constructing digital
elevation models, hydrology functions, hybrid rater-vector analysis and map overlays.
This approach will minimize manual efforts considerably.
Roo (1993) used Areal Nonpoint Source Watershed Environment Response
Simulation (ANSWERS) coupled with GENEMAP GIS to model runoff and soil loss
simulations in selected catchments in South Limburg, Netherlands. Validation of results
was carried out by comparing data from field tests and GIS simulated results show large
percentage errors. This is not unexpected as the ANSWERS model was modified to
enable simulations within the GIS software. The fundamental change in model
organization will almost certainly alter the output of the model. In this study, modeling
result obtained from the GIS analysis will be validated against published data to
determine its accuracy and applicability.
Saunders (1996) studied the application of GIS in a watershed-scale assessment
of nonpoint source pollutants for the San Antonio Nueces Coastal Basin. This
assessment was performed by the associating land uses and rainfall runoff using the
Arc/Info GIS GRID tool to simulate rainfall runoff throughout the basin. This method
resembles closely the method applied in this study. However this study models rainfall
47
runoff differently as opposed to the mathematical relationship established using data
from the United States Geological Survey (USGS) flow gauges.
Features in building better integrated environmental information systems include
effective interfaces in a technical and organizational sense of efforts with policy and
decision making process (Fedra 1993). For this study, an interactive, menu-driven
interface will prompt and explain messages through the application. Additional
interfaces like pop-up windows and icons will help in navigating through the system.
2.12
Previous Study
Referring to the previous study regarding water quality monitoring of Sungai
Tebrau, Johor by using Mapinfo software, Sharon S.S, 2005 has proved that GIS plays
an important role in river quality management and the tools that been applied, MI Pro is
able to handle the task in managing water quality data. It is able to support both spatial
data and attribute data. However, it is suitable for small to moderate data storage size.
This is because data need to manually entered and data entry for the bigger data storage
will be a laborious task and time consuming. This is different from some other software
for example ArcGIS, which will be used in this study, where related lines or objects can
be detected and subsequently their details checked.
Tim and Jolly (1994) refer to three potential levels of integrating GIS with
hydrologic and water quality models. They control the sediment pollution using the
combined AGNPS-Arc/Info model for the 417-hectare Bluegrass watershed in Audubon
County, Iowa. For the first level of integration, known as Ad-hoc integration, the GIS
and the Model are developed separately and are executed independently. The GIS serves
only as a pre-processor of the input data for the model. The second level is the result of
establishing an interactive interface between the GIS and the model. The third level of
integration is typically referred to as complete integration or modeling within GIS. This
48
model is technically preferred by most modelers, but is often difficult to implement, due
to incompatibilities in the data structures of the model and the GIS, or due to proprietary
rights of commercial GIS software limiting the introduction of additional processing
routines.
Completely integrated GIS models of the Universal Soil Loss Equation (USLE)
have also been created to control soil loss during project construction which can affect
the water quality. Hession and Shanholtz (1988) created the Virginia Geographic
Information System (VirGIS), incorporating the USLE and a sediment delivery ratio, for
the estimation of potential sediment loadings to streams from agricultural lands.
Separate land use-based map layers were created for rainfall erosivity factor, soil
erodibility factor, slope length, cover and management factor, and conservation practice
factor. Each of these parameters is components of the USLE, and a value for soil loss
per unit area was determined by combining them. Sediment delivery ratio for each land
use cell was also determined as a function of the relief and slope in each cell. The model
run smoothly but at the end it has been found to be extremely sensitive to rainfall input,
indicating that care must be taken for temporally and spatially variable events. The
model is also sensitive to infiltration variables for small events (Von Euw et al., 1989)
2.13
Summary
The previous sections review the implementation and status of integrated river
basin management concept. Numerous nonpoint source pollution models are also
reviewed to understand the different approaches taken to pinpoint and quantify this type
of pollution. The use of GIS in this area is increasing as can be seen from the various
examples previously. These examples will provide valuable insights as we discover the
advantages and shortcomings of this techno centric approach.
52
CHAPTER III
METHODOLOGY
3.1
Introduction
The following sections describe the tasks taken to achieve the objectives and
scope of work respectively. Figure 3.1 shows the flow chart that depicts the stages of
work implemented in this project
53
Figure 3.1: Methodology Flow Chart
The following sections describe each stage of figure 3.1 in details.
3.2
Identification of Study Area
This study has been conducted monitoring of runoff from Segamat Construction
Road project to establish the status of water quality, detect changes and identify
pollution sources. Water quality data were used to determine the water quality status
weather in clean, slightly polluted or polluted category and to classify the rivers in Class
I, II, III, IV or V based on Water Quality Index (WQI).
54
3.3
Data Acquisition
There are two types of data acquisition which are primary and secondary data.
3.3.1
Primary data
In line to obtain primary data, site visit must be done to do water quality sampling.
i.
Water Quality Sampling
Sampling sites will be selected at appropriate location to best identify any
impacts of the construction works on the receiving waters. Upstream and downstream
sampling locations as well as area near cut and fill areas will be selected to allow the
assessment of site discharge against receiving water quality at the time of discharge. The
method used is in-situ measurement.
The sampling frequency of water quality is taken at eight stations with three
times of frequency for during both dry and rainy days. Two samples will be taken for
each major catchment area. There are six major parameters that recommended by the
Department of Environment, Malaysia in order to determine river classification which
consist of dissolved oxygen (DO), Biochemical Oxygen Demand (BOD), Ammoniacal
Nitrogen (NH3-N), Suspended Solid (SS) and Ph.
The in-situ measurements are dissolved oxygen (DO), temperature, salinity, pH
and conductivity. To determine the pH value, Consort 535 Analyzer will be used where
YSI 550 probe will be use for dissolved oxygen, salinity, temperature while the
laboratory measurement test includes SS, BOD and Ammoniacal Nitrogen (NH3-N).
55
3.3.2
Secondary Data
The secondary data that will be use in this study are rainfall data,
topography map, land use and geology map. Details on the hydrology data such as
runoff and discharge calculation value of the study area refer to the project’s Hydrology
and Hydrological Capacity Calculation Report from consultant company, KFI Engineers
Sdn. Bhd.
3.4
Laboratory Work
3.4.1
Biochemical Oxygen Demand (BOD)
Biochemical oxygen demand (BOD) is a measure of the quantity of oxygen used
by microorganisms in the aerobic oxidation of organic matter. In rivers with high BOD
levels, aerobic bacteria, robbing other aquatic organisms of the oxygen they need to live,
consume much of the available dissolved oxygen. BOD measures the rate of oxygen
uptake by microorganisms in a sample of water at a temperature of 20°C and over an
elapsed period of five days in the dark. The sample is kept at 20 °C in the dark to
prevent photosynthesis (and thereby the addition of oxygen) for five days before the
dissolved oxygen is measured again.
The BOD test is carried out by diluting the sample with oxygen saturated deionized water. Table 3.2 shows measurable BOD using various dilutions of samples.
56
Table 3.0: Measurable BOD using Various Dilutions of Samples.
VOLUME SAMPLE BOD VALUE (mg/L)
0.02
30 000 - 105 000
0.05
12 000 – 42 000
0.10
6 000 – 21 000
0.20
3 000 – 10 500
0.50
1 200 – 4200
1.00
600 – 2100
2.00
300 – 1050
5.00
120 – 420
10.00
60 – 210
20.00
30 – 105
50.00
12 - 42
100.00
6 – 21
200.00
0-7
BOD5 at 20oC TEST PROCEDURE (diluted)
1. 100 mL sample is paced in BOD bottle.
2. 200 mL of BOD nutrient is added into the BOD bottle.
3. Initial DO ( DOi) is measured after 15 minutes.
4. After that, the bottle is closed and placed in incubator for 5 days, at temperature
20oC
5. After 5 days, the DO (DOt)in the bottles is measured.
Calculation of BOD
BODt = ( DOi – DOt )/ P
Where:
BODt = biochemical oxygen demand, mg/L
(eq. 3.1)
57
DOi
= initial DO of the diluted sample about 15 minutes after
preparation mg/L
Where P =
DOt
= final DO of the diluted sample after incubator for 5 days, mg/L
P
= dilution factor
Volume of sample
Volume of sample + Volume of distilled water
When BOD has been determined in an undiluted sample,
BOD (mg/l) = DO before incubation (mg/l) - DO after incubation (mg/l)
Figure 3.2: Apparatus Used to Measure Dissolved Oxygen in BOD Test.
3.4.2
Chemical oxygen demand (COD)
Chemical oxygen demand (COD) test is commonly used to indirectly measure
the amount of organic compounds in water. Most applications of COD determine the
amount of organic 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.COD value was determined using Hach 5000
Spectrophotometer (HACH, 2005).
58
COD Test Procedure
1. Turn on the COD reactor. Preheat to 150OC. Place the plastic shileeed in front of
the reactor.
2. Pipet 3 mL of COD reagent into the vial.
3. Hold the vial at 45O angle and pipet 2.00 mL of sample into the vial.
4. Replace the vial cap tightly. Rinse the COD vial with deionized water and wipe
the vial clean with a paper towel.
5. Hold the vial by the cap and over a sink. Invert gently several times to mix the
contents. Place the vial in the preheated COD reactor. The vial will become very
hot during mixing.
6. Prepared a blank by repeating step 2 to 5. For blank use deionized water as
sample. Place the blank in the preheated COD reactor.
7. Heats the vials for 2 hours.
8. After 2 hours, turn off the reactor and wait about 20 minutes for the vials to cool
to 120OC or less.
9. Invert each vial several times while still warm. Place the vial into a rack. Wait
until the vials have cooled to room temperature.
10. Use Hach DR 5000 Spectrophotometer to get the result.
Figure 3.3: Apparatus Used to Measure COD
59
3.4.3
Total Suspended Solid Test
Total Suspended Solid (TSS) is a measurement to measure water clarity for
water quality assessment. TSS includes all particles suspended in water that not pass
through a filter. These suspended solid may comes from various sources such as urban
runoff, agricultural land, industrial waste, erosion, algal growth and wastewater
discharge. Suspended solids are an important indicator of water quality. Although
turbidity purports to measure approximately the same water quality property as TSS, the
latter is more useful because it provides an actual weight of the particulate material
present in the sample. Increase of TSS directly reduced DO concentration in water,
hence, reducing the ability of a water body to support life.
Apparatus
Forceps, glass fiber filter, filter paper, aluminum weighing dish, analytical balance and
100mL of sample.
Procedure
1. Aluminum dish and the filter paper are weighed on the analytical balance and the
weight is recorded.
2. Using forceps, the filter paper is placed on filter holder.
3. 100 mL of sample is poured into the graduated cylinder through the filter.
4. Apply vacuum and 100 mL sample is filtered.
5. Then filter paper is dried in an oven at 103 c for 1 hour
6. After that, the filter paper and the aluminum dish is weighed and the data is
recorded.
Calculation of TSS
Suspended Solid
Where :
= [ A-B ] x 1000 / C
A = Weight of filter and dish + residue, (mg)
(eq. 3.2)
60
B = Weight of filter and dish, (mg)
C = Volume of sample filtered, (mL)
a) Glass fiber filter
b) Analytical balance
Figure 3.4: Apparatus Used in TSS Test
3.4.4
Ammonical nitrogen
Nitrogen pollution in hydrosphere is attracting increasing attention for
eutrophication of lakes and rivers all over the world. Ammonium is the inorganic ion
form of nitrogen pollution contained in municipal sewage, industrial wastewater and
agricultural wastes or decomposed from organic nitrogen compounds in those
wastewater and wastes. Higher concentration of ammonium will cause a sharp decrease
of dissolved oxygen and obvious toxicity on aquatic organisms (Wen et al., 2006).
Concentration
of
ammonical
nitrogen
was
measured
using
Hach
Spectrophotometer (HACH, 2005)
Ammonical Nitrogen Test Procedure Using Hach 5000.
1. Set Hach DR 5000 Spectrophotometer to Ammonia Nessler programme.
2. Fill 25 mL mixing graduated cylinder to the 25 mL mark with sample.
3. Fill another 25 mL mixing graduated cylinder with deionized water (blank).
5000
61
4. Add three drops of Mineral Stabilizer to each cylinder. Stopper. Invert several
times to mix.
5. Add three drops of Polyvinyl Alcohol Dispersing Agent to each cylinder by
holding the dropping bottle vertically. Invert several times to mix.
6. Pipet 1.0 mL of Nessler Reagent into each cylinder. Stopper. Invert several times
to mix.
7. Press START TIMER at Hach DR 5000 Spectrophotometer.
8. Pour each solution into a sample cell.
9. When the timer beeps, place the blank into the cell holder. Close the light shield.
10. Press ZERO.
11. Place the prepared sample into the cell holder. Close the light shield. Result in
mg/L ammonia expressed as nitrogen (NH3-N) will be displayed.
3.4.5
Oil and grease (O&G)
Oil and grease is an important parameter for water quality and safety. The term
‘oil and grease’ encompasses a broad family of chemical compounds such as fatty
material of biogenic origin, or petroleum hydrocarbon constituents. These compounds
can be cause environmental degradation and induce related public health risks when
discharged in surface or ground water (E.Farmaki, et al, 2005).
Oil and grease (O&G) test procedure
1.
Fill a clean 500 ml graduated separatory funnel to the 350 ml mark with
sample.
2.
Using a pipet filter and pipet, add 4.0 ml of 14.5 N Sulfuric Acid Standard
Solution to the separatory funnel. Stopper and shake.
3.
Using an analytical balance, weigh a dry, previously cleaned 125 ml
distillation flask to the nearest 0.1 mg. record the weight of this flask.
62
4.
Add 20 ml of 1,1,2-tricloro-1,2,,2-trifloroethane (Freon 113) to the
separatory tunnel.
5.
Shake the stoppered separatory funnel vigorously for two minute.
6.
Stand the separatory funnel upright in a support. Wait 10 minutes.
7.
Insert a small cotton plug soaked with Freon 113 into delivary tube of
separatory funnel. Drain the Freaon 113 layer into the distillation flask.
8.
Repeat step 4 to 7 two times, then discard the water layer.
9.
Rinse the separatory funnel with three separate 10 ml increments of fresh
Freon 113 to remove any oil film left on the funnel walls. Add each
increment to the distillation flask.
Maintaining a constant temperature of 70oC, distill off all but approximately
10.
10 ml of Freon 113
11.
Remove the flask from the water bath and wipe the outside with a paper
towel dampened with acetone. Cover the water bath and adjust the
temperature to 80oC. Dry the residue and the outside of he flask by heating
on the covered water bath for 15 to 20 minutes. Remove the remaining Freon
113 vapor by attaching the vacuum connector.
12.
Place the flask in a desiccator for 30 minutes.
13.
Weigh the flask to the nearest 0.1 mg using analytical balance and the weight
is recorded.
Thoroughly clean the flask with Freon 113, place in a drying oven at 103oC
14.
for one hour and then transfer to a desiccator.
Calculation of test:
Total oil and Grease ( mg/L)
= A-B-C
(eq. 3.3)
Sample volume in liter
Where: A
= weight of residue and flask (mg)
B
= weight of flask (mg)
C
= weight of blank (mg)
= weight of flask with blank residue-weight of flask without blank
63
residue
Figure 3.5: Apparatus for Oil and Grease Test
3.5
GIS Integration
The processes were divided into two part where the first part is to get the digital
river network to be burn-in into DEM and the second part was to produce loading of
pollutant where overlying with flow accumulation to get the accumulated loading of the
pollutant. In the first part, digitizing of the topography maps was done for the rivers and
contours. Contour lines were converted to grids or digital elevation model (DEM) using
3D extension. In the second part, interpolation was carried out to generate runoff grid.
EMC is defined as mass of pollutant transported per volume of runoff. EMC of
various pollutants are directly related to land uses in the drainage areas (Saunders and
Maidment, 1996). The calculated EMC based on the sampling data, linked to land use
maps to generate the EMC grids. With the EMC grids and runoff grid, loading of the
pollutants were generated using map calculator. To assess accumulative pollutant
loadings, the flow direction of the DEM grid and a weighted flow accumulation of the
pollutant loading grid must be process. The output of the flow accumulation represents
64
the amount of pollutant that would flow through each cell upslope to the downstream of
the river network. Figure 3.6 show the GIS integration overview.
Figure 3.6: The GIS Integration Overview
3.6 Nonpoint Source Pollution Modeling Description
Modeling runoff consists of determining the flow at the watershed outlet
generated by the storm, while modeling nonpoint source pollution consists of
determining the pollution transport at the outlet. Nonpoint source pollution is strongly
related to the runoff process since it is the runoff which transports the pollutants. This
relationship implies that understanding the water quantity problem is essential to
understanding the water quality problem. There is a difference between modeling flow
and transport or equivalently between modeling water quantity and quality (Maidment
1993b). Thus, it is necessary to model or map the flow domain before modeling the
transport of constituents, since their motion is driven by the motion of the flow domain.
Modeling the transport of pollutants will utilize the Expected Mean
Concentration (EMC) method. The measure of pollutant level that occurs during a
65
runoff event is the EMC; define as the mass of pollutant transported per volume of
runoff. It is generally assumed that the expected mean concentration of various
pollutants is directly related to the land use. Sounder ( 1996) utilized the EMC technique
within the GIS while assessing nonpoint pollution in San Antonio-Nueces basin and
results generally match well with average observed values.
The overview of the modeling frameworks in figure 3.7 provides an overall
illustration on implementing and modeling the NPS in ArcGIS by illustrates the step
taken to establish the river basin GIS database, generation of Digital Terrain Model
(DTM), construction of the runoff grid, Expected Mean Concentration (EMC) grid and
at last, determining the pollutant loadings. The modeling is run through model builder
that build using ArcGIS Model Builder (Appendix B).
Figure 3.7: The Nonpoint Source Pollutant Modeling
66
3.6.1
Establishing a River Basin GIS Database
Establishing of the database consists of data collection, editing and building
topological data. This study uses a hybrid approach to data structure, as vector and raster
data are utilized simultaneously. Most of the data was obtained from various
government agencies such as Department of Survey and Mapping and Soil Management
Division in Department of Agriculture. Published reports from Local Authority of Muar
provided more detailed information about the study area. Hardcopy maps are converted
into digital data by digitizing using AutoCAD and Arc Digitizing System while
attributes are entered into Microsoft Access database. PC Arc/Info version 3.5 from
Environmental System Research Institute (ESRI) was used to edit and build cleaned
topological data as coverage.
Vector data based on the arc-node model include point, line and polygon
coverage. Point coverage includes data represented by single positional values such as
water quality sampling points. Line coverage like river networks are represented by a
series of interconnected nodes. Polygon coverage such as land use contains arcs which
compose its border. Spatial modeling and analysis are then performed by calling
coverage into ArcGIS 9.1 and its extension ArcGIS Spatial Analyst which is a raster
based spatial modeling and analysis tool that enables integrated vector-raster analysis.
The digitized river network is shown in figure 3.8. For grid themes, each grid cell size is
2500 square meters. Map projection used for the digital data is based on Malaysia
National Mapping Rectified Sew Orthomorphic (RSO). Figure 3.9 show the database
development approach for each stage of the modeling.
67
Figure 3.8: The Digitized River Network
68
Figure 3.9: Construction of the Runoff Grid, Expected Mean Concentration (EMC) Grid
and Assessment of Pollutant Loading
69
3.6.2
Generation of DTM
Elevation data was obtained from the Department of Survey and Mapping L7030
series topographic maps at the scale of 1: 50 000. 2 topographic maps are needed to
cover the study area. The published dates for these maps are 2003. Contour lines were
digitized using AutoCAD software at 20 intervals. Contour lines are assigned to
AutoCAD layers according to their elevation. Edge matching was then performed to
match adjacent map sheets. Consequently, the AutoCAD drawing file is exported to the
AutoCAD Exchange Format (DFX).
The exported file is then imported into Arc/Arc using the DFXARC command.
Contour line topology was then created using the BUILD command with the LINE
option. In order to append attributes kept in Acode.dbf to the contour coverage, the
command JOINITEM is used. This is to ensure that the DFX_layer field will be updated
to the counter features. It will be used later as the height value for creating the DTM.
The contour coverage is then added to ArcVGIS 9.1 as line features. As ArcGIS
Spatial Analyst can only interpolate surface from point or multipoint themes, the line
features are converted to point features using the Avenue script, PolyPoint.eve. By using
the Interpolate Surface command from the Surface menu, the Inverse Distance Weighted
(IDW) interpolator was used to generate a continuous DTM grid theme.
Sinks are elevation grid cell surrounded by grid cell which has a higher
elevation. This represents the major difficulty as NPS model is based on the existence of
the downslope flow routing concept. The depressions are problematic because any water
that flow into them cannot flow out. This is cause by the interpolation error or
insufficient precision in elevation values (Garbrecht et. Al 1996). The solution is to
remove all sink in the DTM by raising the elevations within the sink to the elevation of
70
its lowest outlet. The Flow Direction, Sink, Watershed, ZonalFill and Con grid Avenue
request are used to fill the sinks. The process of filling sinks can create sink, so a looping
process is used until all sinks are filled. To accomplish it, an Avenue program,
Demmfill.ave, available from ESRI website was used. Figure 3.10 shows the elevation
ranges of the DTM created for the study site.
Figure 3.10: Contour Line Digitizing using ArcGIS
71
3.6.3
Rainfall-runoff analysis
Runoff is that portion of precipitation that flows over land surfaces toward larger
bodies of water. Although there are elaborate methods in determining runoff, the lack of
data and complexities hinder their continued widespread use and popularity (Yevjevich
1992). For purpose of this study, runoff is estimated based on Tank Model (Sugawara
1972) which was used to generate long term discharge data from daily rainfall data by
Johor Meteorological Department (Refer Appendix C2).
In order to maintain a similar time frame with the sampling data set, daily
generated discharge for 13 February, 21 April and 1 September 2009 was used.
Table 3.1: Monthly Average of Generated Discharge for 2009
Month
Stesyen
Stesyen
Stesyen
Stesyen
Stesyen Stesyen Stesyen
1
2
3
4
5
6
7
Feb
14.42
13.33
55.14
67.17
63.81
133.5
136.9
Mar
19.95
19.34
58.23
85.53
73.02
161.93
140.6
Apr
35.25
28.63
33.22
25.61
67.11
56.32
70.23
May
18.96
23.54
78.31
25.78
36.9
23.8
79.43
Jul
8.34
10.44
4.67
13.78
12.46
23.56
15.88
Sep
21.53
23.55
23.897
43.55
20.53
32.56
32.14
To obtain annual runoff based on the Tank Model, a time-series chart is
constructed and the area below the chart lines is the total runoff for that particular area.
The values of runoff for each particular station is shown in figure 3.11.
72
Figure 3.11 : Chart for Discharge Value for Each Station
Consequently, the value are used as interpolation values and using ArcGIS
Spatial Analyst, interpolation was carried out using the Spline Interpolator. The resulting
grid theme and the location of the discharge station are discussed in Chapter IV.
3.6.4
Expected Mean Concentration (EMC)
The measure of pollutant level during a runoff event is the expected mean
concentration (EMC), measured in mg/L, and defined as ratio of the mass of pollutant in
the event divided by the volume of runoff. EMC are frequently used to characterize
nonpoint source pollutant’s loading and can be multiplied by the runoff volume to
estimate the mass discharge. EMC has a statistical distribution and varies in value from
event to event. EMC is directly related to land use in the watershed.
73
Since there is no published data on EMC for this particular study site, water
quality data obtained from sampling were used in conjunction with the land use theme.
This is due to the fact that EMC associates its values with land use. There is four type of
land use in this study area. Generally, the upper reaches of the study are largely covered
by forest, Ma’okil Forest Reserve. Only the lower reaches and along the major
tributaries exist a more diversified land usage.
Table 3.2: Land Use Description and Codes
(Source: Soil Management Division, Department of Agriculture, 2000)(Appendix C1).
74
To associate pollutant EMCs with land use, the land use coverage shown in table
3.1 is used along with the EMC data (Table 3.3). The EMC data is attached to the landuse polygon attribute table through use of Join Data command in ArcGIS, by using the
land se category identifier as the linking item between both tables (Figure 3.12). Then
the concentration grids of pollutants are created by converting land use polygon to grid
with same grid cell of DEM data using EMCs as the cell values.
Table 3.3: EMC Values of Pollutants Based on Land Use.
Types of land uses
Agriculture
BOD (mg/l)
SS (mg/l)
TN (mg/l)
4
107
4.40
+
0.20+
Forest
6*
6
Urban
17.2
57.9
1.57
Grassland
0.5
1
0.7
Water*
0
0
0
Wetland
6*
2
0.83*
Sources: Hydrological Procedure No. 16, DID, 1980
* Naranjo, E., 1996
+ Urban Stormwater Management Manual for Malaysia, DID, Vol. 5
Figure 3.12: Joining of Table land use and EMC
75
The derived values for each water quality parameter for certain land use is shown
in chapter IV.
3.6.5
Pollutant Loading Assessment
Pollutant loading is significant in relation to the volume and circulation of the
receiving water; problem occurs when high loadings occur into receiving waters with
limited assimilative capacity. Each cells’ contribution of pollutant towards the basin
down stream outlet can be estimated by circulating the product of runoff and expected
mean concentration. Conceptually, the EMC model works in the following manner:
1. Runoff is first determined as depth/time. Since the study is based on average
annual assessment, the value is mm/year. This is derived in Figure 3.2.
2. By multiplying the runoff and the cell size, the volume of runoff can be known.
Runoff flow (volume/time)= Runoff depth (mm/year) X cell size (meter)
3. After obtaining the Runoff flow, the population loading can be calculated using
the EMC grid
Load (mass/time) = Runoff flow (volume/time) * EMC (mass/volume) (eq. 3.3)
In equation wise,
Load = Runoff * EMC*A*C
Where
Load is in kg/year
(eq.3.4)
76
Runoff is in mm/year
EMC is in mg/Liter
A (area of 1 grid cell) = 2500 m2
C is the coefficient to establish Load in kg/year = 10-6
The next step is to assess the accumulative effects of pollutant loads.
3.6.6
Accumulative Pollutant Loading Assessment
To assess accumulative pollutant loading, the flow direction of the DTM grid and
a weighted flow accumulation of the pollutant loading grid must be processes.
3.6.6.1 Flow Directions
Flow directions are basically assigned by considering each cell’s elevation and
the elevation of the 8 surrounding cells known as The Eight-direction Pour Point Model.
For most cells, the neighbor cell with the largest elevation drop determines the flow
direction (Maidment 1993a). Figure 3.2 show the resulted grid of the model.
3.6.6.2 Flow Accumulations
Flow accumulations are found by counting the number of cells by “tracing” each
cell’s path (flow direction) to the point that it eventually leaves the data set. The flow
accumulations used for this area is for a simple 5x5 data set.
A variation of the accumulation is the use of weight grids. Typically, a weight
grid is used to represent the “density” of each assigned cell. In the case of this study, the
77
weight grid is used to determine how much pollutant load may run out of a given river
basin. Hence, the weight grid is a selected pollutant EMC grid. The output of flow
accumulation would then represent the amount of pollutant that would flow through
each cell upslope to the basin outlet.
3.7
Summary
In this chapter, a detailed explanation on the implementation of the EMC model
using GIS technology is given. The methodology outlined for the assessing nonpoint
source pollution is rather long and repetitive. This can led to longer processing times and
human errors. As such, the development of a GIS application streamlines the entire
process via a custom user interface. Further details are described in Chapter 4.
81
CHAPTER IV
RESULT AND ANALYSIS
4.1
Introduction
In this study, Water Quality Index (WQI) used to determine the quality of river
nearby the construction area involved six parameters included dissolved oxygen (DO),
Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Ammoniacal
Nitrogen (AN), Suspended Solid (SS) and pH. Moreover, assessment on BOD, TSS and
NH3N been carried out in order to evaluate the status of the river. The water samples
analyzed based on the in situ and lab-scale experimental analysis during dry and wet
season. The result will correlate whether the construction project become threatened to
the river located nearby.
4.2 Water Quality Index (WQI)
WQI for the area was calculated according to the values of each parameter. The
calculation of WQI is shown in Appendix A. Table 4.1 below shows the WQI value
obtained for each sampling time.
82
Table 4.2 a: Water Quality Index for First Sampling, 13 February 2009
Sampling Point
Parameter
Unit
Ph
W1(Upstream) W1(Downstream)
W2U
W2D
W3U
W3D
W4U
W4D
7.5
7.5
6.8
6.6
6.32
6.59
7.58
7.57
Dissolve Oxygen
mg/l
11.3
10.8
7.43
7.2
5.04
4.68
9.3
6.86
TSS
mg/l
32.7
32.9
13.65
14
43.6
31.7
54.3
56.5
BOD
ppm
5.4
5.85
5.12
4.5
5.28
5.99
6.12
8.98
Salinity
µs
45.8
33.5
89.6
90.3
88.9
54.3
45.6
45.2
COD
mg/l
30
33
13.7
22.5
10.7
22.1
27.2
32.91
NH3-N
mg/l
0.51
0.44
0.41
0.42
1.36
1.88
0.41
0.77
Turbidity
FTU
5.92
6.04
6.06
6.41
34.56
32.55
24.84
39.55
Oil and Grease
mg/l
0
0
0
0
0.01
0
0
1.23
Temperature
C
29.4
28.8
27.2
27.5
28
28.5
28.5
29.4
80.98
80.68
86.50
84.16
71.01
71.28
73.53
73.52
II
II
II
II
III
III
III
III
WQI
CLASS
Table 4.2b: Water Quality Index for Second Sampling (Dry Season) 21 April 2009
83
Sampling Point
Parameter
Unit
Ph
W1(Upstream) W1(Downstream)
W2U
W2D
W3U
W3D
W4U
W4D
6.57
6.65
5.53
5.61
6.65
6.59
7.46
7.57
Dissolve Oxygen
mg/l
5.71
6.8
5.58
5.29
6.55
5.68
6.55
6.86
TSS
mg/l
21.44
20.09
12
12
32.87
31.7
134.86
311
BOD
ppm
6.57
6.85
5.23
2.37
7.56
5.99
8
8.98
Salinity
µs
30.4
33.5
40.2
40.3
43.2
44.3
45.3
45.2
COD
mg/l
NA
NA
30.45
34.55
40
32.11
98.48
213.91
NH3-N
mg/l
0.34
0.34
0.22
0.22
2.01
1.88
0.51
0.77
Turbidity
FTU
6.02
6.31
5.99
6.41
21.33
32.55
35.89
39.55
Oil and Grease
mg/l
0
0
0
0
0
0
1.09
1.23
Temperature
C
29.4
28.8
27.2
27.5
28
28.5
29.6
29.4
83.46
86.37
78.53
78.95
71.01
71.28
65.93
58.16
III
III
III
III
III
III
III
III
WQI
CLASS
84
Table 4.2c: Water Quality Index for Third Sampling (Dry Season) 1 September l 2009
Sampling Point
Parameter
Unit
Ph
Dissolve Oxygen
mg/l
TSS
mg/l
W1(Upstream) W1(Downstream) W2U
W2D
W3U
W3D
W4U
W4D
6.7
6.8
6.2
10.7
6.56
8.0
7.7
7.9
5.37
6.53
6.58
7.5
6.9
7.13
6.55
6.86
121.1
231.3
100
100
32.4
34.1
144.5
445.4
BOD
ppm
3.4
3.4
1.0
1.3
4.5
5.4
3
4.5
Salinity
µs
29.6
34.5
41.2
43.4
39.9
41.4
44.6
40.5
COD
mg/l
12
12
12.3
10.1
40
40
66.3
89.3
NH3-N
mg/l
0.32
0.32
1.4
2.4
14.4
18.4
0.41
2.1
Turbidity
FTU
37.4
65.2
56.8
77.3
38.89
78.8
45.3
56.7
Oil and Grease
mg/l
0
0
0
2.43
1.2
2.33
1.0
1.0
Temperature
C
27.8
27
24.1
24.4
25.6
25.7
27.6
27.4
77.60
79.40
77.53
69.11
69.33
68.50
72.16
59.46
II
II
III
III
III
III
III
III
WQI
CLASS
85
4.3 GIS Modeling result and analysis
4.3.1
Digital Elevation Model
In the first part of the process a Digital Elevation Model (DTM) was generated from
the digitizing process of topography map. The elevation surface map of the study area is
shown in figure 4.0.
Figure 4.0: Elevation Surface of Study Area
The elevation surface is then overlaid with the stream network to show clearly the
location for each sampling station as shown in figure 4.1.
86
Figure 4.1: Stream network overlaid with elevation surface
4.3.2
Rainfall-Runoff Analysis
Using ArcGIS Spatial Analysis, interpolation was carried out using the Spline
interpolator that result in a rough surface that tighly conforms to the input data points.
The resulting grid theme of the river discharge stations are shown in figure 4.2. The
value of the runoff for each particular reference station is shown in table 4.3.
87
Figure 4.2: The Yearly Runoff Grid
Table 4.3: Annual Runoff
Station
Annual Runoff (mm/year)
Station 1
14.42
Station 2
13.33
Station 3
55.14
Station 4
67.17
Station 5
63.81
Station 6
133.5
Station 7
136.9
Generally, runoff values increase towards the south of the river network. Runoff
values range from 58-329 mm/year in the upper reaches, 329-932 m/year in the middle
reaches and reach a peak at the lower of the river network towards the river mouth in the
88
range of 932- 1495 mm/year. Rainfall isohyets also indicate that rainfall in the upper
area is around 1700 mm/year which gradually increases to maximum value of 2904 mm
in the lower reaches. From the runoff grid, we can see that the overall runoff grids show
conformity with rainfall patterns.
4.3.3
Expected Mean Concentration value (EMC)
The derived values for each water quality parameter for selected category of land
use are shown in table 4.4.
Table 4.4: Expected Mean Concentration (EMC) Table for Study Area.
Land Use Code
BOD(mg/l)
SS (mg/l)
NH3N (mg/l)
1E
4.20
45.0
3.7
3O
3.9
114.0
3.7
6
3.00
20.0
3
7F
1.00
10.0
5.3
4.3.4
Accumulative Pollutant Loading Assessment
For this study, flow direction and flow accumulation are processed using the
eight direction pour point model. The result of the model is shown in figure 4.3.
89
Figure 4.3: The Flow Analysis using GIS
4.3.4.1 Flow Direction
For this study, the flow direction process is performed on the DTM grid to model
the flow domain as shown in figure 4.4. This resultant grid will be used later during the
modeling of pollutant constituent’s movement.
Figure 4.4: Flow Direction Grid of the Study Area
90
4.3.4.2 Flow Accumulations
The result of the weighted flow accumulation is shown in figure 4.5. The output
of flow accumulation would then represent the amount of pollutant that would flow
through each cell upslope to the basin outlet.
Figure 4.5: Flow Accumulation Grid of the study area
4.4 Assessment Approach
The most important output element of this system would be the accumulated loading
map grid for each pollutant; total suspended solid, BOD and ammonia nitrogen as
shown in figure 4.6. The final output, which is overlaid with DEM, shows a color-coded
spatial representation where the darker colors denote higher values. A distinct
resemblance of the darker lines with the river network pattern can be seen. This is due to
the movement of pollutant flow across the basin which hinges heavily on the terrain
model. As such, river is the lowest elevation value during the creation of the terrain
model. Therefore the pollutant flows and accumulates in the rivers. This explains why
91
the highest value is along the lower river network and the greatest at the downstream of
river confluence for each sampling station. The grid pattern of BOD and NH3N is the
same pattern, just the value is different.
Figure 4.6: Pollutant Accumulated Loading Map
The pollutant grid generated by ArcGIS Spatial Analyst is stored in point-floated
data. Therefore, the grid is converted into an integer grid using menu Analysis- Map
Calculator. Cell values can then be obtained for the particular points by zooming in and
clicking on the cell grid that coincide with these points.
The gross weight of pollutant load released from each source Is estimated by
multiplying the quantity of dominant pollutant with its corresponding unit weight of
92
pollutant load. The values of unit weight for each pollution source and corresponding
estimated pollutant load are shown in table 4.5.
To assess the annual accumulated pollutant load, the runoff ratio factor must be
taken into account as not all of the gross pollution load is carried off as some amounts
will be discharged into ground (Nyon 1999). In addition a multiplication of 365 is
performed to synchronize to the time frame set in the modeling of EMC using GIS
before deriving the final values of pollutant load in units, kg/day.
Table 4.5: Pollutant Load Calculation
Station
BOD
TSS
NH3N
Discharge Load (kg/day)
8.86
143.56
4.78
Runoff ratio of Pollutant load
0.6
0.9
0.8
Pollutant Load (kg/day)
5.316
1292.04
3.824
Pollutant Load (kg/year)
1940.34
471 594.6
1395.76
W1U
The values of annual accumulative pollutant load (kg/year) for three parameters
for each sampling point are shown in figure 4.7 and table 4.6.
93
Figure 4.7a: Annual Accumulative Pollutant Load for BOD and NH3N
Figure 4.7b: Annual Accumulative Pollutant Load for TSS
94
Table 4.6: The Annual Accumulative Pollutant Load (kg/year)
Station
BOD
SS
NH3N
W1U
1856.4
299 432
1023.4
W1D
1940.3
471 594.6
1395.8
W2U
1321.1
123 633.1
123.4
W2D
1311.1
121 345.6
123.4
W3U
1637.2
294 401.2
43.9
W3D
1831.1
304 210.5
45.9
W4U
2304.7
495 642.3
325.3
W4D
2402.5
843 349.3
452.3
4.5
Results Assessment
4.5.1
Biochemical Oxygen Demand (BOD)
Biochemical Oxygen Demand (BOD) is a chemical procedure for determining
how fast biological organisms use up oxygen in a body of water. In general, a high BOD
reflects high concentration of substances that can be biologically degraded. The highest
BOD recorded is during April (Figure 4.8) since heavy rainfall during this month which
can be proved in Figure 4.9; show that the maximum rainfall is on April. Maximum
surface runoff had occurred and caused the pollutant’s to be washed out to the river.
95
Figure 4.8: Differences in BOD Loading
Figure 4.9: Daily Rainfall for the Study Area
On top of that, W4 show highest value since it located at the cut and fill area are
(Figure 4.10). There are heaps of human activities there and it can also contribute to the
increasing of BOD.
96
Figure 4.10: BOD Pollutant Laoding for W4
Referring to figure 4.10, the increasing of the BOD pollutant indicates that the
pollutant loading accumulated downstream. Therefore the downstream BOD is greater
than the upstream of that point. Beside, since there are no aquatic animals and plants,
bacteria easily degrade organic matter with the present of dissolved oxygen because the
least the amount of organic matter, the least the amount of oxygen utilized.
Based on WQI, most the average value of BOD in every point fall into Class III
which means the water is in average but for the second time of sampling, the BOD value
show the increasing reading which it can categories in Class III based on WQI which
same as the modeled BOD pollutant for the second sampling; also show the highest
value of BOD pollutant loading.
97
4.5.2
Total Suspended Solid (TSS)
Suspended matter consists of silt, clay, fine particles of organic and inorganic
matter, soluble organic compound, plankton and other microscopic organisms
(Chapman, 1996). TSS refers to the concentration of suspended solid matter in water.
The highest value of TSS is on April, during the raining day while the lowest value is on
Feb (Figure 4.11). The high value of TSS during April also associated with the stage of
the project, whereby during April the project in on its construction stage. Therefore
there are a lot of cut and fill activity been done during this stage.
Figure 4.11: Differences in TSS Loading
It also resulted from the damaged drainages and seepages from excess rainwater
runoff. Therefore the downstream of W4 recorded the highest value of TSS since all the
pollutant loading were accumulated and fall into the downstream.
98
Figure 4.12: BOD Pollutant Laoding During Construction Stage
Based on WQI, all the average value of TSS in every point fall into Class III
which means the water is in average but for W4, the TSS value show the worst reading
which it can categories in Class V based on WQI which same as the modeled TSS
pollutant for the third sampling; also show the highest value.
4.5.3
Ammonical Nitrogen (NH3-N)
In environment, inorganic nitrogen occurs in a range of oxidation states as nitrate
and nitrite, the ammonium ion and molecular nitrogen. The measurements of ammonia
nitrogen specify the mass of nitrogen contained in ammonia. At certain pH levels, high
concentrations of nitrogen in this form which ranging from 0.53 to 22.8 mg/L are toxic
to aquatic life. Higher concentrations could be an indication of organic pollution such as
from domestic sewage, industrial waste and fertilizer run-off. According to Figure 4.13,
the highest value of NH3-N is at W3 during September.
99
Based on WQI, the W3 value for September fall into Class V, which means the
water, contained high concentration of ammonia. This may brought harmful effects to
aquatic life as ammonia is a toxic pollutant. The sampling value for February, it showed
it falls into Class III. This may be due to the fertilizer that been use in the oil palm
during that time contained high concentration of ammonia such as synthetic fertilizer
since W3 located at the oil palm plantation.
Figure 4.13: NH3N Loading for Each Sampling Station
According to the assessment, the project construction contributed pollutant loading
to the river especially in BOD, TSS and NH3N loading. This study has shown that
technology such as GIS is capable of use in assessing the loads of non point source
pollution. With such technology in hand, the manual calculation which is tedious was
reduced. EMC in modeling non point source pollution has been proven as the simplest
model used and is applicable using ArcGIS with Spatial Analyst. Comparison with WQI
value gives a creditable reliability for GIS generated result. As nonpoint source
pollution is diffuse in nature, there are no conclusive values. The remedial measures to
minimize nonpoint sources should be incorporated.
100
4.6 Corrective Measure
It is important to ensure that water quality management during the construction
phase be closely related to management of soil erosion and sedimentation, as excessive
erosion and siltation will contribute to water quality deterioration. Other related aspects
of water quality management include management of waste disposal from site clearing
works, solid waste and sewage as well as potential pollution of oil and grease from
waste oils, fuels and lubricants from machinery. There are several corrective measures
that can be applied such as:
i.
Avoid dumping or allowing any waste oil and grease from the construction site
into any water body and the waste shall be stored in proper drums or containers
and sent out for proper disposal at landfill approved by local authority.
ii.
Always provide temporary drainage or earth drain system for surface runoff to
flow into the silt fence.
iii.
Use temporary sanitation facilities such as modular self contained septic tank
systems or portable toilets at the project site. These facilities shall be built in
accordance with the specifications prescribed by the Ministry of Housing and
Local Government.
iv.
Skid tanks at the construction site shall be bunded in order to prevent
contamination of water resources due to spillage and the bund shall be designed
to contain 110% of the volume.
v.
Always ensure that maintenance yards at the project site be managed to prevent
contamination of water resources.
101
vi.
Biomass and construction debris shall not be disposed in any waterway and if
discovered, the relevant contractor shall take such necessary measures to remove
and collect the waste.
vii.
Planning consideration - sequencing the construction to reduce the amount and
duration of soil exposed to erosion by wind, rain, surface runoff and vehicle
tracking. It is necessary to divide construction work into several stages to avoid
uncontrolled large scale earthworks.
viii.
Diversion of surface runoff - a temporary earth bank is proposed for a temporary
berm or ridge of compacted soil used to divert surface runoff or channel the
water to a desired location. It helps reducing the potential for erosion and site
sedimentation.
ix.
Vegetative stabilization - seeding the grass and planting of trees, shrubs and
ground cover to provide long term stabilization of an exposed surface and bare
soil.
x.
Sediment fencing - temporary sediment barrier consisting of filter fabric should
be installed at the lowest point of the slope to protect surface runoff flows
directly to the existing channel. It shall be inspected regularly and sediment shall
be removed when reaches 1/3 of the fence height.
xi.
Embankment - to mitigate erosion and sedimentation then reduce sediment / run
off flows to the stream during earthworks.
xii.
Gabion structure - permanent structure for slope protection in a caged filled with
boulders.
102
xiii.
Silt fence - to minimize the amount of sediment from construction site before
entering rivers, streams and other water bodies. Silt fence is a temporary
structure and shall be removed upon completion of the project and the removed
sediment shall be located on ground away from drain and other water bodies
4.7 Summary
According to this study, the project construction contributed pollutant loading to the
river especially in BOD, TSS and NH3N loading. This study has shown that technology
such as GIS is capable of use in assessing the loads of non point source pollution. With
such technology in hand, the manual calculation which is tedious was reduced. EMC in
modeling non point source pollution has been proven as the simplest model used and is
applicable using ArcGIS with Spatial Analyst.
Comparison with WQI value gives a creditable reliability for GIS generated result.
As nonpoint source pollution is diffuse in nature, there are no conclusive values. The
remedial measures to minimize nonpoint sources should be incorporated. Despite the
advantage of GIS, there are still some inherent shortcomings. In time, these can be
overcome with better data structure and technology advancement that depict the real
world better.
103
CHAPTER V
CONCLUSION
5.0
Introduction
As preceding chapters have discussed the literature, methodology and results of
modeling nonpoint source pollution using GIS, examining the major finding and
determining future research will enable further inquiry to be conducted in establishing
this assessment method.
5.1
Major Finding
The major findings of this study are described here
i.
As shown in the results of the EMC modeling, nonpoint source pollution is
very much present and evident for the study area. Therefore, the use of GIS
in the assessment on nonpoint source pollution proves to be a viable method
throughout a hydrologic unit. Trying to achieve the same using conventional
methods will prove to be a major undertaking in terms of numerical
calculations and data manipulation.
104
ii.
The accuracy of modeling nonpoint source pollution is hindered by the lack
of field data available for use. Therefore there is a wide margin of difference
when results from the GIS modeling were compared to other similar studies.
Indirectly, this research highlights the lack of qualitative and quantitative
environment field data for any reliable computational modeling. Relevant
agencies responsible for the collection and dissemination environmental field
data must work to rectify this weakness.
iii.
The integration of the Expected Mean Concentration model wholly within
ArcGIS suited well in terms of data structure and modeling. Although many
environmental models that are integrated with GIS uses an ad-hoc or partial
integration method, this research shows nonpoint source pollution modeling
can be executed solely within a GIS. However, this may not be the case
where models requires complex statistical analysis.
iv.
The GIS-centric approach to non-point source pollution modeling can be
adopted
in
regulating
pollutant
discharge.
For
example,
DOE’s
Environmental Quality Regulation which limits the concentration of
pollution, but not the amount of discharge. For example, a project
construction complies with the regulation if the TSS level are below
150mg/L. The hindsight is what if the construction activity is discharging
200mg/L daily and there are several other similar construction project in that
particular area. This will certainly pollute the river severely. The newly
proposed method which is currently used by the United States Environmental
Protection Agency is where the total load that the river can handle is
determined first. The construction projects are then issued discharge permits.
The GIS methodology used in this study can certainly be applied effectively
to determine the total pollution loadings.
105
6.3
Future Research
The assessment of non-point source pollution using GIS has proved to be a
viable and promising technique. Further research can be carried out by examining these
following areas;
i.
Having better EMC values will certainly improve the accuracy of the
assessment value. At the present moment, EMC value area used intensely in
the United States. Calibrating the EMC values for a particular specific area is
not within the scope of this study. To extend this research further, field tests
must be carried out in this study area to yield accurate EMC values.
Consequently, this value will be entered into the customized GIS application
to perform model calculations. Therefore, a higher degree of accuracy of
pollutant loadings values will be obtained.
ii.
Point sources should be taken into consideration in analyzing nonpoint
source pollution. These effects greatly the estimated nonpoint source
loadings especially in area where point sources like animal farms and
factories are present. With the area getting more developed, the impact of
point sources cannot be ignored in this study area.
iii.
The remedial measures to minimize nonpoint sources should be incorporated
using the analytical functions of GIS. For example, the effectiveness of
techniques such as Best Management Practice (BMP) can be performed with
ease using GIS. This function can be built into the interface using Avenue
programming. Before a state or designed planning agency carry out the BMP
plan, simulations can be run to assess its effectiveness on minimizing
pollution for sustainable development.
106
GIS-based compute models provide hydrologist and planners with important
tools, which is able to view data spatially. In this study, GIS prove to be an
indispensable tool in aiding the assessment of nonpoint source pollution. With the
improvement suggested above, GIS will provide even more comprehensive analysis.
106
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110
APPENDIX
111
Appendix A: Excel Sheet of WQI Calculation
112
Appendix B: The Modeler for the NPS Modeling
113
Appendix C
Data Collection
Appendix C1: Johor Land Use Map
114
Appendix C2: The rainfall data
115
Appendix D
GIS Processing
Appendix D1: Tables of Attribute
116
Appendix D2
Metadata of GIS Output Grid (FDGC ESRI Xml)
Metadata: Digital Elevation Model
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