EFFECTS OF LANDUSE CHANGES TO WATER QUALITY AND GREEN
MUSSELS (Perna viridis) DUE TO DEVELOPMENT ALONG
DANGA-PENDAS COASTAL AREA
NORLIYANA BINTI ADNAN
A project report submitted in partial fulfillment of the
requirements for the award of the degree of
Master of Engineering (Civil - Environmental Management)
Faculty of Civil Engineering
Universiti Teknologi Malaysia
November 2009
iii
DEDICATION
“…To Whom I Love and Those Who Love Me…”
I sorrowfully dedicate this project to loving memory of my mother, grandmother and
grandfather
Mak, Tok and Aki
May their souls rest in peace
Special dedicate to my beloved parent, sisters and little brother.
Abah, Along, K. Linda, K. Ayu, K. Awin, Wan
and family
My sweetheart
Syed
and
Friends
for all the love, prayers, sacrifices, patience, supports and encouragement
iv
ACKNOWLEDGEMENT
Alhamdullillah… First of all I thank the almighty Allah for his endless Grace
and Blessing on me to fulfill this project. For the successful of this work and my
study, I am entirely indebted to many their love, support, sacrifices and
encouragement.
First and foremost, I would like to express my heartfelt gratitude to my
Project Supervisor Dr. Shamila Azman for the guidance, constant encouragements
and invaluable help given to me during my project study. I am also very thankful to
my co-supervisor, Assoc. Prof Dr. Razali Ismail for his support.
The assistance and co-operation during this study, provided by the staffs of
the Faculty of Civil Engineering and Faculty of Science, UTM, especially Mr. Mohd.
Daniel is also deeply acknowledged. Special thanks are extended to all lecturers of
Environmental Engineering Department, master and undergraduate students who as
direct and indirectly involved in this project and study. Also, I would like to thank to
all my course mate for MAK 2008/2009 for their supports.
I would also like to thank my parents, sisters and brother for their prayers and
encouragement. The love, patience, supports and encouragement by my dearest, also
gratefully acknowledged. Last, but not least, I would like to thank all the wonderful
individuals who have, one way or another, generously contributed their knowledge,
expertise and talents.
v
ABSTRACT
The effects of landuse changes have become significant with reduction in
water quality and thus affecting aquatic life in their ecosystem. A large area in Johor
Straits starting from Singapore-Malaysia Second Link Bridge to Danga Bay have
been identified as a suitable location for the expansion of mussels and caged-fish
industries by the National Fisheries Institute of Malaysia. However, this area has
been chosen as a part of the massive development projects of Iskandar Development
Region (IDR). Therefore this would definitely increase the level of pollutants in
coastal water. The objectives of this study are to determine the effects of landuse
changes to water quality along this river as well as to analyze the effects of water
quality on green mussels (Perna viridis). This study was carried out along DangaPendas coastal area with eleven sampling points for water quality and four mussels
sampling points. Study parameters include dissolved oxygen (DO), temperature,
salinity, pH, ammoniacal nitrogen, total dissolved solid (TDS), biochemical oxygen
demand (BOD), chemical oxygen demand (COD) and heavy metals for zinc, lead,
cadmium and nickel. Sampling was carried out from November 2008 to April 2009.
All water and mussels samples were then analyzed in laboratory based on the
standard procedures. In order to observe the variation on landuse change, satellite
image; Landsat TM and ETM for year 1991, 2000, 2005 and 2008 were used.
Processes includes geometric correction, atmospheric correction, images
classification and change detection were carried out for each image to obtain the final
output of landuse changes statistics. Results obtained from the processes and
laboratory analysis indicate that there were reduction in water quality especially in
heavy metals when compared to Interim National Water Quality Standard (INWQS)
and Interim National Marine Water Quality Standard (INMWQS) with increased of
build-up and open area due to development. Lead and cadmium recorded in green
mussel were mostly higher than the permissible limit allowed by Malaysian Food
Regulation 1985 whereas zinc concentration was below than the limit. Generally,
there are significant relationship between landuse and water quality and thus with
green mussel. As development increased, quality of water decreased and heavy
metals accumulation in green mussel were also increased. The severity of the water
quality along study area was classified as slightly polluted and proper management
and prevention planning should be carried out to preserve the coastal environment.
vi
ABSTRAK
Kesan perubahan gunatanah amat berkait rapat dengan penurunan kualiti air
dan secara tidak langsung mempengaruhi ekosistem hidupan akuatik. Kawasan
sepanjang Selat Tebrau dari Jambatan Link-Kedua Malaysia-Singapura hingga ke
Teluk Danga telah dikenal pasti sebagai kawasan yang sesuai bagi perkembangan
industri kupang dan ikan sangkar oleh Lembaga Kemajuan Ikan Malaysia. Namun
demikian, kawasan ini juga telah dipilih sebagai salah satu kawasan pembangunan
besar-besaran dibawah Wilayah Pembangunan Iskandar (WPI) yang dijangka akan
meningkatkan bahan cemar ke dalam air di pesisiran pantai tersebut. Objektif kajian
ini adalah untuk menganalisis kesan perubahan gunatanah terhadap kualiti air dan
analisa kesan kualiti air terhadap kupang hijau (Perna viridis). Kajian ini telah
dijalankan di sepanjang kawasan persisiran Danga-Pendas merangkumi 11 stesen
persampelan air dan empat stesen persampelan kupang. Parameter yang terlibat
dalam kajian ini adalah oksigen terlarut (DO), suhu, kemasinan, pH, nitrogen
ammonia, jumlah pepejal terlarut (TDS), permintaan oksigen biokimia (BOD),
permintaan oksigen kimia (COD) dan logam berat seperti zink, plumbum, cadmium
dan nikel. Kajian ini telah dijalankan pada November 2008 hingga April 2009.
Kesemua sampel air dan kupang dianalisis di makmal berdasarkan kaedah piawai.
Dalam memantau variasi perubahan guna tanah, imej satelit, Landsat TM dan ETM
bagi tahun 1991, 2000, 2005 dan 2008 telah digunakan. Proses seperti pembetulan
geometri, pembetulan atmosfera, topengan, pengkelasan imej dan penentuan
perubahan telah dijalankan bagi mendapatkan hasil akhir statistik perubahan guna
tanah di kawasan kajian. Hasil yang diperolehi dari pemprosesan dan analisis
makmal menunjukkan penurunan terhadap kualiti air terutama bagi logam berat
dibandingkan dengan Interim National Water quality Standard (INWQS) dan Interim
National Marine Water Quality Standard (INMWQS) dengan penambahan kawasan
pembangunan bandar dan kawasan terbuka. Pada masa yang sama juga, plumbum
dan kadmium yang direkodkan dalam kupang kesemuanya adalah melebihi tahap
minimum berbanding Malaysian Food Regulation 1985 manakala kandungan zink
adalah kurang dari tahap tersebut. Secara amnya, terdapat kaitan antara perubahan
guna tanah terhadap kualiti air dan kupang. Dengan penambahan pembangunan,
terdapat penurunan terhadap kualiti air dan logam berat yang menumpuk dalam
kupang juga bertambah. Dari pemerhatian, tahap pencemaran air di sepanjang
kawasan kajian adalah sedikit tercemar dan pengurusan and pelan pencegahan yang
sewajarnya haruslah dilakukan bagi memelihara kawasan persekitaran persisiran
pantai ini.
vii
CONTENT
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
CONTENTS
vii
LIST OF TABLES
xiii
LIST OF FIGURES
xv
LIST OF ABBREVIATIONS
xix
LIST OF APPENDICES
xxi
INTRODUCTION
1.1
Introduction
1
1.2
Problem Statement
3
1.3
Objectives
4
1.4
Scope of Study
5
1.5
Significant of Study
6
viii
2
LITERATURE REVIEW
2.1
Introduction
7
2.2
Danga – Pendas Coastal Area
7
2.2.1
9
Water Quality at Danga- Pendas
Coastal Area
2.2.2
The Distribution of P.viridis
10
at Danga-Pendas coastal area
2.2.3
Existing and Future Planning Landuse
12
2.3
Iskandar Development Region (IDR)
13
2.4
Landuse Impact to Water Quality
16
2.5
Water Quality Parameters and Heavy Metals
17
2.5.1
Dissolved Oxygen (DO)
17
2.5.2
Temperature
18
2.5.3
pH
19
2.5.4
Salinity
20
2.5.5
Total Dissolved Solid (TDS)
20
2.5.6
Biochemical Oxygen Demand (BOD)
21
2.5.7
Chemical Oxygen Demand (COD)
21
2.5.8
Nutrients
22
2.5.8.1 Ammoniacal nitrogen
22
Heavy Metals
23
2.5.9.1 Nickel (Ni)
23
2.5.9.2 Zinc (Zn)
24
2.5.9.3 Lead (Pb)
24
2.5.9.4 Cadmium (Cd)
25
2.5.9
2.6
Water Quality Standard
25
2.6.1
26
Interim National Water Quality
Standard (INWQS)
2.6.2
Interim National Marine Water Quality
26
Standard (INMWQS)
2.6.3
2.7
Water Quality Index (WQI)
27
Asian Green Mussel, Perna viridis
28
2.7.1
31
P.viridis as Bioindicator and
ix
Biomonitor
2.7.2
Heavy Metal Contamination on Green
32
Mussels (Perna viridis)
2.7.3
Studies of Heavy Metal Contaminants in
33
Green Mussels at Johor Rivers and Estuaries
2.8
Remote Sensing and Geographic Information
34
System (GIS)
2.8.1
Remote Sensing
34
2.8.1.1 Remote Sensing Data Landsat
39
Thematic Mapper (TM) and Enhanced
Thematic Mapper (ETM)
2.8.2
2.9
3
Geographic Information System (GIS)
41
Integration of Remote Sensing and GIS
42
2.9.1
Landuse Monitoring Using Satellite Data
43
2.9.2
Landuse Change Detection
44
METHODOLOGY
3.1
Introduction
45
3.2
Literature Review
45
3.3
Data Collection and Sampling
46
3.4
Sampling Processes and Laboratory Analysis
46
3.4.1
Study Area and Sampling Stations
47
3.4.2
Sampling Frequency and Parameters
50
Analyzed
3.4.3
Sampling Techniques
51
3.4.4
Laboratory Analysis
52
3.4.4.1 Sample Pretreatment
52
3.4.4.2 Laboratory Analysis for BOD,
53
COD and Ammoniacal Nitrogen
3.4.4.3 Laboratory Analysis for Heavy Metals
3.5
54
Integrated Remote Sensing and GIS
55
3.5.1
55
Remote Sensing Data, Landsat TM
x
3.5.2
4
Landuse Extraction from Satellite Imagery
57
3.5.2.1 Geometric Correction
58
3.5.2.2 Atmospheric Correction and Masking
59
3.5.2.3 Subset
60
3.5.2.4 Image Classification
60
3.5.3
Landuse Change Detection
61
3.7
Flowchart of Methodology
63
EFFECT OF LANDUSE ON WATER QUALITY
4.1
Introduction
64
4.2
Landuse Classification
64
4.2.1
Landuse Classification for 1991
65
4.2.2
Landuse Classification for 2000
66
4.2.3
Landuse Classification for 2005
67
4.2.4
Landuse Classification for 2008
68
4.3
Landuse Change Detection
69
4.3.1
Differential in 1991 and 2008 images
70
4.3.2
Statistical Difference
74
4.3.2.1 Statistical Changes Report for
75
1991 and 2005
4.3.2.2 Statistical Changes Report for
76
2005 and 2008
4.4
Analysis of Water Quality
77
4.4.1
77
Physical and Chemical Water Quality
Parameters
4.4.1.1 Dissolved Oxygen (DO)
78
4.4.1.2 Temperature
79
4.4.1.3 Salinity
79
4.4.1.4 pH
81
4.4.1.5 Total Dissolved Solids (TDS)
82
4.4.1.6 Ammoniacal Nitrogen
82
4.4.1.7 Biochemical Oxygen Demand (BOD)
83
xi
4.4.2
4.5
4.4.1.8 Chemical Oxygen Demand (COD)
85
Heavy Metals
86
4.4.2.1 Zinc (Zn)
86
4.4.2.2 Lead (Pb)
88
4.4.2.3 Cadmium (Cd)
89
4.4.2.4 Nickel (Ni)
90
Analysis of Previous Water Quality Data Compared
92
to Present Data
4.6
Analysis on the Effects of Landuse Change to
99
Water Quality along Danga-Pendas coastal area
5
EFFECT OF WATER QUALITY ON GREEN MUSSELS
(Perna viridis)
5.1
Introduction
102
5.2
Analysis on Effects of Water Quality on Green
102
Mussels Vs Water Samples
5.2.1
5.2.2
5.3
Physical Parameters
103
5.2.1.1 Dissolved Oxygen (DO)
103
5.2.1.2 Salinity
104
5.2.1.3 Temperature
105
5.2.1.4 pH
107
5.2.1.5 Total Dissolved Solids (TDS)
108
5.5.1.6 Ammoniacal Nitrogen
109
5.5.1.7 Biochemical Oxygen Demand (BOD)
110
5.5.18 Chemical Oxygen Demand (COD)
111
Heavy Metals
111
5.2.2.1 Cadmium (Cd)
112
5.2.2.2 Lead (Pb)
114
5.2.2.3 Zinc (Zn)
115
Comparison of Cd, Pb and Zn Concentrations
117
in P.viridis from Previous Studies
5.4
Effects of Water Quality on Green Mussel
122
xii
6
CONCLUSIONS AND RECOMMENDATIONS
6.1
Conclusions
124
6.2
Recommendations
126
REFERENCES
128
Appendix A – D
145 - 162
xiii
LIST OF TABLES
TABLES NO.
TITLE
PAGE
2.1
Salinity represents the types of water.
20
2.2
INWQS Classification
26
2.3
DOE- INMWQS parameters and standard
27
2.4
Water Quality Index
28
2.5
Scientific classification of Perna viridis
28
2.6
Description of seven elements in remote sensing
36
Processes
2.7
Landsat satellite sensor characteristic and specification
39
2.8
Landsat’s band descriptions
40
3.1
Data collection and sampling
46
3.2
Coordinates of the eleven sampling stations at the study area
48
3.3
Date of sampling
51
3.4
Recommended condition for Flame Atomic Absorption
55
Spectrophotometer
3.5
Landsat TM Specifications
57
3.6
Projection specifications
59
3.7
Coordinates for image subset.
60
4.1
Overall statistical landuse for 1991, 2000, 2005
75
and 2008
4.2
Statistical changes based on landuse types between
76
2005 and 1991
4.3
Statistical changes based on landuse types between
77
2005 and 2008
4.4
Water quality data for year 1991, 2006 and 2009
5.1
Guidelines of maximum permissible limits of heavy
94
118
xiv
metals (µg/g) in seafood from different countries
5.2
Mean concentration of Cd, Pb and Zn in P.viridis
collected from Pantai Lido in present and previous studies.
119
xv
LIST OF FIGURES
FIGURE NO.
2.1
TITLE
PAGE
(a) Location of Danga-Pendas coastal area
8
(b) Developed Danga coastal area
(c) Mangrove along Pendas coastal area
(d) Development progress of IDR near coastal
area of Nusajaya.
2.2
Figure shows the tributaries which empty into
9
Danga – Pendas coastal area.
2.3
(a) Green mussels harvested area from satellite view.
11
(b) and (c) show the harvested mussels along
Danga-Pendas Area.
2.4
Existing landuse along Danga-Pendas coastal area
12
2.5
Existing landuse types along Danga-Pendas.
13
2.6
Proposed land use under IDR
14
2.7
Proposed for major development of coastal zone
15
under Iskandar Development Region
2.8
Development of IDR near Tebing Runtuh
16
2.9
Green Mussel
29
2.10
The geographical distribution of P.viridis worldwide
30
2.11
Seven elements in remote sensing processes consisting of
35
A is energy source or illumination, B is radiation and the
atmosphere, C is interaction with the target, D is recording
of energy by the sensor, E is transmission, reception and
processing, F is interpretation and analysis and G is applications
2.12
Example of (a) spatial variations (b) spectral
38
xvi
resolution and (c) temporal resolution in remote
sensing data.
2.13
Data integration from different forms of input through GIS
42
3.1
Flowchart of sampling and laboratory works
47
3.2
Location of study area with eleven sampling stations
48
3.3
Sampling station S2 at Sungai Danga estuary
49
3.4
Sampling station M1 for mussels collection station near
49
Sungai Melayu estuaries
3.5
Sampling stations M3 for mussel’s collection
50
3.6
Sampling station M4 and S9 near Nusajaya area
50
3.7
(a) YSI-USA multiparameter probe (b) HACH DR
54
5000 Spectrophotometer for water quality analysis
3.8
Perkin Elmer model Analyst 400 Atomic Absorption
54
Spectrometer
3.9
Landsat TM and ETM images
56
3.10
Flowchart of Landuse Classification
58
3.11
Flowchart of Landuse Change Detection.
62
3.12
Overall flowchart of methodology
63
4.1
Landuse classification for 1991
66
4.2
Landuse classification for 2000
67
4.3
Landuse classification for 2005
68
4.4
Landuse classification for 2008
69
4.5
Series of landuse types at Johor Bahru city center
71
4.6
Series of landuse information at Sungai Danga and
72
Sungai Skudai estuaries.
4.7
Series of landuse information at Nusajaya.
73
4.8
Landuse information at Sungai Pendas coastal area.
74
4.9
DO concentration measured at station S1 to S11 from
78
November 2008 to April 2009.
4.10
Temperature level measured at station S1 to S11 from
79
November 2008 to April 2009.
4.11
Salinity measured at station S1 to S11 from November
80
2008 to April 2009.
4.12
pH measured at station S1 to S11 from November 2008
81
xvii
to April 2009.
4.13
TDS concentration measured at station S1 to S11 from
82
November 2008 to April 2009.
4.14
Ammoniacal nitrogen concentration measured at station
83
S1 to S11 from November 2008 to April 2009.
4.15
BOD concentration measured at station S1 to S11 from
84
November 2008 to February 2009.
4.16
COD concentration measured at station S1 to S11 from
86
November 2008 to February 2009.
4.17
Zinc concentration measured at station S1 to S11 from
87
November 2008 to April 2009.
4.18
Lead concentration measured at station S1 to S11 from
89
November 2008 to April 2009.
4.19
Cadmium concentration measured at station S1 to S11
90
from November 2008 to April 2009.
4.20
Nickel concentration measured at station S1 to S11 from
91
November 2008 to April 2009.
4.21
Water quality data at Danga and Pendas area for year 1991,
96
2006 and 2009 (a) pH (b) temperature (c) DO and
(d) ammoniacal nitrogen (AN).
4.22
Heavy metals concentration data at Danga and Pendas
98
area for year 1991, 2006 and 2009 (a) zinc (b) cadmium
(c) lead and (d) nickel.
4.23
Concentration of manufacturing in and outer of
100
Johor Bahru.
5.1
DO concentration at mussel sampling stations, M1 to
104
M4 from November 2008 to April 2009.
5.2
Salinity level at mussel sampling stations, M1 to M4 from
105
November 2008 to April 2009.
5.3
Water temperature at mussel sampling stations, M1 to
106
M4 from November 2008 to April 2009.
5.4
pH at mussel sampling stations, M1 to M4 from November
107
2008 to April 2009.
5.5
TDS concentration at mussel sampling stations, M1 to
108
xviii
M4 from November 2008 to April 2009.
5.6
Ammoniacal nitrogen concentration at mussel sampling
109
stations, M1 to M4 from November 2008 to April 2009.
5.7
BOD concentration at mussel sampling stations, M1 to M4
110
from November 2008 to February 2009.
5.8
COD concentration at mussel sampling stations, M1 to
111
M4 from November 2008 to April 2009.
5.9
Cadmium concentration in water and P.viridis samples at
113
mussel sampling stations, M1 to M4 from November 2008
to April 2009.
5.10
Lead concentration in water and P.viridis samples at mussel
114
sampling stations, M1 to M4 from November 2008
to April 2009
5.11
Zinc concentration in (a) water and (b) P.viridis samples
116
at mussel sampling stations, M1 to M4 from November
2008 to April 2009.
5.12
Cadmium concentration in P.viridis samples at Pantai Lido
120
from 1991 to 2009
5.13
Lead concentration in P.viridis samples at Pantai Lido
121
from 1991to 2009.
5.14
Zinc concentration in P.viridis samples at Pantai Lido
from 1991to 2009.
121
xix
LIST OF ABBREVIATIONS
µg
-
micro gram
AN
-
Ammoniacal nitrogen
BOD
-
Biochemical Oxygen Demand
Cd
-
Cadmium
COD
-
Chemical Oxygen Demand
DDDW
-
Double Distilled Deionised Water
DEIA
-
Detail Environmental Impact Assessment
DN
-
Digital Number
DO
-
Dissolved Oxygen
DOE
-
Department of Environment
DOF
-
Department of Fisheries
ETM
-
Enhanced Thematic Mapper
g
-
gram
GCP
-
Ground Control Point
GIS
-
Geographic Information System
GPS
-
Global Positioning System
ha
-
Hectares
IDR
-
Iskandar Development Region
INMWQS
-
Interim Marine National Water Quality Standard
INWQS
-
Interim National Water Quality Standard
JUPEM
-
Jabatan Ukur dan Pemetaan Malaysia
m
-
meter
MACRES
-
Malaysia Center for Remote Sensing
mg
-
milligram
Mid-IR
-
Middle Infrared
NA
-
Not Available
NH3
-
Ammonia
xx
Ni
-
Nickel
NIMPIS
-
National Introduced Marine Pest Information System
NIR
-
Near Infrared
NO2
-
Nitrite
NO3
-
Nitrate
NST
-
New Straits Times
P.viridis
-
Perna viridis
Pb
-
Lead
ppm
-
part per million
ppt
-
part per thousand
RADAR
-
Radio Detection and Ranging
ROI
-
Region of Interest
RSO
-
Rectified Skew Orthomorphic
SEC
-
Special Economic Corridor
SJER
-
South Johor Economic Region
SPOT
-
Systeme pour l’Observation de la Terre
TDS
-
Total Dissolved Solids
TM
-
Thematic Mapper
US EPA
-
United State Environmental Protection Agency
USGS
-
United State Geological Survey
WQ
-
Water quality
WQI
-
Water Quality Index
xxi
LIST OF APPENDICES
APPENDIX
TITLE
PAGE
A
Water Quality Standard
145
B
Laboratory Analysis
147
C
Accuracy Assessment for Landuse Classification
152
D
Water Quality Data
154
CHAPTER 1
INTRODUCTION
1.1
Introduction
Landuse changes are very significance with changes from pure green land
area to developed urban area. Most of the countries in the world face the same
dilemma where the initial landuse have to be transformed as the populations,
technologies and economics increases. In Malaysia, landuse has undergone many
changes since the country achieved its independence in 1957. Landuse changes were
driven by a number of economical, socio-political and biophysical factors. Over the
last two decades, the evolution of landuse became drastic in the urban and rural
areas. Especially, more land areas have been displaced or converted to nonagricultural activities particularly for industry, housing and commercial activities
(Khaled, 2005).
In Malaysia, for example, the total urban population has increased to 59
percent in the year 2000 (Hadi, 2000) and some of the states in Peninsular Malaysia
has achieved the urbanization level of developed countries with 80 percent of total
population. Foreign and local investment in the agricultural, commercial and mining
sectors are the main factors leading to the growth of urban population in Malaysia.
Most urban areas in developing countries are located on the coast or on major rivers
in Malaysia. The uncontrolled growth of urban development has adversely affected
Malaysian river basin ecosystems (Jahi and Nordin, 1996). The same problem is
being faced in Johor, it is one of the most developed state in Malaysia and the one
2
closest to Singapore, it also faces massive development especially along the coastal
area in South of Johor. One of the projects under rapid progress which lead to rapid
changes in landuse, water quality and aquaculture life is the development of Iskandar
Development Regions (IDR).
Iskandar Development Regions or also known as South Johor Economic
Region (SJER) is a developing corridor in the South Johor region. From a physical
planning perspective, SJER is defined as a geographic area in the southern part of
Johor that will benefit from economic opportunities that will be promoted within the
region. Iskandar Malaysia is planned to become Southern Peninsular Malaysia’s
most developed region where it includes living, entertainment, environment and
business (SJER CDP, 2006). The new development of SJER will cause massive
impact to water quality of the river along the area involved. The Johor Straits
waterway leads into two open waters which are the Straits of Malacca on the west
and the South China Sea on the east (SJER CDP, 2006). Specifically, it will affect
the discharge to the nearest river at Johor Straits such as Sungai Melayu, Sungai
Pendas and Sungai Skudai. This may be one of the contributing factor to drastic
reduction in water quality. The changes of water quality will also affect the aquatic
life living in the river along this area such as the green mussel.
Green mussels (Perna viridis) occur widely in shallow waters along the west
coast of Peninsular Malaysia. It have become a food resource and Malaysia once
exporter green mussel. The green mussel is harvested commercially in the IndoPacific region as a human food resource due to its dense and fast growth (Knott and
De-Victor, 2007). P.viridis can have economic, ecological and human health impact.
Human consume mussels as it is a cheap source of protein and tasty. Besides of
economic importance, it is also edible therefore analysis of mussels is important to
provide information about possible contaminants or toxicity level that may harm
public health. Green mussel is important as a bioindicator because it is an efficient
filter feeder and has been used widely as bio-indicator species for pollutants
(Sivalingam, 1977). P. viridis has become the best candidate for bioindicator surveys
in South-East Asia since 1980’s (Phillips, 1980). While, other important factor which
had led to the use of green mussels as a biomonitoring agent for heavy metals is that
3
they are commercially an important seafood species worldwide (Farrington et al.,
1987).
1.2
Problem Statement
Currently, the coastal area of Danga-Pendas is affected by rapid development
of Danga Bay and Iskandar Development Region (IDR). These massive
developments will cause changes in landuse types along this area. The changes in
landuse also affect the water quality of rivers, estuaries and coastal water. This is due
to the discharge from industry, agriculture or sewage such as suspended sediments,
pathogens, nutrients, heavy metals and oil. Urban areas also diffuse sources of
suspended sediments, pathogens, nutrients and pesticides in agricultural land uses.
Many of these anthropogenic influences are part of a larger process of catchment
landuse change that can affect water quality in both rivers and lakes as well as
downstream estuarine and coastal waters.
IDR is a developing corridor in the South Johor region. IDR covers a land
size of 2217 sq. km which is estimated to have 1.35 million people or 43% of Johor’s
population. Four of five Flagship Zones of the focal points in IDR will be located in
the Nusajaya-Johor Bahru-Pasir Gudang corridor also known as the Special
Economic Corridor (SEC). The development of newly developing IDR will cause
massive impact to river water quality along this area. It will affect the discharge to
the nearest river such as Sungai Melayu, Sungai Pendas and Sungai Skudai. Effluent
from several polluted rivers in Johor Bahru such as Sungai Melayu, Sungai Skudai
and Sungai Segget may also be one of the contributing factor to the drastic reduction
in water quality. Water quality of these rivers will decrease with increase of nutrients
and heavy metals.
From previous studies carried out by Department of Environment (DOE), the
Water Quality Index (WQI) for Sungai Danga and Sungai Skudai were classified as
Class III according to DOE-WQI. The main pollutants are organics, ammoniacal
4
nitrogen, nitrate, phosphate, faecal coliform and a number of heavy metals such as
iron, nickel and mercury which are above the levels recommended by the proposed
Interim National Marine Water Quality Standards (INMWQS) (DOE, 2007). The
new developing SJER will boost up the pollutants such as nutrient and heavy metal
discharge to these rivers thus resulting in reduction of water quality and low WQI.
The changes of water quality will also affect the aquatic lives living in the river along
this area such as green mussel.
Green mussel has been identified as potential organism for aquaculture in
Pacific Island (Uwate et al., 1984). In Malaysia, Johor is the largest state producing
P.viridis and produces approximately 92% of total mussel’s production. The
National Fisheries Institute of Malaysia have identified a large area in Johor Straits
estuaries starting from the Singapore-Malaysia second link to Danga Bay as a
suitable area for the expansion of mussels and caged-fish industry. P.viridis occurs in
estuarine or coastal water which are rich with plankton, warm water in range of 26 to
32˚C with high salinity around 27-33 ppt (Hickman, 1989; Power, 2004).The growth
rates of P.viridis are mostly influenced by environmental factor such as temperature,
food availability such as chlorophyll and also water movement. Generally, the
pollution from the effluent may affect changes in the condition of water quality along
this area. Specifically, valuable water ecosystems in this area especially green
mussel, P.viridis will also be affected. Thus it will disrupt the growth and reduces the
production rate of P.viridis.
1.3
Objectives
The objectives of this study are:
i.
To determine the effect of landuse changes on water quality along
Danga-Pendas coastal area using Remote Sensing and Geographic
Information System (GIS)
5
ii.
To analyze the effects of water quality on green mussels (Perna
viridis) along Danga-Pendas coastal area.
iii.
To assess the severity of pollution level due to massive development
along Danga-Pendas coastal area.
1.4
Scope of Study
The scope of this study consists of collecting samples at designated stations,
extensive laboratory analysis on water quality and green mussel, data processing
using integrated technology of remote sensing and GIS and finally analysis of the
results. Sampling were carried out along the coastal and estuary from Danga Bay
coastal area up to the Second Link Bridge. Sampling was conducted monthly starting
from September 2008 to April 2009. The samples were then tested in laboratory and
analyses were carried out based on validated standard procedures.
The parameter analyzed in this study includes physical, nutrient and heavy
metals analysis. Physical data comprise of dissolved oxygen (DO), temperature, pH,
total dissolved solid (TDS), salinity, biochemical oxygen demand (BOD) and
chemical oxygen demand (COD). Nutrient was analyzed for ammoniacal nitrogen
and specifically lead, cadmium, nickel and zinc for heavy metals.
Data processing were then applied using typical remote sensing and GIS’s
software. Landsat TM satellites data for series of 1991 to 2008 were used in order to
evaluate landuse changes and water quality effects. For remote sensing processing
purposes, software such as Envi 4.2 and Erdas Imagine 9.1 were used. As for GIS
processing purposes, ArcView 3.2 and ArcGIS 9.2 softwares were used to process
the data. All the results were then analyzed in term of environmental monitoring such
as effect of landuse changes to water quality and the effect of changes in water
quality to mussel’s growth.
6
1.5
Significant of Study
Effects of landuse changes are very significant contributor to changes in
water quality of rivers, estuaries or coastal waters. This is because the replacement of
the existing landuse to other development landuse which is mostly urban area will
create large interference in term of pollutants such as organics, pesticides and
chemicals residues which lead to polluted river. Therefore it is important to study the
correlation between the changes of landuse due to the development and its impact on
water quality as well as green mussel along Johor straits. The correlation can be used
to monitor the water quality along the straits which also may lead to other effects in
environmental aspects and disturb the aquatic ecosystem.
Green mussel is an economically important seafood for Malaysia where it
may lead to million in revenues in domestic consumption or exports. Beside that, the
capability of modern technology such as GIS and remote sensing in adapting with the
environmental aspects gives more valuable results. This then can be used as a basis
monitoring and future prediction. Among others, the main importance of this study is
to ensure that sustainability of the environment is still maintained and controlled
along with the development.
CHAPTER 2
LITERATURE REVIEW
2.1
Introduction
This chapter explains the background related to studies on the impacts of
nutrient, heavy metals and pollutant from development to water quality and green
mussels. The information on water quality, monitoring landuse change as well as
integration analysis using remote sensing and GIS will also be included.
2.2
Danga – Pendas Coastal Area
Danga-Pendas coastal area is situated along the south coast of Johor. From
the observation, Danga areas are more developed than Pendas area with the
urbanization and expansion of Danga Bay. Most of Pendas area are still covered with
vegetation and view along the coastal area are green with such a healthy mangrove
trees. There are also some parts near Sungai Bahan which have been developed with
slight IDR progresses. Most of the local resident along this area work either as
fisherman or with aquaculture such as fish, prawns or mussels. Figure 2.1 shows the
location of Danga-Pendas coastal area.
8
Peninsular
Malaysia
Danga
Johor
Pendas
(a)
(b)
(c)
(d)
Figure 2.1:: (a) Location of Danga-Pendas
Danga Pendas coastal area (b) development along Danga
coastal area (c) Mangrove along Pendas coastal area and (d) development progress of
IDR near coastal area of Nusajaya.
9
There are several tributaries along Danga-Pendas coastal area. Major
tributaries which empty into this area are Sungai Pendas, Sungai Melayu and Sungai
Skudai. Other tributaries are Sungai Danga, Sungai Perepat and Sungai Bahan.
Figure 2.2 shows the number of tributaries entering into the Danga-Pendas coastal
area.
Sungai Skudai
Sungai Melayu
Sungai Perepat
Sungai Danga
Sungai Bahan
Sungai Pendas
Figure 2.2: Figure shows the tributaries which empty into Danga - Pendas coastal
area.
Previous studies reported that Sungai Skudai and Sungai Segget are among
the four most polluted rivers in Johor. The other two rivers are Sungai Tukang Batu
and Sungai Ayer Baloi (DOE, 2007). Sungai Skudai have been polluted by domestic
wastes and industrial discharge especially ammoniacal nitrogen throughout the years.
While the main pollutants for Sungai Segget is from the waste discharge of small
industries which contain dyes and grease which are released into the drainage
systems and hence flow into Sungai Segget (DOE, 2007).
2.2.1
Water Quality at Danga- Pendas Coastal Area
From DOE DEIA report, coastal seawater was found to satisfy Class III of
the Water Quality Index (DOE-WQI). The main pollutants are organics, ammoniacal
10
nitrogen, nitrate, phosphate, faecal coliform and a number of heavy metals such as
iron, nickel and mercury. The faecal coliform counts in the sea water were about 200
times higher than the level recommended by the INMWQS (DEIA report).
From other reports in New Strait Times, hydrologist, Dr Low Kwai Sim said
that pollution in this area is so bad that it could pose a health hazard unless concerted
efforts are made to break the Malaysia-Singapore Causeway and flush out the “dead
water” (NST, 2006). She also said the pollutants accumulated over the years and
trapped on both sides of the 82-year-old crossing could not be flushed out to the open
sea. The two "dead water" bodies flanking the Causeway will have the worst
ecological conditions of any waterway or rivers in the country if left in the present
state. The danger is that if the Causeway is not demolished to allow for the free flow
of tides and currents, the pollution level will begin to impact the lives of people
(NST, 2006).
2.2.2
The Distribution and Harvested of P.viridis at Danga-Pendas coastal area
Since early 80’s harvesting activities of P.viridis in Malaysia is always
confined to Southern Johor. Most of the mussels are mostly harvested by the
villagers. Large green mussel is a common species found at inter-tidal areas of the
Johor Straits. The culture of shellfish from spat collected from areas of natural spat
fall is a potentially profitable activity for coastal residents. Figure 2.3 shows the
harvested green mussels area along Danga-Pendas estuary.
11
Green Mussels Harvested Area
(a)
(b)
(c)
Figure 2.3: Figure (a) Green mussels harvested area from satellite view. (b) and (c)
shows mussels aquaculture along Danga-Pendas Area.
12
2.2.3
Existing and Future Planning Landuse
Existing landuse along Danga-Pendas coastal area are mostly covered with
mangrove plant. There are only a few parts which have been developed such as
Danga estuaries (Danga Bay) and part of Pendas area located at Second-Link Bridge.
Other parts along this area are still maintained with green mangrove area and
residential area which are located just near the coastal water. Figure 2.4 shows the
existing landuse along Danga-Pendas area, whereas Figure 2.5 shows landuse map
along the area.
(a)
(b)
(c)
Figure 2.4: Existing landuse along Danga-Pendas coastal area
13
Figure 2.5: Existing landuse types along Danga-Pendas. (Source: SJER, CDP 2025)
Danga-Pendas coastal area plan will receive a wide range of developments by
2025. This is due to the planning of Iskandar Development Regions (IDR) which
envelops along Johor Straits starting from Sungai Pulai to the end of Tanjung
Langsat at the east of Johor coastal area. Planning involves the development of parks,
waterfronts, ports and free access zones regions.
2.3
Iskandar Development Regions (IDR)
The Danga-Pendas area have received heavy development these recent years.
The development involves all aspects of socio-economics and urban planning. One
of the biggest and progressive development engaged in this area is the new South
Johor Economic Region (SJER). SJER is one of the 8th Malaysia Plan in order to
improve technology and future economics in the South East Asia region.
14
From a physical planning perspective, SJER is defined as a geographic area
in the southern part of Johor that will benefit from the economic opportunities that
will be promoted within the region (SJER CDP, 2006). Iskandar Malaysia is planned
to become Southern Peninsular Malaysia’s most developed region where it includes
living, entertainment, environment and business. The area covers 221 634.1 hectares
(2 216.3 sq. km) involving five jurisdiction of Majlis Bandaraya Johor Bahru
(MPJB), Majlis Perbandaran Johor Bahru Tengah (MPJBT), Pasir Gudang Local
Authority, Majlis Perbandaran Kulai (MPK) and Majlis Daerah Pontian (MDP).
Figure 2.6 shows the proposed landuse development under IDR.
Figure 2.6: Proposed landuse under IDR (Source: SJER CDP 2025,
www.wpi.com.my)
15
The coastal zone for IDR encompasses the waters of Johor Straits within
Malaysian boundary and a 3 km inland zone along the coastline of Johor Straits,
which also lies within the Special Economic Corridor (SEC). Special Economic
Corridor (SEC) consists of four focal points that will be located in Nusajaya - Johor
Bahru-Pasir Gudang area. It extends all the way into Pontian and Kota Tinggi where
the Johor Straits waterway leads into two open waters which are the Straits of
Malacca on the west and the South China Sea on the east (SJER CDP, 2006). Figure
2.7 shows the proposed major development of coastal zone under IDR.
Figure 2.7: Proposed for major development of coastal zone under Iskandar
Development Region (Source: SJER CDP 2025, www.wpi.com.my)
The development would not only create an attractive new feature for the city
but would also provide the opportunity to create new public spaces for the benefit of
the growing population. From the proposed coastal zone physical planning provided
by Khazanah National, 100 acres of parks and open spaces would be created, in
addition to another 180 acres of land for mixed use development and infrastructure
which have already been created. The opportunity to reshape the coastal area will
result in a new and vibrant waterfront that will link Johor Bahru City Centre to
16
Danga Bay development. The Johor Bahru Coastal development will reclaim 250
metres of land to create a new shoreline. While, Nusajaya will create a seamless
work and living environment between Johor and Singapore (SJER CDP, 2006).
Figure 2.8 shows IDR development near Tebing Runtuh which is one of the study
location.
Figure 2.8: Development of IDR near Tebing Runtuh – under construction
2.4
Landuse Impact to Water Quality
By definition, landuse is the development of land for any use. It also mean
human modification of natural environment into built environment. Agricultural and
urban land uses are widely known to impair water quality in streams and other water
bodies (US EPA, 2000). However, landuse change beyond the metropolitan fringe is
increasingly recognized as an emerging mode of development in critical need of
ecological and water quality assessments (Theobald, 2004).
Basically, agriculture or urban landuse has both direct and indirect impacts on
water resources. Some impacts result from the direct modification or destruction of
streams, lakes and wetlands. Other impact occur primarily offsite due to changes in
the quality and quantity of runoff from urban development and construction activities
(Dreher and Price, 1992).
17
Landuse areas generate both nonpoint and point sources pollution. Point
sources that have an impact on surface water include industrial and municipal waste
discharges which those that affect groundwater quality include leaky underground
storage facilities, as well as miscellaneous accidental spills of organic or inorganic
contaminants. Nonpoint sources include runoff or infiltration of water from roads,
industrial areas and golf courses. Contaminants include metals, industrial organic
chemicals, nutrients and pesticides. Naturally vegetated areas are increasingly
converted to agricultural and urban cover, however it is important to look at the
impact of these transformations on water quality (Lohani, 2008).
2.5
Water Quality Parameters
Water quality measurements include physical, chemical and biological
characteristics of water. The complexity of water quality as a subject is reflected in
many types of measurements of water quality indicators. Some water quality
measurement can be done on site during sampling process mean while other more
complex parameters can be obtained by laboratory analysis. Simple measurements
that can be made on-site are temperature, pH, dissolved oxygen, conductivity and
salinity. More complex measurements that must be made in a laboratory setting
requires water sample to be collected, preserved, and analyzed at the laboratory such
as biological oxygen demand (BOD), chemical oxygen demand (COD), suspended
sediment and all heavy metals components. Water quality parameters provide
important information about the health of a water body. The following is a brief
description of some commonly used parameters.
2.5.1
Dissolved Oxygen (DO)
Dissolved oxygen (DO) analysis measures the amount of gaseous oxygen
(O2) dissolved in an aqueous solution. Oxygen gets into water by diffusion from the
surrounding air, by aeration and as a waste product of photosynthesis. In simple, DO
18
refer to the amount of oxygen that is present in the water. It is measured in units of
milligrams per liter (mg/L), or milligrams of oxygen dissolved in a liter of water. The
amount of oxygen that can dissolve in water is strongly limited by the temperature of
the water. The colder the water, the more oxygen it can hold. The concentration of
DO is high if the water quality is good (CRC Sugar, 2002).
There are several factors that affects DO levels in water body. Among the
factors are volume and velocity of water flowing in the water body. In slow, stagnant
waters, oxygen only enters the top layer of water. While deeper water is often low in
DO concentration due to decomposition of organic matter by bacteria that live on or
near the bottom of the reservoir. Climate and season also affects DO level where the
colder the water, the more oxygen can be dissolved in the water. Therefore, DO
concentrations at one location are usually higher in the winter than in the summer
(CRC Sugar, 2002).
Other factors that also influence DO are the concentration of dissolved or
suspended solids, amount of nutrients and type and number of organisms in water
body. Oxygen is more easily dissolved into water with low levels of dissolved or
suspended solids. Nutrients can also lead to increased plant growth. This can lead to
high DO concentrations during the day as photosynthesis occurs, and low DO
concentrations during the night. The types of organisms present in plant, bacteria or
fungi affects the DO concentration in a water body. If many plants are present, the
water can be supersaturated with DO during the day, as photosynthesis occurs.
Concentrations of oxygen can decrease significantly during the night, due to
respiration (Metcalf and Eddy, 2004).
2.5.2
Temperature
Temperature impacts both chemical and biological characteristics of surface
water. It affects the DO level in water, photosynthesis of aquatic plants, metabolic
rates of aquatic organisms, and the sensitivity of these organisms to pollution,
19
parasites and disease. Variables that affect a waterway temperature include the color
of water, depth, amount of shade from shoreline vegetation and also volume of
water.
Most heat warming surface waters comes from the sun, thus waterways with
dark-colored water or those with dark muddy bottoms absorb heat best. However,
deep waters usually are colder than shallow waters simply because they require more
time to warm up. Trees overhanging a lake shore or river bank shades the water from
sunlight. Some narrow creeks and streams are almost completely covered with
overhanging vegetation during certain times of the year. The shade prevents water
temperatures from rising too fast on bright sunny days. As for the volume of water,
more water there is, the longer it takes to heat up or cool down (CRC Sugar, 2002).
2.5.3
pH
pH is a measure of acidic or alkaline basic nature of water. The concentration
of the hydrogen ion [H+] activity in a solution determines the pH. The balance of
positive hydrogen ions (H+) and negative hydroxide ions (OH-) in water determines
how acidic or basic the water is. In pure water, the concentration of positive
hydrogen ions is in equilibrium with the concentration of negative hydroxide ions,
and the pH measures exactly 7. The pH of water determines the solubility which is
amount that can be dissolved in the water and biological availability which is the
amount that can be utilized by aquatic life of chemical constituents such as nutrients
like phosphorus, nitrogen, and carbon also heavy metals such as lead, copper,
cadmium, and etc. A pH range of 6.0 to 9.0 appears to provide protection for the life
of freshwater fish and bottom dwelling invertebrates. Rivers in Malaysia have pH
values lower than 7.0 with 60% of them are in the average of pH 6.0 to 8.5 (Jarvie et
al., 2006).
20
2.5.4
Salinity
Salinity is a measure of the mass of dissolved salts or ionic constituents in a
given mass of solution and usually expressed as parts per thousand (ppt). The
electrical conductivity of seawater is strongly dependent on its salinity. The degree of
salinity can represent the classification of seawater. It can be divided into freshwater,
brackish water, saline and brine water. Table 2.1 shows the salinity measurement for
each of them.
Table 2.1: Salinity represents the types of water.
Water salinity
Fresh Water
2.5.5
Brackish Water
Saline Water
Brine
< 0.05 %
0.05 – 3 %
3–5%
>5%
< 0.5 ppt
0.5 – 30 ppt
30 – 50 ppt
> 50 ppt
Total Dissolved Oxygen (TDS)
Total Dissolved Solids (TDS) refers to all of the constituents dissolved in
water. Dissolved solids refer to any minerals, salts, metals, cations or anions
dissolved in water. The term TDS often used interchangeably with the term salinity.
Total dissolved solids (TDS) comprise of inorganic salts (principally calcium,
magnesium, potassium, sodium, bicarbonates, chlorides and sulfates) and some small
amounts of organic matter that are dissolved in water. TDS can also represent the
water according its content. Freshwater correspond to TDS content which is lower
than 1000 mg/L. TDS of 1000 to 10000 mg/L will represent brackish water. While
for salty water TDS is about 10000 to 100000 mg/L. TDS above 100000 mg/L are
represented for brine water (US EPA, 2000).
21
2.5.6
Biochemical Oxygen Demand (BOD)
Biochemical oxygen demand (BOD) is a measure of the quantity of oxygen
used by microorganisms such as aerobic bacteria in the oxidation of organic matter.
When organic matter decomposes, microorganisms such as bacteria and fungi feed
upon this decaying material and eventually the matter becomes oxidized. BOD
measures the amount of oxygen consumed by microorganisms in the process of
decomposing organic matter in water. The harder the microorganisms work, the more
oxygen they use and the higher the measure of BOD, leaving less oxygen for other
life in the water (Vynavi, 2005).
The test for BOD is especially important in waste water treatment, food
manufacturing, and filtration facilities where the concentration of oxygen is crucial
to the overall process and end products. High concentrations of dissolve oxygen
(DO) predict that oxygen uptake by microorganisms is low along with the required
break down of nutrient sources in the medium of sample. On the other hand, low DO
readings signify high oxygen demand from microorganisms, and can lead to possible
sources of contamination depending on the process.
2.5.7
Chemical Oxygen Demand (COD)
Chemical oxygen demand (COD) test is commonly used to indirectly
measure the amount of organic compounds in water. Most cations of COD determine
the amount of organic pollutant found in surface water such as lakes and river,
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 part per million (ppm). It is a measure of the
capacity of water to consume oxygen during the decomposition of organic matter and
the oxidation of inorganic chemicals such as ammonia and nitrite. COD
measurements are commonly made on samples of waste waters or of natural waters
contaminated by domestic or industrial waste and COD is related to BOD.
22
However, BOD only measures the amount of oxygen consumed by microbial
oxidation and is most relevant to waters rich in organic matter. It is important to
understand that COD and BOD do not necessarily measure the same types of oxygen
consumption. For example, COD does not measure the oxygen-consuming potential
associated with certain dissolved organic compounds such as acetate. However,
acetate can be metabolized by microorganism and would therefore be detected in an
assay of BOD. In contrast, the oxygen-consuming potential of cellulose is not
measured during a short term BOD assay, but it is measured during a COD test
(Nathanson, 1986).
2.5.8
Nutrients
Nutrients, nitrogen and phosphorus are essential for plant growth. High
concentrations indicate potential for excessive weed and algal growth. Total nutrients
are made up of a dissolved component and an organic component, which is bound to
carbon. Ammoniacal nitrogen is one of the nutrients which are important to take into
consideration.
2.5.8.1 Ammoniacal Nitrogen
Ammonia (NH3) refers to inorganic substance that is found in abundance on
surface water, soil and easily catered through plant tissue decaying and composed of
animal waste. Ammonia that is rich with nitrogen will be oxidized to nitrite (NO2-)
by soil bacteria; Nitrosomonas with the absence of high dissolve oxygen in water.
Then, nitrification occurs when Nitrobacter bacteria oxidize the nitrite to form nitrate
(NO3-) (Cech, 2003). Surface water may be polluted when ammonia level reach 0.1
mg/L and when the level increase to 0.2 mg/L, water bodies are considered no longer
a safe place for aquatic life because of high toxicity. There are a lot of contributors to
increase ammonia level in river. Improper management of sewerage services, animal
waste especially pig farm and waste from palm oil mill are the main contributors.
The decay of dead algae and other organic material can also produce ammonia that
23
can be toxic to many forms of aquatic life. According to Jack (2006), when dissolved
oxygen decrease, ammonia levels tend to increase. Ammonia is also recognize as the
number one killer of tropical fish. As the level of ammonia rises, the death rate
climbs even higher. Ammonia affects fish by causing the blood to lose its ability to
carry oxygen. This creates stress and lowers the resistance of fish to such recurrent
bacterial infections such as fin and tail rot, body slime, eye cloud, mouth fungus, and
body sores (Nurhidayah, 2007).
2.5.9
Heavy Metals
Toxic metals can be present in industrial, municipal, and urban runoff which
can be harmful to humans and aquatic life. Increased in urbanization and
industrialization are to blame for an increased level of trace metals, especially heavy
metals in our waterways. The toxicity of metals is dependent on their solubility
which in turn depends heavily on pH and on the presence of different types of anions
and other cations. The heavy metals linked most often to human poisoning are lead,
mercury, arsenic and cadmium. Other heavy metals, including copper, zinc, and
chromium, are actually required by the body in small amounts, but can also be toxic
in larger doses. Brief descriptions of heavy metals are discussed as follows.
2.5.9.1.Nickel (Ni)
Nickel is a transition metal with a lustrous, metallic and silvery with a gold
tinge appearance. Boiling point and atomic number of nickel is 2913ºC and 28
respectively. Nickel plays numerous roles in the biology of microorganisms and
plants. Nickel compounds are hazardous because it is suspected to be carcinogenic
for example nickel sulfide fume and dust is believed to be carcinogenic. Nickel
levels in water varies as for sea water the concentration is 0.1 to 0.5µg/L, drinking
water and surface water concentrations average is less than 20µg/L and 15 to 20µg/L,
respectively. The concentration of Ni in shellfish ranging from 0.2 to 2.2ppm (wet
24
weight) has been determined in hardshell clams and 0.5 to 1.9ppm in eastern oysters
(Stephan and Shetty, 1981).
2.5.9.2Zinc (Zn)
Zinc having the atomic number 30 and it is a transition metal with bluish pale
grey solid appearance. Boiling point for zinc is 907ºC. Zinc mainly applies in
platting to prevent corrosion and rust of other metal. Zinc is an essential nutrient,
necessary for sustaining all life. Green mussels assimilate 21 to 36% of zinc from
ingestion of five species of marine phytoplankton. (Chong and Wang, 2000). The US
recommendated dietary allowance of zinc from puberty on wards is 11 mg for males
and 8 mg for females. Zinc deficiency may also be associate with a lot of chronic
illness such as sickle cell disease, liver disease and renal disease. Even though zinc is
an essential requirement for a healthy body, too much zinc can be harmful (Vazquez
et al., 1999).
2.5.9.3Lead (Pb)
Lead is a soft, malleable, poor metal also considered to be one of the heavy
metals. Lead has a bluish-white color when freshly cut, but tarnishes to a dull grayish
color when exposed to air. It has a shiny chrome-silver luster when melted into a
liquid (Sax, 1976). Seawater contains trace amounts of lead (2 to 30 ppt). On average
rivers contain between 3 and 30 ppb of lead. Phytoplankton contains approximately 5
to 10 ppm lead (dry mass), freshwater fish approximately 0.5 to 1000 ppb, and oyster
approximately 500 ppb.
Lead and lead compounds are generally toxic pollutants. Lead (II) salts and
organic lead compounds are most harmful ecotoxicologically. Lead salts are
attributed to water hazard Class 2, and consequently are harmful. The same applies to
lead compounds such as lead acetate, lead oxide, lead nitrate, and lead carbonate.
Lead limits plant chlorophyll synthesis. Nevertheless, plants can take up high levels
of lead up to 500 ppm from soils. Higher concentrations negatively influence plant
25
growth. Through plant uptake, lead enters food chains. Consequently, lead pesticide
application is prohibited in most countries. Lead accumulates in organisms,
sediments and sludge (Vynavi, 2005).
2.5.9.4 Cadmium (Cd)
Cadmium is a metal found in natural deposits as ores containing other
elements. The greatest use of cadmium is primarily for metal plating and coating
operations, including transportation equipment, machinery and baking enamels,
photography and television phosphors. It is also used in nickel-cadmium and solar
batteries and in pigments. Nowadays, about 70 percent of cadmium produced
worldwide is used in nickel-cadmium (nicad) batteries. Nicad batteries can be used
over and over. When a nicad battery has lost some or all of its power, it is inserted
into a unit that plugs into an electrical outlet. Electricity from the outlet recharges the
battery (US EPA, 2000).
Cadmium occurs naturally in zinc, lead, copper and other ores which can
serve as sources to ground and surface waters, especially when in contact with soft,
acidic waters. Major industrial releases of cadmium are due to waste streams and
leaching of landfills, and from a variety of operations that involve cadmium or zinc.
In particular, cadmium can be released to drinking water from the corrosion of some
galvanized plumbing and water main pipe materials (US EPA, 2000)
2.7
Water Quality Standard
Standard is a term for an established norm or requirement. Water quality
standard refers to the standard applied and required for water quality in every water
levels such as river, lake or marine. There are numerous numbers of standards
establish for water quality whether by international or local agency. The aim of water
quality standard is to ensure that the quality and specifications of water quality in
river, lake or marine water are safe for human uses. In Malaysia, some of the
26
standard used in water quality include Water Quality Index (WQI), Interim National
Water Quality Standard (INWQS) and Interim National Marine Water Quality
Standard (INMWQS).
2.6.1
Interim National Water Quality Standard (INWQS)
Based on Interim National Water Quality Standard (INWQS) evaluation,
there are six main classes which comprise of Class I, IIA, IIB, III, IV and V. All the
classes are recommended and classified based on water quality parameters
concentration that suits the classes (refer Appendix A).Table 2.2 shows the INWQS
classification.
Table 2.2: INWQS classification (DOE, 1986)
Classes
Class I
Descriptions
Water supply I – No treatment needed (except
disinfection and boiling only)
Class IIA
Water supply II – Conventional treatment
Fishery II – sensitive aquatic species
2.6.2
Class IIB
Recreational use with body contact
Class III
Extensive treatment. Moderately tolerant species
Class IV
Irrigation
Class V
Unsuitable for specified beneficial use
Interim National Marine Water Quality Standard (INMWQS)
Interim National Marine Water Quality Standard (INMWQS) is the standard
review specifically for marine water. This standard provides the standard
concentration of selected water quality parameters regarding marine water. Table 2.3
shows INMWQS parameters and standards.
27
Table 2.3: Department of Environmental’s INMWQS parameters and standard
(DOE, 1986)
Parameters
2.6.3
Standard
E. coli (MPN/100ml)
100
Oil & Grease (mg/l)
0
Suspended Solids (mg/l)
50
Cadmium (mg/l)
0.01
Chromium (mg/l)
0.5
Lead (mg/l)
0.1
Nickel (mg/l)
0.01
Copper (mg/l)
0.1
Water Quality Index (WQI)
Water Quality Index (WQI) is specifically used to classify river water. The
index range from 0 to 100 where the values of WQI have to be calculated from six
water quality parameters. Among the six parameters are DO, BOD, COD,
ammoniacal nitrogen, suspended sediment and pH. All parameters are based on the
concentration in mg/l except for pH. These concentrations then are multiply with its
weight and calculated to get the final WQI index value. Equation 2.1 shows the
calculation for WQI. Table 2.4 shows the WQI index table with its range and degree
of pollution.
WQI = (0.22*SIDO) + (0.19*SIBOD) + (0.16*SICOD) +
(0.15*SIAN) + (0.16*SISS) + (0.12*SIPH)
Equation 2.1
where SI refers to sub index for each parameters in table (refer Appendix A).
28
Table 2.4: Water Quality Index (DOE, 1986)
2.7
WQI range
Degree of Pollution
< 31.0
Severely Polluted
31.0 – 51.9
Slightly Polluted
51.9 – 76.5
Moderate
76.5 – 92.7
Clean
>92.7
Very Clean
Asian Green Mussel, Perna viridis
The Asian Green Mussel, Perna viridis (Linnaeus, 1758) is a kind of species
from the family of Mytililidae. The classification of P.viridis is as shown in Table
2.5;
Table 2.5: Scientific classification of Perna viridis (Source: National Introduced
Marine Pest Information System (NIMPIS, 2000)
Scientific Classification
Name
Kingdom
Animalia
Phylum
Mollusca
Class
Bivalvia
SubClass
Lamellibranchia
Order
Mytiloida
Family
Mytilidae
Genus
Perna
Species
viridis (Linnaeus, 1758)
The ranges of P.viridis are from 80 to 100 mm in length which occasionally
reach 165 mm (Rajagopal, 2006). Green mussels are bivalves, which typically have
two hinged shells closed by one or two adductor muscles. A strong ligament holds
the two valves together at the hinge (Knott and De-Victor, 2007). The family
29
Mytilidae, to which this species belongs, is characterized by narrow, elliptical, fanshaped, thin valves which are of the same size (NIMPIS, 2000). The shell of P.
viridis is bright green as a juvenile, and become darker to brown with green edges as
it matures (USGS, 2008). The inner shell surface is bluish green and smooth, and the
adductor muscle is kidney shaped.
Figure 2.9: Green Mussels (P.viridis)
Sexes in this species is separated where fertilization occur externally.
Spawning occur generally twice a year between early spring and late autumn.
However, in Thailand and Philippines spawning occurs year round (Walter, 1982). In
Malaysia, spawning typically occurs twice a year which is in April and September.
Sexual maturity normally occurs at 15 to 30 mm shell length that is 2 to 3 months
age. The life cycle for P.viridis is typically 2 to 3 years (Power et al., 2004).
Although the reported native thermal range of the green mussel is broad, reduced
temperatures have been demonstrated to significantly have negatively impact on their
growth rates. The native habitat of P.viridis is the Indo-Pacific region, primarily
along Indian and southeast Asian coasts. Figure 2.10 shows the geographical
distribution of P.viridis in the world.
30
Introduced
Native
Unknown
Figure 2.10: Geographical distribution of P.viridis worldwide. (Source: National
Introduced Marine Pest Information System (NIMPIS))
The optimum habitats for green lipped mussels are within settlements with
the depth of 0 to 4 meters depth. However, it may live in a settlement up to 10 meters
depth. Within a square meter there may be a presence of 35000 mussels in the some
place. Good adaptations of green mussels make it able to survive at a broad range of
temperature (7 to 37.5ºC) and salinities for a short period. It is primarily found in
estuarine habitats with salinities optimum range from 27 to 33 ppt. P.viridis is also
able to tolerate both hyper saline condition 80 ppt to reduced salinities until 12 ppt.
Green mussels occur in environments with temperatures ranging from 10 to 35ºC and
exhibit optimal response at temperatures between 26ºC and 32ºC (Power et al.,
2004).
This species is an efficient filter feeder, feeding on small zooplankton,
phytoplankton and other suspended fine organic material. Numerous species preys on
P.viridis are crustaceans, fish, seastars and molluscs (octopus). It is also a food
source for human and aquaculture species throughout south-east Asia. P.viridis also
has ecologically impact. Ecologically, P. viridis is able to out compete many other
fouling species, causing changes in community structure and trophic relationships.
P.viridis has also been recorded with high levels of accumulated toxins and heavy
metals and is linked to shellfish poisoning in humans (NIMPIS, 2000).
31
2.7.1
P.viridis as Bioindicator and Biomonitor
Biomonitor is an animal or plant that is sensitive to changes in the
environment and thereby helps us to assess the overall health of its habitat. It
describes species that accumulate trace metals or other substances in their tissues and
therefore can be used to monitor the bioavailability of these substances in a particular
environment (Wagner and Boman, 2004). Biomonitors exclusively provide time
integrated measures of the bioavailable levels of substances (Camusso et al., 1994) a
feature that makes them superior compared to water or sediment samples. As we can
see, minor variation in the environment may lead to wide differentiation in the
concentration of water or sediments sample. To assess heavy metal contamination in
the marine environment different types of organisms may be used, such as seaweeds
and filter-feeding mollusks. Bivalve mollusks have an ability to accumulate heavy
metals to various orders of magnitude with respect to the levels found in their
environment (Rainbow et al., 2000). On the other hand, bioindicator is an organism
that functions as a signpost or calendar, indicating environmental factor such as
seasonal change or proximity of a vital resource. It gives us a single piece of
information such as the presence of water, whereas biomonitor help us to measure
long term trends.
Green mussels can be used as both biomonitor and bioindicator. Because of
green mussel is an efficient filter feeder, it has been used widely as bioindicator
species for pollutants (Sivalingam, 1977). Since the 1980’s, the best candidate for
bioindicator surveys in South-East Asia is the green-lipped mussel Perna viridis
(Phillips, 1980). Due to the wide distribution of green-lipped mussel Perna viridis in
the coastal waters of the Asia-Pacific region (Tanabe, 2000) it is also regularly used
as a biomonitoring agent for scientific purposes. Yet, there are criteria to be fulfilled
in order to be a biomonitor such as the capacity to accumulate pollutants without
being killed by the encountered levels, and sedentariness in order to be representative
of the area (Wagner and Boman, 2004). Furthermore, biomonitors should be
abundant and available in the area of study throughout the years, sufficiently longlived, tolerant of natural environmental fluctuations and pollution, and be of a
reasonable size to provide enough tissue for analysis (Yap et al., 2004a).
32
In addition, one of the major requirements is that all species to be used as
bioindicators should exhibit the same correlation in their elemental contents with
those in the surrounding marine environment namely water. The body burdens of
trace metals in most bivalves have been used to identify and map areas with
exceedingly high levels of trace metals and organic pollutants, hence they can be
used as biomonitors for aquatic environment.
2.7.2
Heavy Metal Contamination on Green Mussels (Perna viridis)
Green mussels provide a cheap source of protein for human consumption. For
P. viridis, it had been reported that there are about 60% protein in every 100 g (dry
weight) of mussel soft tissues (Choo and Ng, 1990). From the nutritional point of
view, mussel is an important food source for supplying essential trace metals such as
Ca, Fe and certain vitamins such as niacin, thiamin and riboflavin (Cheong and Lee,
1984). Moreover, fish and shellfish may also contain polyunsaturated n-3 fatty acids
which are biologically important and have been associated with a decreased risk of
cardiovascular disease (Kromhout et al., 1985; Yap et al., 2004a).
From the toxicological point of view, excessive consumption of metalcontaminated seafood may cause toxicity to humans. Since heavy metals are
inorganic chemicals that are non-biodegradable, cannot be metabolized and will not
break down into harmless forms (Kromhout et al., 1985), the measurement of levels
of metals in the soft tissues of P.viridis is becoming more significant. They could
simply accumulate through time, becoming more and more of a toxic threat as their
concentrations increase. Levels of metals above the permissible limits would
certainly create a notorious food image from the public health point of view. Chronic
exposure to heavy metals such as Cu, Pb and Zn is associated with Parkinson’s
disease and the metals might act alone or together over time to cause the disease
(Gorell et al., 1997). Some metals, such as Cd and Pb, have long been known to
accumulate within the aquatic food chain.
33
Mussels have been well known to accumulate a wide range of contaminants
in their soft tissues (Goldberg et al., 1978). Intertidal areas are natural habitats of
marine mussels and they are usually close to estuaries. Therefore, the chance of
exposure to many contaminants from land-based activities through the riverine
system as well as sea-based sources, is high (Yap et al., 2004a).
2.7.3
Studies of Heavy Metal Contaminants in Green Mussels at Johor Rivers
and Estuaries
There are a number of studies carried out regarding the level, distributions
and concentrations of heavy metals contaminant in green mussels in this area.
Previous studies reported that, the critical metals that lead to green mussel’s
contamination are Cd, Cu, Pb and Zn. Yap et el., (2003), found that the concentration
of heavy metals in these area are higher above the baseline literature of Tomlinson
Pollution Load Index (PLI). The Tomlinson Pollution Load Index (PLI) was selected
because it can be used as an index of bioavailability of heavy metals for mussels in
coastal waters (Tomlinson et al., 1980; Angula, 1996; Yap et el., 2003).
Aminah et al., (2003) reported regarding their study of the application of
green mussels as bioindicator in assessing pollution at coastal area of Peninsular
Malaysia. It can be valuable method for monitoring pollution in coastal sediment due
to some trace and heavy metal elements. The feeding habits of this species heavy
and trace elements present in the sediment makes them potential bioindicators.
P. viridis have also demonstrated to be a good bioindicator for Cr.
Based on studies done by Ibrahim (1993), marine aquatic life along South
Johor coastal areas are contaminated by many chemicals and heavy metals. It is so
relevant since most of rivers in these areas are used for wastewater and industrial
waste discharge. Moreover, the advanced and popular industrial country, Singapore
is just around the corner. It can also be one of the possibilities that contribute to
heavy metals pollution in the area.
34
2.8
Remote Sensing and Geographic Information System (GIS)
Remote sensing and geographic information system (GIS) can be powerful
tools in analyzing landuse change thus can also monitor water quality of river. The
combined use of GIS and Remote Sensing with other statistical techniques, such as
modeling offers a lot of promise in water quality analysis (Lohani et al., 2008).
Furthermore, they are also capable in monitoring the production of green mussels.
With integration in using these technologies, water quality parameters that are
important for mussel growth such as chlorophyll-a, salinity, temperature, pH and
water depth are identified and analyzed in order to monitor the mussel production
area.
2.8.1
Remote Sensing
Remote sensing is the science and art of obtaining information about an
object, area or phenomenon through the analysis of data required by a device such as
by way of aircraft, spacecraft, satellite, buoy or ship that is not in contact with the
object, area, or phenomenon under investigation (Lillesand and Kiefer, 2000). It is
parallel with the definition given by Natural Resources Canada (NRC) which defined
remote sensing as the science of acquiring information about the earth's surface
without actually being in contact with it. This is done by sensing and recording
reflected or emitted energy and processing, analyzing, and applying that information.
There are two kinds of remote sensing, which are passive and active. Passive
sensors detect natural radiation that is emitted or reflected by the object or
surrounding area being observed. Reflected sunlight is the most common source of
radiation measured by passive sensors. Examples of passive remote sensors include
film photography, infra-red, charge-coupled devices and radiometers. Active sensor,
on the other hand, emits energy in order to scan objects and areas whereupon a
passive sensor then detects and measures the radiation that is reflected or
backscattered from the target. RADAR is an example of active remote sensing where
35
the time delay between emission and return is measured, establishing the locations,
height, speed and direction of an object (Lillesand and Kiefer, 2000).
Most of remote sensing, processes involve interaction between incident
radiation and the targets of interest. This is exemplified by the use of imaging
systems where seven elements are involved (CCRS). Figure 2.11 shows the
fundamental of seven important elements in remote sensing processes (Lillesand and
Kiefer, 2000). All the seven elements are tabulated in Table 2.6.
Figure 2.11: Seven elements in remote sensing processes consisting of A is energy
source or illumination, B is radiation and the atmosphere, C is interaction with the
target, D is recording of energy by the sensor, E is transmission, reception, and
processing, F is interpretation and analysis and G is applications (source: Lillesand
and Kiefer, 2000).
36
Table 2.6: Description of seven elements in remote sensing processes (Lillesand and
Kiefer, 2000).
Elements
Descriptions
Energy source or
The first requirement for remote sensing is to have an
illumination (A)
energy
source
which
illuminates
or
provides
electromagnetic energy to the target of interest.
Radiation and the
As the energy travels from its source to the target, it will
atmosphere (B)
come in contact with and interact with the atmosphere it
passes through. This interaction may take place a second
time as the energy travels from the target to the sensor.
Interaction with the
Once the energy makes its way to the target through the
target (C)
atmosphere, it interacts with the target depending on the
properties of both the target and the radiation.
Recording of energy
After the energy has been scattered by, or emitted from the
by the sensor (D)
target, we require a sensor to collect and record the
electromagnetic radiation.
Transmission,
The energy recorded by the sensor has to be transmitted,
reception, and
often in electronic form, to a receiving and processing
processing (E)
station where the data are processed into an image in form
of hardcopy or digital.
Interpretation and
The processed image is interpreted, visually and/or
analysis (F)
digitally or electronically, to extract information about the
target which was illuminated.
Applications (G)
The final element of the remote sensing process is achieved
when we apply the information we have been able to
extract from the imagery about the target in order to better
understand it, reveal some new information, or assist in
solving a particular problem.
37
Generally, the quality of remote sensing data consists of its spatial, spectral,
radiometric and temporal resolutions as discussed below (Ranganath et al., 2007).
i.
Spatial resolution
Spatial resolution is the size of a pixel that is recorded in a raster image
typically as pixels may correspond to square areas ranging in side length from 1 to
1000 meters. Figure 2.12 (a) show different spatial resolution of satellite images.
ii.
Spectral resolution
Spectral resolution is the number of different frequency bands recorded
usually, this is equivalent to the number of sensors carried by the platforms. For
example, Landsat collection consists of seven bands, including several in the infrared spectrum. Figure 2.12 (b) show different spectral resolution of satellite images.
iii.
Radiometric resolution
Radiometric resolution is the number of different intensities of radiation the
sensor is able to distinguish. Typically, this ranges from 8 to 14 bits, corresponding
to 256 levels of the gray scale and up to 16,384 intensities or shades of color, in each
band.
iv.
Temporal resolution
Temporal resolution is the frequency of flyovers by the satellite or plane, and
is only relevant in time-series studies or those requiring an averaged or mosaic
image. Figure 2.12 (c) show different temporal resolution of satellite images.
38
(a)
(b)
(c)
Figure 2.12: Example of (a) spatial variations (b) spectral resolution and (c)
temporal resolution in remote sensing data. (Source: Ranganath et al., 2007)
Remote sensing are applied for sustainable agriculture, ocean color and
fishery, water security, environmental assessment and monitoring, disaster
monitoring and mitigation, weather and climate studies, infrastructure development
and etc (Ranganath et al., 2007).
39
2.8.1.1 Remote Sensing Data Landsat TM and ETM
The Thematic Mapper (TM) is an advanced, multispectral scanning, earth
resources sensor designed to achieve higher image resolution, sharper spectral
separation, improved geometric fidelity, and have greater radiometric accuracy and
resolution. This sensor also images a swath that is 185 km (115 miles) wide with
each pixel in a TM scene represents a 30 m x 30 m ground area, except in the case of
the far-infrared band 7. Band 7 in TM sensor uses a larger 120 m x 120 m pixel. The
TM sensor has seven bands that simultaneously record reflected or emitted radiation
from the earth's surface. Table 2.7 lists a brief description of sensor specification in
LandsatTM (Lillesand and Kiefer, 2000).
Table 2.7: Landsat satellite sensor characteristic and specification (Lillesand and
Kiefer, 2000).
Spatial Resolution
30 meters
Orbit
705 +/- 5 km (at the equator) sun-synchronous
Orbit Inclination
98.2 +/- 0.15
Orbit Period
98.9 minutes
Grounding track Repeat Cycle
16 days (233 orbits)
Resolution
15 – 90 meters
Band
1
(Blue) : 0.45 – 0 52 µm
2
(Green) : 0.52 – 0.60 µm
3
(Red) : 0.63 – 0.69 µm
4
(NIR) : 0.76 – 0.90 µm
5
(NIR) : 1.55 – 1.75 µm
6
(TIR) : 10.40 – 12.50 µm
7
(MIR) : 2.08 – 2.35 µm
Landsat Enhanced Thematic Mapper (ETM) stresses the provision of data
continuity with Landasat 5-TM. The characteristics and number of bands are similar
to Landsat TM. However, Landsat ETM requires advanced one bands with make it 8
bands with addition of panchromatic bands. The wavelength of this band is 0.52 -
40
0.90 µm with 15 m resolution. Table 2.8 show the 7 bands description with addition
of 8th band laid in Landsat ETM.
Table 2.8: Landsat’s band descriptions (Lillesand and Kiefer, 2000).
Band
Descriptions
Band 1
Band 1 is in the visible portion of the electromagnetic spectrum that
corresponds with blue-green light. The principal applications are for
coastal
mapping,
differentiation
of
soil/vegetation
or
deciduous/coniferous.
Band 2
Band 2 is in the visible portion of the electromagnetic spectrum that
corresponds with green light. Green reflectance is from healthy
vegetation.
Band 3
Band 3 is in the visible portion of the electromagnetic spectrum that
corresponds with red light. This is used to show chlorophyll absorption
for plant species differentiation.
Band 4
Band 4 is in the Near Infrared (NIR) portion of the electromagnetic
spectrum. This form of radiation is reflected to a high degree off leafy
vegetation, since chlorophyll has a high albedo in this band. It is used for
biomass surveys and water body delineation.
Band 5
Band 5 is in the Middle Infrared (Mid-IR) portion of the electromagnetic
spectrum. This portion of the spectrum is sensitive to variations in water
content in both leafy vegetation and soil moisture.
Band 6
Band 6 is in the Thermal Infrared portion of the electromagnetic
spectrum. Thermal infrared is radiation that is detected as heat energy;
therefore, the thermal IR band effectively measures the temperature of the
surfaces it scans. Using this band, researchers can discriminate and help
assist with drought planning, flood forecasting, and agricultural
assessment.
Band 7
Band 7 is in the Middle Infrared (Mid-IR) portion of the electromagnetic
spectrum. This portion of the electromagnetic spectrum is sensitive to
moisture and thus responds to the moisture contents in soils and
vegetation. Hydrothermal mapping can detect plant stress.
Band 8
This band is used to merge with other spectral band to enhance the spatial
resolution of the data.
41
2.8.2
Geographic Information System (GIS)
GIS is a computer system capable of capturing, storing, analyzing, and
displaying geographically referenced information that is data identified according to
location. Practitioners also define GIS as a system that includes procedures,
operating personnel and spatial data that go into the system (USGS, 2007).
Ranganath et al. (2007) defined GIS as a computer assisted system for capture,
storage, retrieval, analysis and display of spatial data and non-spatial attribute data.
Analysis models comprise of simple user defined views to complex stochastic
models. Some of these are reclassifications, aggregation, overlays, suitability
analysis, network and route analysis, optimization, allocation or sitting.
The power of a GIS comes from the ability to relate different information in a
spatial context and to reach a conclusion about this relationship. Most of the
information in the world contains a location reference and placing that information at
some point on the globe. Applications of GIS range from simple database query
systems to complex analysis and decision support systems.
GIS techniques are playing an increasing role in facilitating integration of
multi-layer spatial information with statistical attribute data to arrive at alternate
developmental scenarios. A GIS makes it possible to link, or integrate, information
that is difficult to associate through any other means. Thus, a GIS can use
combinations of mapped variables to build and analyze new variables. Figure 2.13
shows the capability of GIS in data integration.
42
Figure 2.13: Data integration from different forms of input through GIS. (Source:
USGS, 2007)
A critical component of GIS is its ability to produce graphics on the screen or
on paper to convey the results of analyses to people who make decisions on
resources. Wall maps, interactive maps and other graphics can be generated, which
allow the decision makers to visualize and thereby understand the results of analyses
or simulations of potential events.
2.9
Integration of Remote Sensing and GIS
Developments of remote sensing and GIS technologies have lead to the
betterment of mapping and interpretation techniques as a means of understanding and
effectively managing the present resources for sustainability. Remote sensing is a
powerful technique for surveying, mapping and monitoring earth resources. This
technique has become indispensable and increasingly more meaningful because of
the synoptic coverage of satellites over large areas rendering its cost and time
effectiveness. Furthermore, in areas that are difficult to access, this technique is
perhaps the only method of obtaining the required data more affectivity. In addition
of GIS, the analysis of mechanisms of landuse pattern changes and monitoring plays
an important role in not only forecasting changes but also formulating local
development policies (Lillesand and Kiefer, 2000).
43
2.9.1
Landuse Monitoring using satellite data
Landuse refers to the purpose the land serves for example recreation, wildlife
habitat or agriculture. One of the important applications of remote sensing is the
generation of landuse and landcover maps from satellite imagery. Compared to more
traditional mapping approaches such as terrestrial survey and basic aerial photointerpretation, land-use mapping using satellite imagery has the advantages of low
cost, large area coverage, repetitively, and computability (Franklin, 2001).
Consequently, land-use information products obtained from satellite imagery such as
land-use maps, data and GIS layers have become an essential tool in many
operational programs involving land resource management.
Remote sensing application has very powerful tools in accessing landuse
information. By the use of remotely sensed data, information of landuse are available
for various times, resolutions and ranging from general to specifically landuse types
based on the application needed. There is a number of researches done in extracting
landuse information using various multispectral satellite images such as Landsat TM,
SPOT, IKONOS, aerial photograph and so on. It have been proved that the use of
this application give numerous benefits and advantages.
Remotely sensed data are able to provide time series landuse information
which is important in monitoring aspect. This is the main advantage of satellite data
which offer large range of landuse monitoring. Beside, the resolutions of the data
range from small, fine and to the higher ones. Users or researchers can choose
suitable data based on the application of landuse itself. Where else, the information
generate can also be decided by user whether it is generally classified of specifically
classified landuse information.
Landuse studies using remote sensing data have received immense attention
worldwide due to their importance in global climate change analysis (Cihlar, 2000).
Both human-induced and natural land cover changes can influence global change
because of its interaction with terrestrial ecosystem (Miller and Pool, 2002),
44
biodiversity (Sala et al., 2000) and landscape ecology (Reid et al., 2000). In addition,
it reflects the impacts of humans on environment at various temporal and spatial
scales (Lopez et al., 2001). Therefore, accurate and up-to-date landuse information
is essential for environmental planning to understand the impact on terrestrial
ecosystem and to achieve sustainable development.
2.9.2
Landuse Change Detection
Change detection is the process that helps in determining the changes
associated with landuse and landcover properties with reference to geo-registered
multi temporal remote sensing data. Landuse and landcover change can play a vital
role in environmental changes and contribute to global change (Meyer and Turner,
1991; Dale, 1997). Beside that, the study of the landuse patterns and the monitoring
of changes are also very important for economic planning and country development.
(Lwin and Ryosuke, 1997). The basis of using remote sensing data for change
detection is that changes in landuse result in changes in radiance values which can be
remotely sensed. Techniques to perform change detection with satellite imagery have
become numerous as a result of increasing versatility in manipulating digital data and
increasing computer power.
Landcover can be altered by forces other than anthropogenic. Natural events
such as weather, flooding, fire, climate fluctuations and ecosystem dynamics may
also initiate modifications on land cover. Globally, landcover today is altered
principally by direct human use which is by agriculture and livestock raising, forest
harvesting and management and urban and suburban construction and development.
There are also incidental impacts on land cover from other human activities such as
forest and lakes damaged by acid rain from fossil fuel combustion and crops near
cities damaged by tropospheric ozone resulting from automobile exhaust (Meyer,
1995).
CHAPTER 3
METHODOLOGY
3.1
Introduction
This chapter explains the methodology and steps taken in this study. It can be
divided into four main tasks. The methodology starts with collection of information
from primary sources and secondary sources (literature review). The second task is
data collection and sampling including primary data and historical data. The third
task is sampling processes and laboratory test. In this stage, processes consist of the
collection of water quality parameters and green mussel and laboratory analysis. The
final task involves process of integrated remote sensing and GIS technology
application on water quality along study area. This process will be carried out using
several remote sensing and GIS software.
3.2
Literature Review
Methodology starts with gathering and collection of information related with
this study in order to understand the processes and methodology that will be used.
The reviews is based on primary and secondary sources such as journal articles and
previous dissertations, textbooks and articles from Department of Fisheries (DOF)
Malaysia, Department of Environment (DOE) or other related departments,
newspapers and internet to find the relevant background information.
46
3.3
Data Collection and Sampling
Data collection involves the collection of primary and secondary data that
were used in this study. Primary data includes the data from sampling process,
laboratory works and satellite images. Secondary data consist of historical water
quality data from various years gathered from journals, dissertations and research
conducted at the study area. Additional information on green mussels, water quality
and landuse were also gathered from related Departments. Table 3.1 shows data
collection and sampling.
Table 3.1: Data collection and sampling
Data
Source
Year
Satellite Images
MACRES,
1991,2000,2005
Landsat.org/TRFIC
2008
Sampling
Present
Journals, Dissertations, Research
1991- 2008
Water Quality
Reports, DOE
Sampling
Present
Journals, Research Reports
1991- 2008
Landuse Map
JUPEM
1991
Topography Map
JUPEM
2002
Green Mussels
3.4
-
Edisi 2-PPNM Sheet 175
-
Edisi 2-PPNM Sheet 174
Sampling Processes and Laboratory Works
Sampling process entail the data collection for water quality parameters
includes in-situ and laboratory test. Several considerations in this process are
47
sampling stations, sampling techniques and laboratory analysis for water quality
parameters.
Figure 3.1: Flowchart of sampling and laboratory works
3.4.1
Study Area and Sampling Stations
This study was carried out along the coastal and estuaries area starting from
Danga Bay to Second Link Bridge. The area involved is 11 km with total eleven
sampling stations and four mussels collection stations. Eleven sampling stations were
identified along Danga to Pendas coastal area starting from Sungai Skudai estuaries
and ends at Tanjung Kupang which is near to Second Link Johor-Singapore Bridge.
Figure 3.2 to 3.6 shows the location of study area with the sampling points followed
by Table 3.2 showing the sampling stations coordinates.
48
S1 Water Quality Sampling Points
M1 Mussels Sampling Points
Johor Bahru
Singapore
Figure 3.2: Location of study area with eleven sampling stations.
Table 3.2: Coordinates of the eleven sampling stations at the study area.
Sampling Stations
1
Sungai Skudai Estuaries
2
3
Sungai Danga Estuaries
4
5
Sungai Melayu Estuaries
6
Coordinate
Code
N 01 28.265
E103 43.039
S1
N 01 28.011
E103 43.150
S2
N 01 28.108
E103 43.433
S3
N 01 27.662
E103 43.855
S4
N 01 27.454
E103 43.459
S5
N 01 27.328
E103 41.719
S6
7
Nusajaya
N 01 27.574
E103 41.308
S7
8
Tebing Runtuh
N 01 26.825
E103 41.603
S8
9
N 01 26 348
E103 40.640
S9
10 Tanjung Kupang
N 01 24.544
E103 39.440
S10
11 Second Link (Tanjung
N 01 22.225
E103 38.139
S11
Kupang)
49
Figure 3.3: Sampling station S2 at Sungai Danga estuary.
(a)
(b)
Figure 3.4: Sampling station M2 for mussels collection station near Sungai Melayu
estuaries.
50
Figure 3.5: Sampling stations M3 for mussel’s collection
Figure 3.6: Sampling station M4 and S9 near Nusajaya area
3.4.2
Sampling Frequency and Parameters Analyzed
Sampling were carried out six times for a period of 6 months from November
2008 to April 2009 (refer Table 3.3). The sampling strategy incorporated frequent
collections in order to adequately define transient changes in the concentration of the
constituents being measured. Each time sampling commenced from the upper
51
reaches of the river and estuaries at Sungai Skudai (S1) and Sungai Danga up to the
Sungai Pendas (S11) nearest to Second Link Bridge.
Table 3.3: Date of sampling
Sampling Month
Date
Samples
November 2008
20th November 2008
Water and Mussels
December 2008
12th December 2008
Water and Mussels
th
January 2009
14 January 2009
Water and Mussels
February 2009
18th February 2009
Water and Mussels
March 2009
21st March 2009
Water and Mussels
April 2009
25th April 2009
Water and Mussels
A total of 12 water quality parameters were studied involving physical,
nutrient and heavy metals. The parameters measured are dissolved oxygen (DO), pH,
temperature, salinity, biochemical oxygen demand (BOD), chemical oxygen demand
(COD), total dissolved solids (TDS), ammoniacal nitrogen and several heavy metals
parameters such as lead (Pb), cadmium (Cd), nickel (Ni) and zinc (Zn).
3.4.3
Sampling Techniques
Grab sampling method was adopted in this study. Location of each sampling
points were affirmed using Global Positioning System (GPS). Water was sampled at
all station however mussels were only sampled at selected stations. In this aspect,
water quality parameters and green mussels were taken for samples.
In water sampling for water quality analysis, parameters such as DO, pH,
salinity and temperature were recorded at each station. Other basic water quality
parameters such as TDS, BOD, COD were analyzed in the laboratory. Heavy metal
parameters
were
also
sampled
and
analyze
by
Atomic
Absorption
52
Spectrophotometer. Water sample was acidified to pH less than 2 with concentrated
nitric acid.
Wild mussels were also collected from selected areas nearest to the water
collection points. Green mussels obtained were aqua-cultured green-mussels. A
random number of green mussels of several sizes were collected. The different sizes
of green mussels then were grouped according to their estimated age. All mussels
were digested before analyzing for nutrients and heavy metals. Green mussels then
were weighed and digested using wet basis method.
Polyethylene sampling bottle 1 liter was used for water samples whereas
mussels were transported in polyethylene bags. Generally, all polyethylene and
glassware used in the experiment was pre-cleaned and rinsed with double deionised
distilled water (DDDW) to remove any possible trace of contaminants prior to use.
3.4.4
Laboratory Analysis
Laboratory analysis were carried out for both water and mussel samples.
Water samples were analyzed for its physical parameter and nutrients. Both samples
were analyzed for heavy metals in order to evaluate the precedence of the metals in
both water and mussels.
3.4.4.3 Sample Pretreatment
Before both samples were analyzed in laboratory test, they need to be treated
in order to be able for them be analyzed in the lab.
53
i)
Mussels Pre-Treatment
Collected P.viridis was group according to size and before mussels are
shucked, the external shell surface was thoroughly cleaned with brush and water to
remove all sand and dirt, adhering to the shell to prevent contamination of composite
samples. It was then thawed and shucked where the flesh was collected in a clean
dish and homogenized by mixing. Green mussels (2g) were weighed and digested
using wet basis. Green mussels were digested using a modified reflux system by
digesting it overnight using hot plate. Digested samples were then filtered and diluted
using DDDW to ensure the acid in the sample is less than 5%. Digestion method
adopted from APHA 3030E which is a nitric acid digestion method (refer Appendix
B).
3.4.4.4 Laboratory Test for BOD, COD and Ammoniacal nitrogen
Parameters such as DO, pH, salinity and temperature were recorded at each
station using YSI model 556 MPS multi probe system. Analysis for COD and
nutrient were carried out in the laboratory using standard procedures. Figure 3.7
shows the instruments used for analysis.
i)
Biochemical Oxygen Demand (BOD) measurement
The concentration of BOD was determined based on the Standard Method
APHA 5210-B procedures (Appendix B).
ii)
Chemical Oxygen Demand (COD) measurement
The concentration of COD was determined based on the Standard Method
APHA 5220-C procedures (Appendix B).
iii)
Ammoniacal nitrogen (AN) measurement
The concentration of AN was determined based on the Standard Method
APHA 4500-NH3 procedures using HACH DR 5000 Spectrophotometer (Appendix
B).
54
(a)
(b)
Figure 3.7: (a) YSI-USA multiparameter probe (b) HACH DR 5000
Spectrophotometer for water quality analysis.
3.4.4.5 Laboratory Analysis for Heavy Metals
Heavy metals were determined using Flame Atomic Absorption Spectrometer
(Perkin Elmer model Analyst 400) (Figure 3.8). Perkin Elmer HGA 900 Graphite
Furnace Atomic Absorption Spectrometer was used in analyzing arsenic. Shimadzu
analytical balance and GQ calibrated volumetric flasks were used for preparation of
all metal standard stock solution.
Figure 3.8: Perkin Elmer model Analyst 400 Atomic Absorption Spectrometer for
heavy metals extraction and analysis.
Analysis was done using air – acetylene flame atomic absorption
spectroscopy for all metals. Operating condition for each metal are shown in Table
3.4.
55
Table 3.4: Recommended condition for Flame Atomic Absorption Spectrometer
Perkin Elmer Analyst 400
Metal
Wavelength,
nm
Slit width,
nm
Characteristic
Concentration Check
Lead
283.30
0.7
20.0
Nickel
232.00
0.2
7.0
Zinc
213.86
0.7
1.0
Cadmium
228.80
0.7
1.5
The concentrations of metals in the samples were determined by referring to
standards calibration curve which was prepared in standards preparation (refer
Appendix B)
3.5
Integrated Remote Sensing and GIS
Processing of remote sensing data was carried out by integrating both remote
sensing and GIS. Remote Sensing and GIS can be a powerful tool in analyzing
landuse change thus can also monitor water quality of the river. The combined use of
GIS and Remote Sensing with other statistical techniques, such as modeling offers
much promise in water quality analysis (Lohani, 2008). Satellite imageries and
historical water quality data were used in analyzing the Western Johor estuarine
system due to landuse effects. In this part, it requires the used of some remote
sensing and GIS software such as Erdas Imagine, ArcGIS and ArcView also the used
of satellite images such as Landsat TM and ETM.
3.5.1
Remote Sensing Data, Landsat TM and ETM
Remote sensing data for various years were gathered from Malaysia Remote
Sensing Agency (MACRES). Various year data were used to see the landuse changes
56
at the study area. Landsat TM and ETM were used as the main remote sensing data
in this study. Landsat TM and ETM images gathered from MACRES are from year
1991 to 2008.
Landsat TM and ETM are chosen because of the advantages of this data in
large spatial resolution of 30m which can cater the study area with fine spectral
resolution. It has seven bands which can cater the analysis from ground which was
used for landuse extraction and water for analysis of water quality. Figure 3.9 shows
scenes of Landsat satellite images from 1991 to 2008. Table 3.5 shows Landsat TM
classification.
(a)
(b)
(c)
(d)
Figure 3.9: Landsat TM and ETM images for (a)1991 (b)2000 (c) 2005 (d) 2008
57
Table 3.5: Landsat TM Specifications
1991
2000
2005
2008
Landsat-5
Landsat-7
Landsat-5
Landsat-7 ETM
TM
ETM
TM
125/59
125/59
125/59
125/59
11 March
28 April
28 January
24 February 2008
1991
2000
2005
Layer
6 layers
6 layers
6 layers
6 layers
Pixel Size
30.0 m
30.0 m
30.0 m
30.0 m
Data Type
Unsigned-
Unsigned-
Unsigned-
Unsigned-8bit
8bit
8bit
8bit
Format
GeoTIFF
GeoTIFF
GeoTIFF
GeoTIFF
Source
MACRES
MACRES
MACRES
Landsat.org/TRFIC
Specifications
Satellite
Path/Row
Date
3.5.2
Landuse Extraction from Satellite Imagery
For extraction of landuse, processing of satellite images was carried out in
several stages. Each image goes through several processing such as geometric
correction, atmospheric correction, subset to particular coordinate and ROI selection
before obtaining landuse information image. Figure 3.10 display the flowchart for
landuse classification.
58
Figure 3.10: Flowchart of Landuse Classification
3.5.2.1 Geometric Correction
Geometric correction was used to correct the geographical location and
projection of the images to be same as on the earth surface. All the raw remote
sensing data, contains geometry effect that been produced by earth curve,
atmospheric effects and panoramic effects. The main reason for performing
geometric correction is to correct this effect so that the image will be rectified in a
proper way. Geometric correction involves the changes of the original data for
preprocessing by considering the position pixel and system parameter such as
number of ground control points (GCP) and type of interpolation method (Jensen,
1996). It is also known as rectification process.
Both satellite images need to be rectified to the real coordinate as stated in the
Topographic Map. Topographic Map in year 2002 was used as the base map in
running the process. In order to rectify the images, several similar points (GCP) from
59
both image and map were identified. Coordinates of each point that has been
identified from the image were then replaced by the corrected coordinate from the
map. The images were then resampled based on polynomials to the preferred
projection. In processing data in this study, type of projection used for all the satellite
images are RSO Malaya Meters. The details specification of this projection is shown
in Table 3.6.
Table 3.6: Projection specifications
Projection
Rectified Skew Orthomorphic (RSO) Natural Origin
False-Easting
804671.299775
False-Northing
0.00000
Scale Factor
-0.999840
Azimuth
-36.974209
Origin Longitude
102.250
Origin Latitude
4.00
Linear Unit
Meter
Datum
Kertau
3.5.2.2 Atmospheric Correction and Masking
The next step to be taken is atmospheric correction. Atmospheric correction
was used to reduce atmospheric effects in images since these images are taken from
the outer space. The objective of atmospheric correction is to retrieve the surface
reflectance that characterizes the surface properties from remotely sensed imagery by
removing the atmospheric effects (Jensen, 1996). Atmospheric correction has been
shown to significantly improve the accuracy of image classification. Then the heavy
cloud should be mask out so that it will not appear and affect the digital values (DN)
for further processing. In this process, the DN values for cloud were identified by the
infra-red band. Infra-red band (band 4) was used because it can be the best band
which can differentiate clearly cloud with other features in the image. The values
were then run in a modeler maker, which is one of the Erdas Imagine software
function used to calculate and model the data.
60
3.5.2.3 Subset
Due to the large size of Landsat images, it was discovered that the best way
to serve the images and data on study area was to take subsets of the larger image
swaths taken by the Landsat satellite. All the images then were subset only to the
area required for this study.
Each image was subset to the designated subset
coordinate as shown in Table 3.7.
Table 3.7: Coordinates for image subset.
Upper Left X
618545.00
Upper Left Y
170471.00
Lower Right X
645965.00
Lower Right Y
146711.00
3.5.2.4 Image Classification
Region of Interest (ROI) are the process selection for the representative pixels
from each landuse classes. Selection of ROI is the main important part before further
classification process. Six classes were developed which consist of mangrove, urban
and development area, cultivation area, forest or scrubs, open area and water bodies.
Classification processes were then carried out to obtain landuse information
from various years of data. The type of classification algorithm used in this study is
Maximum Likelihood. The Maximum Likelihood decision rule assigns each pixel
having pattern measurement or features X to the class c whose units most probable or
likely to have given rise to feature vector x (Jensen, 1996). In this process, each pixel
of the images were determined or represented by landuse types such as water bodies,
ground, forest, urban and so on. It is also considers that each statistic for ROI for
each class which is normally distributed.
61
To get the classification probability of pixel which represent class i, and it
has vector feature f, then the probability for this class p (i|f) can be measured using
Bayes condition (Schowengerdt, 1997).
P (i|f) = p (f|i) p (i)
P (f)
Where ;
(Equation 3.1)
P (f) = ∑ p (f|i) p (i)
Conditional result is based on the probability on the posterior state as
Equation 3.1. Then pixel is classified to the class if the posterior probability is higher
than other classes.
If p (f|i) > p (j|f), for all j≠i
Then pixel is classified as class i.
The results then were verified by accuracy assessment for classification.
Accuracy assessment is important whenever it is required to validate the derived or
measured parameters compared to the ground truth data. Type of assessment done
was error matrix consist of producer’s and user’s accuracy, overall accuracy and
kappa statistics. Producer’s accuracy corresponds to error of omission (exclusion)
while user’s accuracy refers to error of commission (inclusion). Kappa statistic is a
measure of the proportional or percentage improvement by the classifier over a
purely random assignment to classes.
3.5.3
Landuse Change Detection
For change detection process, both landuse classification images chosen for
changes that have been produced were used. It is important to take notes that, both
images need to be registered to the same projection which enable them to be
62
processed correctly. The process was done using the designated features for change
detection using post-classification comparison image.
Post-classification comparison change detection method is the most
commonly used quantitative method. It requires rectification and classification of
each remotely sensed image. These two maps are then compared on pixel-by-pixel
basis using a change detection matrix (Jensen, 1996). This kind of method is easy to
understand. The advantage include the detailed from-to (before to after) information
that can be extracted and the fact that the classification map for the next base year is
already completed. However, the accuracy of the change is dependent on the
accuracy of the two separate classifications that are required (Jensen, 1996). Figure
3.11 shows the flowchart of the landuse change detection.
Figure 3.11: Flowchart of Landuse Change Detection.
63
3.6
Flowchart of Methodology
The overall flowchart of methodology is shown in Figure 3.12.
Figure 3.12: Overall flowchart of methodology
CHAPTER 4
EFFECT OF LANDUSE ON WATER QUALITY
4.1
Introduction
This chapter shows and explains the results obtained from the works carried
out on the effects of landuse to water quality along Danga-Pendas coastal area. The
results can be classified to several phase of output such as landuse classification
using image processing for year 1991 and 2008, the analysis of the landuse change
and water quality from 1991, 2005 and 2009 and also analysis of difference in water
quality due to landuse change. The final output included in this chapter are landuse
maps for 1991 to 2008, statistic and analysis of landuse change, water quality and
analysis of changes in water quality due to landuse change.
4.2
Landuse Classification
Landuse classification process were carried out for year 1991, 2000, 2005 and
2008 which are considered to be relevant changes to landuse along Danga-Pendas
area. After several image processing such as geometric correction, atmospheric
correction, subset and classification were done, landuse maps for each significant
year for this area were obtained. The landuse image was classified into six types of
major landuse classes which are mangrove area, urban and developed area, open
area, forest or scrubs area, cultivation and water bodies. Landuse classes were chosen
65
based on the most relevant and main landuse covered.
Two standard criteria were used to assess the accuracy of the classifications
which consist of overall accuracy and kappa statistics. The overall accuracy was
defined as the total number of correctly classified pixels divided by the total number
of reference pixels (total number of sample points) (Rogan et al., 2002). On the other
hand, kappa coefficient was defined as a statistical measure of accuracy that ranges
between 0 and 1, it measures how much better the classification is compared to
randomly assigning class values to each pixel (Miller and Pool, 2002).
4.2.1
Landuse Classification for 1991
Figure 4.1 shows the landuse classification map that has been derived from
image classification process using Erdas Imagine software. As we can observe from
the figure, most of the developed areas are distributed at the main area of Johor
Bahru and part of Singapore area (brown color). Whereas the majority of Johor areas
are covered with cultivation area. Cultivation area consists of oil palm and rubber
plantation area where there are also some mix plants. Open area can be seen at
several parts in urban area. Along Danga-Pendas coastal area, mangrove and scrubs
are the main landuse types present in 1991. Only several parts in Danga coastal area
are developed within the town and residential area.
66
Figure 4.1: Landuse classification for 1991
In order to validate the accuracy of landuse classification that has been
obtained from the processing, assessment of the map was carried out. By the used of
accuracy assessment for classification, overall classification accuracy obtained was
80 % with overall kappa statistics of 0.6918. A detail on accuracy assessment is
shown on Appendix C (i).
4.2.2
Landuse Classification for 2000
Figure 4.2 represent landuse map for year 2000. The same landuse types or
classes were developed. In comparison to year 1991 landuse map, major changes
have been observed especially in urban/ developed, open area and scrubs. There are
lines of new developed road, open area for developing region and the main
difference is the development of Second Link Bridge in Pendas. Overall accuracy for
2000 landuse classification was 76.47% with kappa statistic of 0.6699. A detail on
accuracy assessment is shown on Appendix C (ii).
67
Second-Link Bridge
Figure 4.2: Landuse classification for 2000
4.2.3
Landuse Classification for 2005
Result for landuse classification for 2005, display a dense urban/ developed
area compared to open area as shown in Figure 4.3. Most of the open area that
existed in year 2000, have been change into developed area. As illustrated in the
figure, Johor Bahru areas are more intense with development where most of the areas
are brown in color. Overall accuracy for year 2005 landuse classification was
calculated as 82.35% with kappa statistic of 0.7560. A detail on accuracy assessment
is shown on Appendix C (iii).
68
Figure 4.3: Landuse classification for 2005
4.2.4
Landuse Classification of year 2008
Figure 4.4 illustrates the landuse for latest image captured in 2008 for Johor
Bahru area. The Landsat image for 2008 has some interference with stripping line
which consists of no data. This problem is due to an error in the detector or scanner
of the satellite which caused the detector or scanner unable to detect and scan the
ground for specific lines. This however did not affect other scanlines where they can
still be processes to obtain landuse map. Besides lacking of data in some parts, the
image was also heavy covered and distributed with cloud. Although the clouds have
been masked and detected as unclassified, their appearance still gives some effect to
the landuse information.
From the figure, there are much more difference in landuse compared to year
2005. The obvious IDR development region which is in progress can be seen clearly
as an open area in this figure. The region has been classified as an open area because
69
it is still under the progress which consists of site clearing, earthwork, demolition and
also development of new buildings that requires the area to be cleared in open area.
Figure 4.4: Landuse classification for 2008
Overall accuracy for 2008 landuse classification was 81.25% with kappa
statistic of 0.7348. The result of accuracy assessment obtained was quite good. It
represents that more than 80% of the image have been correctly classified as the
actual landuse types as in ground. Detail on accuracy assessment report is enclosed
in Appendix C (iv).
4.3
Landuse Change Detection
Digital change detection is the process helps in determining the changes
associated with landuse and landcover properties with reference to geo-registered
multi temporal remote sensing data (Yacouba et al., 2009). The application of
landuse change using remote sensing data and techniques have been used in
worldwide and become as one of the valuable method in monitoring the landuse
70
changes. From the applications of remote sensing, results were obtained in two parts
which consist of image changes and statistical changes. Image changes will illustrate
the change in landuse against years in term of graphics. Generally, results of image
change can be seen visually. This process was carried out using remote sensing
software. While on the other hand, statistical changes shows more specifically the
measured information for each landuse class in terms of area and percentages
differences. The determination statistical changes were carried out using GIS
software, ArcView 3.2.
4.3.1
Differential in 1991 to 2008 images
In the initial process, we need to figure out in general the changes that have
taken place along Danga-Pendas in a long time frame. Figure 4.5 shows the
differentiation series of landuse information from 1991 to 2008. As illustrated in the
images, there are an obvious changes on landuse that can be monitored. In early
1991, the urban/ developed areas are not as dense as developed areas in 2005 and
2008. Furthermore, the open area that existed in 1991 became lesser in late 2000.
This is because most of the open areas have been developed as urban areas. Beside
that, some of the mangroves that existed in the estuaries in 1991 become lesser and
lesser in 2000, 2005 and 2008.
71
1991
2000
(a)
(b)
2005
2008
(c)
(d)
Figure 4.5: Series of landuse types for year (a) 1991 (b) 2000 (c) 2005 and (d) 2008
at Johor Bahru city center.
Along Sungai Danga and Sungai Skudai estuaries, there are variations of
landuse changes that can be observed. From Figure 4.6, most of the mangrove area
that was present in 1991 becomes lesser in 2005 and 2008. The area was also
recovered by scrubs and urban/ developed area. On the other hand, clear water bodies
observed in 1991 were less with the presence of boats and marine water activities
such as aquaculture, fishing and boating. In addition, there were no particular land
lines that can be observed in Sungai Skudai estuary. It can be clearly observed
starting from year 2000 to 2008. These circumstances could be due to the
embankment of the bay for development.
72
1991
(a)
(b)
2005
(c)
2000
2008
(d)
Figure 4.6: Series of landuse information at Sungai Danga and Sungai Skudai
estuaries for year (a) 1991 (b) 2000 (c) 2005 and (d) 2008.
At other parts near Tebing Runtuh and Nusajaya, there were obvious changes
on landuse from 1991 to 2008 (refer Figure 4.7). The initial scrubs and cultivation
landuse types of the area in 1991 have been developed to open area in 2000 and
become built up in 2005. In 2008, the area received rapid changes due to the
development of IDR. From Figure 2.8 in 2008, it can be seen clearly the open area of
IDR development which is still in construction progress.
73
1991
2000
(a)
(b)
2005
2008
(c)
(d)
Figure 4.7: Series of landuse information at Nusajaya for year (a) 1991 (b) 2000 (c)
2005 and (d) 2008.
Danga to Pendas area, which is located at the end of the study area covers the
Sungai Pendas estuary. The obvious growth of the area is due to the development of
Second-Link Bridge which connects Malaysia and Singapore. The expansion of the
bridge gives numerous changes to the surrounding landuse of the area. First, there
were slightly build-up areas in earlier 1991 and 2000 (refer Figure 4.8). However it
can be seen undoubtedly that there were some developments through out this area.
Nevertheless, the mangrove land area still exists
74
1991
2000
(a)
(b)
2005
2008
(c)
(d)
Figure 4.8: Landuse information at Sungai Pendas coastal area for year (a)1991 (b)
2000 (c) 2005 and (d) 2008.
4.3.2
Statistical Differences of Landuse Changes
Landuse changes can also be evaluated using statistical approach where each
of the landuse type was analyzed in order to see difference of area between year. The
results were obtained in area of changes (ha) and the initial area (ha) for each class
for each year. Results for four years of landuse change is represented on Table 4.1.
For further studies, only significant year were considered such as year 1991, 2005
and 2008 where statistical landuse changes were analyzed. Comparison between
before and after 2005 to 2008 image is very important since the development of IDR
region began in 2006.
75
Table 4.1: Overall statistical landuse for 1991, 2000, 2005 and 2008 along DangaPendas area including Singapore
Class
Landuse Area (ha)
1991
2000
2005
2009
Mangrove
2 311.92
2 596.68
2 273.67
1 371.78
Water Body
7 055.01
7 322.22
7 473.51
5 027.94
17 878.50
21 257.28
22 103.46
28 713.51
4 235.40
9 560.43
9 121.50
4 209.51
30 742.20
23 611.05
21 407.13
20 862.57
2 923.74
4 816.71
2 923.74
3 479.04
Urban/Developed
Forest/Scrubs
Cultivation Area
Open Area
4.3.2.1 Statistical Changes Report for 1991 and 2005
There are major landuse difference obtained between 1991 and 2005. From
Table 4.2, cultivation area experienced major changes with a total of 9,335.07 ha
(30.36%) followed by urban/ developed at 4,224.96 ha (23.63%) and forest/scrubs at
4,886.07 ha (15.36%). Image difference represents the changes of landuse types
whether it more or less than before. The negative sign means that the areas are
reduced while the positive represent increase in the area. Based on the table,
mangrove, open area and cultivation types become lesser in 2005 compare to 1991.
While scrubs and developed types have greatly increased. This may be due to
openings for cultivation and open area for new development to become urban area.
76
Table 4.2: Statistical changes based on landuse types between 2005 and 1991 along
Danga-Pendas area including Singapore
Class
Changes 1991-2005
1991 (ha)
Mangrove
Water Body
Urban/Developed
Forest/Scrubs
Cultivation Area
Open Area
2005 (ha) Difference (ha) Difference (%)
2 311.92
2 273.67
- 38.25
- 1.65
7 055.01
7 473.51
418.50
5.93
17 878.50
22 103.46
4 224.96
23.63
4 235.40
9 121.50
4 886.10
15.36
30 742.20
21 407.13
- 9 335.07
- 30.36
2 923.74
2 923.74
-156.78
- 5.36
Note :
+ increase in area
- reduction in area
4.3.2.2 Statistical Changes Report for 2005 and 2008
The landuse changes between 2005 and 2008 are significant to the
development of IDR. The IDR project region was set up and initiated in 2006. As
mentioned and illustrated in Figure 4.7, there are major differences in the image with
the opening of the new area. Review based on Table 4.3, shows that the open area
and urban/developed become the largest changes compared to other landuse types.
Open area with an increase of 553.30 ha (18.99%) is very significant since there are
many parts of Danga-Pendas area opened for new development or constructions
based on IDR plan. Urban/ developed have also been recorded as major increased
with 6,610.05 ha (29.91%) since progress of development due to IDR and the
expansion of Johor town such as Danga Bay. Scrubs shows a large reduction with
4,912.47 ha (53.86%) compared to other landuse types followed by mangrove with
901.89 ha (39.66%), water body at 2,445.57 ha (32.72%), and cultivation area with
554.56 ha (2.50%).
77
Table 4.3: Statistical changes based on landuse types between 2005 and 2008 along
Danga-Pendas area including Singapore.
Class
Changes 2005-2008
2005 (ha)
Mangrove
Water Body
Urban/Developed
Forest/Scrubs
Cultivation Area
Open Area
2008 (ha) Difference (ha) Difference (%)
2 273.67
1 371.78
-901.89
- 39.66
7 473.51
5 027.94
-2 445.57
- 32.72
22 103.46
28 713.51
6 610.05
29.91
9 121.50
4 209.51
-4 912.47
- 53.86
21 407.13
20 862.57
-544.56
- 2.50
2 923.74
3 479.04
555.30
18.99
Note :
+ increase in area
- reduction in area
4.4
Water Quality Analysis
Water quality along Danga-Pendas coastal area was obtained from samplings
that were carried out from November 2008 to April 2009. The six months sampling
were conducted to see the variation of water quality parameters due to sampling
stations. Eleven sampling stations were analyzed starting from the upper Danga
estuary to final station which is close to SecondLink Bridge. Analysis was carried
out based on physical, nutrient and heavy metals components. Results were then
compared with Malaysian Interim National Water Quality Standard (INWQS) and
Malaysia Interim National Marine Water Quality Standard (INMWQS).
4.4.1
Physical and Chemical Water Quality Parameters
Physical and chemical water quality parameters include in this study are
dissolved oxygen (DO), pH, salinity, temperature, ammoniacal nitrogen biochemical
oxygen demand (BOD) and chemical oxygen demand (COD).
78
4.4.1.1 Dissolved Oxygen (DO)
The content of oxygen is an important indicator of the pollution of a water
body. DO depend on several factors, some of which are the amount of rainfall,
saltiness of the water, the amount of decomposition in the water, the amount of plant
life in the water, type of rock in riverbed and presence of pollutants. The study of
oxygen content plays quite an important role, when evaluating the conditions of the
habitation in a water body (Vynavi, 2005).
Figure 4.9 shows the variation in DO level along Danga-Pendas sampling
stations. Station S1 to S5 which is from Sungai Skudai estuaries to Sungai Melayu
estuaries showed lower DO levels compared to Sungai Perepat to Sungai Pendas.
This maybe due to the development of Danga and Skudai areas which tend to
discharge many pollutants thus decreasing the DO level. Compared to the area from
Sungai Perepat to Sungai Pendas, there is not much development found and most of
the areas were covered with natural mangrove. The time and day also can affect the
solubility of oxygen in water bodies. The more hours of daylight there are, the more
photosynthesis may takes place, resulting in higher concentration of oxygen in
midday. All sampling in this study were done during daytime starting in the morning
at station S1 and end in midday at station S11.
5.00
4.50
4.00
DO (mg/L)
3.50
Nov
3.00
Dec
2.50
Jan
2.00
1.50
Feb
1.00
Mar
0.50
Apr
0.00
1
2
3
4
5
6
7
8
9
10
11
Sampling Stations
Figure 4.9: DO concentration measured at station S1 to S11 from November 2008 to
April 2009.
79
4.4.1.2 Temperature
The temperature of water is one of the most important characteristics that
determines to a considerable extent, the trends and tendencies of changes in the river
water quality. Increased water temperature will decrease the solubility of dissolved
oxygen. Water temperature above 27ºC is unsuitable for public use and at above
32ºC it would be considered unfit for public use (Chapman, 1992). Temperature
influences directly the amount of dissolved oxygen that is available to aquatic
organisms. Water temperature affects the rates of all chemical and biological process.
Figure 4.10 shows the results for temperature obtain from sampling. Temperature
measured for all stations ranged from 27.75ºC to 30.00ºC. April 2009 have the
lowest temperature range for most of the stations. While temperature measured in
February 2009 state the highest for most of the stations.
30.50
Temperature ( ºC)
30.00
29.50
Nov
29.00
Dec
28.50
Jan
28.00
Feb
27.50
Mar
27.00
Apr
26.50
1
2
3
4
5
6
7
8
9
10
11
Sampling Stations
Figure 4.10: Temperature level measured at station S1 to S11 from November 2008
to April 2009.
4.4.1.3 Salinity
One major characteristic of an estuaries system is the variation in the
distribution of water salinity. Many processes, physical, chemical or biological are
related to salinity. Salinity is important in coastal waterways for several reasons.
Salinity is a dynamic indicator of the nature of the exchange system. The salinity of
80
water within estuary determines how much fresh water was mixed with seawater.
sea
Salinity is also an important determinant
de
of mixing regime because of the density
variation associated with
with salinity variation, salinity stratification tends to inhibit
inhibi
vertical mixing in an estuary which can have important implications for dissolved
oxygen concentrations (Wright, 2003).
Salinity is the main index of mixture of seawater with water from major
rivers. It can reflect the types of water and also may influence the measurement of
other water quality parameters such as DO and pH. Figure 4.11 shows the salinity
measured during sampling. The values of salinity seems to fluctuates at four earliest
stations
ions (S1 to S4) and more stable for the rest of the station.
station The three lowest
stations (S1, S4 and S5) are located in the points near the river mouth. Salinity
increase from the river mouth to the coastal areas. Three lower stations (S1, S4 and
S5) were probably
obably situated at the river mouths, whereas the rest were recorded a bit
far from the river mouths thus higher results obtained due to the influence of
seawater.
30.00
25.00
Salinity (ppt)
Nov
20.00
Dec
15.00
Jan
10.00
Feb
Mar
5.00
Apr
0.00
1
2
3
4
5
6
7
8
9
10
11
Sampling Stations
Figure 4.11:: Salinity measured at station S1 to S11 from November 2008 to April
2009.
81
4.4.1.4 pH
pH is another important parameter which affect water quality. A change in
the pH of water can alter the behavior of other chemicals in the water. The altered
water chemistry may affect aquatic plants and animals. Heavy metals such as
cadmium, lead and chromium dissolve more easily in more acidic water. This is
important because many heavy metals also become more toxic when dissolved in
water (Mesner and Geiger, 2005). The measured pH levels correspond to sampling
stations were shown in Figure 4.12. As seen in the figure, the trends of pH levels
were constant and stable. The reading of pH levels is in the range of 7.38 to 9.65.
Human activities can also affect pH values. Several human activities that can
affect the changes in pH are from polluted precipitation which is also known as acid
rain. It increases the acidity of surface water near many industrial or large urban
areas. The main contributors to acid rain are sulfuric acid produced by coal burning
industries and nitric acid that is produced by automobile engines. Furthermore,
dumping industrial pollutants directly into waters is also known as point source
pollution which can have intense and immediate effects on the pH of a water body
(Mesner and Geiger, 2005).
10
pH
9.5
9
Nov
8.5
Dec
8
Jan
7.5
Feb
7
Mar
6.5
Apr
6
1
2
3
4
5
6
7
8
9
10
11
Sampling Stations
Figure 4.12: pH measured at station S1 to S11 from November 2008 to April 2009.
82
4.4.1.5 Total Dissolved Solids
Total dissolved solid (TDS) is one of the parameters analyzed in the
laboratory. The result is illustrated in Figure 4.13, where there is not much variation
in TDS values along the coastal area. The values are less in contrast to the average
range from 21.00 mg/L to 26.5 mg/L. TDS values are a bit higher at three stations,
S1, S2 and S3. This may be due to the deposit of pollutants and sediments entering
from the developed Danga and Skudai area. However, the readings had become
stable for the next stations. The amount of solids in water affects the clarity of the
water where the more TDS concentration, the cloudier and turbid the water.
Fortunately, overall TDS values satisfy Class 1 DOE-INWQS where all stations
recorded less than 500 mg/L.
30.00
TDS (mg/L)
25.00
20.00
Nov
15.00
Dec
Jan
10.00
Feb
Mar
5.00
Apr
0.00
1
2
3
4
5
6
7
8
9
10
11
Sampling Stations
Figure 4.13: TDS concentration measured at station S1 to S11 from November 2008
to April 2009.
4.4.1.6 Ammoniacal Nitrogen
Ammoniacal nitrogen parameter represents pollution from farm wastes,
agricultural waste and domestic sewage effluent (Margerat, 1986; Peavy, 1986).
Ammoniacal nitrogen is often found in water as a result of the discharge of sewage
effluent and the high levels adversely affect the quality of water for fisheries. The
result for ammoniacal nitrogen is illustrated in Figure 4.14. The range of ammoniacal
83
nitrogen during sampling was from 0.012 mg/L to 1.137 mg/L. It is shown that the
values of ammoniacal nitrogen decreased as it approaches Pendas area. It is quite
high at S1 and S3, which maybe due to the discharge of ammonia since ammoniacal
nitrogen occurs partly in the form of ammonium ions and partly as ammonia. The
highest concentration of ammoniacal nitrogen is observed at Station S1 (Sungai
Skudai) possibly due the discharge of pollutants of this river where Sungai Skudai is
known among the most polluted river in Johor. According to Pollution Influx
Observed at Continuous Water Quality Station done by Department of Environment
Malaysia, the main pollution sources in Sungai Skudai are domestic sewage or latex
based industry effluent which high causes ammoniacal nitrogen concentration. This
value reflects the deterioration of water quality at Sungai Skudai (Farihah, 2009).
Ammonical Nitrogen (mg/L)
1.2
1
Nov
0.8
Dec
0.6
Jan
Feb
0.4
Mar
0.2
Apr
0
1
2
3
4
5
6
7
8
9
10
11
Sampling Stations
Figure 4.14: Ammoniacal nitrogen concentration measured at station S1 to S11 from
November 2008 to April 2009.
4.4.1.7 Biochemical Oxygen Demand (BOD)
Biochemical oxygen demand (BOD) is measurement of the amount of
oxygen that bacteria will consume while decomposing organic matter under aerobic
conditions. BOD analysis is advantageous as it provides a direct measure of oxygen
in the environment (Law, 1981). The dissolved oxygen level in water is greatly
affected by the content of biochemical oxygen demand. Elevated organic matter in
84
river waters increases the biochemical oxygen demand (BOD) to oxidize the organic
matter (Vynavi, 2005). In this study however, the data for BOD and COD were only
available for four samplings occasions which is from November 2008 to February
2009.
Figure 4.15 show the results for BOD against sampling stations for
November 2008 to February 2009. As illustrated on the figure, the range of BOD
concentration in water samples was 4.6 mg/L to 6.7 mg/L. The water samples
analyzed for earlier stations near Danga and Skudai recorded higher BOD
concentrations. BOD concentrations for December 2008 and January 2009 were
higher for most of the sampling stations. This might be due to decrease in brackish
water levels which renders the water less turbulence due to less high wind and rain
that can increase dissolution of DO into the water. As DO concentration decreases,
oxygen demand will increase for the use of bacteria to carried out degradation
process. High BOD values indicate a good deal of high oxygen demanding organic
matter was present to be decomposed due to the discharge from industrial areas,
sewage treatment plant, housing and urban areas (Vynavi, 2005). Generally, the
BOD concentrations satisfy Class III of INWQS.
7
6.5
6
BOD (mg/L)
5.5
5
Nov
4.5
Dec
4
Jan
3.5
Feb
3
2.5
2
1
2
3
4
5
6
7
8
9
10
11
Station
Figure 4.15: BOD concentration measured at station S1 to S11 from November 2008
to February 2009.
85
4.4.1.8 Chemical Oxygen Demand (COD)
Chemical oxygen demand (COD) is a test of oxygen demand in which the
organic matter is chemically oxidized instead of being biologically oxidized. COD
values are higher than BOD values because this chemical test oxidizes all of the
organics present in the water samples, while the BOD test which is a biological test
is selective to organisms and microorganisms present in the water samples
(Greenberg et al., 1979). Elevated organic matter in river waters increases the
chemical oxygen demand to decompose chemicals. Increased COD levels in river
waters was attributed to the increase of organic matter and inorganic chemicals in
river waters from agriculture, urban run-off and industrial discharge (Wandan and
Zabik, 1996)
Figure 4.16 show the concentration of COD in both November 2008 to
February 2009. The trends of the COD concentration shows increase against stations.
The lowest concentration was recorded at the first station and starts gradually to
increase up to until station S9 for December 2008. While November reading also
have quite similar trend but there were reduction in COD values in station S6 and S7
and then increase at S8 to S11. The range of COD values was 834 mg/L to 3850
mg/L. COD test shows that oxygen usage was high in water samples. High COD
results may be due to high salt content in the water samples, which causes
precipitation with the added reagents as shown after reflux the solution obtained is
cloudy. COD vials used might affect the results due to reuse of vials that is not
cleaned or scratch causing diffraction of wavelength in colorimeter (Yap, 2009).
86
4500
4000
COD mg/L
3500
3000
Nov
2500
Dec
2000
Jan
1500
Feb
1000
500
0
1
2
3
4
5
6
7
8
9
10
11
Stations
Figure 4.16: COD concentration measured at station S1 to S11 from November 2008
to February 2009.
4.4.2
Heavy Metals
Heavy metals are pollutants that are hazardous to human and aquatic life. The
concentration of heavy metals in water could be influenced by several environmental
aspects which indirectly effects the contamination or accumulation. Tides are one of
the factors which also indirectly affect the concentration values. However, based on
the study done by Ibrahim (1993) at similar study area, he reveals that tides have less
influence on the distribution of heavy metals in water. Therefore the influences of
tides were neglected in this study. There are several types of heavy metals analyzed
in this study with each of them having significant aspect related to human and
development activities.
4.4.2.1 Zinc (Zn)
Zinc is an essential nutrient, necessary for sustaining life. Although zinc is
known as an ubiquitous element in nature, it is supposed that about 96% of its release
into the global environment is the result of anthropogenic activities, like
electroplating, smelting and ore processing, corrosion from alloys and galvanized
87
surfaces as well as erosion from agriculture land (Landner and Reuther, 2004). Most
of the activities that originates zinc to water comes from the development of
industrial and agricultural activities which so significant to Johor Bahru.
Zinc concentration in water samples along Danga-Pendas area are varies with
month. Based on Figure 4.17, the three earliest months (November 2008 to January
2009) were more consistence and lower compare to the other three months later
(February to April 2009). The distribution values of Zn for February to April 2009
show that the concentrations become lesser or decreased when it approaches Pendas
coastal area. The concentration of Zn is higher at several stations and increased at S2
before declining until the last station. The range of Zn observed was from 0.011
mg/L to 0.65 mg/L. Unfortunately, the concentration of Zn in water along this area
were higher compared to the quality criteria concentration that was recommended for
Zn in marine and estuarine waters set by U.S EPA which is 0.09 mg/L (Wu et al.,
2008). Compared to the permissible limits of INWQS for Zn which is 0.5 mg/L,
nearly all samples are under the limits. Only several samples in station S2 and S3
recorded higher than the permissible limits.
0.7
0.6
Zn (mg/L)
0.5
Nov
0.4
Dec
0.3
Jan
0.2
Feb
Mar
0.1
Apr
0
1
2
3
4
5
6
7
8
9
10
11
Stations
Figure 4.17: Zinc concentration measured at station S1 to S11 from November 2008
to April 2009.
.
88
4.4.2.2 Lead (Pb)
Typically, seawater contains trace amounts of lead (2 to 30 ppt) while an
average river contain between 3 and 30 ppb. Mining, smelting and other industrial
emissions and combustion sources and solid waste incinerators are the primary
sources of lead. Another source of lead is paint chips and dust from buildings built
before 1978 and from bridges that source of this metal (Sax, 1974). Lead is one of
the significant metals which affect the water quality along study area since Johor
Bahru is among one of the active industrial towns in Malaysia.
Lead is a hazardous metals that also contaminates water samples. Figure 4.18
shows the variations of Pb concentration in water were much similar to the variations
of Zn concentration. The concentrations were highest at the earlier stations such as in
S2 (Sungai Danga estuaries) and S3 as illustrated. The concentration starts to
decrease moving towards the final station. Range of Pb concentration in water along
Danga-Pendas coastal area was 0.09 mg/L to 3.23 mg/L where both the lowest and
highest concentration were recorded in the same month, i.e February 2009. The
higher concentrations in earlier stations were due to effluent discharges from sewage
treatment plant, pig farms and industries which are the main contributors (Vynavi,
2005). Nevertheless, most of the concentrations of Pb recorded did not meet the
Malaysia Interim National Marine Water Quality Standard (INMWQS) which is 0.1
mg/L. This situation gives consequences that the quality of Danga-Pendas coastal
water is heavily polluted with Pb thus could affect the aquatic life as well as human
health.
89
3.500
Pb (mg/L)
3.000
2.500
Nov
2.000
Dec
1.500
Jan
Feb
1.000
Mar
0.500
Apr
0.000
1
2
3
4
5
6
7
8
9
10
11
Stations
Figure 4.18: Lead concentration measured at station S1 to S11 from November 2008
to April 2009.
4.4.2.3 Cadmium (Cd)
The concentration of cadmium in water samples along Danga-Pendas is
illustrated in Figure 4.19. The trends of the concentrations were different for the first
three months i.e in November 2008 to January 2009 and the next three months in
February 2009 to April 2009. Based on the graph, the first three months show more
stable and constant Cd concentration readings compared to concentration to the last
three final months. The concentration in February to April 2009 recorded more
similar trend as observed for Zn and Pb. The concentration tends to be higher at the
earlier stations and decreased when approaching the final station in Pendas. The
range of Cd concentration was from 0.019 mg/L to 0.505 mg/L. The highest Cd
concentration was recorded at S3 in Danga and the lowest concentration was
recorded at the final station, S11 in Pendas. The same situation was observed for Cd,
where most of the Cd levels recorded exceeded the Interim National Marine Water
Quality Standard (INMWQS) that limits the Cd concentration in water at 0.1 mg/L.
Cadmium compounds enter the water bodies from the discharge of effluents
from paint manufacturing industries where cadmium is used to produce excellent
colour from rechargeable batteries (Herber, 1994). Other major sources of Cd in river
water are from sewerage treatment plants, pig farms and manufacturing industries
(Erdawati, 1997; IWK, 2000; Mazlin et al., 2000). Cadmium can escape from
90
landfills where trash is buried and seep into the ground and groundwater. From there,
it can become part of food and water that humans and animals ingest.
0.6
Cd (mg/L)
0.5
Nov
0.4
Dec
0.3
Jan
Feb
0.2
Mar
Apr
0.1
0
1
2
3
4
5
6
7
Stations
8
9
10
11
Figure 4.19: Cadmium concentration measured at station S1 to S11 from November
2008 to April 2009.
4.4.2.4 Nickel (Ni)
Result for nickel concentrations in water is presented in Figure 4.20. As
shown on the graph, there were predictable trends for the concentration of Ni against
stations along Danga-Pendas. The concentration of Ni increased at the first three
earlier stations (S1 to S3) before decrease towards station S4. Then the
concentrations gradually increase again for few stations and start to decrease at
station S8 and S9. The observed concentration then increase up to the final station in
Pendas. The range of the concentration was 0.08 mg/L to 0.51 mg/L. The
concentration limit of Ni given in Interim National Water Quality Standard (INWQS)
is 0.1 mg/L. Based on the results obtained, nearly all of the samples exceeded the
limit.
91
0.6
0.5
Ni (mg/L)
0.4
Nov
Dec
0.3
Jan
0.2
Feb
0.1
Mar
Apr
0
1
2
3
4
5
6
7
8
9
10
11
Stations
Figure 4.20: Nickel concentration measured at station S1 to S11 from November
2008 to April 2009.
The overall water quality along Danga-Pendas coastal area that have been
measured shows that the status of water is average. For some physical and chemical
parameters such as DO, pH and ammoniacal nitrogen, the average value only satisfy
Class II and III of INWQS. However, the result for TDS satisfies Class I of INWQS.
The most critical parameter that affects water quality along the study area
was heavy metals. Although there was only four heavy metal parameters that were
analyzed in this study, nearly all the metals shows high concentration level. Judging
against four metals, three of them, nickel, cadmium and lead were above the
permissible levels of INWQS and INMWQS. Conversely, zinc recorded good result
as all of the samples analysed fulfilled INWQS limit.
Stations located at Sungai Danga, Sungai Skudai and Sungai Melayu
estuaries, S1 to S5 show higher (worst) physical, chemical and heavy metals
concentrations. This might be due to the developed town and domestic area which
tend to discharge pollutants to this area. Furthermore, agricultural activities in several
parts of the study area such as at Sungai Melayu also give an impact to water quality
of this area since the usage of fertilizers tends to run off to this area compared to
Pendas.
92
The concentration and levels of pollutants in water were also influenced by
physical and biological environment. Tides also play an important role in analyzing
water quality parameters since there were different in concentration due to different
types of tide. However, based on the study done by Ibrahim (1993) at similar study
area, the tides have less influence to the concentration of water quality parameters in
water. Beside, rainfall distributions also play a part in assessing water quality. Wet
season is believed to give more effects of the concentration of pollutant to water than
in dry season. However, in this study, there is no significant relation due to the
statement. As observed, most of the parameters especially heavy metals were
recorded higher in dry seasons compared to wet seasons.
4.5
Analysis of Previous Water Quality Data Compared to Present Data
Analyses of the previous water quality data are useful in order to observe the
variation and differentiation in water quality along Danga-Pendas coastal area. It is
also helpful in analyzing the effects of landuse changes on water quality generally
and to see the effects of the development to water quality for these times specifically.
Previous water quality data have been obtained from several studies that have been
done along this area. Water quality data from 1991 and early 2006 were used in
analyzing the status of water quality along Danga-Pendas coastal area. Two most
significant areas along Danga-Pendas were chosen in order to evaluate the variations
of water quality in these selected years. The first one is located at Danga station and
another one at Pendas station. Danga station is considered very significant since there
were a lot of development in the area since 1991. The major development is
urbanization of Danga Bay and the surrounding areas was predicted to show high
impact on water quality along this area. On the other hand, Pendas station was
significant as this area received less development compared to Danga area.
Water quality parameter that were compared and analyzed in this study
includes several physical and chemical data such as pH, temperature, dissolved
oxygen (DO) and ammoniacal nitrogen (AN) for three years. Other physical and
93
chemical water quality data was not analyzed since there were limited sources and
references. Heavy metal data was also analyzed in order to see the variation trend
and the effects due to development (landuse change) and also to correlate with their
concentration on mussels as analyzed and discussed in more on Chapter 5. Some of
the heavy metal parameters analyzed were zinc, cadmium, lead and nickel. Table 4.4
show the availability description and reference of the data acquired from several
previous studies. Whereas Figure 4.21 and 4.22 illustrate the comparison graphs of
some physical, chemical and heavy metals data for 1991, 2006 and 2009.
Table 4.4: Water quality data for year 1991, 2006 and 2009
Water Quality Parameters
Year
Station
Physical and Chemical
pH
References
Heavy Metals
Temp
DO
AN
Zn
Cd
Pb
Ni
(ºC)
(mg/L)
(mg/L
(mg/L)
(mg/L)
(mg/L)
(mg/L)
)
1991
2006
2009
Danga
0.76
30.5
2.75
NA
31.428
0.082
0.773
NA
Gunasegaran (1991);
Pendas
NA
NA
NA
NA
16.092
0.059
0.596
NA
Ibrahim (1993)
Danga
8.15
28.8
5.46
0.443
0.243
0.001
0.90
0.008
Ridhuan (2006);
Pendas
7.97
NA
5.68
0.187
NA
NA
NA
NA
Shaiful (2006)
Danga
8.59
29.00
3.12
0.242
0.339
0.282
2.253
0.332
Pendas
8.68
29.28
4.33
0.560
0.753
0.107
0.430
0.214
Current study
NA – Not Available
94
95
As illustrated in Figure 4.21(a) and (b), the pH and temperature trend for
1991 to 2009 is constant and much more similar. However, there was a slight
increase in pH from 1991 to 2009. The lowest pH for Danga estuaries area was
recorded in 1991 at 7.64 and increase up to 8.59 in 2009. The same trend was also
observed in Pendas area, where the pH values increased from 7.97 in 2006 to 8.68 in
2009. The variation against year for temperature can only be observed for Danga
station since there were not enough data acquired for temperature for Pendas area.
Temperature recorded in 1991 was slightly higher at 30.5ºC compared to year 28.8ºC
at 2006 and 29.0ºC at 2009. The temperature recorded was 28.8ºC in 2006 before
increasing to 2009. In 2009, the temperature values for Danga and Pendas nearly the
same with a difference of 0.28ºC.
Figure 4.21(c) and (d) represent the concentration of dissolved oxygen and
ammoniacal nitrogen in water for year 1991, 2006 and 2009. As illustrated in the
figure, the concentrations of dissolved oxygen in water were higher in 2006
compared to both 1991 and 2009. However, the lowest DO reading was recorded in
1991 at Danga area at only 2.75 mg/L. This shows that the area might be polluted at
the specific time. In 2006, the concentration of DO at both Danga and Pendas areas
increase to 5.46 mg/L and 5.68 mg/L respectively. However in 2009, DO level at
Danga and Pendas was recorded at 3.12 mg/L and 4.33 mg/L respectively. Although
the concentration of DO was higher in 2006, but the level only satisfy of Class II
Interim National Water Quality Standard (INWQS). In 2009, the DO level only
satisfy Class III of INWQS which indicate that the water quality level for the areas
are in average condition.
However, the trend of ammoniacal nitrogen concentration differs with
increasing years. In Danga, the concentration decreased from 0.433 mg/L in 2006 to
0.242 mg/L in 2009. Meanwhile for Pendas area an increase from 0.19 mg/L in 2006
to 0.56 mg/L in 2009 was detected. Danga station recorded high ammoniacal
nitrogen concentration which might be due to discharge of industrial and domestic
waste from the developed Danga Bay, Johor Bahru town and the surrounding area.
Furthermore there was also discharge of agricultural run offs from the cultivated area
96
near Danga estuaries. Compared to Pendas which was less developed there was less
human activities that influenced to the ammoniacal nitrogen concentration.
10
pH
8
6
Danga
4
Pendas
2
NA
0
1991
2006
2009
Year
(a)
35
Temperature ( ºC)
30
25
20
Danga
15
Pendas
10
5
NA
0
1991
NA
2006
2009
Year
(b)
6
DO (mg/L)
5
4
3
Danga
2
Pendas
1
NA
0
1991
(c)
2006
Year
2009
0.6
AN (mg/L)
0.5
0.4
0.3
Danga
0.2
Pendas
0.1
0
NA
1991
2006
2009
Year
(d)
Figure 4.21:: Water quality data at Danga and Pendas area for year 1991, 2006 and
2009 for (a) pH (b) temperature (c) DO and (d) ammoniacal nitrogen (AN).
97
Figure 4.22 shows the graph of zinc, cadmium, lead and nickel concentrations
for water samples in the Danga-Pendas area. There are no data available for Pendas
area in 2006 for Zn, Cd, Ni and Pb. As observed in 1991, 2006 and 2009, the
concentrations of heavy metals recorded were higher at Danga station compared to
Pendas station. The highest heavy metals concentrations for Cd, Pb and Ni detected
in 2009.
As illustrated in Figure 4.22(a), the concentration of Zn in Danga area was
highest in 1991 with 31.43 mg/L and decreased to 0.24 mg/L in 2006 and increased
to 0.34 mg/L in 2009. The concentration of Zn for Pendas area also show a
decreasing trend from 16.09 mg/L in 1991 to 0.75 mg/L observed in 2009. The
reduced concentrations trend of Zn might be due to less influence from agricultural
run offs. Cadmium concentrations as shown on Figure 4.22(b) have higher
concentration in 2009 compared to 1991. At Danga station, Cd concentration in 1991
was 0.082 mg/L compared to 0.0001 mg/L in 2006. Then, the concentration
increased to 0.282 mg/L in 2009. Although Cd concentration in Pendas was much
lower compared to Danga, increase in concentration was observed from 1991 to 2006
i.e at 0.059 mg/L and 0.107 mg/L respectively. The current Cd concentration is
slightly higher than the limit set by INMWQS at 0.1 mg/L.
Figure 4.22(c) illustrates the concentration of lead in water for Danga and
Pendas area in 1991, 2006 and 2009. The trend for Pb concentration was similar to
Cd concentration. For Danga station, the lowest Pb level recorded in 2006 was at
0.773 mg/L and increased in 2009 to 2.253 mg/L. However, Pendas station show a
slight decrease in 2009 compared to 1991. Nickel concentration is shown in Figure
4.22(d) for Pendas and Danga in 2009 and for Danga only in 2006. Data is not
available for nickel in 1991 both in Pendas and Danga and in 2006 for Pendas only.
In Danga area, the concentrations increased from 0.008 mg/L in 2006 to 0.332 mg/L
in 2009. Both Pb and Ni concentration in 2009 exceeded the required permissible
level of 0.1 mg/L.
98
35
Zn (ppm)
30
25
20
Danga
15
Pendas
10
5
NA
0
1991
2006
2009
Year
(a)
0.3
Cd (ppm)
0.25
0.2
0.15
Danga
0.1
Pendas
0.05
NA
0
1991
2006
2009
Year
(b)
2.5
Pb (ppm)
2
1.5
Danga
1
Pendas
0.5
NA
0
1991
2006
2009
Year
(c)
0.35
0.3
Ni (ppm)
0.25
0.2
Danga
0.15
Pendas
0.1
0.05
NA
0
1991
2006
Year
2009
(d)
Figure 4.22:: Heavy metals concentration data at Danga and Pendas area for year
1991, 2006 and 2009 (a) zinc (b) cadmium (c) lead and (d) nickel.
99
4.6
Analysis on the Effects of Landuse Changes to Water Quality along
Danga-Pendas Coastal Area
Studies on landuse and landcover-water quality relationship has been studied
for a long time by researchers (Reth et al., 1996; Allan et al, 1997; Johnson et al,
1997, Basnyat et al., 1999, Yunus et al., 2003). Such studies revealed that the type
and severity of water contamination is often directly related to human activities,
which can be quantified in terms of the intensity and type of landuse in the source
areas of water to streams and aquifers. One of the most important factors that can
affect the quality of a water body is the landuse within its watershed. Urban sprawl
(particularly the paving of large segments of landscape) can have significant and
usually negative impacts on water resources. Although growth and landuse change
may be inevitable in many communities, the way in which growth takes place affects
its impact on water quality.
Based on the results obtained from both integrated landuse and also observed
water quality data, the negative effects of landuse changes to water quality cannot be
resisted. As years passes by, we can see the reduction or decrease in natural land
surface. Mangrove and forested area have shown significant decrease with increase
in developed build-up and agricultural area. At the same time, decrease in quality of
coastal water was also detected. Metals, specifically recorded extensive increase in
water concentration to increase of build-up area. This situation is significantly related
to land-based activities such as domestic and industrial wastes. The type of landuse
which represent the land-based activities showed significant accumulation results.
The increase of industrial development and agricultural will also increase heavy
metals accumulation to the water (Yunus et al., 2003).
The relationship of landuse-water quality can also be proven based on
location of area observed. In more developed areas of Danga, the level of water
quality parameters recorded for most of all the sampling carried out in 2009 or by
previous studies is worst compared to the water quality observed in Pendas area. This
may be due to rapid increase in population and industrial facilities around the main
town of Danga and Johor Bahru. Heavy metals in untreated municipal and industrial
100
wastewater discharged directly into canals and rivers will eventually find their way
to marine environment. The levels of water quality for most of the parameters
recorded in 2009 are higher than 1991 and 2006. This indicates that the development
of the surrounding land may have impact on water quality along Danga area.
The increase in build-up area with numerous landuse practices can affect the
quality of streams and coastal water in various ways. Industrial activities may
discharge their effluents directly into streams and coastal water. Moreover, Johor
plays an important role as an industrial state. Figure 4.23 illustrate the types of
manufacturing industry in Johor for year 2005. Most of these sectors play a part in
using heavy metals and thus affecting the water quality of the surrounding area.
Furhermore, increase in agriculture sector also degraded the water quality along this
area as fertilizers and pesticides applied to cultivated area can wash into streams. In
addition, the new development of IDR might also degrade the water quality. The
rapid and massive development may introduce pollutants along this area since the
development takes place just beside the coastal area of the Danga-Pendas.
Figure 4.23: Concentration of manufacturing sectors in and outer of Johor Bahru.
(Source: SJER, 2005)
There is also no doubt that pollution from neighbouring country, Singapore
can degrade water quality in Danga-Pendas coastal area since the straits is narrow
and located in the middle of Malaysia-Singapore which acts as a boundary for both
countries. Singapore which is one of the most developed countries in this region is
101
also well-known as an industrial country that might influence decrease in water
quality along this area. In order to assess the effect of development in Singapore, an
integrated landuse change maps derived earlier also includes Singapore land area.
CHAPTER 5
EFFECT OF WATER QUALITY ON GREEN MUSSELS (Perna viridis)
5.1
Introduction
This chapter focuses on the effects of water quality on green mussels
(P.viridis) along Danga-Pendas coastal area. This area has been proven as Malaysia’s
best breeding place for P.viridis by Fisheries Institute of Malaysia. P.viridis is a type
of mollusks that is considered as highly nutritious seafood. However, the undergoing
rapid development in Pendas-Danga area may affect the water quality as well as
P.viridis’s quality along this area. In this chapter, analyses on mussel include heavy
metal contaminants in water and green mussels.
5.2
Analysis on Effects of Water Quality on Green Mussels Vs Water
Samples
Analysis on the effect of contaminants on green mussels was compared to the
concentration in water samples in order to see the correlation between them. Mussels
were not sexed, as sex has been found to be an unimportant factor in influencing
contaminant concentrations in P.viridis (Bayen et al., 2004). Although P.viridis may
be affected by seasonal variation, Malaysia is not a temperate country with four
seasons, therefore it does not play an important role in the study (Yap, 2009).
103
The study has taken into consideration of both in-situ measurement and
laboratory analysis. Parameters measured in-situ such as DO, salinity, temperature
and pH while laboratory analysis involves of ammoniacal nitrogen and heavy metals.
All the parameters were analyzed to see the correlation on water samples and
mussel’s contamination.
5.2.1
Physical Parameters
Physical parameters are among the most important factors for living organism
in water bodies. This is because the suitability of growth condition and survival of
organisms relys on the condition of the physical aspects of the water. DO, salinity,
temperature and pH are the most significant parameters that represent water
condition. Biologically, P.viridis habitats are primarily found at estuarine and coastal
water which is warm (27ºC to 32ºC) and have high salinity ranging from 27 to 33 ppt
(Hickman, 1989).
5.2.1.1 Dissolved Oxygen (DO)
Dissolved oxygen is one of the important components needed by the
underwater organism. The minimum amount of dissolved oxygen required to sustain
a variety of living organism is 5g/m3. From the viewpoint of living organisms, no gas
is more important than oxygen. Because all heterotrophs use oxygen for their
respiration, it tends to be removed continuously from water. This means that it can
enter water through solution at the air-water interface or through photosynthesis by
aquatic plants. High level of animal activity, coupled with an active detritus food
chain, can withdraw a large amount of oxygen from the water (Vynavi, 2005).
As shown in Figure 5.1, the concentration of DO measured gradually
increased from station M2 as it approaches the last station M4. The value however
does not differ much. Dissolved oxygen available for P.viridis is usually affected by
the presence of chromium. In which, the oxygen consumption of P.viridis decreased
104
with increasing exposure duration of chromium (Vijayavel, 2007). However, the DO
values measured are lower than the average of 5 mg/L. The reduction of DO
concentration may also be due to nutrient enrichment of water column from mussels
excretory products (Ferreira et al., 2008; Rlliot et al., 2008).
4.50
DO (mg/L)
4.00
Nov
Dec
3.50
Jan
3.00
Feb
Mar
2.50
Apr
2.00
M1
M2
M3
M4
Stations
Figure 5.1: DO concentration at mussel sampling stations, M1 to M4 from
November 2008 to April 2009.
5.2.1.2 Salinity
Salinity is a major factor influencing the distribution and physiology of
aquatic life in marine systems. Variation of salinity affects the growth, survival and
metabolic physiology of organism (Blackmore and Wang, 2003). Increase in
dissolved salts will also decrease the amount of dissolved oxygen (Wright, 2003).
Figure 5.2 shows the results of salinity measured in-situ for a series of six months.
All stations show the same variation of concentration. The salinity measured at the
M1 decrease for station M2 before increasing again in station M3 and M4. The last
station, station M4 recorded high salinity due to the influence of salts content from
the open sea. The range of salinity values measured was 22.55 ppt to 24.66 ppt.
Salinity suitable for shellfish water is 12 ppt to 38 ppt (European Parliament,
2006). P.viridis has a broad salinity range from 17 ppt to 33 ppt (NIMPIS, 2000).
105
Hickman (1989) reported that the primary growing habitat for P.viridis is within the
range of 27 ppt to 33 ppt. However, numerous studies have shown that there is an
increase in metal uptake by aquatic organisms at reduced salinities condition
(Rajagopal, 1998).
25.00
24.50
Salinity (ppt)
24.00
Nov
23.50
Dec
23.00
Jan
22.50
Feb
22.00
Mar
21.50
Apr
21.00
M1
M2
M3
M4
Stations
Figure 5.2: Salinity level at mussel sampling stations, M1 to M4 from November
2008 to April 2009.
5.2.1.3 Temperature
Temperature plays a vital role in chemical and biochemical reactions. The
metabolic rate of aquatic organisms is related to water temperature. In warm waters,
respiration rates increase leading to increase in oxygen consumption. Thus, growth
rate of aquatic organisms will also increases. This can lead to increase in
decomposition of organic matter, water turbidity, macrophyte growth and algal
blooms, especially when nutrient conditions are suitable (Jackson and Jackson,
1996). Toxic chemicals made more soluble by higher temperature may present an
additional hazard to the organisms in the water (US EPA, 1986). Higher temperature
increases the toxicity of many substances such as heavy metals or pesticides, whilst
the sensitivity of the organisms to toxic substances also increases (Vynavi, 2005).
106
Different aquatic organisms have different abilities to live on temperature
variations in water. Temperature plays a key role in influencing the reproductive
activity of P.viridis (Rajagopal et al., 1998). Figure 5.3 shows the result for
temperature measurement. Temperature values ranged from 28.21ºC to 29.9ºC
during sampling. P.viridis also has a wide range temperature tolerance at 11ºC to
32ºC (NIMPIS, 2000). However, the optimum temperature for P.viridis biological
habitat is in warm water ranging from 27ºC to 32ºC (Hickman, 1989). Although the
thermal range of the green mussel is broad, reduced temperature can give
significantly negative impacts on growth rates. Lower temperature and lower salinity
will induce spawning. Total mortality was reported after 24 hour exposure at 33°C
and 35°C (Sivalingam, 1977). Previous study conducted had proven that there are
inter-relation
between
temperatures,
salinity
with
concentration
of
metal
accumulation in green mussels. High temperature and lower water salinity will
increase the filtration rate of mussels, which leads to a rapid accumulation of metal
contents. Effect of high temperature towards the concentration of heavy metals in
water is similar where higher temperatures generally imply higher solubility of metal
salt (Wong et al., 2000).
30.50
Temperature ( ºC)
30.00
Nov
29.50
Dec
29.00
Jan
28.50
Feb
28.00
Mar
27.50
Apr
27.00
M1
M2
M3
M4
Station
Figure 5.3: Water temperature at mussel sampling stations, M1 to M4 from
November 2008 to April 2009.
107
5.2.1.4 pH
pH is another important water quality parameter that can be measured in-situ.
The pH of water has an important influence on living organisms and on any use of
the water. If the water is too acidic and too alkaline, there will be very limited
aquatic life. It also affects the growth habitat and survival of P.viridis. Study done by
Vijayavel (2009) showed that the mortality of mussels increased with decreasing pH
and increasing hardness and alkalinity variables. In contrast, the mortality decreased
with increasing pH and decreasing hardness and alkalinity values. A decrease in pH
would increase metal availability, rendering itself to greater uptake by organisms and
can cause physiological damage to aquatic life (Connell and Miller, 1984).
From Figure 5.4, there was not much variation in pH level, the values are
recorded in the range of 7.61 to 9.59. Each sampling station recorded similar pH
values and trend. The difference can be seen in terms of month, where the period of
December 2008 and January 2009 pH reading was the highest compared to the other
months.
12
10
Nov
pH
8
Dec
Jan
6
Feb
4
Mar
Apr
2
0
M1
M2
M3
M4
Stations
Figure 5.4: pH at mussel sampling stations, M1 to M4 from November 2008 to April
2009.
108
5.2.1.5 Total Dissolved Solids (TDS)
Total Dissolved Solids (TDS) are solids in water that can pass through a
filter. TDS is a measure of the amount of material dissolved in water. This material
can include carbonate, bicarbonate, chloride, sulfate, phosphate, nitrate, calcium,
magnesium, sodium, organic ions, and other ions. A certain level of these ions in
water is necessary for aquatic life. Changes in TDS concentrations can be harmful
because the density of the water determines the flow of water into and out of an
organism's cells. However, if TDS concentrations are too high or too low, the growth
of many aquatic lives can be limited which will cause death.
Figure 5.5 shows the result of TDS values against mussel sampling stations.
The values are in the range of 22.61 mg/L to 24.64 mg/L and a bit lower at the
earliest station and increased when approaching final station. The dissolved solid
represent the cloudiness or turbidity of water. It affects the amount of light entering
the water and thus restricts photosynthesis process. Therefore it also affects the
amount of dissolved oxygen that indirectly will affect the organism living including
P.viridis.
25.00
TDS (mg/L)
24.50
24.00
Nov
23.50
Dec
23.00
Jan
22.50
Feb
22.00
Mar
21.50
Apr
21.00
M1
M2
M3
M4
Stations
Figure 5.5: TDS concentration at mussel sampling stations, M1 to M4 from
November 2008 to April 2009.
109
5.2.1.6 Ammoniacal Nitrogen
The concentration of ammoniacal nitrogen against sampling stations is
illustrated in Figure 5.6. The trend of the concentration observed is shown in
decreasing trend when approaching towards the final station. The sources of
ammoniacal nitrogen in water originate from the discharge of ammonia such as from
agricultural activities. The range of ammoniacal nitrogen in water at mussel sampling
stations was 0.012 mg/L to 0.199 mg/L. An average of the ammoniacal nitrogen
concentration measured in this study does not comply with the Malaysia Interim
National Water Quality Standard (INWQS) which is at 0.1 mg/L.
Although shellfish aquaculture does not require additional food unlike other
forms of marineculture (Zhang et al., 2009) and less serious than finfish farming,
sedimentation of feces, pseudo feces and fall-off beneath the shellfish farms could
effectively lead to organic enrichment, which is generally similar to finfish
marineculture (Shanmugam et al., 2007). The urine and feces from the aquatic
animals can cause high content of ammoniacal nitrogen in water. Besides that, the
increase of nitrogen and phosphorus loading into water may by virtue increase
primary production and be associated with enhanced shellfish growth (Ferreira et al.,
2007).
Ammoniacal-N (mg/L)
0.25
0.2
Nov
Dec
0.15
Jan
0.1
Feb
Mar
0.05
Apr
0
M1
M2
M3
M4
Stations
Figure 5.6: Ammoniacal nitrogen concentration at mussel sampling stations, M1 to
M4 from November 2008 to April 2009.
110
5.2.1.7 Biochemical Oxygen Demand (BOD)
BOD levels are used as indicators of organic pollution in water quality
monitoring (Law, 1981). A low BOD reading means either that the water is clean or
that the organisms have been killed by toxic pollutants. The results for water samples
of BOD and COD at mussel’s stations were only available for four months,
November 2008 to February 2009. Figure 5.7 shows the concentration of BOD
against mussel collection stations. The BOD values for November 2008 increased
starting from M2 to the final station. Whereas the BOD values for December 2008 is
more stable where there are no much variations on the concentration against stations.
The ranges for BOD values at four mussel stations were 4.6 mg/L to 5.8 mg/L. As
more organic waste is available, the growth of bacteria will increase and decrease the
DO level as the organics are decomposed by microbes. High amounts of nutrients
and organic matters left on seabed can exceed the carrying capacity of water. Overall
the BOD values satisfy Class III of INWQS.
7
6
BOD (mg/L)
5
Nov
4
Dec
3
Jan
2
Feb
1
0
M1
M2
M3
M4
Stations
Figure 5.7: BOD concentration at mussel sampling stations, M1 to M4 from
November 2008 to February 2009.
111
5.2.1.8 Chemical Oxygen Demand (COD)
Figure 5.8 shows the concentration of COD against mussel’s stations from
November 2008 to February 2009. As illustrated in the figure, the values were a bit
higher in M2 and increase starting from station M2 to M4. In December 2008 to
February 2009 water samples shows an increasing trend while in November 2008
samples show a small decrease. The ranges of COD values were 1992 mg/L to 3640
mg/L. High COD values is due to the excessive discharge of organic matter and
inorganic chemicals from agriculture, urban areas and industries from the
surroundings into the coastal water. It could also be due to analytical interference
from chloride in seawater.
4000
3500
COD (mg/L)
3000
2500
Nov
2000
Dec
1500
Jan
1000
Feb
500
0
M1
M2
M3
M4
Stations
Figure 5.8: COD concentration at mussel sampling stations, M1 to M4 from
November 2008 to April 2009.
5.2.2
Heavy Metals
P.viridis is known as the main bioindicator and biomonitor of heavy metals
especially in Asian countries such as Singapore, Thailand and India (Vijayavel,
2009). Three major heavy metals that were analyzed in this study are Pb, Cd and Zn.
112
Most of the studies carried out at international level and specifically in Malaysia
regarding P.viridis used the three metals as their main analysis (Monawwar, 2002;
Yap et al., 2002; Aminah, 2003; Yap et al., 2003; Chee et al., 2003; Yap et al.,
2004a; Liu and Kueh, 2005). Cd, Pb and Zn are widely distributed in the coastal
environment, both from natural geological processes and anthropogenic activities,
this is of much interest to public health, since the metals are readily accumulated in
the soft tissues of P.viridis (Yap et al., 2002). For the used of analysis in this study,
only the concentrations of adult mussels (7 to 9 cm) were analyzed using dry weight
basis. The relative importance of water and food for metal bioaccumulation in
mussels is not influence by mussel’s size. This is because the influx rates of most
metal from water and food both decrease in a similar fashion with increasing size
(Yap et al., 2005b). In order to see the significant correlation of Cd concentration
between seawater and green mussel, water samples were also been collected at
mussels stations (M1 to M4).
5.2.2.1 Cadmium (Cd)
Figure 5.9 show the comparison of cadmium (Cd) concentrations in water
and P.viridis for each mussel sampling stations. The levels of Cd in mussels were
high although the ambient seawater that they were exposed to have low Cd levels.
The untreated waste from the industries may be one of the factors that enhanced the
accumulation of Cd in mussels.
Gradual decreasing trends were observed for concentration of Cd in water
samples. The concentrations were higher at the earlier station of M1 and decreased
when approaching the last station, M4. The high concentration of Cd at the earliest
station may be due to the untreated waste from the industries discharged from the
tributaries of Sungai Skudai, Sungai Danga, Sungai Melayu and part of Sungai
Segget. The first three months (November 2008 to January 2009) shows more
constant concentration values compared to the next three months (February 2009 to
April 2009) which is higher. Concentrations Cd in February to April 2009 water
samples exceeds the Interim National Water Quality Standard (INWQS) for
Malaysia that is at 0.1 mg/L for Cd.
113
The concentration of Cd in P.viridis resulted in different trend for mussel
samples. Lowest concentration of Cd in P.viridis was obtained at station M1 and the
highest gain at station M2. Then it starting to decrease when move towards the last
station, M4. The range of Cd concentration in P.viridis is from 0.11 µg/g to 1.21
µg/g. In comparison with the permissible limits set by the Malaysian Food
Regulation (1985) for Cd (1.00 µg/g), there was one sample of P.viridis that
exceeded the limit while the others were still under the limit.
1.4
1.2
1
Cd
0.8
Water
0.6
Mussel
0.4
Nov Dec Jan Feb Mar Apr
Nov Dec Jan Feb Mar Apr
M4
Nov Dec Jan Feb Mar Apr
M3
Nov Dec Jan Feb Mar Apr
M2
0
M1
0.2
Stations
Figure 5.9: Cadmium concentration in water and P.viridis samples at mussel
sampling stations, M1 to M4 from November 2008 to April 2009.
From Figure 5.9, we can see that the concentrations of Cd in water decreased
from station M1 at Danga to station M4 at Pendas area. In comparison with
concentration of Cd in P.viridis, most of the samples recorded higher concentration
than in water. However the concentrations of Cd in P.viridis for all samples in April
2009 were clearly lower than the ambient water that they were exposed to. This
indicates that the valve closure in response to the toxic levels of metal exposures and
thus preventing accumulation of metals inside the soft tissue of the mussels. Even
though less metal were accumulated in the soft tissue, the homeostatic mechanisms
to counteract the acute metal exposures seemed to have been used up and this
114
resulted in permanent disturbed metabolic function (Abel and Papoutsoglou, 1986;
Yap et al., 2004b).
5.2.2.2 Lead (Pb)
Figure 5.10 shows the comparison between the concentration of Pb in water
and Pb levels in P.viridis for each station. From the figure, there are obviously seen
that the concentration of Pb in mussels were much higher than the concentration in
water. Compare to Cd, the values of concentration for Pb in mussels were higher
than the average concentration of Cd in P.viridis. Generally, there are no exact trends
observed between the concentration of Pb in water and mussels.
30.0
25.0
Pb
20.0
15.0
Water
Mussel
10.0
5.0
Stations
Nov Dec Jan Feb Mar Apr
M4
Nov Dec Jan Feb Mar Apr
M3
Nov Dec Jan Feb Mar Apr
M2
Nov Dec Jan Feb Mar Apr
M1
0.0
Figure 5.10: Lead concentration in water and P.viridis samples at mussel sampling
stations, M1 to M4 from November 2008 to April 2009.
Concentration of Pb in water samples shows decreasing trend for most of the
months. The trend is quite similar to Cd concentration, where the highest level was
at the first station, M1. The reading then starts to decrease when approaching the
final station, M4. Only sample on February 2009 which turn to increase at M3 before
rapidly decreasing at the final station. The range of Pb concentration in water
115
samples were 0.12 mg/L to 3.21 mg/L. The higher Pb concentrations in the earlier
stations were plausibly related to the discharge of effluents from the nearby domestic
and industrial inputs. For all sampling stations in all months, none of the levels
complies with the Interim Water Quality Standard (INWQS) which is at 0.1 mg/L.
Concentration of Pb in mussels samples is shown in the same figure. Pb
concentration does not differ much except for several sampling occasions that began
to fluctuate. Generally, most of the Pb levels were below 10 µg/g and only several
readings show results higher than that. P.viridis showed a wide range of Pb
concentrations. The ratios of the highest to the lowest values among sites were 2.10
µg/g to 24.23 µg/g during the study period. Significantly, higher concentrations of Pb
in P.viridis were found in M2 (near to Sungai Perepat) where as lower concentrations
were recorded at final station, M4 (near to Pendas). Compared to the limits set by
Malaysian Food Regulation (1985), the permissible limit allowed for Pb
concentration is 2.00 µg/g. Pb levels for all sampling exceeded the regulation thus
meaning that it may be harmful to health since P.viridis is a source of seafood in
Malaysia.
5.2.2.3 Zinc (Zn)
The comparison of zinc content in mussels and seawater is shown in Figure
5.11 (a) and (b). Results for Zn are more interesting since it clearly differs from both
previous heavy metals correlations. The concentrations of Zn in P.viridis were
obviously higher than in water. The ranges between both concentrations were too
high. The high Zn concentration could be due to Zn being needed for metabolism in
the different soft tissues of P.viridis (Viarengo et al., 1985) although it might also be
regulated (Phillips, 1985).
Zinc is one of the essential metals for marine organisms and it increases the
enzymatic activity (Vallee, 1978). Zinc concentrations in tissues of marine organism
are usually much higher and more variable than concentration of other metals. The
concentration of Zn in water samples as shown in Figure 5.11(a) was found between
116
0.034 mg/L to 0.56 mg/L. Generally, the trend of concentration for Zn is much
similar to Cd concentration trend. The highest concentration of Zn as expected was
found at the first station, M1 (0.142 mg/L to 0.56 mg/L). While the lowest levels of
Zn is at the final station, M4. Van de Berg (1987) reported that, increases in
dissolved and suspended fractions of zinc in estuarine water were identified in the
mixing zone between fresh and brackish water. The increases were attributed to the
increase in residence time of zinc in estuary compared to freshwater. The
concentrations of Zn are lower at the ocean surface than in deeper water (Bruland et
al., 1978).
0.6
Zn (mg/L)
0.5
Nov
Dec
0.4
Jan
0.3
Feb
0.2
Mar
Apr
0.1
0
M1
M2
M3
M4
Stations
(a)
120
100
Nov
Zn (µg/g)
80
Dec
60
Jan
Feb
40
Mar
20
Apr
0
M1
M2
M3
M4
Stations
(b)
Figure 5.11: Zinc concentration in (a) water and (b) P.viridis samples at mussel
sampling stations, M1 to M4 from November 2008 to April 2009.
117
Uptake of Zn by P.viridis was dependent on both salinity and temperature
(Conti and Cecchetti, 2003). The absorption of zinc by aquatic animals tends to be
from water rather than food. Only dissolved zinc tends to be bioavailable, and
bioavailability depends on the physical and chemical characteristics of the
environment and biological processes (Bruland et al., 1978). Result of the
concentration of Zn for P.viridis is shown in Figure 5.11(b). The concentration of Zn
in P.viridis varied slightly among the study sites where the ratios of the highest to the
lowest Zn concentration being 21.1 µg/g to 98.45 µg/g during study period. These
indicates that the spatial distribution of this metal was not fairly uniform due to area
and months. Slightly higher concentration of Zn was found at station M1 where
average from all sampling was higher than the other stations average. November
2008 became the highest accumulation of Zn compared to other months. In
comparison with the permissible limits by Malaysian Food Regulation (1985) for Zn,
all of the Zn level does not exceed the permissible level of 100 µg/g.
Based on the heavy metals concentration in P.viridis measured in DangaPendas coastal area, it is possible to report that the contamination of Cd and Zn is not
serious. However, concentration levels of Pb are quite high which exceed the
Malaysian recommended permissible limit on food sources. High levels of Pb found
in the P.viridis in this region were plausibly related to discharge of effluents from the
nearby domestic and industrial inputs.
5.3
Comparison of Cd, Pb and Zn Concentrations in P.viridis from Previous
Studies
In order to see the variation of heavy metals concentration in P.viridis, data
for Cd, Pb and Zn concentration from a series of years were gathered. A large
number of sampling stations were analyzed by the previous studies and only
significant stations related with this study area were considered. Pantai Lido station
was the one selected for analyzing the concentration for Cd, Pb and Zn in this study.
Table 5.1 is the list of the maximum permissible limits of heavy metals in seafood
from different countries. The list can be used as guideline to evaluate quality of
Malaysia seafood especially P.viridis. While Table 5.2 is the comparison of metal
118
concentrations in P.viridis collected from the present study and those from the
previous studies (Ibrahim, 1993; Yap et al., 2002, Yap et al., 2003; Chee et al.,
2009). Detail analysis on the variation of the concentration of Cd, Pb and Zn in
P.viridis collected in Pantai Lido will be illustrated as graphs of Cd, Pb and Zn
against year in Figure 5.12, 5.13 and 5.14.
Table 5.1: Guidelines of maximum permissible limits of heavy metals (µg/g) in
seafood from different countries (Adapted from Yap et al., 2004a)
Location
Cd
Pb
Zn
Permissible limits by Malaysian Food Regulation (1985)
1.00
2.00
100.0
International Council for the Exploration of the Sea
1.80
3.0
-
5.00
10.0
250
-
6.67
667
3.7
1.7
-
Australian Legal Requirement (NHMRC, 1987)
10.0
-
750
Permissible limit set by the Hong Kong Environmental
2.00
6.00
-
(ICES, 1988)
Maximum permissible levels established by Brazilian
Ministry of Health (ABIA, 1991)
Permissible limit set by Ministry of Public Health,
Thailand (MPHT, 1986)
Food and Drug Administration of the United State
(USFDA, 1990)
Protection Deoartment (HK EPD, 1997)
119
Table 5.2: Mean concentration of Cd, Pb and Zn in P.viridis collected from Pantai
Lido in the present and previous studies.
Year
Cd (µg/g)
Pb (µg/g)
Zn(µg/g)
Reference
1991
NA
NA
16.143
Ibrahim (1993)
± 1.107
1998
2000
2006
2009
0.70
3.30
80.89
± 0.07
± 0.27
± 2.24
0.68
4.03
116.9
(0.36-1.53)
(1.83-8.98)
(68.1-165)
1.179
22.812
67.767
(0.86-1.53)
(6.47-35.2)
(47.6-92.8)
0.26
6.89
59.632
(0.041-0.39)
(4.30-8.43)
(45.2-76.52)
Yap et al.(2002)
Yap et al.(2003)
Chee et al.(2009)
Current study
Figure 5.12 show the concentration of Cd in P.viridis against year. As seen in
the graph, the concentrations increased up to 1.18 µg/g in 2006 and decreased to the
lowest level of 0.26 µg/g in 2009. Year 1998 to 2000 show more constants Cd
concentration levels with an average of 0.68 µg/g to 0.70 µg/g. It start to increase in
2006 due to the discharge of domestic and industrial wastes entering this area from
Sungai Skudai, Sungai Danga and Sungai Segget. Moreover, 2006 is the starting year
of IDR development area (SJER CDP, 2006). The aggressive and massive
development progress might be one of the reasons which contribute to higher Cd
concentration in this area. However the present study shows drastic reduction of Cd
concentration in P.viridis for this area which is possibly due to the intensive
prevention program in IDR development by the developers and government.
Although most of the concentration is quite high, it is still under the permissible limit
set by Malaysia Food Regulation (1985) which is 1.0 µg/g. Only samples on 2006
recorded Cd concentration more than that. In comparison with regulation from other
countries, the concentrations of Cd were under the limits.
120
1.4
1.2
Cd (µg/g)
1
0.8
0.6
Cd
0.4
0.2
0
NA
1991
1998
2000
2006
2009
Year
Figure 5.12: Cadmium concentration in P.viridis samples at Pantai Lido from 1991
to 2009
Concentrations of Pb in P.viridis against year from 1991 to 2009 is shown in
Figure 5.13. The trend of Pb concentration in P.viridis is much similar to the trend
observed for Cd concentration where the values increased from 1998 to 2006 and
decreased in 2009. The highest Pb value recorded in 2006 with 22.8 µg/g and the
lowest Pb value obtained in early 1998 with 3.30 µg/g. Due to the similar trend for
both heavy metals, the possibly reasons of the high Pb concentration may be similar
to the reasons found in Cd concentration. Even though the trend is much similar, the
level of Pb concentration in P.viridis is much higher. Based on the Malaysian Food
Regulation (1985), none of the samples were under the limit of 2.00 µg/g. The
concentration of Pb seems to be very critical as the comparison
comparison with regulation and
guidelines from other countries shows that the values were still within and above the
limits.
121
25
Pb (µg/g)
20
15
10
Pb
5
0
NA
1991
1998
2000
2006
2009
Year
Figure 5.13: Lead concentration in P.viridis samples at Pantai Lido from 1991 to
2009.
Zn is another heavy metals which is critical to aquatic organisms. The
concentration trends for Zn were obviously different than Cd and Pb. The
concentrations started to increase from year 1991 to 2000 and gradually decreased in
2006 and 2009. The highest concentration obtained for Zn was in 2000 at 116.9 µg/g
while the lowest levels recorded was in 1991 at 16.14 µg/g. Generally, although the
concentrations of Zn were higher for most of the year, it is still under the permissible
limit set by Malaysian Food Regulation (1985) which is 100 µg/g. Comparison made
to other guidelines shows that the concentrations of Zn in P.viridis were far below
the limits which indicate that they are safe as a food source.
140
120
Zn (µg/g)
100
80
60
Zn
40
20
0
1991
1998
2000
2006
2009
Year
Figure 5.14: Zinc concentration in P.viridis sampless at Pantai Lido from 1991 to
2009.
122
5.4
Effects of Water Quality on Green Mussel
The intertidal areas are natural habitats of marine mussels and they are
usually close to estuaries. Therefore, the chance of exposure to many contaminants
from land-based activities through the riverine system as well as sea-based sources,
is high (Yap et al., 2004b). The elevated levels of metals found in the intertidal and
marine aquatic life especially P.viridis could be due to land-based activities and the
areas semi-enclosed topography which may aggravate the pollution problem in
general. Moreover, the rapid development of IDR which plan to end in 2025 will be
the main contributor to drastic boost of heavy metals in this region.
Trace metals are available to aquatic animals through uptake from both
dissolved and dietary phases (Inglis and Gust, 2003). P.viridis are filter feeders and
tends to bioaccumulate heavy metals in their tissue. The heavy metals could simply
accumulate through time, becoming more toxic threat as their concentrations increase
since heavy metals are inorganic chemicals that are non-biodegradable, cannot be
metabolized and will not break down into harmless forms (Yap et al., 2004b).
Consumption of metal-contaminated P. viridis may also cause toxicity to humans.
Other environmental factors are also contributing factors which influence the
accumulation of metals in P.viridis. The factors include temperature, pH, tides,
velocity, wind direction and rain. However, based on the study done by Ibrahim
(1993), at the same area, these factors have less influence on the levels of metals
accumulation by marine organisms including P.viridis.
Based on the analysis and results of effects of water quality on green-mussel,
there are no exact correlation that can prove the statement of the effects of water
quality to this species directly. However, there are significant relations observed, as
the concentration of pollutant such as heavy metals increased, the concentration of
that particular pollutant in green mussel, P.viridis were also increased. As
bioaccumulator and filter-feeder, green mussels accumulate heavy metals and toxins
in its tissues up to hundreds of times more concentrated compared to its habitat.
123
According to Farrington (1987), green mussels have the capability to accumulate the
excess of heavy metals from seawater up to 100,000 times higher than the seawater.
In mussels, they can induce metallothionein-like proteins to defend against heavy
metals or in some cases, mineral concretions can be a way of storage for heavy
metals in the soft tissue of mussels (Phillips and Rainbow, 1993). This has been the
reason why mussel can accumulate high levels of metals (Yap et al., 2004b).
However, exposed to higher concentration in heavy metals can also effect the
growth of green mussels. Study reviewed by Yap et al., (2004b) revealed that, the
percentage of mortality for young mussels (3 to 4 cm) were high when the
concentration of heavy metals in water was high. This indicate that the higher
concentration of pollutants in water, can effect the quality of green mussels itself as it
may be consumed by human as food sources, but it can also effects the growth of
young green mussels as they might die.
CHAPTER 6
CONCLUSION AND RECOMMENDATIONS
6.1
Conclusion
Studies over the last three decades have clearly demonstrated that landuse has
direct and indirect impacts on water quality, stream characteristics, aquatic habitat,
macroinvertebrates, and fish. Although the exact mechanisms for these impacts at
specific sites is not always immediately obvious, the impacts have been
demonstrated to be due to altered hydrology and its water quality and aquatic habitat,
both at the individual site and at surrounding scale. In this study, the changes in
landuse also affects water quality of the surrounding water bodies. The levels of
water quality decreased with increase in development along Danga-Pendas coastal
area especially. The reduced water quality status has major impact with increase in
pollutants, nutrients and heavy metals. Thus, these will also have effects on green
mussel, P.viridis either directly or indirectly.
Monitoring of landuse change along Danga-Pendas provide useful
information in analyzing the effects to water quality along this area. Remote sensing
and Geographic Information System (GIS) techniques can be used to monitor the
conversion of landuse types Landuse studies using remote sensing data have received
immense attention worldwide due to their importance in global change analysis With
time series information and wide range coverage make it useful in monitoring the
landuse change thus helpful in analyzing the effects on water quality.
125
As a result, we can see the degradation in water quality levels in comparison with
INWQS, with the increase of build-up or development along Danga-Pendas coastal
area.
Build-up or developed area have recorded major increased trend with changes
about 4224.96 ha (23.63%) between 1991 to 2005 and 6610.05 ha (29.91%) within
three years from 2005 to 2008. Forest/ scrubs, cultivation area and mangrove
recorded the highest reduction trend. Mangrove area decreased at a total of 41.31%
from 1991 to 2008. Forest/ scrubs and cultivation area changed 38.5% and 32.86%
respectively. Along with the increase of developed area and decreased in vegetated
area (forest, cultivation and mangrove), there were reduction in water quality levels,
where most psychochemical water quality parameter satisfies class II and III of
INWQS while most of the heavy metals exceeded INMWQS limits.
As well as the landuse change affects the water quality, the degradation of
water quality also affects aquatic life such as P.viridis in their ecosystem. Based on
the analysis, there was no exact correlation found between the concentrations of trace
metals in water with green mussels collected from the time being. However, there
was significant relation when high concentrations of metals were recorded in water,
there were also higher concentrations of those metals in green mussels. As
bioaccumulator and bioindicator, P.viridis is able to accumulate up to hundreds times
of the concentrations in water itself. The degradation of water quality level due to
landuse, might also give significant impact to P.viridis since they can accumulate
high concentration of pollutants thus making it hazardous to human as food source. It
also effects the growth of young P.viridis exposed to high metals concentrations.
This may influence the distribution of P.viridis as a native species laid along DangaPendas coastal area.
In assessing the severity of the development along Danga-Pendas coastal area
for the time being, there are huge landuse changes especially in Johor Bahru town
and Nusajaya area which involved IDR development. Based on the SJER planning,
by the time of development ends in 2025, there would be vast landuse changes in
Danga-Pendas and all along the coastal area of south Johor. The worst is that, most
126
of the development will directly and indirectly affects the water quality along this
region. Overall the levels of water quality in this area are slightly polluted. There
were several parameters especially metals recorded in higher concentration as they
should be based on the INWQS and INMWQS. Extensive care and prevention
methods should be applied especially for IDR development in order to limit direct
access of pollutants to coastal water which affects water quality and mussels. This is
because there would be more landuse changes that will decrease water quality levels
and affect mussels by the end of its progress.
As a conclusion, there are significant effects of landuse changes on reducing
water quality levels along Danga-Pendas coastal area. As the area of land-based
activities such as development and agriculture increased, there was decrease in water
quality levels. Although there were no exact correlation of water quality on mussels,
but several previous studies have revealed small relation of water quality on mussel’s
accumulation and growth.
6.2
Recommendations
Regarding the processes and results obtained in this study, there are several
recommendations that can be applied in order to get better results and analysis
concerning the effect of landuse changes in water quality and green mussels along
this area. The recommendations include;
i)
The effects of water quality due to development and landuse changes
along this area also need to take into consideration pollutants originating
from Singapore. The information of landuse types, land-based activities
might be required to support the study. If possible sampling should be
conducted nearest to the coastal area of Singapore. Thus it can be a point
in analyzing the effects from the landuse for both countries that is sharing
the straits.
127
ii)
The extensive data collection as regards to water and mussels with a
longer time frame would be required to better facilitate analysis on both
water and mussel.
iii)
The analysis of uptake of pollutants and heavy metals should also involve
sediment and aquatic plants. Analyzing samples of sediment and plants
provides information to correlate the concentration of metals in sediments
and plants thus analyzing the sources of green mussel consumer.
128
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145
APPENDIX A
WATER QUALITY STANDARD
a)
INTERIM NATIONAL WATER QUALITY STANDARD
Parameters
Unit
Temperature
o
C
pH
I
IIA
IIB
III
IV
V
-
normal
-
normal
-
-
6.5 - 8.5
6-9
6-9
5-9
5-9
-
Conductivity
uS/cm
1000
1000
-
-
6000
-
Colour
Pt -CO
15
150
150
-
-
-
DO
mg/L
7
5-7
5-7
3-5
<3
<1
BOD
mg/L
1
3
3
6
12
>2
COD
mg/L
10
25
25
50
100
> 100
Oil & Grease
mg/L
natural
40; nil
nil
-
-
-
Dissolved solids
mg/L
500
1000
-
-
4000
-
Suspended solids
mg/L
25
50
50
150
300
> 300
Turbidity
NTU
5
50
50
-
-
-
Ammoniacal – N
mg/L
0.1
0.3
0.3
0.9
2.7
> 2.7
nil
nil
nil
-
-
-
nil
nil
nil
-
-
Floatables
Odour
Salinity
10 -3
0.5
1
-
-
2
-
E. coli
MPN/100mL
10
100
400
5000
5000
-
Total coliform
MPN/100mL
100
5000
5000
50000
50000
> 50000
Hardness
mg/L
natural
250
250
-
-
-
K
mg/l
natural
-
-
-
-
-
F
mg/l
natural
1.5
1.5
10
1
>1
NO3
mg/l
natural
7
7
-
5
>5
P
mg/L
natural
0.2
0.2
0.1
-
-
S
mg/L
natural
0.05
0.05
0.001
-
-
Cd
mg/L
natural
0.01
0.01
0.01
0.01
> 0.01
Cu
mg/L
natural
1
1
-
0.2
> 0.2
Fe
mg/L
natural
0.3
0.3
1
1-5
>5
Pb
mg/L
natural
0.05
0.05
0.02
5
>5
Mn
mg/L
natural
0.1
0.1
0.1
0.2
> 0.2
Ni
mg/L
natural
0.05
0.05
0.9
0.2
> 0.2
146
b)
WQI-SI FORMULA
Parameters
Value
Subindex Equation (SI)
COD
If X = < 20
SICOD = 99.1 – 1.33X
If X > 20
SICOD = 103 x [E]-0.0157X - 0.04X
If X = <5
SIBOD = 100.4 – 4.32X
If X >5
SIBOD = 108 x [E]-0.055X – 0.1X
If X = < 0.3
SIAN = 100.4 – 4.32X
If 0.3 < X < 4
SIAN = 94 x [E]-0.573X – 5(X-2)
If X = > 4
SIAN = 0
If X = < 100
SISS = 97.5 x [E]-0.00676X + 0.7X
If 100 < X < 1000
SISS = 71 x [E]-0.0016X – 0.015X
If X= > 1000
SISS = 0
If X < 5.5
SIpH = 17.2 – 17.2X + 5.02X2
If 5.5 = < X < 7
SIpH = -242 + 95.5X – 6.67X2
If 7 = < X <8.75
SIpH = -181 + 82.4X – 6.05X2
If X = > 8.75
SIpH = 536 – 77X + 2.76X2
BOD
AN
SS
pH
DO
X = DO (mg/L) * 12.6577
If X = < 8
SIDO = 0
If 8 > X
SIDO = -0.395 + 0.030X2 – 0.00019X3
Note: (1) X is concentration of parameter in unit mg/L, except for pH and DO
(2) x is symbol of multiply
(3)SIDO, SIBOD, SICOD, SIAN, SISS and SIpH are the Sub Index (SI) of the
respective water quality parameters which isused to calculate the Water Quality
Index (WQI).
147
APPENDIX B
LABORATORY ANALYSIS
a)
MUSSEL DIGESTION (DRY WEIGHT)
i)
Sample Preparation
Weigh about 5 g of sample (dry weight) into a digestion tube. Add 5 mL of HNO3
and then 5 mL of H2SO4 to the sample. Allow the reaction to proceed. When the reaction
slows, place the tubes in a hot-block digestion apparatus and heat at a low temperature
(60°C) for 30 min. Remove the tubes from the hot block, allow to cool, add 10 mL of
HNO3, return tubes to digestion rack and heat slowly to 120°C. Increase the temperature to
150°C. Remove the tubes when the samples go black, allow to cool, then add 1 mL of
H2O2. A vigorous reaction may occur. Return the tubes to the block. Repeat the H2O2
additions until the samples are clear. Remove the tubes and make up to 50 mL with
deionized water.
Most elements can be determined directly; however, to determine Pb and Cd,
solvent extraction is used to concentrate these elements. Take 40 mL of the digest and
make up to 100 mL; add 5 mL of APDC and 5 mL of MIBK. Shake vigorously for 5 min.
Determine Pb and Cd in the MIBK phase.
ii)
Analysis
Standards should be treated in the same way as samples. It is important that the
standards contain the same amount of acid as the samples, especially H2SO4, as it will have
a viscosity effect that will suppress sensitivity. Make up stock solutions as explain in the
heavy metals analysis.
148
b)
BIOCHEMICAL OXYGEN DEMAND (BOD) ANALYSIS
Biochemical Oxygen Demand (BOD5) was determined as soon upon return to
laboratory. BOD5 was determined as the difference between initial and 5-days oxygen
concentration in bottles at 20°C (following APHA 5210 B method). Samples were diluted
five times and seeded.
i)
Preparation of BOD reagent
For preparation of phosphate buffer solution, potassium dihydrogen phosphate
(KH2PO4) (8.5 g), dipotassium hydrogen phosphate (K2HPO4) (21.75 g), disodium
hydrogen phosphate heptahydrate (Na2HPO4 7H2O) (33.4 g), and ammonium chloride
(NH4Cl) (1.7 g) were dissolved in distilled water (about 500 mL) and diluted to 1 liter. The
pH of this buffer should be 7.2 and checked with a pH meter. Magnesium sulfate solution
was prepared by dissolving magnesium sulfate heptahydrate (MgSO4 7H2O) (22.5 g) in
distilled water and diluted to 1 liter. Preparation of calcium chloride solution was done by
dissolving anhydrous calcium chloride (CaCl2) (27.5 g) in distilled water and diluted to 1
liter. While ferric chloride solution was prepared by dissolving ferric chloride hexahydrate
(FeCl3 6H2O) (0.25 g) in distilled water and diluted to 1 liter. All the reagents were
discarded if there were any sign of biological growth in the storage bottle.
ii)
Preparation of dilution water
The distilled water used for dilution water must be of high grade and free from
contaminants (such as copper and chlorine) which could inhibit the growth of bacteria.
Each of phosphate buffer, magnesium sulfate solution, calcium chloride solution and ferric
chloride solution (1mL, 1mg/L) were added in volumetric flask (1000 mL). Double
distilled deionized water was added until the mark.
iii)
BOD5 analysis
149
Sample (60 mL) was placed into the BOD bottle (300 mL). A small amount of seed
(2 mL) was added directly to the sample and dilution water was added until the mouth of
the bottle. The initial DO content of each sample was determined by using the DO meter
together with the blank dilution water. The DO meter was calibrated before being used.
The bottles were placed in the incubator at 20°C, dark and incubate for five days. After the
five days, another dissolved oxygen reading (mg/L) was taken using the DO meter. BOD5
was calculated based on the equation.
BOD5, mg/L = [(DO1 – DO2) – (S1 – S2)] X DF
BOD5 = Biochemical Oxygen Demand
DO1
= Initial dissolved oxygen concentration of sample (mg/L)
DO5
= Sample dissolved oxygen concentration after five days (mg/L)
S1
= Initial dissolve oxygen concentration of blank (mg/L)
S2
= Blank dissolved oxygen concentration after five days (mg/L)
DF
= Dilution factor
c)
CHEMICAL OXYGEN DEMAND (COD) ANALYSIS
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) based on Standard Method APHA 5220-C procedures.
i)
Turn on the COD reactor. Preheat to 150OC. Place the plastic shileed in front of
the reactor.
ii)
Pipet 3 mL of COD reagent into the vial.
iii)
Hold the vial at 45O angle and pipet 2.00 mL of sample into the vial.
iv)
Replace the vial cap tightly. Rinse the COD vial with deionized water and wipe
the vial clean with a paper towel.
150
v)
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.
vi)
Prepared a blank by repeating step 2 to 5. For blank use deionized water as
sample. Place the blank in the preheated COD reactor.
vii)
Heats the vials for 2 hours.
viii)
After 2 hours, turn off the reactor and wait about 20 minutes for the vials to cool
to 120OC or less.
ix)
Invert each vial several times while still warm. Place the vial into a rack. Wait
until the vials have cooled to room temperature.
x)
d)
Use HACH DR 5000 Spectrophotometer to get the result.
AMMONIACAL NITROGEN ANALYSIS
All the reagents used in the analysis of ammoniacal nitrogen test were
manufactured by Merck with analysis reagents grad. The method was analogous to EPA
350.1 and APHA 4500-NH3 D. The procedures comprised of the preparation of sample and
standard solution are described below.
i)
Standard preparation
Ammonium (NH4+) Standard Solution (1000 mg/L) was diluted to stock solution
(100 mg/L) by double distilled deionized water. To prepare standard solution with
concentration 0.5 mg/L, stock solution (250 µL) was pipetted by using micropipette with
capacity 100-1000 µL into the test tube diluted with double distilled deionized water until
the volume reached 5 mL.
ii)
Sample preparation
Pretreated sample (5 mL) was pipetted into a test tube and reagent NH4-1 (0.6 mL)
and sodium hydroxide solution (1 mL, 5 mol/L) was added and mixed. 1 level microspoon
of reagent NH4-2 was then added and shaked vigorously until the reagent was completely
dissolved. The solution was left for five minutes for reaction (time A) to occur. Next,
151
reagent NH4-3 (4 drops) was added, mixed and left to stand for another five minutes for
reaction (time B) to complete. Blank solution was also prepared by using double distilled
deionized water instead of sample but treated using the same procedure as the sample.
iii)
Instrumental analysis
The ammoniacal nitrogen in the samples was filled into the cell and measured using
HACH DR5000 Spectrometer programmed as HACH Program 2460 N-Ammonia LR
TNT, with UV wavelength 655.0 nm.
e)
HEAVY METALS
The standard operational procedures from APHA and US EPA with certain
modification adapted to the working environment was applied to increase the data
reliability.
Applied Standard Operational Procedures
Standard method
APHA
Method
APHA 3111
Preparation of working solutions method
APHA 311 1 B
Direct Air Acetylene Flame Method (Atomic Absorption
Spectrometer)
US EPA
3005 A
Acid digestion of water samples method
i)
Reagents and Solutions
Double distilled deionized water and high purity reagents were used for all
preparations of standards and sample solutions. Cuprum and Plumbum (MERCK) stock
solutions 1000 mg/L were used in preparation of standard solutions. Nitric acid, HNO3 and
hydrochloric acid, HCl manufactured by HmbG Chemicals were used for extraction
method.
152
ii)
Preparation of working solution
The Pb, Cd, Zn and Ni standard solutions were used as a reference in determining
metal concentration in samples. The standards were prepared from certified atomic
absorption reference standards. Dilution of stock solutions were performed to obtained the
desired concentration standard solutions. Dilution was made based on the following
calculation:
M1V1 = M2V2
M1 = Concentration of stock standard solution (mg/L)
V1 = Volume of stock solution needed (mL)
M2 = Concentration of desired standard solution (mg/L)
V2 = Final volume of the desired standard solution (mL)
iii)
Sample preparation
Preparation of water samples by acid digestion was adapted from US EPA 3005A
standard method. A measured of well mixed sample (50 mL) was transferred into a clean
beaker (100 mL). HNO3 (2 mL ) and HCl (5 mL ) were added into the beaker and covered
with watch glass. The sample was heated at 95 ± 5 °C until the volume was reduced to
about 15 mL. The sample was then filtered and the filtrate was collected in volumetric
flask (50 mL), followed by careful adjusted volume with double distilled deionized water
to 50 mL.
iv)
Instrumentation
A Perkin Elmer Model AAnalyst 400 Flame Atomic Absorption Spectrometer was
used for determination of metals in samples. The AAS was equipped with hollow cathode
lamps with specified wavelength and air acetylene burner. The instrumental settings were
153
made based on the manufacturer’s manual guideline. The detection method was adapted
from standard method APHA 311 1 B Direct Air Acetylene Flame Method.
APPENDIX C
ACCURACY ASSESSMENT REPORTS FOR 1991, 2000, 2005 and 2008
CLASSIFICATION
i)
Accuracy Assessment for 1991 Landuse Classification
ii)
Accuracy Assessment for 2000 Landuse Classification
154
iii)
Accuracy Assessment for 2005 Landuse Classification
iv)
Accuracy Assessment for 2008 Landuse Classification
155
APPENDIX D
WATER QUALITY DATA
(a)
Sampling Data – Water Stations (Water Quality Data)
i)
Station
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
Temperature (ºC)
Nov 2008
29.50
29.30
29.18
29.50
29.47
29.48
29.36
29.21
29.00
29.90
29.40
Dec 2008
29.30
28.90
28.63
29.10
29.60
29.36
29.20
29.90
28.40
28.60
29.10
Months
Jan 2009
29.10
28.70
28.50
28.90
29.10
28.20
28.57
28.64
28.67
29.72
29.30
Feb 2009
29.53
29.65
29.39
29.56
29.65
29.55
29.65
29.67
29.64
29.99
29.60
Mar 2009
29.47
29.21
29.18
29.45
29.47
29.52
29.27
29.35
29.36
29.46
29.60
Apr 2009
28.21
27.81
27.75
28.21
28.35
28.90
28.55
28.75
28.64
28.45
28.44
156
ii)
Station
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
iii)
Dissolved Oxygen (mg/L)
Nov 2008
2.81
3.31
2.31
3.31
2.95
3.71
3.81
3.66
4.37
4.37
4.33
Station
S1
S2
S3
Months
Jan 2009
2.65
3.21
3.11
3.22
2.52
3.47
3.51
4.01
4.21
4.27
4.22
Feb 2009
2.95
3.32
3.33
3.11
3.71
4.05
4.11
4.25
4.21
4.35
4.35
Mar 2009
2.06
2.33
2.32
2.32
2.38
4.03
3.99
4.21
4.31
4.48
4.23
Apr 2009
2.66
3.22
3.11
3.11
3.20
4.09
4.21
4.44
4.22
4.22
4.12
Salinity (ppt)
Station Nov 2008
S1
18.17
S2
23.21
S3
23.55
S4
19.21
S5
19.75
S6
24.37
S7
24.56
S8
24.32
S9
24.44
S10
25.61
S11
25.45
iv)
Dec 2008
2.85
3.32
3.31
3.31
2.81
4.05
4.05
4.51
4.32
4.31
4.44
Dec 2008
19.05
23.15
23.12
19.31
19.66
23.32
23.66
24.66
24.61
24.66
24.71
Months
Jan 2009
19.55
23.65
23.26
22.78
21.46
23.22
23.76
24.21
24.26
25.66
24.61
Feb 2009
20.25
23.25
22.36
20.05
19.91
22.77
23.44
23.41
23.45
23.44
23.71
Mar 2009 Apr 2009
18.87
19.20
23.45
23.22
22.35
23.21
19.44
19.40
19.10
19.05
23.25
23.41
23.21
23.44
23.44
24.50
23.64
24.10
23.52
24.55
23.55
24.65
Dec 2008
9.24
9.27
9.19
Months
Jan 2009
9.65
9.62
9.62
Feb 2009
8.67
8.71
8.83
Mar 2009
8.44
8.32
8.23
pH
Nov 2008
7.38
7.52
7.57
Apr 2009
8.18
8.12
8.19
157
S4
S5
S6
S7
S8
S9
S10
S11
v)
7.55
7.61
7.76
8.12
7.96
8.01
8.34
8.57
9.26
9.33
9.34
9.36
9.33
9.21
9.25
9.22
8.31
8.35
8.26
8.24
8.19
8.16
8.18
8.24
8.12
8.09
8.07
8.13
8.13
8.06
8.11
8.14
Dec 2008
24.67
26.45
26.46
21.46
24.61
22.10
22.61
24.67
24.47
22.86
22.73
Months
Jan 2009
24.02
25.41
24.16
23.10
22.40
23.59
23.10
24.55
25.45
23.41
23.59
Feb 2009
23.81
24.90
24.70
23.21
23.70
22.94
22.55
23.67
23.67
23.33
24.43
Months
Jan 2009
0.558
0.199
0.320
0.149
0.244
0.083
0.073
0.092
0.082
0.043
0.036
Feb 2009
0.446
0.162
0.119
0.250
0.214
0.096
0.081
0.056
0.067
0.054
0.096
Mar 2009
24.50
25.20
26.47
24.00
23.57
23.20
23.42
24.67
24.07
23.20
24.00
Apr 2009
23.71
24.45
23.32
23.40
23.21
23.21
22.46
23.57
23.61
24.07
24.68
Ammoniacal-nitrogen
Station Nov 2008
S1
0.311
S2
0.241
S3
0.223
S4
0.080
S5
0.126
S6
0.020
S7
0.012
S8
0.023
S9
0.022
S10
0.045
S11
0.023
vii)
8.65
8.58
8.72
8.56
8.55
8.67
8.66
8.68
Total Dissolved Solid (TDS) (mg/L)
Station Nov 2008
S1
26.40
S2
24.67
S3
24.55
S4
23.98
S5
24.20
S6
22.09
S7
22.99
S8
23.56
S9
24.55
S10
23.56
S11
23.98
vi)
9.58
9.54
9.59
9.52
9.53
9.54
9.57
9.61
Dec 2008
0.965
0.410
0.965
0.162
0.453
0.132
0.095
0.081
0.061
0.078
0.075
Mar 2009
1.137
0.199
0.762
0.230
0.230
0.076
0.091
0.045
0.033
0.056
0.076
Biochemical Oxygen Demand (BOD)(mg/L)
Station
S1
S2
Nov 2008
5.2
5.4
Months
Dec 2008
6.2
6.5
Jan 2009
6.5
6.2
Feb 2009
5.1
5.4
Apr 2009
0.513
0.24
0.424
0.156
0.231
0.074
0.066
0.024
0.078
0.062
0.035
158
S3
S4
S5
S6
S7
S8
S9
S10
S11
viii)
Station
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
x)
Station
S1
S2
5.4
5.4
5.6
5.6
5.2
5.3
5.4
5.6
5.7
6.2
6
5.9
5.8
5
5.6
5.3
4.9
5
5.2
5.9
5.4
5.7
5.3
5.2
5
4.6
4.9
Chemical Oxygen Demand (COD) (mg/L)
Station
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
.
ix)
5
4.6
5
4.6
5.3
5.6
4.9
5.8
6
Nov 2008
976
1672
2370
2164
2642
1992
1526
2400
2745
2646
2840
Months
Dec 2008
836
2405
2150
2255
2400
3315
3470
3640
3850
3000
3745
Jan 2009
834
2404
2088
2232
2284
3215
3745
3125
3480
3470
3647
Feb 2009
1076
1980
2210
2098
2265
2552
3065
3120
3350
3186
3264
Zinc (mg/L)
Nov 2008 Dec 2008
0.201
0.209
0.245
0.209
0.287
0.214
0.178
0.165
0.198
0.211
0.121
0.102
0.098
0.098
0.031
0.021
0.052
0.034
0.019
0.011
0.034
0.032
Months
Jan 2009
0.201
0.207
0.214
0.112
0.209
0.087
0.078
0.076
0.088
0.112
0.1
Feb 2009
0.218
0.216
0.234
0.187
0.271
0.111
0.122
0.131
0.103
0.09
0.09
Mar 2009
0.43
0.65
0.55
0.43
0.45
0.19
0.2
0.23
0.18
0.11
0.1
Apr 2009
0.421
0.512
0.45
0.395
0.43
0.315
0.334
0.21
0.2
0.11
0.12
Months
Jan 2009
0.560
0.980
Feb 2009
2.650
3.230
Mar 2009
1.910
2.230
Apr 2009
2.930
3.000
Lead (mg/L)
Nov 2008
1.510
0.990
Dec 2008
2.030
3.090
159
S3
S4
S5
S6
S7
S8
S9
S10
S11
1.090
1.650
1.210
0.440
0.540
0.250
0.890
0.560
0.210
xi)
3.090
0.890
2.780
1.780
0.450
0.320
0.210
0.340
0.220
2.430
2.090
2.210
0.230
0.900
0.340
0.450
0.090
0.120
2.250
1.650
1.700
1.340
1.230
0.780
0.320
0.830
0.230
3.050
2.780
2.800
0.780
0.560
0.350
0.780
0.550
0.670
Months
Jan 2009
0.09
0.12
0.08
0.06
0.07
0.05
0.07
0.07
0.04
0.05
0.05
Feb 2009
0.41
0.44
0.46
0.36
0.41
0.32
0.22
0.18
0.17
0.18
0.15
Mar 2009
0.45
0.48
0.47
0.38
0.44
0.28
0.265
0.14
0.13
0.125
0.1
Apr 2009
0.475
0.49
0.505
0.41
0.465
0.355
0.24
0.2
0.24
0.23
0.175
Months
Jan 2009
0.19
0.21
0.24
0.26
0.22
0.25
0.25
0.22
0.17
0.27
0.25
Feb 2009
0.32
0.35
0.33
0.2
0.18
0.27
0.21
0.2
0.21
0.19
0.12
Mar 2009
0.45
0.48
0.47
0.38
0.44
0.28
0.265
0.14
0.13
0.125
0.1
Apr 2009
0.475
0.49
0.505
0.41
0.465
0.355
0.24
0.2
0.24
0.23
0.175
Cadmium (mg/L)
Station Nov 2008 Dec 2008
S1
0.065
0.07
S2
0.08
0.08
S3
0.058
0.05
S4
0.053
0.042
S5
0.05
0.061
S6
0.042
0.041
S7
0.04
0.044
S8
0.033
0.033
S9
0.028
0.019
S10
0.026
0.028
S11
0.019
0.02
xii)
Nickel (mg/L)
Station Nov 2008
S1
0.2
S2
0.19
S3
0.22
S4
0.25
S5
0.16
S6
0.21
S7
0.24
S8
0.16
S9
0.13
S10
0.19
S11
0.17
(b)
1.200
1.230
0.490
0.210
0.440
0.670
0.540
0.210
0.240
Dec 2008
0.08
0.27
0.28
0.25
0.1
0.23
0.24
0.25
0.24
0.28
0.28
Sampling Data –Mussel Stations (Water and Mussel Data)
-
Water Samples
i)
Temperature (ºC)
160
Stations
M1
M2
M3
M4
ii)
Stations
M1
M2
M3
M4
iii)
Nov 2008
29.48
29.50
29.50
29.00
Dec 2008
29.90
29.50
29.45
28.40
Months
Jan 2009
28.80
28.40
28.45
29.14
v)
Stations
M1
M2
M3
M4
vi)
Mar 2009
29.44
29.46
29.48
29.44
Apr 2009
28.70
28.35
28.21
28.53
Dissolved Oxygen (mg/L)
Nov 2008
2.66
2.56
3.31
4.27
Dec 2008
2.56
2.45
3.31
4.33
Months
Jan 2009
2.50
2.65
3.45
4.30
Feb 2009
2.66
2.72
3.32
4.15
Mar 2009
2.56
2.51
3.53
4.21
Apr 2009
2.98
2.75
3.66
4.26
Salinity (ppt)
Months
Stations Nov 2008
Dec 2008
Jan 2009
M1
23.95
23.55
23.32
M2
23.65
23.21
23.71
M3
24.14
23.21
22.71
M4
24.65
24.55
24.66
iv)
Total Dissolved Solid (mg/L)
Stations
M1
M2
M3
M4
Feb 2009
29.64
29.53
29.71
29.45
Nov 2008
23.32
23.50
23.52
24.64
Dec 2008
22.46
22.61
23.51
24.07
Months
Jan 2009
23.30
23.40
23.39
23.10
Feb 2009
22.78
22.91
22.73
23.56
Mar 2009
22.65
22.55
23.10
23.66
Feb 2009
23.20
23.20
22.91
23.05
Mar 2009
24.02
23.60
23.20
23.68
Feb 2009
0.199
0.13
0.1
0.08
Mar 2009
0.172
0.126
0.076
0.06
Apr 2009
23.32
23.44
23.61
24.62
Apr 2009
23.20
23.10
23.21
23.11
Ammoniacal-nitrogen (mg/L)
Nov 2008
0.05
0.07
0.02
0.012
Dec 2008
0.165
0.156
0.125
0.074
Months
Jan 2009
0.169
0.102
0.062
0.027
BOD (mg/L)
Station
M1
M2
Months
Nov 2008
5
4.6
Dec 2008 Jan 2009
5.6
5.9
5.6
5.8
Feb 2009
5.4
5.7
Apr 2009
0.178
0.102
0.067
0.034
161
M3
M4
vii)
5.3
5.6
Months
Nov 2008
2642
1992
2400
2470
Dec 2008
2400
3315
3640
3000
5.6
4.6
5.2
4.6
Jan 2009
2284
3215
3125
3470
Feb 2009
2265
2552
3120
3186
COD (mg/L)
Station
M1
M2
M3
M4
-
5.6
5.8
Water and Mussel Samples
i)
Zinc
Water
Nov 2008
0.142
0.112
0.109
0.045
M1
M2
M3
M4
Dec 2008
0.142
0.112
0.123
0.034
Mussel
Nov 2008
Dec 2008
M1
76.23
56.45
M2
90.21
23.45
M3
98.45
45.32
M4
66.34
21.10
ii)
Cadmium (ppm)
Water
M1
M2
M3
M4
Nov 2008
0.042
0.04
0.033
0.028
Dec 2008
0.041
0.044
0.033
0.019
Mussel
Nov 2008
0.22
1.21
0.89
0.23
M1
M2
M3
M4
iii)
Dec 2008
0.21
0.89
0.98
0.63
Lead (ppm)
Months (mg/L)
Jan 2009
Feb 2009
0.167
0.209
0.123
0.123
0.198
0.112
0.098
0.103
Mar 2009
0.56
0.28
0.22
0.17
Apr 2009
0.37
0.37
0.355
0.19
Months (µg/g)
Jan 2009 Feb 2009
45.34
67.34
48.00
57.78
34.23
24.43
24.56
21.33
Mar 2009
67.23
32.00
21.34
24.56
Apr 2009
45.2
32.00
21.3
22.2
Months (mg/L)
Jan 2009 Feb 2009
0.061
0.28
0.041
0.265
0.044
0.14
0.033
0.13
Months (µg/g)
Jan 2009
Feb 2009
0.22
0.29
0.67
0.78
0.23
0.43
0.45
0.21
Mar 2009
0.387
0.335
0.335
0.125
Mar 2009
0.29
0.98
0.45
0.23
Apr 2009
0.38
0.37
0.355
0.3
Apr 2009
0.33
0.23
0.21
0.11
162
Water
M1
M2
M3
M4
Nov 2008
1.020
1.320
1.200
0.380
Dec 2008
2.670
0.980
0.210
0.780
Mussel
M1
M2
M3
M4
(c)
Nov 2008
6.23
12.21
11
7.5
Dec 2008
8.27
6.78
5.43
3.45
Months (mg/L)
Jan 2009
Feb 2009
0.980
1.990
0.450
0.980
0.240
3.210
0.320
0.120
Months (µg/g)
Jan 2009
Feb 2009
5.43
8.27
6.78
6.32
4.34
5.34
3.45
3.45
Mar 2009
1.430
1.400
1.200
0.340
Apr 2009
1.230
1.020
1.030
1.020
Mar 2009
8.43
24.23
5.12
5.23
Apr 2009
4.3
5.4
6.3
2.1
Water Quality Data from Previous 1991, 2006 and Present Studies
Year
1991
2006
2009
pH
Danga Pendas
7.64
8.15
8.59
NA
7.97
8.68
Temp(ºC)
Danga Pendas
30.50
28.80
29.00
NA
NA
29.27
DO (mg/L)
Danga Pendas
2.75
5.46
3.12
NA
5.68
4.33
AN(mg/L)
Danga Pendas
NA
0.443
0.242
NA
0.187
0.560
Year
Zinc (mg/L)
Danga Pendas
Cadmium (mg/L)
Danga
Pendas
Lead (mg/L)
Nickel (mg/L)
Danga Pendas Danga Pendas
1991
2006
2009
31.428 16.092
0.243
NA
0.339 0.753
0.082
0.001
0.282
0.773
0.900
2.253
0.059
NA
0.107
0.596
NA
0.430
NA
0.008
0.033
NA
NA
0.214