estimation and distribution of exhaust ship emission...
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ESTIMATION AND DISTRIBUTION OF EXHAUST SHIP EMISSION FROM
MARINE TRAFFIC IN THE STRAITS OF MALACCA AND SINGAPORE
USING AUTOMATIC IDENTIFICATION SYSTEM (AIS) DATA
HENDRA SAPUTRA
UNIVERSITI TEKNOLOGI MALAYSIA
ESTIMATION AND DISTRIBUTION OF EXHAUST SHIP EMISSION FROM
MARINE TRAFFIC IN THE STRAITS OF MALACCA AND SINGAPORE
USING AUTOMATIC IDENTIFICATION SYSTEM (AIS) DATA
HENDRA SAPUTRA
A project report submitted in partial fulfilment of the
requirements for the award of the degree of
Master of Engineering (Marine Technology)
Faculty of Mechanical Engineering
Universiti Teknologi Malaysia
JANUARY 2013
iii
Special dedication to my beloved family
Mama, Papa, Rian, Dedi, and Dea…..
Thanks for your valuable sacrifice
Love you all…
iv
ACKNOWLEDGEMENT
“In the name of God, the most gracious, the most compassionate”
First and foremost, all praises and thanks to Allah (S.W.T) to give me
strength and ability to complete this project.
I would like to take this opportunity to express my highest appreciation to
Prof. Dr. Adi Maimun Bin Abdul Malik, CEng, my respectful thesis supervisor to
provide better guidance, encouragement, ideas, comment, recommendation and
advice that lead me for the successful completion of this project. My special
appreciation to Dr. Eng Jaswar Koto, CEng (Co-Supervisor) and C. L. Siow for
encouragement, guidance, critics and suggestions.
To my mother and father that always pray for my successes, all these things
cannot pay for all what you all have done. My special thanks also to my fellow
postgraduate friends. Special thanks to Mr. Suardi, Mufti Fathonah Muvariz, Risandi
Dwirama Putra, Eko Prayetno and Arfena Deah Lestari, their willingness to help and
suggest means a lot to me. In addition, my thanks to Kepulauan Riau Government
especially to Education Agency of Riau Archipelago Province (Provinsi Kepulauan
Riau, Indonesia) who have supported my scholarship to do master degree in UTM
Malaysia. Special thanks to Drs. Yatim Mustafa, MPd as Head of Education Agency
of Riau Archipelago Province, Dr. Muhammad Dali, MM as Head of Secondary and
Higher Education Division, Fansuri, ST as Head of Higher Education Subdivision,
Mr. Franto Tonggi, Mr. Hengkie and all the people who are in Education Agency of
Riau Archipelago Province, thousand thanks to all supported of financial and moral.
Above this all, my highest praises and thanks to Almighty Allah Subhanahu
Wa’Talla, the most gracious the most merciful, who gave me the knowledge, courage
and patience to accomplish this project. May the peace and blessings of Allah be
upon Prophet Muhammad Sallallahu Alaihi Wasallam.
v
ABSTRACT
Global warming and air pollution have become one of the important issues to
the entire world community. Exhaust emissions from ships has been contributing to
the health problems and environmental damage. This study focuses on the Strait of
Malacca area because it is one of the world’s most congested straits used for
international shipping where located on the border among three countries of
Indonesia, Malaysia and Singapore. The study seeks to estimate of the exhaust
emission and to know the concentration of emission to several areas around. This is
accomplished by evaluating the density of shipping lanes in the Straits of Malacca by
using the data which obtained by Automatic Identification System (AIS). To show
the movement patterns of ships, operational mode and influence of the ship
movement to the distribution of emissions are integrating of AIS data and GIS
software. MEET methodology is used to estimate emissions from ships and Gaussian
Puff Model used to estimates the concentration in several areas around the Strait. The
results show 813 total number of ships through the Strait of Malacca on 2 September
2011 at 07.00 am-08.00 am produces exhaust emission for NOx, CO, CO2, VOC,
PM and SOx are about 13715.51 g/second, 25461.525 g/second, 11092.99 g/second,
5858.216 g/second, 415.304 g/second and 6921.746 g/second, respectively. The
ships under the Singapore flag contribute approximately 22.72% of total emissions in
the Strait of Malacca followed by Panama and Liberia flag approximately 21.32%,
12.89%, respectively. Ships under Malaysia and Indonesia rank of sixth and seventh
respectively of the emission rates. The most high-risk areas which affected by the
emissions are Sentosa Island (Singapore), Port of Pasir Gudang (Malaysia) and
Jurong Island (Singapore) with approximately contaminated about 47.33%, 21.68%
and 17.69%, respectively of total emission and other areas around the Strait represent
below 1%.
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ABSTRAK
Masalah pemanasan global dan pencemaran udara telah menjadi salah satu
daripada isu-isu yang penting kepada masyarakat seluruh dunia. Pelepasan ekzos dari
kapal telah menyumbang kepada masalah kesihatan dan kerosakan alam sekitar.
Kajian ini difokuskan kepada kawasan Selat Melaka kerana ia adalah salah satu selat
di dunia yang paling sesak digunakan untuk pelayaran antarabangsa di mana terletak
di sempadan tiga negara antara Indonesia, Malaysia dan Singapura. Kajian ini
bertujuan untuk menghitung keluaran emisi dan untuk mengetahui kepekatan
keluaran emisi seperti SOx, Nox, CO, CO2 dan PM kepada beberapa kawasan
sekitarnya. Ini dapat dicapai dengan menilai trafik laluan kapal di Selat Melaka
dengan menggunakan data yang diperolehi dari Automatic Identification System
(AIS). Untuk menunjukkan corak pergerakan kapal, mod operasi kapal dan pengaruh
pergerakan kapal kepada taburan emisi dengan mengintegrasikan data AIS dan
perisian GIS. Kaedah MEET Methodology digunakan untuk menghitung keluaran
emisi daripada kapal dan Gaussian Puff Model digunakan untuk menghitung
kepekatan emisi di beberapa kawasan di sekitar Selat. Hasilnya menunjukkan 813
jumlah kapal yang berlayar di Selat Melaka pada 2 September 2011 pada 07:00-
08.00 dan menghasilkan keluaran emisi NOx, CO, CO2, VOC, PM dan SOx kira-
kira 13715.51 g/second, 25461.525 g/second, 11092.99 g/second, 5858.216
g/second, 415.304 g/second and 6921.746 g/second. Kapal-kapal di bawah bendera
Singapura mewakili hampir 22.72% daripada jumlah pelepasan diikuti oleh kapal-
kapal di bawah bendera Panama dan Liberia dengan jumlah masing-masing 21.32%,
12.89%. Kapal di bawah Malaysia dan Indonesia menduduki tempat keenam dan
ketujuh dari kadar pelepasan. Pulau Sentosa (Singapura), pelabuhan Pasir Gudang
(Malaysia) dan Pulau Jurong (Singapore) adalah kawasan tertinggi terjejas oleh
pelepasan dengan kira-kira 47.33%, 21.68%, 17.69% dari jumlah kepekatan emis
daripada trafik kapal dan kawasan lain di sekitar Selat terjejas dibawah 1%.
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES x
LIST OF FIGURES xii
LIST OF ABBREVIATIO N xvi
LIST OF SYMBOLS xviii
LIST OF APPENDICES xix
1 INTRODUCTION 1
1.1 Background 1
1.2 Problem Statements 3
1.3 Objective 3
1.4 Scope of Study 4
2 STUDY LITERATURE 5
2.1 Literature Review 5
2.2 Automatic Identification System (AIS) 8
2.3 Air Pollution 13
2.3.1 Air Pollution Overview 13
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2.3.2 Air Pollution from Shipping Activity 15
2.4 Nitrogen Oxides 17
2.5 Carbon Monoxides 19
2.6 Carbon Dioxides 20
2.7 Volatile Organic Compounds (VOC) 21
2.8 Particulate Matter (PM) 22
2.9 Sulphur Dioxides 23
2.10 Methodologies for Estimating Air Pollutant Emissions from
Ships 25
2.10.1 Definitions of Hoteling, Maneuvering and Cruising 31
2.11 Gaussian Puff Model 31
3 METHODOLOGY 38
3.1 Introduction 38
3.2 Identification Problem 38
3.3 Data Processing 39
3.3.1 AIS Data Collection 39
3.3.2 Ship Database Collection 39
3.4 Air Emission Assessment 40
3.5 Air Emission Distribution Assessment 40
3.6 Calculation by Programming 41
3.7 Simulation of Emission Distribution 41
3.8 Flowchart 42
4 RESULT AND ANALYSIS 43
4.1 Overview 43
4.2 Investigation Number of Ship from AIS Data 43
4.3 Analysis Procedure 51
4.3.1 Ship Emissions Calculation Procedure 51
4.3.2 Distribution of Emissions Calculation Procedure 52
4.3.3 Calculation by Programming 57
4.4 Results of Emissions Assessment 59
4.4.1 Results of NOx Emissions 59
4.4.2 Results of CO Emission 62
ix
4.4.3 Results of CO2 Emission 65
4.4.4 Results of VOC Emission 68
4.4.5 Results of PM Emission 71
4.4.6 Results of SOx Emission 74
4.4.7 Summary 76
4.5 Results Distribution of Emission 77
4.5.1 Results of NOx Emissions 78
4.5.2 Results of CO Emission 79
4.5.3 Results of CO2 Emission 80
4.5.4 Results of VOC Emission 81
4.5.5 Results of PM Emission 82
4.5.6 Results of SOx Emission 83
4.5.7 Summary 84
4.6 Modeling the Distribution of Emissions by Surfer 84
4.6.1 Distribution of NOx emission by Surfer.8 84
4.6.2 Distribution of CO emission by Surfer.8 87
4.6.3 Distribution of CO2 emission by Surfer.8 89
4.6.4 Distribution of VOC emission by Surfer.8 91
4.6.5 Distribution of PM emission by Surfer.8 93
4.6.6 Distribution of SOx emission by Surfer.8 95
5 CONCLUSION 96
5.1 Conclusions 96
5.2 Recommendations and Future Work 97
REFERENCES 99
APPENDIX A 104
APPENDIX B 105
APPENDIX C 121
x
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Information updates rates for AIS dynamic information 11
2.2 Identifiers to be used by ships to report their type on AIS 11
2.3 Classification of air pollution 14
2.4 Classification of Air Pollutants 15
2.5 Exhaust emissions (million tonnes) from total shipping,
1990–2007 17
2.6 Nitrogen Oxides (NOx) 18
2.7 Average consumption at full power versus gross tonnage 25
2.8 Emission factors (kg/ton of fuel) 26
2.9 Fraction of maximum fuel consumption in different mode 27
2.10 Pollutants classification 28
2.11 Fuels classification 28
2.12 Ship type class 28
2.13 Engine type class 28
2.14 Installed main engine power as a function of gross tonnage 29
(GT)
2.15 Estimated average vessel ratio of Auxiliary Engines / Main
Engines by ship type 30
2.16 Estimated % load of MCR (Maximum Continuous Rating)
of Main and Auxiliary Engine for different ship activity 30
2.17 Constants a, b, c, d depend on Pasquill Stability categories
defined by Turner (1970) 34
2.18 A formula and corresponding parameter values for
determining the lateral diffusion coefficient for a
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Gaussian plume 35
2.19 A formula and corresponding parameter values for
determining the vertical diffusion coefficient for a
Gaussian plume 35
4.1 Total MMSI (Ship) on September 2011 46
4.2 Total MMSI (Ships) on second day of September 2011 47
4.3 Number of ships by type 50
4.4 Number of ships by flag 50
4.5 Number of ships by flag 51
4.6 51 points of receptor 53
4.7 NOx emission by type of ship 59
4.8 NOx emission by flag 60
4.9 NOx emission by mode operational 62
4.10 CO emission by type of ship 62
4.11 CO emission by flag 63
4.12 CO emission mode operational 64
4.13 CO2 emission by type of ship 65
4.14 CO2 emission by flag 66
4.15 CO2 emissions by mode operational 67
4.16 VOC emission by type of ship 68
4.17 VOC emission by flag 69
4.18 VOC emission by mode operational 70
4.19 PM emission by type of ship 71
4.20 PM emission by flag 72
4.21 PM emission by mode operational 73
4.22 SOx emission by type of ship 74
4.23 SOx emission by flag 75
4.24 SOx emission by mode operational 76
5.1 The highest affected area by the emission 97
A.1 Sample of Ship Raw Data from AIS Receiver on September
2nd 2011 at 7.00am-08.00am 104
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LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Automatic Identification System (AIS) operation view 13
2.2 Ship movement characteristics 31
2.3 Transfer coordinates 33
3.1 Flowchart of the study 42
4.1 Data processing of AIS using MySQL 44
4.2 Traffic density for each day on September 2011 45
4.3 Traffic density for each hour on second day of September 2011 45
4.4 Data of ship from marine traffic website 48
4.5 Number of ships by type 49
4.6 Number of ships by flag 49
4.7 Number of ships by mode operational 49
4.8 Mode operational patterns of 813 ships by GIS 51
4.9 51 points of receptor 55
4.10 Programming of emission calculation 57
4.11 Percentage of NOx emission by types of ship 60
4.12 Percentage of NOx emission by flag 61
4.13 Percentage of NOx emission by mode operational 62
4.14 Percentage of CO emission by types of ship 63
4.15 Percentage of CO emission by flag 64
4.16 Percentage of CO emission by mode operational 65
4.17 Percentage of CO2 emission by types of ship 66
4.18 Percentage of CO2 emission by flag 67
4.19 Percentage of CO2 emission by mode operational 67
4.20 Percentage of VOC emission by types of ship 68
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4.21 Percentage of VOC emission by flag 69
4.22 Percentage of VOC emission by mode operational 70
4.23 Percentage of PM emission by types of ship 71
4.24 Percentage of PM emission by flag 72
4.25 Percentage of PM emission by mode operational 73
4.26 Percentage of SOx emission by types of ship 74
4.27 Percentage of SOx emission by flag 75
4.28 Percentage of SOx emission by mode operational 76
4.29 Concentration of NOx emission 78
4.30 Concentration of NOx emission in receptor 34-39 78
4.31 Concentration of CO emission 79
4.32 Concentration of CO emission in receptor 34-39 79
4.33 Concentration of CO2 emission 80
4.34 Concentration of CO2 emission in receptor 34-39 80
4.35 Concentration of VOC emission 81
4.36 Concentration of VOC emission in receptor 34-39 81
4.37 Concentration of PM emission 82
4.38 Concentration of PM emission in receptor 34-39 82
4.39 Concentration of SOx emission 83
4.40 Concentration of SOx emission in receptor 34-39 83
4.41 Distribution of NOx emission by Surfer.8 at Δt= 40 min 84
4.42 Distribution of NOx emission by Surfer.8 and QGIS at
Δt= 40 min 85
4.43 Distribution of NOx emission by Surfer.8 at Δt= 20 min 85
4.44 Distribution of NOx emission by Surfer.8 and QGIS at
Δt= 20 min 85
4.45 Distribution of NOx emission by Surfer.8 at Δt= 1 min 86
4.46 Distribution of NOx emission by Surfer.8 and QGIS at
Δt= 1 min 86
4.47 Distribution of CO emission by Surfer.8 at Δt= 40 min 87
4.48 Distribution of CO emission by Surfer.8 and QGIS at
Δt= 40 min 87
4.49 Distribution of CO emission by Surfer.8 at Δt= 20 min 87
4.50 Distribution of CO emission by Surfer.8 and QGIS at
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Δt= 20 min 88
4.51 Distribution of CO emission by Surfer.8 at Δt= 1 min 88
4.52 Distribution of CO emission by Surfer.8 and QGIS at
Δt= 1 min 88
4.53 Distribution of CO2 emission by Surfer.8 at Δt= 40 min 89
4.54 Distribution of CO2 emission by Surfer.8 and QGIS at
Δt= 40 min 89
4.55 Distribution of CO2 emission by Surfer.8 at Δt= 20 min 89
4.56 Distribution of CO2 emission by Surfer.8 and QGIS at
Δt= 20 min 90
4.57 Distribution of CO2 emission by Surfer.8 at Δt= 1 min 90
4.58 Distribution of CO emission by Surfer.8 and QGIS at
Δt= 1 min 90
4.59 Distribution of VOC emission by Surfer.8 at Δt= 40 min 91
4.60 Distribution of VOC emission by Surfer.8 and QGIS at
Δt= 40 min 91
4.61 Distribution of VOC emission by Surfer.8 at Δt= 20 min 91
4.62 Distribution of VOC emission by Surfer.8 and QGIS at
Δt= 20 min 92
4.63 Distribution of VOC emission by Surfer.8 at Δt= 1 min 92
4.64 Distribution of VOC emission by Surfer.8 and QGIS at
Δt= 1 min 92
4.65 Distribution of PM emission by Surfer.8 at Δt= 40 min 93
4.66 Distribution of PM emission by Surfer.8 and QGIS at
Δt= 40 min 93
4.67 Distribution of PM emission by Surfer.8 at Δt= 20 min 93
4.68 Distribution of PM emission by Surfer.8 and QGIS at
Δt= 20 min 94
4.69 Distribution of PM emission by Surfer.8 at Δt= 1 min 94
4.70 Distribution of PM emission by Surfer.8 and QGIS at
Δt= 1 min 94
4.71 Distribution of SOx emission by Surfer.8 at Δt= 40 min 95
4.72 Distribution of SOx emission by Surfer.8 and QGIS at
Δt= 40 min 95
xv
4.73 Distribution of SOx emission by Surfer.8 at Δt= 20 min 95
4.74 Distribution of SOx emission by Surfer.8 and QGIS at
Δt= 20 min 96
4.75 Distribution of SOx emission by Surfer.8 at Δt= 1 min 96
4.76 Distribution of SOx emission by Surfer.8 and QGIS at
Δt= 1 min 96
xvi
LIST OF ABBREVIATION
AIS - Automatic Identification System
IMO - International Maritime Organization
SOLAS - Safety of Life at Sea
CO - Carbon Monoxides
CO2 - Carbon Dioxides
g - Gram
g/s - gram per second
H2S - Hydrogen Sulphide
HNO3 - Nitric Acid
km - Kilometer
km2 - Kilometre square
m - Meter
m/s - Meter per second
NO - Nitric oxide
N2O2 - Dinitrogen dioxide
N2O3 - Dinitrogen trioxide
NO2 - Nitrogen Dioxides
NOx - Nitric Oxides
N2O - nitrous oxide
N2O4 - Dinitrogen tetroxide
N2O5 - dinitrogen pentoxide
O2 - Oxygen
O3 - Ozone
xvii
PM - Particulate matter
SO2 - Sulphur Dioxides
SO3 - Sulphur Trioxides
SOx - Sulphur Oxides
VOC - volatile organic compunds
WHO - World Health Organization
µg/m3 - microgram per meter cube
xviii
LIST OF SYMBOL
% - Percent
Cr - Concentration of emission
Q - Emission rate
Δh - Effective stack height
∆t - Length of time of the release itself
σy - Horizontal standard deviation
σz - Vertical standard deviation
α - Wind direction
Ei - total emissions of pollutant i
f - Auxiliary engine fuel consumption
Fijlm - Average emission factors of pollutant i from fuel j in engines
type l in mode m
i - Pollutant
j - Fuel
k - Ship class
L - Load factor
P - Auxiliary engine rated output
s - Reference reduction scenario
Sjkm (GT) - daily consumption of fuel j in ship class k as a function of
gross tonnage
tjklm - days in navigation of ships of class k with engines type l using
fuel j in mode operational m
U - Wind speed
xr, yr, zr - Cartesian coordinates downwind of the puff
xix
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Sample of Ship Raw Data from AIS Receiver 104
B Code of Programming with Visual Basic 2010 105
C Sample of Gridding Report (NOx) in Surfer.8 121
1
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
Global warming and air pollution have become one of the important issues to
the entire world community. About 80% of the international trade and movement of
goods carried by ship through the sea (UNCTAD, 2010) cause exhaust emission
from marine traffic which contribute to the global warming and air pollution
problems. Exhaust emission from ship consisted of sulfur oxides (SOx), Nitrogen
oxides (NOx), Green House Gas (GHG) or CO2, Particulate Matter (PM),
hydrocarbons (HC) and carbon monoxide (CO) have been contributing to the health
problems and environmental damage. For instance, Corbett et al (2007) estimated
64.000 cardiopulmonary, lung cancer mortalities and 92% premature death as
globally caused by Particulate Matter (PM) from ship traffic. This emission effect
arises especially in inland seas, territorial waters, canals, port region, and Straits area.
The Straits of Malacca and Singapore is one of the most important shipping
channels in the world connecting the Indian Ocean with the South China Sea and the
Pacific Ocean. The Straits remains as one of the world’s most congested straits used
for international shipping. Approximately over 60,000 vessels pass through The
Straits annually (Mihmanli, 2011) and recorder over 74,000 vessels in 2010 (Khalid,
2
2011), the Strait play role in producing of shipping emission and contributed to air
pollution. Previous attempts, showed by Street et al (1997), shipping emission of
sulfur oxide (SO2) on the Strait of Malacca almost the same in Sumatra, Indonesia
(32,000 t SO2 of Strait of Malacca and 52,000 t SO2 per year of Sumatra) and
approximately one-quarter of the total emissions in each of Singapore and
Malaysia (Arndt et al, 1996 and Bhatti et al., 1992).
The Strait of Malacca is a narrow stretch of water lying between the east
coast of Sumatra Island in Indonesia and the west coast of Peninsular Malaysia, and
is linked to the Straits of Singapore at its Southeast end. Approximately 990
kilometers long and 2.7 kilometers wide, the Straits of Malacca connects The Indian
and Pacific Ocean and it's recognized as one of the most economically, strategically
and the busiest shipping lanes in the world.
This study focuses on the Strait of Malacca and Singapore area because it is
one of the world’s most congested straits used for international shipping where
located on the border among three countries of Indonesia, Malaysia and Singapore.
The strait of Malacca is a narrow strait where major hub is to be found and it is one
of the heavy marine traffic.
The objective of this study is to estimate the air pollution (SOx, Nox, CO,
CO2 and Particulate Matter) resulting from the marine traffic in the Strait of Malacca
and Singapore areas using Automatic Identification System (AIS) data. The same
method, previously conducted by Jalkanen et al (2009; 2011), Perez et al (2009) and
Pitana (2010). AIS is used as an initial data, it is can identify Maritime Mobil Service
Identity (MMSI) of ship, ship speed, initial position and type of ship. This data is
used to evaluate the traffic density of the Strait of Malacca and Singapore area. The
initial data will combine with ship database to obtain gross tonnage (GT) of the ship
for emission estimation consideration.
3
1.2 PROBLEM STATEMENT
The Strait of Malacca and Singapore is one of the world’s most congested
straits used for international shipping, a narrow Strait where major hub to be found
and it is one of the heavy marine traffic. Consequently, that it may cause air pollution
levels are high. Previous studies of ship emissions in the Malacca Strait, performed
by the Street (1997), the study only measured levels of sulfur oxides (SOx) using
ATMOS (Atmospheric Transport Modeling System) as dispersion model for
emission. This project will try to find out how the air emission (SOx, NOx, CO, CO2
and particulate matter) from the ships in the Strait currently.
In addition, because the Straits located on the border among three countries of
Indonesia, Malaysia and Singapore, so it is necessary to know how the dispersion
and how big influence of its emission on border countries alongside the coast of The
Strait of Malacca and Singapore.
1.3 OBJECTIVE
The objectives of this study are:
i. To estimate of exhaust gas emission that is produced by ships which is
passing through The Strait of Malacca and Singapore.
ii. To estimate the distribution of its emissions from ships on hoteling,
maneuvering and cruising mode operation.
iii. To determine the distribution's effect of its emission to several territories
along the coast of the Straits of Malacca and Singapore by using the Gaussian
Puff Model.
4
1.4 SCOPE OF STUDY
The scopes of this thesis are:
i. The estimation of exhaust ship emission will focuses on the Strait of Malacca
and Singapore area because it is one of the world’s most congested straits
used for shipping.
ii. An analysis of actual vessel data will focus only data which are detected by
AIS receiver installed at the Marine Technology Department, Faculty of
Mechanical Engineering, UTM Skudai, Johor Bahru, Malaysia.
iii. The Estimation of exhaust ship emission will focuses on one certain day
which is occurred the highest traffic in the last one year (2011/2012).
iv. Analysis of the range of the most congested shipping hour of choosing a day
that occurs highest traffic in the last one year (2011/2012).
v. Analysis of emission effects of air pollution only on areas which is passed by
ships which is detected by AIS receiver.
99
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