estimation and distribution of exhaust ship emission...

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%.

vi

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

viii

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

xi

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


4.6 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

xii

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


4.7 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

xiii

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



Δt= 20 min 85



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



xiv

Δt= 20 min 88



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


4.56 Distribution of CO2 emission by Surfer.8 and QGIS at

Δt= 20 min 90



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



Δt= 20 min 92



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



Δt= 20 min 94



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



Δt= 20 min 96



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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