PROCEEDINGS OF IPI RESEARCH COLLOQUIUM 2017 · 2018-03-05 · PROCEEDINGS OF IPI RESEARCH COLLOQUIUM 2017 OCTOBER 1-3, 2017 INSTITUTE OF CLIMATE CHANGE (IPI) UNIVERSITI KEBANGSAAN

PROCEEDINGS OF IPI RESEARCH COLLOQUIUM 2017

OCTOBER 1-3, 2017

INSTITUTE OF CLIMATE CHANGE (IPI)

UNIVERSITI KEBANGSAAN MALAYSIA

eISBN 978-967-0829-83-8

Editors:

Rawshan Ara Begum

Fatimah PK Ahamad

Mohammad Rashed Iqbal Faruque

Sabirin Abdullah

Khairul Nizam Abd Maulud

Technical Committee:

Noridawaty Mat Daud

Farhanah Md Isa

Disclaimer: The authors of individual papers are responsible for technical, content and

linguistic correctness.

PUBLISHED BY INSTITUTE OF CLIMATE CHANGE (IPI)

Cetakan Pertama / First Printing February 2018

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E-mel: [email protected]

Sidang Editor / Editorial

Rawshan Ara Begum

Fatimah PK Ahamad


Sabirin Abdullah

Khairul Nizam Abdul Maulud

Jawatankuasa Teknikal / Technical Committee

Noridawaty Mat Daud

Farhanah Md Isa

Rekabentuk oleh / Designed by

Noor Shuhaira Rejab

eISBN 978-967-0829-83-8

PREFACE

The Institute of Climate Change (IPI) Research Colloquium 2017 was held at the Felda

Residence Trolak, Perak, on 1-3 October, 2017 and organised by the Institute of Climate

Change (IPI), Universiti Kebangsaan Malaysia (UKM) in collaboration with the UKM-YSD

Chair in Climate Change. This is the first IPI Research Colloquium focusing on research

progress and articles of the IPI postgraduate students. It is also a continuation of the

ANGKASA Postgraduate Research Seminar and Colloquium from 2014 to 2016.

The IPI Research Colloquium provides an excellent opportunity for all the postgraduate

students, presenters, researchers, supervisors, evaluators and participants to meet, discuss and

share a broad range of issues in terms of research progress and presentation, thesis writing,

challenges and improvements as well as preparing and writing manuscripts for publication. The

proceedings include all the accepted articles consisting of full paper and abstract that were

presented in the IPI Research Colloquium 2017. The papers of the proceedings are arranged

according to the presentation sessions covering the research themes of climate change and

space science.

We would like to thank all the postgraduate students, presenters, participants, researchers,

supervisors, reviewers, evaluators, organising committee members and those who have

contributed to make this colloquium successful. We also acknowledge UKM-YSD Chair in

Climate Change for sponsoring the publication of the proceedings.

We are indeed very happy for the publication of the Proceedings of IPI Research Colloquium

2017. We believe the proceedings will contribute to the improvement and further development

of knowledge and intellectual in the fields of climate change and space science.

Thank you very much!

Best regards,

Editors

February 2018

CONTENTS

NO. TITLE & AUTHORS PAGE

NUMBERS

1 Possibility of UAV Application to Monitor Shoreline Changes 1 Abdul Aziz Ab Rahman


Othman Jaafar

2 Study on Coastal Vulnerability Index (CVI) for Selangor Coastal Area 4 Muhammad Afiq Ibrahim


Fazly Amri Mohd

Mohd Radzi Abdul Hamid

Nor Aslinda Awang

3 GIS-integrated Infrastructure Asset Management System 7 Muhammad Aqiff Abdul Wahid


Mohd Aizat Saiful Bahri

Muhammad Amartur Rahman

Othman Jaafar

4 Assessing of Shoreline Changes by using Geospatial Technique 12 Siti Norsakinah Selamat


Othman Jaafar

5 Heat Stress on Mangrove (Rhizophora apiculata) and Adaptation Options 16 Baseem M. Tamimi

Wan Juliana Wan Ahmad

Mohd. Nizam Mohd. Said

Che Radziah Che Mohd. Zain

6 Terahertz Meta-surface Absorber for Absorbing Application 20 Md. Mehedi Hasan


Mohammad Tariqul Islam

7 Labyrinth Resonator for Wideband Application 24 Md. Jubaer Alam



8 Design and Analysis of a Metamaterial Structure with Different Substrate

Materials for C Band and Ku Band Applications

28

Eistiak Ahamed


Mohd Fais Mansor

9 9th September 2011 Solar Flare to MAGDAS Reading 33

Norhani Muhammad Nasir Annadurai

Nurul Shazana Abdul Hamid

Akimasa Yoshikawa

10 Comparison of the Neural Network and the IRI Model for Forecasting TEC

over UKM Station

35

Rohaida Mat Akir

Mardina Abdullah

Kalaivani Chellappan

Siti Aminah Bahari

11 Variation of EEJ Longitudinal Profile during Maximum Phase of Solar

Cycle 24

39

Wan Nur Izzaty Ismail


Mardina Abdullah

Akimasa Yoshikawa

12 The Impact of High Environmental Temperature on Branchial

Ammonia Excretion Efficiency between Euryhaline and Stenohaline

Teleosts

42

Hon Jung Liew,

Yusnita A Thalib

Ros Suhaida Razali

Sharifah Rahmah

Mazlan Abd. Ghaffar

Gudrun De Boeck

13 Large Scale Wave Structure Prior to the Development of Equatorial

Plasma Bubbles

46

Suhaila M Buhari

Mardina Abdullah

Tajul Ariffin Musa

14 Determining the Probability of Sediment Resuspension in the East Coast of

Peninsular Malaysia through Wind Analysis

49

Shahirah Hayati Mohd Salleh

Wan Hanna Melini Wan Mohtar


Nor Aslinda Awang

15 A Review on Forest Carbon Sequestration as a Cost-effective Way to

Mitigate Global Climate Change

53

Asif Raihan

Rawshan Ara Begum

Mohd Nizam Mohd Said

Sharifah Mastura Syed Abdullah

16 Review of Methodology on Source Apportionment of PM2.5 near a Coal-

fired Power Plant using Multivariate Receptor Modelling

58

Ahmad Hazuwan Hamid

Md Firoz Khan

Mohd Talib Latif

17 Study of Maximum Usable Frequency (MUF) for High Frequency (HF)

Band at Equatorial Region in Malaysia

62

Johari Talib

Sabirin Abdullah

18 Performance Analysis of a Negative-permeability Metamaterial Inspired

Antenna with 1U Cubesat

65

Touhidul Alam

Farhad Asraf

Mohammed Shamsul Alam


Mengu Cho

19 Zonal Velocity Drift of Equatorial Plasma Bubbles Calculated over

Southeast Asia

68

Idahwati Sarudin


Mardina Abdullah

Suhaila M Buhari

20 Effect of Elevated Atmospheric Carbon Dioxide on Mangrove Growth in

Controlled Conditions

71

Baseem M. Tamimi

Wan Juliana Wan Ahmad

Mohd. Nizam Mohd. Said

Che Radziah Che Mohd. Zain

21 Observations of Lightning and Background Electric Field in Antarctica

Peninsula

75

Norbayah Yusop

Mardina Abdullah

Mohd Riduan Ahmad

22 Determination of the GPS Satellite Elevation Mask Angle for

Ionospheric Modeling the Ionosphere over Malaysia

78

Siti Aminah Bahari

Mardina Abdullah

Zahra Bouya

Tajul Ariffin Musa

23 A New Wide Negative Refractive Index Meta-atom for Satellite

Communications

82

Mohammad Jakir Hossain



24 Ionospheric Bottomside Electron Density Thickness Parameter over

Southeast Asian Sector

87

Saeed Abioye Bello

Mardina Abdullah


25 Assessing the Accuracy of Hydrodynamic Parameters using Statistical

Approaches

91

Fazly Amri Mohd


Othman A. Karim

Rawshan Ara Begum

26 Socio-economic Impacts of Climate Change in the Coastal Areas of

Malaysia

95

Mohd Khairul Zainal

Rawshan Ara Begum


Norlida Hanim Mohd Salleh

PRESENTERS PROFILE 100

PROCEEDINGS OF IPI RESEARCH COLLOQUIUM 2017, 1 – 3 OCTOBER 2017,

FELDA RESIDENCE TROLAK, PERAK, MALAYSIA

1

Possibility of UAV Application to Monitor Shoreline Changes

Abdul Aziz Ab Rahman1, Khairul Nizam Abdul Maulud1,2 and Othman Jaafar2

1Earth Observation Centre, Institute of Climate Change (IPI), Universiti Kebangsaan Malaysia, 43600 UKM, Bangi,

Selangor, Malaysia 2Department of Civil & Structural Engineering, Faculty of Engineering & Built Environment,

Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor Malaysia

*corresponding author, E-mail: [email protected]

Abstract

Unmanned Aviation Vehicles (UAV) are recently growing

up fast in the world market. Moreover, it is the first choice

for companies to complete their work especially in survey

work. In fact, conventional survey work is expensive and

takes more time for a complete project. It is used for

mapping and monitoring of air for coastal areas. The

findings show that UAV has been a key tool for conducting

topographic change monitoring works along the coast and

can do good results. This paper focuses on the literature of

the possibility of UAV to monitor the shoreline changes. In

addition, UAV images can generate into orthophoto and the

images also have their own projection because it is

geotagged due to GPS signals from satellites. Consequently,

the rate of physical changes either erosion or acceleration

can be determined using monitoring along coastal area

using this UAV. Hence, this paper presents to show and

prove that shoreline changes can be monitored by UAV

application.

1. Introduction

Generally, landscape changes can help to understand how

certain traits and elements exist and behave. Understanding

functions, relationships and rules can support landscape

management and sustainable development such as the

prevention is the effect of the devastating floods.

Furthermore, the coastal area is experiencing destruction due

to sea action and the causes of nature and humanity caused

by it. Changing topography on the beach and sand dunes

should be assessed, after severe and regular events, to build

a model that can predict the evolution of this natural

environment. This is an essential app for LIDAR airborne,

and conventional photogrammetry is also used for sensitive

monitoring of coastal areas (Gonçalves, & Henriques 2015).

According to Turner, Harley & Drummond (2016) UAV

beach engineering application is used here to illustrate the

practical use and potential benefits of this latest survey

technology. Over the last 2 years, the rapidly expanding

UAV survey has been successfully integrated into a four-

decade coastline surveillance program in Narrabeen Beach,

Australia. This has expanded the scope of the program to

include detailed measurements from the desert and coastal

erosion that covered the 3.5 km long dew on a spatial scale

and temporal resolution previously unprofitable. In fact,

Čermáková, Komárková & Sedlák (2016) mentioned that

Unmanned aerial vehicles are increasingly being used to

monitor small areas, e.g. Small water bodies (ponds). UAV

can yield faster results and usually have higher spatial

resolution. Therefore, this paper presents to show and prove

that shoreline changes can be monitored by UAV

application.

2. Review on UAV Application on Monitoring

Shoreline Changes

All the methods were combined to display the possibility of

UAV application to monitor the physical changes of the

coast.

2.1. Beach topographical changes at the Ligurian Sea

This study was conducted at Region of Liguria, Italy which

is located at the north-western Mediterranean. Based on

Casella et al. (2016) writing state this region has been

monitored three times more than 5 months in autumn 2013-

2014 autumn (November 1, 2013, December 4, 2013, March

17, 2014) to get Digital Elevation Model (DEM) and beach

orthophotos. The coastal topography changes associated

with storm events and human activities are assessed in terms

of either increase or decrease of sediment and the transition

of dry wet boundaries that determine the coastline.

Moreover, the flying height was set up at 70m altitude and

the flight programmed by Microdropter OSD tool software

to cover the entire region coast. In addition, UAV pilots and

observer have the duty to control the mission and carry out

take-off and landing operations. It interfered with GPS

guided flights in the case of unwanted RPAS behaviour and

the most important are the pilot has the duty to follow the

flight from the land station and convey the change from the

designated path to the pilot (Casella et al. 2016).

2.2. The Structure from Motion Approach on Coastal

Environment

Beach geomorphology requires accurate topographical

information on coastal systems called for the

implementation of coastal erosion simulation, flood



2

phenomenon, and coastal sediment budget assessment. For

such a study, the availability of topographic datasets is a

specific basis for systems characterized by complex

morphology. The presence of sand dunes should be carefully

considered because of their role in coastal defence as a

natural protective feature, providing sediment supply to the

shore and protecting the interior from storm surges (Mancini

et al. 2013).

This study stated that the unmanned aerial vehicle

(UAV) for reconstruction of the 3D coastal environment is

being investigated in this study. UAV images in the sandy

beach environment require additional verification

procedures. Tidal plates, beaches, and sewerage systems

show different differences in images obtained by air surveys

near the possibility of responding to the dominant grain size

or with the presence of coastal plants. This study was

successfully held at Ravenna, Italy on the North Adriatic

coast. The Ravenna coastline, stretching less than 40 km in

the direction of N-S, is characterized by the presence of a

natural site and sandy beach equipped, sometimes bordered

with pine forests, and proximate urban areas. Almost all of

these areas are affected by erosive trends as a result of

several factors, such as the reduction of strong river

sediment supply, the destruction of sand dunes system by

tourism-related pressure, the establishment of ports and

poles that affect sedimentation along the coast, land

subsidence, ineffective defensive structure, and rising sea

levels.

Despite, Mancini et al. (2013) also found that UAV

system used is the VTOL (Vertical Take Off and Landing)

hexacopter designed and produced by Sal Engineering (Sea

Air Land) and is equipped with calibrated Canon EOS 550D

digital cameras. The survey line was designed using an

orthophoto air at an average aviation height of 40 m and the

acquisition was automatically set at one shot per second.

Operating operations and landing operations are manually

guided by remote pilots. During the survey, flights are

automatically enabled by waypoints. Acquisition time

provides up to 10 overlapping images for any single land

feature and any attempt to visualize coverage of aerial

imagery for a limited area will result in a somewhat

confused figure.

Further, The NRTKs have been used on May 27, 2013.

The NRTK study has a threefold collection purpose.

Eighteen 3D Land Control Points (GCPs) consisting of

cubes (30×40×30 cm) with 20 cm wide board chess are

printed at the top, 126 Points of Authentication (VP) at a

surface level along five transects across the whole Dots, and

19 Vertical Targets (VTs) designed for georeferenced. The

GNSS-NRTK study performed by multiple frequency GRS1

(Topcon) for the mentioned datasets (GCPs, VPs, and VTs)

each produces RMS values less than 0.018 m and 0.029 m

for horizontal and vertical precision respectively. Horizontal

coordinates are referred to the UTM 33N Zone (ETRF00),

while the vertical values also referred to min sea level using

the ITALGEO2005 geoid model provided by the Italian

Institute of Geography (IGMI) (Teatini, Ferronato,

Gambolati, Bertoni, & Gonella, 2005).

Table 1: Hexcopter Specification (Mancini et al. 2013)

Manufacturer Description

Type Micro-drone Hexacopter

Engine Power 6 Electric Brushles

Dimension & Weight 100 cm, 3.3 kg (total

weight for all equipment

is approximately 5 kg)

Flight Mode Dual, automatic based on

waypoints or base on

wireless control

Endurance Standard 20 min (+5 min

safety

Camera Configurations Digital gimbal, Canon

EOS 550D (focal length

27 mm), res. 5184 ×

3456 Bi-axial roll and

pitch control

2.3. Delineation a Part of Shoreline of the Chosen Pond

at Pohranov Pond, Czech Republic

The attractive area is close to the town of Pardubice, in the

Czech Republic. Case study studies part of Pohranov's beach

shoreline, close to Pohranov municipality. The pond size is

0.4 km2and it is surrounded by forests. This means that the

observation to collect the data is difficult. Satellite

Imagination does not provide data with the appropriate

resolution. Therefore, UAV represents a more appropriate

way of data collection in this case. The UAV provides data

in high contrast and lower costs are also lower. Tarot 690 is

one of UAV type was used for Pohranov pond monitoring. It

can be characterized as follows: vent tool; 6 gears; Average

impeller of 0.985 m; Height of 0.35 m, the maximum speed

of 70 km / h. This UAV has the following restrictions

(conditions where it cannot be used): temperature below -

10ºC; wind spinner from 10 m.s-1; mist with sight below 100

m; frozen creation on airscrew; drizzle, rain and snow. The

conclusion must be done several times in a few days to get a

short time series. The time horizons are selected according

to the weather conditions described above and cover longer

periods of time ie: 7. 7. 2015, 18. 7. 2015, 23. 8. 2015 and 2.

11. 2015. The flight altitude is 80 m (high installed in UAV

software before the flight) for all flights (Čermáková et al.

2016).

This article also mentioned that during the observation,

videos were collected by the UAV cameras are on the

spectrum only. Videos provided from UAV must be initially

processed to create an image of each observation. In

particular, the image must be selected and created from the

video. Software not available Free Video to JPG Converter

is used for this step. Combining all the collected images into

one picture is the next step. Image Composer Editor

(available for free) is used for this step. A Mosaicsgenerated

from the image cannot be distorted as only the central part is

selected for merging. The centre of the image cannot be



3

inferred. The resulting image represents our monitored area

and changes during the monitoring period. Figure 1 shows

the type of UAV used in this observation.

Figure 1: UAV Tarot 690 (dji, 2017)

3. Discussion of the Possibility of UAV Application

Based on the all methods were combined to display the

possibility of UAV application to monitor the physical

changes of the coast, show that UAV is capable for

monitoring coastal changes and it is sufficient to state that

using UAV is good enough to see the physical changes of

the coastal area. Various of UAV methods have been

utilised to monitor the shoreline changes such as based on

the previous literature show that all the images acquisition

was taken at range altitude from 40m to 80m. Furthermore,

show that within that range of altitude, after mosaicking

stage it will produce the orthophoto result to see the physical

changes of the coastal area. The orthophoto result represents

the monitored area. The result can be seen more clearly

when the UAV is used as a major tool to retrieve the data

compared to satellite images where the image is unclear.

Mancini et al. (2013) identified that the coastal change

monitoring method needs to set off some control points

which the Ground Control Point (GCP) to the coordinate x,

y and z to avoid distortion. As example, the study at the

Ravenna, Italy used the GNSS-NRTK to produce RMS

values less than 0.018m for the horizontal and 0.029m for

the vertical precision. Therefore, when the image was

georeferenced by the coordinates the images is easy to

process and it will be placed at exact location. The less RMS

values get the less distortion will affected to the results.

However, the study at Pohranov Pond, Czech Republic

did not use the method of placing GCP in coastal areas

because they already get the reference data from the State

Administration of Land Surveying and Cadastre (CUZK).

The data collection is focused on the video that was taken by

the UAV. The main disadvantage of this method is the

actual value of coordinate for georeferenced cannot get the

real value because there is no in situ observation to get the

real coordinate but still can use to process the data to get the

orthophoto.

Thus, since the possibility of UAV application to

monitor shoreline changes has been proved, I will choose

low cost UAV to monitor shoreline to see the physical

changes at coastal area.

4. Conclusion

In conclusion, this paper is showed and proved that

shoreline changes can be monitored by UAV application.

Based on all the previous study, using UAV for monitor the

shoreline changes is one of the most successful methods for

determining and see the physical changes on the shoreline

area. UAV application is possible to monitor shoreline

changes. Further research can be conducted by using more

high intense of UAV to monitor shoreline changes.

Acknowledgements

Praise be to Allah Almighty for this opportunity. This study

is supported by a Research Discipline Research Grant

Scheme (TRGS/1/201/UKM /02/5/1). The author also

wishes to thank the Earth Observation Centre, Institute of

Climate Change, UKM.

References

[1] Casella, E., Rovere, A., Pedroncini, A., Stark, C. P.,

Casella, M., Ferrari, M. & Firpo, M. 2016. Drones as

tools for monitoring beach topography changes in the

Ligurian Sea (NW Mediterranean). Geo-Marine Letters,

36(2), 151–163. doi:10.1007/s00367-016-0435-9

[2] Čermáková, I., Komárková, J. & Sedlák, P. 2016. Using

UAV to detect shoreline changes: Case study -

pohranov pond, Czech Republic. International Archives

of the Photogrammetry, Remote Sensing and Spatial

Information Sciences - ISPRS Archives, 2016–

Janua(July), 803–808. doi:10.5194/isprsarchives-XLI-

B1-803-2016

[3] Gonçalves, J. A. & Henriques, R. 2015. UAV

photogrammetry for topographic monitoring of coastal

areas. ISPRS Journal of Photogrammetry and Remote

Sensing, 104, 101–111.

doi:10.1016/j.isprsjprs.2015.02.009

[4] Mancini, F., Dubbini, M., Gattelli, M., Stecchi, F.,

Fabbri, S. & Gabbianelli, G. 2013. Using unmanned

aerial vehicles (UAV) for high-resolution reconstruction

of topography: The structure from motion approach on

coastal environments. Remote Sensing, 5(12).

doi:10.3390/rs5126880

[5] Turner, I. L., Harley, M. D. & Drummond, C. D. 2016.

UAVs for coastal surveying. Coastal Engineering, 114,

19–24. doi:10.1016/j.coastaleng.2016.03.011



4

Study on Coastal Vulnerability Index (CVI) for Selangor Coastal

Area

Muhammad Afiq Ibrahim1, Khairul Nizam Abdul Maulud1, 2, Fazly Amri Mohd2,

Mohd Radzi Abdul Hamid3, Nor Aslinda Awang3

1Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM) 2Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM)

3Coastal Management & Oceanography Research Centre, National Hydraulic Research Institute of Malaysia,

Ministry of Natural Resources & Environment, Selangor, Malaysia


Abstract

Sea level rise has high potential on changing and affecting

the ecosystem that already exist in the local area. This also

affects the local residential and local activities at the coastal

area. The rate of sea level rise is greater than the global rate

especially at low ground area. Thus, this research is to study

on coastal vulnerability index (CVI) for Selangor coastal

area. Selangor coastal area has been announced as one of the

area that is affected by erosion due to sea-level rise impact.

This area has been reported to be eroded for the past few

years until today and still on going. The only way to deal with

this is to do some adjustment and adaptation on the coastal

area so that the effect of sea-level rise can be minimized.

Using coastal vulnerability index (CVI) method, which is a

relatively simple and functional method that can be used to

estimate the vulnerability of the coastal area against erosion

due to of sea-level rise phenomena. In this study, six physical

parameters were taken count in coastal vulnerability index

calculation. By ranking the vulnerability of the coastal area,

it is easier to identify the areas that area comparatively more

vulnerable to sea-level rise changes.

1. Introduction

Climate change has causes the change on the environment

such as ice on rivers breaking up earlier, the shrunk of the

glaciers and also plant and animals ranges have shifted. This

will result on melting of ice, sea level rise and global

warming as shown in figure 1 below. The Intergovernmental

Panel on Climate Change (IPCC) has predicted that the

global temperature will rise from 2.5 up to 10 degrees

Fahrenheit over the next century [1]. The increases in global

temperature somehow give beneficial impacts on some area

and harmful ones in the others. As the global temperature

increase over time, the net annual cost also increases. Earth

ecosystem is disturbed because of the global climate change

that occurs regularly today. Humans and other living things

on Earth is threatened by the climate change that causes

many houses and habitats were destroyed and less place left

for living.

Climate change shows the difference on earth atmosphere

condition which is mainly consist of the sea, surface area that

is covered by ice and also all human activities [2]. The

physical impact of sea level rise is explained that sea level

rise leads to flood and also the movement of low-land and

humid-land on the Earth [3]. Due to this, the local community

live nearby coastal area is threatened and disturbance in

economic activities in that area. That’s why it is very

important to know the hydrodynamic behaviour of the sea

based on several aspects includes the beach structure,

sediment transportation and also the beach morphology

change and assessment. The effect of sea level rise from

global warming has cause the coastal area and nearby island

in Malaysia to be affected by flood, coastal erosion and

destruction of ecosystem at wetlands and swamp areas. The

flood incidence at Johor in 2007 might be one of the sea level

rise effect that may cause from the heating temperature in

Malaysia that destroy a large-scale settlement area and also

affecting the economic activities in the area.

2. Coastal Vulnerability Index (CVI)

Coastal vulnerability index (CVI) is a relatively simple and

functional method that can be used to estimate the

vulnerability to erosion of any coastal zone regarding the

future sea-level rise [5]. It is an index representative of six

physical variables to be related in a quantifiable manner that

can be easily understandable. The six physical variables

includes geomorphology, mean tidal range, sea-level rise

rate, erosion and accretion, mean height and significant wave

and also coastal slope. It combines the sensitivity of coastal

zone to changes and also the ability of the coastal to adapt

the changes made. Using numerical data that is arranged by

ranking, this method can highlight the areas where the

various effects of sea-level rise may be the greatest. The

geometric average is quite sensitive to small changes in

individual ranking factors but the square root is used to

reduce the extreme range. Thus, it is important to identify the

coastal vulnerability index of the coastal area before



5

executing any methods of coastal protection at a specific area

in order to prevent any erosion cases.

2.1. CVI Calculation

CVI value can be calculated using the following formula. By

multiplying all the parameters and divide into total number of

parameters then square root of the answer is the CVI value.

The formula can be represented as follows:

6

)*****( fedcbaCVI , (1)

where;

a = geomorphology

b = mean tidal range

c = sea-level rise

d = erosion and accretion

e = mean height and significant wave

f = coastal slope

3. Discussion

The discussion of this paper is focusing on the basic physical

parameters that is used for coastal vulnerability index in

Selangor coastal area. The following parameter are suitable

and has been identified to be used for coastal vulnerability

index study at Selangor coastal area. The parameters are

listed below.

3.1. Geomorphology

Geomorphology is the study of the nature and history of

landforms and the processes which create them. Initially, the

subject was committed to unravelling the history of landform

development, but to this evolutionary approach has been

added a drive to understand the way in which

geomorphological processes operate. In many cases,

geomorphologists have tried to model geomorphological

processes, and, more recently, some have been concerned

with the effect of human agency on such processes.

3.2. Mean Tidal Range

Tidal range is the difference between the high tide and the low

tide. The tidal range is the vertical difference between the

high tide and the succeeding low tide. Tides are the rise and

fall of sea levels caused by the combined effects of the

gravitational forces exerted by the Moon and the Sun and the

rotation of the Earth. The tidal range is not constant, but

changes depending on where the sun and the moon are. The

most extreme tidal range occurs when the gravitational forces

of both the Sun and Moon are aligned, reinforcing each other

in the same direction which is called the new moon or in the

opposite directions which is called the full moon. This type of

tide is known as a spring tide. During neap tides, when the

Moon and Sun's gravitational force are in a right angle to the

Earth's orbit, the difference between high and low tides is

smaller. Neap tides occur during the first and last quarters of

the moon's phases. The largest annual tidal range can be

expected around the time of the equinox, if accidental with a

spring tide.

Tidal data for coastal areas are published by the

Department of Survey and Mapping Malaysia (JUPEM). It is

based on astronomical phenomena and it is predictable. Storm

force winds blowing from a constant direction for a prolonged

time interval combined with low atmospheric pressure can

increase the tidal range, especially in narrow bays. Such

weather-related effects on the tide, which can cause ranges in

excess of predicted values and can cause localized flooding,

are not calculable in advance.

3.3. Sea-level Rise Rate

Sea level rise is an increase in the volume of water in the

world’s oceans which resulting in an increase in global mean

sea level. Sea level rise is due to global climate change by

thermal expansion of the water in the oceans and by melting

of ice sheets and glaciers on land. Sea level rise at specific

locations may be more or less than the global average

depending on the environment of the location. Sea level rise

is expected to be ongoing for centuries. Based on IPCC

Summary for Policymakers, AR5, 2014, indicated that the

global mean sea level rise will continue during the 21st

century, very likely at a faster rate than observed from 1971

to 2010. Sea level rises significantly influence human

populations in both coastal and island regions and also

affecting natural environments like marine ecosystems in the

area.

3.4. Erosion and Accretion

Erosion is the action of surface processes such as water flow

or wind that remove soil, rock, or dissolved material from one

location to another location. Natural rates of erosion are

controlled by the action of geomorphic drivers, such as

rainfall, bedrock wear in rivers, coastal erosion by the sea and

waves, glacial plucking, and mass movement processes in

steep landscapes like landslides and wreckage flows. The

rates of such processes act control the rate of erosion.

Processes of erosion that produce sediment or solutes from a

place contrast with those of deposition, which control the

arrival and emplacement of material at a new location. While

erosion is a natural process, human activities have increased

the rate at which erosion is occurring globally around the

world.

Accretion is the process of coastal sediment returning to

the visible portion of a beach or foreshore following a

submersion event. A sustainable beach or foreshore often

goes through a cycle of submersion during rough weather

then accretion during calmer periods. If a coastline is not in a

healthy sustainable state, then erosion can be more serious

and accretion does not fully restore the original volume of the

visible beach or foreshore leading to permanent beach loss.

3.5. Mean Height and Significant Wave

The wave height value in a forecast, and reported by ships and

buoys is called the significant wave height. The term

significant wave height is historical as this value appeared to

be well correlated with visual estimates of wave height from



6

experienced observers. It can be shown to correspond to the

average 1/3rd highest waves (H1/3).

3.6. Coastal Slope

Coastal slope is an indication of the relative vulnerability to

inundation and the potential rapidity of shoreline retreat

because low-sloping coastal regions should retreat faster than

steeper regions. The regional slope of the coastal zone was

calculated from a grid of topographic and bathymetry

elevations extending about 5 km landward and seaward of the

shoreline.

4. Conclusion

Based on the discussion that has been made, it is clearly seen

that by using the six physical parameters, which are

geomorphology, mean tidal range, sea-level rise, erosion and

accretion, mean height and significant wave and coastal slope

of coastal vulnerability index formula by Gornitz, more

accurate estimation can be obtained regarding the

vulnerability of the coastal area to erosion. It also combines

the sensitivity of the coastal area to changes and also allow

the ability of the coastal area to adapt with the new

conditions. Thus, all the physical parameters would be used

for coastal vulnerability index (CVI) at Selangor coastal area

for further research.

Acknowledgments

I would like to thank the National Hydraulic Research

Institute Malaysia (NAHRIM). I also would like to

acknowledge to Ministry of Education for supporting the

TRGS research grant (TRGS/1/2015/UKM/02/5/1).

References

[1] IPCC. 2013. IPCC Fifth Assessment Report (AR5).

IPCC, s. 10-12.

[2] Md.Jahi, J. 2009. Pembangunan Pelancongan dan

Impaknya terhadap Persekitaran Fizikal Pinggir Pantai.

Malaysian Journal of Environmental Management,

10(2), 18.

[3] Faour, Ghaleb, Fayad, Abbas, Mhawej, Mario. 2013.

“GIS-Based Approach to the Assessment of Coastal

Vulnerability to Sea Level Rise: Case Study on the

Eastern Mediterranean” 1 (i): 41– 48.

[4] Gornitz, V., White, T. W. & Cushman, R. M. 1991.

Vulnerability of the US to future sea level rise.

Proceedings of the 7th Symposium on Coastal and Ocean

Management, 2354–2368.

doi:10.1017/CBO9781107415324.004.



7

GIS-integrated Infrastructure Asset Management System

Muhammad Aqiff Abdul Wahid1, Khairul Nizam Abdul Maulud2,3, Mohd Aizat Saiful Bahri4,

Muhammad Amartur Rahman4, Othman Jaafar4

1Institute of Climate Change, Universiti Kebangsaan Malaysia, Malaysia 2Earth Observation Centre (EOC), Institute of Climate Change, Universiti Kebangsaan Malaysia, Malaysia

3Department of Civil & Structural Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia,

Malaysia 4Prasarana UKM, Universiti Kebangsaan Malaysia, Malaysia

*corresponding auhor, E-mail: [email protected]

Abstract

Infrastructure asset management is a core process in asset

management. An organisation is constantly striving for a

better infrastructure asset management to ensure the

effectiveness in decision making. This paper aims to

investigate how infrastructure asset management can be

integrated with geographic information systems (GIS)

technology. In the previous study, multiple questions were

asked to identify how GIS can be integrated with asset

management, the requirements and the challenges also. The

studies revealed that GIS and asset management can be

integrated with spatial and non-spatial information of the

assets in GIS environment. However, there are requirements

and challenges in the process, such as the data need to be

converted into digital and GIS format. The size of

geodatabase also will mostly be occupied and it is a

necessity to have big storage. GIS technology also needs to

have the ability to absorb new technology which means it is

customizable based on projects and operations. The paper

provides an in-depth overview of how GIS can be integrated

with infrastructure asset management and highlight the

importance of GIS technology in asset management. An

integrated pipeline management systems was develop as a

preliminary prototype. The advantage is that it can improve

the effectiveness of decision making and managing pipeline

network.

1. Introduction

Infrastructure assets such as sewers, water pipes, roads and

electricity lines are the supporting pillars of a society

specifically an organization such as a university.

Infrastructure asset is a multiplex structure with extremely

important and essential elements for an organization [1]. In

addition, [2,3,4] mentioned that economic growth also

depends on the imperative role of the infrastructure asset.

The important roles of infrastructure assets require massive

attention from the management of an organization such as

policy makers, decision makers, asset managers and also

down to technical staff and users.

Investment in the development of the infrastructure

assets for a university is focusing on the maintaining the

good environment. Education institution needs to provide a

very calm and productive environment for their community

to enhance the learning process and to produce the next

generation that can benefit the country. Thus, infrastructure

asset management plays a vital role to support the needs of

the university’s community. The infrastructure assets also

should be uses and pass to many generations. Taken

together, managing asset is not a simple task. It takes a great

responsibility and many decisions can be wrong without

fully recognizing the complexity, diversity, and social and

technological evolution of the system [1]. Furthermore, a

great responsibility comes with great challenges. One of the

purposes of managing infrastructure asset is to extend its life

value. Without a proper method or tools, the inefficiencies

will lead to many negative decisions, profit loss and lastly

the investment becomes a waste.

At the same time, emerging new technology, science and

mathematics are influencing our approaches and

understanding in designing and analyzing infrastructure. The

public is getting aware the importance of good management

practice and its change the philosophy of long term

management responsibility [1,5]. In addition, new

technology such as Intelligent Transportation Systems (ITS),

Supervision, Control, and Data Acquisition (SCADA) and

Geographic Information System (GIS) signal the start of a

new understanding of future management system. This

paper briefly discusses the advantages of GIS technology in

infrastructure asset management as a decision support tool.

2. Methodology

This study was conducted to customise web applications

using ArcGIS Online – WebApp Builder to visualise the

information of pipeline infrastructure in UKM and also to

integrate the information of pipeline infrastructure with GIS

geodatabase. The study will cover UKM, Bangi area. The

study is divided into four phases as a guideline and each

phase needs to be done according to the guideline in order to

ensure the objectives can be achieved. Figure 1 shows the

workflow of the study.

Database design and application design is important

phase where all the spatial and non-spatial data are link

together. Then, the application needs to be able to

understand the database environment and able to translate

the data into a display in the application. Both of database

and application development used desktop and online

application of ESRI’s software.



8

Figure 1: System development framework

3. GIS in Asset Management

Spatial and information system capabilities of GIS

technology becomes an obvious solution to assist in the

management of infrastructure asset [6]. The capabilities to

answer questions about location, patterns, trends and

conditions that is GIS [7]. Many well-known that GIS can be

viewed as a software package, which is used to collect,

store, manipulate, analyze and display output data [8].

In theory Information Technology (IT) in asset

management have three major roles. IT is utilized in

collection, storage, and analysis of information spanning

asset lifecycle processes. Secondly, IT provides decision

support capabilities through the analytic conclusions from

analysis of data. Thirdly, IT provides an integrated view of

asset management through processing and communication

of information and thereby allow for the basis of asset

management functional integration [9]. The minimum

requirements for asset management at the operational and

tactical levels is to provide functionality that facilitates;

knowing what and where the assets that the organization

own and is responsible for are

knowing the condition of the assets

establishing suitable maintenance, operational and

renewal regimes to suit the

assets and the level of service required of them by

present and future customers

reviewing maintenance practices

implementing job/resources management

improving risk management techniques

identifying the true cost of operations and maintenance,

and

optimizing operational procedures [10].

Taking the point of knowing what and where the location

of the assets is where GIS comes to be acknowledged the

transformation of GIS technology from desktop-based

solution to the enterprise system will give the chance for an

organization to use spatial application in asset management

and services. A system with spatial integration is capable to

analyses a complex data structure based on spatial location,

such as visualize data using a map using various relation to

show the proximity, adjacency, and others spatial

relationship [11]. Asset management system with integration

of GIS technology is best suited for spatial asset

management. In addition, GIS technology plays an

important role in asset management within utility, power,

government, transportation, telecommunication, and much

more in asset intensive industry by providing the additional

tools for collecting and updating data with spatial location

[11].

The impact of GIS is increasing as the users and the

organization is keen to know the status of the asset but also

the location of the asset. Furthermore, many previous studies

of GIS integration to computerized maintenance

management systems (CMMS) have concluded that the

system integration will only benefit the user such as:

providing maps of utility with the work orders; tracing water

pipeline infrastructure prior to fieldwork; planning travel

roads for work crews; and scheduling maintenance of

infrastructure assets [12]. The integration of GIS with the

process of asset management will be a very effective

geospatial solution [11]. The process of planning and

making decisions will be better and also it will improve the

productivity and the customer relation will become more

convenient.

4. GIS-Integrated Infrastructure Asset

Management

The key challenge to achieving effective infrastructure asset

management is to improve the effectiveness of decision

making. However, effective infrastructure asset

management seems to be more challenging since: the

function of infrastructure assets is complex; a standard is

needed to define failure and benefits of the assets; and these

standards are hard to quantify or measure [13]. At the same

time, the challenges faced from the complexity caused by

technical, economic, environmental, political and social

factors [14]. Over the years, the expectations in terms of

reliability, safety and availability of the infrastructure

networks also have steadily increased [15]. The crucial

assessment here is infrastructure asset management is a

method of a process to help improve the decision making.

The complexity faces in infrastructure asset management

have continually caused public agencies or an organization

to continually allocated large budgets for the maintenance,

renovation and reconstruction work. However, this situation

has effected many agencies. These agencies are unable to

guarantee a performance level that meets the expectations

of the public because of budgetary constraints [16]. The

new approach has emerged in asset management for public

agencies which to achieve more value with fewer resources

[17]. While these approaches clearly pointed out different



9

kind of models, numbers and decisions focus, there are

three general areas of decision making can be identified:

decisions with regard to the infrastructure objectives of

the public agencies;

decisions with regard to the performance-related

situation of the agency’s infrastructure; and

decisions with regard to the interventions applied by

the agency to the infrastructure [16].

Another approach to improve the decision making is to

integrate infrastructure asset inventory data and spatial data

by using GIS technology. This approach will not only

improve the data access but the management capability with

the information that will make the decision effective.

4.1. Requirements and Challenges

The main purpose of a GIS-integrated infrastructure asset

management system is to maintain an accurate, updated, and

reliable data on the current infrastructure assets. Moreover,

the systems enable users to efficiently access this data to

make future predictions and decisions of the infrastructure

performance, to plan maintenance operations and

maintenance budget [18]. The goal requires as such

requirements:

modeling and management of infrastructure physical,

functional, and performance data as well as gathering

condition data in a timely and effective manner

interoperation and data exchange between different

function-specific software tools

modeling, management, and coordination of

maintenance operations and effective communication of

accurate and timely information

the ability to customize the system to specific project or

organization policies and to accommodate various

operations that reflect industry practices [19].

Each of these requirements has its own challenge to be

addressed. Firstly is the data, probably the most crucial

challenge that needs to be sort before the others. The size,

complexity, and the nature of data present several challenges

that the integrated system needs to address. An efficient data

gathering, analysis, and management techniques are the key

to develop successful GIS-integrated infrastructure asset

management system. Furthermore, the integrated system

should also support different modes of data access and

exchange such as centralized geodatabase, application-to-

application file exchange, and Intranet/Extranet access

[18,19].

To support the integration and interoperability of legacy

software tools a standard module need to be established.

This important implication in reducing the systems

implementation and maintenance time and cost [20]. It is

important not to spend money for a new tools or technology

when you can just upgrade current one by reused its in other

ways. By using this module also will not impact the

operation of the systems in overall.

Infrastructure asset management is not a single

operation, it is a multi-disciplinary process that involves a

lot of different operations but with the same purpose.

Although, it is very important to manage the inter-dependent

operations in a coordinated manner. Integrated systems

should enable the efficient flow of information among

various activities such as efficient access, sharing,

management, and tracking of documents. Infrastructure asset

management team needs to share information to organize

their tasks [18,19].

The integrated systems also should have a modular

architecture to cope with future modification, extension, and

technology improvement. Furthermore, another major

design consideration is the necessity to separate the

responsibilities between the function-specific toolset and

other framework components. Tools would provide users

with the functionality to perform specific tasks, while the

integrated systems components would provide the

functionality to integrate and manage different processes.

5. Implementation of an Integrated Pipeline

Management Systems

A preliminary prototype has been developed on an

integrated pipeline management systems to support the

maintenance management of the National University of

Malaysia, Bangi as shown in Figure 2. The integrated

systems implemented several requirements as described in

the previous topic. Modelling and management of

infrastructure data in timely and effective manner. Second,

the data exchange between different software also can be

achieved. Thirdly, effective and accurate timely information

also can be shared among the management and

stakeholders. Lastly, the ability to customize the systems to

accommodate various operations and projects.

Figure 2: GIS-integrated pipeline management system.

As for the GIS-integrated pipeline management systems,

ESRI software which is ArcGIS has been chosen as a

medium application to integrate all the spatial and non-

spatial information. Moreover, a web GIS application will

be used to access all the pipeline information. The

integrated web GIS applications should provide an

informative solution to the users. Combining the database

that keeps all the information of the infrastructures and a

geodatabase that contain the spatial information of the

infrastructures into one and can access in one application.

ArcGIS Online technology is a convenient method to

use for publishing spatial data online [20]. It is a

collaborative, cloud-based platform that allows members of



10

an organization to use, create, and share maps, apps, and

data, including authoritative basemaps published by ESRI.

Through ArcGIS Online user will get access to ESRI’s

secure cloud, and use it to manage, create, store, and access

data as published web layers, and because ArcGIS Online is

an integral part of the ArcGIS system, user can use it to

extend the capabilities of ArcGIS for Desktop, ArcGIS for

Server, ArcGIS apps, and ArcGIS Web APIs, and ArcGIS

Runtime SDKs.

The applications already provide many templates that

can be used for the web applications and the user also can

choose to build new applications using Web AppBuilder.

Web AppBuilder offers the user more choices in

configuring the appearance, settings and functionality of the

web application. Furthermore, the web application using

visual and compositional themes offer in the Web

AppBuilder and following widgets layer list, attribute table,

print, zoom slider, measurement, home, scalebar, coordinate

and filter are added to provide more options for the user.

Once the web applications are ready it has an option where

it can be shared among the organization members. Only an

authorized member will have an access to the web

application because of the data security issues.

GIS-integrated asset management system is becoming

more of necessity in asset management, generally.

Infrastructure assets information which is previously stored

using conventional methods such as in paper form, paper

maps, CAD drawing and standard database are not efficient

anymore. However, this information can be used by

converting them into a geospatial data format. Converting

these information into digital based in not an easy task and

might take big size of data storage. Furthermore, a

geodatabase is created to store all the information. Spatial

data and attribute data are connected to each other in the

geodatabase. ESRI’s software such as ArcGIS is an

application to create, manage, edit, manipulate, visualize

and publish geospatial data.

The published service would be used in ArcGIS Online

and act as a medium to customise a web map application.

The web-map application is capable to provide and

visualize the spatial and non-spatial information of each

infrastructure asset. In addition, assets information can

easily be shared among the university management and with

the advantage of GIS mapping the information can easily be

interpreted by everyone.

6. Conclusion

Asset management is already existed a long time ago.

Although, the method is difference to what exists today, the

purpose of asset management is still the same. It is to have

an inventory of the assets and to make sure the investment

will only gain profit in the future. GIS capabilities in

providing a good platform for the user to customize and

configure the applications based on the user needs is a

privilege for the user to integrate it with infrastructure asset

management.

The process of storing, editing, manipulating and

visualizing the information of the infrastructure asset

becomes more convenient and efficient. Moreover, users are

able to access the updated data and share it among the

members of the organization. A good infrastructure asset

management will always benefit the organisation in many

ways. It would be a great help to management in making

better planning and decisions for the better future of the

organisation and its customers.

Acknowledgements

The authors acknowledge and thankful for the financial

support given by the Universiti Kebangsaan Malaysia Top

Down Grant through TD-2016-012.

References

[1] A.C. Lemer, Progress Toward Integrated

Infrastructure-Assets-Management Systems: GIS and

Beyond. APWA International Public Works Congress

NRCC/CPWA Seminar Series “Innovations in Urban

Infrastructure, (410), 7–24. 1998.

[2] E. Too, Infrastructure asset: developing maintenance

management capability. Facilities, 30(5/6), 234–253,

2012.

[3] L. Hardwicke, Australian infrastructure report card.

Barton, ACT: Engineers Australia. 2005.

[4] M.V.D. Mandele, W. Walker, S. Bexelius, Policy

development for infrastructure networks: concepts and

ideas, Journal of Infrastructure Systems, 12(2), pp. 69–

76, 2006.

[5] R. Haas, W.R. Hudson, L. Falls, Pavement Asset

Management. Pavement Asset Management, 2015.

[6] G. M. Baird, Leveraging your GIS, Part 1: Achieving a

low-cost enterprise asset management system. Journal,

American Water Works Association, 102(10), 16–20.

2010.

[7] D.I. Heywood, S. Cornelius, S. Carver, An introduction

to geographical information systems, Harlow, England

; N.Y. : Prentice Hall, 2011.

[8] P.A. Burrough, and R.A.McDonnell, Principles of

Geographical Information Systems, Oxford University

Press, Oxford, p.333, 1998.

[9] N.A.J. Hastings, Physical asset management. Physical

Asset Management, 2010.

[10] A.Haider, Governance of IT for engineering asset

management. Business Transformation through

Innovation and Knowledge Management: An

Academic Perspective - Proceedings of the 14th

International Business Information Management

Association Conference, IBIMA 2010, hlm.Vol. 1, 77–

95, 2010.

[11] J. Campbell, A.K.S.J. Jardine, McGlynn, Asset

management excellence: optimizing equipment life-

cycle decisions. Dekker Mechanical Engineering,

2011.

[12] J. McKibben, D. Davis, Integrating GIS, computerized

maintenance management systems (CMMS) and asset



11

management. 22nd Annual ESRI International User

Conference, 2002.

[13] R. Dekker, Applications of maintenance optimization

models: a review and analysis. Reliability Engineering

& System Safety, 51(3), 229–240, 1996.

[14] R.I. Godau, The changing face of infrastructure

management. Systems Engineering, 2(4), 226–236,

1999.

[15] G. Arts, W. Dicke, L. Hancher, New Perspectives on

Investment in Infrastructures, WRR Amsterdam

University Press, Amsterdam, 2008.

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of infrastructure asset management: challenges for

public agencies. Built Environment Project and Asset

Management, 1(1), 61–74, 2011.

[17] F.L. Moon, A.E. Aktan, H. Furuta, M. Dogaki,

“Governing issues and alternate resolutions for a

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management”, Structure and Infrastructure

Engineering, Vol. 5 No. 1, pp. 25-39, 2009.

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[20] P. Seamann, Web mapping application of Roman

Catholic Church administration in the Czech lands in

the early modern period. Geoinformatics FCE CTU

16(1), 2017.



12

Assessing of Shoreline Changes by using Geospatial Technique

Siti Norsakinah Selamat1, Khairul Nizam Abdul Maulud1&2, and Othman Jaafar2

1Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia 2Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment,



Abstract

The changing of the shoreline position has become a major

problem that involve coastal zones around the world.

Therefore, analysing and understanding of shoreline

changes are importance task to address the issues of

shoreline changes. This study focuses on determination

analysis rate of shoreline changes using the geospatial

technique in 1993 to 2014. To archive our objectives multi

temporal data and high spatial resolution imagery used as

investigation data. The rate of shoreline changes was

computed using Digital Shoreline Analysis System (DSAS)

technique, where end point rate (EPR) has been used in this

study to determine the rate of shoreline changes for short

term analysis. Approximately 348 transects along Bagan

Pasir was created with 25 meter interval. Results illustrated

the average rate of shoreline changes between 0.01 to -

33.28 m/year during 1993 and 2006. From 2006 to 2014,

the rate of changes existed from 0.01 to 46.64 m/year. The

research proved that DSAS method can be an effective way

to determine the rate of shoreline changes.

1. Introduction

Climate change issues are the main problem that are often

discussed around the world. According to the [1] climate

change is a weather changing process that is complicated

and time consuming. Generally, climate change is not a

change of weather because the weather naturally changes

daily and even changes every hour. Climate change is a

weather pattern that has changed dramatically in recent

years and long term effects. These phenomena influenced by

two major factors that are natural changes and human

activities that contribute to the increase of greenhouse gases.

Therefore, critical natural disasters such as rising sea levels,

floods, landslides, coastal erosion, drought, forest fires and

haze due to the effects of climate change.

Human activity is a major factor contributing to climate

change from the mid-20th century [2]. Climate change can

also be attributed to the rise in global temperatures, known

as global warming. The phenomenon of global warming has

risen and is forecast to increase over time. Ice melting in the

Arctic is a major factor that causes sea level rise and poses a

threat especially to countries with high population rates and

socio-economic activities on coastal areas. Globally there

are about 400 million people living in the 20 meter sea level

and within 20 km of the beach [3] and stated these

phenomena seriously amplify risks to coastal populations

[4].

Nowadays, National development has been rising over

the years. Regarding that, coastal zones were recognized as a

centre of economy and tourism for the coastal country. The

increase in coastal populations indirectly contributes to the

development of coastal development. Malaysia has also

faced this situation. Hence monitoring coastal zones is

crucial for protecting and maintaining the environment so as

not to be affected by the development of coastal

development [3].

Shoreline change is one of the most dynamic processes

in coastal areas. Shoreline changes occurred caused by two

major phenomena such as natural phenomena and human

activities. In [5], it is found that natural change was due to

the process of unification between waves, currents, tides and

streams that often caused conflicts in the process of erosion.

Besides that shoreline is known as the main component

when determining the territorial boundaries of an area, but

unfortunately these zone is considered fragile area and easy

to change. Therefore, the mapping of shoreline changes

becomes an important process for analysing the history of

change and overcoming these problems.

Shoreline changes studies have been widely studied by

many authors such as [6], [7], [8], and [9]. Traditionally,

shoreline changes have been assessed by survey measuring,

where field measurements are needed to clarify data [10]

and [11]. However, rising technology help overcome this

problem. Geographical Information System (GIS) and

Remote Sensing technology able to cover a wide area and

capable to solve this problem efficiently. It can be proven by

the study conducted by [12], [13], and [14] which proves the

study using this approach is very useful and valuable.

The study area corresponds to the west coast of

Malaysia. It is located in Bagan Pasir, Selangor. These coast

categories as the muddy coast and recognized as density

populated area. Other than that, this area also knows as a

centre of economic for communities. Figure 1 illustrated the

condition of Bagan Pasir coastal area.



13

Figure 1: Location of study Area

This study explores the analysis of shoreline changes

using DSAS approach to investigate erosion and accretion

phenomena and calculate the rate of shoreline changes that

have occurred. The main goals of this study to analysis the

shoreline change over the year and compare patterns of

changes for short term changes.

2. Materials and Methods

This paper focuses on determination shoreline changes using

multi-resolution and multi-temporal data. The study adopted

a methodology for extraction shoreline position and

determine the rate of changes is that used by several authors

[12], [14], and [15]. This methodology is based on three

stage of data process which is extraction shoreline position,

DSAS processing and analysis rate of shoreline changes.

2.1. Data Sources

In this study, SPOT 5 and topographic maps datasets

acquired from 1993 to 2014 were used to determine the rate

of shoreline changes along Bagan Pasir area. Table1 shows

the data sources used for determination of shoreline changes.

Projection systems used in this study are Rectified Skew

Orthomorphic (RSO) in meter unit.

Table 1: Data sources used for this study

Type of data Year Scale/Resolutio

n

Topographic map 199

3

1: 50 000

SPOT 5 200

6

2.5 meter

SPOT 5 201

4

2.5 meter

2.2. Shoreline Extraction

The shoreline dataset from 1993 to 2014 was extracted

using ArcGIS 10.4 software by using manual digitizing

technique.

2.3. Shoreline Analysis

DSAS V4.4 is an extension of ArcGIS 10 software, was

developed by United States Geological Survey (USGS)

[16]. The DSAS provided five statistical methods to

determined rate of changes such as shoreline changes

envelop (SCE), Net Shoreline Movement (NSM), End Point

Rate (EPR), Linear Regression Rate (LRR), and Least

Medium of Square (LMS). This approach can calculate the

rate of shoreline change either short term or long term

changes. In addition, users can choose any method to

address their research objectives because every method has

their own advantages and disadvantages to calculate the

change. In this study used EPR calculation to determined

rate of shoreline changes. The EPR method is an effective

operation to determine short-term changes. This method

consider dividing the distance movement of shoreline by the

time between the older and the most recent time to

calculated rate of changes.

DSAS tool computes the rate of shoreline changes using

four steps: (1) shoreline preparation, (2) baseline creation,

(3) transect generation, and (4) computation rate of

shoreline changes by [16]. In order to determine the rate of

shoreline changes, 348 transects perpendicular to shoreline

were generated with 25 meter interval. The erosion and

accretion were calculated by using the difference between

older and most recent shoreline. At the end of this study, the

rate of erosion and accretion were categorized into six

classes as shown in Table 2.

Table 2: EPR shoreline classification [15]

Rate of shoreline

changes (m/year)

Shoreline classification

> -2 Very High Erosion

> -1 to < -2 High Erosion

> -1 to < 0 Moderate Erosion

0 Stable

> 0 to < 1 Moderate Accretion

> 1 to < 2 High Accretion

> 2 Very High Accretion



14

3. Results and Discussion

Shoreline analysis was conducted for two different periods

which are from 1993 and 2006 and then from 2006 and

2014. The results of the present study show in table 3,

evaluation rate of shoreline changes using EPR method for

short term changes analysis. Based on the results obtained

from year 1993 and 2006 show the highest erosion rate of

33.28 meters per year, while the highest accretion rate only

14.00 meters per year. Minimum readings for erosion rate

also exceed the accretion rate where the erosion rate is 0.06

meters and the accretion rate is 0.01 meters per year. It may

be seen in 13 years, shows that erosion phenomena exceed

those accretion phenomena. Figure 2 illustrated map of EPR

classification based on the rate of changes that occurred

along 1993 and 2006.

Table 3: Rate of shoreline changes using EPR method

1993 - 2006 2006 -2014

Erosion Accretion

Erosio

n Accretion

Maximum 33.28 14 39.56 46.64

Minimum 0.06 0.01 0.01 0.01

Mean 11.7 6.09 13.16 9.26

Other than that, these results also show the rate of

changes that occurred along 2006 and 2014. The rate of

erosion changes from year 2006 and 2014 varied between

0.06 to 33.28 meters per year, while rates of accretion

changes fluctuate between 0.01 to 46.64 meters per year.

Here, the rate of erosion Here, the higher rate of erosion

was recorded is 39.56 meter while the accretion rate as high

as 46.65 meters per year. Based on these results shows both

rates of changes are significantly high recorded. Figure 3

represented map of shoreline classification based on EPR

calculation rate of changes between 2006 and 2014.

Based on these results, the rate of shoreline changes

during year 2006 and 2014 get the highest erosion rate

where applicable 39.56 meters per year compared with the

highest erosion during year 1993 and 2006 is 33.28 meters

per year. While, the highest rate of accretion occurred

during the year 2006 and 2014 compared with 1993 and

2006 where is 46.64 meters and 14.00 meters per year

respectively.

Figure 2: Classification rate of shoreline changes between

1993 and 2006

4. Conclusion

Bagan Pasir was known as high population density area

along the coast. It is also recognized as an economic centre

for some communities working in the fishing industry. The

historical investigation of shoreline changes is an important

task to determine the movement of shoreline for every year.

Monitoring of shoreline changes is easily and effectively

through GIS approach. This study provided the most

valuable information on the rate of shoreline changes

occurring at Bagan Pasir coastal area through DSAS

computation technique. This study has investigated the

changes according to two time period which are from 1993

and 2006 and then from 2006 and 2014. Based on the

analysis, Bagan Pasir experienced more erosion compared

with accretion phenomena. The findings showed that 1993

and 2006 indicated facing the higher erosion phenomena

compared with accretion which is 94.84% and 5.17%

respectively. Meanwhile, for 2006 and 2014 indicated the

same thing where the phenomena erosion still higher than

accretion phenomena with 68.43% and 31.57% respectively.

It may be seen along 21 years, shows that erosion

phenomena exceed that accretion phenomena occurred at

Bagan Pasir area. Therefore, further research and monitoring

are needed to emphasize the problem so that the erosion

phenomenon can be reduced.



15

Acknowledgements

The authors gratefully acknowledge to the Earth

Observation Centre, Institute of Climate Change, UKM for

sharing the satellite data. This study was supported by the

research grants of Trans Disciplinary Research Grant

Scheme (TRGS/1/2015/UKM/02/5/1) and Research

University Grant (AP-2015-009).

References

[1] Johnston. A, Slovinsky. P, & Yates K. L, Assessing the

vulnerability of coastal infrastructure to sea level rise

using multi-criteria analysis in Scarborough, Maine

(USA). Ocean and Coastal Management, 95, 176–188,

2014.

[2] Reyes. S. R. C, & Blanco. A. C, Assessment of coastal

vulnerability to sea level rise of Bolinao, Pangasinan

using remote sensing and geographic information

systems. International Archives of the

Photogrammetry, Remote Sensing and Spatial

Information Sciences, 39(B6), 167–172, 2012.

[3] Rasuly. A, Naghdifar. R, & M. Rasoli, International

Society for Environmental Information Sciences 2010

Annual Conference Monitoring of Caspian Sea

Coastline Changes Using Object-Oriented Techniques,

2(5), 416–426, 2010.

[4] Gornitz. V, Couch. S, & Hartig, E. K. Impacts of sea

level rise in the New York City metropolitan area. In

Global and Planetary Change (Vol. 32, pp. 61–88),

2001.

[5] M. Ekhwan, Hakisan Muara dan Pantai Kuala

Kemaman , Terengganu : Permasalahan Dimensi

Fizikal dan Sosial Erosion in the Estuary and Coastal

Area in Kuala Kemaman , Terengganu : A Physical and

Social Dimension Setback, 69, 37–55, 2006.

[6] Chen. L. C, & Rau. J. Y, (1998). Detection of shoreline

changes for tideland areas using multi-temporal

satellite images. Detection of Shoreline Changes for

Tideland Area Using Multi-Temporal Satellite Image,

19(17), 3383–3397, 1998.

[7] Lipakis. M, Chtysoulakis. N, & Kamarianakis. Y,

Shoreline extraction using satellite imagery.

BEACHMED-e/OpTIMAL - Beach Erosin Monitoring.

2008.

[8] Khairul. N. A. M, & Rafar. R. M, Determination the

Impact of Sea Level Rise to Shoreline Changes Using

GIS, International Conference on Space Sciences and

Comunication (IconSpace), 0–5, 2015

[9] Fitton. J. M, Hansom. J. D, & Rennie. A. F, Ocean &

Coastal Management A national coastal erosion

susceptibility model for Scotland, 132, 80–89, 2016.

[10] Pujotomo. M. S, Coastal changes assessment using

multi spatio-temporal data for coastal spatial planning

parangtritis beach yogyakarta Indonesia, 2009.

[11] Mills, J. P, Buckley. S. J, Mitchell, H. L, Clarke, P. J,

& Edwards. S. J, A geomatics data integration

technique for coastal change monitoring, 2005.

[12] Anand. R, Chandrasekar. B. N, & Magesh. S. K. N. S,

Shoreline change rate and erosion risk assessment

along the Trou Aux Biches – Mont Choisy beach on

the northwest coast of Mauritius using GIS-DSAS

technique. Environmental Earth Sciences, 75(5), 1–12,

2016.

[13] Erener. A, & Yakar. M, Monitoring Coastline Change

Using Remote Sensing and GIS Technologies, 30,

310–315, 2012.

[14] Moussaid. J, Ait. A, Zourarah. B, & Maanan. M, Using

automatic computation to analyze the rate of shoreline

change on the Kenitra coast, Morocco, 102, 71–77,

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[15] Kermani. S, Boutiba. M, Guendouz. M, Guettouche.

M. S, & Khelfani. D, Ocean & Coastal Management

Detection and analysis of shoreline changes using

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[16] Thieler. E.R, Himmelstoss. E.A, Zichichi. J.L, and

Ergul. A, Dsas 4.0. Digital Shoreline Analysis System

(DSAS) Version 4.0—An ArcGIS Extension for

Calculating Shoreline Change, 2009.



16

Heat Stress on Mangrove (Rhizophora apiculata) and Adaptation Options

Baseem M. Tamimi1, Wan Juliana Wan Ahmad1, Mohd. Nizam Mohd. Said1, Che Radziah

Che Mohd. Zain2

1School of Environmental and Natural Resource Sciences, Faculty of Science and Technology,

Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia 2School of Bioscience and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia,

43600 Bangi, Selangor, Malaysia


Abstract

Global climate change has shown to have a significant impact

on critical ecosystems, that in turn has led to elevated CO2

and temperatures that accompany changes in many abiotic

factors, including mangrove forests, facing challenges in

their habitat. This study was conducted to investigate the

morphological and physiological attributes of the mangrove

Rhizophora apiculata in response increased air temperature

for the selection of tree species that are able to adapt to

climate change. The seedlings were grown in controlled

growth chambers with temperature of 38°C, CO2 at 450 ppm

and controlled condition for three months. The plants were

watered with two litres of saline water of 28 ppt every 48

hours. Thus, after two weeks the mangrove recorded positive

results for all parameters to high temperature. The

differences in temperature resulted in significant differences

and negative interaction between CO2 and increased

temperature that led to serious damage to all samples

compared to controlled samples, and decreased growth and

photosynthesis rates. These results suggested that low levels

of photosynthetic capacity may be attributed to the decreased

CO2 fixative reaction system and photosynthetic pigment

contents.

1. Introduction

Elevated atmospheric carbon dioxide concentration (CO2)

and concomitant increasing temperatures are changing the

global environment [1], due to these factors being

determinants in the photosynthetic rates in plants, any

changes they present in the atmospheric composition and

climate will significantly affect planetary ecosystems [2].

Over the last century, atmospheric CO2 concentration has

increased from 280 to 360ppm as previous studies have

indicated making this an eminent and undeniable global

environmental change (GEC), with the current rate of

increase averaging at 1.5 µmol mol–1 year–1 [3]. It’s expected

that CO2 concentrations can reach 700ppm by the end of the

century as global population and economic activity increases,

leading to warmer global temperatures [4]. Recent model

projections suggest a global mean surface air temperature

increase of 1 to 4.5°C by 2100 AD [5] and the 0.3 to 0.6°C

rise of mean annual surface air temperature over the last

century shows the clear effect of recent atmospheric changes

to projected increase in temperature [6]. However, important

details in (a) diurnal and seasonal patterns, (b) frequency,

timing and duration of extremes (e.g. high or low

temperatures, late or early frosts), and (c) climatic variability

can be obscured by these broad mean annual changes in

temperature predictions [7]. One example is that recent

scenarios predict most warming in mid- and high-northern

latitudes in late autumn and winter, and little or none (or even

a cooling in mid-latitudes) in summer [5], which could affect

growing season length. Indeed, there is already evidence of a

change in growing season length [8]. Another example is the

strong evidence that, over land, the increase in night time

minimum temperature has been about twice the increase in

the maximum [6]. Plant growth will be greatly affected by the

continuing changes in diurnal cycles compared to an even

change in temperature over 24 hours but these broad global

mean temperature predictions obscure aspects critical to

natural and managed ecosystems.

The conservation and restoration of mangroves and

associated coastal ecosystems play important roles in climate

change adaptation strategies. Mangroves are not only

valuable in climate change mitigation efforts, but they are

also influential in adaptation to changing climates [9]. Due to

the affect mangroves have in adapting to climate change,

more investments should be funneled to its development

plans as climate change adaptation is a growing concern in

most international development agendas [7]. Thus, the

objective of this study is to determine the effects of increased

temperature on the growth of the most dominant and

commonly distributed mangrove forest from the

Rhizophoraceae family found in Malaysia [10], as the

mangrove forests should be preserved, especially because of

their economic importance and their important role in

preserving the ecosystem and diversity of organisms.


This research study was conducted at the “Tropical

Ecophysiology Lab.”, in UKM, Bangi, Malaysia (2° 55'

12.03"N, 101° 47' 2.99 E). The facility consists of Plant

Growth Chamber model (GC-202C), the plant growth



17

chamber monitored and controlled the relative humidity,

lighting, temperature and CO2 for the whole project duration,

which took three months. The mangrove plant seedlings with

soil were collected at the age of three months from Kuala

Gula in Perak (4.924012, 100.459581). These mangrove

seedlings were transplanted in box size (42-62cm) containers.

The propagules of mangrove seedlings were then planted in

two groups with seven samples in each box. Two weeks later,

the samples were checked in terms of physical growth. All the

plants that were rated as ‘in good health’ were transferred to

the plant growth chambers. The first group was exposed to

levels of the plant growth chamber at temperature 38°C with

CO2 at 450 ppm and the second group was at ambient.

Meanwhile, the plants were watered with two litres of saline

water (28 ppt) every 48 hours and were not given any

fertiliser. All dead or damaged plant material was removed

from the mesocosms, and all visible fauna (e.g. snails and

crabs) were removed to avoid confounding effects of soil

burrowing, herbivory, and other activities. Each mangrove

seedling was labelled according to groups and treatment. Any

changes in the seedling health were also recorded

qualitatively.

2.1. Experimental Design and Growth Measurement

The plant growth parameters were measured to study the

response of the mangrove plants to increase air temperature.

The measurement of the number of leaves, plant height,

number of branches, and diameter of stems, all the

morphological parameters, were done manually using the

graphical method with tools such as the foot rule, and Log

rule calliper, and the photosynthesis rate were measured by

using a Li-cor 6400. Determination of chlorophyll

concentration was conducted using standard procedure by

Nurdin et al. (2009) [11] on the reduction of the acetone

volume where 0.1g of mangrove plants leaves were chopped

into small pieces (about 2 mm), and the leaves were put into

a test tube, after which 20ml 80% acetone was added to the

test tube. The mixture was homogenised by a shaker and then

incubated in the dark for 48 hours. Concentrations of

chlorophyll a and chlorophyll b were analysed using a

spectrophotometer at the wavelength of 663nm and 645nm,

respectively. The chlorophyll concentrations were calculated

using [12; 13] the following equations:

Cchl-a = 12.7A663- 2.69B645

Cchl-b = 22.9 A645 - 4.68 B663

Total chlorophyll = Cchl-a+Cchl-b

The measurement was done three times. The first quantitative

measurement was made on the 1st of July 2015 and the second

on 17th of August 2015 (after 45 days) and the measurements

were made until the final measurements on 1st of October

2015 (after 90 days). The data was then analysed to examine

the plant growth changes within eight weeks.

2.2. Data Analysis

The experimental data was subjected to a variance analysis

(ANOVA) via SAS (Release 9.4) software and Duncan’s

multiple-range tests (DMRT) determined a significant

difference at α=0.05 level.

2.3. Results

2.3.1 Seedlings preparation and growth measurement

Seedlings growth parameters (plant height, the number of

branches, and stem diameter) between treatments of increase

temperature displayed various responses depending on the

number of days of treatments. Observations on plant height,

the number of branches, and stem diameter showed increased

significant differences between the treatments after 1-45 days

of exposure. Subsequent observation after 45-90 days of

treatments revealed various responses depending on different

temperature and number of days of treatments (Table 1).

Table 1: Growth parameters of mangrove seedlings

R. apiculata subjected to different air temperature.

Pa

ram

eters

1 Day 45 Days 90 Days

T 3

8 °C

Co

ntro

lled

T 3

8 °C

Co

ntro

lled

T 3

8 °C

Co

ntro

lled

Plant height

(cm)

57

±0.5b

58

±0.53d

63

±0.45a

60.5

±0.94d

62.3

±0.99b

61.5

±0.93c

Number of

branches

4.7

±0.57b

4.3

±0.56d

6.3

±0.57a

7.3

±1d

7

±0.95b

10.7

±0.57c

Number of

leaves

8.7±0

.57a

7.7

±0.53d

7.3

±0.55b

9

±0.98c

6.3

±1d

13.7±

0.45dc

Diameter of

stems

2.3

±0.26d

2.6

±0.25c

2.5

±0.24d

2.69

±0.27b

2.7

±0.22c

2.76

±0.26a

Note: Mean ± standard deviation (SD) followed by different letter of the same rows parameter of treatment is significantly tested using (DMRT) at

α=0.05 level.

At 90 days of exposure, the mean height of plants under

controlled condition increased, whereas the plants under CO2

concentration and temperature 38°C decreased (Table 1). To

illustrate, the result of Number of branches was not

significant between 45-90 days for the plants under 450 ppm

CO2 and 38°C temperature, the increase in the number of

branches for the plants under controlled condation at 90 days

was slightly significant, Table 1. The difference in

temperature resulted in a significant difference in the number

of leaves in which of the plants under controlled condation at

45 and 90 days was increased. On the another hand, the plants

under CO2 concentration and temperature 38°C continued to

decline. At 90 days of exposure, the mean diameter of stems



18

under controlled condation and under CO2 concentration and

temperature 38 °C increased (Table 1).

2.3.2. Photosynthetic Rate, and Chlorophyll Concentration

Measurement

The result shows that the photosynthesis process was poor

and inefficient under elevated CO2 concentration and

different temperature. Photosynthesis responses declined

gradually and slowed down at 1-45 days depending on

different temperature and the number of days of treatment. At

90 days of exposure, the photosynthesis responses declined

under CO2 concentration and temperature 38°C, whereas the

plants under controlled recovered in photosynthesis responses

Fig.1(A). The result found that the total chlorophyll under

CO2 concentration and different temperature displayed

various responses depending on the number of days of

treatments. Total chlorophyll increased gradually at 1-45

days for all treatments Fig. 1 (B). At 90 days of exposure, the

total chlorophyll declined significantly under CO2

concentration and temperature 38°C, whereas the plants

under controlled showed less increase compared to 1-45 days.

Fig.1 (B).

Figure 1: Comparative responses from ambient and

temperature of (A) photosynthesis rate, and (B) Total

Chlorophyll of mangrove seedlings R. apiculate

3. Discussion

The results showed significant differences in the parameters

studied and affected by CO2 and different temperature, where

various responses were displayed depending on a number of

days of treatments. There was an observed response to CO2

on the morphological parameters, especially after the first 45

days, but the high-temperature presence has a negative impact

on mangrove growth that was clear at the end of the study (90

days). Most of the samples died in this treatment, some

morphological parameters were affected, especially the

number of leaves that saw a significant decrease, which

affected the photosynthesis rate [14] despite the increase in

the chlorophyll concentration. This indicates that the increase

in temperature has a physiological effect on the plant through

the effect on the biological activities within the plant,

especially enzymes [2] (Rubisco enzyme responsible for CO2

Calvin cycle). However, the Rubisco limits photosynthesis

when electron transport limitations dominate [2] and there

can be a rapid fall-off of the photosynthetic rate at high

temperatures [15]. As for the low temperature, its effect was

very slow, leading to slow growth and the survival all of the

plants, which is why the studied morphological parameters

did not show great differences compared to samples in high

temperature, but there was a clear effect on photosynthesis

and enzymes at ambient. The results of this study were

identical to Wataru Yamori et. al. [15]. Climate change on

mangrove plants, especially during the early phases of

growth, can be considered dangerous by interfering between

biotic and abiotic factors in global warming, where these

results provide confirmatory evidence that the effect of the

interaction between the CO2 and temperature is negative and

dangerous, which will not only affect the geographical

distribution of mangrove plants but also their survival.

Moreover, the interaction of the other factors may have a

different effect so studies should be increased in this field to

improve the knowledge on interaction between the factors

which could affect growing season length. Indeed, evidence

of changes in growing season length exists [16], the extent of

heat stress along with time periods have affected on diurnal

cycles, which have greatly affect plant growth compared to

even temperature changes over 24 hours.

4. Conclusion

Generally, this research study showed that the rising CO2 and

temperature levels have a great impact on the growth rate. It

is imperative to understand CO2 responses in varying

temperature ranges due to the history of GEC and its future,

as well as the differing temperature ranges in different regions

of the world. However, the impacts of Temperature x CO2 are

not the only factors affecting plants. Light, water, and nutrient

supply are equally critical in assessing and interpreting the

effects of increased CO2. Indeed, many of these interactions

may be already included in the experiments reported.

Nevertheless, the rapid responses to elevated carbon dioxide

and temperature levels during the early phases of growth as

in seedling establishment may be important determinants in

the regeneration of species.

A

)

B



19

Acknowledgements

We gratefully acknowledged the Sime Darby Foundation for

greenhouse facility, research fund from

FRGS/1/2014/STWN10/UKM/02/1 to fund this project. The

authors also thank staffs of PPSSSA, FST, Universiti

Kebangsaan Malaysia for their contributions in completing

this project.

References

[1] J. I. L. Morison and D. W. Lawlor, Plant, Cell and

Environment, 22, 659-682, 1999.

[2] R.F. Sage, and D.S. Kubien, Plant Cell Environ, 30,

1086–1106, 2007.

[3] D. Schimel, D. Ives, I. Enting, M. Heimann, F. Joos, D.

Raynaud and T. Wigley, Climate Change. (J.T.

Houghton, L.G. Meira Filho, B.A. Callendar, N.Harris,

A. Kattenberg and K. Maskell, IPCC,Cambridge

University Press, Cambridge.1995), pp. 65–131, 1996.

[4] S. Arrhenius, Philosophy Magazine, 41, 237–276, 1896.

[5] A. Kattenberg, F. Giorgi, H. Grassl, G.A. Meehl, J.F.B.

Mitchell, R.J. Stouffer, T. Tokioka, A.J. Weaver and

T.M.L. Wigley, Climate Change. (J.T. Houghton, L.G.

Meira Filho, B.A. Callendar, N.Harris, A. Kattenberg

and K. Maskell, IPCC,Cambridge University Press,

Cambridge.1995), pp. 285–257, 1996.

[6] N. Nicholls, G.V. Gruza, J. Jouzel, T.R. Karl, L.A.

Ogallo and D.E. Parker, Climate Change. Ch.3, pp.132–

192, 1996.

[7] J. I. L. Morison Aspects of Applied Biology 45, 62–74,

1996.

[8] R.B. Myneni, C.D. Keeling, C.J. Tucker, G. Asrar and

R.R. Nemani, Nature, 386, 698–702, 1997.

[9] S.Crooks, D. Herr, J. Tamelander, D. Laffoley, and J.

Vandever, World Bank, 2011.

[10] W.A. Wan Juliana, M. S. Razali, and A. Latiff, Springer,

23-36, 2014.

[11] Nurdin, M. Clara, I. Kusharto, Tanziha, abd M.

Januwati, Journal of Nutrition and Food, 4 (1). 13 – 19,

2009.

[12] D.I. Arnon, Plant Physiology, 24. 1-15, 1949.

[13] G. Mac Kinney, J Biol Chem, 140. 315-322, 1941.

[14] Miklos Nagy, Kazuharu Ogawa and Akio Hagihara. Interactive effect of CO2 enrichment and temperature on the photosynthesis of field-grown hinoki cypress (Chamaecyparis obtusa) branches. Trees. 14:282–288. 2000.

[15] Wataru Yamori, Kouki Hikosaka and Danielle A. Way .Temperature response of photosynthesis in C3, C4, and CAM plants: temperature acclimation and temperature adaptation. Photosynth Res DOI 10.1007/s11120-013-9874-6. 2013.

[16] R.B. Myneni, C.D. Keeling, C.J Tucker., G. Asrar and R.R Nemani .Increased plant growth in the northern high latitudes from 1981 to 1996. Nature 386, 698–702. 1997.



20

Terahertz Meta-surface Absorber for Absorbing Application

Md. Mehedi Hasan1, Mohammad Rashed Iqbal Faruque1, Mohammad Tariqul Islam2

1Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia,

2Dept. of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment,

Universiti Kebangsaan Malaysia, 43600 Bangi.


Abstract

A meta-surface absorber that absorbs waves from all

directions of incident can be realised if the surface

impedance is made from vary as a function of incidence in a

specific manner. In this paper, a terahertz meta-surface with

left handed characteristics for high absorbing application has

been discussed. The designed absorber small unit is

developed by cross metallic connection of two metal strips

printed on the epoxy resin fibre material. Commercially

available Finite integration technique based electromagnetic

simulator CST Microwave Studio has been utilized to

design, simulation and investigate the proposed design. The

proposed meta-surface shows resonance at 39.19 THz, 58.47

THz and 77.80 THz and the left handed characteristics at

15.3 THz and 87.7 THz, respectively. Besides, the absorber

structure presents the highest absorption peak respectively,

99.6% and 89.5% at 16.4 THz and 75.8 THz.

1. Introduction

In recent years, there has been a renewed interest in the

property of near perfect absorption from the scientific

community, originally used in stealth technology to reduce

radar cross section of objects at specific radar frequencies.

The advent of meta-surface with unique properties played a

key role in the development of high quality absorbers

ranging from microwaves to optical wavelengths and their

integration in numerous functional applications such as

imaging, solar energy collection, medical applications,

optical applications, etc. In the past two decades, the field of

terahertz technology has experienced remarkable

development due to advances in laser and semiconductor

technology. This has given rise to various potential

applications including sub-diffraction imaging, cloaking,

and polarization conversion systems. Meta-surface absorbers

can be divided into two broad categories based on their

principle of operation. The primary classification comprises

of device impedance coordinated to free space. If the

material impedance coordinated with substantial and lossy

estimations of permittivity and permeability, then the

surface will be reflection less at normal incidence. The

second classification in view of electrically responsive

metamaterial components firmly coupled to a ground plane.

In 2003, Ziolkowski et al. developed a metamaterial by

capacitor loaded strips and split ring resonators, which

exhibited negative permittivity and negative permeability

both at the X-band frequencies [1]. In particular, meta-atom

absorbers have been studied since Landy et al. introduced

them in 2008 [2]. In 2016, Hasan et al. proposed a z-shaped

DNG metamaterial for wide band applications. The 10×10

mm2 structure metamaterial unit cell was applicable for C-

and X-band operations [3]. In 2017, Hasan et al. projected a

negative index meta-atom, resonance at C-, X- and Ku-band

with bandwidth from 7.0 to 12.81 GHz [4]. In 2017,

Karaaslan et al. introduced a multiband absorber based on

multi-layered square split ring structure. The multi-layered

metamaterial structure was designed to be used in the

frequency bands such as WIMAX, WLAN and satellite

communication. The absorption levels of the proposed

structure were higher than 90% for all resonance frequencies

[5]. A metamaterial absorber in microwave frequency is

shown in [6]. Yao et al. suggested a dynamically lambda-

tunable grapheme based terahertz metamaterial absorber,

which displayed absorption of 99% at 35 μm and 97% at 59

μm, respectively [7]. Microstrip patch antenna was designed

with artificial magnetic conductor for telemedicine

applications by Sneka et al. it was observed the antenna gain

about 6.21 dBi, directivity around 6.37Bi, return loss almost

-29 dB and the radiation efficiency was 96.21% [8]. Wang et

al. developed a U-shaped terahertz absorber in 2016 that had

been shown 98% [9]. Yahiaoui et al. in 2015 designed

metamaterial absorber, which shown at absorption

frequencies of 0.22 THz, 0.48 THz, 0.72 THz and 0.76 THz

the percentage of absorption were respectively, 79%, 80%,

76%, 74% [10].

A new 3D meta-surface absorber at terahertz frequency

has been designed in this study, whereas the working

frequency range is from 0 THz to 100 THz. The proposed

meta-surface shows resonance at 39.19 THz, 58.47 THz and

77.80 THz. The met-surface exhibits left handed

characteristics at 15.3 THz and 87.7 THz, whereas the

permittivity, permeability and refractive index are

respectively being -25.21, -177.5, -68.28 and -42.38, -0.78, -

8.12. Besides, the absorber structure presents absorption at

the resonance peak are respectively, 99.6% and 89.5% at

16.4 THz and 75.8 THz. The paper is decorated in this

manner; design of the proposed meta-surface absorber with

the schematic and 3d view is in section 2, methodology

explained elaborately with the simulated diagram, retrieval



21

methods of effective medium parameters and equivalent

circuit model of the proposed meta-surface absorber in the

section 3. The results are shown in section 4 and section 5

concludes the paper.

2. Design of Meta-Surface Absorber

The schematic view, top view and 3D view of the proposed

meta-surface absorber are shown in figure 1(a-c). The

designed absorber small unit is developed by cross metallic

connection of two metal strips printed on the dielectric

substrate material. Epoxy resin fibre is used as substrate

material, which dielectric constant and loss tangent are

respectively 4.5 and 0.002. The thickness of the substrate

material is considering as 0.1 μm. The total dimension of the

designed meta-surface absorber is 5.1×5.15 μm2, whereas

the small single unit cell is 1×1.1 μm2.

(a) (b)

(c)

Figure 1: Schematic view of the: (a) unit cell, (b) top view of

the designed structure, and (c) meta-surface structure.

Table 1: Design parameters of the meta-surface

absorber single unit cell.

Parameters L W p M N

Dimensions

(μm)

5.4 5.1 0.95 5.5 5.15

Parameters l w d t h

Dimensions

(μm)

1 1.1 0.1 0.1 0.017

3. Methodology

Finite integration technique based commercially available

CST Microwave Studio is adopted for all the numerical

investigations. Boundary conditions are usually used in most

of the computer simulations to speed up the computation

process. For the simulation from 0 to 100 THz, the

electromagnetic waves are propagating along the z-axis,

whereas the x- and y-axis are respectively considered as a

perfect electric conductor and perfect magnetic conductor

boundaries. The equivalent circuit of proposed design,

where the shunt branches of the proposed meta-surface

absorber circuit model are purely inductive. The inductive

effect raises for the metal part shifted towards the resonance

to the lower frequency, whereas the gaps are accountable for

capacitive effect. The inductive and capacitive effect is

minimized together and set up resonance at a fixed point. In

addition, there is a parasitic coupling effect for the mutual

inductance and capacitance. However, are represents as

respectively capacitance, inductance and external source of

the lumped LC-circuit model.

4. Results and Analysis

The surface current distribution on the proposed absorber at

77.8 THz is displayed in the figure 2(a). The arrows on the

structure are showing the direction of the current and colour

state the intensity of the current. In the current distribution

several dominating current paths have been found, which are

causes the resonating modes of the structure when the

propagating electromagnetic waves are along z-axis. The

current on the absorber structure are flowing opposite

direction and nullify each other. Stop bands are found for

minimizing the surface current together. However, the

electric field density at 77.8 THz is exhibited in figure 2(b).

(a) (b)

Figure 2: (a) Surface current distribution, and (b) Electric field, in 77.8 THz of the designed meta-surface absorber.

Figure 3(a) depicts, the magnitudes of the reflection (S11)

and transmission (S21) coefficient. The figure shows the

resonance at 39.19 THz (magnitude of -41.90), 58.47 THz

(magnitude of -48.18) and 77.80 THz (magnitude of -51.93).

Figure 3(b) reveals, the real magnitude of the effective

permittivity curves, whereas the negative peaks from 4.4

THz to 46.9 THz and from 87.6 THz to 100 THz. From the

figure 3(c) the negative permeability curve from 14.7 THz to

18.8 THz and from 45.7 THz to 89.6 THz. In figure 3(d), the

negative refractive index from 6.4 THz to 9.2 THz, 13.1

THz to 18.7 THz, 44.2 THz to 46.8 THz and 85.5 THz to 90

THz. If the permittivity and permeability are simultaneously

negative, then refractive index is also negative. Here at 15.3

THz and 87.7 THz the designed meta-surface absorber

exhibits the permittivity, permeability and refractive index



22

parameters are respectively -25.21, -177.5, -68.28 and -

42.38, -0.78, -8.12. As a result, the meta-surface structure

can be characterized as a left handed meta-surface at 15.3

THz and 87.7 THz.

Table 2: Value of effective medium parameters of the

meta-surface for the left handed characteristics.

Resonance

Frequency

Permeability

(µ)

Permittivity

(ε)

Refractive

Index (ƞ)

15.3 THz -25.21 -177.5 -68.28

87.7 THz -42.38 -0.78 -8.12

(a) (b)

(c) (d)

Figure 3: (a) Reflection and transmission coefficient,

Effective: (b) permittivity, (c) permeability, and (d)

refractive index, of the suggested meta-surface.

In figure 4, the result of the absorption has been discussed.

The absorption at the resonance peak are respectively,

99.6% and 89.5% at 16.4 THz and 75.8 THz. However, the

nature of the absorption can be easily understood by

observing the current density in the absorber structure from

the surface current distribution curves.

Table 3: Percentage of absorption of the meta-

surface absorber.

Resonance of the

reflection (S11)

Absorption rate

16.4 THz 99.6%

75.8 THz 89.5%

Figure 4: Calculated absorption of the recommended meta-

surface absorber.

5. Conclusion

This paper focused on a new left handed meta-surface

absorber for absorption application. The dielectric material

epoxy resin with woven glass fabric composite is used as

substrate material to construct the meta-surface absorber

structure. The designed structure exhibits left handed

properties at 15.3 THz and 87.7 THz. The absorber structure

also presents the highest absorption peak respectively,

99.6% and 89.5% at 16.4 THz and 75.8 THz. In addition, the

finite integration technique and the equivalent lumped

inductance-capacitance circuit model of the proposed design

have been explained elaborately.

Acknowledgements

This work was supported by the Research -Universiti Grant,

Geran Universiti Penyelidikan (GUP), code: 2016-028.

References

[1] R.W Ziolkowski, Design, fabrication, and testing of

double negative metamaterials, IEEE Transactions on

Antennas and Propagation, 51:1516–1529, 2003.

[2] N.I Landy, S. Sajuyigbe, J.J. Mock, D.R. Smith, W.J.

Padilla, A perfect metamaterial absorber, Physical

Review Letter, 100:1–4, 2008.

[3] M.M. Hasan, M.R.I. Faruque, S.S. Islam, M.T. Islam,

A New Compact Double-Negative Miniaturized

Metamaterial for Wideband Operation, Materials,

9(10):830, 2016.

[4] M.M. Hasan, M.R.I. Faruque, M.T. Islam, A Single

Layer Negative Index Meta Atom at Microwave

Frequencies, Microwave and Optical Technology

Letters, 59:1450–1454, 2017.

[5] M. Karaaslana, M. Bagmancıa, E. Unala, O. Akgola,

C. Sabahb, Microwave energy harvesting based on

metamaterial absorbers with multi-layered square split

rings for wireless communications, Optics

Communications, 392:31–38, 2017.

[6] M.M. Hasan, M.R.I. Faruque, M.T. Islam, A tri-band

microwave perfect metamaterial absorber, Microwave

and Optical Technology Letters, 59: 2302–2307, 2017.



23

[7] M.H.G. Yao, F. Ling, J. Yue, C. Luo, J. Ji, J. Yao,

Dual-band tunable perfect metamaterial absorber in the

THz range, Optics Express, 24:1518-1527, 2016.

[8] N. Sneka, K.R. Kashwan, Design and implementation

of a metasurface patch antenna array for medical

applications, International Conference on Research

Advances in Integrated Navigation Systems, India,1-4,

2016.

[9] B.X. Wang, G.Z. Wang, L.L. Wang, Design of a Novel

Dual-Band Terahertz Metamaterial Absorber,

Plasmonics, 11:523–530, 2016.

[10] R. Yahiaoui, S. Tan, L. Cong, R. Singh, F. Yan, W.

Zhang, Multispectral terahertz sensing with highly

flexible ultrathin metamaterial absorber, Journal of

Physics, 118:083103-6, 2015.



24

Labyrinth Resonator for Wideband Application

Md. Jubaer Alam1, Mohammad Rashed Iqbal Faruque1, Mohammad Tariqul Islam2

1Space Science Centre (ANGKASA), Institute of Climate Change (IPI), Universiti Kebangsaan Malaysia,

43600 Bangi, Selangor, Malaysia 2Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor,

Malaysia


Abstract

The paper presents the structure of a labyrinth resonator

double negative metamaterial unit cell that is suitable for dual

band of microwave frequency. A relation was made on the

performance after the analysis of unit cell, 1 × 2 array and 2

× 2 array structures. A great transmission coefficient of

almost 13GHz with a 500MHz band gap at the middle is

demonstrated for all of these configurations. The resonator

covers C, X and Ku-band separately with double negative

phenomena at X and Ku-band. To justify the performance of

the proposed resonator an analogy is conferred. Having a

compact design, double-negative characteristics and the

proposed metamaterial has potential to be used for wideband

application.

Keywords Array Structure, Double negative,

Metamaterial, Wideband application.

1. Introduction

Metamaterials are the special type of materials that are

usually not available in nature. They are actually engineered

materials, they need to embed periodic unit cell for their

formation to create naturally unavailable electromagnetic

properties. Moreover, these materials have the power to

control the electromagnetic wave beams to show their

unorthodox characteristics. These unusual features of the

metamaterials totally depend on the geometry of the atomic

construction. It has been started from the year 1968, Veselago

et al [1] observed unique properties of materials having

negative permittivity (ε) and permeability (μ).

But it was not appreciated until 2000 when Smith et al.

fortunately validated a new unreal with these unconventional

properties (both permittivity and permeability were negative)

is called left-handed metamaterial. In case of negativity, it has

been categorized as Single-negative (either permittivity is

negative or permeability is negative), Double-negative (both

permittivity and permeability are negative). There is also a

term called near-zero refractive index metamaterial (NZRI)

where the permittivity and permeability of a material become

approximately to zero on a particular range of frequency.

Having these captivating electromagnetic phenomena,

necessary applications, like SAR reduction [2], super lenses,

antenna design [3-4], filters [5], invisibility cloaking [6],

electromagnetic absorber, and electromagnetic band gaps etc

can be employed by metamaterials. In some cases, intrinsic

negative permittivity is found. It is really difficult to get the

negative refractive indices. Currently, multi-band

metamaterial absorbers have become an auspicious

application in the detection of explosives, even in bolometers

and thermal detectors. Moreover, a very few studies have

been made in designing this type of materials [7]. Different

alphabetic shapes have become popular for particular

operations [8]; like, Benosman et al. [9] introduced a double

S-shaped metamaterial that showed negative values of η from

15.67 to 17.43GHz. Mallik et al. proposed various U-shaped

rectangular array structures left-handed aspect at

approximately 5, 6 and 11GHz. A V-shaped metamaterial

was presented by Ekmekci et al. the architecture showed

double-negative characteristic. Zhou et al. designed an S-

shaped 15 × 15 mm2 chiral metamaterial for X- and Ku-band

application. Though the EMR was not higher than 4. For the

purpose of application on S and C bands, Hossain et al. [10]

design G-shaped DNG for different unit cells and array sizes.

A metamaterial unit cell of labyrinth resonator has been

proposed in this paper. The structure covers multiple bands

(C, X, and Ku) of frequencies for the transmission coefficient.

And for effective parameters, it covers the X and Ku bands

with double negative characteristic.

2. Cell Design

The diagram of the prospective resonator is itemized in Fig.

1. Here both front and back sides of the substrate are

comprised of labyrinth resonators. Each unit cell comprises

with 20mm in length and 20mm in width. All elements have

the thickness of 0.35mm. Each split resonator has the width

of 1mm with a same split gap. The outer length of the

resonator is 18mm. The entire patch (made of copper) is

developed on a substrate called FR-4. It has a dielectric

constant of εr = 4.3, a dielectric loss-tangent of tanδε = 0.025.

Sides of the substrate are L = W = 20mm and the thickness is

t = 1.6mm. Designed parameters of the proposed

metamaterial are enlisted in Table 1.



25

CST Microwave Studio is used to get the result of S11 and S21

with the help of hundred frequency samples. Two waveguide

ports are used to propagate the electromagnetic waves to

excite the configuration on two opposite direction of Z-axis.

PEC and PMC were used along the vertical direction of x and

y axis respectively. And for the free-space simulation

purposes, a frequency domain solver was utilized. Moreover,

for the analysis purpose of these configurations, a tetrahedral

mesh was used with a flexible mesh. The normalized

impedance was 50ohms and the system was performed from

1 to 18GHz.

Fig.1 Geometry of Metamaterial Unit cell

Table 1: Parameters of the unit cell

Parameters Dimensions (mm)

L 20

W 20

a 1

b 1

By settling the perspective unit cell in between, the

waveguides as per the Fig. 2 to actuate the parameters

accurately of the metamaterial unit cell. To determine the

parameters, we used a vector analyzer commonly known as

Agilent N5227A. To calibrate perfectly, an Agilent N4694-

60001 was utilized.

To differentiate the effective permittivity ( 𝜀𝑟 ) and

permeability ( 𝜇𝑟 ) with 𝑆11 and 𝑆21 , the NRI method is

applied. To such a degree the 𝜀𝑟 and 𝜇𝑟 can be determined by

𝜀𝑟 = 𝑐

𝑗𝜋𝑓𝑑×

(1−𝑉1)

(1+𝑉1) (1)

𝜇𝑟 = 𝑐

𝑗𝜋𝑓𝑑×

(1−𝑉2)

(1+𝑉2) (2)

The effective refractive index (𝜂r) can also be calculated from

𝑆21 and 𝑆11:

𝜂𝑟 =𝑐

𝑗𝜋𝑓𝑑× √

(𝑆21−1)2−𝑆112

(𝑆21+1)2−𝑆112 (3)

By settling the perspective unit cell in between, the

waveguides as per the Fig. 2 (a) to determine the scattering

parameters accurately of the split metamaterial.


There are plenty of ways to find out the effective parameters

of a unit cell like NRW method, DRI, etc. This paper

highlights the electromagnetic properties using the real values

of ε, μ, and η using S11 and S21.

W

L

a b

(a) Front view

(b) Back view

(a)

Vector Network Analyser

Sample

Waveguide Ports

1

Fig.2: (a) Experimental set up for measuring S parameter;

(b) Current distribution of the unit cell at various

frequencies

(b)

9.0 GHz 10.3 GHz



26

3.1. Analysis of Unit Cell

As the unit cell is fabricated on a Fr-4 which has an area of

18 × 18 mm2, it has been measured within a frequency range

of 1 to 18GHz. The simulation was performed by CST MWS

to get the result of the transmission coefficient (S21). The

transmission coefficient exhibits a wide band with a coverage

of C, X and Ku-band. The first resonance is found in the L-

band at frequency 5.07GHz. Then a wide band from 5.07GHz

to 13.96GHz with a little band gap of 500MHz.

However, the optimized resonance frequency is 9.68GHz.

Fig.2 (b) shows the current distribution of the unit cell at 9.0

and 10.3GHz. Fig.3 (a) shows magnitude of the transmission

coefficient (S21).

Fig.3 (b), (c) and (d) show negative permittivity,

permeability and refractive index at resonating points.

Table 2 shows the frequency range of refractive indices with

effective parameters of the unit cell at different resonating

frequency bands. Hence, the designed unit cell has significant

portions, where all the three effective parameters becomes

negative. Therefore, this configuration can be allegated as

double-negative metamaterial as it has negative peaks at 8.14

and 14.01GHz in all the three effective parameters which is

shown in Table 2 with bandwidths.

Table 2: Parameters of the unit cell

Effective

parameters

Frequency

Range(GHz)

Covered

Bands

Values at

9.68GHz

Permittivity

(ε)

2.60 to 5.16, 6.63

to 10.31 & 13.03

to 16.18 GHz

S, C, X

& Ku -1.15

Permeability

(μ)

7.74 to 13.07 &

13.88 to

16.55GHz

C, X &

Ku -78.6

Refractive

Index (η)

8.13 to 12.14,

13.01 to 15.22 &

16.73 to

16.95GHz

X &

Ku

-6.99

Fig. 3 (a) Measured and simulated results of S21 ; Real and

imaginary values of (b) effective permittivity (ε) vs

frequency; (c) effective permeability (μ) vs frequency; (d)

refractive index (η) vs frequency

3.2. Array Analysis

Fig. 4 describes the array formation of 1 × 2 and 2 × 2 arrays

on the basic unit structure for higher degrees of arrays on the

same Fr-4 substrate. The array structure is measured within

the frequency range of 1 to 18GHz. For unit structure, both

the patches are placed 1mm apart from each other on the

substrate. Fig.4 (a) shows array formation and (b) shows the

transmission coefficient of the array structures. It is apparent

that the resonances of the frequencies are found at the same

points as the unit cell, but having greater negative

magnitudes. The S21 improves in case of 1 × 2 and 2 × 2 array.

Fig.4 (c) shows the real values of the permittivity,

permeability and refractive index as a function of frequency

of array structures. All the effective parameters of these array

structures are summarized in table 3.

Table 3: Frequency range of effective parameters of array

structures

Effective

parameters

Array

Structu

res

Frequency Range

(GHz)

Covered

Bands

Permittivity

(ε)

1 × 2

1.83 – 4.76, 6.55 –

10.19 & 13.92 – 16.45

L, S, C,

X & Ku

2 × 2

1.78 – 4.61, 6.44 –

10.19 & 12.96 – 16.27

Permeability

(μ)

1 × 2

7.71 – 13.01 & 13.92 –

16.57

C, X &

Ku

2 × 2

7.71 – 13.01 & 13.85 –

16.58

(b)

(d)

(c)

(a)



27

1×2 array

(b)

Refractive

Index (η)

1 × 2

7.92 – 12.32, 12.98 –

15.56 & 16.63 – 17.18

C, X &

Ku

2 × 2

8.10 – 11.98, 12.94 –

15.24 & 16.73 – 17.11

Fig. 4 Unit structure (a)Different array formation; (b) S21 vs

frequency; (c) Effective parameters vs frequency for the 1 ×

2, 2 × 2 array.

4. Conclusion

This paper presents the framework of the labyrinth resonator

and a correlation is contrived on transmission coefficient,

relative permeability, permittivity and refractive index. The

analyses and the comparisons are made on unit cell, 1 × 2, 2

× 2 array structures. The transmission coefficient (S21) is

calculated and compared with different array formations. The

transmission coefficient covered C, X and Ku bands for all

the configurations. Negative effective parameters are also

found in all the structures. However, unit cell, 1 × 2, 2 × 2

array structures shown good commitment to the effective

parameters. Even the negative values of each of the effective

parameters are found on the X and Ku bands at 8.14 and

14.01GHz with a bandwidth of more than 1.20 and 1.32GHz

respectively. It certainly represents the wide band double

negative characteristic of the proposed compact design.

Thus, these structures are valid for wide band and dual bands

applications. These can also be a promising choice for double

negativity. This resonator can be an auspicious alternative to

new metamaterials, especially in utilizations where

metamaterials are the only requirement.

References

[1] V. G. Veselago, The electrodynamics of substances with

simultaneously negative values of ε and μ, Sov. Phys. 10:

509–514, 1968

[2] M.R.I. Faruque, M.T. Islam, N. Misran, Design analysis

of new metamaterial for EM absorption reduction, Prog.

Electromagn. Res. 124: 119–135, 2012

[3] M. M. Islam, M.T. Islam, M. Samsuzzaman, M.R.I.

Faruque, Compact metamaterial antenna for UWB

applications, Electron. Lett. 51: 1222–1224, 2015

[4] O.M. Khan, Z.U. Islam, Q.U. Islam, F.A. Bhatti,

Multiband High-Gain Printed Yagi Array Using Square

Spiral Ring Metamaterial Structures for S-Band

Applications, IEEE Antennas Wirel. Propag. Lett. 13,

2014

[5] R. Singh, I. Al-Naib, W. Cao, C. Rockstuhl, M. Koch, W.

Zhang, The fano resonance in symmetry broken terahertz

metamaterials, IEEE Trans. Terahertz Sci. Technol. 3: 1–

7, 2013

[6] S.S. Islam, M.R.I. Faruque, M.T. Islam, A Near Zero

Refractive Index Metamaterial for Electromagnetic

Invisibility Cloaking Operation, Materials 8: 4790–

4804, 2015

[7] B. Gong, X. Zhao, Numerical demonstration of a three-

dimensional negative-index metamaterial at optical

frequencies, Opt. Express. 19: 289–296, 2011.

[8] S. S. Islam, M. Rashed, I. Faruque, and M. T. Islam, The

Design and Analysis of a Novel Split-H-Shaped

Metamaterial for Multi-Band Microwave Applications,

Materials 7pp. 4994–5011, 2014.

[9] H. Benosman, N.B. Hacene, Design and Simulation of

Double “S” Shaped Metamaterial. Int. J. Comput. Sci. 9:

534–537, 2012.

[10] Z. Zhou, H. Yang, Triple-Band asymmetric transmission

of linear polarization with deformed S-shape bilayer

chiral metamaterial, Appl. Phys. A 119: 115–119, 2015

1×2 array

(a)

2×2 array

2×2 array



28

Design and Analysis of a Metamaterial Structure with Different Substrate Materials

for C Band and Ku Band Applications

Eistiak Ahamed1, Mohammad Rashed Iqbal Faruque1, Mohd Fais Mansor2

1Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia 2Dept. of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment,

Universiti Kebangsaan Malaysia, 43600 Bangi,

Selangor, Malaysia


Abstract

A modified square shape resonator structure based

metamaterial is introduced that works for C and Ku band

applications in microwave regime. Commercially available

computer simulation technology CST microwave studio is

utilized to investigate the proposed structure retrieval

parameters characterization. At first, the FR-4 substrate is

used to investigate the proposed metamaterial design, and

its characteristics. Further investigation is done by replacing

Rogers RT 6006 and Polyimide substrate materials instead

of FR4 substrate material. Different metamaterials

properties are achieved like double negative characteristics,

near refractive index zero, epsilon negative and mu negative

within the C and Ku bands by changing the substrate.

Among all the substrates, the metamaterial characteristics

are showed in best results in terms of effective parameter.

Therefore, the proposed design can be used for different

microwave applications within C and Ku band.

Key words: Metamaterials, DNG, C band, Ku band,

Satellite.

1. Introduction

Metamaterial is a composite structure material unimagined

in nature with extraordinary electromagnetic properties

controlled by the geometrical features, but not controlled by

the composite of the materials. The periodic metamaterial

unit cell dimensions are much smaller than wavelength that

created plasmonic resonances [1]. Unusual electromagnetic

properties like negative permittivity, negative permeability

and negative refractive index are shown in metamaterial so

it is different from the other natural materials.

In 1968, the Russian physicist Victor Veselago first

introduce with negative permittivity ( ε < 0) as well as

negative permeability (μ < 0) in a material with a certain

frequency range [2]. J.B Pendry et al explained negative

estimated permittivity (ε) and negative estimated

permeability (μ) for thin wire configuration and split ring

resonator respectively [3]. And later smith et al, invented

some special type of metamaterial that exhibit negative

permittivity, negative permeability that has some exotic

properties like inverted Snell’s law, negative refractive

index, reversed Doppler effect etc [4]. Metamaterials are

mainly divided into three categories zero-index materials,

single negative materials and negative materials.

Permittivity (ε) and permeability (μ) are equal to zero over a

certain frequency range is called zero index materials [5-6].

When permittivity or permeability only one is negative,

then it said to be single negative materials and when

permittivity only negative then it called epsilon negative

(ENG) and when permeability negative only then it called

mu negative materials (MNG) [7]. Besides, when

permittivity and permeability both are negative in a material

then it called double negative materials [8].

Metamaterials are used in several important applications

depending on unavailable electromagnetic properties such

as antenna designing for high gain and minimize the its size,

absorber design, filter design, increasing photonic

absorption rate of solar cell, invisible cloaking, SAR

reduction etc. Some of metamaterial unit cell structures are

proposed depending on the exceptional properties of its like

V-shape, U-shape, Z-shape, SRR, double SRR, F-shape,

triangular shape and so on. Determining low frequencies

and negative magnetic properties, split ring resonator

structure is used [9]. In 2007, for X band application E.

Ekmekci introduce an SNG matamaterial [10]. In 2012,

Benosman showed a metamateril that works in Ku band

[12].

A new unit cell structure to form metamaterial is

introduced in this paper. With this proposed design,

resonance was found in C-band (2–4 GHz) and Ku-band

(12-18GHz) of the microwave frequency region, and it

appears as a SNG metamaterial. C and Ku band has

promising applications in the satellite communications. In

addition, material substrate is replaced by Rogers RT 6006

and Polyimide instead of FR-4 to get better properties. The

proposed unit cell is compact in size as the effective

medium ratio is only 5 therefore the manufacturing cost is

also low.



29

2. Structure of the Unit Cell

The schematic view of the proposed modified two pair of

split ring resonator specified in Figure 1. The structure is

modified by the two pair of square split ring resonator

consist of copper with thickness (h) 0.035mm. Flame

Retardant-4 (FR-4) is used as a substrate material which has

4.3 dielectric constant and tangent loss 0.025. The length

and width of substrate are taken as 8×8 mm2 with a height of

1.6 mm. CST Microwave studio is used to design the unit

cell where incident electromagnetic wave travels along the

positive z-axis to negative z-axis. The length, width,

thickness of the substrate are a, b and h, respectively, and

unit cell metal strip length is defined by L1, L2, L3, L4 and

width as well as W1, W2, W3, W4. The overall diagram of

the modified design is illustrated in Figure1 and

Table1demonestrate the design parameters of the proposed

unit cell.

Table 1: Specification of the proposed unit cell structure

Parameters Dimension

(mm)

Parameters Dimension

(mm)

a 8 L4,W4 1.06

b 8 d1 0.50

h 1.6 d2 0.35

L1,W1 7 g1 0.50

L2,W2 5 g2 0.25

L3,W3 2.47 G1 0.50

Figure 1: Unit cell Construction

3. Methodology

In order to simulate and determine the transmission

parameter, finite integration time domain based

electromagnetic simulator CST microwave studio is used.

The proposed design is placed between the two waveguide

ports in the direction of the positive and negative Z-axis and

energized by the electromagnetic force. For boundary

condition, perfect electric and perfect magnetic boundaries

are applied in x and y direction. Standardized impedance

and simulation frequencies are 50 ohm and 2 to 16 GHz,

respectively. The NRW (Nicolson-Ross-Weir) method is

used to find the effective parameters from the complex S11

(refraction coefficient) and S21 (transmission coefficient).

For these square shape resonators metal strips are used for

inductance and split gap are used for capacitance. When

length of metal strip is increased then LC resonator

frequency of the unit cell decreased and when split gap

increased then capacitance can be decreased and that is

responsible for the increase in LC resonance frequency.


4.1. Analysis with FR-4 Substrate Material

The numerical magnitude of the transmission coefficient

(S21) which obtained from the simulation for the proposed

unit cell are shown in the figure 2 (a). From the simulation,

it demonstrate that S21 displays resonances at 7.502 GHz

under C band and 13.671 GHz under Ku band of microwave

spectra respectively. From figure 2(b), the negative

permittivity from 3.86 to 7.95 GHz (bandwidth of 4.09

GHz), 9.084 to 11.20 GHz (bandwidth of 2.11 GHz), and the

negative permeability from 8.68 to 16 GHz (bandwidth of

7.32 GHz). Furthermore, the negative refractive index from

6.283 to 7.19 GHz (bandwidth .90GHz) and 8.296 to 11.21

GHz (bandwidth 2.904 GHz).

Due to the internal architecture of the materials

permittivity and permeability properties are affected by the

polarization as well as refractive index is also affected by

that. Due to the negative permittivity at 7.501 GHz (ε = -

2.154), it can be called as ENG (epsilon negative)

metamaterial where the negative permeability is positive.

Also a near-zero refractive index appears with positive

values of n = 0.092, at near resonance frequency of 13.67

GHz shown in Figure 4(B). It maintains a bandwidth of

13.25 GHz to 14.18 GHz as the value of S21 remains under -

10 dB within that range of frequency with a near zero

refractive index (NZRI).



30

Figure 2: For FR-4 substrate (a) Transmission coefficient S21

(b) Amplitude of permittivity, permeability and refractive

index

4.2. Analyze with Rogers RT 6006 Substrate Material

To verify the effect of other substrate material Rogers RT

6006 dielectric material is used as a substrate replaced by

the FR-4 substrate with the same dimension. The value of

dielectric loss-tangent and dielectric constant of that Rogers

RT 6006 is 0.0027 and 6.15, respectively. By the substrate

material, there are three different resonance points found in

the plot of S21 after simulation and shown in Figure 3(a).

The permittivity (ε) graph exhibits negative characteristics at

frequency range of 3.66 GHz to 7.236 GHz (bandwidth

3.576 GHz), 8.3 GHz to 10.554 (bandwidth 2.25 GHz) and

14.44 GHz to 15.418 GHz (bandwidth 0.978 GHz) that was

shown in Figure 3(b).The permeability graph also exhibits a

negative value at the frequency of 8.566 GHz to 16 GHz

(bandwidth 7.44 GHz) shown in Figure 3(b). Therefore, in

this case, we can declare the material as a DNG

metamaterial at the frequency of 15.3 GHz because of at that

point the permittivity and permeability both are negative.

Moreover, it exhibits negative permittivity at 6.63 GHz

frequency, so in this point of frequency, it acts as an ENG

(epsilon negative) metamaterial. The DNG (double

negative) property can be further justified from the

refractive index graph shown in Figure 3(b), as refractive

index exhibits negative value for the frequency range of

12.584 GHz to 15.496 GHz which is clearly indicate that at

frequency 15.3 GHz the modified design act like as double

negative metamaterial. Therefore, by changing the substrate

material, the property of metamaterial can be changed and it

behaves like double negative metamaterial. The dielectric

constant of a material depends on internal structure and raw

compositions of it.

Figure 3: For Rogers substrate (a) Transmission coefficient

S21 (b) Amplitude of permittivity, permeability and

refractive index

4.3. Analyze with Polyimide Substrate Material

By following a previous strategy, we carried further

investigation by replacing earlier substrate material with a

new dielectric consisting of a lossy polyimide substrate that

contains dielectric constant of 3.5 and loss-tangent of

0.0027. The dimension of this new substrate material is

considered similar as the previous substrates. By this

substrate material after simulation, two different points of

resonance are found also in the graph of S21 at 8.15 GHz and

at 14.696 GHz and are shown in Figure 4 (a).

However, in the case of FR-4 substrate material, one

resonance point shifted from C-band to X-band. The



31

permittivity (ε) graph exhibits negative characteristics at a

frequency range of 4.11 GHz to 8.412 GHz, 9.742 GHz to

11.772 and 15.874 GHz to 15.918 GHz and it was shown in

Figure 4 (b). The permeability graph also exhibits a negative

value at the frequency of 8.916 GHz to 16 GHz shown in

Figure 4 (b). Therefore, in this case, we can declare the

material as a DNG metamaterial at the frequency of 15.87

GHz. Moreover, it exhibits negative permittivity at 8.15

GHz frequency, so in this point of frequency, it acts as an

ENG metamaterial where permeability is negative.

The DNG property can be further justified from the

refractive index graph shown in Figure 4(b), as it exhibits

negative value for the frequency range of 15.784 GHz to 16

GHz. Therefore, by changing the substrate material, only the

property of metamaterial can be changed. Therefore, it is a

further evidence of our previous statement that, by changing

the substrate material, only the property of metamaterial can

be changed.

Figure 4: For Polyimoid substrate (a) Transmission

coefficient S21 (b) Amplitude of permittivity, permeability

and refractive index

4.4. Comparative Analyze of Different Types of

Substrates

In Table 1, we see the significant comparisons due to the

effect of different substrate materials. It is seen from the

table that with increasing value of dielectric constant of

different substrates, the resonant frequency range is

decreasing without rogers last resonance frequency.

However, the proposed metamaterial structure shows double

negative properties if we use Rogers RT-6010 substrate and

lossy polyimide substrate material. Another mentionable

point is, at a frequency of 13.671 GHz for FR-4 substrate,

the material shows NZRI characteristics, whereas around

15.87 GHz polyimide substrate material shows double

negative characteristics.

In this case, the difference between the dielectric

constant of FR-4 and polymide is 0.7. So, it demonstrates

that only 20% change in dielectric constant of the substrate

has turned the ENG (or single negative) metamaterial to

double negative metamaterial. However, it is clear from

these analyses that using the above structure, we can have

different types of metamaterial by changing the substrate but

all in C and Ku band microwave spectra. Moreover, from

the analysis, it is seen that due to the change in dielectric

property (from high to low), the material shows ENG,

MNG, NZRI, and DNG characteristics shown at the

minimum points of resonance frequency.

Table 2: Comparison of the effects of substrates on the

metamaterial.

Substrate

material

Dielectric

constant

Frequency Metamaterial

type

Rogers RT

6006

6.15 6.63,12.668,15.3 ENG,MNG,

DNG

FR-4 4.3 7.50,13.671 ENG,NZRI

Polyimide 3.5 8.15,15.87 ENG,DNG

5. Conclusion

In this paper, a novel metamaterial structure is proposed that

resonates at the frequency of 7.50 GHz and 13.671 GHz,

which is in the C-band and Ku- band of microwave spectra.

It acts as a single negative metamaterial at that frequency.

For the same design on Rogers RT 6010 substrate and

polyimide substrate material, it shows double negative

characteristics. Besides, satellite application C band is used

for weather radar application, data communication like Wi-

Fi etc and Ku band is used for satellite application.

Therefore, this material can be a promising one for satellite

applications and other applications of this range.

Acknowledgements

This work was supported by the Research -Universiti Grant,

Geran Universiti Penyelidikan (GUP), code: 2016-028.



32

References

[1] Zhongya nSheng, Vasundara V. Varadan Tuning the

effective properties of metamaterials by changing the

substrate properties, Journal of Applied Physics 101,

014909, 2007.

[2] Veselago, V.G. The electrodynamics of substances

with simultaneously negative values of and m.Sov.

Phys. Uspekhi, 10, 509, 1968.

[3] J. B. Pendry, A. J. Holden, D. J. Robbins, and W. J.

Stewart,“Magnetism from conductors and enhanced

nonlinearphenomena", IEEE Transactions on

Microwave Theory andTechniques, Vol. 47, pp. 2075-

2084, 1999.

[4] D. R. Smith, W. J. Padilla, D. C. Vier, S. C. Nemat-

Nasser, andS. Schultz, “Composite medium with

simultaneously negative permeability and

permittivity”, Physical Review Letters, Vol.84, pp.

4184-4187, 2000.

[5] Huang, X.Q.; Lai, Y.; Hang, Z.H.; Chan, C.T. Dirac

cones induced by accidental degeneracy in

photonic crystal and zero-refractive-index materials.

Nat. Mater., 10, 582–586, 2011.

[6] Ziolkowski, R.W. Propagation in and scattering from a

matched metamaterial having a zero index of

refraction. Phys. Rev. E, 70,

doi:10.1103/PhysRevE.70.046608, 2004.

[7] Cui, T.J.; Smith, D.; Liu, R. Metamaterials: Theory,

Design, and Applications; Springer: Berlin,

Germany, 2009.

[8] Karamanos, T.D.; Dimitriadis, A.I.; Kantartzis, N.V.

Compact double-negative metamaterials

based on electric and magnetic resonators. Antennas

Wirel. Propag. Lett. IEEE, 11, 480–483, 2012.

[9] S. Linden, C. Enkrich, M. Wegener, J. Zhou, T.

Koschny, and C. M.Soukoulis, Science 306, 1351,

2004.

[10] E. Ekmekci, G. Turhan-Sayan, “Investigation of

effective permittivity and permeability for a novel V-

shaped metamaterial using S-parameters” proceedings

on 5th International Conference on Electrical and

Electronics Engineering, Bursa, Turkey, 2007.

[11] H. Benosman, N. B. Hacene, “Design and Simulation

of Double “S” Shaped Metamaterial” IJCSI

International Journal of Computer Science Issues, Vol.

9, 2012.



33

9th September 2011 Solar Flare to MAGDAS Reading

Norhani Muhammad Nasir Annadurai1 , Nurul Shazana Abdul Hamid1* and Akimasa Yoshikawa2,3

1School of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi,

Selangor ,Malaysia. 2Department of Earth and Planetary Sciences, Faculty of Sciences, 33 Kyushu University, 6-10-1 Hakozaki, Higashi-ku,

Fukuoka 812-8581. 3International Center for Space Weather Science and Education, Kyushu University 53, 6-10-1 Hakozaki, Higashi-ku, Fukuoka

812-85811.


Abstract

Solar flare can immidietly enhance magnitude of ionosphere

current and the investigation of this phenomena using

ground based magnetometer is generally applied in the

previous study. Rather than normal disturbance, peculiar

effect can occur in equatorial magnetometer data where the

magnitude of magnetic readings decreases. This study is an

observation on the effect of solar flare class X that can

cause depletion of EUEL magnitude. Readings of ground

based magnetometer at equatorial stations from MAGDAS

network is use to study this event. Results show 9th

November X class flare 6.9 cause depletion of magnetic

data in all magnetometer data for stations facing the Sun.

1. Introduction

An intense eastward current is confined at dip equator is

known as equatorial electrojet, EEJ. Normally the solar

flare would increase the magnitude of current as ionization

increases without changing the direction of the current.

However there are some studies found the different effect in

certain location or region of solar flare such as findings by

[1,4, 6] in where they observed the westward current during

solar flare event. In [6], they found two events on 18 June

2000 and 3 July 2002 that were rather shocking as depletion

was found at some dip equatorial station as a high intensity

solar flare occur at noontime. In their work, they concluded

that the solar flare effect is limited to local time and the

depression of H component magnetic field shows

occurrence of westward current. Few years later, [3]

reexamined the events using more equatorial station. Their

work uses more data from magnetometer networks. They

found out that the counter EEJ does not occur according to

the intensity of the flare and the direction of magnetic field

carried by the solar wind. Latest report by [7] in their

review paper stated that the cause of depletion of H-

component event is still a question until today. In all

previous study, only solar flare 23 and older was

considered. As our present solar cycle 24 is special (with

long solar minimum), different solar flare effect might be

observed.

2. Methodology

We analysed effect of solar flare for the whole year from

2005 to 2013. Only one event that catches our attention

which is on 9th August 2011 at 0805UT where an intense

solar flare class X6.9 detected by GOES 15 X-ray flux.

Magnetic component from magnetometer are taken from

Magnetic Data Acqusition System/Circum-pan Pacific

Magnetometer Network (MAGDAS/CPMN). Table 1 shows

the geographic information of MAGDAS stations used and

Figure 1 ilustrates the location the stations.

Table 1: Longitude and latitude information of stations.

Code Latitude Longitude

ANC -77.13 -11.71

ILR 8.5 4.68

TIR 8.7 77.80

DAV 7.00 125.40

YAP 9.56 138.14

Figure 1: MAGDAS dip equator stations.

Instead of using the raw H component data, we converted

to EUEL index [5] as it is the most suitable index to

observe solar activity effect to the ionosphere.


Top panel of Figure 2 shows variation of X-ray flux from

GOES 15 and bottom panel shows EUEL index on 9th

August 2011 as stated before. Immediately after the solar



34

flare, depletion of EUEL magintude was detected. This

event produces depression magnetic data to all reading of

magnetometer at equatorial stations. Enlargement of EUEL

for occurance of solar flare is plot in Figure 3.

Figure 2: Variation of X-ray flux by GOES 15 satellite (up)

and EUEL index of stations during event (bottom).

Figure 3: Enlargement of EUEL index.

Depressions can be seen clearly to all stations except station

ANC. Data at ILR experienced highest negative EUEL

followed by DAV, TIR, YAP and MUT. Data at ANC does

not have significant solar flare effect as it is located

nighttime. This is the first time for such event reported in

which all daytime equitorial stations data from different

longitude experienced depletion. This indicate that EEJ

current at all location is turning westward [3,6].

4. Conclusion

Event on 9th November 2011 is supprisingly uniqe. We can

see that solar flare effect the normal EEJ flow. Futher study

using other stations located outside EEJ band from various

network should be done. We also suggest one to plot

equivalent current to show the turning direction of EEJ

current.

Acknowledgements

Author thanks the MAGDAS group for all their

collaborations. Financial resource are sponsored by

Universiti Kebangsaan Malaysia and Malaysian Ministry of

Education using grant FRGS/1/2015/ST02/UKM/02/1 and

GUP-2016-016.

References

[1] Rastogi, R. G., M. R. Deshpande, and N. S. Sastri.

1975. Solar flare effect in equatorial counter electrojet

currents. Nature. 258, 218–219.

[2] Rastogi, R. G., M. R. Deshpande, and N. S. Sastri.

1975. Solar flare effect in equatorial counter electrojet

currents. Nature. 258, 218–219.

[3] Rastogi, R.G., Chandra, H. and Yumoto K. 2013.

Unique examples of solar flare effects in geomagnetic

H field during partial counter electrojet along CPMN

longitude sector. Earth Planets Space, 65, 1027-1040.

[4] Sastri, J. H. 1975. The geomagnetic solar are of 6 July

1968 and its implications. Ann. Geophys., 31, 481–

485.

[5] Uozumi, T., Yumoto, K., Kitamura, K., Abe, S.,

Kakinami, Y., Shinohara, M., Yoshikawa, A., Kawano,

H., Ueno, T., Tokunaga, T., McNamara, D., Ishituka, J.

K., Dutra, S. L. G., Damtie, B., Doumbia V., Obrou,

O., Rabiu, A. B., Adimula, I. A., Othman, M., Fairos,

M., Otadoy, R. E. S., & MAGDAS Group1. 2008. A

new index to monitor temporal and long term variation

of the equatorial. Earth Planets Space 60: 785-790.

[6] Yamazaki, Y., Yumoto, K., Yoshikawa, A., Watari, S.

and Utada, H. 2009. Characteristics of counter-Sq SFE

(SFE*) at the dip equator CPMN stations. Journal of

Geophysical Research, 114, A05306.

[7] Yamazaki Y. & A Maute. 2016. Sq and EEJ—A

Review on the Daily Variation of the Geomagnetic

Field Caused by Ionospheric Dynamo Currents. Space

Sci Rev.



35

Comparison of the Neural Network and the IRI Model for

Forecasting TEC over UKM Station

Rohaida Mat Akir1, 3, Mardina Abdullah1,2, Kalaivani Chellappan1,2, Siti Aminah Bahari1,2

1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti

Kebangsaan Malaysia, 43600 Bangi 2Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor

Darul Ehsan, Malaysia 3Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Parit Raja, Johor,

Malaysia.

*corresponding author, E-mail: [email protected], [email protected]

Abstract

One of the ionospheric parameters that affects the

propagation of radio waves is total electron content (TEC).

This paper presents a study on forecasting of TEC values

using neural network (NN) model over the GPS Ionospheric

Scintillation and TEC Monitor receiver at Universiti

Kebangsaan Malaysia (UKM) station, Malaysia. The

reliability of the NN model and the International Reference

Ionosphere, (IRI) model in comparison to the observed

GPS-TEC was measured through root mean square error

(RMSE). As a preliminary result, the maximum peaks of

the GPS–TEC were observed during the post noon time and

the minimum was observed during the early morning time.

The IRI model RMSE (25.5 TECU) was compared to the

NN model (11.5 TECU). The NN model was found to be

suitable for predicting TEC over the UKM station compared

to the IRI model.

1. Introduction

Variation in ionospheric electron density has a major effect

on propagation of radio signals through the atmospheric

layer which ranges between 60 km to 1000 km above the

Earth’s surface. One of the quantities which can describe the

ionospheric ionization content is Total Electron Content

(TEC). GPS TEC can be defined as the integral number of

electrons in one cross sectional area (1 m2) unit along the

path of the GPS satellite to the receiver on the ground. TEC

is measured in TECU, where 1 TECU=1x1016 el.m-2. TEC

can be derived from a dual frequency L1 frequency at

1575.42 MHz and L2 frequency at 1227.60 MHz from GPS

Ionospheric Scintillation and TEC Monitor (GISTM)

receivers.

TEC forecasting can be performed using neural network

(NN) model. An NN is able to learn and make

simplifications. The simplification refers to the ability of a

neural network to create acceptable outputs for a set of

inputs not used during training (learning) [1]. NN has been

applied in TEC modelling using GPS data, including for

GISTM stations at different locations and periods with

proper outcomes [2]–[7].

The IRI model is a data driven model where the accuracy

of the model in a specific region or time period depends on

the availability of reliable data for the specific region and

time given. It is stated that IRI01-corr and NeQuick

performed well compared to IRI-2001 [8], [9]. Thus in this

paper, the IRI01-corr was selected to be compared with the

prediction result using NN.

2. Data and Methodology

Available data during medium to high solar activity for a

period of five years, namely from 2011 to 2015, were used

for this study. Data were utilized from a GISTM receiver

installed at Universiti Kebangsaan Malaysia, UKM (2°55' N,

101°45’ E). The GPS receiver can track up to 11 GPS

satellites at L1 and L2 simultaneously and convert these

slant TEC (STEC) to vertical TEC (VTEC) at the sub-

ionospheric pierce point (IPP) by assuming the ionosphere

to be a single layer, by using a modified single layer model

as follows [10], [11]:

VTEC = STEC cos (x′) (1)

sin x′ =RE

RE + hm

sin x (2)

where x′and x are the satellite’s zenith angle at the IPP and

the receiver’s position, respectively. RE is the radius of the

earth (6371 km) and hm is the height of the ionosphere (450

km).

The training data sets were the TEC data from 2011 to

2014, while the TEC data in 2015 was reserved for the

testing data sets. The input space for the neural network was

selected from the parameters that affect the TEC value such

as solar activity (sunspot number) and both seasonal

variation (day number) and diurnal variation (hour number).



36

Solar activity was indicated by the mean of 81 days of

sunspot number (SSN). The seasonal and diurnal variations

were represented by four components. The hour and the day

number of the year were expressed in both cosine and sine

components to allow a continuous trend in the data [12], as

follows:

DNS = sin (2π × DN

365.25) (3)

DNC = cos (2π × DN

365.25) (4)

HRS = sin (2π × HR

24) (5)

HRC = cos (2π × HR

24) (6)

where DN and HR are the day number of the year and time

of the day in hour, respectively. The factor 0.25 stands for

leap years. Therefore, the predicted TEC by the NN model

can be stated as follows:

TECNN = f (DNS, DNC, HRS, HRC, SSN) (7)

where, TECNN is the predicted TEC data by using the NN

model, DN is day number which was split into sine and

cosine, and HR is hour number which was represented in

sine and cosine, respectively.

For the NN model development, the training data set was

used by the NN to learn the relationship between different

input and output variations and for validation to improve the

NN model generation. Data testing sets were used for

evaluation of the NN performance on patterns that were not

trained during learning and assessment of the NN final

outcome, respectively [2]. The type of NN used was a feed

forward neural networks. During training, the Leverberg-

Marquardt back propagation algorithm was used for its time

saving advantage during training [2], [6]. In order to define

the most suitable number of neurons in the hidden layer, the

root mean square error (RMSE) values between the

observed and predicted outputs were used. The smaller the

RMSE, the better the model. The RMSE was computed

using the formula:

RMSE = √1

N∑(TECmod − TECobs)2

N

i=1

(8)

where TECmod and TECobs represent the modelled TEC using

NN and observed TEC, respectively. Figure 1 shows the

RMSE values computed after training the NN with hidden

neurons from 6 up to 20. After considering the different

numbers of hidden neurons during the NN training, 10

hidden numbers that provided the lowest RMSE of 7.30

TECU and optimum solution for the NN model were

chosen. There is no clear and straight forward way of

determining the number of hidden neurons. However,

similar statistical methods have been used in determining

hidden nodes for optimization [4], [7], [12], [13].

Figure 1: RMSE values using the NN model with the

corresponding number of hidden nodes from 6 to 20

nodes.


Figure 2 shows the comparison between the diurnal

variations of the VTEC obtained from the observed GPS–

TEC with the NN model and the IRI01-corr for the year

2015 at UKM station during the quiet days with Kp < 3.

Local time (LT) in Malaysia is eight hours ahead of

universal time (UT). The observed minimum VTEC value

at sunrise was between 0500 LT and 0600 LT. Then it

gradually increased until it reached its maximum between

1500 LT and 1700 LT shortly in the afternoon, followed by

another decrease in TEC value at sunset, at 1800 LT. As

illustrated in Figure 2, it can be observed that the NN shows

good agreement trends with the observed GPS-TEC data

throughout the day. In the early morning from 0100 to 0400

LT, all models showed underestimation of TEC values in

comparison to the observed GPS–TEC value. Between

0900 and 2400 LT, the IRI01-corr model exhibited an

underestimation of TEC values which is in agreement with

the observed data. The IRI01-corr model underestimated

the observed TEC from the GPS-TEC data because the

GPS-TEC computed the TEC from the ground all the way

up to the plasmasphere, but the IRI model included the

ionosphere only.

Figure 3 shows the monthly variation between the VTEC

from the observed GPS–TEC and the ones modelled using

the NN model and the IRI01-corr model. As shown in

Figure 3, the VTEC trend gradually increased during noon

time starting from January to April. In contrast, from May

to August, the VTEC trend gradually decreased and in June,

it attained a minimum value. Starting from September, the

VTEC began to gradually increase again until December.

For the whole year of 2015, the IRI01-corr model showed

underestimated values compared to the observed GPS-TEC

after 0900 LT, while in October, the NN model showed

overestimated values compared to the GPS-TEC.



37

Figure 3: Monthly variation of VTEC from the observed GPS–TEC in comparison to the IRI01-corr and the NN

model for the year 2015.

Figure 2: Diurnal hourly variation of VTEC from the

observed GPS-TEC with the IRI01-corr and the NN

model

To investigate the VTEC seasonal variations, the seasons

were divided into three seasons, namely the equinoxes

(March, April, September, October), summer (May, June,

July, August) and winter (January, February, November,

December). From the observation, the best agreement from

the NN model with the observed GPS-TEC was during the

summer seasons, followed by winter and then the

equinoxes. During the equinoxes, higher RMSE values

were observed. According to [8], during the equinox

months, the sun will be directly over the equatorial region

and during the June solstice, the ionospheric plasma

densities are generally low.

Referring to Table 1, the RMSE of the NN model ranged

from 1.90 and 11.50 TECU while the RMSE for the IRI01-

corr model was from 8 to 25.50 TECU for the whole year in

2015. The NN model in February gave higher RMSE of

11.43 TECU compared to the other months. The lowest

RMSE was 1.91 TECU in June followed by August with

1.95 TECU. In contrast, for IRI01-corr the highest RMSE

occurred in April with 25.42 TECU and the lowest RMSE

was 8.15 TECU in July. During the summer months, the

IRI01-corr model showed good prediction throughout the

whole day, but in other months, it had good prediction

during the morning and night time.



38

Table 1: Comparison of RMSE between the observed

GPS-TEC and predicted TEC (NN and IRI01-corr)

model for the year 2015.

Month RMSE (TECU) between the

observed GPS-TEC and

NN model IRI01-corr model

January 5.75 22.03

February 11.43 25.12

March 8.31 25.08

April 7.51 25.42

May 6.19 19.49

June 1.91 10.79

July 2.15 8.15

August 1.95 10.22

September 2.06 10.33

October 4.81 11.48

November 2.47 13.55

December 2.03 12.68

4. Conclusion

The results indicate that the NN model can be a good tool in

predicting TEC values. The reliability of the NN model and

the International Reference Ionosphere (IRI) model in

predicting TEC in comparison to the observed GPS-TEC

was measured through root mean square error (RMSE). The

IRI model gave the highest value of RMSE (25.5 TECU)

compared to the NN model (11.5 TECU). From the

averaged RMSE, the NN model provided good agreement

with the observed GPS-TEC during summer followed by

winter, and lastly, the equinox months. Future work will

involve data over a longer period of time and include other

locations within the region of Malaysia.

Acknowledgements

The author would like to extend her gratitude to Universiti

Tun Hussein Onn Malaysia (UTHM) for giving her study

leave, enabling her to conduct this research. In addition, the

authors would like to acknowledge World Data Center

(WDC) and National Oceanic and Atmospheric

Administration (NOAA) for the solar and geomagnetic data

and express appreciation to Universiti Kebangsaan

Malaysia (UKM) for the installation and maintenance of

GISTM in UKM. This work was partially supported by

Universiti Kebangsaan Malaysia grant, GUP-2015-052.

References

[1] S. Haykin, Neural Networks: A Comprehensive

Foundation, 2nd ed. Indian: Pearson Education, 1999.

[2] J. B. Habarulema, L.-A. McKinnell, and P. J. Cilliers,

“Prediction of global positioning system total electron

content using Neural Networks over South Africa,” J.

Atmos. Solar-Terrestrial Phys., vol. 69, pp. 1842–

1850, 2007.

[3] J. B. Habarulema, L. A. McKinnell, and B. D. L.

Opperman, “Towards a GPS-based TEC prediction

model for Southern Africa with feed forward

networks,” Adv. Sp. Res., vol. 44, no. 1, pp. 82–92,

2009.

[4] J. B. Habarulema, L. A. McKinnell, P. J. Cilliers, and

B. D. L. Opperman, “Application of neural networks to

South African GPS TEC modelling,” Adv. Sp. Res.,

vol. 43, no. 11, pp. 1711–1720, 2009.

[5] R. F. Leandro and M. C. Santos, “A neural network

approach for regional vertical total electron content

modelling,” Stud. Geophys. Geod, vol. 51, pp. 279–

292, 2007.

[6] M. J. Homam, “Prediction of Total Electron Content of

the Ionosphere using Neural Network,” J. Teknol., vol.

78, no. 5–8, pp. 53–57, 2016.

[7] K. Watthanasangmechai, P. Supnithi, S. Lerkvaranyu,

T. Tsugawa, T. Nagatsuma, and T. Maruyama, “TEC

prediction with neural network for equatorial latitude

station in Thailand,” Earth, Planets Sp., vol. 64, pp.

473–483, 2012.

[8] A. O. Akala, E. O. Somoye, A. O. Adewale, E. W.

Ojutalayo, S. P. Karia, R. O. Idolor, D. Okoh, and P. H.

Doherty, “Comparison of GPS-TEC observations over

Addis Ababa with IRI-2012 model predictions during

2010-2013,” Adv. Sp. Res., vol. 56, no. 8, pp. 1686–

1698, 2015.

[9] N. A. Elmunim, M. Abdullah, A. M. Hasbi, and S. A.

Bahari, “Comparison of GPS TEC variations with

Holt-Winter method and IRI-2012 over Langkawi ,

Malaysia,” Adv. Sp. Res., vol. 60, no. 2, pp. 276–285,

2017.

[10] Z. M. Radzi, M. Abdullah, A. M. Hasbi, J. S.

Mandeep, and S. A. Bahari, “Seasonal variation of total

electron content at equatorial station, Langkawi,

Malaysia,” in International Conference on Space

Science and Communication, IconSpace, 2013, no.

July, pp. 186–189.

[11] R. M. Akir, M. Abdullah, K. Chellappan, and A. M.

Hasbi, “Preliminary vertical TEC prediction using

neural network: Input data selection and preparation,”

2015 Int. Conf. Sp. Sci. Commun., no. August, pp. 283–

287, 2015.

[12] L. A. McKinnell and A. W. V Poole, “The

development of a neural network based short term foF2

forecast program,” Phys. Chem. Earth, Part C Solar,

Terr. Planet. Sci., vol. 25, no. 4, pp. 287–290, 2000.

[13] E. O. Oyeyemi, L. A. McKinnell, and A. W. V Poole,

“Near-real time foF2 predictions using neural

networks,” J. Atmos. Solar-Terrestrial Phys., vol. 68,

no. 16, pp. 1807–1818, 2006.



39

Variation of EEJ Longitudinal Profile during Maximum Phase of

Solar Cycle 24

Wan Nur Izzaty Ismail1, Nurul Shazana Abdul Hamid1*, Mardina Abdullah2,3,

Akimasa Yoshikawa4,5

1School of Applied Physics, Faculty of Science and Technology 2Space Science Centre (ANGKASA), Institute of Climate Change

3Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment,

Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia 4Department of Earth and Planetary Sciences, Faculty of Sciences, 33 Kyushu University

5International Center for Space Weather Science and Education (ICSWSE), 53 Kyushu University , 6-10-1 Hakozaki,

Higashi-ku, Fukuoka 812-8581, Japan


Abstract

It has been well reported that the equatorial electrojet (EEJ)

varies with longitude. This paper present the longitudinal

variation of the EEJ strength based on maximum phases of

solar cycle-24 (SC-24) in 2012, 2013 and 2014. This

analysis was carried using EUEL index calculated from he

northward H component of geomagnetic field. Data used in

this study were taken from ground-based magnetometer

networks including MAGDAS, INTERMAGNET and IIIG.

The results obtained show that the EEJ varies with

longitude where is found strongest at two sector which are

South American sector and Southeast Asian sector.

1. Introduction

The current that flows eastward with very high intensity is

known as the equatorial electrojet, EEJ [1,2]. This EEJ

current flows at the altitude 90-120 km within the latitude

of ±3° at dip equator. Previous study have reported that the

EEJ current varies with longitude. Study by [3] using

empirical model from six longitudes sector found that the

magnitude of EEJ current were different according to the

longitude. Their results indicate that the EEJ current is the

strongest in South American sector which is between 80° to

100° west and weakest in Indian Sector at 75° east.

However in their study, they does not emphasize the

contribution of Sq current that might influenced the EEJ

measurement.

Study by [4] agrees with previous study where the EEJ

current is higher at American sector. Their work is based on

solar minimum data. In the present study, we want to clarify

the longitudinal profile of EEJ during solar maximum in

SC-24. Hence, we adopt the method of using the average

data in order to get the longitudinal variation of the EEJ

[4,5].

2. Method and Analysis

The longitudinal profile of EEJ was constructed using the

average of normalized data from the year of 2012 until 2014.

The magnetometer data was taken from four longitude

sectors. This involves fourteen station that located at South

American sector (ANC-FUQ), African sector (ILR-TAM

and AAB-NAB), Indian sector (TIR-ABG) and Southeast

Asian (LKW-KTB and DAV-MUT) sector. Figure 1 shows

the positions of the selected magnetic observation. Each pair

of the station are the combination of two stations that

located at off dip and dip equator. The analysis carried out

using the equatorial electrojet index, EUEL [6]. On the other

hand, Figure 2 illustrate the reading of sunspot number of

SC-24 from the year 2005 until 2016. The yellow box

represent the solar maximum period.

Figure 1: Map of geomagnetic observation



40

Figure 2: Solar cycle-24


In this study we are covering the maximum phases of SC-24

which is in 2012, 2013 and 2014 as illustrated in Figure 3.

The blue line represent the linear interpolation while the

dotted red line shows the spline interpolation. EEJ is

represent by the average data. Results show that the EEJ was

strongest at South American sector which is in ANC-FUQ

station and Southeast Asian Sector which located at LKW-

KTB. Furthermore, trend of EEJ longitudinal profile shows

the same pattern through all year of solar maximum. On top

of that, in 2012, the lowest value of EEJ was recorded at

AAB-NAB stations that located in African sector. Since

there is no data available at ILR-TAM and AAB-NAB

stations in 2013 and 2014, we cannot compare the lowest

value of EEJ at particular year. Table 1 shows the average of

EEJ magnitude from the year of 2012 until 2014.

Figure 3: Longitudinal profile of EEJ for solar

maximum (2012, 2013 and 2014)

Table 1: The average value of EEJ current

Station/Year 2012 2013 2014

ANC 89.81 79.98 102.55

ILR 55.5 NaN NaN

AAB 43.8 NaN NaN

TIR 25.11 26.23 44.55

LKW 83.78 101.1 104.81

DAV 53.17 55.21 70.79

4. Conclusion

We investigate the longitudinal profile of EEJ using the

average yearly data from year 2012 until 2014. During

maximum phase, the EEJ value was stronger at South

American sector and Southeast Asian sector. This is

different with previous study which reported that EEJ

current calculated from ground based data is highest at

South American sector. Furthermore, in 2012, lowest value

of EEJ strength was located at AAB station. Future work is

necessary to compare the variability of EEJ current between

ground and satellite based.

Acknowledgement

The authors would like to thank all the member of the

MAGDAS project for their cooperation and contribution to

this study. We thank the national institutes that support them

and INTERMAGNET for promoting high standards of

magnetic observatory practices (www.intermagnet.org).

Financial support was provided by Universiti Kebangsaan

Malaysia and Ministry of Education, Malaysia, using grants

FRGS/1/2015/ST02/UKM/02/1A and GUP-2016-016.

Yoshikawa were supported in part by JSPS Core-to-Core

Program (B. Asia-Africa Science Platforms), Formation of

Preliminary Center for Capacity Building for Space Weather

Research, and JSPS KAKENHI grants 15H05815. We also

acknowledge the National Oceanic and Atmospheric

Administration (NOAA) for providing Kp index data,

Goddard Space Flight Center/Space Physics Data Facility

(GSFC/SPDF) OMNIWeb at http://omniweb.gsfc.nasa.gov

for providing F10.7 data, and the National Geophysical Data

Center (NGDC) for the estimated values of the magnetic

inclination component.

References

[1] S., Chapman, & K. S. Raja Rao, The H and Z

variations along and near the equatorial electrojet in

India, Africa and the Pacific. Journal of Atmospheric

and Terrestrial Physics, 27(4), 559–

581.https://doi.org/10.1016/0021-9169(65)90020-6,

1965

[2] C. A. Onwumechili . Study of the Return Current of

the Equatorial Electrojet On the other hand , the

continuous distribution of current density model fitted

very well the horizontal field of the equatorial

electrojet observed on the ground in its entire range ,

the altitude. J. Geomag. Geoelectr, 44, 1–42 , 1992.



41

[3] V. Doumouya, B.R. Arora, Y. Cohen, and K. Yumoto,

Local time and longitude dependence of the equatorial

electrojet magnetic effects, Journal of Atmospheric and

Terrestrial. 65 (2003), 1265-1285.

[4] N. S. A. Hamid, W. N. I. Ismail, & A. Yoshikawa,

Longitudinal Profile of the Equatorial Electrojet

Current and Its Dependence on Solar Activity. Adv.

Sci Lett 23,1357-1360, 2017

[5] W. N. I. Ismail, N. S. A.Hamid, M. Abdullah,

A.Yoshikawa, & T. Uozumi, Longitudinal Variation of

EEJ Current during Different Phases of Solar Cycle.

Journal of Physics: Conference Series, 852(1).

https://doi.org/10.1088/1742-6596/852/1/012019, 2017

[6] T., Uozumi, K. Yumoto, K. Kitamura, S. Abe, Y.

Kakinami, M. Shinohara, and the MAGDAS group. A

new index to monitor temporal and long-term

variations of the equatorial electrojet by MAGDAS /

CPMN real-time data : EE -Index. Earth, Planets and

Space, 60(7),785–790.

https://doi.org/10.1186/BF03352828, 2008.



42

The Impact of High Environmental Temperature on Branchial

Ammonia Excretion Efficiency between Euryhaline and Stenohaline

Teleosts

Hon Jung Liew*1, Yusnita A Thalib1, 2, Ros Suhaida Razali1, Sharifah Rahmah2, Mazlan Abd.

Ghaffar2, 3, Gudrun De Boeck4

1Institute of Tropical Aquaculture 2School of Fisheries and Aquaculture Sciences

Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu, Malaysia 3School of Environmental and Natural Resource Sciences, Faculty of Science and Technology,

Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia 4Systemic Physiological and Ecotoxicological Research, Department of Biology, University of Antwerp,

Groenenborgerlaan 107, BE-2020, Antwerp, Belgium


Abstract

As fish is ectotherms where their body temperature depend on

ambient temperature limit, which made them vulnerable to

the changes of surrounding environment. Temperature

beyond physiological tolerance limit is known to alter

biological processes disturbance that affect species survival

and ultimately cause imbalance ecosystem, which have been

widely studies in temperate region and oceanic. But, lack of

attention being focus on the comparative species in tropical

species. Therefore, this study was designed to investigate the

impact of temperature on physiological response of

stenohaline-freshwater (ST-FW), stenohaline-seawater (ST-

SW) and euryhaline (EU). The main focus in the present study

was to elucidate the impact of temperature on branchial

osmorespiration efficiency on different categories of fishes

which were ST-FW (Hoven’s carp), ST-SW (Grouper) and

EU (Tilapia). Experimental specimens were exposed to

temperature at between 28oC and 32oC for two weeks before

the measurements. Our results showed that ammonia

excretion (Tamm) increased significantly in Tilapia but not in

Hoven carp and Grouper in high temperature. While

metabolic oxygen intake (MO2) in Hoven’s carp and Grouper

increased significantly with temperature. While, Tilapia

shows no significant difference in MO2 when expose to high

temperature. Through this study, it revealed a new insight of

understanding the effect of high temperature on three

different habitats of teleost.

1. Introduction

Rising of atmosphere temperature pose a direct impact to

aquatic animals. As fish is ectotherms organisms, their body

temperature depend on ambient temperature limit, which

made them vulnerable to the changes of surrounding

environment (Pang et al., 2011). Temperatures beyond the

optimal limit of a particular species adversely influence

physiological responses of fishes (Dalvi et al., 2009; Singh et

al., 2013), subsequently threatened aquatic ecosystem

balancing (Dallas and Rivers-Moore, 2014). The effects of

high environmental temperature (HET) on biological

metabolism in fish has been well documented (Das et al.,

2005; Manush et al., 2004; Kim et al., 2005; Zheng et al.,

2008). But most of previous studies were focused on

temperate species, not much attention being highlighted on

tropical species. Different species at different geographical

region would have different tolerance strategy towards

changing environment in order to survive. Thus, pose

necessary gate to investigate this impact on tropical or warm

water species at different level such as stenohaline-freshwater

(ST-FW), stenohaline-seawater (ST-SW) and euryhaline

(EU). With all this information, we hypothesized that at HET,

the excretion of branchial ammonia by fish increases as their

metabolic rate increase due to HET exposure provoke high

metabolic rate, thus induce high ammonia production.

Therefore, high excretion rate was expected in line with

respiration rate. In order to understand the impact of HET on

metabolic responses of teleost, this study was designed with

objective to investigate the effect of HET (28oC vs. 32oC) on

osmorespiration and branchial ammonia excretion efficiency

between stenohaline and euryhaline teleost.


2.1 Fish and Maintenance

Hoven carp (21.7 ± 1.9 g), Grouper (29.0 ± 1.7 g) and Tilapia

(40.8 ± 3.3 g) were purchased from the commercial fish farm.

Fish were transferred and kept in Hatchery facilities of the

Institute of Tropical Aquaculture at the Universiti Malaysia

Terengganu. Fish were maintained in plastic tanks equipped

with aeration and biological filters. The experimental fish

were fed thrice a day until satiation with commercial pellets.

The water parameter in the tank was monitored regularly

where the temperature is within 27-28 °C (for maintenance



43

purpose), pH range at 6-8 and DO2 > 5 mg/L. The water in

the tank was replaced once a week at about 40% to maintain

the water quality.

2.2 Metabolic Oxygen Intake (MO2)

For MO2 measurements, 10 fish were randomly selected from

each of acclimation temperature. Fish were allowed to

acclimatize to the respirometers 2 h prior experimentation.

During acclimation period, each respirometry chamber were

supplied with continuous influx of oxygenate water and

gentle aeration. After acclimation period, 1 ml of initial water

sample was sampled from all chambers in triplicate and

calibrated oxygen electrodes (CyberScan DO 300, Portable

Dissolved Oxygen Meter, USA) was inserted to record initial

dissolve oxygen reading. After that, the aeration in

respirometry chambers was removed. Without stressing the

fish, the lid of the chamber was sealed to prevent water and

gas exchange. The respirometry assay was performed for 1 h.

After that, the final oxygen concentration (mg/L) was

recorded and the final water samples were sampled to

measure the excretion rate of ammonia. MO2 were calculated

as MO2=(ΔO2i−O2f)×V×1000× (1/O2MW)×(1/BW)×T, where

O2i is first oxygen concentration (mg/L) and O2f is second

oxygen concentration (mg/L); V is total water volume in

respirometer; O2MW is molecular weight of oxygen; BW is

body weight (g) and T is time (h); and expressed as μmol/g/h

[9] .

2.3 Total Ammonia Excretion (Tamm)

The ammonia assay was prepared according to phenol–

nitroprusside method [9]. The ammonia excretion was

calculated by following formula as Tamm excretion =

(ΔNH4+

f – NH4+

i) × V (1/NH4+

MW) × (1/BW) × (1/T). Where,

NH4+

i and NH4+

f referred as initial and final ammonia

concentration (μg/L); V = the total water volume in

respirometer (L); NH4+

MW = the molecular weight of

ammonia; BW = body weight (g); T = time (h). The ammonia

excretion is expressed as μmol/g/h.

2.4 Statistical Analysis

All data are expressed as means ± SEM (n = number of fish

tested) and significance was accepted at P<0.05. Significant

differences within temperature 32oC vs. 28oC on each

stenohaline and euryhaline species on ammonia excretion

(Tamm) and metabolic oxygen intake (MO2). Data normality

were checked with Shapiro-Wilk test. The significant

differences between temperature on each species of

Euryhaline (EU), Stenohaline freshwater (ST-FW) and

Stenohaline seawater (ST-SW) were assessed by unpaired

two-tail student t-test. Significance within temperature

acclimations among species-specific were analyzed using

One-way ANOVA. If the ANOVA indicated a significance

level at P<0.05, a Tukey multiple post-hoc test were done.

3. Results

Total ammonia excretion (Tamm) exposed at temperature

between 28oC and 32oC on Hoven carp, Grouper and Tilapia

is presented in Table 1. The current findings showed Tilapia

had significantly increased (P<0.05) the ammonia excretion

in both acclimation temperature which higher than in Grouper

and Hoven carp. While, the lowest trend of ammonia

excretion was observed in Grouper when conjugate in both

acclimation temperatures where the values ranging from 0.05

to 0.08 μmol/g/h. Contrastly, the reversal of ammonia

excretion pattern was displayed in Hoven carp, where low

excretion rate was noticed with elevated temperature, but no

significant difference was found within the temperature

exposure. Hence, this difference reflecting the temperature

had significantly (P<0.05) affected the ammonia excretion.

Table 1. MO2 and Tamm pattern of ST-FW Hoven carp (n=10),

EU Tilapia (n=10) and ST-SW Grouper (n=10) exposed to

temperature at 28oC and 32oC. Value are expressed as

mean±SEM. An asterisk (*) indicates a significant difference

between temperature. Lower case letter denote significant

differences on species-specific within temperature.

Species MO2 (µmol/g/h) Tamm (µmol/g/h)

28oC 32oC 28oC 32oC

Hoven

carp

a*7.21±

1.43

10.47±

0.67

a0.53±

0.05

a0.48±

0.03

Tilapia

a11.82±

1.04

12.29±

1.01

b*0.52±

0.03

a0.95±

0.13

Grouper

b*7.77±

0.49

9.99±

0.51

c0.05±

0.01

b0.08±

0.01

The Metabolic oxygen intake (MO2) of Hoven carp,

Grouper and Tilapia that exposed to temperature at 28 oC and

32 oC is shown in Table 1. In the present study, it was found

that exposure of HET had significantly increased (P<0.05)

the oxygen consumption in both Hoven’s carp (10.47

μmol/g/h) and Grouper (9.99 μmol/g/h). As compared to

28oC, Hoven carp and Grouper only consume 7.21 and 7.77

μmol/g/hr. However, no significant difference (P>0.05) was

observed in Tilapia in both acclimation temperature.

Although it was clearly seen that Tilapia consumed more

oxygen (11.82 to 12.29 μmol/g/h) compared to the other two

species. In contrast, at ambient temperature (28 oC), all three

teleost exhibited difference oxygen consumption needs

(P<0.05) which not found in HET.

4. Discussion

The results obtained showed that acclimation in HET has

induced MO2 differently in Hoven carp, Grouper and Tilapia.

MO2 in Hoven’s carp and Grouper increased significantly

with increment of temperature as expected (Table 1). High

temperature provokes high metabolic rate in fish have

reported previously in fish Common carp [10], Pacific cod

[11], Asian catfish [3], Guppies [12] and Nurse Shark [13]. In

contrast, Tilapia in HET was able to maintain MO2 within

acclimated temperature. Tilapia is euryhaline which also a

osmoregulator, capable to maintain homeostasis without

much effort in different environments [10, 14]. It may explain

that Tilapia can modify metabolism needs and conserve their



44

energy during HET. The previous study revealed Tilapia able

to adjust their mechanisms to cope with temperature changes

[15, 16].

Reviewing the current study results, the differential

response of ammonia excretion (Table 1) were observed in all

three teleost under high environmental temperature (HET).

The ability of Tilapia to excrete more ammonia in HET was

strongly believed because the adaptability of the species to

modify gills mechanisms in different environment as they are

osmoregulator fish. It has been suggested that mitochondrian-

rich cells (MRCs) in the gill epithelium play a pivotal role in

enabling Tilapia to adapt to the changing environment [10,

17]. In the previous study, it has been found that MRCs in the

euryhaline fish have a capacity to adjust the branchial ion

ultrastructure and ion-transporting cells, such as Na+/K+-

ATPase [18]. Therefore, Tilapia can minimize the retention

of endogenous ammonia during HET by excreting the large

volume of ammonia in the external environment which was

in parallel with the current study. To counteract with

ammonia toxicant, various methods of uptake, elimination

and detoxification was deployed in order to survive in harsh

environments [20].

Contradictory, the present study found that rates of

ammonia excretion in Grouper were lower than in Hoven carp

and Tilapia. This difference could be due to the species-

specific excretory mechanism. Sayer and Davenport [21]

reported that marine fish only excrete 50-70% of nitrogen

across the gills compared to 90% in freshwater fish. This an

in agreement with the results obtained in Grouper, where

ammonia excretion rate was less than Hoven carp.

Additionally, the lower excretion rates of ammonia in

Grouper might be due to interference with electron potential

gradient in water chemistry in combination with temperature

stress. Goldstein et al. [22] and Ip and Chew [23] that

ammonia excretion efficiency in marine fish is lower than

freshwater fish due to the leaky tight junctions between

mitochondrion-rich cells that increase permeability for Na+

secretion. Thus, only small portion ammonia can be excreted

through Na+ diffusion. Further, this may lead to elevated

ammonia accumulation that might disturb the ionoregulatory

function [24, 25].

Surprisingly, present study shown the reversal of

ammonia excretion pattern in Hoven carp when conjugate in

HET which was unexpected, a low ammonia excretion rate

was found (Table 1). This strategy illustrates that Hoven carp

able to cope with the changes of temperature by lower down

the metabolic rate to avoid nitrogen metabolic waste

production. According to Randall and Tsui [26] under HET

condition, in order to avoid endogenous ammonia production,

fish reduce feed intake was reported. The similar findings also

seen in black bullheads where the reduction of feeding intake

was observed to compensate with the increased water

temperature and reduced metabolic rate [27]. Thus, in the

present study, it was found that Hoven carp able to re-

strategies basal metabolic needs to cope with HET by

reducing the feed intake to avoid ammonia accumulation.

5. Conclusion

High environmental temperature has induced differential

physiological responses among three teleost. It was found

that, Tilapia is the species to compromise with HET exposure.

In ST-FW (Hoven carp), metabolic rate has been minimized

to prevent ammonia toxication. While the ST-SW (Grouper)

is among the sensitive species that affected under HET

exposure with high metabolic rate. Overall, EU (Tilapia) has

the higher capability to cope with the warming environment

stress. Thus, through the findings, our hypotheses are

accepted where excretion of branchial ammonia increased

with increasing temperature in response of high metabolic

demand was seen in investigated species. We suggest a need

for future studies on ion-transporter, ion ventilation, blood

properties and the structure of gill morphology to investigate

the overall impact of HET on teleost. It will be interesting to

compare the most and less sensitive species when exposed to

HET.

Acknowledgement

This study was supported by the UKM-YSD Chair in Climate

Change Research Grant (Project Code ZF-2016-012) and

Fundamental Research Grant Scheme (Vot. No. FRGS-

59386). Authors also would to thank to Institute of Tropical

Aquaculture AKUATROP, UMT and staffs who help during

conducting this experiment.

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11: 13p, 2001.



46

Large Scale Wave Structure Prior to the Developmet of Equatorial

Plasma Bubbles

Suhaila M Buhari1,3, Mardina Abdullah2, Tajul Ariffin Musa,3

1Scientific Computing and Instrumentation, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia 2Space Science Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Selangor, Malaysia

3Geomatic Innovation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Johor, Malaysia


Abstract

The large scale wave structure (LSWS) is believed to seed

equatorial plasma bubble (EPB) through Rayleigh-Taylor

instability process. The onset time and location of successive

EPBs during post sunset hours was successfully observed

using high-density GPS receivers in Malaysia. This study

aims to detect the LSWS using GNU Radio Beacon

Receiver (GRBR) at Kuala Lumpur (KLP). The GRBR

receives beacon data from low Earth orbit satellite (LEOS)

such as Communications/Navigation Outage Forecasting

System (C/NOFS). C/NOFS transmits beacon data at 150

and 400 MHz from 400 – 800 km altitude. TEC can be

derived from phase difference between the transmitted

frequencies. The LSWS at the bottomside of the F layer is

detected from large TEC perturbation. The results show that

the GRBR is capable of detecting LSWS before sunset

hours. Further study on the spatial relation between the

LSWS and EPB will be carried out in the near future.

1. Introduction

The equatorial ionosphere most often shows a nighttime

plasma irregularity that is commonly referred as equatorial

plasma bubble (EPB). The occurrence of EPB could cause

rapid fluctuations in the amplitude and phase of the

propagation radio signals and crucial to communication and

navigation systems. The EPBs normally occur successively

where one structure rising after another during the sunset

time. However, the onset time and location of the EPBs are

ubiquitous because the seed of the initial perturbation is not

completely understood.

The horizontal modulation in a form of wavelike

structures along the observed longitudes might be

responsible for the development of successive EPBs [1]. The

wavelike structures at the bottom-side of ionospheric layer

could be easily amplified into successive EPBs due to high

growth rate of the Rayleigh-Taylor instability (RTI) during

high solar activity. The wavelike structures in the zonal

direction could be present in the late evening plays an

important role in the development of successive EPBs

during sunset time.

The wavelike structures that appear at the bottom-side of

the ionospheric layer could not be detected from

geostationary satellite such as the GPS, where the signal is

integrated from the satellites at 22,200 km altitude. In this

study, the existence of the wavelike structures prior to the

development of the EPBs will be investigated using radio

beacon data from low earth orbit satellite (LEOS).

2. Data Observation

In this study, the properties at the bottomside of the F layer

is investigated using GNU Radio Beacon Receiver (GRBR)

installed at Kuala Lumpur (KLP) (2.92oN, 101.77oE; dip

latitude 6.86oS) as shown in Figure 1. The GRBR receives

beacon data from low earth orbit satellite (LEOS) such as

Communications/Navigation Outage Forecasting System

(C/NOFS) which orbits at 400 - 800 km altitude. The

GRBR receives beacon data from C/NOFS at 150 and 400

MHz frequencies. The total electron content (TEC) at the

bottom-side of the F layer can be derived from phase

different between both frequencies as equation below [2]:

, (1)

Where Ψ1 and Ψ2 are phase at both frequencies, 150 MHz

and 140 MHz. Next, p = 3, q = 8, fr = 50 Mhz, A = 80.6 m3

s-2, c is speed of light, η’ is system phase bias and ∫ N dx =

TEC.

The GRBR data was collected from KLP station on 18

March 2013. TEC was calculated from GRBR data using

equation (1). Then, the LSWS was determined by

subtracting the TEC with 2.5 minutes running average. The

large perturbation inside the TEC could cause the

development of EPBs. The probability of EPB occurrence

becomes 100% when the LSWS amplitude is more than 3

TECu at Southeast Asian and African sectors [3].



47

Figure 1: The location of GRBR at Kuala Lumpur

(KLP) (2.92oN, 101.77oE; dip latitude 6.86oS).


For the first time, TEC and LSWS from GRBR data at KLP

station on 17 April 2017 are shown in Figure 2 (a), (b) and

(c). Each figure depicted TEC and LSWS from three

C/NOFS orbits at approximately 0921 UT (1st orbit), 1104

UT (2nd orbit) and 1247 UT (3rd orbit). The red and blue

lines in the upper panel for each Figure 2 shows TEC and

(a)

(b)

(c)

Figure 2: TEC and LSWS from three C/NOFS orbit at (a)

0921 UT, (b) 1104 UT and (c) 1247 UT.

2.5 minutes running average of TEC, respectively. The blue

lines in the bottom panel for each Figure 2 depicted the

LSWS, where the TEC is substracted by 2.5 minutes

running average.

The upper panel of Figure 2 (a) shows the decreasing of

TEC from 50 TECu 30 TECu for the 1st C/NOFS orbit. The

LSWSs from the 1st C/NOFS orbit are shown in the bottom

panel of Figure 2(a), where the small depletions of the TEC

can be clearly seen around 95o East and 102o East. Noted

that the vertical axis of the LSWS is between -2 to 2 TECu.

Two LSWSs can be seen as early as 0921 UT at 95o East,

which is 3 hours before sunset.

Figure 2 (b) illustrates TEC and LSWS for the 2nd

C/NOFS orbit at 1104 UT. The green and red lines denote E

and F region sunset, respectively. The upper panel shows

TEC decreasing from 40 TECu to 10 TECu. Lower TEC

around the sunset hours is due to low recombination rate at

the bottomside of F layer. At the same time, we can see that

LSWSs at the bottom panel of Figure 2 (b) have different

shape as compared to LSWSs in the 1st C/NOFS orbit.

Furthermore, both LSWSs in Figure 2 (b) are slightly

shifted to the East.

Figure 2 (c) presents TEC and LSWSs from the 3rd

C/NOFS orbit at 1247 UT. TEC is plotted in upper panel is

decreases from slightly more than 20 TECu to around 20

TECu. The E and F region sunset are located at 90o East and

94o East. Interestingly, the two LSWS structures are behind

the F region sunset. Noted that the vertical axis of LSWS is

from 5 to –5 TECu. The amplitude of both LSWS depleted

rapidly from around 1 TECu in the 2nd C/NOFS orbit to

approximately 5 TECu in the 3rd orbit. The larger depletion

after F region sunset hout might be associated with the

occurrence of EPB. However, the observation of the EPB

using radar, imager or GPS receiver should be carried out in

the future.

4. Conclusion

The TEC and LSWS have been successfully derived from

the GRBR data at KLP station on 18 March 2017. The

results show that the LSWS exist 3 hour ahead of E-region

sunset. This showed that the GRBR system is capable of

detecting the existence of LSWS prior to the occurrence of

EPB. The GRBR would be beneficial to predict the

occurrence of EPB in the future.

Acknowledgements

The authors would like to thank Roland Tsunoda, Mamoru

Yamamoto and Tulasi Ram Sundarsanam for the GRBR

data. The data can be obtained from Space Science Centre,

Universiti Kebangsaan Malaysia and SRI International. We

are grateful for the funding that supported this work;

Fundamental Research Grant Scheme -

FRGS/1/2016/WAB08/UKM/01/1 from Ministry of Higher

Education and Potential Academic Staff – PY/2017/00125

from Universiti Teknologi Malaysia.

GRBR station (KLP)



48

References

[1] S. M. Buhari, M. Abdullah, T. Yokoyama, Y. Otsuka,

M. Nishioka, A. M. Hasbi, S. A. Bahari and T.

Tsugawa, Climatology od successive equatorial plasma

bubbles observed by GPS ROTI over Malaysia, J.

Geophys. Res. Space Physics, 122, 2174-2184, 2017.

[2] S. V. Thampi, M. Yamamoto, R. T. Tsunoda, Y.

Otsuka, T. Tsugawa, J. Uemoto, and M. Ishii, First

observations of large-scale wave structure and

equatorial spread F using CERTO radio beacon on the

C/NOFS satellite, Geophys. Res. Lett., 36, L18111,

2009.

[3] S. Tulasi Ram, M. Yamamoto, R. T. Tsunoda, H. D.

Chau, T. L. Hoang, B. Damtie, M. Wassaie, C. Y.

Yatini, T. Manik, and T. Tsugawa, Characteristics of

large-scale wave structure observed from African and

Southeast Asian longitudinal sectors, J. Geophys. Res.

Space Physics, 119, 2288–2297, 2014.



49

Determining the Probability of Sediment Resuspension in the East

Coast of Peninsular Malaysia through Wind Analysis

Shahirah Hayati Mohd Salleh1, Wan Hanna Melini Wan Mohtar1, 2, Khairul Nizam Abdul

Maulud1,2, Nor Aslinda Awang3

1Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment,

Universiti Kebangsaan Malaysia, Bangi, Malaysia 2Earth Observation Centre (EOC), Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Malaysia

3Coastal Management and Oceanography Research Centre, National Hydraulic Research Institute of Malaysia, NAHRIM


Abstract

This paper discusses the possibility of sediment

resuspension events due to variation in wind speed along

the Pahang Shoreline. The data for observed mean wind

speed for a period of four years and 3 months from 2011 till

2015 was statistically analysed. Two probability density

functions were fitted to the measured probability

distributions on a yearly basis. The mean wind speed for the

entire data set was found to be 5.92 m/s with a standard

deviation of 2.71. The monthly variation in wind speed

determined by using the Weibull power density and

Rayleigh distribution is presented. They show that the wind

speed along the Pahang shoreline is 5 m/s 40% of the time

and 7.5 m/s for the remaining 70% of the time. Wind

direction is predominantly determined by the Northeast

monsoon and ranges between 10ο to 80ο, and it brings with

it more than 150 W/m2 wind energy.

1. Introduction

The Pahang shoreline is very susceptible to global climate

change and the challenges in sustaining the natural

resources along this shoreline for future generation is real.

Wind speed associated with climate change plays a major

role in inducing wave and tidal current which mobilize

sediment [1-2]. The East coast of Peninsular Malaysia is

subject to the hydrodynamics and wind from the South

China Sea. The North East Monsoon season (between

November and March) has a profound impact on the east

coast of peninsular Malaysia and can often cause severe

flooding [3]. Additionally, the South west Monsoon season

(between May and September) also plays a significant role

in changing the morphology of the east coast shoreline [4]

through the accumulation and deposition of sediment via

littoral transport [5]. Booth et al. (2000) has shown that an

average critical wind speed of 4 m/s may induce a

suspension of up to approximately 50% of the bottom

sediment. Shi (2002) studied sediment behavior in Tampa

Bay and found that a maximum of 3.21 x 10-3 kg/m/s

sediment had been transported by a wind speed of 20 m/s.

It is cruical to have an in-depth understanding of wind

speed distribution and its impact on sediment transport.

Hence, this study aims to gain a better understanding of

wind speed characteristics and its variations along the

Pahang shoreline, and their associated effects on sediment

transport. Statistical analysis was done by using the Weibull

distribution to analyze wind speed. In general, Weibull

distribution is the best method for describing coastal wind

speed analysis and is widely used for analyzing wind power

energy [8-9].

2. Study Area

Figure 1: Peninsular Malaysia

The east coast of peninsular Malaysia comprises of four

states, Kelantan, Terengganu, Pahang and Johor (as shown

in Figure 1). Two major rivers (i.e. Sungai Pahang and

Sungai Kelantan) along this coast, which have the widest

coastal plain, mobilized high sediment yield from the river

discharge to the shore [5]. For instance, Kuala Pahang

received 1755.242 tons/km2/year of suspended sediment

load from the Pahang River. Dawi et al. (2013) have

determined that, in the months of November and December,

wind magnitude and direction exert a significant influence

on the river plume of the Pahang River. A Coastal Wind



50

(U10) data along the Pahang shoreline for the period from

October 2011 to December 2015 was compiled using the

data provided by Jabatan Meteorologi Malaysia. The data

was gathered through ship observation at latitude longitude

between 2.5N 103E - 4.2N 105E. The wind speed in East

coast of Peninsular Malaysia is higher than 5 m/s during the

Northeast monsoon [11].

3. Methodology

Weibull and Rayleigh Distribution Analysis

Two important parameters for analysing wind speed by

using the Weibull distribution function are shape and scale

factors, which is expressed mathematically as [12]

𝑓(𝑣) =𝑘

𝑐(

𝑣

𝑐)𝑘−1 exp (− (

𝑣

𝑐)

𝑘

), (1)

where,

ν = wind speed (m/s)

k = shape factor (dimensionless)

c = scale factor (m/s)

Rayleigh distribution is categorised when the k value of the

Weibull distribution is 2 [13]. The cumulative probability

function of the Weibull distribution is given by

𝑓(𝑣) = 1 − exp [− (𝑣

𝑐)

𝑘

]. (2)

Wind Power Density Analysis

A wind power density analysis was done to determine the k

value for the best fit of the Weibull distribution with the

observed data. The Weibull power density analysis of time

series was used to calculate wind energy from wind speed

data by using the following equation [12]

𝑃 =1

2𝑛𝜌 ∑ 𝑣3 =

1

2

𝑛𝑖=1 𝜌𝑣𝑚𝑒𝑎𝑛

3 (3)

where,

ρ = air density 1.225 kg/m3

vmean = mean wind speed (m/s)

n = number of time series wind speed data


In this study, the probability of wind speed influence

towards sediment resuspension had analysed. Figure 2

shows the wind speed distribution for the period from 2011

to 2015 along the Pahang shoreline based on wind direction.

The histogram shows that the highest wind speed occurs

from the 20ο direction with a frequency higher than 0.09.

The frequency pattern of wind speed is higher between the

directions of 0ο to 80ο which occurs between October and

March (northeast monsoon season). The wind during the

Southwest monsoon (April to September) comes from the

direction of between 90ο to 260ο, as shown in Table 1.

From figure 3, the variation in wind speed clearly shows

a diurnal pattern, with a high wind speed of between 5 m/s

to 10 m/s between the periods from December to February.

The velocity of wind speed is much lower between Jun and

August and ranges between 5 m/s and 8 m/s. Wind speed is

relatively lower in April and May and is typically less than 5

m/s. Wind speed from September to October is almost

consistent every year, hence indicating no significant impact

of climate change (at least for the years being studied).

Moreover, the variation in wind speed for each month

differs, particularly for the year 2013, where the mean wind

speed is much lower than all the other years with the

exception of during the Northeast Monsoon season. The

mean wind speed is higher for the year 2014 and decreased

slightly in 2015.

Figure 2: Wind speed distribution frequency based on wind

direction

Table 1: Wind direction range based on months

Wind direction, degree Months

0-80 Oct, Nov, Dec, Jan, Feb,

Mar

90-260 Apr, May, Jun, Jul, Aug,

Sep

270-350 Oct, Nov, Dec, Jan, Feb,

Mar

Figure 3: Mean wind speed by month and year

Table 2 shows the mean wind speed, standard deviation

and Weibull parameters for each month for the period from

2011 to 2015. The highest mean wind speed of 7.75 m/s

occurs in January and the minimum mean wind speed of

4.00m/s occurs in April. As the mean speed is typically

higher than 4 m/s, there is a high possibility of the wind



51

speed mobilizing sediment, either from the coast to the

shore or vice versa. However, the probability of sediment

mobility via wind speed is lower in April, as indicated by

the lowest shape factor (k = 1.46).

Table 2: Mean wind speed, standard deviation, and

Weibull parameters

Month ν (m/s) σ k c (m/s)

Jan 7.75 3.41 2.59 8.39

Feb 6.50 2.32 3.14 7.24

Mar 5.75 2.95 2.32 6.06

Apr 4.00 2.90 1.46 4.39

May 4.25 2.47 2.15 4.91

Jun 6.50 3.11 2.22 7.18

Jul 6.50 2.30 3.13 7.16

Aug 6.50 2.38 3.13 6.93

Sep 6.25 2.88 2.56 6.93

Oct 5.00 2.42 2.44 5.49

Nov 4.60 2.26 2.22 5.16

Dec 7.40 3.07 3.40 8.15

Total 71.00 32.47 30.74 77.98

Mean/year 5.92 2.71 2.56 6.50

Wind speed analysis based on both Weibull and

Rayleigh distribution functions were examined by

comparing the probabilities predicted by both models to the

actual frequencies of measured data. Figures 4 and 5 present

the annual observed wind speed data, Weibull power

density, and Rayleigh distribution, as well as the cumulative

distribution of the observed wind speed and the Weibull

analysis. Analysis shows that the mode of frequency for the

wind speed to reach a velocity of 5 m/s is 0.12. Both the

Weibull and Rayleigh distributions analysis show a well-fit

distribution with an RMSE value of 0.02 and R2 of 0.9996

for Weibull and 0.9998 for Rayleigh distributions, as shown

in Table 3.

Figure 4: Comparative histogram of observed wind speed

distribution, Weibull distribution analysis, (solid line), and

Rayleigh distribution (dotted green line).

Furthermore, the probability of the wind speed in the

East coast of Malaysia reaching between 4 m/s and 8 m/s is

high based on the Weibull and Rayleigh lines (red lines in

Figure 4). It can be inferred, based on the study conducted

by Booth et al. (2000), that the bottom sediment in shallow

areas will be resuspended by wind speed of 4 m/s. It can be

seen from Figures 4 and 5 that higher wind speed will result

in increased suspended sediment in water column, especially

during the Northeast monsoon. Figure 5 shows that 40 and

70% of the cumulative distribution frequency have wind

speeds of 5 and 7.5 m/s, respectively.

Table 3: Weibull and Rayleigh parameters and

root mean squared error

Weibull Rayleigh

RMSE 0.02 0.02

R2 0.9996 0.9998

k 1.99 2.00

c 6.77 6.77

Figure 5: Cumulative distribution frequency of observed

data (dashed line) and Weibull analysis (solid line)

Figure 6 illustrates the monthly variation in wind speed

and wind power density for the years 2011 to 2015. The

maximum energy of wind speed occurs in December and

January with energy greater than 200 W/m2. On the other

hand, the months of April and May have the lowest energy,

which is well below 50 W/m2. Wind energy analysis could

potentially be used to describe the possibility of harvesting

wind energy for use as green energy. Further exploration

and analysis, including numerical model, need to be done in

this area.

Figure 6: Variation of mean wind speed and wind power

density for the period from 2011 to2015



52

5. Conclusion

This study presents the results of a statistical analysis of

wind speed data for the period from 2011 to 2015 for the

region along the Pahang shoreline. The results of the

analysis show that the Weibull power density distribution

and Rayleigh distribution are able to describe wind speed

distribution very well. There is a high probability of wind

speed reaching a velocity of between 4 m/s and 8 m/s which

will promote sediment transport along the Pahang shoreline.

Statistical analysis is a cost effective method for determining

the probability of wind energy in coastals area in

comparison to numerical modelling. However, a

combination of statistical and numerical model can be used

to gain a better understanding of the dynamic process of

wind speed on sediment transport since tidal and wave

influences can also be included in the model.

Acknowledgements

The authors wish to thank Jabatan Meteorologi Malaysia for

providing the wind data for the state of Pahang.

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53

A Review on Forest Carbon Sequestration as a Cost-effective Way to

Mitigate Global Climate Change

Asif Raihan1, Rawshan Ara Begum1, Mohd Nizam Mohd Said1, 2, Sharifah Mastura Syed Abdullah1

1Institute of Climate Change (IPI), Universiti Kebangsaan Malaysia

2School of Environmental and Natural Resource Sciences, Faculty of Science and Technology,



Abstract

This article provides a review on forest carbon sequestration

as a low-cost option for climate change mitigation strategy.

Several studies have analyzed the costs of forest carbon sink

programs by estimating their cost effectiveness and carbon

sequestration capacity in a variety of settings. Increasing of

greenhouse gas (GHG) emissions has led to climate change

which is dominated by carbon dioxide (CO2). Forestry

sector has a huge potential in reducing carbon emissions,

atmospheric accumulation of GHGs as well as the negative

impacts of climate change. Forests absorb a huge amount of

atmospheric CO2 in the process of photosynthesis and

carbon remains stored as biomass in trees for a long periods.

Because of such capacity to store carbon which is called

carbon sequestration, interest in using forests for climate

change mitigation has been growing. The question is

whether the carbon sequestration process is a cost effective

way to mitigate climate change or not. However, it is found

that carbon sequestration through various forestry activities

can be a cost-effective way to mitigate climate change.

1. Introduction

Over the past few decades, rising atmospheric accumulation

of GHGs causes global warming and changes in all

components of the climate system. CO2 is the major GHG

which is the main reason for rising global average surface

temperature. Deforestation and burning of fossil fuels are

the major anthropogenic sources of carbon dioxide emission

that increase the negative effects of climate change. Thus,

limiting climate change will require substantial and

sustained reduction of CO2.

However, the world’s forests play a critical role in the

global carbon cycle [1] by fixing, storing, and emitting vast

quantities of atmospheric carbon. Terrestrial ecosystems

store approximately 1 trillion tons of CO2 in the biomass of

living trees and plants [2]. Reducing forest carbon

emissions and increasing forest carbon stocks (carbon

sequestration) are potentially important elements of a global

climate change mitigation strategy [3]. It would be possible

to increase this carbon efficiently to reduce the future

damages of climate change by different mitigation options

such as afforestation, reduced deforestation, regeneration,

agroforestry and sustainable forest management [4].

Sequestering carbon in the forests will allow the

implementation of more permanent options for the

avoidance of greenhouse gas emissions, and stabilization of

climate change.

Concern about rising carbon emission and atmospheric

concentrations of greenhouse gases [5] has inspired the

search for tactics of sequestering carbon in plant biomass.

The economics of carbon sequestration have been analyzing

for approximately two eras and proved that carbon

sequestration can play a substantial economic role in

climate change mitigation through reducing the greenhouse

gas emissions. Thus, this article provides a review on forest

carbon sequestration as a low-cost option for climate

change mitigation strategy.

2. Forest Carbon Sequestration and Carbon

Stocks

Forests draw carbon from the atmosphere in the process of

photosynthesis, and the carbon may remain stored for long

periods in trees and other forest vegetation (in above and

below ground biomass and in forest soils). This process of

absorbing atmospheric CO2 by trees and storing as carbon

biomass is called carbon sequestration. For the past dozen

years there has been a growing interest in the possibility of

mitigating the global warming effects of carbon dioxide by

increasing the carbon stocks in biomass and soils. Deveny

et al. [6] reviewed six studies on global estimates of forest

carbon stokes to compare the global forest carbon stock

estimates. IPCC (2006) showed that maximum 1,145,348

MtCO2eq can be sequestered globally by the forests (Table

1). On an average this amount is 837,206 MtCO2eq where

Forest carbon index (2009) found it 762,634 MtCO2eq.



54

Table 1: Global Estimates of Forest Carbon Stocks [6]

Global Forest

Carbon Stocks

(MtCO2-eq)

References

762,634 Forest carbon index (2009)

856,511 Kindermann et al. (2008)

373,838 Gibbs and Brown (2007)

1,145,348 IPCC (2006)

726,483 Olson et al. (1983); Gibbs (2006)

983,747 Houghton (1999); DeFries et al. (2002)

777,834 Brown (1997); Achard et al. (2002,

2004)

693,815 FAO (2006)

837,206 Average

Malaysia has a large forested area, estimated at 17.7 M ha

which offers an opportunity for carbon sequestration. The

forest ecosystem of Peninsular Malaysia alone is reported to

contain 23.48 Million tons of Carbon (or 86.17M to CO2

equivalent) and a carbon sequestration potential of 4 tons of

carbon ha-1year-1 [7]. Both aboveground and belowground

carbon density in the forests of Malaysia was decreased

from the year 2000 to 2010 (Figure 1) while a little bit of

carbon biomass has recovered in 2015 [8].

Figure 1: Trend of forest carbon sequestration in Malaysia

3. Cost of Forest Carbon Sequestration

Several studies over the past two decades have analyzed the

costs of forest carbon sequestration. The studies vary

according to geographic scope. For example, Nordhaus [9]

and, Sedjo and Solomon [10] provided global analyses,

Dixon et al. [11] analyzed costs of sequestration on three

continents, Alig et al. [12], van Kooten et al. [13], and

Masera et al. [14] considered sequestration costs in the

United States, Canada, and Mexico, respectively. Lubowski

et al. [15] reported that almost 1/3 of the US carbon

reduction commitment would be achieved in a cost-

effective solution by forest carbon sequestration.

The estimation of carbon sequestration cost is a

necessary input for determining its potential in relation to

other climate change mitigation measures. Sedjo et al. [16]

carry out a review of a handful studies which consider

conversion of land into forests, long-rotation periods, forest

management, long-lived wood products, biomass for energy

production, and urban forestry. The cost estimates vary

within and between forests in tropical, temperate and boreal

zones. Table 2 shows that the marginal cost ranges from 1.5

US$/tonC to 133 US$/tonC which represents the cost of

reducing 1 ton carbon emission through forest carbon

sequestration. Richards and Stokes [17] make a

comprehensive and thorough review of 36 studies on carbon

sequestration in forests at different geographical scales

(global, regional, national, and subnational). They find that

the cost per ton carbon sequestration varies between 13 and

188 US$ per ton carbon.

Table 2: Marginal cost ranges of forest carbon

sequestration, in 2011 prices [adapted from 18]

Marginal cost range

US$/ton C

References

1.5 – 133 Sedjo et al. (1995)

13 – 188 Richards and Stokes (2004)

4.5 – 24 Van Kooten et al. (2004)

0 – 60 Van Kooten et al. (2009)

0.4 – 171 Phan et al. (2014)

Van Kooten et al. [19] include 55 studies, and

investigate the impact of forest activity (tree planting and

agroforestry) and the use of forest product (wood and

bioenergy). They obtain a baseline estimate that varies

between approximately 4.5 and 24 US$/ton C. Van Kooten

et al. [20] is a follow up meta-analysis where the number of

studies have been increased to 68, and the results are used

to predict carbon sequestration costs in different countries

and for different forest sink activities. The marginal cost

ranges from near zero to 60 US$/ton C. Locations in

tropical regions are found in the lower range, and the higher

range costs are found for activities in Europe. Tree

plantation and use of harvested biomass for energy seem to

be the least costly forest project. Phan et al. [21] make a

meta-analysis on 32 studies on avoidance of deforestation in

developing countries. They found that the avoidance cost

ranges between 0.4 and 171 US$/ton C with an average of

10.3.

A study by Michetti and Rosa [22] presented that, the

inclusion of carbon sink could reduce the cost of meeting

European Union (EU) 2020 carbon dioxide (CO2)

mitigation commitment in an emission trading system

(ETS) by at least 25%. Vass et al. [23] used a non-linear

programming model to calculate the net cost of emission

reduction for 27 EU member countries with and without

forest carbon sequestration, and emissions when EU targets

are met in a cost-efficient manner. France, Germany, Italy

and Spain have the highest net cost in both scenarios (Table

3). These are also the countries with the highest GDP and

therefore have a larger national abatement burden as well as

fewer emission allowances. The EU countries that

experience the highest cost saving by including forest

carbon sequestration are Austria, Estonia, Latvia, Slovenia

and Sweden. Table 3 also shows cost-efficient emissions in

2020 with and without sequestration. Altogether, the total

0

1,000

2,000

3,000

4,000

1990 2000 2005 2010 2015Car

bo

n (

Mil

lio

n m

etri

c

tons)

Carbon in aboveground biomass

Carbon in belowground biomass



55

emission level is reduced by 11.4% when including forest

carbon sequestration.

Table 3. Net costs and net emissions in the cost-efficient

solutions with and without forest carbon sequestration in

some of the EU Countries [23]

EU

Countries

Net cost

of

emission

reduction

without

sequestra

tion

(Million

Euro)

Net cost

of

emission

reduction

with

sequestra

tion

(Million

Euro)

Emission

s 2020

without

sequestra

tion

(Thousan

d ton

CO2)

Emissio

ns 2020

with

sequestr

ation

(Thousa

nd ton

CO2)

Austria 989 128 64172 51572

Estonia 42 -3 11668 8300

Finland 399 211 43840 13949

France 2813 1098 330060 264670

Germany 7224 2752 626800 609300

Greece 367 364 82790 80185

Hungary 580 170 49121 47232

Ireland 622 609 39403 38451

Italy 6346 4054 417980 329000

Latvia 366 -30 7224 -13260

Lithuania -82 -66 10589 3305

Netherlands 1505 1460 155840 155280

Poland 991 562 230650 191440

Portugal 318 127 59808 55784

Slovakia 413 98 31931 30620

Slovenia 227 41 13000 9547

Spain 3503 1350 303970 288050

Sweden 2290 9 45360 25362

Therefore, it is crucial that forests play a duel role by

acting as both sink and source of carbon emission.

Reducing carbon emission by both decreasing deforestation

and storing carbon as biomass are possible only through

forest carbon sequestration. Thinking about reducing carbon

emission without forest carbon sequestration is so expensive

that it’s almost impossible for most of the countries over the

world. Compare to other mitigation options, carbon

emission can be reduced by increasing carbon sink through

forest carbon sequestration within short time duration with

the lowest cost.

Tree growth rate in the tropical and sub-tropical areas is

faster than the other regions. Moreover, most of the tropical

and sub-tropical countries are developing countries and tree

planting cost along with the land cost on that areas are

cheaper than temperate or boreal region. Due to cost

effectiveness, high potential rates of carbon uptake, and

associated environmental and social benefits, much

attention has focused on promoting tropical forestry for

offsetting carbon emissions. Malaysia is one of the tropical

countries with a huge percentage of forest land. Malaysia's

Second National Communication (NC2) assumed the

carbon price RM 16 (US$ 3.68) per ton CO2eq [1]. Thus,

carbon sequestration cost in Malaysia can be cheaper than

Europe or North American countries. Forestry sectors in

Malaysia could play a key role in enhancing cost-effective

carbon sequestration and sinks while reducing global

greenhouse gas emissions and thereby mitigate climate

change.

4. Discussion and Conclusion

Due to the increase of carbon emission, current impacts and

future risks of climate change become more apparent.

Forests act both as sources and sinks of greenhouse gases

(GHG), through which they have significant influence on

the climate on earth. Approximately 17.4 percent of annual

global carbon dioxide emissions are caused by deforestation

and forest degradation and it will be impossible to solve the

climate change problem without addressing these emissions.

Forests and other terrestrial systems annually absorb

approximately 2.6 GtC (9.53 GtCO2eq), while deforestation

and degradation of forests emit approximately 1.6GtC (5.87

GtCO2eq), for net absorption of 1GtC (3.67 GtCO2eq) [24].

Thus, reducing emissions from deforestation by forest

carbon sequestration could be one of the most cost-effective

tools for reducing GHG emissions as well as climate change

mitigation.

Forests are at the heart of the transition to low-carbon

economies. Forests and forest products have a key role to

play in mitigation and adaptation, not only because of their

double role as sink and source of emissions, but also

through the potential for wider use of wood products to

displace more fossil fuel intense products. Forests have

potential for climate change mitigation in both developed

and developing countries, through a range of activities.

While mitigation potential and costs of forest carbon

sequestration differ greatly by activity, region, system

boundaries and time horizon, FAO [25] indicate that the

total economic mitigation potential of afforestation,

reducing deforestation and forest management could range

from 1.9 to 5.5 Gt CO2eq per year in 2040 at a carbon value

of less than US$20 per ton CO2eq.

Establishment of carbon prices can accelerate the

transition to low-carbon economies and would incentivize

increases in forest area and use of wood products. At the

moment, market incentives for forest mitigation are almost

non-existent. The Kyoto Protocol has fostered a carbon

market; its accounting rules and project guidelines for

generation of carbon credits defined the activities eligible

for mitigation and hence shaped the main investments in

mitigation in developed and developing countries. Globally,

however, the combined value of the regional, national and

subnational carbon pricing instruments was less than US$50

billion in 2015, of which almost 70 percent was attributed to

emission trading systems and the rest to carbon taxes.

Carbon prices vary significantly, from less than US$1 to

US$130 per ton CO2eq. About 85 percent of emissions are



56

priced at less than US$10 per ton CO2eq. This is

considerably lower than the price estimated as needed to

meet the recommended 2°C climate stabilization goal.

From the last several decades, there has been a growing

interest to mitigate global warming and climate change by

increasing the carbon stocks in tree biomass and soils. The

literature reviewed demonstrated the differences on the

costs of capturing and storing carbon in forest ecosystems

among the global, national and regional level. It becomes

apparent that the cost of emission reduction with forest

carbon sequestration is much lesser compared to the cost of

emission reduction without forest carbon sequestration. The

emission reduction rate is also higher for the forest carbon

sequestration. Therefore, forest carbon sequestration can be

the most cost-effective way to mitigate climate change

which provides an indication for further studies in relation

to climate change mitigation cost and carbon sequestration

through various forestry activities in Malaysia.

Acknowledgements

The authors are thankful for the research grants from the

project ‘Assessing Coastal Vulnerability due to Climate

Change towards Sustainable Community in Malaysia’

(Project Code: AP-2015-009) and Trans Disciplinary

Research Grant Scheme (TRGS/1/2015/UKM/02/5/3).

References

[1] Malaysia's Second National Communication (NC2),

Second National Communication to the UNFCCC

2011, Malaysia, 2011.

[2] FAO (Food and Agriculture Organization), Global

forest resources assessment 2005, FAO, Rome, 147 pp.

2006.

[3] B.C. Murray, Economics of Forest Carbon

Sequestration as a Climate Change Mitigation Strategy.

Encyclopedia of Energy, Natural Resource and

Environmental Economics 1: 41-47, 2013

[4] B. Sohngen, An analysis of forestry carbon

sequestration as a response to climate change,

Copenhagen Consensus Center, Fredriksberg,

Dinamarca. 28p. 2009.

[5] T.M.L. Wigley, Climate change and forestry,

Commonwealth Forestry Review 72: 256-264, 1993.

[6] A. Deveny, J. Nackoney, N. Purvis, M. Gusti, R.J.

Kopp, E.M. Madeira, A.R. Stevenson, G. Kindermann,

M.K. Macauley, M. Obersteiner, Forest carbon index.

Resources for the Future (RFF), Washington DC,

USA, 2009.

[7] A.A. Chinade, C. Siwar, S.M. Ismail, A. Isahak, A

Review on Carbon Sequestration in Malaysian Forest

Soils: Opportunities and Barriers, International

Journal of Soil Science 10(1): 17, 2015.

[8] FRA, Global forest resources assessment 2015,

Country report Malaysia, The food and agriculture

organization of the United Nations’ Global Forest

Resources Assessment, 2015.

[9] W. Nordhaus, The Cost of Slowing Climate Change: A

Survey, Energy 12 (1): 37-65, 1991.

[10] R. Sedjo, A. Solomon, Greenhouse Warming:

Abatement and Adaptation, in Crosson, P.,

Darmstadter, J., Easterling, W., and Rosenberg, N.

(eds.), RFF Proceedings, July 1989, pp. 110-119,

1989.

[11] R. Dixon, J. Winjum, K. Andrasko, J. Lee, P.

Schroeder, Integrated Land-Use Systems: Assessment

of Promising Agroforestry and Alternative Land-Use

Practices to Enhance Carbon Conservation and

Sequestration, Climate Change 30: 1-23, 1994.

[12] R. Alig, D. Adams, B. McCarl, J.M. Callaway, S.

Winnett, Assessing Effects of Mitigation Strategies for

Global Climate Change with an Intertemporal Model

of the U.S. Forest and Agriculture Sectors,

Environmental & Resource Economics 9: 259–274,

1997.

[13] G. van Kooten, L. Arthur, W. Wilson, Potential to

Sequester Carbon in Canadian Forests: Some

Economic Considerations, Canadian Public Policy

18(2): 127-138, 1992.

[14] O. Masera, M. Bellon, G. Segura, Forest Management

Options for Sequestering Carbon in Mexico, Biomass

and Bioenergy 8(5): 357-368, 1995.

[15] R. Lubowski, A. Plantinga, R. Stavins, Land-use

change and carbon sinks: econometric estimation of the

carbon sequestration supply function, Journal of

Environmental Economics and Management 51 (2):

135-152, 2006.

[16] R.A. Sedjo, J. Wisniewski, A.V. Sample, J.D.

Kinsman, The economics of managing carbon via

forestry: assessment of existing studies, Environmental

and Resource Economics 6(2): 139-165, 1995.

[17] K.R. Richards, C. Stokes, A review of forest carbon

sequestration cost studies: a dozen years of research,

Climatic change 63(1): 1-48, 2004.

[18] A. Aklilu, I.M. Gren, Economic incentives for carbon

sequestration: A review of the literature, No. 2014: 8,

2014.

[19] G.C. van Kooten, A. J. Eagle, J. Manley, T. Smolak, A

meta-analysis of carbon forest sinks, Environmental

Science & Policy 7(4): 239-251, 2004.

[20] G.C. van Kooten, S. Laaksonen-Craig, Y. Wang,

Carbon offset credits via forestry activities: A meta-

regression analysis, Canadian Journal of Forest

research 39: 2153-2167, 2009.

[21] D. T. Phan, R. Brouwer, M. Davidson, The economic

costs of avoided deforestation in the developing world:

A meta-analysis, Journal of Forest Economics 20: 1-

16, 2014.

[22] M. Michetti, R.N. Rosa, Afforestation and timber

management compliance strategies in climate policy. A

computable general equilibrium analysis, Nota di

Lavoro 04-2011 Sustainable Development Series,

Fondazione Eni Enrico Mattei, 2011.



57

[23] M. Vass, M. K. Elofsson, I.M. Gren, Costs and

fairness of forest carbon sequestration in EU climate

policy, No. 2013: 05, 2013.

[24] Intergovernmental Panel on Climate Change (IPCC),

Climate Change 2007: The Physical Science Basis.

Contribution of Working Group I to the Fourth

Assessment Report of the Intergovernmental Panel on

Climate Change, Cambridge University Press,

Cambridge, United Kingdom and New York, NY,

USA, 2007.

[25] FAO, Forestry for a low-carbon future: Integrating

forests and wood products in climate change

strategies, Food and Agriculture Organization of the

United Nations, Rome, 2016.



58

Review of Methodology on Source Apportionment of PM2.5 near a

Coal-fired Power Plant using Multivariate Receptor Modelling

Ahmad Hazuwan Hamid1,*, Md Firoz Khan1, Mohd Talib Latif2

1Centre for Tropical Climate Change System, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Malaysia 2School of Environmental and Natural Resource Sciences, Faculty of Science and Technology,

Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia

*Corresponding author, Email: [email protected]

Abstract

Coal-fired power plant releases various hazardous pollutants

into the atmosphere. This study reviews various

methodologies to examine the composition of PM2.5 near a

coal-fired power plant which includes trace metals, ionic

compositions, elemental carbon and organic carbon. The

possible sources of the PM2.5 can be predicted using positive

matrix factorization (PMF) model and validated using

trajectory based modelling. A health risk assessment can also

be performed to know the health impact towards the

population living near the power plant.

1. Introduction

The increasing global temperature and climate change

coincide with the rise of the industrial era. The demand and

consumption of energy have been increasing to satisfy the

growing demand for the rapid development. Therefore, the

number of power stations has also increased. In Malaysia,

coal accounts for 43% of the total energy input in power

stations or 13,591.44 ktoe in 2014 [1]. However, coal is not a

completely clean fuel as it produces various pollutants such

as ash, carbon dioxide (CO2), sulphur dioxide (SO2), nitrogen

oxides (NOx) and other particulate matter (PM) mixed with

hazardous elements during the combustion process at the

power plants [2].

Among the pollutants, PM2.5 (less than or equal to 2.5 μm

in aerodynamic diameter) can cause damage towards the

respiratory and cardiovascular systems, particularly to the

elderly and sensitive groups of population [2]. Moreover,

recent studies discovered that PM2.5 can also damage the

nervous system [3]. Thus, further studies and baseline data

are required to identify the toxic chemical profiles and their

related emission sources.

Multivariate receptors modelling have been well known

used to separate the potential sources of the observable air

pollutant. It is a tool that is able identifies the source of the

pollution by separating each component and associated them

with each potential sources of pollution [4]. Therefore, this

article aims to review the method of analysing, and

determining the source apportionment and health risk

assessment from PM2.5 dust collected near a coal-fired power

plant.

2. Application of Methodology

2.1 Chemical Analysis

2.1.1 Trace Metals Composition

In order to analyse the chemical composition of PM2.5, a

portion of the filter samples can be cut into smaller pieces and

placed inside a Teflon vessel. The reagent, 12 mL of nitric

acid (65% Merck KGaA, Germany) and 3 mL of hydrogen

peroxide (40% Merck KGaA, Germany) can be used which

was also mentioned by Khan et al. [5]. The Teflon vessel,

containing the reagent and portion of the sample can be

placed inside a microwave and operates in two stages: (1) 180

°C for 20 min and (2) 220 °C for 15 min. If the samples are

less than three, the power can be set at 500 watts and 1000

watts for more than three. Upon completion, the Teflon vessel

can be left to cool down at room temperature before filtered

using a syringe filter and transferred into a 50 mL centrifuge

tube. The sample solution can be diluted with ultrapure water

(UPW; 8.2 M Ωcm, Easypure® II, Thermo Scientific,

Canada). The sample usually needs to be preserved in a

refrigerator at 4 °C if further chemical analysis is delayed. A

study by Khan et al. [5] and Ali et al. [6], also preserved the

prepared samples at a similar temperature. The trace metals

including the rare earth element (REE) (Al, Ba, Ca, Fe, Mg,

Pb, Zn, Ag, As, Cd, Cr, Li, Be, Bi, Co, Cu, Mn, Ni, Rb, Se,

Sr, V, In, Tl, U, Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sc,

Sm, Tb, Th, Tm, Y, and Yb) are required to be determine

using an inductively coupled plasma-mass spectrometry

(ICP-MS; PerkinElmer ELAN 9000, USA). In order to rely

on the quality of the data, detection limit (DL) of the

instrument is required. The convenient procedure as reported

widely in the literatures to determine the DL was three times

of the standard deviation of the trace elements in the filter

blanks. Several researchers introduced an analytical step to

produce an accurate result [4, 7]. As part of the procedures,

two (2) sets of analysis can be applied: (a) a set of high metal

concentration (Al, Ca, Fe, Mg, Zn, and Mn), and (b) a set of

low metal concentration (Ba, Pb, Ag, As, Cd, Cr, Li, Be, Bi,

Co, Cu, Ni, Rb, Se, Sr, and V). For the construction of

external calibration lines, Multi-Element Calibration

Standards 2 and 3 (PerkinElmer Pure Plus; PerkinElmer,



59

USA) are commonly used as calibration standards [4]. It was

found in the literatures that the calibration concentration was

chosen as 10 ppb to 100 ppb in the ambient samples collected

in Malaysia based on the above group of the elements.

2.1.2 Ionic Compositions

The water-soluble ionic (Na+, NH4+, K+, Ca2+, Mg2+, Cl-, NO3

-

, and SO42-) (WSI) compositions are significant to describe

mainly the secondary inorganic and marine borne

compositions. An ion chromatograph (IC) (Metrohm 850

model 881 Compact IC Pro, Switzerland) is widely proposed

in the literatures to determine their concentration level. The

following cationic and anionic columns were seen in the

published articles to the above IC analysis [8, 9]. Metrosep

A-Supp 5–150/4.0 and C4–100/4.0 columns can be used to

determine cations and anions, respectively. The 1.7

mmol L-1 nitric and 0.7 mmol L-1 dipicolinic acids can be

prepared for use as eluents for cations. Eluents of 6.4 mmol

L-1 sodium carbonate (Na2CO3) (Merck, Germany) and 2.0

mmol L-1 sodium bicarbonate (NaHCO3) (Merck, Germany)

can be prepared and used to measure anions (Cl-, NO3-, and

SO42-) with a flow rate of 0.7 mL min-1. The 100 mmol L-

1 Suprapur® sulfuric acid (H2SO4) (Merck, Germany) can

also be prepared to use as a suppressor regenerant, and ions

can be detected by a conductivity detector. The detailed of the

analysis procedure was described by Khan et al. [4].

2.1.3 Elemental Carbon and Organic Carbon

This analysis can determine the EC and OC fractions from the

PM2.5 samples. The ratio of EC/OC can then determine the

source of pollutant where a high OC can indicate the source

is from biomass burning. The OC and EC concentration can

also be correlated with the trace metals or ionic compositions

to further classify the sources [4].

2.2 Health Risk Assessment

Health risk assessment (HRA) usually involves four steps

which are, hazard identification, estimation of dose response,

exposure assessment and risk characterization. We can follow

the methods introduced by the United States Environmental

Protection Agency (US EPA).

The hazardous air pollutants emitted from sources are As,

Cr, Cd, Hg and Pb, which can cause dangerous health

problems. The hazardous elements are also further grouped

into carcinogenic and non-carcinogenic elements for better

classification of the health threats. The estimation of the dose

response can identify the relationship of exposure amount and

the adverse health effect. Reference dose (RfD) and reference

concentration (RfC) are used to determine the toxicological

risk. The common acceptable cancer risk threshold is one in

a million (10-6) but it still varies among different countries

[10].

2.3 Receptor Modelling

Multivariate receptor modelling can help to identify the

potential sources of PM2.5. The commonly used receptor

models are principal component analysis (PCA), absolute

principal component analysis (APCS), positive matrix

factorization (PMF), chemical mass balance (CMB),

UNMIX and other statistical modelling approaches. There

have been several studies reviewed by Park and Oh [11] that

compare the performance of the different receptor models.

However, each of the study has its own context and purpose

as well as advantages/disadvantages. Pant and Harrison [12]

mentioned that the use of PCA, CMB and PMF alone produce

a high correlation for source identification with overall

similar consistency but different in the percentage of the

contribution of sources. It is suggested that the combined

approach could possibly increase the robustness of the results.

Thus, a comparative source apportionment can be considered

using PMF, PCA/APCS and CMB to produce a trusted result.

2.4 Trajectory Modelling

The calculation of the backward trajectories (BTs) is an

important tool to discover the transport pathway of the air to

the sampling site. The trajectory path can be used to further

justify the source apportionment from the receptor modelling

and highlight the any influence of the meteorology factors

towards the pollution concentration on a site. The Hybrid

Single-Particle Lagrangian Integrated Trajectory Model

(HYSPLIT) was used by several researchers [4, 13-16]. To

increase the visualization of the illustrations, IGOR PRO, a

graphical software can also be used to modify or add

additional information to the trajectories as suggested by

Khan et al. [4, 17].

3. Discussion

Coal has been formed from plant life after millions of years

under high pressure and heat. During the process, it absorbs

impurities from the surrounding. Some of these impurities are

Hg, Ni, As, and Pb which are recognized as hazardous

elements. Coal can be classified into four types based on their

heating value, ash content and moisture. This characteristic

can also reflect on the amount of impurities present in the

coal. The most common type of coals is bituminous and sub-

bituminous due to their abundance. Thus, the major

hazardous pollutants are present in the coal material, which

can pose human, and ecosystem damage [18]. Medina et al.

[19] described the level of elements (ppm) released from coal-

fired power plant as Cs (5), Mn (153), Tl (5), Sc (14), Ga (39),

Y (29), Zr (236), Nb (26), Sn (5), La (40), Ce (79), Pr (10),

Nd (37), Sm (5), Eu (1), Gd (6), Tb (1), Dy (6), Ho (1), Er (3),

Yb (4), Hf (5), Ta (7), W (5), and Bi (1). The hazardous

elements determined in PM2.5 can be used as input parameters

to estimate the non-cancer and cancer risk.

The composition profiles of PM2.5 are essential to conduct

source apportionment of the hazardous pollutants. A study by

Yang et al. [20] have identified the coal combustion sources

based on the high concentration of Cl, Br, Pb, As, Mn, and Cu

mixed with moderate concentration of OC, SOx, and NOx and

Fe [20]. Song et al. [16] reported that the high concentration

of OC, EC and Cl can also be an indication of coal

combustion. As, Se and Cs are also indicators to identify a

coal combustion sources as reported widely in the literatures.

Khan et al. [4] and Moreno et al. [21] identified coal

combustion sources referring As a tracer. The ratio of OC/EC



60

has also widely used to classify the coal combustion source

as suggested by Watson and Chow [22].

4. Conclusion

By applying several receptor modelling techniques, a robust

and reliable result can be obtained. The use of the multivariate

techniques can help to identify and pinpoint the source of

PM2.5. A comparison of the several receptor models can

produce an appropriate result. Source apportionment is not

only able to pin point the main source of pollution of an area,

but also help to plan for any countermeasure to stop or reduce

the negative impact towards the human health and ecosystem

around it.

Acknowledgements

The authors would like to thank the Universiti Kebangsaan

Malaysia for Research University Grant GGPM-2016-034

and FRGS/1/2017/WAB05/UKM/02/6.

References

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(Energy Commision), 2014.

[2] S.A.M. Din, N.N.-H.N. Yahya, and A. Abdullah, Fine

Particulates Matter (PM2.5) from Coal-fired Power Plant

in Manjung and its Health Impacts, Procedia - Social

and Behavioral Sciences 85(Supplement C): 92-99,

2013.

[3] E. Underwood, Brain pollution: Evidence builds that

dirty air causes Alzheimer’s, dementia. 2017.

[4] M.F. Khan, N.A. Sulong, M.T. Latif, M.S.M. Nadzir, N.

Amil, D.F.M. Hussain, V. Lee, P.N. Hosaini, S.

Shaharom, N.A.Y.M. Yusoff, H.M.S. Hoque, J.X.

Chung, M. Sahani, N. Mohd Tahir, L. Juneng, K.N.A.

Maulud, S.M.S. Abdullah, Y. Fujii, S. Tohno, and A.

Mizohata, Comprehensive assessment of PM2.5

physicochemical properties during the Southeast Asia

dry season (southwest monsoon), Journal of

Geophysical Research: Atmospheres 121(24): 14,589-

14,611, 2016.

[5] M.F. Khan, M.T. Latif, W.H. Saw, N. Amil, M.S.M.

Nadzir, M. Sahani, N.M. Tahir, and J.X. Chung, Fine

particulate matter in the tropical environment:

monsoonal effects, source apportionment, and health

risk assessment, Atmospheric Chemistry and Physics

16(2): 597-617, 2016.

[6] M.Y. Ali, M.M. Hanafiah, M.F. Khan, and M.T. Latif,

Quantitative source apportionment and human toxicity

of indoor trace metals at university buildings, Building

and Environment 121: 238-246, 2017.

[7] N. Amil, M.T. Latif, M.F. Khan, and M. Mohamad,

Seasonal variability of PM2.5 composition and sources

in the Klang Valley urban-industrial environment,

Atmos. Chem. Phys. 16(8): 5357-5381, 2016.

[8] N.A. Sulong, M.T. Latif, M.F. Khan, N. Amil, M.J.

Ashfold, M.I.A. Wahab, K.M. Chan, and M. Sahani,

Source apportionment and health risk assessment

among specific age groups during haze and non-haze

episodes in Kuala Lumpur, Malaysia, Science of The

Total Environment 601-602(Supplement C): 556-570,

2017.

[9] M.F. Khan, S.W. Hwa, L.C. Hou, N.I.H. Mustaffa, N.

Amil, N. Mohamad, M. Sahani, S.A. Jaafar, M.S.M.

Nadzir, and M.T. Latif, Influences of inorganic and

polycyclic aromatic hydrocarbons on the sources of

PM2.5 in the Southeast Asian urban sites, Air Quality,

Atmosphere & Health 10(8): 999-1013, 2017.

[10] M.M. Mokhtar, M.H. Hassim, and R.M. Taib, Health

risk assessment of emissions from a coal-fired power

plant using AERMOD modelling, Process Safety and

Environmental Protection 92(5): 476-485, 2014.

[11] E.S. Park and M.-S. Oh, Bayesian quantile multivariate

receptor modeling, Chemometrics and Intelligent

Laboratory Systems 159: 174-180, 2016.

[12] P. Pant and R.M. Harrison, Critical review of receptor

modelling for particulate matter: A case study of India,

Atmospheric Environment 49: 1-12, 2012.

[13] L.A. Chen, J.G. Watson, J.C. Chow, D.W. DuBois, and

L. Herschberger, PM2.5 Source Apportionment:

Reconciling Receptor Models for U.S. Nonurban and

Urban Long-Term Networks, J Air Waste Manag Assoc

61(11): 1204-1217, 2011.

[14] J.K. Choi, J.B. Heo, S.J. Ban, S.M. Yi, and K.D. Zoh,

Source apportionment of PM2.5 at the coastal area in

Korea, Sci Total Environ 447: 370-80, 2013.

[15] C.H. Jeong, M.L. McGuire, D. Herod, T. Dann, E.

Dabek–Zlotorzynska, D. Wang, L. Ding, V. Celo, D.

Mathieu, and G. Evans, Receptor model based

identification of PM2.5 sources in Canadian cities,

Atmospheric Pollution Research 2(2): 158-171, 2011.

[16] Y. Song, Y. Zhang, S. Xie, L. Zeng, M. Zheng, L.G.

Salmon, M. Shao, and S. Slanina, Source apportionment

of PM2.5 in Beijing by positive matrix factorization,

Atmospheric Environment 40(8): 1526-1537, 2006.

[17] M.F. Khan, M.T. Latif, C.H. Lim, N. Amil, S.A. Jaafar,

D. Dominick, M.S. Mohd Nadzir, M. Sahani, and N.M.

Tahir, Seasonal effect and source apportionment of

polycyclic aromatic hydrocarbons in PM2.5,

Atmospheric Environment 106: 178-190, 2015.

[18] E. Burt, P. Orris, and S. Buchanan, Scientific Evidence

of Health Effects from Coal Use in Energy Generatio.

University of Illinois at Chicago School of Public

Health, 2013.

[19] A. Medina, P. Gamero, X. Querol, N. Moreno, B. De

León, M. Almanza, G. Vargas, M. Izquierdo, and O.

Font, Fly ash from a Mexican mineral coal I:

Mineralogical and chemical characterization, Journal of

Hazardous Materials 181(1): 82-90, 2010.

[20] L. Yang, S. Cheng, X. Wang, W. Nie, P. Xu, X. Gao, C.

Yuan, and W. Wang, Source identification and health

impact of PM2.5 in a heavily polluted urban atmosphere

in China, Atmospheric Environment 75: 265-269, 2013.

[21] T. Moreno, A. Karanasiou, F. Amato, F. Lucarelli, S.

Nava, G. Calzolai, M. Chiari, E. Coz, B. Artíñano, J.

Lumbreras, R. Borge, E. Boldo, C. Linares, A. Alastuey,

X. Querol, and W. Gibbons, Daily and hourly sourcing

of metallic and mineral dust in urban air contaminated



61

by traffic and coal-burning emissions, Atmospheric

Environment 68(Supplement C): 33-44, 2013.

[22] J.G. Watson and J.C. Chow, Source characterization of

major emission sources in the Imperial and Mexicali

Valleys along the US/Mexico border, Science of The

Total Environment 276(1): 33-47, 2001.



62

Study of Maximum Usable Frequency (MUF) for High Frequency

(HF) Band at Equatorial Region in Malaysia

Johari Talib1,*, Sabirin Abdullah1

1 Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia.


Abstract

In this paper, the study of maximum usable frequency (MUF)

has been conducted for high frequency (HF) band at the

equatorial region in Malaysia. HF propagate through

skywave and reflected by the ionosphere. However, the HF

communication facing the issue as the user unable to

determine the right frequency to use in the HF radio

according to the time of day, year and location. The highest

possible frequency that can be used to transmit over a

particular path under given ionospheric conditions is called

the Maximum Usable Frequency (MUF). The aim of this

research is to develop the MUF model base on Malaysian

region and subsequently to improve HF communication. The

research refers to the appropriateness of HF frequency that

can be an account by the users according to the time of day,

year, sunspot cycle and location. An approach to study using

theoretical and data collecting to determine MUF models has

been performed using DXLab and MATLAB software,

modeling and simulation MUF model and validation of MUF

model.

1. Introduction

The process of selecting the best frequency according to the

prevailing conditions is known as frequency management.

Successful frequency management depends upon the ability

to predict, measure and react to a range of parameters that

characterize both the propagation path and the noise [1]. The

need for frequency management as an aid to improve radio-

circuit operations has been pointed out by King and Slater

(1973) and the implications of the daily variations of HF

communications circuits has been studied by Rush et al.

(1974). There are several methods of frequency

management, i.e. by computer prediction, experience,

ionospheric sounders and others. A computer prediction is

very popular amongst the other. In the USA has an

ionospheric prediction program called the Ionospheric

Communications Enhanced Profile Analysis and Circuit

(ICEPAC) [3]. This program is a full system performance

model for HF communications circuits. This program has

been chosen by many researchers as an ionospheric model.

Malaysia is located in the equatorial region (2° 30' North

latitude and 112° 30' East longitude), which the diurnal and

monthly variations may perform differently in the mid-

latitude region. Therefore, there is the need to predict

ionosphere and HF frequencies in the equatorial region [4].

The aim of this research is to build MUF model based on

Malaysian environment and used the model to predict the

MUF. The parameters of MUF equations will be obtained

from generated frequency, experimental setup and

International Reference Ionosphere (IRI) model, a standard

empirical model that was developed based on all the

available data sources from various measurement location in

Malaysia. The MUF results from the models can be

compared with MUF data obtain from actual transmission

between HF base station (ANGKASA) and selected mobile

station. The minimum error model will be selected as a MUF

model for Malaysian.

HF communication in Malaysian environment is not fully

explored compared to other Asian regions such as China,

Australia, Indonesia and Thailand, and another part of the

world. However, there are a few type of researches on HF

communications in Malaysia, focusing on HF data

communication [5], on automatic link establishment (ALE)

capability of HF radio [6] and secured HF image

transmission [7]. The study of HF communication on the

Malaysian ionosphere would help in the improvement of the

prediction of the HF frequencies at Malaysian region and

obtain better quality HF communication links.

HF radio waves reflected by the ionosphere can provide

a relevant amount of information with the composite

received signal. The ionospheric layer can be measured

through the technique known as Vertical Sounding

Technique [8]. This technique able to evaluate the positions

of the ionospheric layer resulted in the height and electron

density of ionosphere. Furthermore, the virtual height in

kilometer KM (PVH) and power observation of the small-

scale disturbance (SD) effect on signal fading at ionospheric

region also can be determined using Vertical Ionospheric

Sounding (VIS) technique [9]. The massive issue in HF radio

is the brisk change in the ionospheric characteristic, resulting

in the need for operating frequencies to be transposed from

time to time to get decent performance. Hence, MUF is

imperative for HF radio users to obtain good frequency

management. Solar phenomenon such as solar flares, solar

wind and coronal mass ejection (CMEs) can give a massive

impact towards the HF communication [10]. Based on

Kennedy and Davis (1983), it reported that immense increase

of MUF variability is checked after unusual solar

phenomenon. The sunlight intensity also effect towards the



63

HF communication as the electron in the ionosphere change

prior to solar intensity, while the layer of ionosphere also

changes from day and night.

2. Methodology

The methodology for this research is divided into two ways.

2.1. Data Collection

For the data collection, a transceiver is applied which consist

a mobile manpack RF radio transceiver and receiving station

which located at rooftop UKM building. The manpack will be

carry to the designated area around Peninsular Malaysia and

certain HF frequencies will be used in order to communicate

with the receiving station.

Figure 1: Mobile manpack RF transceiver

Figure 2: RF Ground Station

Figure 3: Yagi Uda antenna for RF ground station

2.2. Simulation

The simulation will be executed using DXLab Launcher.

DXLab Launcher is a freeware that able to simulate the HF

signal that being used for Amateur Radio. By simulate using

this software, it can be compared to signal that obtain from

the transceiver.

Figure 4: DXLab Launcher software interface

2.3. Preliminary Results

The preliminary results of this study are:

1. Able to determine MUF model in Malaysian

environment because until now there is no MUF

model based on Malaysia environment.

2. Knowledge of the ionospheric parameters to be used

in the MUF model which is not being fully

explored. The parameters are as follows:

a. Critical frequency (foE, foF2)

b. Height of ionosphere (hmE, hmF2)

c. Propagation / M factor (M(3000)F2)

d. MUF (MUF(3000)F2)



64

Figure 5: Example of hourly and monthly median of

MUF for (a) 2009, (b) 2010 and (c) 2011 [10]

3. Discussion

HF that raging between 3-30 MHz is imposed in this analysis,

whereas certain desired frequency will be selected, from

lowest to highest value in HF band. The frequency also needs

to be determined before using it to prevent using the

frequency that already using either by Amateur Radio or air

force. The simulation will be executed upon the selected

frequency from the fieldwork.

4. Conclusion

The research may be able to help HF user to plan their HF

frequencies, and then to ensure the readiness of HF

communication in disaster events. Thus, the research will

have a big impact on HF user, especially government

agencies, i.e. can improve HF links and make the

communications possible all the time.

HF Communication System using the ionosphere is still

widely used as a form of radio communications technology.

Although not reliable as satellite communications, it is

inexpensive and can provide a useful back-up in case the

Satcom is failing. Moreover, research and development on

the HF communication base on the Malaysian environment

should be more explored due to unique equator region.

Acknowledgements

The authors express gratitude to ANGKASA grant

FRGS/1/2016/TK04/UKM/02/4 for funding and supporting

this research.

.

References

[1] Maslin, N. M. 2004. HF communications: A Systems

Approach. CRC Press.

[2] King, J. W. & Slater, A. J. 1973. Errors in predicted

values of foF2 and hmF2 compared with the observed

day-to-day variability. ITU Telecommunication Journal,

40, 766 – 770.

[3] Li, W., Su, D., Wang, J. & Liu, Y. 2013. Prediction of

Short-wave Communication Effects based on ICEPAC

Model. Proceedings of the 2013 2nd International

Conference on Intelligent System and Applied Material,

295–298. doi:10.12696/gsam.2013.1018

[4] Liu, L., Yang, J., Le, H., Chen, Y., Wan, W. & Lee, C.

C. 2012. Comparative study of the equatorial ionosphere

over Jicamarca during recent two solar minima. Journal

of Geophysical Research: Space Physics, 117(A1),

1978–2012.

[5] Hassan, N., Sha’ameri, A. Z., Sidek, A. R. M. & Sarif,

N. M. 2009. HF radio communication with automatic

link establishment capability at Universiti Malaysia

Pahang. Malaysian Technical Universities Conference

on Engineering and Technology (MUCEET), Universiti

Malaysia Pahang.

[6] Sha’ameri, A. Z. 2010. ALE Radio Technology For

Public Protection And Disaster Relief Operations. .my

Convergence, 04, 34–42.

[7] Sha’ameri, A. Z. 2006. Secured HF Image Transmission

System pp.1–201.

[8] Baskaradas, James Arokiasami, et al. "Description of

ionospheric disturbances observed by Vertical

Ionospheric Sounding at 3MHz." Annals of Geophysics

57.1, 2014 [9] G. Vertogradov and E. Vertogradova, "The investigation

of ionospheric response to total eclipses on 29th March

2006 and on 20th March 2015

based on HF oblique sounding data", Journal of

Atmospheric and SolarTerrestrial Physics, vol. 147, pp.

28-36, 2016. [10] Malik, R. A., Abdullah, M., Abdullah, S., & Homam, M.

J. (2016). Comparison of maximum usable frequency

(MUF) variability over Peninsular Malaysia with IRI

model during the rise of solar cycle 24. Journal of

Atmospheric and Solar-Terrestrial Physics, 138, 87-92.



65

Performance Analysis of a Negative-permeability Metamaterial

Inspired Antenna with 1U Cubesat

Touhidul Alam1, 2, Farhad Bin Ashraf1, Mohammed Shamsul Alam2, Mohammad Tariqul Islam1,3,

Mengu Cho3

1 Dept. of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia 2 International Islamic University Chittagong (IIUC), Bangladesh

3 Kyushu Institute of Technology, Kitakyushu, Japan

* corresponding author, E-mail: [email protected]

Abstract

Nano-satellite offers an accessible and effective platform for

a wide diversity of space-based applications. The

nanosatellite components need to be miniaturized due to

limited volume and power. There is great need for compact,

lightweight and stable performing antenna is a requirement

for smooth operation of the nanosatellite mission. In this

paper, high realized gain antenna is proposed for 1U

nanosatellite communication system. The antenna achieves

impedance bandwidth of 1.1 GHz (7.7 GHz to 8.8 GHz) with

overall dimension of 29.80×30.30×2.66 mm3. The antenna

has been integrated with 1U satellite body and investigated

antenna performances.

1. Introduction

Nano-satellite are revolutionizing in the modern satellite

industry because of their size and cost minimization with

shorter development time features. 1U nanosatellite is one of

the smallest form of the satellites with size of 10×10×10cm3

having multiple subsystems. Several types of antenna are

studied for satellite application. Deployable antenna is one of

the widely-used antenna in nanosatellite [1-2]. The adverse

fact of using deployable antennas in nanosatellite is, they are

required to be deployed mechanically. This might increase

the chance of mission failure. To avoid the deployment

complexity patch antennas were used in many satellite

missions[3].Metamaterials are artificially formed structures

which have shown great potential to engineer the

unconventional properties of the material. The unit cell forms

a two-layer metamaterial structure used as a substrate for

gain enhancement of a stacked antenna at 8.55 GHz [4].

This paper presents a metamaterial based high gain

antenna for 1U CubeSat transmission system. The antenna

operates at 7.7 to 8.8 GHz with 11.3dB of maximum realized

gain.

2. Antenna Design and Methodology

The geometry of the proposed stacked antenna is presented

in Fig. 1. The 1st layer and 2nd layer of the proposed stacked

antenna is designed using Rogers RT5880 substrate material

having relative permittivity of 2.2, height of 0.58mm and

1.575 mm respectively. Four spacers are used to separate the

two layers. The conventional ground plane of the 2nd layer

is replaced by metamaterial ground plane. The overall

antenna dimension is 29.80×30.30×2.66 mm3. Metamaterial

ground plane is shown 1(c). The metamaterial characteristics

characterization has been performed using CST microwave

studio. Perfect electric conductor (PEC) and perfect magnetic

conductor (PMC) boundary conditions are applied in x and y

plane respectively. Two electromagnetic waveguide port is

placed between unit-cell, propagating direction k is along the

z-plane.

(a) (b)

(c) (d)

Fig. 1: Schematic layout of the proposed antenna (a) Top view

(b) Bottom view (c) Ground plane and (d) Side view



66


The metamaterial characterization has been retrieved using

constitutive parameters retrieval method [5], shown in Fig. 2.

The proposed stacked antenna exhibits relative permeability

(µ) from 5.78 GHz to 11.4 GHz, shown in fig. 3. The feeding

potential difference between patch and ground is considered

caused by electric field in-stead of magnetic field. The

reflection coefficient of the proposed antenna has been

shown is shown in Fig. 3. Moreover, the antenna reflection

coefficient with satellite body is also investigated to ensure

compatibility of the antenna for nanosatellite application.

The metamaterial antenna achieves -10dB reflection

bandwidth of 1.1 GHz (7.7 GHz to 8.8 GHz).

Fig. 2: Permeability of the proposed metamaterial unit-cell

Fig. 3: Reflection coefficient of the proposed antenna

The realized gain with and without metamaterial ground

plane has been analyzed, shown in Fig. 4. From Fig 4, it is

noticed that realized gain has increased by 37.13% using

metamaterial ground plane at 8.2 GHz. The radiation

efficiency with and without satellite body has also been

investigated, presented in Fig. 5. It is shown from Fig. 5 that

the antenna shows about 60% radiation efficiency at center

frequency, which can ensure the feasibility of the antenna

with 1U satellite body.

(a) (b)

Fig. 4: 3D realized gain of the proposed stacked antenna at

8.2 GHz (a) without metamaterial and (b) with metamaterial

Fig. 5: Radiation efficiency of the proposed stacked antenna

4. Conclusion

In this paper, a negative indexed metamaterial inspired

stacked antenna is proposed for 1U nanosatellite application.

The antenna achieves fractional bandwidth of 13.33% with

overall antenna dimension of 0.78λ×0.76λ×0.067λ at lower

end frequency of 7.7 GHz. The Antenna has been integrated

with 1U nanosatellite structure and analyzed the antenna

performances. The simulation results show that the antenna

might a good candidate for communication engineering X-

band 1U nanosatellite.

Acknowledgements

This research was supported by the Ministry of Education

Malaysia (MOE) under grant no.

PRGS/2/2015/TK04/UKM/01/1 and Universiti Kebangsaan

Malaysia (UKM) under grant no. GP- K016889.

References

[1] Y. Rahmat-Samii, "Special Issue on Antenna

Innovations for CubeSats and SmallSats [Guest

Editorial]," IEEE Antennas and Propagation Magazine,

vol. 59, pp. 16-127, 2017.

[2] J. S. Silva, M. GarcÍa-viGueraS, T. Debogović, J. R.

Costa, C. A. Fernandes, and J. R. Mosig,

"Stereolithography-Based Antennas for Satellite

Communications in Ka-Band," Proceedings of the IEEE,

vol. 105, pp. 655-667, 2017.



67

[3] M. T. Islam, M. Cho, M. Samsuzzaman, and S. Kibria,

"Compact Antenna for Small Satellite Applications

[Antenna Applications Corner]," IEEE Antennas and

Propagation Magazine, vol. 57, pp. 30-36, 2015.

[4] D. Li, Z. Szabo, X. Qing, E.-P. Li, and Z. N. Chen, "A

high gain antenna with an optimized metamaterial

inspired superstrate," IEEE transactions on antennas and

propagation, vol. 60, pp. 6018-6023, 2012.

[5] U. C. Hasar, A. Muratoglu, M. Bute, J. J. Barroso, and

M. Ertugrul, "Effective Constitutive Parameters

Retrieval Method for Bianisotropic Metamaterials Using

Waveguide Measurements," IEEE Transactions on

Microwave Theory and Techniques, 2017.



68

Zonal Velocity Drift of Equatorial Plasma Bubbles Calculated over Southeast Asia

Idahwati Sarudin1, Nurul Shazana Abdul Hamid1, Mardina Abdullah2, 3*, and Suhaila M Buhari4

1School of Applied Physics, Faculty of Science and Technology,

2Space Science Centre (ANGKASA), Institute of Climate Change, 3Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Malaysia.

4Department of Physics, Faculty of Science, Universiti Teknologi Malaysia, Malaysia.


Abstract

The zonal velocity of equatorial plasma bubbles (EPBs) have

been studied using various techniques in the past few years.

However, the derivation of the zonal drift of EPBs using GPS

ROTI have not been studied before. This study aims to

investigate the zonal velocity drifts of EPBs using GPS ROTI

keogram. The Malaysia Real–Time Kinematic GNSS

Network (MyRTKnet) which consists of 78 GPS receivers

were used to study the occurrence of EPBs along 96°E -

120°E longitude. The EPBs are detected from daily ROTI

keogram that derived from east-west cross section of two

dimension of ROTI maps at 5°N for every 5 minutes. On the

night of 10 April 2013, EPBs with periodic spacing between

50 km to 100 km were recorded by MyRTKnet. In this study,

we obtained that the highest drift velocity is about 194.4 m s-

1 at 1430 UT to 1500 UT whereas the lowest drift velocity is

111.1 m s-1 at 1330 UT to 1400 UT. Besides, the EPBs are

propagated towards the east from 200 km to 2800 km.

1. Introduction

The equatorial plasma bubbles (EPBs) is defined as depletion

of total electron content (TEC) in the ionosphere. The

observation of the EPBs have been carried out using ground

and space based instruments. The first observation of EPBs

over Southeast Asia by using GPS data was made by Buhari

et al in 2014 [1]. The zonal velocity drift is one of the

characteristics of EPBs which have been studied using

various techniques in the past few years. Most of the previous

study observed velocities of EPBs using imaging techniques

[2]. They observed that the velocity of EPBs decreased as

time passed. Unlike these ground based techniques, the

manipulation of space based data such as satellite and GPS

data is very limited. The previous studies made through these

space based instrument is the zonal plasma drift speeds of

EPBs observed using the imager aboard high apogee IMAGE

satellite during March-May 2002 and it had a strong

longitudinal dependence and with a maximum over the

Indian sector [3]. In this paper, we present the zonal drift

velocity of EPBs calculated from high-density GPS receivers

in Southeast Asia (SEA) on night of 10 April 2013. These

drift velocities are calculated from longitudinal change at

significant time that can be seen from plotted ROTI keogram.

2. Methodology

2.1. Data Collection

In this study the zonal velocity of EPBs were calculated using

rate of TEC change index (ROTI) derived from high density

GPS data in Southeast Asia sector [2]. The GPS data was

obtained from Malaysia Real-Time Kinematics GNSS

Network (MyRTKnet) that was installed by Department of

Survey and Mapping Malaysia (JUPEM).

Figure 1: Distribution of GPS receivers from MyRTKnet,

SuGAr, and IGS networks in Southeast Asia.

Since 2003, about 78 GPS receiver stations over Malaysia

called MyRTKnet has been installed by JUPEM. Also, we

have 49 GPS receivers used from IGS and SuGAr networks

through the Scripps Orbit and Permanent Array Center

website (sopac.ucsd.edu/dataArchive) which covered

Indonesia, Singapore and Thailand. The black circle in

Figure 1 represent the distribution of GPS receivers in SEA.

In order to show the longitudinal and temporal variations in

the EPBs structure, the keogram was created by taking an

east-west cross-section of ROTI at 5°N for every 5 minutes.



69

2.2. Analysis

The present study examined one day ROTI keogram derived

from the MyRTKnet in order to identify the occurrence of

EPBs in Malaysia area. The occurrence of EPBs was verified

if ROTI is larger than 0.1 TECU/min at a location on the

keogram. High threshold value (0.06 TECU/ min) was taken

into account to confirm the EPBs are truly present in the

observational area. Then, the zonal drift velocity of EPBs can

be calculated from its spatial displacement divided by time

[5]. The zonal drift velocity is calculated for each EPBs from

the onset highest ROTI values to the final highest ROTI

values as can be seen from the keogram.


Based on the method described earlier, we have calculated

zonal velocity drift on the night of 10 April 2013. By using

GPS ROTI measurement in SEA, we can be observe the

temporal and spatial variations of EPBs.

Figure 1 shows a keogram generated from the two-

dimensional maps of ROTI and their longitudinal variations.

The figure shows a clear overview of the characteristics of the

EPBs, which is a cross section of ROTI by choosing the

horizontal profiles of the ROTI at 5°N latitude with several

times and longitudes. The white gap in Figure 1 shows the

missing data during certain period.

Figure 1: ROTI keogram at 5°N latitude obtained from GPS

networks in SEA from 1000 UT to 2230 UT on night of 10

April 2013.

Figure 2: The zonal velocity drift calculated of EPB for red

error shown in Figure 1.

We further select one striations of EPBs from the keogram

to calculate the drift velocities that denoted by red error in

Figure 1. This EPB is choosen based on it striation that can

be seen fully from the ROTI keogram.

Figure 2 shows plots of the zonal velocity drift from GPS

ROTI keogram. Based on Figure 2, we can see roughly the

pattern of the graph were decrease from 1230 UT to almost

1350 UT and suddenly increase until 1500 UT but then, the

drift velocity were decrease to 1530 UT. Our results agreed

with the some of the previous studies [2],[3], and [4,5] where

drift velocity of EPBs gradually decrease with time. The

zonal drift of the EPBs shows significant difference during

1300 UT to 1500 UT. The highest drift velocity is about 194.4

m s-1 at 1430 UT to 1500 UT whereas the lowest drift velocity

is 111.1 m s-1 at 1330 UT to 1400 UT.

4. Conclusion

In this work, we have presented zonal drift of EPBs from

high-density GPS receivers in SEA on 10 April 2013. In

general, the EPBs propagated towards the east from 200 km

to 2800 km. Our results agreed with previous study that

shows the drift velocity of EPB gradually decrease with time.

Besides, we found that the highest drift velocity is about

194.4 m s-1 at 1430 UT to 1500 UT whereas the lowest drift

velocity is 111.1 m s-1 at 1330 UT to 1400 UT.

Acknowledgements

The GPS data was collected from Department of Survey and

Mapping Malaysia (JUPEM) and downloaded from SOPAC

via (http://sopac.ucsd.edu/). This work was supported by

Fundamental Research Grant Scheme-

FRGS/1/2016/WAB08/UKM/01/1 from Ministry of

Education Malaysia, and GUP-2016-016 from Universiti

Kebangsaan Malaysia.

References

[1] S.M. Buhari, M. Abdullah, A.M. Hasbi, Y. Osuka, T.

Yokoyama, M. Nishioka., T. Tsugawa, Continuous

generation and two-dimensional structure of equatorial

plasma bubbles observed by high-density GPS receivers

in Southeast Asia, Journal of Geophysical

Research:Space Physics, 2014.

[2] D. Fukushima, K. Shiokawa, Y. Otsuka, M. Kubota, T.

Tsugawa, T. Nagatsuma, Geomagnetically conjugate

observation of plasma bubbles and thermosheric neutral

winds at low latitudes, Journal of Geophysical

Research:Space Physics, 2015.

[3] T.J. Immel, H.U. Frey, S.B. Mende, E. Sagawa, Global

observations of the zonal drift speed of equatorial

ionospheric plasma bubbles, Annales Geophysicae 22

3099-3107 doi: 10.5194/angeo-22-3099-2004.

[4] I. Sarudin, N.S.A. Hamid, M. Abdullah, S.M. Buhari,

Investigation of Zonal Velocity of Equatorial Plasma

Bubbles (EPBs) by using GPS data, Journal of Physics:

Conference series, 2017.

[5] D.P. Nade, A.K. Sharma, S.S. Nikte, P.T. Patil, R.N.

Ghodpage, M.V. Rokade, S. Gurubaran, A. Taori, Y.

Sahai, Zonal velocity of the equatorial plasma bubbles



70

over Kolhapur, India, Annales Geophysicae 31 doi:

10.5194/angeo-31-2077-2013.

[6] Igo Paulino, Amauri Fragoso de Medeiros, Ricardo Arlen

Buriti, Hisao Takahashi, Jose Humberto Andrade Sobral,

Delano Gobbi, Plasma bubble zonal drift characteristics

observed by airglow images over Brazilian tropical

region Brazilian, Journal of Geophysics 29(2) 239-246,

2011.

[7] T. Yokoyama, S. Fukao, Upwelling backscatter plumes in

growth phase of equatorial spread F observed with the

Equatorial Atmosphere Radar, Journal of Geophysical

Research 33, 2006.

[8] N.P. Chapagain, M.J. Taylor, K. Nielsen, M. Jarvis,

Airglow observations and modelling of F region

depletion zonal velocities over Christmas Island, Journal

of Geophysical Research 116 A02301

doi:10.1029/2010JA015958, 2011.



71

Effect of Elevated Atmospheric Carbon Dioxide on

Mangrove Growth in Controlled Conditions

Baseem M. Tamimi1, Wan Juliana Wan Ahmad1, Mohd. Nizam Mohd. Said1, Che Radziah

Che Mohd. Zain2

1School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan

Malaysia, 43600 Bangi, Selangor, Malaysia 2School of Bioscience and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600

Bangi, Selangor, Malaysia


Abstract

The objective of this research study is to determine the

effects of the occurrence of expected elevated carbon

dioxide on the growth of mangroves by the end of the

century. This study focuses on two mangrove species

(Rhizophora apiculata and Rhizophora mucronata) that

were planted in a controlled open roof green house for two

months in two groups (monoculture and mixed-culture). The

CO2 injection ratio fixed at 600 ppm was applied from 9-11

am daily. Meanwhile, the plants were watered with two

liters of tap water every 48 hours. The morphology

measurements include the height of the plant, number of

leaves and size of the leaf. The height of plants and number

of leaves were measured weekly. However, the size of

leaves was only measured at the beginning and at the end of

the study. The results showed the rapid growth of both

groups of R. apiculata. After one month, the monoculture of

R. apiculata recorded positive results, while the mixed-

culture of R. apiculata recorded higher growth rate.

However, at the end of the study, the plants in both the

cultures showed a decline in growth with extensive

yellowing of the leaves followed by defoliation. As for the

R. mucronata species, the growth rate was very slow. The

results showed that the mixed-culture of the species

recorded the most unfavorable growth rate. The results

imply that the growth of mangrove plants may face tough

challenges ahead.

1. Introduction

One significant topic in ecological research is the biological

effects of worldwide climate change, while one of the

greatest challenges currently faced is the impact of the

occurrence of elevated CO2 on climate change[1]. The

atmospheric concentration of CO2 was recorded at 270 parts

per million (ppm). This has remained almost constant for at

least 1000 years. However, the advent of the Industrial

Revolution has brought change to the atmospheric

concentration of CO2 whereby accumulation of CO2 in the

global atmosphere has accelerated to an alarming rate.

Today, the atmospheric concentration of CO2 stands at 400

parts per million (ppm), which is 40% higher than any time

in the last 20 million years [2]. The current annual rate of

CO2 is expected to increase yearly by 0.5%. So the

atmospheric concentration of CO2 would exceed 600 parts

per million (ppm) by the end of this century [3]. The

recently observed global CO2 increase has been significantly

faster than the increase anticipated by the Intergovernmental

Panel on Climate Change [4] Fourth Assessment Report

(AR4) [5]. It is widely known that carbon dioxide (CO2) is

one of the main greenhouse gases that has contributed to

global warming. In addition to having an influence on the

climate, CO2 has a direct, measurable effect on the growth

of plants. There is a tendency to plants grow better in

conditions whereby the level of CO2 is high. However,

plants are facing a future that portrays uncertain

consequences of ever-increasing concentration of CO2 [6].

Plants have shown considerable abilities to acclimatize to

long term increase in temperature as well as CO2. Therefore,

these two changes in the atmospheric composition and

climate are very likely to cause significant effects on

planetary ecosystems, because both CO2 and temperature are

vital determinants of the rate of photosynthetic in the plants

[6].

The conservation and restoration of mangroves and

associated coastal ecosystems play important roles in

climate change adaptation strategies. Mangroves are not

only valuable in climate change mitigation efforts, but they

are also influential in adaptation to changing climates. As

climate change adaptation is becoming an increasingly

important part of international development agenda [7], it

will require a lot more investment than the present

development plans for mangrove wetland. Thus, the

objective of this study is to determine the effects of different

concentrations of CO2 on the growth of two most dominant

and commonly distributed mangrove species from the

Rhizophoraceae family found in Malaysia[8].



72


Growth Facility: This research study was conducted at the

“Kompleks Rumah Tumbuhan”, in UKM, Bangi, Malaysia

(2° 55' 12.03"N, 101° 47' 2.99 E). The facility consists of

greenhouses of sizes 4 m x 8 m, a gutter height of 2.5 meters

and a control room that houses CO2 control panels. The

mangrove plant seedlings with soil were collected at the age

of three months with four leaves from Kuala Gula in Perak

(4.924012, 100.459581). These mangrove seedlings were

transplanted in box size containers (42-62cm) in a shaded

house at UKM. The mangrove seedlings were then planted

in two groups (monoculture and mixed-culture) with five

samples in each box. Two weeks later, the samples were

checked in terms of physical growth. All the plants that were

rated as in good health were transferred to the greenhouse.

The first group was put in a shaded house, where, the

mangrove plants were subjected to the natural environment.

Meanwhile, the second group was exposed to levels of

elevated carbon dioxide at 600 ppm.

Experimental Design and Growth Measurement: This

research study examines two species of mangrove plants,

namely Rhizophora apiculata and Rhizophora mucronata.

Later, two cultures, namely monoculture and mixed-culture

were assembled from each of the species to obtain

monoculture for Rhizophora apiculata and Rhizophora

mucronata besides mixed cultures for R. mucronata and R.

apiculata. These cultures were placed at two different

locations in a shaded house with ambient levels of CO2 and

inside a greenhouse of elevated levels of CO2. The rate of

elevated carbon dioxide was approximately 600 ppm inside

the greenhouse. The first injection of CO2 was carried out on

the 4th of November. This was followed by subsequent

injection of CO2 until the 9th of November (6 days). Due to a

technical failure, the injection of CO2 was stopped after the

9th of November. The injection continued on the 19th of

November after the problem was rectified and the injection

was carried on until 6th of December. Every day, the

injection of CO2 was performed from 9.30 am to 11.30 am at

600 ppm. The plants were watered with two liters tap water

every 48 hours and the plants were not given any fertilizer.

The morphology measurements (height of plants and the

number of leaves) were measured weekly. Only the size of

the leaves was measured at the beginning and at the end of

the study.

The growth parameters of the plants were measured in

order to study the response of the mangrove plants to

exposure to elevated concentrations of carbon dioxide. Each

mangrove seedling was labeled according to groups and

treatment. All the changes that took place in the health of the

seedlings were recorded qualitatively. The first quantitative

measurement was made on the 17th of October and the

second on the 24th of October. Weekly measurements were

conducted until the final measurement on the 6th of

December. All the morphological parameters were done

manually using the graphical method with tools such as the

foot rule and Log rule caliper. Then an analysis was

performed on the data of examining the changes that took

place in the growth of the mangrove plants during the eight

periods.

RGR= (Parameter week 8−Parameter week 1)

Parameter week 1Time⁄

However, the RGR (Relative Growth Rate) in the presented

in cm -1 (within 8 weeks)

Rate = consider the time factor in the calculation[9].

2.1. Data Analysis

Data were collected and were subjected to normality test

prior to data analysis for all three independent variables,

including the height of the plant, leaf number and leaf size.

To analysis, the data a two way analysis of variance was

used following mean comparison by Duncan multiple range

tests (DMRT) at 0.05 levels. Descriptive statistics such as

mean and standard error were applied. All statistical analysis

was done using SPSS ver. 19[15].

2.2. Results

2.2.1. Growth Response to CO2 by species

The propagules of the mangrove plants of species

Rhizophora apiculata and Rhizophora mucronata that were

sown into mesocosms became rooted and upright within 5

months. The mangrove seedlings in the monoculture showed

the most rapid growth. The observation showed a marked

increase in the height, the number of leaves, branching and

as well as the diameter of the stem over a period of two

months (Fig. 1). All mangroves seedling grew in

monoculture and mixed-culture (Fig. 1) showed extensive

branching and canopy development, particularly for seedling

in the greenhouse with elevated CO2. By the end of the

experiment, the plants almost exceeded 30 cm tall with over

10 leaves.

Figure 1: Effect of elevated CO2 on (A) height of plant (B)

the number of leaves for both monoculture and mixed-

culture conditions of mangrove species seedlings compared

to ambient.

Species

Species

A



73

The growth of both species was evident after the second

and third week. The growth continued in terms of the height

of the plants for the subsequent weeks. It was clear that the

growth of R. mucronata species was superior in terms of

height. As for the growth of R. mucronata species cultured

in elevated CO2, it was found that the R. apiculata species in

elevated conditions outperformed the R. apiculata in

ambient conditions. Also, the performance of mixed culture

of R. apiculata in both elevated and ambient conditions was

better than the performance of monoculture of R. apiculata

in both elevated and ambient conditions. The comparison

between the performance of monoculture of R. mucronata

showed that the culture in ambient condition

outperformance the culture in an elevated condition. As for

the comparison between performance of monoculture and

mixed culture of R. mucronata in both ambient and elevated

conditions, it was found that the latter was better the former.

From these findings, it can be concluded that the

performance of mixed culture for both species (R. apiculata

& R. mucronata) in both conditions (ambient &elevated)

bear positive responses to growth rate due to interactions of

the species in the culture. The results also showed that the

two mangrove species were able to adapt to elevated levels

of carbon dioxide after a duration of a few months.

Table 1: Effect of elevated CO2 on height, leaf number and

leaf size of mangrove species seedlings in the elevated and

ambient greenhouse.

Note: Different alphabet in each column denotes a significant

difference using t-test at (p< 0.05).

2.2.2. Growth Pattern

In the statistical analysis of the average leaf area, the results

showed two different significant interactions (Table 1.) First,

there were interspecific differences in the ratio of the

average leaf area. The results pointed out that the R.

mucronata typically had a greater average leaf area than the

R. apiculata. Secondly, there were interactive effects of

salinity and humidity in the average leaf area. The leaves of

the R. apiculata monoculture grown in the greenhouse with

elevated CO2 were affected largely through interactions

between elevated CO2, low salinity and humidity. These

interactions bear a significant impact on the size of the

leaves with noticeable wilting and yellowing of leaves. As

for the mixed culture of the R. apiculata species, the plants

were not affected significantly. This indicated the adaptation

of plants in their resistance to changing weather conditions.

As for the R. mucronata species, the response of growth of

the leaves of the monoculture was very slow with no

evidence of the effects of interactions among elevated CO2,

low salinity and humidity. On the contrary, the R.

mucronata of mixed culture showed significant effects in

elevated CO2 levels where by the interactions affected the

size of the leaves. Therefore, it is apparent (Fig. 2) that

elevated CO2 levels and low salinity of (0-5 ppt) effects to

the growth mangrove species.

Figure 2: Effect of elevated CO2 shows the relative growth

rate (RGR) on leaf size of mangrove species seedlings

elevated (600 ppm) and ambient.

3. Discussion

This research study aims to compare the growth traits of two

species of mangroves, R. apiculata and R. mucronata. The

outcomes of the findings would be utilized for identification

and recommendation of the better species. In this research

study, the following results were obtained. First, the

monocultures for R. apiculata and R. mucronata were

affected by the increase of CO2. It was noted that the growth

rate of the R. apiculata showed a positive increase. On the

other hand, the growth rate of the R.mucronata showed a

negative outcome. Secondly, the mixed-culture was affected

by the increase of CO2, with a positive outcome for the

mixed-culture of the R. apiculata species. It was noted that

the R. apiculata species grow better in a mixed situation.

Third, by comparing elevated and ambient location, it was

noted that the R. apiculata species grown in the greenhouse

showed a faster growth rate than the R. apiculata species

grown outside the greenhouse. In the case of R. mucronata

species, the results showed that the R. mucronata species

grown outside the greenhouse (without CO2 enrichment)

showed a higher growth rate than the species grown inside

the greenhouse (with CO2 enrichment).

Like many halophytes, the growth of mangroves is

enhanced under moderate saline conditions. However, the

dominant mangrove species, R. apiculata is found in

abundance in fewer saline sites along estuarine floodplains.

With an increasing aridity in the seasonally dry tropics, the

0

20

40

60

R.A. mon R.M. mon R.A MIX R.M MIXRel

ativ

e G

row

th R

ate

(RG

R)

Species

elevated l. size ambient l. size

Species Location Height

(cm)

Leaf

number

Leafsize

(cm2)

R. apiculata

Monoculture

Elevated 31.98 ±

0.22d

12.96 ±

0.14a

8.01 ±

0.23f

Ambient 29.40 ±

0.22e

11.60 ±

0.14b

17.00 ±

0.23d

R.mucronata

Monoculture

Elevated 52.68 ±

0.22d

12.02 ±

0.14b

22.68 ±

0.23c

Ambient 55.34 ±

0.22b

14.02 ±

0.14a

25.33 ±

0.23b

R. apiculata

Mixculture

Elevated 33.52 ±

0.22c

13.73 ±

0.14a

9.50 ±

0.23f

Ambient 31.06 ±

0.22d

11.63 ±

0.14b

12.05 ±

0.23e

R.

mucronata

Mixculture

Elevated 55.07 ±

0.22b

12.00 ±

0.14b

19.73 ±

0.23b

Ambient 58.66 ±

0.22a

14.03 ±

0.14a

41.42 ±

0.23a



74

growth of R.apiculata has become increasingly restricted to

habitats where salinities are relatively low throughout the

year[11]. The findings of that both agree the growth of the

mangrove species R. apiculata is favorable in low salinity.

The findings in this study showed that the growth rate for R.

apiculata inside the greenhouse is better than that of the R.

mucronata species. The results of these two studies clearly

showed these are inter-specific differences in terms of

changes in the leaf area and/or net assimilation rate that

brings about changes the relative growth rates with

decreasing salinity levels. However, the similarity of these

studies is that a decrease in the rate of net assimilation

accounted for much of the decrease in the growth rate with

decreasing salinity levels. Hence, the implication of a strong

correlation between the rates of net assimilation and growth

is that the levels of carbon restrict the growth of mangrove

species in saline conditions. If limited growth is due to the

effects of reduced stomata conductance on carbon

assimilation, then the enhancement of growth can be

expected under an elevated CO2 [11]. However, plants that

grow slowly due to salinity stress may be inherently slow to

respond to elevated CO2 levels. This is in line with findings

by Farnsworth [12] who stated that the effects of elevated

CO2 on the growth of R. mucronata species in 100%

seawater only became evident 8 months after the planting of

the species. These results were consistent with the findings

in other studies that compared the growth of closely related

species, i.e. Ceriops australis and C. tagal [11]. Clearly, the

increasing tolerance for salt occurred at the expense of

growth. Therefore, most species grow best under low

salinity conditions. Another finding pointed out that under

the most favorable growth conditions, the less salt-tolerant

species, R. apiculata, had a greater number of branches and

leaves in comparison to the more salt-tolerant species, R.

mucronata. Finally, the results demonstrated differences in

the behavior of some plants in the face of environmental

challenges and changes depending on other species in

nature. It is rather common to see the same species

coexisting with other species in nature as they adapt to each

other for survival.

4. Conclusion

Generally, this research study showed that the rising CO2

levels have a great impact on the growth rate. This is

because different species of mangrove respond differently to

varying levels of CO2. The differences in the growth rate in

elevated conditions in CO2 may further increase disparities

in the forest structure and productivity of mangrove species

found in low and high salinity sites. It is evident that the

varying growth rate of mangrove species that may occur at

salinities near the limits of tolerance of a particular species

is unlikely to have a significant effect on the ecological

patterns. Nevertheless, the rapid responses to elevated

carbon dioxide levels during the early phases of growth as in

seedling establishment may be important determinants in

competition between species, as well as regeneration of

species.

Acknowledgements

We gratefully acknowledged the Sime Darby Foundation for

greenhouse facility, research fund from

FRGS/1/2014/STWN10/UKM/02/1 to fund this project. The

authors also thank staffs of PPSSSA, FST, Universiti

Kebangsaan Malaysia for their contributions in completing

this project.

References

[1] T. R., Cavagnaro, R. M. Gleadow, and R. E.. Miller,

Functional Plant Biology, 38, 87–96, 2011.

[2] D. B.,Andrew, A.A.,Elizabeth, C. J., Bernacchi, R.,

Alistair, P.L. Stephen, and R.O.Donald,. Journal of

Experimental Botany. 60, 2859– 2876, 2009.

[3] D., Schimel, D., Alves, I., Enting, M., Heimann, F.,

Joos, D.Raynaud, and Wigley, T.. IPCC, 65-86, New

York, Cambridge University Press, 1996.

[4] R.K.Pachauri,A. Reisinger,IPCC, Geneva, Switzerland,

2007.

[5] P. J., Hanson, A.Classen, and L. Kueppers,. Biological

and Environmental Research, 2008.

[6] R.F.Sage, and D.S.Kubien,..Plant,Celland

Environment, 30, 1086–1106, 2007.

[7] S. Crooks, D. Herr, J. Tamelander, D.Laffoley, and

J.Vandever. World Bank, 2011.

[8] W.A.Wan Juliana, M. S. Razali. and A. Latiff,

Springer, 23-36, 2014.

[9] W.A. Hoffmann, and H. Poorter,. Annals of Botany, ,

90 (1): 37. 2002.

[10] A. Bryman, and D.Cramer.: A guide for social

scientists: Rout ledge, 2012.

[11] M.C., Ball, M.J. Cochrane, and H.M. Rawson,. Plant,

Cell and Environment, , 20, 1158–1166.8,1997.

[12] E.J., Farnsworth, A.M. Ellison, and W.K. Gong,. Oecologia, ,

108, 599–609, 1996.



75

Observations of Lightning and Background Electric Field in Antarctica Peninsula

Norbayah Yusop1, 2, Mardina Abdullah1, 3, Mohd Riduan Ahmad2,

1Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor 2Atmospheric and Lightning Research Lab, Centre for Telecommunication Research and Innovation, Faculty of Electronics

and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka 3Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti

Kebangsaan Malaysia

*corresponding authors, E-mail: [email protected], [email protected], [email protected]

Abstract

This paper presents observations of lightning occurrence and

the associated atmospheric electric field mill in Antarctic

Peninsula. The measurement was conducted at Carlini Base,

Argentina (CARL: 62o 24''S, 58o 54"W) between February

and April 2017 using Boltek LD-350 lightning detector and

EFM-100 electric field mill. A total of 109,072,753

individual lightning pulses have been detected within three

months measurement campaign. Cloud lightning pulses were

counted to be around 98% from the total lightning pulses

detected while only 2% were cloud-to-ground (CGs)

lightning pulses. The electric field record has peaked 4 times

at around –20.480 kV/m on the 9th, 11th, 12th and 26th

February 2017.

1. Introduction

Lightning is an electrical discharge that occurs during a

thunderstorm. It can occur within a cloud called intra-cloud

(ICs), between two clouds called cloud-to-cloud (CCs) but

this is less and most common is between cloud-to-ground

(CGs) lightning. In general, the thundercloud charge

structure contains of two main charge center positive on top

of negative charge and one pocket positive charge located at

the base of the cloud [1]. Cloud-to-ground (CG) lightning

lowered down electrical charges from thundercloud to the

surface of the Earth. It can be categorized to four major types

of CG lightning known as downward negative, downward

positive, upward negative and upward positive lightning. But

the most common type of the CG lightning is downward

negative accounting for 90% and less than 10% is downward

positive lightning [2,3]. On average, the negative CG

lightning produces a sequence of three to five return strokes

and sometimes the return strokes occurred as short as 1 ms

or less in the same lightning channel [4]. While the cloud

lightning is a lightning discharge developed inside the

confines of the cloud and never hits direct to the Earth

surface. Two types of cloud lightning are intra-cloud (IC) and

cloud-to-cloud (CC) discharge.

In Antarctica, more than 70% of the Earth’s freshwater

are configuring by ice sheet and due to thick of ice sheet

make only small moisture falls from the sky over Antarctica

[5]. This makes polar region becomes one of the challenging

regions for conducting research due to the geographical

remoteness and climate extremes different from other

regions in the world. One of the continental Antarctica which

frequent receive rainfall in summer is Antarctic Peninsula.

The coastal area of the peninsula receives an average

precipitation of 203 mm per year [6]. It has experienced rapid

climate warming during last 50 years with the atmospheric

temperature increases considerably greater than others

continent [7]. The factor involves on the warming is due to a

local strengthening of circumpolar westerly winds driven by

changes in the summer Southern Hemisphere Annular Mode

(SAM) in response to anthropogenic forcing [8]. This makes

lightning phenomena interesting topic to be studied in

Antarctica.

In this work, we report for the first time the observations

of lightning occurrence and background electric field

between February and April 2017 in Antarctica Peninsula.

The location of the lightning occurrences and types of

lightning have been observed using the lightning detector

(LD-350) and the background electric field has been

recorded by using atmospheric electric field monitoring

system (EFM-100).

2. Methodology

2.1. Instrument Setup

The lightning detector (LD-350) and atmospheric electric

field monitor (EFM-100) installed at Carlini Base Station in

mid of January 2017. Both devices used for long and short

range detection of storm respectively. The LD-350 detects

strikes up to 300 miles (480 km) away and sometimes it can

go broad as far as 600 miles (960 km) cause by the strong

storm. It also able to plot the location of strikes occurs from

the station and provide a relevant strikes information such as

time, bearing, distance and coordinate. The capability to

capture the exact time of receives lightning with the accuracy

of 100 ns by using the combination of the LTS-3 timestamp

card (installed) and GPS receiver. While the EFM-100 used

to monitor and alert for weather conditions that precede

lightning. The high accuracy of lightning detection is 0.1 s

and able to detect strikes from 0 to 24 miles (0 to 38 km)

away.

mailto:[email protected]




76

2.2. Data Processing

In this study, we used two types of LD and EFM data. Data

was collected from February until April 2017 for analysis.

Both data need to be converted before it can be interpreted

using MATLAB software while the LD data need to be

converted using the windows command line (DOS) before it

can be read in MATLAB. One minute average was used for

the EFM data to closely study the variation of electric field.

The process flow to analyse the data has shown in Figure 1.

Figure 1: The process flow of data analysis.

3. Preliminary Result and Discussion

In this study, we observed the individual radiated impulse

from the lightning discharge which is only the maximum

pulses will be detected by lightning detector (LD-350) and

electric field produced by the atmospheric electric field mill

(EFM-100) installed at Carlini Base station in Antarctic

Peninsula.

Table 1 show the number of pulses and electric field

recorded from both equipment’s within three months

observation. The total number of pulses detected increase

significantly around 16,519,357 from February to April 2017

and the maximum vertical electric field observed from -0.013

to -20.480 kV/m. The atmospheric electric field was found

higher in February around -20.48 kV/m during the summer

season compared to March and April in autumn season

around -19.654 kV/m and -16.575 kV/m respectively. It was

expected that the value maximum due to the intense of

lightning activity occurred nearby the station. In Antarctica

Peninsula, rainfall was more frequent to be received due to

the depressions come in from the west bringing cloud

precipitation and winds make it falls in summer season

compared to other seasons in polar region.

Table 1: The data on the pulses and electric field.

Month Total of pulses Electric Field

(kV/m)

February 28,940,762 - 0.249 to -20.480

March 34,671,872 - 0.055 to -19.654

April 45,460,119 - 0.013 to -16.575

Figure 2(a) shows the LD screen display majority of the

strikes was come from three different location at northwest

(top left), centre and southeast (bottom right) of the station on

16 February 2017. The types of lightning detected from the

pulses was 3% for the cloud-to-ground (CG) discharge and

the rest of 97% was cloud lightning discharge. All the

distance of the strikes was detected from 0 to 920 miles. From

the screen captured by the EFM-100 show that lightning was

detected nearby the station as shown in Figure 2(b).

(a) (b)

Figure 2: The screenshot of (a) LD-350 and (b) EFM-100

displayed on 16 February 2017.

The electric field recorded on 16 February 2017 were

analysed and illustrated in Figure 3 which is similar to the

signal captured by EFM-100 in Figure 2(b). It was clearly

shown that there was lightning occurs below 30 km with

intense electric field from the station. We found a total of

364,479 pulses recorded by the lightning detector (LD-350)

between 06:00 LT to 14:00 LT and the distance of the pulses

travel as far as 908 miles. Most of the bearing of the pulses

detected origin from centre of the station. Two electric field

found around -4.289 kV/m at 09:00 LT and -8.605 kV/m at

12:00 LT pointing downward.



77

Figure 3: Electric field recorded on 16 February 2017.

Figure 4 show the total number of pulses, type of lightning

and electric field observed from February to April 2017. It

was found that the number of pulses gradually increased in

February 2017 before going down on 22 February 2017 and

keep increasing back until decrease end on 28 February 2017

during summer season. While the pulses was consistent on

March and April 2017 except on 12 March, 13, 20, 28, 29 and

30 April 2017 during autumn season. There have a missing

for the LD data from 1st to 11th March 2017 which not

displayed any values in Figure 4(a) and (b). The cloud

lightning (CF) discharges was dominated compare to CGs

along the observation in the percentage of 98% and 2%

respectively. From Figure 4(c) show that the electric field was

much more closely disturbed by lightning activity at mid of

February 2017.

Figure 4: The (a) total number of pulses, (b) type of lightning

and (c) electric field from February to April 2017.

4. Conclusion

Almost 2 million of lightning pulses have been recorded by

the lightning detector (LD-350) per day at Carlini Base in

Antarctic Peninsula. This made a total of 109,072,753

lightning pulses have been observed within three months

observation. The atmospheric electric field monitor (EFM-

100) produced higher electric field reading up to -20.480

kV/m when there a lightning occurred especially during

summer season on February 2017 compare to March and

April 2017. Lightning was detected by the LD-350 occur

below 30 km from the station on 16 February 2017 and this

is first time lightning was discovered which clearly refutes

the classical hypothesis that lightning flashes are rare

phenomena in Antarctica. Most of the lightning strikes

detected was mainly occurs from northwest, centre and

southeast from the Carlini Base station. Classifying on the

cloud to ground (CG) lightning flash and cloud flash from

the lightning pulses will be analysed for future research.

Acknowledgements

This research is funded by the Ministry of Science,

Technology and Innovation Malaysia (MOSTI) through the

Flagship Program under ZF-2014-016 grant. The authors

would like to thank to Dr. Wayan Suparta and the Instituto

Antartico Argentino (IAA) for the expedition to Antarctica

at Carlini Base during the summer campaign 2016/2017,

Universiti Teknikal Malaysia Melaka and the Ministry of

Higher Education for their moral, operational and financial

support.

References

[1] Joseph R. D. and Martin A. U, The physics of lightning,

Physics Reports 534 (2014) 147–241, 2014.

[2] Rakov, V.A., Uman, M.A., 2003. Lightning: Physics and

Effects. Cambridge University Press.

[3] Akinyemi M. L., Boyo A. O., Emetere M. E., Usikalu M.

R. and Olawole F. O., Lightning a Fundamental of

Atmospheric Electricity, International Conference on

Environment Systems Science and Engineering, 2014.

[4] Rakov VA. Lightning phenomenology and parameters

important for lightning protection, 9th International

Symposium on Lightning Protection, 2007.

[5] Alvarinho J. L., Past, present and future climate of

Antarctica, International Journal of Geosciences, 2013.

[6] P. Uotila, Lynch A. H., Cassano J. J. and Cullather R. I.,

“Changes in Antarctic Net Precipitation in the 21st

Century Based on Intergovernmental Panel on Climate

Change (IPCC) Model Scenarios,” Journal of

Geophysical Research, Vol. 112, No. D10, 2007.

[7] Steig, E., D. Schneider, S. Rutherford, M. Mann, J.

Comiso, and D. Shindell, Warming of the Antarctic ice-

sheet surface since the 1957 International Geophysical

Year, Nature, 457, 2009.

[8] Marshall, G., Orr A., Van Lipzig N., and King J., The

impact of a changing Southern Hemisphere Annular

Mode on Antarctic Peninsula summer temperatures, J.

Clim., 19, 5388–5404, 2006.



78

Determination of the GPS Satellite Elevation Mask Angle for

Ionospheric Modeling the Ionosphere over Malaysia

Siti Aminah Bahari1, 2, Mardina Abdullah1,2, Zahra Bouya3,

Tajul Ariffin Musa4

1Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering and Built Environment,

Universiti Kebangsaan Malaysia, Selangor, Malaysia 2Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia, Selangor, Malaysia

3Space Weather Services, Australian Bureau of Meteorology, Sydney, Australia 4Department of Geoinformation, Faculty Geoinformation & Real Estate, Universiti Teknologi Malaysia, Johor, Malaysia


Abstract

Signals from Global Positioning System (GPS) satellites at

low elevation masks angle are often excluded from a GPS

solution because they experience considerable ionospheric

delays and multipath effects. Their exclusion can degrade

the overall satellite geometry for calculations, resulting in

large error. This paper presents the effects of choice of

elevation mask angle in modeling the regional ionosphere

over Malaysia. Spherical cap harmonic analysis (SCHA)

was used for modeling and mapping of the regional

ionospheric TEC over Malaysia. Ionospheric pierce point

(IPP) of satellite was converted into spherical coordinate

system. The Vertical Total Electron Content (VTEC) was

calculated and mapped based on the SCHA. Utilizing the

myRTK network over Malaysia, GPS data owned by

JUPEM was processed and used to map the TEC. The result

shows that the elevation mask angle of 30° is suitable to be

used as a cut off elevation mask angle for regional

ionospheric modeling over Malaysia.

1. Introduction

The ionosphere affects modern technologies such as civilian

and military communications, navigation systems and

surveillance system. For many communication and

navigation systems, this increases because the systems use

signals transmitted to and from satellites, which must pass

through the ionosphere. For the most reliable

communication and navigation, it is necessary to correct the

signals for effects imposed by the ionosphere.

It is difficult to model the TEC with high precision

because it depends on the sunspot activity, seasonal, diurnal

and spatial variations and the line of sight which includes

knowledge of the elevation mask and azimuth of the

satellite etc. Furthermore, horizontal gradients of electron

density make TEC modelling and prediction more difficult.

Slant TEC is measured at different elevation mask

angles, usually, the vertical TEC (VTEC) or simply the

TEC is modeled. The choice of elevation mask angle in

modeling the ionosphere plays an important role since the

determination of TEC also depends on the elevation mask

angle.

Different elevation mask angles have been used in a

number of studies such as:DasGupta et.al.(2006) and

Noguiera et al.(2015) used 30° elevation mask angle in his

analysis [1-2]. Seif et al. (2015) using 15° of elevation mask

angle [3]. While Buhari et al. (2017) and Idrus et. al. (2013)

is using 35° of elevation mask angle in order to reduce the

multipath error [4-5]. Elmunim et al. (2017) and Akir et. al.

(2017) used 20°, while Hussein et. al. (2011) used 40° of

elevation mask angle [6-8]. Based on the previous studies,

the range of elevation mask angle is between 15° – 40°.

The aim of this paper is to discuss the choice of

elevation mask angle by comparing the VTEC for different

elevation mask angle and their root mean square error.

Ionosphere over equatorial are more affected directly by the

solar activity. In order to avoid the impact of solar activity

on the ionosphere, low solar activity was considered. This

study uses the data of 1 January 2010, where the Sun is

considered in its low activity.


In this study, 78 GPS receiver stations owned by the

Department of Mapping and Surveying Malaysia (JUPEM)

were used. Figure 1 shows the MyRTKnet network over

Malaysia.

Figure 1:MyRTKnet network over Malaysia [10].



79

2.1. Total Electron Content

GPS signals are broadcast on two L-band frequencies, f1 =

1575.42 MHz and f2 = 1227.60 MHz. The signals

transmitted from satellites to the receivers on Earth

experience a phase delay and pseudorange advanced when

propagated through the ionosphere. The effect on

pseudorange and carrier phase is the same but opposite in

sign. The ionospheric delay in the GPS signals is

proportional to the total number of electrons along the

signal path and is known as TEC. The TEC can be tracked

by differencing the phase delays Lt = L1-L2 [11].

TEC can be defined as equation 1 below:

TEC (1)

where is the electron density along the signal path,

while a minus sign is used for calculating the range error

using pseudorange (code) data. TEC is often expressed in

units of TEC units (TECU), where 1 TECU equals to 1016

electrons/m2. Equation 1 represents the slant TEC. To

achieve independence from the elevation mask angle, slant

measurements have to be projected to the VTEC and vice

versa using a mapping function. This is commonly done by

assuming a spherically stratified single layer ionosphere.

This simple assumption provides the possibility of locating

the measurement at the IPP of the radio link with the

ionospheric layer. The slant TEC at a given point in the

ionospheric shell is related to the equivalent vertical TEC at

that point by

TEC (t) = (2)

where

is the slant factor at satellite i,

is the elevation mask angle of the GPS

satellite,

is the vertical TEC, and

is the receiver and satellite bias.

The inversion from slant TEC to vertical TEC is

available when the satellites are at zenith, = 0. The zenith

angle of the satellite must be taken into account since the

path length in the ionosphere varies with changing zenith

angle. The slant factor or also known as model mapping

function can be written as

(3)

with

(4)

where

: is the Earth’s mean radius, 6371 km,

: is the height of maximum electron density,

: zenith angles at the receiver site, and

: zenith angles at the IPP.

Based on previous research, the value of at the

equatorial region ranges from 300 – 450 km. In this study,

the value of was set to 350 km.

Assuming that the geographic latitude and longitude of

the receiver are known, the coordinate of the IPP

can be obtained based on the observed azimuth and

elevation mask angle to the tracked satellite and the single

layer model. The latitude of the IPP can be calculated using

equation 5 below:

(5)

where

: is the latitude of the GPS receiver (radian),

: angle subtended at the center of the Earth

between the user position vector, and

: azimuth angle of the satellite at the user’s

position (radian).

angle is calculated as follows:

(6)

Longitude of IPP, can be calculated using equation 7

below:

(7)

The latitude and longitude of the IPP were then converted

into the spherical coordinate for further analysis.

2.2. Spherical Cap Harmonic Analysis

of colatitude and longitude of IPP defined

over a sphere can be represented as an expansion of

spherical harmonics:

(8)

where

: associated Legendre function of non-

integer degree and integer order

,

: is the maximum degree-index,

: are the constant fitting coefficients

for each degree-index/order pair.

Details on SCHA can be found in Haines (1988), and Fiori

et al. (2010) [12-13].



80


For the Malaysian region, equal to 3 was used. In the

spherical cap harmonic model, the coefficient

represents the average of the regional TEC [9]. In order to

evaluate the performance of the elevation mask angle,

for different elevation mask angles were plotted and

compared. Two types of analysis were carried out where (1)

the location of IPP at the same point were averaged and (2)

the location of IPP at the same point was not averaged.

Analysis based on different elevation mask angles ranging

from 20° to 40° was used. Figure 2 (a-b) shows an example

of for the elevation mask angle of 30° for 1st January

2010.

(a) Averaged data at the same point with elevation mask

angle of 30°

(b) Non-averaged data at the same point with elevation

mask angle of 30°

Figure 2: SCHA coefficient, for 1st January 2010

In order to identify which elevation mask angle is suitable

for ionospheric modeling over Malaysia, the accuracy of the

method was compared using root mean square error

(RMSE) as shown in equation 9 below:

(9)

where is number of measurements used in this study,

is the actual measurement of VTEC and

is the VTEC from the model.

The result is presented in Table 1. Based on the RMSE,

the elevation mask angle of 40° has the lowest error;

however, the average graph is not well presented for the

regional TEC compared to other elevation mask angles.

Based on Figure 2 and Table 1, the result whereby the data

have been averaged is more likely to represent the variation

of TEC over Malaysia compared to method (2).

Table 1: Root mean square error for different elevation

mask angles

No Elevation

mask

angle

Average

Data

No Average

Data

1 20 0.58 0.70

2 25 0.53 0.59

3 30 0.48 0.51

4 35 0.44 0.46

5 40 0.44 0.44

Based on the result above and also the analysis carried

out by Otsuka et al. [11], elevation mask angle of 30° with

slant factor of 1.73 is suitable for modeling the ionosphere.

High elevation masks can typically reduce the multipath

and ionospheric delay, in addition to reducing the number

of satellites in view. Relying on too few satellites can make

it difficult to model and map the regional ionosphere [14].

Due to that, average data with elevation mask angle of 30°

was chosen for modeling the regional ionosphere over

Malaysia.

4. Conclusion

This paper has investigated the elevation mask angle that is

suitable for modeling the regional ionosphere over

Malaysia. Based on the result, an elevation mask angle of

30° with averaged data produced the smallest RMSE and

similar pattern of VTEC variation of Malaysia. Further

analysis using data from different solar activities should be

performed.

Acknowledgements

The GPS data were collected from the Department of

Survey and Mapping Malaysia (JUPEM). This work was

supported by GUP-2015-052 University Grant (GUP) made

available through Universiti Kebangsaan Malaysia.

References

[1] A. DasGupta, A. Paul, S. Ray, A. Das, S.

Ananthakrishnan, Equatorial bubbles as observed with

GPS measurements over Pune, India, Radio Science

41: RS5S28, 2006.



81

[2] P.A.B. Nogueira, J.R. Souza, M.A. Abdu, R.R. Paes, J.

Sousasantos, M.S. Marques, G.J. Bailey, C.M.

Denardini, I.S. Batista, H. Takahashi, R.Y. C. Cueva,

S.S. Chen, Modeling the equatorial and low-latitude

ionospheric response to an intense X-class solar flare,

Journal of Geophysical Research : Space Physics 120 :

1-12, 2015.

[3] A. Seif, R.T. Tsunoda, M. Abdullah, A.M. Hasbi,

Daytime gigahertz scintillations near magnetic equator:

relationship to blanketing sporadic E and gradient-drift

instability, Earth, Planets and Space 67: 177 – 190.

[4] S.M. Buhari, M.Abdullah, T. Yokoyama, Y. Otsuka,

M. Nishioka, A.M. Hasbi, S.A. Bahari, T. Tsugawa,

Climatology of successive equatorial plasma bubbles

observed by GPS ROTI over Malaysia, Journal of

Geophysical Research, 122 (2) : 2174 – 2184, 2017.

[5] I.I. Idrus, M. Abdullah, A.M. Hasbi, A. Husin, B.

Yatim, Large-scale traveling ionospheric disturbances

observed using GPS receivers over high-latitude and

equatorial regions, Journal of Atmospheric and Solar-

Terrestrial Physics, 102 : 321-328, 2013.

[6] N.A. Elmunim, M. Abdullah, A.M. Hasbi, S.A. Bahari,

Investigation on the implementation of the Holt-Winter

method for ionospheric delay forecasting, Advanced

Science Letters 23 : 1325 – 1328, 2017.

[7] R.M. Akir, M. Abdullah, K. Chellapan, A.M. Hasbi,

S.A. Bahari, Comparative study of TEC for GISTM

stations in the Peninsular Malaysia region for the

period of January 2011 to December 2012, Advanced

Science Letters 23 : 1304 – 1309, 2017.

[8] A. Husin, M. Abdullah, M.A. Momani, Observation of

medium-scale traveling ionospheric disturbances over

Peninsular Malaysia based on IPP trajectories, Radio

Science, 46: RS2018, 2011.

[9] J. Liu, R. Chen, J. An, Z. Wang, J. Hyyppa, Spherical

cap harmonic analysis of the Arctic ionospheric TEC

for one solar cycle, Journal of Geophysical Research :

Space Physics, 119 : 601 – 619.

[10] Jabatan Ukur dan Pemetaan Malaysia (JUPEM),

www.jupem.gov.my, [access : 14 September 2017].

[11] Y. Otsuka, T. Ogawa, A.Saito, T. Tsugawa, S. Fukao,

S. Miyazaki, A new technique for mapping of total

electron content using GPS network in Japan, Earth,

Planets and Space 54 : 63-70, 2002.

[12] G.V. Haines, Computer programs for Spherical Cap

Harmonic Analysis of potential and general fields,

Computer and Geosciences, 14(4) : 413-447, 1988.

[13] R.A.D. Fiori, D.H. Boteler, A.V. Koustov, G.V.

Haines, J.M. Ruohoniemi, Spherical cap harmonic

analysis of Super Dual Auroral Radar Network

(SuperDARN) observations for generating maps of

ionospheric convection, Journal of Geophysical

Research, 115 : A07307, 2010.

[14] J.A.R. Rose, J.R. Tong, D.J. Allain, C.N. Mitchell, The

use of ionospheric tomography and elevation mask to

reduce the overall error in single-frequency GPS

timing applications, Advances in Space Research, 47 :

276-288, 2011.



82

A New Wide Negative Refractive Index Meta-atom for Satellite

Communications

Mohammad Jakir Hossain1, Mohammad Rashed Iqbal Faruque1, Mohammad Tariqul Islam2

1Space Science Centre (ANGKASA), Institute of Climate Change (IPI), Universiti Kebangsaan Malaysia, 43600 Bangi,

Selangor, Malaysia 2Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor,

Malaysia


Abstract

In this paper, a new wideband negative refractive index

meta-atom structure was designed and simulated. The

suggested structure displays epsilon-negative, mu-negative

and wide refractive index negative at the resonant frequency

that was indicated X-band microwave regime. An analysis

and comparison of the different structures were performed

that follows better effective medium ratio (EMR) for multi

band operations in microwave regime. The FDTD based

commercially available CST microwave studio was adopted

to investigate the design scattering parameters. The results

demonstrate the double negative characteristics and wide

negative refractive index (7.26-14.33) GHz of the unit-cell

and arrays over X- and Ku-band application which leads the

long-distance radio telecommunication like satellite

communications.

Keywords: Effective medium ratio; Meta-atom;

Miniaturized; satellite communications;

1. Introduction

Metamaterials are attractive engineered composite materials

that can manipulate the electromagnetic wave at surprising

manners. Negative permittivity properties of the materials

could be found, but to make engineered material with

negative permeability is still a challenging work. US

physicists D. R. Smith et al. achieved success to develop a

new man-made meta-atom with peculiar characteristics,

namely negative permeability and permittivity practically in

2000 [1]. Composite material can be mentioned double

negative (DNG) and negative index metamaterial (NIM)

when the electric permittivity (ε) and magnetic permeability

(μ) of both of them show negative properties that are not

naturally available. This kind of negative characteristic

materials is called left-handed media (LHM), backed wave

media (BW media) and negative index media (NIM) [2].

Metamaterial structures of different types such as U-shape

and ∆-shape etc. suggested to different applications. On the

other hand, some of them are applicable for X-band

microwave regime namely, satellite communications [3-4].

The ratio between the wavelengths to unit-cell dimension is

termed as EMR which is important to design miniaturized

meta-atom. Discovering the practical meta-atom, researchers

are paying more attention at the multi-band meta-atoms and

arrays of the meta-atoms with high effective medium ratio

and wide negative index bandwidth. On the other hand, few

structures have been focused on constructing such

metamaterials [5-7]. In long distance radio

telecommunication like satellite communications, a new

double negative metamaterial unit-cell structure analysed

whereas the design structure of unit-cell was very big [8].

Hossain et al. recommended a design structure of 12×12

mm2 “double C-shape” metamaterial for multi-band

operation and reported EMR was 7.44 with negative

refractive index from 11.304 to 13.796 GHz [9]. The

proposed new wideband negative refractive index meta-

atom dimension is 10 mm × 10 mm × 1.6 mm which

includes all structural parameters to fit the design inside the

substrate area. In this paper, the circular shape meta-atom

exhibits, multi resonance at L-, S-, C-, X-, and Ku-bands

with wider bandwidth 1.96-2.01 GHz, 3.73-4.16 GHz, 6.45-

7.13 GHz, 8.77-10.77 GHz, and 13.03-13.83 GHz

respectively. The negative indices of the proposed meta-

atom are 5.64-7.36 GHz (1.72 GHz bandwidth), 7.9-13.44

GHz (5.54 GHz bandwidth), and 14.09-15.65 GHz (1.56

GHz bandwidth), that are a larger from [10, 11]. To compute

the scattering parameters, namely the reflection coefficient

(S11) and transmission coefficient (S21), the commercially

available CST electromagnetic simulator 2014 was used.

The effective medium parameters, namely effective

permittivity, permeability and refractive index were also

retrieved using well-established Nicolson-Ross-Weir

method.

2. Methodology

2.1. Design of Negative Refractive Index Meta-atom

A combination of multiple concentric split ring resonators

was utilized to achieve unconventional characteristics of

metamaterials that were usually not found in nature. The

proposed meta-atom unit cell and structural parameters are

shown in Fig 1(a). The dimension of the substrate is

10×10×1.6 mm3 where the substrate material is low cost




83

(a)

Figure 1:(a) Simulation setup, (b)Boundary condition of

proposed structure.

(b)

port1

port2

PEC

PMCFigure 2: Simulated S-parameters curve of meta-

atom structure.

Figure 3: Effective Refractive index values of real

and imaginary curves of meta-atom.

FR4 lossy material. All elements of the resonators are made

of copper with conductivity of 5.8×107 S/m and the

thickness of copper resonators are 0.035 mm that is printed

on a substrate with standard effective permittivity ɛ= 4.3 as

well as loss tangent δ = 0.025. The width of each ring is 0.7

mm and the split of each ring is 0.4 mm. The inner radius of

the each CSRR along the x-direction are 4.2 mm (outer one),

3.2 mm (middle one), and 2.2 mm (inner one), respectively.

In this paper, the finite-difference time-domain method

based CST simulator is adopted to examine this design

structure. The electric field and magnetic field have been

polarized along the x-axis and the y-axis, respectively,

whereas z-axis has been utilized for electromagnetic wave

travelling. The boundary conditions of perfect magnetic

conductor (PEC) and the perfect electric conductor (PMC)

are utilized along the x-axis and y-axis, individually, and

two waveguide ports are placed on the positive and negative

z-axis. The simulation setup and schematic diagram of the

proposed design is illustrated in Fig 1(a) and (b). To

determine the transmission coefficient and the reflection

coefficient in simulation a frequency domain solver is

utilized. The impedance matching was set to fifty ohms. The

frequency range 1-15 GHz was used to simulate the design

of meta-atom.

2.2. Effective Scattering and Medium Parameters

Calculation

The Nicolson-Rose-Weir (NRW) method is utilized to

determine the medium parameters like effective

permeability (µeff) and permittivity (εeff) from simulated

scattering parameters such as transmission coefficient (S21)

and reflection coefficient (S11). The direct refractive index

method is applied to calculate the effective refractive index

(n) from the simulated complex S-parameters [12].


There are many methods that are used to extract effective

parameters, namely Nicolson-Rose-Weir (NRW) method,

Direct-Retrieval method, Transmission–Reflection (TR)

method, and Direct Refractive Index, etc. The real and

imaginary both values of the refractive index are justified to

characterize the proposed meta-atom. In this paper, meta-

atom structure and various elevation angles (00, 900, and

1800) of different split rings, for instance, inner, middle and

outer rings of meta-atom have been analyzed.

3.1. Meta-atom Structure Analysis

The simulation result of multiple concentric miniaturized

meta-atom has been offered. The simulated reflection

coefficient (S11), and transmission coefficient (S21) of the

unit-cells are demonstrated in Fig2. Fig2illustrates the

numerical values of the five frequency ranges of resonance

frequencies such as 1.96-2.01 GHz, 3.73-4.16 GHz, 6.45-

7.13 GHz, 8.77-10.77 GHz, and 13.03-13.83 GHz that

designates L-, S-, C-, X-, and Ku-bands applications.



84

Figure 5: Simulated S-parameters curve of meta-atom

structure.

Figure 6: Simulated S-parameters curve for 00, 90

0

and 1800 elevation angle of middle ring meta-atom

structure.

Fig. 3 reports the effective negative refractive index 2.96-

3.674 (0.714 GHz bandwidth), and7.258-14.328 (7.07 GHz

bandwidth) of design structure of meta-atom. The curves of

the effective refractive index become negative when the

curves of the permittivity and permeability are negative,

simultaneously. The design structure of meta-atom has

shown negative-index properties above frequencies because

the permittivity, permeability and refractive index were

negative at that point simultaneously.

3.2. Meta-atom Parametric Analysis

There are three types of elevation angle of inner, middle and

outer rings such as, 00, 900, and 1800 that have been

investigated. The scattering parameters, effective medium

parameters and effective medium ratio of elevated meta-

atoms are observed for 00 elevation angles, 900 elevation

angles, and 1800 elevations angle of the rings of design

structure. The meta-atom structure has capacitive and

inductive elements that increase the response of the material

to the incident electromagnetic wave. The splits of the ring

make capacitance that prevents current flow around the

rings, however, the mutual capacitance between the two

rings enables the flow of the current through the structure.

Total capacitance depends on the splits of the individual

rings and gap between the concentric rings, whereas, the

total inductance is created by conducting rings and gap

between the rings. In this section, various elevation angles

(00, 900, and 1800) of the concentric rings (inner, middle,

and outer) of meta-atom structure have been investigated.

3.2.1. Effect of the Elevation Angle of Inner Ring of

Design Structure

The magnitudes of transmission parameters for an elevation

angle of 00, 900 and 1800 of the inner ring are shown in Fig

4.

Fig. 5 describes the effective negative refractive index 2.96-

3.674GHz, and7.258-14.328GHz for 00 elevation angles;

2.96-3.982 GHz, 5.424-5.55GHz, 6.502-8.546GHz,9.428-

11.192GHz, and 14.72-14.93GHzfor 900 elevation angle;

2.96-4.57GHz, 6.67-8.476GHz, and 9.54-14.44GHzfor 1800

elevation angle of inner ring of design structure. The values

of the negative index are 7.356-8.616 GHz and 11.318-

12.018 GHz for 00 elevation angles; 7.146-8.546GHz, and

10.352-10.926GHzfor 900 elevation angle; 7.118-8.476GHz,

and 11.36-12.284GHzfor 1800 elevation angle of inner ring

of design structure. The design structure of meta-atom has

shown negative-index properties above frequencies because

the permittivity, permeability and refractive index were

negative at that point simultaneously.

3.2.2. Effect of the Elevation Angle of Middle Ring of

Design Structure

The amplitudes of transmission parameters for an elevation

angle of 00, 900 and 1800 of middle ring are shown in Fig 6.

By keeping other rings constant, only altered the middle ring

at different elevation angle like 00, 900, and 1800.Figure 6

displays the numerical of transmission spectra of ring

elevated meta-atom. The position with a dip of resonance

frequency in the transmission spectra has been observed for

elevation angle of middle ring of meta-atom. The numerical

values of the resonance frequencies with dip are 1.994 GHz

at -12.869 dB, 4.004 GHz at -22.776 dB, 6.911GHz at -

21.451 dB, 10.002 GHz at -29.224 dB, and 13.447 GHz at -

18.081 dB for 00elevation angles of middle ring; 2.159 GHz

at -16.948 dB,3.889 GHz at -18.24 dB, and 9.939 GHz at -

31.671 dB for 900elevation angle of middle ring; and 2.904


0

and1800 elevation angle of inner ring meta-atom

structure.



85


curves of middle ring elevated meta-atom.


0

and 1800 elevation angle of outer ring meta-atom

structure.


curves of outer ring elevated meta-atom.

GHz at -26.106 dB,10.064 GHz at -22.233 dB, and 12.76

GHz at -23.006 dB for 1800 elevation angle of middle ring of

meta-atom. The scattering parameters of meta-atom with

elevated angle of middle ring have been marginally shifted

towards the higher frequency and little bit fluctuation of dip.

The little bit difference has been occurred for altering the

middle ring at different elevation angle that causes

polarization effects on the interior construction.

Fig. 7 designates the effective negative refractive index

2.96-3.674GHz, and7.258-14.328GHz for 00 elevation

angles; 3.296-3.492GHz, 4.724-8.196GHz, and 9.708-

14.552GHzfor 900 elevation angles; 5.074-5.228GHz,

5.634-8.574GHz, 9.82-11.234 GHz, and 12.55-

14.496GHzfor 1800 elevation angle of middle ring of design

structure. The values of the negative index are 7.356-8.616

GHz and 11.318-12.018 GHz for 00 elevation angles; 7.342-

8.196 GHz, and 12.984-13.6GHzfor 900 elevation angle;

7.188-8.574GHz, 10.632-10.926GHz, and 13.712-14.076

GHz for 1800 elevation angle of middle ring of design

structure. The design structure of meta-atom has shown

negative-index properties above frequencies because the

permittivity, permeability and refractive index were negative

at that point simultaneously.

3.2.3. Effect of the Elevation Angle of Outer Ring of

Design Structure

The amplitudes of transmission parameters for an elevation

angle of 00, 900 and 1800 of the outer ring are shown in Fig

8. By retaining other rings constant, only changed the outer

ring at different elevation angle like 00, 900, and 1800.Figure

8 presents the numerical values of transmission spectra of

ring elevated meta-atom. The location with a dip of

resonance frequency in the transmission spectra has been

detected for elevation angle of the outer ring of meta-atom.

The numerical values of the resonance frequencies with dip

are 1.994 GHz at -12.869 dB, 4.004 GHz at -22.776 dB,

6.911GHz at -21.451 dB, 10.002 GHz at -29.224 dB, and

13.447 GHz at -18.081 dB for 00elevation angle of outer

ring; 5.712 GHz at -24.162 dB,7.496 GHz at –32.613 dB,

13.15 GHz at -18.519 dB, and 12.743 GHz at -16.06 dB for

900elevation angle of outer ring; and 2.554 GHz at -22.853

dB,3.379 GHz at –12.271 dB,6.861 GHz at -21.289 dB,

10.034 GHz at -23.197 dB, and 13.148 GHz at -25.903 dB

for 1800 elevation angle of outer ring of meta-atom. The

scattering parameters of meta-atom with elevated angle of

outer ring have been slightly lifted towards the higher

frequency and small part fluctuation of dip. The small part

difference has been happened for altering the outer ring at

different elevation angle that causes polarization effect on

the interior structure.

Fig. 9 labels the effective negative refractive index 2.96-

3.674GHz, and7.258-14.328GHz for 00 elevation angles;

2.722-5.508GHz, 6.25-7.342GHz, 7.482-11.374GHz, and

12.648-14.356GHzfor 900 elevation angles; 4.332-

6.684GHz, 7.146-8.742GHz, 9.764-11.276 GHz, and

12.914-14.342GHzfor 1800 elevation angle of outer ring of

design structure. The values of the negative index are 7.356-

8.616 GHz and 11.318-12.018 GHz for 00 elevation angles;

7.482-7.496 GHz, 9.624-11.108 GHz, and 13.628-13.936

GHz for 900 elevation angles; 7.328-8.742GHz, 10.632-

10.982GHz, and 13.936-14.342 GHz for 1800 elevation

angle of the outer ring of design structure. The design

structure of meta-atom has shown negative-index properties

above frequencies because the permittivity, permeability and

refractive index were negative at that point simultaneously.



86

Table 1: The values of transmission parameters with dip at

different resonance frequencies, number of resonance

frequency, and EMR for meta-atom and different structure

with angle elevated rings.

Elevation angle of rings

Number of

resonance

frequency

EMR

Meta-atom 5 15.05

900 Elevation of the

inner ring

5 14.84


inner ring

5 14.83


middle ring

3 13.89


middle ring 3 10.33


outer ring 4 5.25


outer ring 5 11.75

The number of resonance frequency and EMR of meta-atom

and different structure with elevated rings are observed from

Table 1. It is seen from the table 1, the little bit differences

of the parameters have been changed for different elevation

angles. However, Meta-atom without elevation angle of any

concentric rings has achieved higher EMR which indicates

the compactness of meta-atom, and more cover band.

Finally, in this paper, the circular meta-atom has been

analysed with elevation of the different individual ring and

achieved higher EMR (15.05). It is seen from the new

analysis, the effect of the rotation of the different individual

ring alters the miniaturized factor and cover band of the

metamaterials. The proposed meta-atom has attained simple,

miniaturized and negative-index comparing all mentioned

references that are suitable for microwave regime.

4. Conclusion

A new design of circular miniaturized negative-index meta-

atom structure is proposed for satellite communications,

namely, X-, and Ku-band applications in this paper. These

designs exhibited higher EMR such as 15.05, and negative-

index characteristics. The CST electromagnetic simulator

was utilized to determine the metamaterials properties. The

proposed meta-atom is applicable for amateur radio, space

communication, radar, terrestrial broadband for X-band and

satellite communications for Ku-band. A comparative

analysis also carried out for 00 to 1800 elevation angles of

individual ring of the incident electromagnetic waves

consistent with applicable band, the size of the unit cell,

metamaterials characteristics and effective medium ratio for

dual-band applications. Hence, the meta-atom structure is

miniaturized in size, negative-index and follows better

EMR which is more suitable in microwave spectra.

References

[1] P. T. Bowen, A. Baron, and D. R. Smith, Theory of

patch-antenna metamaterial perfect absorbers, Phys.

Rev. A, 93: 063849, 2016.

[2] N.Engheta, and R. W. Ziolkowski, Metamaterials:

Physics and Engineering Explorations, Wiley & Sons.

Chapter 1, New York, 2006.

[3] B.Gallas, K. Robbie, M. Abdeddaim, G. Guida, J.

Yang, J. Rivory and A. Priou, Silver square nanospirals

mimic optical properties of U shaped metamaterials,

Opt. express, 18:16335–16340, 2010.

[4] M. R. I.Faruque, and M. T. Islam, “Novel design of

triangular metamaterial for electromagnetic

absorptionin human head,” Progress In

Electromagnetics Research, Vol 141, 463–478, 2013.

[5] J. Pendry, Perfect cylindrical lenses, Opt. Express, 11:

755-760, 2003.

[6] S. il Kwak, D.-U. Sim, J. H. Kwon, and Y. J. Yoon,

Design of PIFA with metamaterials for body-SAR

reduction in wearable applications, IEEE Trans.

Electromagn. Comp., 59: 297-300, 2017.

[7] D. R.Smith, W. J. Padilla, D. C. Vier, S. C.Nemat-

Nasser, and S.Schultz, Composite medium with

simultaneously negative permeability and permittivity,

Phys. Rev. Lett., 84:4184–4187, 2000.

[8] Islam, S. S., M. R. I. Faruque and M. T. Islam, “design

and analysis of a new double negative metamaterial,”

Journal of Microelectronics, Electronic Components

and Materials, 44:218–223, 2014.

[9] M. J.Hossain, M. R. I.Faruque, S. S.Islam, and M.

T.Islam, Design and analysis of a new composite

double negative metamaterial for multi-band

communication, Curr. appl phys., 17:931-939, 2017.

[10] M. J.Hossain, M. R. I.Faruque, S. S.Islam, and M.

T.Islam, An effective medium ratio following

miniaturized concentric meta-atom for S- and C-band

applications,Microw. Opt. Tech. Lett., 59:1233-1240,

2017.

[11] M. J.Hossain, M. R. I.Faruque, S. S.Islam, and M. T.

Islam, A new double T-U-shaped biaxial compact

double-negative meta-atom for multiband applications,

Microw. Opt. Tech. Lett., 59: 2551-2557, 2017.

[12] A. M. Nicolson, and G. F. Ross, Measurement of the

intrinsic properties of materials by time-domain

techniques, IEEE Trans. Instrum. Meas., 19:377–382,

1970.



87

Ionospheric Bottomside Electron Density Thickness Parameter over

Southeast Asian Sector

Saeed Abioye Bello1, 2, Mardina Abdullah1,3, Nurul Shazana Abdul Hamid4,*

1Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia. 2Faculty of Physical Sciences, Department of Physics, University of Ilorin, Nigeria.

3Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment,

Universiti Kebangsaan Malaysia, Malaysia. 4School of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi,

Selangor, Malaysia.


Abstract

The thickness of the electron density profile below the

ionospheric F2-layer peak (B2bot) was study over the Chiang

Mai (CMI; 98.9°E, 18.8°N, dip latitude: 13.2°N), in the

Southeast Asian sector. To estimate the values B2bot, the

experimental F2-layer peak values were used as an input into

the NeQuick 2 bottomside thickness model during maximum

solar cycle of the year 2014. The NeQuick model is one of

the widely use empirical model for estimating ionospheric

electron density over a region. The experimental ionospheric

peak parameters used for this study are measurement data

obtained from FM-CW (Frequency-Modulated Continuous

Wave) ionosonde at CMI station. Result from our analysis

shows that B2bot exhibit diurnal variation. Furthermore, the

B2bot is highest in the daytime than at night time. This implies

that the ionospheric electron density during the daytime is

thicker than the rest of the day.

1. Introduction

The ionospheric B2bot is a term used to describe the

bottomside thickness of the electron density of the

ionosphere below the F2-layer peak height. The values of

B2bot can directly be estimated from the bottomside electron

density profile Ne(h) by calculating the difference between

the peak height of the F2 layer (hmF2) to the height (h0.24)

where the electron density is 0.24 x NmF2 (peak electron

density of F2-layer) if F1-layer does not exist or to the peak

height of F1-layer (hmF1). This techniques requires scaling

the ionogram trace (or electron density) and inverting the

h’(F) curve into the true height profile. However, the

ionogram record of the FM-CW ionosonde at CMI station is

an analogue data that is somewhat difficult to scale using

manual method. This is because some of the ionogram can

present complex instances and might introduce possible

systematic error during scaling procedure. The success rate

of scaling a more complicated ionograms may likely not

exceed 70% in spite of ever increasing coding efforts [1].

Currently, the modern ionosonde are fully becoming digital

(e.g. digisonde portable sounder, DPS) [2] and capable of

automatically scaling digitized ionogram trace by assigning

a confidence score to each trace in order to determine

attributed uncertainty of each profile points. The ARTIST

auto-scaling ionogram software [2] is an example of this

advancement in ionospheric sounding procedure. Though,

the software mainly works for digitized ionogram.

The development of the NeQuick model [3] provides the

opportunity to study the electron density profile of the

ionosphere. The NeQuick model is one of the widely use

empirical model for estimating ionospheric electron density

over a region [3]. The model was developed at two

laboratories; namely Aeronomy and Radio Propagation

Laboratory (ARPL) of the Abdus Salam International Centre

for Theoretical Physics (ICTP), Trieste, Italy and Institute for

Geophysics, Astrophysics and Meteorology (IGAM) of the

University of Graz, Austria [4]. To estimate the thickness of

the ionospheric electron density (B2bot) above 90 km and up

to the peak height of ionospheric F2 layer, we used the

improved version of the NeQuick model (NeQuick 2) which

is a modified version of ‘Di Giovanni and Radicella’ (DGR)

formulation [5]. We compute the B2bot parameter using the

ionospheric peak parameters: peak frequency of F2-layer

(foF2) and peak height of F2-layer (hmF2) as an anchor point

for the estimate. The ionospheric peak parameters used for

this study are measurement data obtained from FM-CW

(frequency-modulated continuous wave) ionosonde at

Chiang Mai (CMI) station. The ionosonde send sweep of

frequency in the range of 2-30 MHz with maximum power

of 150 W [6].

This study is a preliminary result over the CMI station

and aimed to contribute for further understanding of the

ionospheric electron density thickness parameter at the

Southeast Asian region. The process of the NeQuick 2 model

used for estimating B2bot in this present study is described in

detail in the data and methodology section (Section 2). The

results and discussions are given in Section 3. Finally, in

Section 4, the conclusions are made.



88


The study focuses on an equatorial station located at Chiang

Mai (CMI; 98.9°E, 18.8°N, dip latitude: 13.2°N), in the

South-East Asia sector. The ionospheric dataset are obtained

from the FM-CW ionosonde installed at CMI station which is

one of the three South East Asia Low-latitude Ionospheric

Network (SEALION) along the 100°E meridian [6]. The

location of the station is given in Fig. 1. The CMI station and

Kotatobang (KTB) station are nearly magnetic conjugate [7].

Figure 1: SEALION- The South East Asia Low-latitude

Ionospheric Network [7].

The peak frequency (foF2) and maximum usable

frequency refracted from the ionospheric F2-layer that can be

received at a distance of 3000 km (MUF(3000)) were

experimental measurement from the ionosonde dataset

(ionogram) used in the present study. These parameters is

analyse for the year 2014 which is a period of high solar

activity. The 27-day averaged solar index F10.7 of ~146(sfu

= 10-22x m-2x Hz-1) and average sunspot number (avg_R) of

~113. Table 1 provides the summary of the monthly daily

averages of F10.7 for the year 2014. The bar chart in Fig. 2

shows the number of days in the month of 2014 for which

data are available in the CMI station.

Table 1: Monthly daily averages of F10.7 for the year 2014.

Months F10.7 (sfu)

January 155

February 172

March 148

April 145

May 133

June 126

July 142

August 128

September 149

October 154

November 151

December 154

The bottomside thickness parameter (B2bot) for the

ionospheric F2-layer is estimated using NeQuick 2 model.

The B2bot is calculated using the expression given in equation

(1)

𝐵2𝑏𝑜𝑡 =0.385 𝑥 𝑁𝑚𝐹2

(𝑑𝑁 𝑑ℎ⁄ )𝑚𝑎𝑥

where NmF2 (1010el.m-3)is the peak electron density of

ionospheric F2-layer and can be calculated from experimental

foF2 (MHz) using;

𝑁𝑚𝐹2(= 1.24 × 1010(𝑓𝑜𝐹2)2 (2)

ln((𝑑𝑁 𝑑ℎ⁄ )𝑚𝑎𝑥 = −3.4567 + 1.714𝑙𝑛(𝑁𝑚𝐹2) + 2.02𝑙𝑛(𝑀(3000)𝐹2) (𝑑𝑁 𝑑ℎ⁄ )𝑚𝑎𝑥 is the maximum gradient inflection point of

ionospheric electron density Ne(h) below the F2 layer.

M(3000)F2 is the propagation factor:

𝑀(3000)𝐹2 = 𝑀𝑈𝐹(3000)𝐹2

𝑓𝑜𝐹2

Figure 2: Bar chart showing the number of days in each month

with available data at CMI station in the year 2014.


The diurnal variation of bottomside thickness parameter

(B2bot) for the ionospheric F2-layer for the ten (10) quietest

days in each month of the year 2014 is shown in Fig.3. The

quietest days are defines as period with no geomagnetic

disturbances and are obtained from the catalogue of the World

Data Center (WDC) for geomagnetism, Kyoto, Japan

(http://wdc.kugi.kyoto-u.ac.jp/). There is no data for the

months of March to July and this is largely due to technical

failure of the measuring instrument. For this reason, the

periods of the month without data are the white empty space

in Fig. 3. The magnitude of B2bot estimated by NeQuick 2

model typically shows a maximum value during the day time

and lowest during the night time. The model reproduces the

September equinox peak with values reaching 60 km during

the daytime.

The diurnal hourly monthly averages of B2bot over CMI

station for the year 2010 is given Fig. 4. The figure gives the

scatter plot of the hourly values of the ten quietest days in the

months of January to April and August to December and their

averages in orange line plot. The hourly data points for the

month of April are few during the selected quiet days. The

daily values of the B2bot are represented by the circle marker

and their monthly averages (B2bot_avg) is the orange bold line

(error bar is calculated from the standard deviation) in Fig.

(1)

(3)

(4)



89

4(a-h). It can be observed that the B2botshows both diurnal

variations. The values of B2bot are found to be at maximum

during the daytime and lowest at night time towards the pre-

sunrise period. In all the observed month, the values of B2bot

gradually increase from 0800h LT towards the midday. The

value thereafter reduces towards the night time from 1600h

LT. The deviation of the values of B2bot is greatly spread out

around 1200h to 1600h LT. This suggests the variability of

B2bot is largely related to the daytime photoionization and

daytime plasma drift. The mechanism that controls the peak

height of F2-layer (hmF2) equally contributes to the observed

behaviour of B2bot [8]. It is seen from Fig. 4 that the highest

midday value B2bot is found during the equinoctial months

(February, April, and August to October). The magnitude of

B2bot during equinoctial months was found to be similar with

the values of B2bot during winter months (December to

January). Largely due to paucity of data, a complete

description of B2bot seasonal variation cannot be concluded

using the current data. A more simplify description between

the values of B2bot during the equinox and winter seasons are

given in Fig. 5. The observed morphology of B2bot is similar

to the results of previous findings [4, 7].

Figure 5: Seasonal variation of B2bot during the month of

equinox and winter of the year 2014.

4. Conclusion

The experimental data obtained at Chiang Mai during a period

of high solar activity have been used to study the behaviour

of the ionospheric electron density thickness parameter below

the F2-layer. The thickness parameter was estimated using the

NeQuick bottomside thickness model. The experimental

values of foF2 and M(3000)F2 have been used as an input

into the model. The conclusion can be drawn as follows:

1) B2bot was found to exhibit diurnal variation.

2) The magnitude of B2bot during the daytime is highest

than at night time.

3) The value of B2bot during the equinoctial month is

almost similar to that of winter season.

Figure 3: Diurnal monthly variation of B2bot Chiang Mai

(CMI) for the year 2014.

Figure 4: The diurnal monthly averages of B2bot at Chiang Mai (CMI) station for the year 2014.



90

Acknowledgements

The authors are grateful to National Institute of Information

and Communications Technology (NICT), Japan for the

ionosonde data used in this study. This work is supported by

the grants FRGS/1/2015/ ST02/UKM/02/1 of Universiti

Kebangsaan Malaysia.

References

[1] B. Nava, P. Coisson, S. Radicella, A new version of the

NeQuick ionosphere electron density model, J. Atmos.

Sol.-Terr. Phys., 70(15): 1856-1862, 2008.

[2] S. Wang, J. Shi, X. Wang, G. Wang, Validation of B2bot

in the NeQuick model during high solar activity at

Hainan station, Adv. Space Res., 46(9): 1094-1101, 2010.

[3] G. Di Giovanni, S. Radicella, An analytical model of the

electron density profile in the ionosphere Adv. Space

Res., 10(11): 27-30, 1990.

[4] T. Maruyama, M. Kawamura, S. Saito, K. Nozaki, H.

Kato, N. Hemmakorn, T. Boonchuk, T. Komolmis, C. H.

Duyen, Low latitude ionosphere-thermosphere dynamics

studies with ionosonde chain in Southeast Asia, Ann.

Geophys., 25: 1569-1577, 2007.

[5] T. Maruyama, J. Uemoto, M. Ishii, T. Tsugawa, P.

Supnithi, T. Komolmis, Low‐ latitude ionospheric

height variation as observed by meridional ionosonde

chain: Formation of ionospheric ceiling over the

magnetic equator, J. Geophys. Res. Space Phys.,

119(12): 10595-10607, 2014.

[6] C.-C. Lee, B.W. Reinisch. Variations in equatorial F2-

layer parameters and comparison with IRI-2007 during a

deep solar minimum, J. Atmos. Sol.-Terr. Phys., 74: 217-

223, 2012.

[7] P. Coïsson, B. Nava, S. Radicella, O. Oladipo, J.

Adeniyi, S. G. Krishna, P.V.S. Rama Rao, S. Ravindran,

NeQuick bottomside analysis at low latitudes. J. Atmos.

Sol.-Terr. Phys., 70(15): 1911-1918, 2008.



91

Assessing the Accuracy of Hydrodynamic Parameters using

Statistical Approaches

Fazly Amri Mohd1, Khairul Nizam Abdul Maulud1&2, Othman A.Karim1, Rawshan Ara Begum3

1Department of Civil & Structural Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan

Malaysia, 43600 UKM, Bangi, Selangor, Malaysia. 2Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor,

Malaysia. 3Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.

*corresponding author, Email: [email protected]

Abstract

This study simulates the hydrodynamic characteristics at

Pahang coastal area which is located at the South China Sea

by using MIKE 21 Hydrodynamic FM. The numerical

modelling normally applies complicated mathematical

equations, which have coefficients that are site specific.

Therefore, the model simulations are important to calibrate

and validate against measured conditions by collecting in-

situ data such as water level, current direction and current

speed within two weeks period at the study area. In this

study, the device used to record the tidal reading at Kuantan

and Kuala Pahang Jetty is tide gauge, meanwhile Acoustic

Wave and Current Profiler (AWAC) are used to record the

current direction and current speed at two stations nearshore

at Pahang shoreline. This objective of this paper is to verify

the statistical methods used to assess the accuracy of the

simulation models by comparison between calibrated and

validated model results using RMSE and Brier Skill Score

(BSS). BSS for the water level at Kuantan and Kuala

Pahang Jetty are 0.90 and 0.97 respectively while current

speed and current direction are approximately around 0.86

to 0.98. These values show that the simulation model results

can be accepted.

Keywords: Hydrodynamic, RMSE, BSS, MIKE 21,

simulation model

1. Introduction

Coastal zone is one of the most vital zones for human

activities and infrastructure development [1]. Nevertheless,

this system is dynamic and must to be studied widely before

any infrastructure is planned to avoid damage caused by

natural processes such as erosion. The main natural

elements responsible for coastal hydrodynamics are waves,

currents and tides [2,3,4]. This information is very

significant for various coastal engineering designs and

applications for new modifications to coastal protection

structures.

To understand coastal hydrodynamics over geographic

areas, many numerical modelling has been shown to be the

best method. These models are recently being used as a

prediction tool to help in decision making. MIKE21 is such

an interconnected modelling module, commercially

presented by DHI (known as Danish Hydraulic Institute)

[5]. It includes modules that represent various processes in

coastal dynamics. The output of numerical hydrodynamic

model are used to study complex systems of various

processes in coastal areas that may occur simultaneously. The main objective of the study was to assess the

accuracy of the simulation models of water level, current

speeds and current directions data at the Balok to Kuala

Pahang coastal using different of statistical methods. These

simulations have been carried out using the software MIKE

21, which includes module of Hydrodynamic FM [5].

2. Methods

2.1 Numerical Model

MIKE 21 Flow Model FM is a modelling system for 2D free

surface flows based on a flexible mesh approach. The

modelling system has been developed for applications

within oceanographic, coastal and estuarine environments.

The Hydrodynamic module is one of the basic

computational components of the entire MIKE 21 Flow

Model modelling system [5]. This module is applicable for

the simulation of hydraulic and environmental phenomena

in lakes, estuaries, bays, coastal areas and seas.

2.2 Model Input

By using MIKE 21 Hydrodynamic FM, the data required for

the modelling consists of bathymetry data from the

computational domain, wind speeds and wind directions,

significant wave heights, mean wave directions and bed

resistance. The input data for this model has been simulated

with the current condition during inter monsoon as well as

wind speed and wind direction data with 8 m/s and 2400

respectively. This model also used 14 days tidal data and



92

hydrographic data that was obtained from The Department

of Survey and Mapping Malaysia (JUPEM).

2.3 Bathymetry

Bathymetry survey with the fine resolution was conducted

along Beserah to Kg. Tanjung Agas in Pekan covering an

area approximately 48 km x 5 km. The interval between

sounding lines is within 500 m of each line. The bathymetric

survey was carried out during the spring tide. The data

observation recorded includes depth from -0.01m to -

19.77m from the average sea level (MSL). In addition,

bathymetry data of the ocean region was generated using C-

MAP 2014.

2.4 Boundary Condition

The purpose of boundary condition is used to allow energy

of the water level through into and out of the model domain.

The specification of boundary information for each code is

made subsequently. When mesh was generated using the

MIKE Zero Mesh Generator, a code value for open water

boundaries can be defined. In this study, the mesh file

specified in the domain parameters: code 2 (South), code 3

(East) and code 4 (North).

2.5 Model Calibration and Validation

Two units of Acoustic Wave and Current Profiler (AWAC)

were installed at two locations at Pahang Coastal which

located the South China Sea coast within spring tide and

neap tide period as shown in Figure 1. The device was

utilised to measure the current characteristics including

current speeds and current directions. For the water level

reading at Kuantan and Kuala Pahang jetties were deployed

and recorded using Tide Gauge with 10 minute intervals in

the project site.

Table 1: Locations of Tide gauge, AWAC 2 and AWAC 3

devices at Pahang Coastal

The result of this simulation model was determined using a

statistical method based on the standard error allowed for

hydraulic study by Department of Irrigation and Drainage

(DID) guidelines on year the 2013 (JPS, 2001). The quality

of the simulation modelling was evaluated the performance

of the numerical modelling systems using Brier Skill Score

(BSS) [6].


The in-situ measurement consists of the water level, current

speed and current direction at the Pahang coastal was

collected during spring tide and neap tide period on 24th

May 2014 until 7th June 2014. On Figure 2, the pattern of

water levels obtained from hydrodynamic simulations for

the Kuantan Jetty and Kuala Pahang Jetty have a good

agreement with the field measurements. The water level

range at both jetty are approximately – 1.5 to 1.5 meter.

(a)

(b)

Figure 2: Pattern of water level at Kuantan Jetty and Kuala

Pahang Jetty

Based on Figure 3 and Figure 4, it is evident that the

current speeds and current directions for the Station B and

Point C at the Balok to Kuala Pahang area during spring tide

and neap tide conditions were approximately 0 to 0.40 m /s

and 0 to 0.44 m /s, respectively which located at southwest

direction with ranges between 1800 - 2000.Thus, the current

speeds, current directions and water levels obtained from

hydrodynamic simulations have a good agreement with the

field measurement.

No Station Latitude

(Y)

Longitude

(X)

1 Kuantan Jetty 3.809889 103.336056

2 Kuala Pahang Jetty 3.530073 103.462840

3 Station B 3.673722 103.480896

4 Station C 3.601107 103.480896



93

Figure 3: The pattern of Current Speed result at Station B

and C near Pahang Coastal.

Figure 4: Pattern of Current Direction result at Station B

and C near Pahang Coastal.

Table 2 summarises the minimum values of RMSE and

Brier Skill Score (BSS) in model calibration and validation

process. The minimum values of Root Mean Squared Error

(RMSE) for calibration and validation of water level at

Kuantan Jetty and Kuala Pahang Jetty are 7.92 and 7.86.

The value of current speeds and current direction by RSME

method for Station B and Station C is representing 18.5 m/s

and 17.590, and 14.06 m/s and 18.830 respectively.

Regarding Brier Skill Score (BSS) method, the values of

water level, current speed and current direction are

approximately 1, which means this simulation model gives

good result of the prediction. The range of these

hydrodynamic parameters using statistical method is 0.89 to

0.97.

Based on standard error allowed for hydraulic study by

Department of Irrigation and Drainage (DID) guidelines on

the year 2013, the RSME of current speed should be not

more than 20% and the current direction is not more than

200. For the water level, the tolerance of JPS requirement

for RMSE is not more than 10%. Therefore, most of these

statistical methods from this study prove that the model is

well calibrated and validated and accepted.

Table 2: The types of Statistical Method for hydrodynamic

parameters

Statistical Method

No Hydrodynamic

Parameters

RMSE Brier Skill

Score (BSS)

1 Water Level (m)

1.Kuantan Jetty

2.Kuala Pahang Jetty

7.90

7.86

0.97

0.90

2. Current Speed (m/s)

1.Station B

2.Station C

18.5

14.06

0.98

0.89

3. Current Direction

(Degree, 0 )

1.Station B

2.Station C

17.59

18.83

0.92

0.95

4. Conclusion

The statistical analysis applied in the numerical model for

this study gave a high agreement between the model results

and the measured data. The BSS method had been

successfully applied in the numerical model for identifying

the accuracy data of the hydrodynamic parameters at

Pahang Coastal. The result shows that the numerical model

is in good performances as the BSS ranged from 0.90 to 0.97

for the water level, meanwhile the value ranging from 0.89

to 0.98 are representing the current speed and current

direction for Station B and Station C at the Pahang Coastal

respectively. Based on the simulation results, the current

speed and current directions at the Balok to Kuala Pahang

coastal between 24th May 2014 until 7th June 2014 are an

approximately 0 – 0.44 m/s and 1800 - 2000.

Acknowledgement

The authors greatly acknowledge Earth Observation Centre,

Institute of Climate Change, UKM and relevant agency

such as NAHRIM in providing the information and field

data. This work was supported by Research Fund (AP-



94

2015-009, TRGS/1/2015/UKM/5/1 and

TRGS/1/2015/UKM/5/3) by Research University Grants

from Universiti Kebangsaan Malaysia and Ministry of

Higher Education, Malaysia

References

[1] Kulkarni, R. R. Numerical Modelling of Coastal

Erosion using MIKE21. Master Dissertation,

Norwegian University of Science and Technology,

(2013).

[2] Fitri, A., Hashim, R. and Motamedi, S.. Estimation and

Validation of Nearshore Current at the Coast of Carey

Island, Malysia. Science and Technology, 25(3),

1009–1018, (2017)

[3] Jabatan Pengairan & Saliran (JPS). Guidelines for

Preparation Of Coastal Engineering Hydraulic Study

And Impact Evaluation Malaysia, December 2001,

(2001)

[4] V, Noujas. Coastal Hydrodynamics and Sediment

Transport Regime of the Central Kerala Coast in

Comparison to Southern Kerala. Ph.D. Dissertation,

Cochin University of Science and Technology, (2015).

[5] DHI (Danish Hydraulic Institute). MIKE 21 FLOW

MODEL FM. User Guide, (2011)

[6] Sutherland, J., Walstra, D. J. R., Chesher, T. J., Rijn,

L. C. Van, & Southgate, H. N.. Evaluation of coastal

area modelling systems at an estuary mouth. Coastal

Engineering, 51, 119–142, (2004)



95

Socio-economic Impacts of Climate Change in the Coastal Areas of

Malaysia

Mohd Khairul Zainal 1, Rawshan Ara Begum 1, Khairul Nizam Abdul Maulud 1&2,

Norlida Hanim Mohd Salleh 3

1Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia 2Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

3 Faculty of Economic and Management, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia


Abstract

This paper provides an overview of the socio-economic

impacts of climate change in the coastal areas of Malaysia.

Malaysia has a 4,800 kilometre coastline which rich in

natural resources that provide opportunities for socio-

economic activities. Sectors in coastal areas such as

agriculture, fisheries, and oil and gas contribute 8%, 1% and

20% of GDP in Malaysia. However, the impacts of climate

change such as sea level rise, flooding, erosion, inundation,

and salt water instrusion bring problems and vulnerability to

coastal areas and communities. These include decreasing

crop yields by as much as 80%, mangrove forest loss about

0.8% per year, decreasing in fisheries industries production,

declining tourism and recreation activities, loss of land,

infrastructure damages, affected health and life, loss of

physical properties and livelihood damages. Furthermore, it

has been estimated that 30% of the coastline is subject to

varying degrees of erosion that affected to the socio-

economic along the coastal areas. For example, if the flood

frequency is doubled, the annual flood damage would

increase by 1.67 times which might cost RM1.3 billion per

year for mitigating floods. Therefore, socio-economic

assessment on the adaptation measures is crucial in order to

reduce the damages of climate change impacts and identify

the efficient adaptation measures in the coastal areas of

Malaysia.

1. Introduction

Malaysia is located in Southeast Asia and situated in the

equatorial region. It is divided into two similarly sized

region which consists of Peninsular Malaysia and East

Malaysia where Peninsular Malaysia lies between latitudes

1.5°N and 7°N and longitudes 99.5°E and 104°E.

Meanwhile, East Malaysia is located between latitudes 1°N

and 6.5°N, and longitudes 108.5°E and 120°E [1]. Malaysia

is a coastal nation with a 4,800 kilometre coastline [2],[3]

which is rich in natural resources that provide opportunities

for socio-economic activities such as agriculture, fisheries,

mangrove, oil and gas, seaports and marine transport,

tourism, recreation, etc. Moreover, there are a lot of people

living around the coastal area due the various resources and

biodiversity which attract large number of immigrants, and

hence increasing the demand for housing, energy, goods and

services.

Most of the population in Malaysia is located in the

coastal areas and support a major portion (about 60%) [2]-

[4] of the total population. However, coastal areas are

constantly facing tremendous development pressures both

from natural and anthropogenic factors. Demands on coastal

and marine resources such as urbanization process, primary

sector, good and services sectors are rapidly increasing.

Hence, those activities are intrinsically linked to climate

change especially in sea level rise events. Consequently,

these situations could create problems to coastal areas and

the vulnerability of human settlements to erosion,

inundation, storm surges, and flooding events also increases.

As a consequence, it will affect the socio-economic

activities around the coastal areas. Therefore, this paper

provides an overview of the socio-economic impacts of

climate change in the coastal areas of Malaysia.

2. Sectoral and Socio-economic Contribution of

the Coastal Area

Coastal area is an important interface between land and sea

with rich potential for biodiversity and natural resources.

Malaysian coastal areas include Peninsular (West and East

Coast) and East Malaysia (Sabah and Sarawak). Demands

on coastal resources have resulted in coastal development

and brings the socio-economic activities around the

coastline including primary sector such as agriculture and

fisheries, secondary sector like ports and marine transport,

and tertiary sectors as well like tourism activities. About

21% of the coastal areas have been developed for

residential, housing, transportation and tourism purposes [2].

Malaysian economy have become highly dependent on

revenues generated from coastal activities such as

agriculture, oil and gas, tourism and recreational. Thus,

coastal areas is an important resource that contribute to the

economy such as national income, employment, trade, and

business. There are several socio-economic activities around

the coastal areas that contributed to Malaysian economy



96

such as oil palm, oil and gas and etc. Table 1 shows the

contribution of socio-economic activities around the coastal

areas in Malaysia.

Table 1: Summaries of Sectoral and Socio-economic

contribution of the Coastal Area

Sectoral Socio-economic Contribution

Agriculture

plantation

Agriculture industries contribute about 8%

to Malaysian GDP [2].There are a lot of

agriculture activities including oil palm,

coconut, mangrove along the coastal areas

such as in Johor,[5,6] and rice cultivation in

the coastal areas of the northwestern states

of Perak, Penang, Kedah and Perlis.

Fisheries

industry

Fisheries production has peaked at around

one million tones [5] for more than a

decade that contribute in 1% of Malaysia

GDP. Landings of marine fish (including

shellfish collection) were 1.483 million

tonnes in 2013, compared with 1.286

million tonnes in 2000 [6].

Mangrove

forest activity

Mangrove forest provided about 1,400

workers and 1,000 indirect employment in

Matang and total value of mangrove forest

about $20.7 million. Besides, mangrove in

Sabah contribute $11 million from 260,000

tonnes wood chips and created 3,000

employment. [7].

Oil and gas The discovery of oil and gas in Johor,

Kelantan, Terengganu, Sarawak and

Labuan has resulted in the development of

a significant component of the Malaysian

economy that contribute in 20% of national

income. [7,8].

Seaports and

marine

transport

Important to transportation for the export

and import goods to distribute around

Malaysia, for instance, Klang Port

(Selangor) and Port Tanjung Pelepas

(Johor).In year 2004, Malaysia have

exported various types of fish equivalent to

RM1.293 billion ringgit to Singapore,

Japan and Europe. At the same time, it

imported up to RM1.217 billion ringgit for

own consumption. [2,4].

Maritime

activity

Coastline as major training bases for ships

and submarines such as in Malacca and

Perak to protect the Malaysian coastline

could contribute in security and safety for

country [9].

Urban

development

There are about 22 urban settlements along

the coastline of Malaysia consists of some

Sectoral Socio-economic Contribution

major towns such as Georgetown, Malacca,

Johor Bharu, Kuantan, Kuala Terengganu

etc. that create employment and jobs that

reducing about 3% of unemployment rate

[1,2].

Culture and

Historical

place

There are various cultural , historical

coastal areas such as Lembah bujang,

Kuala Kedah, Kuala Muda, Malacca, Kota

Tinggi andJohore Lama that are significant

in Malaysian history [2]. A portion of the

coast has also been gazetted as a Ramsar

site like Tanjung Piai [10].

Tourism and

recreational

In year 2004, about 4.07million tourist

visited Malaysia compared to 8.1 million in

year 2003 and this expected to increase in

coming years [2]. Meanwhile, in 2009

tourist arrivals was over 23.6 million

people in which their presence contributes

to the economy [11].

Table 1 shows that coastal areas are importantant to the

socio-economic activities and development in Malaysia.

Nevertheless, the coastal areas are vulnerable due to the

impacts of climate change and accelerated sea level rise such

as shoreline erosion, saltwater intrusion, flooding,

inundation, and affects to communities, cultural and historic

resources as well as infrastructure which might jeopardise

the socio-economic development in Malaysia.

3. Socio-economic Impacts of Climate Change

Malaysia is experiencing changing climates for the past few

decades. Most of the coastal areas in Malaysia are low-lying

areas less than 0.5 m above the highest tide or are within 100

m inland of the high-water mark. Hence, these areas are

vulnerable to sea level rise [9]. It has been proven that

Malaysian coastal areas will face the rise in sea level of

about 13-94 cm in 100 years [1]. In Perak and Pulau Penang,

sea-level rise of 6.45 mm per year; 4.26 mm at Perhentian

Island, Terengganu; and 2.73 mm at Mersing water was

identified [12]. Sea level rise is one of the major problem to

coastal erosion and the destruction of mangrove forests in

Malaysia [13]. It has been estimated that some 30% of the

coastline is subject to varying degrees of erosion [2] where

about 288 km of coastline is subject to erosion, which

indicates that the areas are facing erosion which poses

immediate danger of collapse or damage to shore-based

facilities and infrastructure, [14] plus, 65 coastal areas in

Malaysia are also facing serious coastal erosion [13].

As a consequence, climate change has the potential to

increase the intensity and severity of extreme coastal

impacts such as sea level rise, shoreline erosion, salt water

intrusion, inundation of wetlands and estuaries, high tides,

strong storms, and coastal flooding [9] and even worst is

tsunami event. Next, it will be a threat to socio-economy



97

such as agriculture, cultural and historic resources as well as

infrastructure. Table 2 shows the impact of climate change

on socio-economy in Malaysia.

Table 2: Socio-economic impacts due to Climate Change

Climate

Change

phenomena

Sectoral impacts

Coastal

Flooding and

Sea level rise

Agriculture loss:

Floods and droughts during the early

stage of the growing season decrease

yields by as much as 80% [12].

RM46 million for Western Johor

Agricultural Development Project

area. The West Johor Project area

accounts for about 25% of the national

drainage areas [3].

Coastal

Erosion

Mangrove loss:

Food and Agricultural Organization

(FOA) (2007) mentioned that the

destruction of mangrove forests in

Malaysia has been occurring at a rate

of 0.8% per year [12].

About 25% of mudflats and

mangroves under threat of erosion and

flooding in north west of Tanjung Piai,

Johor [10].

It will cost US$9,990 (RM37,962) per

hectare per year to use technology to

replace the naturally available

mangroves [2].

Coastal

Erosion

Loss of fisheries production :

RM300 million loss based on 20% loss

of mangrove resulting in a loss of

about 70,000 tonnes of prawn

production valued at RM4,500/tonne

[15,16].

Coastal

Erosion

Land loss:

Batu Pahat Johor, it has been reported

that the coast has eroded by 2 m every

year, and this affects local agriculture

activities and causes a loss of

investment to farmers [12].

Land loss varies from 3% to 19% due

to flooding and river bank overtopping

at Kg. Lubok Buaya, Kedah [17].

Coastal

Erosion

Residential/ Housing loss :

Pengkalan Atap village, which is

located in Kuala Besut, Terengganu; in

2011, a total of 41 families from the

village were

relocated, as their houses were

destroyed by coastal erosion and

Climate

Change

phenomena

Sectoral impacts

extreme waves [13].

More 2000 families along coastal areas

in west coast of Malaysia lost their

home and properties [2].

Coastal

Erosion and

Sea level rise

Insfrastructure loss :

About 7 – 8km of coastal road under

threat of erosion and flooding between

Tanjung Piai and Tanjung Bin [18];

[19].

Overtopping of coastal bund south of

airport runway is predicted in

Kampung Kuala Muda Airport to

Kampung Chenang, Kedah [3,15].

Number of infrastructure facilities

were destroyed due to extreme waves

and coastal erosion in Malacca [12].

Sea level rise Health and life affected :

In the extreme flood of 2014, 25 lives

were lost, half a million people were

affected and damage to public

infrastructure amounted to RM2.9

billion [20].

It is also in line with Malaysian

government’s call for preservation of

mangrove swamps following the

tsunami disaster in 26 December 2004

which caused 69 death and more than

RM200 million ringgit losses to the

country [2].

Coastal

Erosion and

Sea level rise

Cultural and Heritage loss :

28.5% potential loss of the world

heritage in Tanjung Piai, Johor [23].

Table 2 clearly shows that climate change could bring

negative impacts on the socio-economy around the coastal

areas and could affect income of households,

unemployment, properties damages and will increase cost

and public expenditure. The average cost by the Government

to mitigate floods over the past 40 years has risen from

about RM3 million per year during the Second Malaysia

Plan period (1971-1975) to RM1.3 billion per year during

the Tenth Malaysia Plan period (2011-2015) [24]. Thus, it is

recommend that adaptation measures is needed to prevent

the more damages in future.

4. Concluding Remarks

A large number of human population is living along the

coastlines because coastal areas in Malaysia are rich in

resources and biodiversity that contributes to the socio-

economic activities. Most of coastal region countries earn



98

their revenue from the coast resources such as primary

sectors; agriculture, fisheries, secondary sector; oil and gas

and tertiary sector; tourism and recreation that could

contribute in national income, unemployment rate and

trading[2,6,24,25].

Nevertheless, the impacts of climate change could

jeopardise economic growth and affect social activities in

Malaysia. Consequently, coastal areas are sensitive areas

and tend to be vulnerable to various threats such as erosion,

sea level rise, salt water intrusion, flooding and inundation

[12,26].

The impacts of climate change pose a direct threat to the

vulnerable communities and people. As a result, other

sectors are also affected by climate change including

agriculture and mangrove forest loss, fisheries industries

production reduction, tourism and recreation industries

declining, land loss, infrastructure damages, affected health

and life, loss of physical properties and livelihood damages

[1,15]. These impacts could increase the cost of public and

private expenditures.

However, there is a lack of comprehensive studies in

socio-economic impacts of climate change in Malaysia.

Therefore, socio-economic assessment on the adaptation

measures is crucial in order to reduce the damages of

climate change impacts and identify the efficient adaptation

measures in the coastal areas of Malaysia.

Acknowledgement

In arranging this research, the author intended to express

gratitude and appreciation to Ministry of Higher Education

Malaysia through its projects Transdisciplinary Research

Grant Scheme TRGS/1/2015/UKM/02/5/3 and The National

University of Malaysia (UKM) for Arus Perdana Grant

Scheme AP-2015-009 that has been funded the research

project.

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