passive biomonitoring of thulium, lanthanum, and several heavy metals in air by using tree bark...
DESCRIPTION
An undergrad thesis regarding atmospheric monitoring of air by using tree bark sample.TRANSCRIPT
PASSIVE BIOMONITORING OF THULIUM, LANTHANUM AND SELECTED HEAVY METALS IN AIR BY USING TREE BARK
SAMPLE
SITI MARIAM BINTI ABDUL KADIR
BACHELOR OF SCIENCE (Hons.) CHEMISTRY FACULTY OF APPLIED SCIENCES
UNIVERSITI TEKNOLOGI MARA
JANUARY 2014
PASSIVE BIOMONITORING OF THULIUM, LANTHANUM AND
SELECTED HEAVY METALS IN AIR BY USING TREE BARK
SAMPLE
SITI MARIAM BINTI ABDUL KADIR
Final Year Project Report Submitted in
Partial Fulfillment of the Requirements for the
Degree of Bachelor of Science (Hons.) Chemistry
in the Faculty of Applied Sciences
Universiti Teknologi MARA
JANUARY 2014
ii
This Final Year Project Report entitled “Passive Biomonitoring of Thulium,
Lanthanum and Selected Heavy Metals in Air by Using Tree Bark Sample” was
submitted by Siti Mariam Abdul Kadir, in n partial fulfillment of the requirements
for the Degree of Bachelor of Science (Hons.) Chemistry, in the Faculty of Applied
Sciences, and was approved by
Dr. Mohd Zahari Abdullah @ Rafie
Supervisor
B.Sc. (Hons.) Chemistry
Faculty of Applied Sciences
Universiti Teknologi MARA
26400 Jengka
Pahang
Aiza binti Harun
Project Coordinator
B.Sc. (Hons.) Chemistry
Faculty of Applied Sciences
Universiti Teknologi MARA
26400 Jengka
Pahang
Prof. Madya Mohd Supi bin Musa
Ketua Pusat Pengajian
Faculty of Applied Sciences
Universiti Teknologi MARA
26400 Jengka
Pahang
Date: 3rd
January 2014
iii
ACKNOWLEDGEMENT
I would like to express my deepest appreciation to my supervisor, Dr. Mohd Zahari
Abdullah @ Rafie for his priceless supervision and guidance throughout this study. I
am in awe of his dedication, constant support, motivation and his assistance with
insightful comments that certainly influenced the quality of this study. I would also
like to extend my gratefulness to Mrs. Siti Norhafiza Mohd Khazaai from Faculty of
Applied Science for providing assistance with ICP-MS analyses. I also want to
recognize and acknowledge Dr Nurlidia Mansor from Universiti Teknologi Petronas
(UTP) for the priceless resources. My sincere gratitude goes to Mr. Mohd Fauzie
Idrus from Physical Chemistry Laboratory for his expertise and aid with FAAS
analyses as well as Mr. Rudaini Mohd. Nawawi for his help with the equipment in
the wood workshop. I would also like to express my gratitude to Ms. Asiah Ismail
and Ms. Siti Nadzifah Ghazali for the guidance in the earlier study. They had taught
me multitudinous lessons of academic research in general. I am thankful to God for
the endless support from my friends. Their endless encouragements, great inspiration
and motivation deserve special appreciation. To my wonderful parents, Abdul Kadir
Alias and Rosenah Abd Maulod earn particular appreciation for always believing in
me and instilling in me love of learning.
Siti Mariam Abdul Kadir
iv
TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS iii
TABLE OF CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
LIST OF ABBREVIATIONS ix
ABSTRACT xi
ABSTRAK xii
CHAPTER 1 INTRODUCTION
1.1 Background of study 1
1.2 Problem statement 5
1.3 Objectives of study 8
1.4 Significance of study 8
CHAPTER 2 LITERATURE REVIEW
2.1 Air Pollution 9
2.2 Rare Earth Elements 10
2.2.1 Lanthanum 11
2.2.2 Thulium 12
2.3 Heavy metals 12
2.4 Biomonitoring 15
2.5 Tree bark sampling 17
2.6 Samples analysis 20
2.6.1 EF and Igeo 24
2.6.2 Pollution Load Index 27
CHAPTER 3 RESEARCH METHODOLOGY
3.1 Materials 28
3.2 Sampling site 28
3.3 Geological characteristics 30
3.4 Sample collection 30
3.5 Sample pre-treatment 31
3.6 Statistical treatment and data presentation 32
CHAPTER 4 RESULTS AND DISCUSSION 4.1 Calibration curve 35
4.2 Validation of the analytical technique
4.2.1 Flame Atomic Absorption Spectrometer 35
v
4.2.2 Inductively Coupled Plasma Mass Spectrometer 36
4.3 Concentration of selected REEs and heavy metals in tree bark samples
4.3.1 General trends of the elements 37
4.3.2 Enrichment Factor and Geoaccumulation Index 40
4.3.2.1 Iron 48
4.3.2.2 Aluminum 49
4.3.2.3 Vanadium 51
4.3.2.4 Manganese 53
4.3.2.5 Nickel 55
4.3.2.6 Copper 57
4.3.2.7 Lead 59
4.3.2.8 Lanthanum 62
4.3.2.9 Thulium 63
4.3.3 Pollution Load Index 66
CHAPTER 5 CONCLUSION AND RECOMMENDATIONS 68
CITED REFERENCES 70
APPENDICES 78
CURRICULUM VITAE 80
vi
LIST OF TABLES
Table Caption Page
2.1 Several elements and their respective anthropogenic
sources.
15
2.2 The instruments used to analyze tree bark 21
2.3 The detection limits of the selected elements 22
2.4. The mean concentration of several elements in tree bark
of various environments
23
2.5 The enrichment factor and enrichment degree. 25
2.6 The contamination level categories based on Igeo. 26
3.1 Wind Statistics and Weather Information in June at
Kuantan Airport
30
4.1 Calibration Curve by FAAS. 35
4.2 Percentage Recovery of Fe in CRM. 36
4.3 Percentage Recovery of selected REEs and heavy metals
in CRM.
36
4.4 Sampling Locations 39
4.5 Concentration of the Analyzed Elements. 39
4.6 Enrichment Factor and the enrichment degree of the
selected elements in the sampled tree barks.
46
4.7 Geo-accumulation Index (Igeo) and the contamination
levels of the selected elements in the samples tree barks.
47
4.8 The Pollution Index of the sampling locations. 66
vii
LIST OF FIGURES
Figure Caption Page
2.1 Schematic diagram of pine tree bark 19
2.2 Schematic diagram of earth’s crust 26
3.1 The sampling stations 29
3.2 Wind Direction Distribution (%) in June at Kuantan Airport 30
3.3 The sampled Acacia mangium tree bark 31
3.4 The flowchart of the methodology throughout the study 34
4.1a Enrichment Factor and the enrichment degree of the selected 43
elements in the sampled tree barks (lower extremities).
4.1b Enrichment Factor and the enrichment degree of the selected 44
elements in the sampled tree barks (upper extremities).
4.2 Geoaccumulation Index and the contamination level of the 45
selected elements in the sampled tree barks.
4.3 EF contour map for Fe. 49
4.4 a. The EF and b. Igeo contour map for Al. 51
4.5 a. The EF and b. Igeo contour map for V. 53
4.6 a. The EF and b. Igeo contour map for Mn. 55
4.7. a. The EF and b. Igeo contour map for Ni. 57
4.8. a. The EF and b. Igeo contour map for Cu. 59
4.9. a. The EF and b. Igeo contour map for Pb. 61
4.10. a.The EF and b. Igeo contour map for La. 63
viii
4.11. The EF contour map for Tm. 65
4.12 The Pollution Index of sampling locations. 67
ix
LIST OF ABBREVIATION
Al : Aluminium
AREs : Asian Rare Earths
As : Arsenic
BASF : Baden Aniline and Soda Factory
Cd : Cadmium
CaSO4 : Gypsum
Ce : Cerium
CRM : Certified Reference Material
Cr : Chromium
Co : Cobalt
Cu : Copper
DNA : Deoxyribonucleic acid
EDXRF : Energy Dispersive X-ray Fluorescence Spectrometry
EF : Enrichment Factor
Eu : Europium
FAAS : Flame Atomic Absorption Spectrometry
Fe : Iron
GIA : Gebeng Industrial Area
HNO3 : Nitric acid
ICP-MS : Inductively Coupled Plasma-Mass Spectrometry
ICP-OES : Inductively Coupled Plasma Atomic Emission Spectrometry
x
Igeo : Geoaccumulation Index
INAA : Instrumental Neutron Activation Analysis
KIG : Kawasan Industri Gebeng
La : Lanthanum
Hg : Mercury
Mn : Manganese
MSW : Municipal solid waste
Nd : Neodymium
Ni : Nickel
Pb : Lead
PIXE : Particle-Induced X-Ray Emission
PLI : Pollution Load Index
REE : Rare Earth Element
Tm : Thulium
UiTM : Universiti Teknologi MARA
V : Vanadium
Zn : Zinc
xi
ABSTRACT
PASSIVE BIOMONITORING OF LANTHANUM, THULIUM AND
SELECTED HEAVY METALS IN AIR BY USING TREE BARK SAMPLE
Rapid urbanization had caused global attention on the air borne contamination due to
their link to health hazard and risk. Several other anthropogenic activities contribute
to the readily high pollutant exist in the atmosphere. Biomonitoring was proven to be
effective and applicable for assessing elements in the air. In this study, Acacia
mangium tree was used as the bioindicator. Over the years, tree bark had been used
as a medium to assess the concentration of the targeted elements due to several
advantages. The superficial deposition and absorption of the elements on the tree
bark surface of several trees was sampled on the experimental stations surrounding
Gebeng Industrial Area (GIA). The aims of this study were to evaluate the
concentrations, enrichment factor (EF) and Geoaccumulation index (Igeo) of Fe, Al, V,
Mn, Ni, Cu, Pb, La and Tm in the tree bark samples used as the bioindicator of air
pollution. The Pollution Load Index (PLI) was also used to determine whether the
sampling locations is polluted or otherwise. Several selected element concentrations
in the tree bark of 7 trees allow to elucidate the impact of past and present
atmospheric pollution at the industrialized environment surrounding GIA. Tree bark
sample was also collected from an uncontaminated area at Universiti Teknologi
MARA (UiTM) Pahang considered as a control site. The element concentrations
were determined by Flame Atomic Absorption Spectroscopy (FAAS) and
Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-OES) after
digested with 65% HNO3. The metal pollution by Fe, Al, V, Mn, Cu and Pb was
principally in barks sample closed to GIA. The average concentrations of the
analyzed elements were; Fe (28.51), Al (84.78), V (0.095), Mn (6.14), Ni (3.04), Cu
(0.014), Pb (21.53), La (0.014) and Tm (< 0.0000002) mg/kg. The EF values
surrounding the GIA did not comply with the expectations. The mean EF values of
each element were Al (1.92), V (29.26), Mn (3884.53), Ni (2144.98), Cu (2.12), Pb
(3.04), La (662.58) and Tm (0.85). The values were found to be fluctuating and
scattered in lieu of higher at a closer distance with the GIA. The Igeo values proved
that almost all of the sampling locations were uncontaminated when compared with
the chemical composition found in the Upper Continental Crust (UCC). The highest
Igeo values of Fe, Al, V, Mn, Ni, Cu, Pb, and La were at C (Igeo = -10.18), B (-9.96), B
(-7.46), C (-5.39), F (-1.86), D (-11.23), D (1.59) and E (-8.81) respectively. Based
on the PLI values, all of the sampling sites were polluted in the range of 1.69 to
16.43. The assessment of air pollution is very crucial and continuous studies should
be done in order to evaluate the contamination level of the atmosphere as air
pollution is known to be one of the main environmental problems that greatly affect
human beings and the environment.
xii
ABSTRAK
PENGGUNAAN SAMPEL KULIT POKOK BAGI PEMONITORAN PASIF
TULIUM, LANTANUM DAN LOGAM BERAT TERPILIH DI UDARA
Arus pembangunan yang semakin pantas telah menarik perhatian masyarakat dunia
terhadap pencemaran udara yang sering dikaitkan dengan risiko kesihatan. Beberapa
aktiviti antropogenik telah menyumbang kepada nilai pencemar yang sediakala tinggi
di atmosfera. Permonitoran biologi telah terbukti efektif dan boleh diaplikasikan bagi
meninjau unsur-unsur logam di udara. Dalam kajian ini, pokok Acacia mangium
telah digunakan sebagai bioindikator. Kulit pokok telah lama digunakan sebagai
medium bagi peninjauan kepekatan unsur sasaran kerana ia mempunyai beberapa
kelebihan. Sampel diambil dari beberapa batang pokok (Acacia mangium) di
Kawasan Industri Gebeng (KIG), Pahang yang mewakili pemendapan luaran dan
penjerapan unsure logam di permukaan kulit pokok tersebut. Tujuan kajian ini adalah
untuk menilai kepekatan, EF dan Igeo bagi unsur-unsur Fe, Al, V, Mn, Ni, Cu, Pb, La
dan Tm yang terdapat di dalam kulit pokok tersebut yang digunakan sebagai
biomonitor untuk pencemaran udara. Indeks Beban Pencemaran (PLI) juga
diaplikasikan untuk menentukan sama ada kawasan kajian dicemari atau tidak.
Unsur-unsur logam tersebut yang terdapat di kulit pokok membolehkan penilaian
terhadap impak pencemaran udara yang telah berlaku dan sedang berlaku di kawasan
sekitar KIG. Sampel kulit pokok juga telah diambil di luar kawasan persekitaran KIG.
Sampel dari kawasan yang dianggap tidak tercemar di Universiti Teknologi Mara
(UiTM) Pahang, telah digunakan sebagai sampel kawalan. Kepekatan unsur telah
ditentukan oleh FAAS dan ICP-MS setelah sampel dihancurkan menggunakan 65 %
asid nitrik (HNO3). Pencemaran oleh beberapa unsur termasuk Fe, Al, V, Mn, Cu
dan Pb telah didapati berlaku di kawasan berdekatan dengan KIG. Kepekatan purata
bagi unsur-unsur yang telah dianalisis adalah masing-masing Fe (28.51), Al (84.78),
V (0.095), Mn (6.14), Ni (3.04), Cu (0.014), Pb (21.53), La (0.014) dan Tm (<
0.0000002) mg/kg. Nilai EF berdekatan KIG tidak seperti yang dijangkakan. Nilai
purata EF bagi setiap unsur yang telah dianalisis adalah seperti berikut Al (1.92), V
(29.26), Mn (3884.53), Ni (2144.98), Cu (2.12), Pb (3.04), La (662.58) dan Tm
(0.85). Nilai-nilai tersebut didapati tidak seragam apabila dibandingkan dengan
komposisi bahan kimia yang terdapat di dalam kerak benua teratas. Nilai tertinggi
Igeo bagi unsur Fe, Al, V, Mn, Ni, Cu, Pb, dan La adalah masing-masing terdapat di C
(Igeo = -10.18), B (-9.96), B (-7.46), C (-5.39), F (-1.86), D (-11.23), D (1.59) dan E
(-8.81). Berdasarkan nilai PLI (dalam lingkungan 1.69 to 16.43), kesemua kawasan
yang telah disampel didapati dicemari oleh unsur-unsur yang dikaji. Pengukuran
tahap pencemaran udara adalah sangat penting dan kajian secara berterusan perlu
dijalankan kerana pencemaran udara dianggap salah satu masalah persekitaran yang
memberi impak yang besar kepada manusia dan persekitarannya.
1
CHAPTER 1
INTRODUCTION
1.1 Background of study
Rare Earth Elements, or can be abbreviated as REEs are the 17 elements lies
at the bottom of the periodic table which majorly conquered by the
lanthanides. Studies on REEs are not widely done especially towards humans
even though several cases reported back in early 1980s, claiming REEs had
caused illness, mental retardations, and other health issues, still, no specific
scientific confirmation had been reported yet up to this date (Bradsher, 2011).
REEs are globally being applied in numerous fields including chemical
industry, medicine, metallurgy and electronics (Schwabe et al., 2012). Due to
the elevating demands worldwide, REEs are being produced to meet the
needs, and even being exploited and had caused awareness among the
researchers. Several well-known REEs are Lanthanum (La) and Cerium (Ce)
which are among two REEs that could become a threat to plants. These two
REEs had been used in China mainly in the fertilizers to promote plant
growth but at a higher level of concentration of REEs are believed to be toxic
to certain plants. The accumulation of REEs does not only toxic to plants but
also had been proven to be toxic to the community of macrofauna (Li et al.,
2010).
2
The other interested group of parameters is heavy metals. The studies on
heavy metals are increasing as most heavy metals are known to be toxic and
carcinogenic (Fu and Wang, 2011). Heavy metals can be produced naturally
as well as anthropogenically but the latter is the main concern as our country
is experiencing a rapid urbanization and Malaysia is not exceptional in being
affected by regional and local air pollution (Latif et al., 2012). Sawidis et al.
(2011) listed several examples of anthropogenic sources of heavy metals,
inter alia, incomplete fossil-fuel combustion from diesel powered vehicles,
energy production and industrial processes.
Early 2007, Lynas Corporation Sdn. Bhd. had alarmed many Malaysians as it
decided to land its REEs refinery at Gebeng, Kuantan, Malaysia. Lynas had
been known to its specialty in producing REEs. In 2011, the world’s leading
chemical company, Baden Aniline and Soda Factory (BASF) had signed an
agreement with Lynas on the distribution of La to BASF which had involved
11,000 tons of La distribution from the Gebeng, Kuantan REEs separation
unit (BASF, 2011). Such huge participation had elevated the researchers’
awareness as the enormous chemical distribution had to be studied intensively
before the distribution or separations of the chemicals are being done.
What had actually become the main problem in allowing the massive
production of REEs is the health concern to the human. Back in 1986, Asian
Rare Earths (AREs) at the Bukit Merah, Perak case had caused a stir as the
3
post operation of the REEs refinery allegedly caused mental retardation,
leukemia and fatality to the neighboring residents’ child, though no scientific
research had confirmed these cases, but the Bukit Merah refinery had been
closed due to its inability to adhere with the safety guidelines (Bradsher,
2011). Therefore, the Lynas project had to be taken seriously in order to avoid
any unwanted and repetitive issues in the future. By studying the level of the
anthropogenic emissions, little can be done to degrade the emissions quantity
and perhaps curbing the production at once.
Biomonitoring had been widely used to estimate airborne contamination and
its alterations over a long time (Catinon et al., 2009). Bioindicator, the
medium for the biomonitoring, such as tree bark, soil and lichen had been
used for years to indicate the airborne contamination degree at a specific area.
One of the bioindicators is tree bark. Its outer layer has been found to be an
effective passive accumulator of airborne particles which are settled through
wet and dry deposition (Škrbić et al., 2012). Tree bark has the best ability to
accumulate a huge amount of atmospheric dust, thus making it a good
bioindicator of air pollution (Catinon et al., 2009). Tree bark could represent
several years of accumulations thus, it would be the best bioindicators
compared to the other bioindicators. Several main factors of the adsorption of
the suspended particles at the tree bark are moist, roughness of the tree bark
surface, and electrically charged surface making tree bark a highly effective
accumulator of the suspended particles (Catinon et al., 2009). Though the
4
main concern of the accumulation is not through the physical contact, these
factors might help in analyzing the REEs content in the tree bark as a whole,
as the retention of REEs in the plant may be too low in concentration to be
analyzed.
For the analysis purpose, Flame Atomic Absorption Spectrometry (FAAS)
had been chosen as its effectiveness and reliability in analyzing traces
elements. Several other methods had been used for analyzing the tree bark
sample which were Instrumental Neutron Activation Analysis (INAA) (El
Khoukhi et al., 2004) and Energy Dispersive X-ray Fluorescence
Spectrometry (EDXRF) (Ferreira et al., 2012). These methods are not
preferable in this study as it may consume more time and they are lacking in
accessibility. Meanwhile, there were two other techniques that commonly had
been used, namely Inductively Coupled Plasma Atomic Emission
Spectrometry (ICP-AES) and Inductively Coupled Plasma-Mass
Spectrometry (ICP-MS) (Guéguen et al., 2012). Both techniques had been
considered as successful multielemental techniques in analyzing
environmental sample. Nevertheless, the concentration of the selected heavy
metals are expected to be detectable by FAAS while the REEs will be in the
range where ICP-MS can detect, which is lower in concentration.
Several statistical models have been used and proposed for a better
characterization of atmospheric particulate matters. Enrichment Factor (EF)
5
and Geoaccumulation Index (Igeo) were used in the study to represent the
concentrations of the targeted sample elements. EF was used to determine the
degree of anthropogenic pollution of the targeted elements. Whereas, Igeo
represented the minimal anthropogenic impacts of the elements to the
sampled location (Kong et al., 2011). This study also covered the application
of Pollution Load Index (PLI) in order to certify the pollution of the targeted
elements at the sampling locations (Guéguen, et al., 2012).
1.2 Problem statement
It has been reported that there are eight leukemia cases within five years in a
community of 11,000 after many years with no leukemia cases without
declaring specifically which REEs had caused the damages (Bradsher, 2011).
In 2007, Lynas Corporation Ltd (Lynas), specialized in producing REEs, had
decided to locate its REEs refinery in Kuantan, Malaysia. This project had
caught many attentions from the Malaysian experts especially the
environmentalists. In 2011, it had announced to have held 11,000 tons of
REEs by the end of 2011 and the amount will be doubled up by 2012 (BASF,
2011). Lynas also had agreed to distribute the REEs to the world’s leading
chemical group, BASF (BASF, 2011). There are 17 elements in the REEs
group which two of the elements, Tm and La, had caught the attention as the
studies on those two elements had not yet been done on the specified area.
6
Even so, the reports of the biological effects of REEs available is too little
and superficial, however the occupational and environmental exposure to
REEs had been widely spread and several ill effects had been reported (Qiang
et al., 1994) with no specific elements had caused such illness.
The accumulation of lanthanum in bone have been showing confirmatory
negative effects to the laboratory rats such as loss of bone minerals (Huang et
al., 2006) though the impacts on humans are still have not been reported.
Hence, biomonitoring of these elements also allow the environmentalist to
take action in curbing the emission of La and Tm.
In addition, the study area covered Gebeng Industrial Area. This area was
reported to have discharged several heavy metals to the wastewater, including
cadmium (Cd), chromium (Cr), mercury (Hg), nickel (Ni), and zinc (Zn)
among others (Hossain et al., 2012; Norzatulakma, 2010) . Another study
done by Sobahan et al. (2013) confirmed that there were presence of lead
(Pb), copper (Cu), cobalt (Co), cadmium (Cd) and arsenic (As) in the surface
water at the Gebeng Industrial Area. The health impacts of heavy metals to
the humans have not failed to attract a global attention due to their prominent
hazardous properties among others non-biodegradable nature, tendency to
accumulate in the food chain as well as long-biological half-lives for
excretion from the body (Chabukdhara and Nema, 2012).
7
Therefore, a study was done to determine the level of La, Tm and selected
heavy metals at the targeted area. The samples were obtained near the Gebeng
Industrial Area where the Lynas refinery is located. The obtained results
would be the baseline for future studies of the same field. The findings would
be significant to the environmentalists to evaluate the air quality around the
targeted area.
8
1.3 Objectives of study
The main objectives of this research include:-
1. To evaluate the distribution profile of La, Tm and selected heavy
metals emission surrounding Gebeng Industrial Area.
2. To determine the Geoaccumulation Index, Pollution Index and
Enrichment Factor of La, Tm and selected heavy metals around Gebeng
Industrial Area using tree bark sample.
1.4 Significance of study
1. To utilize the local plant as one of the bioindicators to extract
the information about the air surrounding Gebeng Industrial Area.
2. To evaluate the baseline composition of the selected REEs elements
and selected heavy metals surrounding Gebeng Industrial Area.
3. The findings would be useful for future studies regarding the selected
REEs and selected heavy metals pollutions around Gebeng Industrial Area.
9
CHAPTER 2
LITERATURE REVIEW
2.1. Air Pollution
The world today is facing one of the largest problems that are related to
environmental pollution. One of the main environmental pollutions is air
pollution. Air pollution can be defined as complex mixtures of particles
which include particulate matters; nitrogen, carbon monoxide, oxides, sulfur
oxides ozone, methane, and other gases, volatile organic compounds and
metals which are produced naturally and anthropogenically (Block et al.,
2012). Air pollution had been a globally recognized as one of the major
problem over the last 50 years (Dominick et al., 2012). Chung et al. (2011)
compared the potential health effects of the elevating air pollution in Europe
and North America with Asia and concluded that Asia’s potentials remain
largely unmeasured.
One of the emitters of heavy metals is fluidized-bed municipal solid waste
(MSW) incinerator (Li et al., 2003). The heavy metals had been emitted to the
air during the incineration of MSW. Despite being in the information
technology era, most countries still rely on industrial sectors, thus making
industrial activities to play a big role. A study in Argentina confirmed that
industrial activities contributed majorly in the emission of the heavy metals
10
(Wannaz et al., 2012). While in China, other sources include coal burning,
iron and steel industry and vehicle emission (Duan and Tan, 2013).
Malaysia is located in the middle of South East Asia. The country itself
experiences a rapid urbanization and it is affected by local and regional air
pollution (Latif et al., 2012). Traffic is the largest air pollution contributor in
urban areas in most developing countries (Dominick et al., 2012; Azmi et al.,
2010) including Malaysia. A study had proved that severe air quality
problems exist on the Penisular Malaysia especially at the urbanized areas
(Azmi et al., 2010). A study done by Afroz et al. (2003) shows that the main
sources of air pollutants in this country were contributed by mobile sources
stationary emissions and open burnings.
2.2. Rare Earth Elements
Rare Earth Elements (REEs) are 17 elements with atomic number starting
with 57(lanthanum) to 71(lutetium) on the periodic table which can be further
divided into two types which are light earth metals and heavy rare earth
elements (Chakhmouradian and Wall, 2012). REEs production had been
widely demanded with its production increment up to approximately 8% per
annum due to much wider applications of REEs in consumer products,
automobiles, aircraft and other advanced technology products (Long et al.,
2012). REEs are also being used in every car, computer, smart phone, energy-
11
efficient fluorescent lamp, colour TV, as well as in lasers, lenses and ceramics
(Chakhmouradian and Wall, 2012).
REEs also had been produced from quarries or factories producing artificial
fertilisers (Ptaszyński and Zwolińska, 2001). Despite the fear of the unknown
negative effects of these lanthanides, Ptaszyński and Zwolińska (2001)
claimed that lanthanide had been used in plantation which helped in
absorption of nitrogen, phosphorus and potassium, thus promoting the
ripening process of plants and eventually helped in enhancing the growth of
their mass.
2.2.1 Lanthanum
The main concerns in this study are thulium (Tm) and lanthanum (La). La has
been the attention of several fields as its usages and applications had been
revealed. La has been used in optical and semiconductor applications and also
in the production of metals with different yet special properties (Briner et al.,
2000). La also has been associated with the other beneficial REEs such as
cerium (Ce), neodymium (Nd) and europium (Eu) in providing several
promoting effects to the plants which had been widely used in China for
agricultural purpose (Zhang et al., 2013).
La shows positive benefits to non-living things but not the mortals in a high
dosage. Studies revealed that La has the ability to interfere with
12
neurotransmitter release and response, both crucial for normal memory
formation (Briner et al., 2000). Briner et al. (2000) also reported that at a high
dosage of La exposure may lead to fatality to the more susceptible embryos in
utero of mice. Another study revealed that La is a potential behavior
teratogen, which is an agent that causes malfunction to the embryo, which is
due to the research reported that La had caused impairment in memories,
alterations on DNA of the brain and deterioration of learning abilities (Feng
et al., 2006).
2.2.2 Thulium
Tm falls under the “heavy rare earths” category, which makes it to be rarer
than La. Unsurprisingly, fewer studies have been reported on the health effect
of Tm to the mortals. However, Böhlandt et al. (2012) reported that by
inhaling the lanthanides, one may be associated with various acute and
chronic systemic toxicological impacts with the respiratory system as the
main target. Tm is a part of the lanthanides, thus it cannot be neglected
simply because there are lack of studies on the health effects caused by Tm. It
is proven that wide application of REEs in the fertilizers will lead to bio-
accumulation and has the possibility of endangering public health (Turra et
al., 2011).
2.3 Heavy metals
One of the definitions that defines heavy metal is elements having atomic
weights ranging between 63.5 and 200.6, and a specific gravity greater than
13
5.0 (Fu and Wang, 2011). The term “heavy metals” has been often used as a
group name for metals and metalloids that have been regarded with
contamination and potential toxicity or ecotoxicity (Ataabadi et al., 2010).
Nevertheless, several known heavy metals are essential to plant, animals and
humans as a part of the nutrients such as Zn, Cu, Mn and Ni (Ataabadi et al.,
2010).
The wide ranges of human activities as well as natural geochemical processes
contribute to the elevation of heavy metals especially in the urbanized area
(Lu et al., 2010). Human activities contribute dominantly in heavy metals
production. There are two types of anthropogenic sources which are mobile
and stationary. The latter type of anthropogenic sources includes industrial
plants, waste incineration, construction and residential fossil fuel burning,
among others (Lu et al., 2010). In related to the field of study, industrial
plants will be focused as the study site will involve one of the largest
industrial areas in Malaysia.
One of the major metal emitters is petrochemical industries. A study done in
Tarragona County, Spain confirmed that petrochemical industries emit
several metals include Pb and Cd (Nadal et al., 2011). Nadal et al. (2011) also
stated that this industry have been identified as a fundamental emitters of
chemical substances including heavy metals. Another dominant heavy metal
source is chemical industries. The aforementioned study at Spain also
14
reported that chemical industry emitted several heavy metals with As and V
showed the highest concentration at the chemical industries area (Nadal et al.,
2011).
The aforesaid hazardous properties of heavy metals are the main reason why
heavy metals’ study needs to be done extensively. The bioaccumulated heavy
metals could end up in our food and would have potentials in giving us
undesirable complications. Lead toxicity, for example, had been proven in
many studies to cause central nervous system deficits that can persist into
primary adulthood (Ma and Singhirunnusorn, 2012). Besides that, the toxicity
of Cu, Cd, and Zn is acknowledged to cause alteration in human central
nervous system and respiratory system as well as having the ability to cause
disruptions in endocrine system (Ma and Singhirunnusorn, 2012). In addition,
Sawidis et al. (2011) stated that urban air particulates are ubiquitous in
potentially toxic heavy metals for example Pb, Cr, Fe etc. and can be a
genuine hazard to health. Table 2.1 shows the anthropogenic sources for the
heavy metals that have been proven to give detrimental effects to the
biosphere.
15
Table 2.1 Several elements and their respective anthropogenic sources.
Element Sources
Antimony Fossil fuel combustion ,mining, smelting
Arsenic Fossil fuel combustion, geothermal energy production, mining,
pesticides, phosphate fertilizer, smelting, steel making
Cadmium Fossil fuel combustion, incineration, mining, motor vehicles,
phosphate fertilizer, smelting, , sewage sludge
Chromium Fossil fuel combustion, phosphate fertilizer, sewage sludge, smelting,
steel making
Cobalt Mining, smelting, fossil fuel combustion
Copper Fossil fuel combustion, manure, mining, pesticides, sewage sludge,
smelting
Fluorine Aluminum refining, brick making, glass and ceramic manufacture,
fossil fuel combustion, mining, phosphate fertilizer, steel making
Lead Fossil fuel combustion, mining, , motor vehicles, pesticides, sewage
sludge, smelting
Mercury Fossil fuel combustion, incineration, smelting, , sewage sludge
Nickel Fossil fuel combustion, mining, motor vehicles, oil refining, smelting,
steel making, sewage sludge
Selenium Fossil fuel combustion, smelting
Thallium Fossil fuel combustion, smelting
Uranium Phosphate fertilizer, fossil fuel combustion
Vanadium Fossil fuel combustion, oil refining, steel making
Zinc Fossil fuel combustion, galvanized metal, manure, mining, , motor
vehicles, smelting pesticides, phosphate fertilizer, sewage sludge, steel
making
Source: Selinus et al. (2005).
2.4 Biomonitoring
Bioindicators are biological elements used as the indicator that could provide
information regarding the state of air pollution at the particular area. The most
commonly used bioindicators are lichens, mosses, tree barks, pine needles
16
and soils. Moss was the first bioindicator introduced by Ruhling and Tyler to
monitor the lead presence in the air in 1968 (Rühling and Tyler, 1968).
Application of other bioindicators to monitor heavy metal deposition since it
has been an established fact that plants are “living filters”, leaves and any
other exposed parts of a plant acts as persistent absorbent in a polluted
atmosphere (Das and Prasad 2010).
There are two types of biomonitoring which are active and passive. Active
biomonitoring involves the transplantation of organism in containers to the
areas to be tested for ecotoxicological parameters (Lu et al., 2010). While the
passive mode of biomonitoring is proved useful for monitoring contamination
trends for metals as well as several organic contaminants (Besse et al., 2012).
Therefore, passive sampling is much preferred due to the method of obtaining
the sample is much easier and faster. Also, passive sampling is less expensive
compared to conventional high-volume, active air sampling (Salamova and
Hites, 2010).
Active biomonitoring requires a “mode” which known as transplant as a
medium that will be collected at the end of the sampling period to be
analyzed. The most common transplant used is moss bag. The moss will be
placed in a nylon bag and will be brought to the sampling site (Lodenius,
2013) and left for several months or years and the bag will be collected and
the moss will be analyzed (Vuković et al., 2013).
17
Unlike active mode of biomonitoring, passive mode’s procedure is much
simpler, as foretold. In situ bioindicators, lichens, for example, will be
sampled on the sampling area on the same day. The lichens had been exposed
to the atmosphere for a considerable period so that they are considerably
equilibrium with the environmental stressors in the sampling site (Kularatne
and Freitas, 2013). Then the lichens will be harvested carefully on the
particular day using suitable tools like precleaned forceps (Kularatne and
Freitas, 2013). After the samples had been collected, they will be kept in a
clean bag, and then will be brought to the laboratory for pre-treatment.
Samples will be then digested according to the suitability and will be
analyzed.
2.5 Tree bark sampling
The most commonly used biological materials are lichens, mosses, tree bark,
grass or leaves. Using natural vegetation biomonitoring for passive sampling
purpose enables procurement of a well-defined sample at inexpensive costs In
addition, Salamova and Hites (2010) reported that tree bark sampling is easy,
time saving and advantageous in remote settings. Moreover, Acacia mangium
is available throughout the season, which means the trees do not need special
season to thrive in. One interesting study done by Ang et al. (2010) was A.
mangium accumulated the highest total amount of Pb per hectare basis due to
its advantageous factors which are fast growing and relatively high uptake of
18
Pb. Also, A. mangium has wider distribution throughout the study area,
making it to be the chosen tree species.
Tree bark has been one of the best choices of bioindicators because it has
high lipid content, large surface area (Salamova and Hites, 2010) and
represents degree of pollutants over period of several years. Tree bark had
been used to represent the accumulation of air-borne mercury (Lodenius,
2013), organic and inorganic pollutants. Some organic and inorganic
pollutants include heavy metals (Celik et al., 2010), polychlorinated
biphenyls (Guéguen et al., 2011), polycyclic aromatic compounds (Salamova
and Hites, 2010), brominated and chlorinated flame retardants (Salamova &
Hites, 2012).
Tree bark, had been widely used as bioindicator due to its ability to absorb
and adsorb as well as accumulate airborne contaminants (Harju et al., 2002).
Moreover, tree bark’s structural porosity and potential for efficient
accumulation of aerosol particles make it to be a shortlisted indicator in
monitoring air pollution (Berlizov et al., 2007). The main concern of this
study is to determine the airborne Tm, La and heavy metals in the inner bark;
however, the inner bark represents the metal ion flow within a tree, unlike the
outer bark which mainly reflects the airborne pollutants (Harju et al., 2002).
The following schematic diagram, Figure 2.1, shows the cross-section of the
pine bark which reveals the outer bark, inner bark and the wood (xylem). The
19
cross-section is similar to the sampling tree, Acacia mangium. Due to the
unavailability of pine tree at the sampling area, A. mangium will be used in
this study.
Figure 2.1 Schematic diagram of pine tree bark.
Source: Harju et al. (2002).
Based on the Figure 2.1, tree bark consists of two layers which are the inner
layer of bark (phloem) and the outer layer (rhytidome) which the latter is
composed of dead cork cells (Poikolainen, 2004). In the bark, the
accumulation of environmental stressors is purely a physiological-chemical
process (Poikolainen, 2004). The pollutants will be deposited on the bark by
two processes which are passively being accumulated on the surface of the
bark surface or being absorbed through ion exchange processes in the outer
parts of the dead cork layer (Poikolainen, 2004).
20
Tree bark sampling is widely used to determine the air borne contamination,
hence, no interaction from the nutrients uptake from the soil shall be included
since there is no significant migration of elements from the surface of bark
into the underlying wood (xylem), or vice versa (Poikolainen 2004). In
addition, the heavy metals migration from the soil through the roots into the
bark is usually insignificant (Poikolainen, 2004). Škrbić et al. (2012) stated
that the metal species deposited on the outer bark are separated physically
from the taken up trace elements from the soil in the trees and their xylem is
by a layer of phloem and cambium. Furthermore, foreign contamination from
the soil is limited to the 1.5 m of the trunk, starting from the tree’s base
(Škrbić et al., 2012).
Different pollutants have different behavior in the bark, for example, sulphur.
Sulphur accumulated in bark as sulphuric acid and most of it will react with
calcium to form gypsum (CaSO4), whereas for heavy metals, the substance
accumulates depending on their particle size and on the form in which the
metals occur (Poikolainen, 2004). Heavy metals form compounds with other
elements or occur in particles together with compounds of similar size of
particle (Poikolainen, 2004).
2.6 Sample analysis
Updated and high technologies for analytical techniques measurement are
available and had been progressively modified and upgraded from time to
21
time. However, the selection of the best analytical method would be based on
a few factors that include the operational cost, the availability, the sensitivity
of the instrument and time. ICP-OES, PIXE, INAA are several highly
sensitive instruments that could provide accurate results but there are several
limitations to these instruments such as, time consuming, costly and
unavailability. Thus, in this study, FAAS and ICP-MS will be used to analyze
all of the interested elements as it could meet the best requirements among
others.
The knowledge about REE accumulation in plants had elevated with the
availability of more sensitive techniques for high-quality determination for
example. Table 2.2 shows the list of studies done to analyze tree bark by
using different instruments.
Table 2.2 The instruments used to analyze tree bark.
INSTRUMENT REFERENCES
Inductively Coupled Plasma-Mass Spectrometry
(ICP-MS)
(Catinon et al., 2011)
Scanning Electron Microscope Coupled to an
Energy Dispersive X-Ray (SEM-EDX)
(Catinon et al., 2011)
Energy Dispersive X-Ray Fluorescence
Spectrometry (ED-XRF)
(Ferreira et al., 2012)
Flame Atomic Absorption Spectroscopy (FAAS) (Mansor et al., 2010)
It is expected that the presence of the interested elements would be low;
hence, the samples were analyzed by FAAS and ICP-MS. In addition to the
chosen instruments based on the concentration of the samples, those two
22
instruments have their own detection limits on the targeted samples. Table 2.3
shows the detection limits of the targeted elements provided by Elmer (2008)
and the Table 2.4 shows concentration of the selected elements in the tree
bark in various environments.
Table 2.3 The detection limits of the selected elements.
Elements Detection limit of instruments (µg/L)
FAAS ICP-MS
Iron 5.0 0.0003
Aluminium 45.0 0.005
Vanadium 60.0 0.0005
Manganese 1.5 0.00007
Nickel 6.0 0.0004
Copper 1.5 0.0002
Lead 15.0 0.00004
Lanthanum N/A 0.0009
Thulium N/A 0.00006
Source: Elmer (2008).
Tree bark has high lipid content (Salamova and Hites, 2010), therefore a good
choice of acid to digest the tree bark is crucial. Thus, by leaving the grinded
sample in HNO3 for overnight before heating it at 100°C (Fujiwara et al.,
2011) would give the best dissolution of the sample. Also, wet digestion is
chosen due to the several factors in preserving the existing concentration of
the targeted elements. High temperature will not be employed in order to
avoid loss of elements through volatilization or splashing of elements on the
wall of the furnace (Hseu, 2004).
23
Table 2.4. The mean concentration of several elements in tree bark of various environments.
Area of sampling
Mean concentration, mg/kg
Reference Fe Al V Mn Ni Cu Pb La Tm
Relatively unpolluted
area in Finland
(P.sylvestris L.)
nd nd nd 29.2–432 0.54–1.71 267-340 1.00-2.10 nd nd Baltrėnaitė et al.
(2013)
Upper crust continental 35 000 84 700 60 600 20 25 15 30 0.33 Taylor et al., (1981)
City in Sheffield, UK
(Sycamore, oak, cherry) 5712 nd nd 280 65.0 47.3 226 nd nd
Schelle et al.
(2008)
Highly air polluting
industry, Belgrade
(Platanus sp. and Pinus
sp.)
327.277 nd nd nd nd 37.900 15.567 nd nd Sawidis et al.
(2011)
Note: Information in the bracket represents the tree species.
nd: not defined
24
2.6.1 EF and Igeo
For the purpose of the analysis of the trace elements, Enrichment Factor (EF)
and Geoaccumulation Index (Igeo) were used. EF is based on the reference
element as a standard to evaluate the man-made impact activities on the level
of enrichment of atmospheric particulate matter (Fang et al., 2013). It enables
to distinguish between metals originating from man-made activities and those
produced naturally, as well as to assess the degree of human activities’
influence (Li et al., 2013). The formula to calculate EF is shown as the
following;
EF=
n is to measure the elements, while r is the reference element. This can be
further explained with the fraction of (Xn/Xr)atmosphere which represents the
ratio of the measured concentration of measuring the element with the
selected reference element in the total suspended particles in the atmosphere.
Whereas (Xn/Xr)background is for the ratio used to measure the element with the
selected reference element concentration in the reference system (Fang et al.,
2013).
EF can be used to estimate how much the sample is impacted with metals. In
the application of EF, pollution will be measured as the ratio or amount of the
sample metal enrichment above the concentration present in the reference
25
sample (Abdullah et al. 2011). Abdullah et al. (2011) also proposed that the
value of EF is directly proportional to the contribution of anthropogenic
origins. There are five contamination categories which can be shown in the
Table 2.5. Ghrefat et al. (2011) suggested that if the EF value lies between 0.5
and 1.5 it indicates the metal is entirely from natural processes or crustal
materials. If the value exceeds 1.5, it indicates the origin of the elements are
more likely to be anthropogenic. EF explained the enrichment or the pollution
of the targeted elements at the sampling locations by comparing it with the
background values, which was considered as uncontaminated.
Table 2.5. The enrichment factor and enrichment degree.
EF Value Enrichment Degree
<2 Deficiency to minimal enrichment
2-5 Moderate enrichment
5-20 Significant enrichment
20-40 Very high enrichment
>40 Extremely high enrichment
Source: Abdullah et al. (2011).
Igeo can be calculated by using the following equation (Kong et al., 2011);
Cn represents the measured metal concentration and Bn is representing the
value of the geochemical background (Kong et al., 2011). The constant 1.5 is
the background matrix correction factor which allows the analysis of natural
fluctuations in the content of a given environment and to detect minimal
anthropogenic influences as well as to account for possible differences in the
background values due to lithogenic effects (Kong et al., 2011; Omoniyi, et
al., 2013). Geoaccumulation Index (Igeo) for contamination levels in samples
26
can be categorized as shown in Table 2.6. In this study, the background value
was obtained from the chemical composition of the upper continental crust
(UCC) (Taylor et al., 1981). UCC is considered “untouched” region of the
earth which can be used to determine whether manmade activities exceeds, or
in other word, pollute the sampling locations. Figure 2.2 shows a schematic
diagram of the layer of the earth’s crust. The upper crust represents UCC.
Figure 2.2 Schematic diagram of earth’s crust.
Source: Wedepohl (1995).
Table 2.6. The contamination level categories based on Igeo.
Igeo class Igeo value Contamination level
0 Igeo ≤0 Uncontaminated
1 0< Igeo ≤1 Uncontaminated/moderately contaminated
2 1< Igeo ≤2 Moderately contaminated
3 2< Igeo ≤3 Moderately/strongly contaminated
4 3< Igeo ≤4 Strongly contaminated
5 4<Igeo≤5 Strongly/extremely contaminated
6 Igeo > 5 Extremely contaminated
Source: Kong et al. (2011) and Omoniyi et al. (2013).
27
2.6.2 Pollution Load Index
Pollution Load Index (PLI) was used together with the Contamination Factor
(CF) and is as shown:
CF = C metal / C background value
PLI=
where, CF is the contamination factor, n is the number of metals, C metal is
the metal concentration in polluted tree bark, C Background value =
background value of that metal. The PLI value of > 1 is polluted, whereas <1
indicates unpolluted (Nameer, 2011). PLI enables the evaluation of the
studied locations extensively according to their classifications according to
their pollution classes.
28
CHAPTER 3
RESEARCH METHODOLOGY
3.1 Materials
The list of chemical used:
65% HNO3 (Grade: A.R.)
Standard Refrence Material
Multielement Standard Solution
3.2 Sampling site
Samples of tree bark of Acacia mangium tree were used as the bioindicator in
this study. The samples were collected in June 2013 at the points surrounding
Gebeng Industrial Area Xs illustrated in the Figure 3.1. The sampling areas
are divided into two; namely Area X and Area Y. Area X is in the red
polygon as illustrated in Figure 3.1 where its sampling locations were in the
radius of less than 4 km from the centre point of GIA, whereas the Area Y
represented the sampling locations that are approximately 9 km from the
GIA. All of the samples were obtained at seven stations as illustrated in the
Figure 3.1. Two samples were obtained in the GIA itself while the other five
samples were taken outside of the GIA in a distance of approximately less
than 9 km each from GIA. There were two locations located in Area X,
29
namely C and D, whereas the other five locations, namely A, B, E, F and G
were located in the Area Y.
Figure 3.1 The sampling stations.
Source: Google Earth (2013).
30
3.3 Geological characteristics
Figure 3.2. Wind Direction Distribution(%) in June at Kuantan Airport.
Source: Windfinder (2013).
Table 3.1. Wind Statistics and Weather Information in June at Kuantan
Airport.
Month June 2013
Dominant wind direction
Average wind speed (m/s) 2.5
Average air temperature (°C) 29
Source: Windfinder (2013).
During the month of sampling, the wind direction was majorly favoring to
south west. However, according to Windfinder (2013), the dominant wind
direction was toward north with a an average wind speed at 2.5 m/s, as
tabulated in the Table 3.1
3.4 Sample collection
Tree barks from a total of seven Acacia mangium trees were collected.
Samples of approximately 10 cm2 were chiseled by using a precleaned chisel
(Tye et al., 2006) as shown in Figure 3.3. At each sampling station, the
31
samples were taken on two opposite sides of the tree at the height of 1.5 m
from the ground. This height was chosen specifically to avoid areas where
soil particles may be splashed onto the trunk during rainfall (Mansor, 2008).
The samples were sealed in aluminum foil and were fastened with care by
using plastic bag to avoid the degradation of the elements compositions in the
samples. All handling procedures including chiseling of the tree barks were
done by wearing gloves to avoid unexpected contaminations.
Figure 3.3 The sampled Acacia mangium tree bark.
3.5 Sample pre-treatment
Any unwanted foreign traces of matter were removed including soil, lichens
as well as small insects. Before crushing the tree bark samples, the outermost
32
layer of the bark was superficially brushed, but the innermost layer was kept
untouched. The sample was not washed in order to avoid the material that had
been absorbed by the bark surface to be lost (Ferreira et al., 2012) and to
avoid contributing moisture to the tree bark.
The samples were dried in the oven for 24 hours at the temperature of 70
before being grinded into powder form by using grinder. Two gram of the
sample was digested in a 15 mL concentrated HNO3 (69-70%) in a PTFE
beaker (Fujiwara et al., 2011). The digested sample was left for 24 hours at
room temperature and was heated at 100 °C to almost dryness (Fujiwara et
al., 2011). The residue was then dissolved in 10 mL of deionized water and
the obtained solution was centrifuged at 3500 rpm for approximately 4
minutes (Fujiwara et al., 2011).
After completed, the sample was filtered by using prewashed Whatman
No.42 filter paper and the filtrate was placed in the 100mL of volumetric
flask and made up to the mark by using deionized water. The standard was
prepared by using the same procedure but without the presence of the sample.
Three replicates of each sample were prepared. All traces of elements were
determined against the blank solution through Inductively Coupled Plasma
Mass Spectrometry (ICP-MS) and Flame Atomic Absorption Spectrometry
(FAAS). All of the glassware and sampling vials involved were soaked
33
overnight with 10% HNO3 and were cleaned thoroughly with double distilled
water.
3.6 Statistical treatment and data presentation
The heavy metal concentrations data were analysed by using Enrichment
Factor (EF) and Geoaccumulation Index (Igeo). The data obtained from these
formulas revealed the concentrations of the selected REEs and selected heavy
metals enabled the determination of their sources, whether they are produced
naturally or anthropogenically. Pollution Load Index (PLI) also was
calculated in order to decide whether the selected sampling locations are
polluted with the targeted elements or not. The outline of the methodology
throughout the study is presented as shown in Figure 3.4.
34
Figure 3.4 The flowchart of the methodology throughout the study.
Sample analysis by FAAS and ICP-MS
Filtrate will be placed in 100 mL volumetric flask and made up to the mark
Filter with prewashed Whatman No. 42
Centrifuge at 3500 rpm for 4 minutes
Dissolve in 10 mL deionized water
Heated at 100°C to almost dryness
Digest 2 g of sample in 15 mL conc. HNO3
Grinding of tree bark
Drying of samples in oven for 24 hours at 70 °C
Brushing of outer bark and removal of unwanted foreign matters
Sample pre treatment
Collection of the barks of Acacia mangium at the sampling site
Selection of the sampling locations
35
CHAPTER 4
RESULTS AND DISCUSSION
4.1 Calibration curve
The first and foremost fundamental step in running Flame Atomic Absorption
Spectrometer (FAAS) is calibrating the instrumental. The element of interest,
Fe was diluted in deionized water from its initial concentration of 1000mg/L
into a set of 5 concentrations which are; 5 ppm, 10 ppm, 15 ppm, 20 ppm and
25 ppm. The calibration of FAAS was achieved by running these set of
concentrations and the results obtained are shown in Table 4.1 below:
Table 4.1. Calibration Curve by FAAS.
Element Regression Coefficient, R2
Fe 0.9980
4.2 Validation of the analytical technique
4.2.1 Flame Atomic Absorption Spectrometer
FAAS was used to analyze the standard solution and the certified reference
material (CRM) for the purpose of calibration and verification of the correct
use of the method applied in the study. The purpose of analyzing CRM is to
evaluate the reliability and accuracy of the analytical technique used in this
study (Abdullah et al., 2011). By analyzing these materials, the percentage
recoveries of the interested elements can be further determined by using the
formula:
36
Table 4.2 shows the percentage recovery obtained for Fe when the CRM was
analyzed using FAAS. The percentage recovery for Fe was 85.30% which
was considered as good recovery percentage.
Table 4.2 Percentage Recovery of Fe in CRM.
Metal Measured Value
(mg/kg)
Certified Value
(mg/kg)
Percentage
Recovery
Fe 39.24 46±2 85.30
4.2.2 Inductively Coupled Plasma Mass Spectrometer
A much sensitive instrument, ICP-MS was used to analyze multiple elements
at once. As what was done in FAAS, the same CRM were analyzed by using
ICP-MS for Al, V, Mn, Ni, Cu, Pb, La and Tm. The percentage recoveries of
the set of elements are shown in the Table 4.3:
Table 4.3 Percentage Recovery of selected REEs and heavy metals in CRM.
Metal Measured Value
(mg/kg)
Certified Value
(mg/kg)
Percentage
Recovery
Al 515.30 580±30 88.85
V 20.00 NC -
Mn 424.00 488±12 86.89
Ni 1.480 1.47±0.1 100.68
Cu 2.300 2.8±0.2 82.14
Pb 0.175 0.167±0.015 104.79
La 0.0700 NC -
Tm 0.0400 NC -
Average 92.67
NC: Not certified.
The mean percentage obtained for the set of elements was 92.67% which
ranged of 82.14 to 104.79%, representing good to excellent recovery. It was
37
found that the percentage recovery of a couple of elements were greater than
100%, which was possibly caused by random and systematic errors during the
sample preparation.
4.3 Concentration of selected REEs and heavy metals in tree bark samples
4.3.1 General trend of the elements
Table 4.4 shows the coordinate representation for the seven sampling
locations The concentrations of the studied elements in the tree bark samples
obtained in this study are shown in the Table 4.5. The concentrations of the
elements were found as: 13.25 to 45.20 mg/kg for Fe, 0.0022 to 180.94 mg/kg
for Al, 0.003050 to 0.51 mg/kg for V, 0.00224 to 21.50 mg/kg for Mn,
0.00277 to 8.24 mg/kg for Ni, 0.01111 to 0.01564 mg/kg for Cu, 8.47 to
67.54 mg/kg for Pb, 0.10 to less than 0.00002 mg/kg for La and the
concentrations of Tm in each sampling location was less than 0.0000002
mg/kg. The study clearly showed that the concentrations of the analyzed
elements were in a wide range of concentrations. The mean concentrations of
the analyzed metals (in mg/kg) at all the sampling sites are as follows: Fe
(28.51), Al (84.78), V (0.095), Mn (6.14), Ni (3.04), Cu (0.014), Pb (21.53),
La (0.014) and Tm (< 0.0000002) mg/kg.
Based on the concentrations of each element for the whole sampling
locations, almost all of the elements were found higher in the Area X, except
for two elements, Ni and La. For some prominent reasons including the
38
presence of industrial activities in the GIA and the lesser dense forest stand at
GIA majorly contributed to the high value of these two parameters in the
Area X. Based on the results, the contaminations are therefore considerably
consistent with the expected pattern that very high levels of contaminations
are restricted in the GIA, thus resulting in higher contamination of Fe, Al, V,
Mn, Cu and Pb in the Area X than in the Area Y. Other than the distance
aspect, another contributing factor that gave significantly low value of
concentration is the denser forest stand at the sampling location G.
The data obtained were mapped into a contour visualization using Surfer 8
software to give a clearer view on the distribution patterns of the selected
elements surrounding GIA. The spatial distribution maps for each element
from the sampling sites are denoted in their respective specific sections in the
following. The maps also revealed the pattern of Enrichment Factor (EF) and
Geoaccumulation Index (Igeo) of the elements at the sampling area.
The Pollution Load Index (PLI) was also applied to qualify the impact of
pollution at the sampling locations (Guéguen et al., 2012). The contour map
of PLI was also included to give a clearer perspective on the pollution degree
of the sampling locations. Based on the data obtained, the average
concentrations of the investigated elements showed anomaly-to-background
contrasts with their respective chemical compositions in the Upper
Continental Crust (UCC).
39
Table 4.4. Sampling Locations.
Location Latitude Longitude
A 4°3'39.94"N 103°23'16.30"E
B 4°0'43.29"N 103°23'4.54"E
C 3°58'39.46"N 103°22'17.61"E
D 3°58'55.90"N 103°23'45.00"E
E 3°56'17.94"N 103°22'10.01"E
F 3°53'51.43"N 103°21'22.09"E
G 3°58'31.35"N 103°18'31.10"E
Table 4.5. Concentration of the Analyzed Elements.
Sampling Element Concentration, mg/kg
Location Fe Al V Mn Ni Cu Pb La Tm
A 13.25 99.680 0.003580 13.490 6.1100 0.01415 11.00 < 0.00002 < 0.0000002
B 35.40 180.94 0.5100 0.00351 0.003420 0.01500 33.38 < 0.00002 < 0.0000002
C 45.20 127.94 0.07000 21.500 0.003356 0.01334 10.56 < 0.00002 < 0.0000002
D 15.90 41.05 0.003050 0.00334 6.9100 0.01564 67.54 < 0.00002 < 0.0000002
E 16.35 104.04 0.07000 5.890 0.001530 0.01111 8.47 0.10 < 0.0000002
F 14.70 39.78 0.003580 0.00224 8.2400 0.01452 10.54 < 0.00002 < 0.0000002
G 13.55 0.0022 0.003520 2.110 0.002770 0.01425 9.25 < 0.00002 < 0.0000002
Mean 28.51 84.78 0.09500 6.140 3.04000 0.01400 21.53 0.014 0.0000002
Control 15.15 30.63 0.001568 0.00112 0.001470 0.00524 5.82 < 0.00002 < 0.0000002
UCC 35000 84700 60 600 20 25 15 30 0.33
UCC: Upper continental crust
40
4.3.2 Enrichment Factor and Geoaccumulation Index
By assessing the pollution level of the selected sampling locations and
comparing these values with the crust composition is not adequate because of
the presence of local lithological anomalies. In a similar aspect, a high EF
value does not exigently represent an additional source that is due to
manmade activities. But in this study, EF was based on the background
values obtained from the location which was considered to be
uncontaminated. The EF values were calculated by using the formula;
EF=
The general graph of EF is segregated into two due to the extreme differences
on the values. Figure 4.1(a) shows the sampling locations with lower
extremities of EF values, whereas Figure 4.1(b) represents the upper
extremities of the EF value. Lee et al (2012) suggested that if the value of EF
significantly greater than 1, it indicates that the site is heavily contaminated
since the control area’s sample is considered to be uncontaminated. In
addition, Rashed (2010) suggested that the value of EF can be generally
categorized into three groups which are, for EF value less than 1.0, it suggests
a possible mobilization or depletion of metals, if EF value is greater than 1.0,
it indicates that the source of element is from manmade activities whereas if
the EF value exceeds 10, it is considered to be non-crusted origin.
41
However, Ghrefat et al. (2011) distinguished groups of sources of the metals
into two which are from natural processes and human activities based on the
EF values alone. If the EF value lies in the range of 0.5 to 1.5, it indicates the
metals are from natural sources, but if the EF value exceeds 1.5, the sources is
more likely came from manmade activities (Ghrefat et al., 2011). In this
study, the classification of the sources of the elements were based on the
study done by Gherafat et al (2011).
Tessier et al. (2011) stated that in order to avoid overestimation or
underestimation of the enrichment, geochemical normalization based on the
concentration of a conservative element should be applied. Geochemical
normalization also enables the correction of the changes in the nature of the
sample which may affect the contaminant distribution normalization (Tessier
et al., 2011). Therefore, in this study, Fe was used as the conservative element
for normalization.
From the Figure 4.1(a) and 4.1(b), on average, EF values were found to be
scattered. Area X was proven to be less impacted with high values of EF, as
expected. Only several elements showed extreme enrichment at the sampling
area, including V, Mn, Ni and La. A more specified data on the EF values,
including the respective enrichment degrees is tabulated in Table 4.6.
42
Since the background values for Igeo were based on the UCC, the outcome
was different than the pattern of EF, but somehow several elements showed
indistinguishable pattern. The Igeo values were calculated by using the
formula;
Table 4.7 shows the specific data of each element on each sampling locations
based on its Igeo values, its contamination levels as well as the Igeo classes.
Figure 4.2 gives a clearer view on the Igeo values based on the available data.
From the Figure 4.2, almost all of the sampling locations were classified as
uncontaminated. By assuming the trees in the Area X were receiving direct
dispersal from the industrial area, it would be expected that the contamination
level of the elements in this area would be constantly higher in Area X than
Area Y. Mansor (2008) suggested that not all tree from all area experience
similar stem flow due to canopy resistance.
Figure 4.1a Enrichment Factor and the enrichment degree of the selected elements in the sampled tree barks (lower extremities).
44
Figure 4.1b Enrichment Factor and the enrichment degree of the selected elements in the sampled tree barks (upper extremities).
45
Figure 4.2. Geoaccumulation Index and the contamination level of the selected elements in the sampled tree barks.
UC: Uncontaminated, UMC: Uncontaminated/moderately contaminated, MC: Moderately contaminated, MSC: Moderately/strongly contaminated, SC: Strongly
contaminated
46
Table 4.6. Enrichment Factor and the enrichment degree of the selected elements in the sampled tree barks.
Metals/
Stations Al V Mn Ni Cu Pb La
Tm
Enrichment Factor and the enrichment degree
A 3.72
ME
2.61
ME
13771.9
EE
4752.97
EE
3.09
ME
2.16
ME
1.14
DE
1.14
DE
B 2.53
ME
139.20
EE
1.34
DE
1.00
DE
1.23
DE
2.45
ME
0.43
DE
0.43
DE
C 1.40
DE
14.96
SE
6434.2
EE
0.78
DE
0.085
DE
0.61
DE
0.34
DE
0.34
DE
D 1.28
DE
1.85
DE
2.84
ME
4479.41
EE
2.84
ME
11.06
SE
0.95
DE
0.95
DE
E 3.15
ME
41.37
EE
4872.99
EE
0.97
DE
1.96
DE
1.35
DE
4633.03
EE
0.93
DE
F 1.34
DE
2.35
ME
2.06
ME
5777.63
EE
2.86
ME
1.87
DE
1.03
DE
1.03
DE
G 8.03 x 10
-5
DE
2.51
ME
2106.40
EE
2.11
ME
3.04
ME
1.78
DE
1.12
DE
1.12
DE
Mean 1.92
DE
29.26
EE
3884.53
EE
2144.98
EE
2.12
ME
3.04
ME
662.58
EE
0.85
DE
DE: Deficiency to minimal enrichment, ME: Moderate enrichment, SE: Significant enrichment, EE: Extreme/high enrichment.
47
Table 4.7 Geo-accumulation Index (Igeo) and the contamination levels of the selected elements in the samples tree barks.
Metals/
Stations Fe Al V Mn Ni Cu Pb La Tm
Geo-accumulation Index (Igeo) value, Igeo classes and the contamination levels
A -11.95
UC (0)
-10.32
UC (0)
-14.62
UC (0)
-6.06
UC (0)
-2.30
UC (0)
-11.37
UC (0)
-1.03
UC (0)
-21.1
UC (0)
-21.24
UC (0)
B -10.53
UC (0)
-9.46
UC (0)
-7.46
UC (0)
-17.97
UC (0)
-13.10
UC (0)
-11.29
UC (0)
0.57
UMC (1)
-21.1
UC (0)
-21.24
UC (0)
C -10.18
UC (0)
-9.96
UC (0)
-10.33
UC (0)
-5.39
UC (0)
-13.13
UC (0)
-11.46
UC (0)
-1.09
UC (0)
-21.1
UC (0)
-21.24
UC (0)
D -11.69
UC (0)
-11.60
UC (0)
-14.85
UC (0)
-18.04
UC (0)
-2.12
UC (0)
-11.23
UC (0)
1.59
MC (2)
-21.1
UC (0)
-21.24
UC (0)
E -11.65
UC (0)
-10.25
UC (0)
-10.33
UC (0)
-7.26
UC (0)
-14.26
UC (0)
-11.72
UC (0)
-1.41
UC (0)
-8.81
UC (0)
-21.24
UC (0)
F -11.80
UC (0)
-11.64
UC (0)
-14.62
UC (0)
-18.62
UC (0)
-1.86
UC (0)
-11.33
UC (0)
-1.09
UC (0)
-21.1
UC (0)
-21.24
UC (0)
G -11.92
UC (0)
-25.78
UC (0)
-14.64
UC (0)
-8.74
UC (0)
-13.40
UC (0)
-11.36
UC (0)
-1.28
UC (0)
-21.1
UC (0)
-21.24
UC (0)
Values in the bracket indicate the Geoaccumulation Index classes
UC: Uncontaminated, UMC: Uncontaminated/moderately contaminated, MC: Moderately contaminated
48
4.3.2.1 Iron
The analyzed samples showed that the concentrations of Fe were ranged from
13.25 to 45.20 mg/kg with the mean of 28.51 mg/kg, while the concentration
of Fe for the control sample is 15.15 mg/kg. Based on the Table 4.5., the
highest concentration of Fe was measured at sampling location C with the
value of 45.20 mg/kg, denoting an almost threefold higher concentration
compared to the concentration found in the control sample. The possibility of
the sampling location C to have the highest Fe concentration is due to its
position located in the GIA, where vast industrial activities are present. In this
study, Fe was used as the conservative element for normalization, thus the EF
for this element is excluded from this study.
Igeo for Fe was determined and the minimum value of it was found to be -
11.95 at sampling location A to a maximum -10.18 at sampling location C.
All of the sampling locations fall under class 0, proving that Fe metal did not
cause any contamination to the study area. Based on Figure 4.3, Igeo is clearly
focused in the Area X and the indices decrease in the Area Y.
49
Figure 4.3 Igeo contour map for Fe.
4.3.2.2 Aluminum
Aluminum is a major metallic element found in the earth crust, thus its
concentration is high in sediments and would probably not affected by
anthropogenic activities (Dong et al., 2013). In the UCC, Al composition was
found to be 84 700 mg/kg (Taylor et al., 1981). The concentration range of Al
is 0.0022 to 180.94 mg/kg with an average of 84.78 mg/kg. The control value
of Al is 30.63 mg/kg. Referring to the Table 4.5, the highest concentration of
Al is at sampling location B with a value of 180.94 mg/kg while the lowest
value was measures as 0.0022 mg/kg, at the sampling location G. By
comparing the two interested areas; A and B, Al is highly distributed in the
Area X compared to the Area Y except for the location D, which showed
50
approximately half of the most of the concentration shown by the other
sampling locations in Area X.
Based on Figure 4.4(a), the enrichment pattern did not comply with the
expected pattern, which is from higher enrichment at the Area X to lower
enrichment at Area Y. Instead, the enrichment level fluctuates in one
sampling location to another. According to the EF value for Al, the highest
enriched value was measured as 5.91 at sampling location B, indicating
significant enrichment of Al. Based on the EF values of all of the sampling
locations, A, B and E exceeded 1.5, hence, suggesting that these locations
were anthropogenically impacted with Al.
Even though most of the sampling locations were found to be in the same
class (class 0), based on their Igeo values, the range within the class itself is
still applicable. The highest Igeo value for Al was investigated at B (-9.46),
followed by C (-9.96) and the lowest was at G (-25.78). B was located very
close to Area X, thus, the possibility to display the highest value of Igeo is
great. Referring to the Figure 4.4(b), the Igeo values shown is more intense to
the east side of the sampling area while a lesser intense of Igeo can be found at
the west side of the sampling area.
51
a. b.
Figure 4.4. a. The EF and b. Igeo contour map for Al.
4.3.2.3 Vanadium
Generally, vanadium has a broad and varied industrial usage in textile,
dyeing, metallurgy electronics as well as petroleum productions which clearly
indicated that V is produced anthropogenically especially from the industries
nearby the study area. Taylor et al. (1981) reported that in the UCC, the
composition of V is 60 mg/kg.
In this study, V was measured in the concentration range from 0.003050 to
0.51 mg/kg with 0.095 mg/kg in average. The control concentration of V was
found to be very low, 0.001568 mg/kg. The sampling location B was found to
have the highest concentration with a value of 0.51 mg/kg while the lowest
52
was at sampling location D with a value of 0.003050 mg/kg. However, the
lowest concentration of V was actually represented by other three sampling
locations due to their relatively similar concentration. The concentrations of
V at locations A, F, D and G were 0.003580, 0.003580, 0.003050 and
0.003520 mg/kg respectively.
The enrichment pattern for V was close to the expected pattern. The EF
values for this element were indicated from strong to extreme contamination
with a mean of 29.26, that ranging from 1.85 to 139.20. Three of the
sampling locations, which are B, C and E were found to be extremely
contaminated with V. Figure 4.5(a) represents the EF contour map that
clearly indicate the intensity of EF were higher at sampling locations B and C
but gradually decreased when approaching the Area Y. Based on their
respective EF values, all of the sampling locations experienced anthropogenic
impacts of V.
The Igeo indicated that the tree barks in the area fall under class 0 ranging from
a minimum of -14.85 to -7.46, practically uncontaminated with V. Figure
4.5(b) revealed that the Igeo pattern is indistinguishable with that of the EF’s
pattern shown in Figure 4.5(a).
53
a. b.
Figure 4.5. a. The EF and b. Igeo contour map for V.
4.3.2.4 Manganese
As of 2010, the world mine production of Manganese was 13,000,000 mg/kg
per annum (Frisbie, et al., 2012). This element is a powerful neurotoxin,
which can cause deficit in the intellectual function in children and learning
disabilities, compulsive behaviors as well as Mn-induced parkinsonism in
adults (Frisbie et al., 2012). Even though lacking in notoriety which causes
Mn to exert lower toxic properties compared to Pb, the pollution of
manganese is particularly common due to its ubiquitous natural occurrence,
extensive association with industry and ease of mobilization (Alloway, 2012).
The value of Mn in the UCC was 600 mg/kg (Taylor et al., 1981). In this
study, it was found that the concentration range for Mn was 0.00224 to 21.50
mg/kg with a mean of 6.14 mg/kg. The control value of Mn was 0.00112
54
mg/kg. From the Table 4.5, the highest concentration of Mn was recorded as
21.50 mg/kg at the sampling location C, which is located in the Area X. The
second highest concentration of Mn was represented by the sampling location
A with the value of 13.49 mg/kg.
Referring to their respective EF values, there were four locations that were
extremely high enriched with Mn. These locations were at A, C, E and G.
Figure 4.6(a) shows that EF is very intense at the northern area of the
sampling site. It clearly shows that the area was highly contaminated with Mn
compared to the rest of the sampling locations. Area X did not denote
extreme contamination compared to the other sampling locations in Area Y.
The Igeo value for Mn lies mainly in class 0, indicating uncontamination at all
locations. According to the Figure 4.6(b), the contamination of Mn is much
higher around Area X compared to Area Y. However, the highly intense Igeo
was found at the sampling location A, which was in contrast with the
hypothesis. Generally, the intensity pattern of Igeo for Mn is similar with the
EF pattern shown in Figure 4.6(a)
55
a. b.
Figure 4.6. a. The EF and b. Igeo contour map for Mn.
4.3.2.5 Nickel
Nickel, having a value of 20 mg/kg in UCC (Taylor et al., 1995), is naturally
present in all types of rock and present in the pedosphere in a range from
trace amounts to relatively high concentrations, as compared to other trace
elements (Alloway, 2012). Ni can also be found in the form of sulphide,
silicate minerals and oxide. Due to its high abundance, human beings are
constantly exposed to nickel in various amounts (Nazzal, et al., 2013).
The concentration for Ni was observed in the range of 0.00277 to 8.24 mg/kg
with an average of 3.04 mg/kg. The concentration of Ni for the control
sample is 0.00147 mg/kg. From Table 4.5, Ni was highly deposited at the
sampling location F with a concentration of 8.24 mg/kg. The sampling
location F was located at the southern part of the sampling area. During the
56
sampling month, the wind direction was favored to south west. This might
explained the higher concentration of Ni at the sampling location F.
In contrary, the sampling location E had recorded the lowest concentration of
Ni with a value of 0.00153 mg/kg. This can possibly be related to its position,
which is located outside of the GIA. Based on the Figure 4.7(a), it was shown
that Ni was not evenly distributed over the whole sampling area. From this
study, it was found that there were 3 locations that were extremely to highly
enriched with Ni, namely A, F and D according to their respective EF values.
From the same contour map, the EF intensity did not comply with the
hypothetical intensity pattern. The EF values were rather higher in the Area Y
in lieu of Area X. Judging from the EF values, most of the sampling locations
in the Area Y was anthropogenically impacted with Ni, that ranging from
0.97 to 5777.63. At the Area X, Ni concentration was found to be originated
from natural sources.
The Igeo values for Ni ranged from -14.26 to -2.30, which fall under class 0.
This indicates uncontamination of Ni at all of the sampling locations. As
mentioned, the concentration and the EF values of Ni did not agree with the
hypothetical pattern, therefore, this anomaly directly affected the Igeo values.
Figure 4.7(b) shows that the Igeo of the Ni was higher at the Area Y compared
to at the Area X.
57
a. b.
Figure 4.7. a. The EF and b. Igeo contour map for Ni.
4.3.2.6 Copper
Several industries that contributed the composition of Cu to the atmosphere
are blast furnace, steel manufacture, waste dumps and application of
agrochemicals in the agro based industry (Gowd et al., 2010). Taylor et al.
(1981) reported that the composition of Cu in the UCC was found to be 25
mg/kg.
Table 4.5 shows that Cu was uniformly distributed at all the sampling
locations with relatively similar concentrations ranging from 0.01111 to
0.01564 mg/kg with an average concentration of 0.01400 mg/kg. Among
these locations, the one that experienced the highest concentration of Cu was
the sampling location D with a value of 0.01564 mg/kg, while the lowest
concentration of Cu was recorded as 0.01111 mg/kg at the sampling location
58
E. However, these values reported were further lower than the concentration
of Cu in the earth’s crust.
Based on the Figure 4.8(a), the EF intensity pattern is not very significant.
This can be further confirmed with their respective Cu enrichments on each
location. The enrichment degree ranged from deficiency contamination to
moderately contamination. Based on the value of EF as tabulated in the Table
4.6, only two sampling locations, which were stations B and C, were not
anthropogenically impacted with Cu. All of the sampling locations are
moderately enriched with Cu, with the value of EF ranging from 0.085 to
3.09. From the EF values, A, D, E, F, and G were found to be
anthropogenically impacted with Cu while the other two locations, B and C
experienced natural emissions of Cu.
Cu showed relatively indistinguishable values of Igeo, which ranged from -
11.72 to -11.29. All of the sampling locations fall under class 0, denoting
uncontamination of Cu. However, based on Figure 4.8(b), the intensity of Igeo
was found to be higher at B compared to the other locations. It can be
explained that all of the sampling locations were not contaminated with Cu.
59
a. b.
Figure 4.8. a. The EF and b. Igeo contour map for Cu.
4.3.2.7 Lead
A naturally occurring bluish gray Pb can be found in the Earth’s crust in
small quantity (Nazzal et al., 2013), with a concentration of 15 mg/kg (Taylor
et al., 1981). However, in the environment, much of Pb comes from mining,
manufacturing and burning fossil fuels (Nazzal et al., 2013). In recent years,
unleaded petrol has been increasingly used due to the health concerns rose
from the applications of Pb in vehicle fuel. Stankovic et al. (2013) stated that
besides atmospheric deposition, agricultural practices are a source of Pb input
to soils from organic and mineral fertilizers.
Several other anthropogenic activities that contribute to the production of Pb
are motor-vehicle exhaust fumes, smelting and from corrosion of lead pipe
work (Gowd et al., 2010). However, the levels of Pb in the environment are
60
not stable and could vary according to urbanization, climate changes as well
as industrial production (Krystofova et al., 2009). Harmens et al. (2008)
found that the main sources of Pb emissions come mainly from the
manufacturing industry by 41% while road traffic contributed 17%. The level
of Pb in the environment vary between 4000 and 20000 mg/kg of dust
(Stankovic et al., 2013).
The concentration of Pb at the sampling sites ranged from 8.47 to 67.54
mg/kg with an average of 21.53 mg/kg. The control sample concentration of
Pb was 5.82 mg/kg. From the Table 4.5, the location that experienced the
highest concentration of Pb was the D, with a concentration of 67.54 mg/kg.
This value exceeded approximately fivefold the natural abundance of Pb in
the Earth’s crust. D is located in the Area X, thus the high value by referring
to the Figure 4.9(a) and 4.9(b), the spatial distribution of Pb came out as
expected. B and D appeared to be having a higher enrichment of Pb compared
to the other sampling locations.
These two locations experienced a significant Pb enrichment with the value of
EF; 11.06. The sampling locations were mainly impacted with Pb by
manmade activities. Only a couple of the sampling locations were found to be
not anthropogenically impacted with Pb, which were C and E with the EF
values 0.61 and 1.35 respectively. Based on the Figure 4.9(a), the EF
intensity was higher at the east side of the sampling Area Xnd gradually
61
decreased when approach to the west, north and south sides of the sampling
area.
Attempting to understand that the sampling location D to be the only one
location to be significantly contaminated with Pb, the situation can be
justified with the location of the sampling station which is located in the Area
X. It is no surprise that the aforesaid location to be contaminated with Pb. The
Igeo for the tree bark sample lies in the class 0 to 2, indicates uncontaminated
to moderately contaminated. The minimum value of Igeo in this study was
recorded at -1.41 while the maximum value was 1.59. Figure 4.9(a) and
4.9(b) showed that the pattern of the EF level and Igeo level are similar.
a. b.
Figure 4.9. a. The EF and b. Igeo contour map for Pb.
62
4.3.2.8 Lanthanum
Fuge (2013) stated that lanthanum belongs to the resistate minerals, minerals
that do not weather to any significant extent. They are retained in that mineral
form in the soil and subsequent erosion products (Fuge, 2013). La present in
the UCC with a concentration of 30 mg/kg (Taylor et al., 1981).
The concentration of the analyzed La was in the range between less than
0.00002 to 0.10 mg/kg with an average of 0.014 mg/kg. The concentrations of
La at most of the sampling sites were identical, which was less than 0.00002
mg/kg. The control sample also gave the same value. However, it was found
that the concentration of La was the highest at the sampling location E, and
had shown the highest enrichment of La at that area. The sampling location E
was also investigated to be anthropogenically impacted with La, based on its
extremely high EF value (EF = 4633.03). The possible cause of the high
contamination at the particular location may be due to the dominant wind
direction (south west) during that month as well as unknown quarry activities
present near the sampling location. The EF values of La ranged from 0.34 to
4633.03 with a mean value of EF was 662.58. All of the sampling locations
were found to be impacted by only natural processes of La except at the
sampling location E.
Based on the Igeo value, all of the sampling locations were in class 0.
However, E was represented the highest Igeo value among the rest, but still
63
classify itself as uncontaminated with La, based on its Igeo value. The
distribution of La was expected to be high at the D due to the fact that it was
located very near to the Lynas, the REE refinery company, but the study did
not accept this hypothesis.
What can be observed from the EF and Igeo contour maps as shown in Figure
4.10(a) and 4.10(b), La was mainly contaminated at E. Both maps show an
almost homogenous pattern of the La distribution.
a. b.
Figure 4.10. a. The EF and b. Igeo contour map for La.
4.3.2.9 Thulium
The lesser abundant lanthanide (Humphries, 2010), thulium showed the
lowest concentration among all of the elements analyzed. The distribution of
64
Tm in the UCC is 0.33 mg/kg, making it to be the lowest among the other
elements studied (Taylor et al., 1981).
Based on Figure 4.11, the EF values were much higher surrounding Area Y,
in lieu of Area X. It was expected that the EF values would be higher in the
Area X, not the otherwise. Even though the concentration of Tm in each
sampling locations were similar, which was 0.0000002 mg/kg, they exert
different EF values due to the normalization using concentration of Fe. The
EF values ranged from a minimum of 0.34 at C to a maximum of 1.14 at A.
Nonetheless, all of the EF values were less than 1.5, denoting the source of
Tm was only from natural processes.
The concentration of Tm on each sampling location was found to be very low
at a value of less than 0.0000002 mg/kg. Due to the indistinguishable
concentrations of Tm, no difference between concentrations on each sampling
locations exist to establish a contour map for Igeo. The natural abundance of
Tm is readily low, thus does not contribute to the value obtained from the
study. Though, the Igeo values for Tm at all locations were found to be having
a homologous value of Igeo (-21.24). Hence, all of the sampling locations fall
under class 0, suggesting uncontamination of Tm.
65
Figure 4.11. The EF contour map for Tm.
4.3.3. Pollution Load Index
Guéguen et al. (2012) suggested in order to confirm the level of pollution of
the selected sampling locations, the Pollution Load Index (PLI) system was
used. PLI is useful to certify the pollution effect of the diversified elements at
each different sampling location. PLI was calculated based on the
Contamination Factor (CF) values shown in the Appendix A and the formula
for calculating CF and PLI is as the following;
CF = C metal / C background value
PLI=
Table 4.8. The Pollution Index of the sampling locations.
Sampling Location PLI Pollution Status
A 16.43 Polluted
B 4.07 Polluted
C 7.12 Polluted
D 4.12 Polluted
E 13.11 Polluted
F 3.31 Polluted
G 1.69 Polluted
The PLI of each location and their respective pollution status is tabulated as
in Table 4.8. Based on the Table 4.8, all of the sampling locations exceeded
1, suggesting the pollution existed with different PLI values. Even though the
degree of pollution is not available, by distinguishing the degree of pollutions
into several classes, still, the level of pollution can be interpreted based on the
numerical value obtained. The level of pollution in ascending order is G < F
<
B < D < C < E < A. The degree of pollution can be presented in the Figure
4.12 . The highly polluted location was the sampling location A (PLI =
67
16.43), which was located in the Area Y, approximately 9 km northing from
the Gebeng Industrial Area. The second highly polluted location was found to
be at the sampling location E, with a value of 13.11. E was also in the Area
Y, with approximately 4 km southing the GIA.
The two sampling locations that are in the Area X were found to be polluted
with a moderate value ranging from 4.12 to 7.12, which both PLI values
indicate polluted condition. The condition is possible due to the industrial
activities that are present in the area.
The anomalies could not be explained and to point out the major sources of
elemental emissions other than in the GIA is beyond the study’s scope. Still,
these locations might have been impacted with other anthropogenic sources
such as traffic, open burning, urbanization, agriculture, construction, etc.
Figure 4.12 The Pollution Index of sampling locations.
68
CHAPTER 5
CONCLUSION AND RECCOMENDATIONS
This study focused on the possibilities of implementing the existing
biological procedure, passive biomonitoring using the tree bark of Acacia
mangium to determine the distribution of La, Tm and selected heavy metals
surrounding Gebeng Industrial Area, Pahang, Malaysia. In general, the
concentrations and distribution pattern for most of the analyzed elements
were found to be higher in Area X compared to Area Y, which is clearly due
to the direct dispersal of the elements on the tree barks in the Area X.
Nonetheless, the EF and Igeo patterns did not comply with the hypothesis; a
higher values of EF and Igeo in the Area X compared to the Area Y. Several
arguments are raised which include the effect of prevailing wind direction,
the forest stand density as well as other contributing anthropogenic activities
apart from the industrial activities.
The EF values concluded that approximately more than half of the sampling
locations were anthropogenically impacted. However, the Igeo values
classified almost all of the sampling locations to be uncontaminated with the
analyzed elements. By applying the PLI, all of the sampling locations were
found to be polluted with different degrees.
69
It is hoped that the result gained from this study can be used as a valuable
baseline data for future studies which are related to the levels of La, Tm and
the selected heavy metals in industrial areas. The results also can be used as a
reference data for the other monitoring study related to REEs distribution
especially La, Tm and selected heavy metals. The suggested monitoring
technique could be applied to replace the conventional method which used
electronic device to monitor trace elements depositions especially for large
sampling area purpose.
It is recommended that for future study, an in-depth study on the pollution
degree at a particular area that associate with human health studies should be
done in order to assess the health degree outcomes and enable the setting of
priorities in taking environmental control measures.
In recapitulation, future research in the developing world should emphasize
and utilize the sharing of technical resources and communications between
different countries regardless the backgrounds of the country to enhance,
strengthen and validate the data obtained to be compared with the data
obtained from the other countries extensively.
70
CITED REFERENCES
Abdullah, M. Z., Saat, A., and Hamzah, Z. (2011). Optimization of energy dispersive
x-ray fluorescence spectrometer to analyze heavy metals in moss samples.
American Journal of Engineering and Applied Sciences, 4(3).
Afroz, R., Hassan, M.N., and Ibrahim, N.A. (2003). Review of air pollution and
health impacts in Malaysia. Environmental Research, 92(2), 71-77.
Alloway, B. J. (2012). Environmental Pollution: Heavy Metals in Soils: Trace
Metals and Metalloids in Soils and Their Bioavailability (Vol. 22): Springer.
313 and 335 pp.
Ataabadi, M., Hoodaji, M., and Najafi, P. (2010). IC Conferences. Journal of
Environmental Studies, 35(52), 83-92.
Azmi, S.Z., Latif, M.T., Ismail, A.S., Juneng, L., and Jemain, A.A. (2010). Trend
and status of air quality at three different monitoring stations in the Klang
Valley, Malaysia. Air Quality Atmosphere and Health, 3, 53-64.
Baltrėnaitė, E., Baltrėnas, P., Lietuvninkas, A., Šerevičienė, V., and Zuokaitė, E.
(2013). Integrated evaluation of aerogenic pollution by air-transported heavy
metals (Pb, Cd, Ni, Zn, Mn and Cu) in the analysis of the main deposit media.
Environmental Science and Pollution Research, 1-15.
BASF. (2011). BASF signs lanthanum supply agreement with Lynas. Focus on
Catalysts, 2011(11), 3.
Berlizov, A.N., Blum, O.B., Filby, R.H., Malyuk, I.A., and Tryshyn, V.V. (2007).
Testing applicability of black poplar (Populus nigra L.) bark to heavy metal
air pollution monitoring in urban and industrial regions. Science of The
Total Environment, 372(2–3), 693-706.
Besse, J., Geffard, O., and Coquery, M. (2012). Relevance and applicability of
active biomonitoring in continental waters under the Water Framework
Directive. TrAC Trends in Analytical Chemistry, 36(0), 113-127.
Block, M. L., Elder, A., Auten, R. L., Bilbo, S. D., Chen, H., Chen, J.-C., Cory-
Slechta, D. A., Costa, D., Diaz-Sanchez, D., Dorman, D. C., Gold, D. R.,
Gray, K., Jeng, H. A., Kaufman, J. D., Kleinman, M. T., Kirshner, A.,
Lawler, C., Miller, D. S., Nadadur, S. S., Ritz, B., Semmens, E. O., Tonelli,
L. H., Veronesi, B., Wright, R. O., andWright, R. J. (2012). The outdoor air
pollution and brain health workshop. NeuroToxicology, 33(5), 972-984. doi:
http://dx.doi.org /10.1016/j. neuro.2012.08.014
71
Böhlandt, A., Schierl, R., Diemer, J., Koch, C., Bolte, G., Kiranoglu, M., Fromme,
H., and Nowak, D. (2012). High concentrations of cadmium, cerium and
lanthanum in indoor air due to environmental tobacco smoke. Science of the
Total Environment, 414(0), 738-741. doi: http://dx.doi.org/10.1016/j.scitotenv
. 2011. 11.017
Bradsher, K. (2011) Mitsubishi Quietly Cleans Up Its Former Refinery. Available at:
http://www.nytimes.com/2011/03/09/business/energy-environment/09raresi
de.html. [Accessed 30th
March 2013].
Briner, W., Rycek, R.F., Moellenberndt, A., and Dannull, K. (2000).
Neurodevelopmental effects of lanthanum in mice. Neurotoxicology and
Teratology, 22(4), 573-581.
Catinon, M., Ayrault, S., Clocchiatti, R., Boudouma, O., Asta, J., Tissut, M., and
Ravanel, P. (2009). The anthropogenic atmospheric elements fraction: A new
interpretation of elemental deposits on tree barks. Atmospheric Environment,
43(5), 1124-1130.
Catinon, M., Ayrault, S., Spadini, L., Boudouma, O., Asta, J., Tissut, M., and
Ravanel, P. (2011). Tree bark suber-included particles: A long-term
accumulation site for elements of atmospheric origin. Atmospheric
Environment, 45(5), 1102-1109. doi: http://dx.doi.org/10.1016/j.atmosenv.
2010.11.038
Celik, S., Yucel, E., Celik, S., Gucel, S., and Ozturk, M. (2010). Carolina poplar
(Populus x canadensis Moench) as a biomonitor of trace elements in Black
sea region of Turkey. Journal of Environmental Biology, 31(1), 225.
Chabukdhara, M., and Nema, A.K. (2012). Assessment of heavy metal
contamination in Hindon River sediments: A chemometric and geochemical
approach. Chemosphere, 87(8), 945-953.
Chakhmouradian, A.R., and Wall, F. (2012). Rare earth elements; minerals, mines,
magnets (and more). Elements, 8(5), 333-340.
Chung, K.F., Zhang, J., and Zhong, N. (2011). Outdoor air pollution and respiratory
health in Asia. Respirology, 16(7), 1023-1026.
Dominick, D., Juahir, H., Latif, M.T., Zain, S.M., and Aris, A.Z. (2012). Spatial
assessment of air quality patterns in Malaysia using multivariate analysis.
Atmospheric Environment, 60(0), 172-181.
Dong, Chen and Chen. (2013). Frontier of Energy and Environmental Engineering.
Taylor and Francis Group, LLC, Florida. 380-383 pp.
Duan, J., and Tan, J. (2013). Atmospheric heavy metals and Arsenic in China:
Situation, sources and control policies. Atmospheric Environment, 74(0), 93-
101.
72
El Khoukhi, T., Cherkaoui, R.M., Gaudry, A., Ayrault, S., Senhou, A., Chouak, A., .
Chakir, E. (2004). Air pollution biomonitoring survey in Morocco using k0-
INAA. Nuclear Instruments and Methods in Physics Research Section B:
Beam Interactions with Materials and Atoms, 213(0), 770-774.
Elmer, P. (2008). Atomic Spectroscopy: A Guide to Selecting the Appropriate
Technique and System. PerkinElmer Inc., US Available at: http://www.
perkinelmer. com/PDFs/Downloads/BRO_WorldLeaderAAICPMSICPMS.
pdf [Accessed: 24th
April 2013].
Fang, C.S., Qu, Z., Wang, D.L., Wang, J., and Bi, L.J. (2013). Comparative Study
of Geo-Accumulation Index and Enrichment Factor in Source Apportionment
of Atmospheric Particulate Matter. Advanced Materials Research, 72, 634-
638.
Feng, L., Xiao, H., He, X., Li, Z., Li, F., Liu, N., Chai, Z., Zhao, Y., andZhang, Z.
(2006). Long-term effects of lanthanum intake on the neurobehavioral
development of the rat. Neurotoxicology and Teratology, 28(1), 119-124. doi:
http://dx.doi.org/10.1016/j.ntt.2005.10.007
Ferreira, A.B., Santos, J.O., Souza, S.O., Júnior, W.N. S., and Alves, J.P.H. (2012).
Use of passive biomonitoring to evaluate the environmental impact of
emissions from cement industries in Sergipe State, northeast Brazil.
Microchemical Journal, 103(0), 15-20.
Frisbie, S. H., Mitchell, E. J., Dustin, H., Maynard, D. M., and Sarkar, B. (2012).
World health organization discontinues its drinking-water guideline for
manganese. Environmental Health Perspectives, 120(6), 775.
Fu, F., and Wang, Q. (2011). Removal of heavy metal ions from wastewaters: A
review. Journal of Environmental Management, 92(3), 407-418.
Fuge, R. (2013). Anthropogenic sources. In Essentials of medical geology. Springer
Netherlands. 59-74 pp.
Fujiwara, F.G., Gómez, D.R., Dawidowski, L., Perelman, P., and Faggi, A.
(2011).Metals associated with airborne particulate matter in road dust and
tree bark collected in a megacity (Buenos Aires, Argentina). Ecological
Indicators, 11(2), 240-247.
Ghrefat, H. A., Abu-Rukah, Y., and Rosen, M. A. (2011). Application of
geoaccumulation index and enrichment factor for assessing metal
contamination in the sediments of Kafrain Dam, Jordan. Environmental
Monitoring and Assessment, 178(1-4), 95-109.
Gowd, S., Ramakrishna Reddy, M., and Govil, P. K. (2010). Assessment of heavy
metal contamination in soils at Jajmau (Kanpur) and Unnao industrial areas
of the Ganga Plain, Uttar Pradesh, India. Journal of Hazardous Materials,
174(1–3), 113-121.
73
Guéguen, F., Stille, P., and Millet, M. (2011). Air quality assessment by tree
barkbiomonitoring in urban, industrial and rural environments of the Rhine
Valley: PCDD/Fs, PCBs and trace metal evidence. Chemosphere, 85(2), 195-
202.
Guéguen, F., Stille, P., Lahd Geagea, M., and Boutin, R. (2012). Atmospheric
pollution in an urban environment by tree bark biomonitoring – Part I: Trace
element analysis. Chemosphere, 86(10), 1013-1019.
Harju, L., Saarela, K.E., Rajander, J., Lill, J.O., Lindroos, A., and Heselius, S.J.
(2002). Environmental monitoring of trace elements in bark of Scots pine by
thick-target PIXE. Nuclear Instruments and Methods in Physics Research
Section B: Beam Interactions with Materials and Atoms, 189(1–4), 163-167.
Harmens, H., Norris, D. A., Koerber, G. R., Buse, A., Steinnes, E., and Rühling, Å.
(2008). Temporal trends (1990–2000) in the concentration of cadmium, lead
and mercury in mosses across Europe. Environmental Pollution, 151(2), 368-
376.
Hossain, M., Mir, S.I., Nasly, M., Wahid, Z., and Aziz, E.A. (2012). Assessment of
Spatial Variation of Water Quality of Tunggak River Adjacent to Gebeng
Industrial Estate, Malaysia. Assessment, 501(47), A1-07.
Hseu, Z.Y. (2004). Evaluating heavy metal contents in nine composts using four
digestion methods. Bioresource Technology, 95(1), 53-59.
Huang, J., Zhang, T.L., Xu, S.J., Li, R.C., Wang, K., Zhang, J., and Xie, Y.N.
(2006). Effects of lanthanum on composition, crystal size, and lattice
structure of femur bone mineral of Wistar rats. Calcified Tissue
International, 78(4), 241-247.
Humphries, M. (2010). Rare earth elements: The global supply chain: Diane
Publishing. 2 pp.
Kong, S., Lu, B., Ji, Y., Zhao, X., Chen, L., Li, Z., Han, B., and Bai, Z. (2011).
Levels, risk assessment and sources of PM10 fraction heavy metals in four
types dust from a coal-based city. Microchemical Journal, 98(2), 280-290.
doi: http://dx.doi.org/10.1016/j.microc.2011.02.012
Krystofova, O., Shestivska, V., Galiova, M., Novotny, K., Kaiser, J., Zehnalek, J.,
Babula, P., Opatrilova, R., Adam, V., and Kizek, R. (2009). Sunflower plants
as bioindicators of environmental pollution with lead (II) ions. Sensors, 9(7),
5040-5058.
Kularatne, K.I.A., and Freitas, C.R. (2013). Epiphytic lichens as biomonitors of
airborne heavy metal pollution. Environmental and Experimental Botany,
88(0), 24-32.
74
Latif, M.T., Huey, L.S., and Juneng, L. (2012). Variations of surface ozone
concentration across the Klang Valley, Malaysia. Atmospheric Environment,
61(0), 434-445.
Lee, H.-Y., Chon, H.-T., Sager, M., and Marton, L. (2012). Platinum pollution in
road dusts, roadside soils, and tree barks in Seoul, Korea. Environmental
Geochemistry and Health, 34(1), 5-12.
Li, J., Hong, M., Yin, X., and Liu, J. (2010). Effects of the accumulation of the rare
earth elements on soil macrofauna community. Journal of Rare Earths, 28(6),
957-964.
Li, J.X., Yan, J.H., Chi, Y., Ni, M.J., and Cen, K.F. (2003). Heavy Metals Emission
From A Fluidized-Bed Msw Incinerator [J]. Proceedings of the Csee, 12, 033.
Li, P.-H., Kong, S.-F., Geng, C.-M., Han, B., Lu, B., Sun, R.-F., Zhao, R.-J., and Bai,
Z.-P. (2013). Assessing the Hazardous Risks of Vehicle Inspection Workers’
Exposure to Particulate Heavy Metals in Their Work Places. Aerosol and Air
Quality Research, 13(1), 255-265.
Lodenius, M. (2013). Use of plants for biomonitoring of airborne mercury in
contaminated areas. Environmental Research(0).
Long, K., Gosen, B., Foley, N., and Cordier, D. (2012). The Principal Rare Earth
Elements Deposits of the United States: A Summary of Domestic Deposits
and a Global Perspective. In R. Sinding-Larsen and F.-W. Wellmer
(Eds.), Non- Renewable Resource Issues. Springer, Netherlands. 131-155
pp.
Lu, G.H., Ji, Y., Zhang, H.Z., Wu, H., Qin, J., and Wang, C. (2010). Active
biomonitoring of complex pollution in Taihu Lake with Carassius auratus.
Chemosphere, 79(5), 588-594.
Lu, X., Wang, L., Li, L., Lei, K., Huang, L., and Kang, D. (2010). Multivariate
statistical analysis of heavy metals in street dust of Baoji, NW China. Journal
of Hazardous Materials, 173(1–3), 744-749.
Ma, J., and Singhirunnusorn, W. (2012). Distribution and Health Risk Assessment
of Heavy Metals in Surface Dusts of Maha Sarakham Municipality. Procedia
Social and Behavioral Sciences, 50(0), 280-293.
Mansor, N. (2008). Investigation of lead and zinc dispersion from an abandoned
mine site at Tyndrum, Scotland. Ph.D. Thesis. University of Glasgow. 199 pp.
Nadal, M., Schuhmacher, M., and Domingo, J.L. (2011). Long-term environmental
monitoring of persistent organic pollutants and metals in a
chemical/petrochemical area: Human health risks. Environmental Pollution,
159(7), 1769-1777.
75
Nameer, M. (2011). Using Pollution Load Index (PLI) and Geoaccumulation Index
(I-Geo) for the Assessment of Heavy Metals Pollution in Tigris River
Sediment in Baghdad Region. Journal of Al-Nahrain University 14 (4), 108-
114.
Nazzal, Y., Rosen, M. A., and Al-Rawabdeh, A. M. (2013). Assessment of metal
pollution in urban road dusts from selected highways of the Greater Toronto
Area in Canada. Environmental monitoring and assessment, 185(2), 1847-
1858.
Norzatulakma, M. K. (2010). Treatment of industrial wastewater at Gebeng area
using Eichornia Crassipes sp.(Water Hyacinth), Pistia Stratiotes sp.(Water
Lettuce) and Salvinia Molesta sp.(Giant Salvinia). Undergraduates Project
Report (PSM) thesis. Universiti Malaysia Pahang, Malaysia.
Omoniyi, I. M., Oludare, S. M., and Oluwaseyi, O. M. (2013). Determination of
radionuclides and elemental composition of clay soils by gamma-and X-ray
spectrometry. SpringerPlus, 2(1), 1-11.
Poikolainen, J. (2004). Mosses, epiphytic lichens and tree bark as biomonitors for air
pollutants-specifically for heavy metals in Regional surveys: University of
Oulu.
Ptaszyński, B., and Zwolińska, A. (2001). Synthesis and Properties of Solid
Complexes of Lanthanum, Cerium, Neodymium and Erbium with N-
Phosphonomethylglycine. Polish Journal of Environmental Studies 10(4),
257-262.
Qiang, T., Xiao-rong, W., Li-qing, T., and Le-mei, D. (1994). Bioaccumulation of
the rare earth elements lanthanum, gadolinium and yttrium in carp (Cyprinus
carpio). Environmental Pollution, 85(3), 345-350.
Rashed, M. N. (2010). Monitoring of contaminated toxic and heavy metals, from
mine tailings through age accumulation, in soil and some wild plants at
Southeast Egypt. Journal of Hazardous Materials, 178(1–3), 739-746.
Rühling, A., and Tyler, G. (1968). An ecological approach to the lead problem.
Botaniska Notiser, 121, 321-342.
Salamova, A., and Hites, R. (2010). Evaluation of Tree Bark as a Passive
Atmospheric Sampler for Flame Retardants, PCBs, and Organochlorine
Pesticides. Environmental Science and Technology 44(16), 6196–6201.
Salamova, A., and Hites, R.A. (2012). Brominated and Chlorinated Flame Retardants
in Tree Bark from Around the Globe. Environmental Science and
Technology, 47(1), 349-354.
76
Sawidis, T., Breuste, J., Mitrovic, M., Pavlovic, P., and Tsigaridas, K. (2011). Trees
as bioindicator of heavy metal pollution in three European cities.
Environmental Pollution, 159(12), 3560-3570.
Schelle, E., Rawlins, B. G., Lark, R. M., Webster, R., Staton, I., and McLeod, C. W.
(2008). Mapping aerial metal deposition in metropolitan areas from tree bark:
a case study in Sheffield, England. Environmental Pollution, 155(1), 164-173.
Schwabe, A., Meyer, U., Grün, M., Voigt, K.D., Flachowsky, G., and Dänicke, S.
(2012). Effect of rare earth elements (REE) supplementation to diets on the
carry-over into different organs and tissues of fattening bulls. Livestock
Science, 143(1), 5-14.
Selinus, O., Alloway., B, Centeno, J., Finkelman, R., Fuge, R., Lindh U. and
Smedley, P (2013). Essentials of Medical Geology: Revised Edition, Springer
Science+ Business Media Dordrecht, London, United Kingdom. 73 pp.
Škrbić, B., Milovac, S., and Matavulj, M. (2012). Multielement profiles of soil, road
dust, tree bark and wood-rotten fungi collected at various distances from
high-frequency road in urban area. Ecological Indicators, 13(1), 168-177.
Sobahan, M., Mir, S.I., Zakaria, I., and Hossain, M. (2013). Surface Water
Contamination Due To Industrial Activities in Gebeng Area, Kuantan,
Malaysia. International Conference on Civil and Architecture Engineering
(ICCAE'2013) 6-7 May 2013 Kuala Lumpur, Malaysia. 53-61.
Stankovic, S., Kalaba, P., and Stankovic, A. R. (2013). Biota as toxic metal
indicators. Environmental Chemistry Letters, 1-22.
Taylor, S., McLennan, S., Armstrong, R., and Tarney, J. (1981). The composition
and evolution of the continental crust: rare earth element evidence from
sedimentary rocks [and discussion]. Philosophical Transactions of the Royal
Society of London. Series A, Mathematical and Physical Sciences, 301(1461),
381-399.
Tessier, E., Garnier, C., Mullot, J.-U., Lenoble, V., Arnaud, M., Raynaud, M., and
Mounier, S. (2011). Study of the spatial and historical distribution of
sediment inorganic contamination in the Toulon bay (France). Marine
Pollution Bulletin, 62(10), 2075-2086.
Turra, C., Fernandes, E., and Bacchi, M. (2011). Evaluation on rare earth elements of
Brazilian agricultural supplies. Journal of Environmental Chemistry and
Ecotoxicology, 3(4), 86-92.
Tye, A.M., Hodgkinson, E.S., and Rawlins, B.G. (2006). Microscopic and chemical
studies of metal particulates in tree bark and attic dust: Evidence for historical
atmospheric smelter emissions, Humberside, UK. Journal of Environmental
Monitoring, 8(9), 904-912.
77
Vuković, G., Urošević, M.A., Razumenić, I., Goryainova, Z., Frontasyeva, M.,
Tomašević, M., and Popović, A. (2013). Active moss biomonitoring of small-
scale spatial distribution of airborne major and trace elements in the Belgrade
urban area. Environmental Science and Pollution Research, 1-10.
Wannaz, E.D., Carreras, H.A., Rodriguez, J.H., and Pignata, M.L. (2012). Use of
biomonitors for the identification of heavy metals emission sources.
Ecological Indicators, 20(0), 163-169.
Wedepohl, H.K. (1995). The composition of the continental crust. Geochimica et
Cosmochimica Acta, 59(7), 1217-1232. doi: http://dx.doi.org/10.1016/0016-
7037(95)00038-2
Windfinder.com (2013) Wind and Weather Statistic Kuantan Airport (Statistics
based on observations taken between 1/2008 - 11/2013 daily from 7am to
7pm local time). Available at: http://www.windfinder.com/windstats/wind
statistic_balok_k uantan.htm [Accessed 9th
October 2013].
Zhang, C., Li, Q., Zhang, M., Zhang, N., and Li, M. (2013). Effects of rare earth
elements on growth and metabolism of medicinal plants. Acta Pharmaceutica
Sinica B, 3(1), 20-24.
78
APPENDIX A
Contamination Index (CF)
Fe
Location CF Contamination Classification
5 km North 2.34 C3 Slight contamination
10 km North 0.87 C1 No contamination
10 km West 0.89 C1 No contamination
5 km South 1.08 C2 Suspected contamination
10 km South 0.97 C1 No contamination
2 km East 1.05 C2 Suspected contamination
4 km East 2.98 C3 Slight contamination
Al
Location CF Contamination Classification
5 km North 5.91 C4 Moderate contamination
10 km North 3.25 C3 Slight contamination
10 km West 0.00718 C1 No contamination
5 km South 3.40 C3 Slight contamination
10 km South 1.30 C2 Suspected contamination
2 km East 1.34 C2 Suspected contamination
4 km East 4.18 C4 Moderate contamination
V
Location CF Contamination Classification
5 km North 325.26 C6 Extreme contamination
10 km North 2.28 C3 Slight contamination
10 km West 2.24 C3 Slight contamination
5 km South 44.64 C6 Extreme contamination
10 km South 2.28 C3 Slight contamination
2 km East 1.95 C3 Slight contamination
4 km East 44.64 C6 Extreme contamination
Mn
Location CF Contamination Classification
5 km North 3.13 C3 Slight contamination
10 km North 12044.64 C6 Extreme contamination
10 km West 1883.93 C6 Extreme contamination
5 km South 5258.93 C6 Extreme contamination
10 km South 2.00 C3 Slight contamination
2 km East 2.98 C3 Slight contamination
4 km East 19196.43 C6 Extreme contamination
79
Ni
Location CF Contamination Classification
5 km North 2.33 C3 Slight contamination
10 km North 4156.46 C6 Extreme contamination
10 km West 1.88 C2 Suspected contamination
5 km South 1.04 C2 Suspected contamination
10 km South 5605.44 C6 Extreme contamination
2 km East 4700.68 C6 Extreme contamination
4 km East 2.28 C3 Slight contamination
Cu
Location CF Contamination Classification
5 km North 2.86 C3 Slight contamination
10 km North 2700.38 C6 Extreme contamination
10 km West 2.72 C3 Slight contamination
5 km South 2.12 C3 Slight contamination
10 km South 2.77 C3 Slight contamination
2 km East 2.98 C3 Slight contamination
4 km East 2.55 C3 Slight contamination
Pb
Location CF Contamination Classification
5 km North 5.74 C4 Moderate contamination
10 km North 1.89 C2 Suspected contamination
10 km West 1.59 C2 Suspected contamination
5 km South 1.46 C2 Suspected contamination
10 km South 1.81 C2 Suspected contamination
2 km East 11.60 C5 Severe contamination
4 km East 1.81 C2 Suspected contamination
La
Location CF Contamination Classification
5 km North 1.00 C1 No contamination
10 km North 1.00 C1 No contamination
10 km West 1.00 C1 No contamination
5 km South 50000 C6 Extreme contamination
10 km South 1.00 C1 No contamination
2 km East 1.00 C1 No contamination
4 km East 1.00 C1 No contamination
Tm
Location CF Contamination Classification
5 km North 1.00 C1 No contamination
10 km North 1.00 C1 No contamination
10 km West 1.00 C1 No contamination
5 km South 1.00 C1 No contamination
10 km South 1.00 C1 No contamination
2 km East 1.00 C1 No contamination
4 km East 1.00 C1 No contamination
80
Personal profile
Full name : Siti Mariam Abdul Kadir NRIC no. : 900430-14-6012 Birth Date : 30th April 1990 Citizenship : Malaysia Place of Birth : Wilayah Persekutuan Kuala Lumpur Gender : Female Correspondence : 55 Jalan Pesona 10, Taman Pelangi Indah, address 81800 Johor Bahru, Johor. Telephone no. (H) : 607-861 8875 Telephone no. (HP) : 6013-232 7986 Email address : [email protected]
Hobbies and interests
- Interacting with new people and expanding my networks is my cup of tea.
- Venturing to new places and gathering new knowledge and broaden my experience.
- I have no difficulties in writing and conversing in Malay and English.
Academic qualifications
Degree Area Institution Year awarded
B. Sc (Hons.) Chemistry Universiti Teknologi MARA 2014
Matriculation Life Science Negeri Sembilan Matriculation College
2009
S.P.M - SMK IJ Convent Johor Bahru 2007
Projects Passive Biomonitoring of Thulium, Lanthanum and Selected Heavy Metals in Air by Using Tree Bark Sample March 2013 to January 2014
Members: Dr. Mohd. Zahari Abdullah & Siti Mariam Abdul Kadir
An undergrad research project which focused on environmental study and chemistry. A
study on the Rare Earth Elements(REE) and selected heavy metals emitted by Gebeng
Industrial Area, Kuantan, Pahang, Malaysia.
81
Skills & Expertise
- Microsoft Word, Excel & PowerPoint - UV/Vis - ICP - NMR Spectroscopy - FAAS/GAAS - FT-IR - Surfer 11
Awards
Type Name of award/ awarding organization Date
Ceritificate Dean’s List Award 2011, Universiti Teknologi MARA, 26400 Jengka, Pahang.
April 2011
Certificate Dean’s List Award 2011, Universiti Teknologi MARA, 26400 Jengka, Pahang.
January 2012
Certificate Dean’s List Award 2013, Universiti Teknologi MARA, 26400 Jengka, Pahang.
March 2013 Certificate Dean’s List Award 2013, Universiti
Teknologi MARA, 26400 Jengka, Pahang. August 2013
Activities
Activity Date
Chemistry Student Association (CHEMSA) Member 2011 - 2014
Discussion Program on Non-destructive Test (NDT)
Field, UiTM Pahang 31st October 2013
1Citizen Program May 2013
Program Deputy Director of the Visit For Environmental Studies at Kualiti Alam Sdn. Bhd., Negeri Sembilan
November 2013
Program Deputy Director of the Visit for Environmental Studies at Solid Waste and Public Cleansing Management Corporation, Pahang
April 2013
Volleyball Interdegree Tournament UiTM Pahang May 2012
Sukma XV Pahang Baton Race June 2012
Tennis Club Member March 2012 – July 2012
Sport Event Management September 2011 – January 2012
Outward Bound Kesatria UiTM Pahang January 2010 – April 2011
Netball Coach, Mini Kakom, Negeri Sembilan Matriculation College
2009
Biodiversity Research Team Member at Jelebu by
Department of Wildlife and National Parks February 2009
23
Table 2.4. The mean concentration of several elements in tree bark of various environments.
Area of sampling
Mean concentration, mg/kg
Reference Fe Al V Mn Ni Cu Pb La Tm
Relatively unpolluted
area in Finland
(P.sylvestris L.)
nd nd nd 29.2–432 0.54–1.71 267-340 1.00-2.10 nd nd Baltrėnaitė et
al. (2013)
Upper crust continental 35 000 84 700 60 600 20 25 15 30 0.33 Taylor et al.,
(1981)
City in Sheffield, UK
(Sycamore, oak, cherry) 5712 nd nd 280 65.0 47.3 226 nd nd
Schelle et al.
(2008)
Highly air polluting
industry, Belgrade
(Platanus sp. and Pinus
sp.)
327.277 nd nd nd nd 37.900 15.567 nd nd Sawidis et al.
(2011)
Note: Information in the bracket represents the tree species.
nd: not defined
23
39
Table 4.1. Sampling Locations
Location Latitude Longitude
A 4°3'39.94"N 103°23'16.30"E
B 4°0'43.29"N 103°23'4.54"E
C 3°58'39.46"N 103°22'17.61"E
D 3°58'55.90"N 103°23'45.00"E
E 3°56'17.94"N 103°22'10.01"E
F 3°53'51.43"N 103°21'22.09"E
G 3°58'31.35"N 103°18'31.10"E
Table 4.2. Concentration of the Analyzed Elements.
Element Concentration, mg/kg
Fe Al V Mn Ni Cu Pb La Tm
A 13.25 99.680 0.003580 13.490 6.1100 0.01415 11.00 < 0.00002 < 0.0000002
B 35.40 180.94 0.5100 0.00351 0.003420 0.01500 33.38 < 0.00002 < 0.0000002
C 45.20 127.94 0.07000 21.500 0.003356 0.01334 10.56 < 0.00002 < 0.0000002
D 15.90 41.05 0.003050 0.00334 6.9100 0.01564 67.54 < 0.00002 < 0.0000002
E 16.35 104.04 0.07000 5.890 0.001530 0.01111 8.47 0.10 < 0.0000002
F 14.70 39.78 0.003580 0.00224 8.2400 0.01452 10.54 < 0.00002 < 0.0000002
G 13.55 0.0022 0.003520 2.110 0.002770 0.01425 9.25 < 0.00002 < 0.0000002
Mean 28.51 84.78 0.09500 6.140 3.04000 0.01400 21.53 0.014 0.0000002
Control 15.15 30.63 0.001568 0.00112 0.001470 0.00524 5.82 < 0.00002 < 0.0000002
UCC 35000 84700 60 600 20 25 15 30 0.33
UCC: Upper continental crust
39
43
Figure 4.1a Enrichment Factor and the enrichment degree of the selected elements in the sampled tree barks (lower extremities).
44
Figure 4.1b Enrichment Factor and the enrichment degree of the selected elements in the sampled tree barks (upper extremities).
45
Figure 4.2. Geoaccumulation Index and the contamination level of the selected elements in the sampled tree barks.
UC: Uncontaminated, UMC: Uncontaminated/moderately contaminated, MC: Moderately contaminated, MSC: Moderately/strongly contaminated, SC: Strongly
contaminated
46
Table 4.6. Enrichment Factor and the enrichment degree of the selected elements in the sampled tree barks.
Metals/
Stations Al V Mn Ni Cu Pb La
Tm
Enrichment Factor and the enrichment degree
A 3.72
ME
2.61
ME
13771.9
EE
4752.97
EE
3.09
ME
2.16
ME
1.14
DE
1.14
DE
B 2.53
ME
139.20
EE
1.34
DE
1.00
DE
1.23
DE
2.45
ME
0.43
DE
0.43
DE
C 1.40
DE
14.96
SE
6434.2
EE
0.78
DE
0.085
DE
0.61
DE
0.34
DE
0.34
DE
D 1.28
DE
1.85
DE
2.84
ME
4479.41
EE
2.84
ME
11.06
SE
0.95
DE
0.95
DE
E 3.15
ME
41.37
EE
4872.99
EE
0.97
DE
1.96
DE
1.35
DE
4633.03
EE
0.93
DE
F 1.34
DE
2.35
ME
2.06
ME
5777.63
EE
2.86
ME
1.87
DE
1.03
DE
1.03
DE
G 8.03 x 10
-5
DE
2.51
ME
2106.40
EE
2.11
ME
3.04
ME
1.78
DE
1.12
DE
1.12
DE
Mean 1.92
DE
29.26
EE
3884.53
EE
2144.98
EE
2.12
ME
3.04
ME
662.58
EE
0.85
DE
DE: Deficiency to minimal enrichment, ME: Moderate enrichment, SE: Significant enrichment, EE: Extreme/high enrichment.
47
Table 4.7 Geo-accumulation Index (Igeo) and the contamination levels of the selected elements in the samples tree barks.
Metals/
Stations Fe Al V Mn Ni Cu Pb La Tm
Geo-accumulation Index (Igeo) value, Igeo classes and the contamination levels
A -11.95
UC (0)
-10.32
UC (0)
-14.62
UC (0)
-6.06
UC (0)
-2.30
UC (0)
-11.37
UC (0)
-1.03
UC (0)
-21.1
UC (0)
-21.24
UC (0)
B -10.53
UC (0)
-9.46
UC (0)
-7.46
UC (0)
-17.97
UC (0)
-13.10
UC (0)
-11.29
UC (0)
0.57
UMC (1)
-21.1
UC (0)
-21.24
UC (0)
C -10.18
UC (0)
-9.96
UC (0)
-10.33
UC (0)
-5.39
UC (0)
-13.13
UC (0)
-11.46
UC (0)
-1.09
UC (0)
-21.1
UC (0)
-21.24
UC (0)
D -11.69
UC (0)
-11.60
UC (0)
-14.85
UC (0)
-18.04
UC (0)
-2.12
UC (0)
-11.23
UC (0)
1.59
MC (2)
-21.1
UC (0)
-21.24
UC (0)
E -11.65
UC (0)
-10.25
UC (0)
-10.33
UC (0)
-7.26
UC (0)
-14.26
UC (0)
-11.72
UC (0)
-1.41
UC (0)
-8.81
UC (0)
-21.24
UC (0)
F -11.80
UC (0)
-11.64
UC (0)
-14.62
UC (0)
-18.62
UC (0)
-1.86
UC (0)
-11.33
UC (0)
-1.09
UC (0)
-21.1
UC (0)
-21.24
UC (0)
G -11.92
UC (0)
-25.78
UC (0)
-14.64
UC (0)
-8.74
UC (0)
-13.40
UC (0)
-11.36
UC (0)
-1.28
UC (0)
-21.1
UC (0)
-21.24
UC (0)
Values in the bracket indicate the Geoaccumulation Index classes
UC: Uncontaminated, UMC: Uncontaminated/moderately contaminated, MC: Moderately contaminated
66
4.3.3. Pollution Load Index
Guéguen et al. (2012) suggested in order to confirm the level of pollution of
the selected sampling locations, the Pollution Load Index (PLI) system was
used. PLI is useful to certify the pollution effect of the diversified elements at
each different sampling location. PLI was calculated based on the
Contamination Factor (CF) values shown in the Appendix A and the formula
for calculating CF and PLI is as the following;
CF = C metal / C background value
PLI=
Table 4.8. The Pollution Index of the sampling locations.
Sampling Location PLI Pollution Status
A 16.43 Polluted
B 4.07 Polluted
C 7.12 Polluted
D 4.12 Polluted
E 13.11 Polluted
F 3.31 Polluted
G 1.69 Polluted
The PLI of each location and their respective pollution status is tabulated as
in Table 4.8. Based on the Table 4.8, all of the sampling locations exceeded
1, suggesting the pollution existed with different PLI values. Even though the
degree of pollution is not available, by distinguishing the degree of pollutions
into several classes, still, the level of pollution can be interpreted based on the
numerical value obtained. The level of pollution in ascending order is G < F
<
B < D < C < E < A. The degree of pollution can be presented in the Figure
4.12 . The highly polluted location was the sampling location A (PLI =