Distribution and Sources Apportionment of the Selected Heavy Metal Pollutants in Urban
Roadside Mosses
Mohd Zahari Bin Abdullah @ Rafie
Department of Chemistry
Faculty of Applied Sciences, UniversitiTeknologi MARA (Pahang), 24600 Pahang, Malaysia
e-mail: umizah@pahang.uitm.edu.my
Abstract
The present study was undertaken as a preliminary survey on moss contamination around
the five main roads in Bandar Kuantan, Malaysia. Moss samples were taken along the five
selected busiest roads around the town. The concentrations of nine elements in epiphyte
moss species were analyzed by using ICP-OES. The objectives of the study were: (1) to
determine the average concentration of some heavy metals deposited close to the busy road
in developed city; (2) to evaluate the possible sources that were contributed these metals in
the surrounding air. Multivariate statistic approach (Principal Component Analysis) was
adopted for data treatment, allowing the identification of three foremost factors controlling
the heavy-metal variability in mosses. Generally, the results showed that the studied
elements can be divided among three groups which indirectly representing the possibility
of three different sources. The first group comprises of Cr, Mo, Cd, As, Co and Ni, while
Fe is the only element dominated in second group. The metals of Cu and Zn was occupied
the third group. Based on the factor loading and correlation coefficients for all elements in
these three principal components, it can be simplified that the elements in component one
were derived from anthropogenic sources, including transportation activities while the
other two components were considerably influenced by agricultural activities and steelrelated industries.
Keywords: Heavy metals, Air pollutants, mosses, PCA, ICP-OES.
INTRODUCTION
The large inputs of anthropogenic pollutants into surrounding air are mostly contributed by both
stationary sources (power plants, and industries) and mobile sources related to traffic. Huge quantities
of heavy metals are widely emitted at all time and space, and their interactions with other natural
components result in toxic effects on the biosphere. In order to maintain a functioning economy, people
must be able to circulate between the various points that are important to them and do so with ease.
Since then, the increase in the number and the uses of motor vehicles has been among the most
conspicuous features of the modern industrial economy, as well as one of the most influential forces to
the natural and built environment. For all of their advantages, automobiles, especially in large numbers,
bring with them an array of negative effects. Throughout the world, motor vehicles are a major source
of pollution, particularly in urbanized areas, where vehicle concentration is the greatest, and where
pollution from all sources is most severe. Motor vehicles are collectively a significant contributor to
greenhouse-gas emissions because they run on fossil fuels. Finally, by reducing the cost of
transportation, motor vehicles have contributed to the decentralization of urban areas, which is
generally thought to have negative consequences.
Cars, buses, lorries and motorbikes are among the very popular vehicles to the Malaysian populations.
However, in general, road traffic factor was considered potentially to contribute some negative effect
regarding to air quality, noise and land consumption. Moreover, at a certain concentration, some heavymetals highly potential to poses a threat to plants, animals, and human beings (Dockery, 2001). An
exposure to heavy metals is a significant problem of environmental toxicology. These metals, if
deposited constantly in small rates over long period of time, will accumulate in the environment and
probably pose an increasing major environmental and human health hazard in the future.
Atmospheric air pollution related to the vehicle’s exhaust has become important environmental issues
around the world, especially for the developing countries. Some groups of people, particularly babies
and children are more susceptible to the ambient air pollution exposure because of their fast growth and
development. When a car’s engine is running, several different types of gasses and particles are emitted
that can have detrimental effects of the environment. In general, there are a number of air pollutants
that have been given particular concern based on their potential to induce some health effect to the
acceptors. Among the importance of air pollutants are carbon dioxide, a greenhouse gas, hydrocarbons
and a dozen volatile organic compounds which are known carcinogens, nitrogen oxides, sulfur oxides,
particulate matter, tiny particles of solids, such as metal and soot (Harrington and McConnell, 2003).
Other emissions that affect human health and create smog include ozone and carbon monoxide
(Rossman, 2013). Various types of chemicals released from the vehicles such as carbon monoxide,
nitrogen dioxide and lead have been reported potentially to give effects on the neurobehavioral
functions (Wang, et al., 2009). A series of substances that were emitted by road vehicles have been
identified as toxic. The US EPA highlights 21 toxic substances that can mainly be contributed by road
traffic. Some of the most important heavy metals that normally being related to the road emission are
included Pb, Cu, Sb, Cd Ni and Zn (Johansson, et al., 2009). Emissions from mobile sources
considered the major source of air pollution in Malaysia that contributing to at least 70 % to 75 % of
the total air pollution. A result from the study done by the Department of Environment (DoE) Malaysia
in 1996 clearly showed that motor vehicles have contributed almost 82 % to the air pollution in this
country (DOE, 2000). The DoE of Malaysia also has recorded that the transport sector accounted for
about 36 % of the total particulate matter (PM) emissions to the country. The related study done by
Duong et al. (2011) clearly showed that the contamination levels of the heavy metals in the road dust
were highly dependent on traffic volume and atmospheric dispersion from traffic rotaries. Moreover,
the results from the study have also shown that the frequency of brake use and vehicles coming to a
complete stop were additional factors that affected the contamination levels in downtown areas. Land
traffic has a potential to contribute some impact on the spatial distribution of heavy metals in urban
soils as proved by Guney (2010). The highly correlations of the metal concentrations in road dust,
surface and 20-cm depth soils clearly suggests the presence of a common pollution source contributing
the metals. However, if the metal concentrations measured in the deeper soils were substantially lower
than those observed on the surface this indicates the low mobility of heavy metals, especially for Pb
and Zn.
In general, heavy-metals can be defined as the elements that having atomic weights ranging between
63.5 and 200.6, and a specific gravity greater than 5.0 (Fu & Wang, 2011). The term “heavy-metals”
have been often used as group names for metals and metalloids that have been regarded with
contamination and potential toxicity or exotoxicity [8]. 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,
Hoodaji, & Najafi, 2010). The aforesaid hazardous properties of heavy-metals are the main reasons
why heavy-metals’ study needs to be done extensively. The bio-accumulated heavy-metals can end up
in our food and would have potentials in giving us undesirable complications. Lead toxicity, for
example, was proven in many studies to cause central nervous system deficits that can persist into
primary adulthood (Ma & Singhirunnusorn, 2012). Besides that, the toxicity of Cu, Cd, Zn is
acknowledged to cause alteration in human central nervous system and respiratory system as well as
having the ability to cause disruptions in the endocrine system. In addition, Sawidis et al. (2011) in his
study has stated that urban air particulates are rich in potentially toxic heavy metals, for example, Pb,
Cr, Fe, etc. and can be a genuine hazard for human health.
Bio indicators are biological elements that can be used as the indicator to provide some information
about the state of air pollution at the particular area. The most commonly used bio indicators are
lichens, mosses, tree barks, pine needles and soils. Moss was the first bio indicator introduced by
Ruhling to monitor the lead presence on the air in 1968 (Ruhling and Steinnes, 1998), leading to more
adverse application of other bio indicators to monitor heavy-metal deposition since it has been
considered that plants are “living filters." Leaves and any other exposed parts of a plant acts as
persistent absorbent in a polluted atmosphere. Biological monitoring (bio-monitoring) of air quality is a
politically correct approach, which allows direct and active involvement of people in detecting the
conditions within the environment. Particularly, the use of plants for environmental diagnosis should
be regarded as a necessary complementary tool to be integrated with classical instrumental monitoring.
In this context, many approaches may be followed, and many techniques may be applied according to
specific needs.
One of the main objectives of this study was to group the selected heavy-metal content in the moss
samples based on its primary sources and to identify the origin of fall-out of the metals. In order to
obtain the raw data of the heavy metal's distribution in this study, the passive bio-monitoring technique
has been applied. The data of heavy metal's concentration obtained for this study then has been
analyzed by multivariate analysis. The method applied is probably the simpler approach and could be
the first attempt to determine the heavy metal's deposition concentration and its origin by mosses in this
area.
METHODOLOGY
Sampling Location
The town of Kuantan in Pahang, Malaysia was selected as the sampling location in this study. The
town is one of the fast-growing commercial centres in the East Coast of Peninsular Malaysia, with a
population approaching 400,000 people and heavy traffic volumes. The town is located between
latitude 3o 48’ 0“N and longitude 103o 20’-1E with a temperate climate and sometimes influenced by
two monsoon seasons, the southwest monsoon (late May to September) and the Northeast Monsoon
(November to March) resulting heavy rainfall to the area. All the moss samples were collected around
the main street in Kuantan Town that covered the Jalan Telok Sisek, Jalan Besar, Jalan Mahkota, Jalan
Bukit Ubi and Jalan Beserah. The five main streets are always in high loading with various types of
vehicles and considered congested at all time. Figure 1 show the position of Kuantan Town and its
surrounding activities.
Figure 1: The position of Kuantan Town and Sampling Locations : (A) Jalan Besar, (B) Jalan Mahkota, (C)
Jalan Telok Sisek, (D) Jalan Beserah and (E) Jalan Bukit Ubi
Moss Sampling
Sampling technique and related procedures were based on the Scandinavian guidelines (Ruhling,
2002). However, there are some modifications have been done to adapt with the local conditions,
especially the things that related to the area of the sampling points and the quantity of sample taken.
Moss samples were collected along and the outside ring road at each selected main streets. A total of 15
moss samples has been collected from all the sampling sites to represent three replication for each
sampling site. Only epiphytic moss species were considered for this study in order to avoid some
contamination from the soil. Moreover, the epiphytic mosses could survive even at extremely dry sites
(Halleraker, 1998) and were available for sampling in the selected study area. Sites influenced by
pollution factors other than local traffic (e.g. factories and local combustion) were excluded and
avoided whenever possible. Each sampling point had covered 25 x 25 m2 area in which a two types of
mosses (Hypnum Plumaeforme and Taxithelium Instratum) have been mixed together to form a single
sample. All sample materials were collected in plastic bags and transported to the laboratory (Universiti
Teknologi MARA, Pahang) to sample treatment and analysis.
Samples pre-treatment and preparation
Any foreign materials adhering to the surface of the moss samples such as tree bark, lichens, soil dust
and dead materials were removed carefully in dry condition. For the analysis, only the green and
greenish brown parts of the moss were used, as they generally are intended to represent a period of
about 3-4 years. Their metal content is generally considered to reflect the atmospheric deposition
during that period. The samples were dried at 40 o C in the force-air oven for 24 hours. The
representative samples of each moss were used for analyses in triplicate.
Analytical method
For the analysis, all moss samples were digested by wet digestion. In this study, the representative
samples (1 gm dry weight) were placed in an open quartz tube during the digestion process. Five ml
solution of concentrate HNO3 (Merck) was added to each test tube, and the mixture was left to room
temperature for overnight. The samples were heated at 40 o C for two hours and reheated at 160 o C for
another two hours. The nearly dried moss sample (slurry) was cooled to room temperature and was
diluted with 0.1M HNO3 to the final volume. The solution was then filtered through Whatman type
filter papers, and the filtrate was maintained to 25 ml with double deionized water. All the selected
heavy metals, Cr, Mo, Cd, Fe, As, Co, Ni, Cu and Zn has been analyzed by using inductively couple
plasma–mass spectrometer, ICP-OES. The accuracy of the analyses was checked by analyzing the
NIST standard Reference Material No 1575 “Trace elements in pine needle." The results obtained
shows that all the measurements were not deviated exceeding 15 %.
Statistical analysis
The heavy metal concentration data were then subjected to PCA and CA by the package SPSS 19. The
the loadings (weight of metal concentrations on the linear combination PCs), and the plot of score
summarize the information about the similarities between the sites and between the metal
concentrations and highlight the sites of greater concern.
RESULTS AND DISCUSSIONS
Distribution of Heavy Metals Road Traffic Related in Moss
The measured concentration of heavy metals deposited in the moss samples from the five different
sampling locations are listed in Table 1. There were notable differences in the distribution and
bioaccumulation of metals in moss. The result showed that the concentration of Cr was found to range
between 4.5 mg/kg dry weight (Jalan Beserah) and 6.05 mg/kg dry weight (Jalan Telok Sisek) while
the highest concentration of Mo was recorded in the moss sample collected at Jalan Besar with 3.9
mg/kg dry weight and the lowest concentration was recorded for the moss sample collected at Jalan
Mahkota with 0.8 mg/kg dry weight. The moss samples from Jalan Telok Sisek also have recorded the
high concentration of Fe and Cu with 137.3 and 21.6 mg/kg dry weight respectively. The lowest
concentration of Cu metal was measured as 4.3 mg/kg dry weight at Jalan Beserah.
Table 1: The concentration (mg/kg dry wt.) of heavy metals deposited in mosses measured by ICPOES and its respective relative standard deviation.
Metal
Jalan Beserah
Jalan Besar
Jalan
Mahkota
Jalan Telok
Sisek
Jalan Bukit
Ubi
% STDEV
Cr
Mo
Cd
Fe
As
Co
Ni
Cu
Zn
4.5
2.7
0.2
56.7
2.00
3.5
6.5
4.3
13.9
4.00
3.9
0.4
50.9
1.3
5.4
11.6
8.1
3.4
5.8
2.8
0.1
62.8
1.8
3.9
5.7
16.6
11.9
6.05
1.9
0.1
47.9
1.4
2.9
4.5
21.6
12.9
6.3
1.2
0.4
62.1
0.9
3.2
5.6
18.3
13.7
19
40
63
12
29
26
41
53
39
The concentrations of metal Ni and Co in moss were found highest for moss samples collected at Jalan
Besar with 11.6 mg/kg dry weight and 5.4 mg/kg dry weight respectively. Meanwhile, the minimum
concentrations for Ni and Co were recorded in the moss sample obtained from Jalan Telok Sisek (4.5
mg/kg) and Jalan Bukit Ubi (3.2 mg/kg). In most cases, the results showed that the concentration of Fe
in moss sample from all of the five different sampling locations were extremely high compared to other
studied elements. Concentration of Fe was measured to range between 47.9 mg/kg dry weight (Jalan
Telok Sisek) and 62.8 mg/kg dry weight (Jalan Mahkota). In general, the results obtained clearly show
that the Fe metal content deposited in moss at all sampling locations are almost identical with relative
standard deviation of 12 %. Meanwhile, the concentration of Zn in moss collected from Jalan Besar
was recorded the lowest concentration with 3.4 mg/kg dry weight compared to the other four locations;
Jalan Beserah, Jalan Mahkota, Jalan Telok Sisek and Jalan Bukit Ubi that were recorded 13.9, 11.9,
12.9 and 13.7 mg/kg dry weight respectively.
For As, the deposition concentrations were found ranging from 0.9 mg/kg dry weight (Jalan Bukit Ubi)
to 2.00 mg/kg dry weight (Jalan Beserah). Meanwhile the concentration of As at the other three
locations were found to be evenly deposited within 1.3 mg/kg to 1.8 mg/kg dry weight. Cadmium, Cd
metal had shown very constant levels of its existence in all moss samples for all different sampling
locations in this study where they were deposited just in a small range concentrations, 0.1 and 0.4
mg/kg dry weight.
Principal Component Analysis, PCA
In order to assess the contribution of emission sources to the heavy metals pollution in the area, the
principal component analysis was used. The results obtained from the PCA analysis will explain the
relationships among the deposited heavy metals in moss. The correlation data then has been used to
detect the possible origin for the elements interest. The total variance explained by the first three
principal components (PC) is given in Table 2.
Table 2: Total Variance Explained for the analyzed mosses
Initial Eigenvalues
Component
Total
Rotation Sums of Squared Loadings
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
5.109
56.771
56.771
4.772
53.021
53.021
2
2.053
22.810
79.581
2.153
23.925
76.946
3
1.151
12.789
92.370
1.388
15.424
92.370
4
.687
7.630
100.000
5
3.327E-16
3.697E-15
100.000
6
1.790E-16
1.989E-15
100.000
7
2.093E-17
2.326E-16
100.000
8
-1.077E-16
-1.196E-15
100.000
9
-3.894E-16
-4.327E-15
100.000
Extraction Method: Principal Component Analysis.
The first three PCs are chosen according to Kaiser's criterion which considered only PCs with the
eigenvalues more than one as the significant point. As shown in the Table 2, the total variance
explained by the first three components (PCs) in this study was almost 93 %. The high percentage of
variance contributed by these three components indirectly indicates that the distribution pattern of the
studied elements highly depends on the factor loading of all elements in these three components. The
component matrices (factor loading) for all the studied elements are given in Table 3. The first
component, PC1 which contributed almost 57 % of the total variance has considered the most
important component compared to PC2 and PC3. The component matrix for the analyzed heavy metals
clearly indicated that Mo, Co and Ni were closely correlated due to their high positive loading in the
first PC while Cr, Zn and Cu were also closely related due to the high negative loading in the same PC.
The negative values of the component matrix indicate that they are conversely correlated (opposite
direction). The metal of Cr is considered the most dominated element in this group followed by Mo,
Co, Ni, Cu and Zn. The second component, PC2 which contributed almost 23 % of the total variance
had a positive loading of Cd and As and for the less important component that contributed almost 13 %
of the total variance is dominated by the metal Fe that recorded the highest positive loading in the third
PC. Cluster analysis of metal concentrations revealed the degree of correlation of all the studied
elements. The results obtained are represented as dendrogram as shown in Figure 2. The distance
cluster represents the degree of correlation between the elements. The smaller the distance of the two
clusters the more similar of the elements and the more significant was the correlation between the
elements. From the dendrogram, three clusters were revealed: the first were included Mo, Cr, Co, Ni,
Zn and Cd, the second cluster contained the single element Fe while the last cluster contained Cu and
Zn. These results were in accordance with those obtained by PCA, confirming the different sources of
these three groups of elements.
Figure 2: Dendrogram obtained by hierarchical cluster analysis of heavy metal content in mosses.
In order to provide the variation views of the heavy-metal distribution in this study, the data of the
measured heavy metals has been analyzed and treated by PCA. From the previous study, the use of
this statistical method is enabled to reveal the origin than the interest elements based on some factors
(Dragovic and Mihailovic, 2009). Based on the PCA data gained through this study, it is possible to
highlight the sites with high concentrations of particular elements and gain insight into the origin
contributors of the pollutants. Factor loadings which were normally shown on the form as the
component matrix can be used to measure how strong each metal is associated with each axis
(component). The data on the degree of correspondence between the variable (metal) and the PCs, it is
possible to draw some conclusions as to their potential sources.
The first PC was characterized by Cr, Mo, Ni, Co, Cu and Zn. The Zn metal is considered the most
important element in the first PC which was recorded the highest loading with 0.939 followed by Mo,
Cu, Ni, Co and Cr with 0.901, 0.895, 0.869, 0.859 and 0.715 loading respectively. The trend of the
individual heavy-metal distributions at the studied location shows that Mo, Ni and Co are in the same
direction in the first PC while Cr, Cu and Zn is in the opposite direction. With small differences of
factor loading recorded between all the elements in this group it clearly suggested that they were
possibly contributed by the similar emitters. As what has been mentioned in some literatures, the
increasing levels of Cr, Ni, Cu, Zn and Co in the surrounding atmosphere normally were related to the
anthropogenic factor and atmospheric deposition (Gramatica, et al ., 2006; Gerdol, et al., 2000). With
the absence of any important industries located close to all the sampling station, it was highly believed
that the existence of these elements in the studied areas mostly influenced by vehicles and small local
combustion activities.
The second PC was characterized by two elements namely As and Cd. High loading contributed by As
in this second PC with 0.196 clearly attributed to the factor of air pollution (atmospheric deposition)
and a small fraction of the soil factors (Poykio and Torvela, 2000). Some studies also have shown that
Cadmium, Cd did not accumulate strongly in mosses and can therefore, not be considered mainly
derived from road traffic (Wei and Yang, 2010). The metal industry, phosphate fertilizers, waste
incineration and fossil burning were identified as the main sources of Cd in atmosphere (Dragovic and
Mihailovic, 2009). Cd by road traffic sources seems to be low in comparison with other sources (e.g.
industrial). Therefore, it could be assumed that the present of the As and Cd in the moss sample was
associated with the dust blown factor and the small local activities. Meanwhile, for the third
component, PC3, that only shows a strong association with Fe metal would strongly reflect to the soil
factor.
Table 3: Factor loadings of the selected heavy metals in the moss samples. Extraction Method:
Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Metal
Cr
Mo
Cd
Fe
Zn
PC1
-.715
.901
.354
-.120
-.939
PC2
-.422
-.120
.876
.062
.125
PC3
.499
-.330
.201
.890
.061
Factor Loading
Metal
PC1
.370
As
Co
.859
Ni
.868
Cu
-.895
PC2
-.916
.341
.432
.179
PC3
.124
-.232
-.244
-.256
CONCLUSIONS
In general, the use of multivariate analysis has revealed much about the origin of fallout of some heavy
metals around the study area. From this study, it was strongly believed that the existences of the Cr,
Co, Mo, Cu, Ni and Zn in moss samples are highly correlated to the road transportation activities. For
the rest elements, especially Fe, As and Cd, it could be concluded that the existences of these elements
in the studied areas were influenced by the factors of soil dust, vegetation activities and also from fossil
burning.
ACKNOWLEDGEMENT
The author is indebted to MOSTI (Ministry of Science, Technology and Innovation) and RMI UiTM
Shah Alam, Malaysia for providing financial support for this project.
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