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International Journal of Multidisciplinary Approach
and Studies ISSN NO:: 2348 – 537X
Volume 05, No.2, Mar – Apr 2018
Pag
e : 6
8
Spatial Distribution of Criteria Pollutants in Klang Valley from 5
Monitoring Stations
Jabir Abdullah Hussein*, & Ahmad Makmom Abdullah**
Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Darul Ehsan,
Malaysia
ABSTRACT
Over the last decade, air pollution has become Malaysian highest environmental issue due to
the rapid development of industry, commercial and urbanization. Air pollution impacts
human health, well-being and the environment and has been significantly deteriorated the
environmental atmosphere. The aim of this study is to determine the distribution of PM10
concentration in the Klang Valley as well as the number of unhealthy days due to high
ambient air concentration of air pollutants. The five major air pollutants (PM10, CO, NO2,
SO2 and O3) recorded at Nilai (S1), Petaling Jaya (S2), Shah Alam (S3), Putra Jaya (S4), and
Cheras (S5) by Continuous Air Quality Monitoring System (CAQMS) managed by Alam
Sekitar Sdn Bhd (ASMA) were used in this study. The data for these five stations were
referred by Recommended Malaysian Ambient Air Quality Standard (RMAAQS). Statistical
analysis was used to compare the air pollution concentration with the recommended
Malaysian Ambient Air Quality Standard (MAAQG). Analysis of variance was carried out to
see the differences between five stations by using one-way ANOVAs. Time series on daily
averaged data of PM10, CO, NO2, SO2, and O3 in the Klang Valley were all below the
(RMAAQS) throughout the whole five year period. Although different locations recorded
different PM10 concentration, Nilai experienced the highest level, while Putra Jaya recorded
the lowest of PM10 level. The highest concentration of CO and NO2 was recorded at Petaling
Jaya, while the highest level of SO2 was identified at Nilai and Cheras for O3. There is
significant correlation between the pollutants. In addition, the number of unhealthy, very
unhealthy and hazardous days increases gradually from 4 days in 2010 to 131 days in 2014
respectively. Hence, the government must initiate strategies to adopt an integrated
approaches to control and mitigate the emission of air pollutants especially during haze
episode.
KEYWORDS: PM10, CO, NO2, SO2 and O3; Ambient Air Pollution; Klang Valley; GIS;
Malaysian Ambient Air Quality Standard (MAAQS)
1.0 INTRODUCTION
Air pollution impacts human health and well-being, and the environment. It has been a
widely recognized problem of the last five decades. Located in center of South East Asia,
Malaysia experiences industrial development, vehicle emission and urbanization
transformation that has contributed to high amounts of atmospheric haze pollutants [21-11].
The Klang Valley is the mainstream economic region in Malaysia with the extensive physical
development of the infrastructure, industrialization and urbanization that have significantly
deteriorated the environmental atmosphere [20]. For instance, the air pollution in this area
International Journal of Multidisciplinary Approach
and Studies ISSN NO:: 2348 – 537X
Volume 05, No.2, Mar – Apr 2018
Pag
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9
was related to the increase rate of respiratory diseases which are among the 10 principal
causes of death in Malaysia in 2009 [13-16]. The haze phenomenon also reduced the
visibility in large scale areas and expected to lead more incidences of asthma and pneumonia,
and even lung cancer. Negative impacts associated with haze include the increased
emergency room attendance for respiratory tract symptoms, bronchial asthma, eye irritation,
nasal discharge, sore throat and coughing [4]. Transboundary forest fires have been
considered as a regular source of haze since the early 1900s with the first serious episode
occurring in 1997 when farmers adopted the slash and burn techniques of clearing land 1802
square kilometers (Km2) for agricultural usage [6]. The burning of carbon-rich peat land can
cause acid smoke, dust, and dry particle releasing into the atmosphere which results in the
formation of haze. For the past five years, the three major sources of air pollution in Malaysia
are mobile sources (c.a. 70-75% of total air pollution), stationary sources (c.a. 20-25%), and
open burning sources (c.a. 3-5%) [2]. The Klang Valley also was among the worst hit areas in
Malaysia from the widespread forest fires in Kalimantan and Sumatra in 1997 [1-19-14]. This
study aims to explore the trend of ambient air pollution (i.e. PM10, CO, NO2, SO2, O3) and a
number of days exposed to haze within the five selected Malaysian air monitoring stations in
the Klang Valley of five years database, ranging from 2010 to 2014. The air quality data were
compared to the recommended Malaysian Ambient Air Quality Standard (MAAQG) and the
distribution of PM10 concentration was also analyzed. In addition, this study interpolates the
pollutant distribution pattern across the Klang Valley to determine the influence of these
seasons on the trend. Geographic Information System (GIS) is a fundamental and applicable
computer-based tool for capturing, transforming, managing, analyzing and presenting the
spatially distributed phenomena related to the earth [22]. Therefore, integrating spatial
analysis in GIS and statistical modeling can help the researcher to expand the understanding
concerning the distribution of the pollutants in some locations or areas and understand the
factors that influence the trends and significance. Inverse Distance Weighted (IDW) was
employed in this study to estimate the unknown value at a point by averaging the value of
sample data points in the neighborhood of known value [20]. The spatial map can provide an
initial overview of the potential health risks experienced by people who are exposed to high
air pollutants in certain areas.
2.0. MATERIALS AND METHODS
2.1. Description of the Study Area
The Klang Valley consists of Kuala Lumpur, Putra Jaya and adjoining cities and towns in the
State of Selangor [1]. This area with the size of 2,843 square kilometers consists of Petaling
Jaya, Shah Alam, Cheras, Putra Jaya and Nilai which are outside the Klang Valley but
located close to the border with Negeri Sembilan (Figure 1). Its central coordinates are
101.7078° E and 2.9992° N. There were 6. 9 million occupants in the Klang valley to 2013.
Klang valley is standard financial investment location in Malaysia as it contributed 23. 5% of
the Growth Domesticated Product from claiming Malaysia clinched alongside 2012[8].
International Journal of Multidisciplinary Approach
and Studies ISSN NO:: 2348 – 537X
Volume 05, No.2, Mar – Apr 2018
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Figure 1 Location of the five continuous air quality monitoring stations
2.2. Data Acquisition
The Department of Environment monitors the status of air quality throughout the country to
detect any significant change which may cause harm to the human health and environment. In
this study, the air quality data used for this analysis were obtained from the air quality
division of the department of Environment, Malaysia, (DOE) with continuous air quality
monitoring stations controlled by a private company, Alam Sekitar Sdn. Bhd. (ASMA) [10].
The geographical locations of the five stations are shown in Fig. 1. Air pollutant parameters
(PM10, CO, No2, So2 and O3,) from haze episodes between January 2010 and December 2014
were used in this study.
2.3. Air Quality Monitoring Stations
Table 1 Details the information of five stations. These stations are ranked as urban,
residential and industrial areas (Table 1). Nilai is located in the west of Peninsular Malaysia
and bounded by Selangor in the north is categorized as residential areas and have a
population of 38,612 (2010) [8]. Nilai is located in the Negeri Sembilan state while other four
stations are located in Selangor. The stations in Shah Alam and Cheras are located near the
main roads and highly congested urban areas, which are frequently affected by traffic-related
pollution [20]. Petaling Jaya station is also located near the main roads of industrial areas.
The Putra Jaya station is located in the less populated area compared to the other four areas
[8].
International Journal of Multidisciplinary Approach
and Studies ISSN NO:: 2348 – 537X
Volume 05, No.2, Mar – Apr 2018
Pag
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Source: Department of Environment (DOE)
2.4. Research Framework
Trend Series Comparison with Spatial Distribution
of Air Pollutants (RMAAGS) of Air Pollutants
PM10 Identifying:
CO
No2 Unhealthy
So2 Very Unhealthy &
O3 Hazardous days
3.5. Data Analysis
3.5.1. Comparison to the Malaysian Ambient Air Quality Guidelines (MAAQG)
This study was analyzed using statistics utilizing SPSS statistical software version 21 and
excel for graphics. Averaged daily concentration of PM10, CO, NO2, SO2 and O3 ranging
from 2010 to 2014 were compared to the Malaysian Ambient Air Quality Guidelines
(MAAQG) by the DOE [10] in Table (2). The Malaysian Ambient Air Quality Guideline
values are the minimum requirements for outdoor air quality to assess the human health and
environment [7]. This study is based on daily recorded data with dominant pollutants. PM10
concentration of more than 150g/m3
is considered to be unhealthy for everyone peculiarly
Air quality monitoring station site location Area Coordinates Area Category
Sekolah Kebangsaan TTDI Jaya
Shah Alam
N 3.104710
E 101.556179
Urban
Sekolah Menengah Kebangsaan Seri
Permaisuri
Cheras N 3.106222
E 101.717909
Urban
Sekolah Kebangsaan Presint 8 (2) Putra Jaya N 2.931862
E 101.681775
Urban
Taman Semarak Phase (II) Nilai N 2.49246
E 101.48877
Residential
Sekolah Kebangsaan Bandar Utama Petaling Jaya N 3.109474
E 101.638829
Industrial
Interpolation
Using
Geographic
Information
System (GIS) Inverse Distance
Weighted Method
Distribution of Criteria Pollutants
International Journal of Multidisciplinary Approach
and Studies ISSN NO:: 2348 – 537X
Volume 05, No.2, Mar – Apr 2018
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2
the sensitive group such as children, elderly and pregnant women who may experience
serious health effects while the exposure level at 100 g/m3
is rated to be moderate for
sensitive groups [10].
Table 2 Malaysian Ambient Air Quality Standards
Pollutant
Averaging
Time
Ambient Air Quality Standard
IT-1(2015) IT-2(2018) Standard (2020)
µg/m3 / ppm µg/m
3 µg/m
3
Particulate Matter (PM10) 1 Year
1 Day
50
150
45
120
40
100
Carbon Monoxide (CO) 1 Hour
8 Hours
30.0
9.0
35
10
30
10
Nitrogen Dioxide (NO2) 1 Hour
1 Day
0.17
0.04
300
75
280
70
Sulphur Dioxide (SO2) 1 Hour
1 Day
0.13
0.04
300
90
250
80
Ozone (O3) 1 Hour
8 Hours
0.10
0.06
200
10
180
10
2.5.2. Interpolation using Geographical Information System (GIS)
Inverse Distance Weighted Method was employed for the interpolation of air quality data to
find out the spatial and temporal distribution of the pollutants. Geographic Information
System (GIS) was used as a instrument in this study to visualize the dispersion of air
pollutants and assess the relationship between the high and low concentrations as well as
exposure level. Inverse distance weighted (IDW) estimates the value that could not be
measured using the inverse of the distance to each surrounding value as a weighting factor, so
that closer values are given a greater weight than those from farther away. IDW gives those
low root intend square value contrasted with different systems. [17]. The general concept of
IDW is to estimate the unknown value of Y(XO) in location XO given the observed Y value at
sampled locations Xi according to the formula in equation 1.
The IDW use assumes that the estimated rate of concentration at Y(XO) will have more load
if it is located near the sampled locations compared to its location at farther points [17].
International Journal of Multidisciplinary Approach
and Studies ISSN NO:: 2348 – 537X
Volume 05, No.2, Mar – Apr 2018
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3.0. Results and Discussions
3.1. Trend and Status of Air Quality Parameters
Fig. 1 illustrates the annual average trend of PM10, CO, No2, So2 and O3, in Petaling Jaya,
Shah Alam, Putra Jaya and Cheras in the Klang Valley from 2010 to 2014. Even though the
pollutants in this study were all within the RMAAQG. The highest concentration of PM10
was recorded in Nilai 59. 15 µgm-3
( 55.45 µgm-3
- 63.56 µgm-3
), which is the center of so
many industrial activities and businesses and very close to the Klang Valley which is the
main economic region in Malaysia with the extensive physical development of infrastructure,
industrialization, and urbanization [8]. It is also noticed that Shah Alam has recorded the
second highest PM10 concentration with an average of 49.41 µgm3 (45.59 µgm-3
- 54.56
µgm-3
) compared to Petaling Jaya, Putra Jaya, and Cheras. This is because as reported in the
DOE Environmental Quality Report 2012, the urban areas contribute to the high average
concentration of PM10 [9]. It can be further proven that the concentration of particulate matter
(PM10) in Klang (urban area) is significantly higher compared to the suburban and rural areas
as stated by Abdullah et al. [1]. The lowest concentration of PM10 was recorded by Putra Jaya
with a mean average of 40.45 µgm-3
(36.84 µgm-3
- 46.32 µgm-3
) compared to other four
stations. This is due to that Putra Jaya is urban and less populated area.
International Journal of Multidisciplinary Approach
and Studies ISSN NO:: 2348 – 537X
Volume 05, No.2, Mar – Apr 2018
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Figure 1 Trend and status of Air Quality Parameters
The most astounding focus of CO and NO2 might have been recorded on Petaling Jaya with a
mean value of 1.200 µgm3 (1.101 µgm
3 - 1.272 µgm
3). This is due to that it is located in the
center of many commercial and residential areas and categorized as an industrial area in
Table 1 [5]. Abdullah et al. (2012) [1] also mentioned that motor vehicle emissions are
probably the highest factors related to the high-level concentration of CO and NO2 in Petaling
Jaya. Petaling Jaya is an area with high traffic; therefore, the most important sources of NO2
which include open burning, fuel sources, all forms of transport [17]. Fuel utilization with
high engineering what’s more temperature oxidizes those nitrogen in the fuel to process NO2
and all forms of industrial processes [20-23]. Since Putra Jaya is not an industrial area, it is
among the areas with the lowest concentration of CO and NO2 compared to the other four
stations. Even though there is a clear difference of SO2 between air pollutants, Nilai was
recorded with the highest level of SO2 with an average of 0.042 µgm3 (0.033 µgm
3 - 0.006
µgm3). This is due to that it is a residential and densely populated area compared to other
stations. Nilai has a population of 38,612. Therefore, the main source of SO2 is expected to be
International Journal of Multidisciplinary Approach
and Studies ISSN NO:: 2348 – 537X
Volume 05, No.2, Mar – Apr 2018
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5
from motor vehicles predominantly diesel engine trucks and buses [5]. Although Putra Jaya
also recorded a high concentration in O3 with an annual average of 0.017 µgm3
(0.016 -
0.019), Cheras showed the highest concentration of Ozone (O3) with annual average of 0.018
µgm3
(0.15 µgm3 - 0.203 µgm
3). This is because Cheras has abundant sunshine over a period
of time, mild wind, and high temperature. Therefore, it is most likely to experience the
photochemical smog of Ozone (O3) compared to the other four stations. The result of graph 1
shows that PM10, CO, NO2, SO and O3 started to increase from 2010 to 2011 except Ozone
which is decreasing in all the five stations in 2010. Most of the monitoring locations and
pollutants were decreasing due to the low level of haze in 2011 to 2012, increasing in 2012 to
2013 and increased dramatically to its peak from 2013 to 2014. This indicates that haze was
at its highest level in 2013 and 2014 compared to the other three years. PM10 concentration
distribution can be observed higher during the inter-monsoon followed by the southwest
monsoon and lower in northeast inter-monsoon [21].
3.2. The Comparison of Pollutants with the recommended Malaysian Ambient Air
Quality Guidelines (RMAAQG)
Table 3 Comparisons of Pollutants with (RMAAQS)
Parameters Averaging
Time Stations Average Minimum Maximum Standard
Deviation Medium RMAAQG
PM10 (µgm
3)
2010-2014
S1
S2
S3
S4
S5
59.15
48.87
49.41
40.45
49.24
55.45
41.83
45.59
36.84
42.70
63.56
54.71
54.56
46.32
59.02
3.09
4.70
4.27
3.74
6.02
58.88
49.06
47.74
39.16
48.64
150 (µgm3)
CO (ppm) 2010-2014
S1
S2
S3
S4
S5
0.553
1.200
0.706
0.573
0.812
0.469
1.101
0.608
0.540
0.761
0.617
1.272
0.799
0.601
0.879
0.053
0.069
0.712
0.022
0.042
0.561
1.206
0.712
0.580
0.804
30(ppm)
SO2 (ppm) 2010-2014
S1
S2
S3
S4
S5
0.042
0.003
0.003
0.002
0.001
0.033
0.003
0.001
0.002
0.001
0.006
0.004
0.004
0.002
0.002
0.001
0.000
0.000
0.000
0.000
0.003
0.003
0.003
0.002
0.002
0.13(ppm)
NO2 (ppm)
2010-2014
S1
S2
S3
S4
S5
0.014
0.027
0.018
0.012
0.019
0.012
0.025
0.014
0.009
0.017
0.015
0.029
0.022
0.015
0.021
0.001
0.001
0.002
0.002
0.001
0.014
0.028
0.018
0.013
0.020
0.17(ppm)
O3 (ppm) 2010-2014
S1
S2
S3
S4
S5
0.014
0.014
0.012
0.017
0.018
0.012
0.012
0.011
0.016
0.015
0.015
0.014
0.013
0.019
0.203
0.001
0.0011
0.000
0.000
0.001
0.014
0.014
0.012
0.017
0.018
0.10(ppm)
RMAAQG= Recommended Malaysian Ambient Air Quality Guidelines
International Journal of Multidisciplinary Approach
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Note: The concentration of all the five pollutant parameters based on the annual average of
daily concentrations.
The compilation of data from the five air quality monitoring stations (Nilai, Petaling Jaya,
Shah Alam, Putra Jaya and Cheras) and the five recognized air quality parameters (PM10, CO,
NO2, SO2 and O3) statistics, and their comparison to the Recommended Malaysian Ambient
Air Quality Guideline are summarized in Table 1. The average concentration of PM10 at Nilai
was recorded at 59.15 µgm-3
(55.45 µgm-3
- 63.56 µgm-3
) and Petaling Jaya, Shah Alam,
Putra Jaya and Cheras are 48.87 µgm-3
(41.83 µgm-3
- 54.71 µgm-3
), 49.41µgm-3
(45.59 µgm-
3 - 54.56µgm
-3), 40.45 µgm
-3 (36.84 µgm
-3 - 46.32 µgm
-3) and 49.24 µgm
-3 (42.70µgm
-3 -
59.02µgm-3
) respectively. The hourly average concentration of these five stations was found
to be far below the recommended Malaysian Ambient Air Quality Guideline (RMAAQS) of
(150µgm-3
).
However, the average concentration of PM10 at these five stations did not exceed the
European Commission for PM10 (50µgm-3
) except Nilai. This is due to Nilai being
categorized as an industrial and residential area compared to Shah Alam, Putra Jaya and
Cheras which are categorized as urban areas. This is expected to increase the amount of PM10
in the atmosphere. The average concentration of all five parameters was still under the
required limit by the Malaysian Ambient Air Quality Standard (RMAAQS). In Table 5 there
are significant differences between the different air pollutants recorded at the five stations.
This implies that the local surroundings have great influence on the concentrations of gasses
at each monitoring station.
Table 4 Number of unhealthy Days in the Klang Valley from 2010-2014
Stations
API Category
2010
2011
2012
2013
2014
Nilai Hazardous
Very unhealthy
Unhealthy
0
0
4
0
0
10
0
0
14
0
4
12
0
2
34
Petaling Jaya Hazardous
Very unhealthy
Unhealthy
0
0
0
0
0
6
0
1
5
2
1
7
1
1
27
Shah Alam
Hazardous
Very unhealthy
Unhealthy
0
0
0
0
0
11
0
1
4
1
1
8
0
2
28
Putra Jaya Hazardous
Very unhealthy
Unhealthy
0
0
0
0
0
1
0
0
2
1
1
8
0
2
14
Cheras Hazardous
Very unhealthy
Unhealthy
0
0
0
0
0
5
0
1
10
0
2
6
0
1
19
Note: Hazardous = >300 Very Unhealthy = 201-300 and Unhealthy = 101-200
International Journal of Multidisciplinary Approach
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Figure 2 Number of Days Expose unhealthy and Hazardous level of API in the Klang Valley
Table 4 demonstrates the number of days in which PM10 exceeded the air pollution index
(API) value categorized as Hazardous >300, Very Unhealthy (201-300) and Unhealthy (101-
200) in the five-year database and five stations as well. Nilai was the city with the highest
number of unhealthy and very unhealthy days with PM10 in 80 days for the five years. In
2010, only 4 unhealthy days were recorded while in 2011 the number of unhealthy days
increased to 10 and in 2012 increased to 14 but in 2013 the number of unhealthy days
decreased to 12 compared to the gradual increase during the last three years. The highest
unhealthy days in Nilai was recorded in 2014 as it reached 34 days. The most interesting
thing is that the number of unhealthy, very unhealthy and hazardous days noticed in Klang
was not the same; 2014 recorded the highest unhealthy days in each station compared to other
four years as it reached 131 out of 260 days. This number indicates that haze reached its peak
in 2014 as it recorded more than half of the unhealthy days in the five years. This is related to
the transboundary haze from the forest fires in Indonesia. In addition, during the haze
episodes, the southwest monsoon wind transported the suspended particulate PM10 from
Sumatra to the west coast of Peninsular Malaysia, which resulted in severe haze conditions in
the Klang Valley [3-20].
Table 5 Analysis of Variance between Monitoring Stations
Locations
Comparison
Categories
Sum of
Squares
Df
Mean
Square
F-
Value
Significant
Nilai
Between groups
Within groups
Total
877.893
400.969
1278.862
4
20
24
219.473
20.048
10.947
0.000
Petaling
Jaya
Between groups
Within groups
1.386
0.060
4
20
0.346
0.003
114.772 0.000
International Journal of Multidisciplinary Approach
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Correlation between Air Parameters and Sampling Stations
The correlations between air pollutants parameters at all stations are shown in Table 6. As
indicated in the table, almost all parameters recorded in Nilai were found to be positively
correlated with each other except NO2 and O3. It was also found that CO was significantly
positively correlated to PM10 and SO2 (r=0.881 and r=0.840, p<0.01) respectively. In Petaling
Jaya, SO2 and PM10 showed strong positive significant correlation (r=0.782, p<0.01), while
O3 and NO2 indicate a strongly negative correlation between them (r=-0.799, p<0.01). In
Shah Alam, PM10 had a strong positive correlation with CO and NO2 while moderate
negative correlation was found between O3 and CO (r=0.410, p<0.05). In contrast with Shah
Alam, Putra Jaya showed the strongest significant correlation between O3 and CO (R=0.848,
P<0.01). CO and O3 were found to have a strong negative relationship in Cheras (r=0.039
p<0.05). This finding is empowering the findings by Real et al. (2008) [18] which indicate
that hydrocarbon oxidation uses O3 and produces CO [18]. This strong correlation indicates
that the PM10, CO and SO2 in Nilai came from the same source. Therefore, this result proves
that different locations have different air pollutant concentrations.
Table 6 Correlation between air parameters at sampling stations
Total 1.446 24
Shah Alam Between groups
Within groups
Total
0.001
0.000
0.01
4
20
24
0.000
0.000
44.655 0.000
Putra Jaya Between groups
Within groups
Total
0.000
0.000
0.000
4
20
24
0.000
0.000
6.926 0.001
Cheras Between groups
Within groups
Total
0.000
0.000
0.000
4
20
24
0.000
0.000
15.957 0.000
Monitoring
Stations
Air Pollutants
PM10
CO
NO2
SO2
O3
Nilai
PM10
CO
NO2
SO2
O3
1
0.881
0.497
0.782
0.279
1
0.575
0.840
0.124
1
0.138
-0.799
1
0.166
Petaling Jaya
PM10
CO
NO2
SO2
O3
1
0.401
0.503
0.793
1
-0.381
0.242
1
0.138
1
International Journal of Multidisciplinary Approach
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** Correlation is significant at p>0.01, * Correlation is significant at p<0.05
The analytical distribution of air pollutants in this study is based on the seasonal variation
from 2010 to 2014. The interpolation of all the five major pollutants was performed using the
Inverse Distance Weighted Method (IDW) to see the distribution of air pollutants across
Klang Valley as illustrated in Figures 3-12. There are remarkable seasonal variations in the
mean of all pollutant concentrations. PM10, CO and SO2 were all highly present and
concentrated in 2014 compared to the other four years. This is due to the increasing number
of motor vehicles, industries as well as the result of increasing rate of biomass burning from
Sumatra, Indonesia. NO2 were highly present in the Klang Valley in 2013 and O3 in 2010.
Cheras and Nilai were among the areas that were highly affected by PM10 in 2012. However,
PM10 was highly concentrated in Nilai throughout the years. In contrast, CO and NO2 were
both highly concentrated in Petaling Jaya throughout the years, except NO2 which was also
high in 2014. It is noteworthy that SO2 have different trend compared to others which
demonstrated very high concentration in Shah Alam and Petaling Jaya in 2010 and 2011
while SO2 concentration moved to Nilai from 2012 to 2014. The concentration of O3 was
changing continuously across the Klang Valley. In 2010 and 2011, O3 was highly
concentrated in Putra Jaya, Shah Alam, and Cheras, while in 2012 only concentrated in
Cheras and Shah Alam. Putra Jaya only showed the highest concentration of O3 in 2013 while
Shah Alam shared Putra Jaya concentration of O3 in 2014.
-0.065 0.477 -0.799 0.166 1
Shah Alam PM10
CO
NO2
SO2
O3
1
0.756
0.777
0.286
0.116
1
0.679
-0.218
-0.410
1
0.271
0.374
1
0.655
1
Putra Jaya PM10
CO
NO2
SO2
O3
1
0.798
-0.285
0.645
0.682
1
0.129
0.211
0.848
1
-0.050
0.193
1
0.270
1
Cheras PM10
CO
NO2
SO2
O3
1
-0.404
0.209
-0.909
0.019
1
0.410
0.308
-0.039
1
-0.500
0.698
1
-0.423
1
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3.5. Spatial Distribution of Air Pollutants by Different Years
Figure 3 PM10 Concentration in 2014
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Figure 4 CO Concentration in 2014
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Figure 5 SO2 Concentration in 2014
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Figure 6 NO2 Concentration in 2014
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Figure 7. O3 Concentration in 2014
3.5. Spatial Distribution of Air Pollutants by Different Years
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Figure 8 PM10 Concentration during a) 2010 b) 2011c) 2012 d) 2013
d) 2014 e) All Years Average
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Figure 9 CO Concentration during a) 2010 b) 2011c) 2012 d) 2013
d) 2014 e) All Years Average
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Figure 10 NO2 Concentration during a) 2010 b) 2011 c) 2012 d) 2013 d) 2014
e) All Years Average
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Figure 11 SO2 Concentration during a) 2010 b) 2011 c) 2012 d) 2013 d) 2014
e) All Years Average
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Figure 12 O3 Concentration during a) 2010 b) 2011 c) 2012 d) 2013
d) 2014 e) All Years Average
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4. CONCLUSION
This study analyzed the average concentration of all air pollutants at five monitoring stations
in the Klang Valley covering Nilai, Petaling Jaya, Shah Alam, Putra Jaya and Cheras
CAQMS from 2010 to 2014. Although the average pollutants measured were within the
recommended Malaysian Ambient Air Quality Standard( RMAAQS), the Klang Valley
showed a gradual increase in outdoor air pollution from 2010 to 2014 in all the five pollutants
measured; PM10, CO, NO2, SO2 and O3. In addition, the highest number of unhealthy, very
unhealthy and hazard days in the five stations were recorded in 2014 while the least was
recorded in 2010. The results also showed that the haze exposure rate increased from 2010 to
2014 respectively. This is due to the increasing number of motor vehicles near the monitoring
stations as well as the result of the increasing rate of biomass burning from Sumatra,
Indonesia. PM10, CO and NO2 levels remained highly concentrated in 2014 while NO2 was
highly present in 2013 and O3 in 2010.This study analyzed and visualized the spatial
concentration distribution of five air pollutants in five years. The implementation of GIS in
this paper is a useful way to envisage the trend of air pollutants in the Klang Valley.
Therefore, the Malaysian government has its paramount in playing the important role in
addressing the issues related to haze.
ACKNOWLEDGEMENT
The author’s appreciation goes to the Faculty of Environmental Studies of the University
Putra Malaysia and the Malaysian Department of Environment (DOE) for providing the air
quality data. Moreover, I will not forget to acknowledge my great family for providing me
psychological and financial support to finish this project paper smoothly.
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