jabir abdullah hussein*, & ahmad makmom abdullah**ijmas.com/upcomingissue/09.02.2018.pdf · )...

International Journal of Multidisciplinary Approach

and Studies ISSN NO:: 2348 – 537X

Volume 05, No.2, Mar – Apr 2018

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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




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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].




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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].




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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




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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].




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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.




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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




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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




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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




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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




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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




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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





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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





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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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