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International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 04 43

115604-8282 IJET-IJENS @ August 2011 IJENS I J E N S

Optimization of Remote Meteorological

Parameters in Predicting the Air Pollutant

(NO2) Distribution by Petrochemical Industry

along Coastal Zone at East Coast of

Peninsular Malaysia

Mohd H. Ibrahim1,4

, Ahmad M. Abdullah1, Nor M. Adam

2, Juliana Jalaludin

3,

W.M. Norhisyam W. Mamat4, Mohd N.Ibrahim

4, Noraniah Abdul Aziz

4

1Faculty of Environmental Studies,

2Faculty of Mechanical and Manufacturing Engineering,

3Faculty of Medicine and Health Science,

Universiti Putra Malaysia, 43000 UPM Serdang, Selangor. MALAYSIA 4Kolej Universiti TATI, Jalan Panchor, Teluk Kalong,

24000 Kemaman, Terengganu, MALAYSIA

Email: [email protected]

Abstract-- As commonly observed throughout the world, the

meteorological parameters at coastal area are influenced by

both rotation of wind direction and sea breezes wind vectors

features. Theoretically, this atmospheric condition describes

difficulties in predicting on ground concentration of pollutant

using the acceptable method of dispersion under the turbulence

properties. This research applies the air dispersion modeling

using ISCT3 software in order to predict on ground

concentration of NO2 from selected petrochemical plants in

Kertih, Terengganu, located at North East of Peninsular

Malaysia Meteorological data of year 2008 obtained from the

Kuala Terengganu Meteorology Station was used as input to the

ISCT3 software. This meteorology station is located

approximately 95 km north-west off the study site which

contains the pollutant sources and verification point. The

modeling domains covered a 20 x 20 km2 area centre of the

petrochemical industry with grid spacing of 500 meter each as

dummy receptors. During verification process, the significance

improvement through the optimization analysis of wind

direction proven that the correlation coefficient of predicted

over the actual NO2 concentration improve from 0.68 to 0.91.

The average maximum monthly and yearly on ground

concentration NO2 obtained is at 13.97 ug/m3 and 6.91 ug/m3

respectively. The annual value is much below the Malaysian

and WHO guidelines which is at 90 ug/m3 and 40 ug/m3

respectively. No benchmarking could be gauged on the monthly

value since no guideline is available.

Index Term - Air dispersion modeling, optimization

analysis, correlation coefficient, NO2, ISCT3.

I. INTRODUCTION

AS COMMONLY observed throughout the world that the

meteorological parameters at coastal area are influenced by

sea breezes [1] and both by rotation of wind direction and sea

breezes wind vectors features [2]. It has been highlighted that

it is hard to describe theoretically the acceptable method to

predict pollutant dispersion under the turbulence properties

[3] which this atmospheric condition could influence the

concentration of air pollutant [4].

There are two types of sources of pollutants emissions;

mobile sources and stationary sources [5]. Mobile sources

are including highway vehicles and other mode of

transportation while stationary sources are categorized as

stationary fuel combustion and industrial processes.

Generally, pollutants emitted controlled under the

international standard are the NOx, O3, PM, SOx, and CO [6].

In Malaysia these pollutants are controlled by the Malaysian

standard which is referred to Air Pollution Index (API) as to

guide the community on the air quality status [7]. From these

commonly controlled pollutants, the international researchers

tend to specifically identify NO2 as an indicator to gauge and

predict the air quality [8][9][10][11][12][13]. In 2007, The

Colorado Department of Public Health and Environment has

reported that about 44% percent of the NO2emissions

in the Denver area contributed by large

combustion sources such as power plants [14]. The

significance amount to the NO2 emission may result

to the use of the natural gas as the primary feed stock in

industries [15][16]. The petroleum sector which is very

much related to the petrochemical industry has also utilized

the natural gas instead of oil fuel that produces high content

of sulfur [17].

mailto:[email protected]



The petrochemical industry is one example of air pollutant

contributed by stationary sources in Malaysia. This sector has

been considered as the second major industrial emission

sources of SO2, NOx and CO2 in Asia after steel and iron

industry [18]. It is also noted that part of the major

contribution to the stationary emission sources are from

petrochemical plants which are among the 27,000 of the

major air pollution in the United States [19]. Historically,

petrochemical industry at East Coast of Peninsular Malaysia

has started in mid 90‟s [20] and continue to expand since then.

The Petrochemical industry area in this region is capable of

producing 6.3 million tons per year of various petrochemical

products that is equivalent to 50 % of the total Malaysian

petrochemical product [21]. From those figures, 4.2 million

tons per year or 33% of the national petrochemical products

are produced in the State of Terengganu located at Kertih

Petrochemical Industry Area (Figure 1). This high

productions capacity figure may represent high contribution

to air pollution. Geographically, this petrochemical industry

located along the coastal zone and very much influenced by

North East Monsoon with relatively uniform equator climate

throughout the year [6][22]. On top that, this petrochemical

industry located within the two main state districts known as

Kemaman and Dungun that consist of the second and third

largest population after the capital district of Kuala

Terengganu [23]. As many sources indicate that air pollution

could cause harmful effect to human health, therefore it

requires to be controlled [24][25][26]. In Malaysia, this

controlled realizes through enforcement of the Environmental

Quality Act and Regulation 1974 [27].

Fig. 1. Research location – Kertih, A major petrochemical industry

site at North-East of Peninsular Malaysia located

In order to predict the air pollutant, researchers have

applied specific tools for specific pollutant emission sources.

One of the common tools that being widely used throughout

the world is the air dispersion modeling software consist of

various models that fit into the research requirement [28]. It

is also noted that limited of the predictions using different

software models could be specifically recognized as the

optimum result due to several factors. One of those are due

to combination of very complex meteorological parameters

such as the application of various techniques to estimate

surface mixed layer depth above the ground. The various

estimation of mixing height techniques has its own

advantages that do not guarantee one is above to the other

[29]. However, with the above-mentioned limitation,

researchers have agreed that the monthly standard of

correlation coefficient value of 0.50 and above is considered

acceptable [8].

Looking into the scenario above and with the availability

of the relevant and acceptable tools, it would be a need to

predict on the magnitude of NO2 emitted by the

petrochemical plants at Kertih Industrial Area located North

East of Peninsular Malaysia.

II. METHODOLOGY

This paper will utilize the air dispersion modeling known

as Industrial Source Complex Short-Term Model (ISCT3).

There are four main components which are required in order

to simulate and predict on ground concentration of NO2. They

are the meteorological input parameter, point source and NO2

emission estimation, geographical domain set up and finally

the verification process through optimization analysis of the

meteorological parameters.

A Air Dispersion Modeling

An atmospheric dispersion modeling is a mathematical

simulation of the physics and chemistry governing the

transport, dispersion and transformation in the atmosphere.

The model estimates downwind air pollution concentrations

given information about the pollutant emissions and nature of

the atmosphere. The model calculates the pollutant

concentration using information of the given meteorological,

contaminant emission rate and characteristics of the emission

source together with the local topography features. The

ISCT3 is commonly used air dispersion software among

researchers [30][31] and it is recommended by the United

States Environmental Agency to perform atmospheric

dispersion modeling [32]. The ISCT3 is incorporated with

the steady state Gaussian Plume Model and capable of

evaluating the pollutant concentration emitted by point

sources as follows (Figure 2).

po

int

so

urc

e

ground

SID

E

TO

P

pollutant concentration increases

along the ground



2

2

2

2

22exp,,

zyzy

hy

u

QzyxC

(1)

(at z = 0)

Where

C =pollutant concentration (ug/m3)

x =distance downwind from point source (m)

y =cross-wind distance from plumb centerline

(m)

z = vertical distance (m); z = 0 is for

concentration at ground level

Q = contamination emission rate (gm/s)

σy(x) =“cross-wind dispersion coefficient” (m), a

function of x

σz(x) =“vertical dispersion coefficient” (m), a

function of x

u = mean wind velocity (reorient the coordinate

system so the wind always blows in the x-

direction; m·s-1)

h =“stack height” = height above the ground the

pollutant is released (m)

Fig. 2. Steady state Gaussian Plume Model shows formula

applied and dispersion of pollutants

This steady state Gaussian Plume Model above utilizes by

the ISCT3 may not be an ideal tool to apply under turbulence

sea and land breeze condition [28]. As known, the turbulence

factors are critical role in determining the pollutant

deposition pattern around sources [4]. However, it was noted

that ISCT3 being widely used in the United States coastal

region, for example at Apalachicola, Daytona, Tampa and

West Palm Florida [33] as in need of immediate attention to

the pollutants dispersion. Evidence has shown that

meteorological database from these locations are available for

modeling purposes using the ISCT3 software. The

requirements to the input of ISTC3 are the meteorological

parameters, point source and NO2 emission estimation and

geographical domain set up are explained accordingly as

follows.

B. Meteorological Input Parameter

Hourly meteorological data of year 2008 from Kuala

Terengganu Meteorological Station would be processed into

the ISCT3 Software. The Kuala Terengganu Meteorological

Station located at approximately 95 km north of the study site

(Figure 3). Meteorological data from this station is applied in

this research since there is no local meteorological station

available at study site. This specific station is selected due to

its similarity topography and climate condition with the study

site. As an example, both of these sites located at

approximately 2 km off the coastal zone which is influenced

by sea and land breeze circulation that create turbulent

diffusion [1][28]. The Kuala Terengganu Meteorological

Station and study site also experience relatively the same

atmospheric condition, as Malaysia known to have a uniform

climate condition throughout the year at mean temperature

ranging between 26 Co to 20 C

o [6][23]. Beside the above-

mentioned factor, the application of using remote

meteorological data is practicality relevant as other researcher

has successfully applied the same principle of remote

meteorological data, as far as 500 km off the research site

[34].

Fig. 3. Remote distance of Kuala Terengganu Meteorological

Station north of the study site

The ISCT3 requires two separate meteorological data files

in order to execute its programmes; the hourly surface data

file (SCRAM MET 144) and daily a.m. and p.m. mixing

height file (SCRAM). The SCRAM MET 144 file consists of

meteorological parameter which are the ceiling height, wind

direction, wind speed, dry bulb temperature and cloud cover

with respect to the station number, year month, day and hour

concerned. The SCRAM MET 144 file would generate the

Wind Rose Plot (WRP) that provides an overview of wind

blow direction. The SCRAM file consists of maximum a.m.

and p.m. mixing height value. The mixing height is the

variation value of the atmosphere height above the ground

where the vertical mixing takes place due to mechanical or

convection turbulence obtained through the application of the

(x,y)

plumb centerline

y



Venkatram (1980) formula under stable boundary layer as

follows [29].

h = 2300U*1.5

(2)

Where;

h = mixing height

U* = friction velocity = kUref / In(Zref/Zo)

Uref = wind speed at reference height

Zref = reference height for wind = 14 meter

Zo = roughness height = 0.15 meter

The friction velocity is the combination of Von Karman

constant (k), wind speed (Uref,), reference height of wind (Zref)

and surface roughness (Zo). The Zo at 0.15 meter was selected

considering the study site topography to be between “Tree

Covered” and “Low-density Residential” which is at 0.1 and

0.2 respectively [35]. The Zref at 14 meter refer to the

anemometer height of the Kuala Terengganu Meteorology

Station. Combination of these atmospheric elements has been

widely implemented previously [36][37][38].

Finally, combination of hourly surface data file (SCRAM

MET 144) and daily a.m. and p.m. mixing height file

(SCRAM) will generate the hourly MET file. This final

meteorological output combine with the point source

emission are required to simulate and predict the average on

ground NO2

C. Point Source and NO2 Emission Estimation

This paper focuses on the emission estimation of NO2

from point sources emitted by the petrochemical industry.

NO2 is the well known pollutant controlled under the

international and local regulatory [6][7]. There are wide

range of acceptable techniques of estimating the emission

inventory including the source sampling, source emission

model, surveying, material balance, emission factors and

extrapolation [39][40]. Since data obtained from

petrochemical plants are varies, this study will apply the

source sampling and material balance techniques in

estimating the emission rate of NO2 which consists of 28

points source as follows.

1. Estimation emission rate of NO2 based on actual sampling

data; the conversion of volumetric flow rate of g/m3

to

emission rate of g/s. [41][28].

ER (g/s) = C (g/m

3) x A (m

2) x V (m/s) (3)

Where;

ER = Emission rate pollutant

C = Concentration of pollutant

A = Area of point emission based on internal

diameter

V = Velocity

2. Estimation emission rate of NO2 based on natural gas flow

rate using material balance techniques; the ultimate fuel

analysis [42];

ER (g/s) = Qf (kg/hr) x PCf (%) x (MWp/MWf) x

(1000/60 x 60) (4)

Where;

ER = Emission rate pollutant

Qf = Fuel flow rate

PCf = Pollutant concentration in fuel N2 = 0.9

%*

MWp = Molecular weight of pollutant emitted

(g/g-mole) NO2 = 46

MWf = Molecular weight of pollutant in fuel

(g/g-mole) N2 = 28

*[43]

Other point source parameters which compulsory in order

to perform atmospheric simulation in this study are including

the release height, exit temperature, exit velocity and inside

diameter of release point as input into the ISCT3. Simulation

result by the ISCT3 will be super-imposed onto the domain

set up leads to the overview formation of on ground NO2

distribution contour.

D. Geographical domain set up

The geographical domain set up in this study covers 20 x

20 km2 area surrounding the petrochemical plants with 500

meter each of grid spacing as dummy receptors. The point

source location coordinates are identified by matching the

longitude and latitude to AutoCAD coordinate. The final part

of this study will be the optimization of the meteorological

parameter particularly the wind direction. This process is

necessary in order to determine the optimum correlation

coefficient of the predicted over the actual data of NO2 which

lead to the identification of the maximum magnitude value

within the set up domain.

E. Optimization and Verification Analysis

The optimization process is performed through wind

direction analysis as part of the verification of the predicted

over actual NO2 data. This process is performed due to

unavailability of local meteorological at the study site and

after careful consideration of the atmospheric and topography

similarity of both sites as explained. This technique has been

successfully implemented by the local researcher in his recent

study to gauge the monthly predicted NO2 against the actual

data from a specific petrochemical plant within the same

study site [44].

The optimization process of the predicted over actual data

are performed through simulation of wind direction at the



increment of 5o rotation. The rotation angle is refined at 1

o

angle towards the optimum condition. The optimum

condition is obtained from the maximum correlation

coefficient value obtained from formula defined as follows.

[∑ ( ̅) ̅

] (5)

Where;

A = actual data

P = predicted data

σ = standard deviation

N = number of data

III. RESULT AND DISCUSSION

A. The Wind Rose Plot (WRP)

As mentioned, wind condition is one the factor that

determines the pollutant distribution. From the WRP

generated shows that the majority frequencies of wind

directions are blowing from two directions. They are from

north-east and south-west directions. The highest wind speed

range at 5.40 m/s to 8.49 m/s are blowing from north-east

shows that the study site is much influenced by the north-east

monsoon (Figure 4). The average wind speed encountered for

year 2008 is at 2.08 m/s with 11.18% of those is in calm

condition.

Fig. 4. The wind rose plot shows spokes of wind blowing direction measured at Kuala Terengganu Meteorology Station for year 2008 .

B. The Mixing Height

The application of the Venkatram formula leads to

the establishment of the mixing height value at the study

location (Figure 5). It is discovered that the minimum

and maximum value of the mixing height for year 2008

range from 60 to 1212 meter and 100 to 1414 meter

according to a.m. and p.m. period. The average a.m

mixing height is at 320 meter while the average p.m.

value is at 393 meter. The application of the Venkatram

formula in identifying the mixing height value has

proven that it is in line with the traditional theory

understood in general where the p.m mixing height is

higher than the a.m value mainly due to the influence of

WIND ROSE PLOT

Station #48618 - ,

NORTH

SOUTH

WEST EAST

4%

8%

12%

16%

20%

Wind Speed (m/s)

> 11.06

8.49 - 11.06

5.40 - 8.49

3.34 - 5.40

1.80 - 3.34

0.51 - 1.80

UNIT

m/s

DISPLAY

Wind Speed

CALM WINDS

11.18%

MODELER

DATE

9/29/2009

COMPANY NAME

COMMENTS

WRPLOT View 3.3 by Lakes Environmental Software - www.lakes-environmental.com

PLOT YEAR-DATE-TIME

2008 Jan 1 - Dec 31Midnight - 11 PM

AVG. WIND SPEED

2.08 m/s

ORIENTATION

Direction(blowing from)

PROJECT/PLOT NO.



the surface heat flux generated during the day time is

higher than night time.

(a)

(b)

Fig. 5. Distribution of the maximum daily a.m and p.m mixing height for year 2008

C. Point Source Specification and NO2 Emission

Estimation

Data gathered and analyzed shows that the NO2

emission rate emitted by 28 point sources range from

0.02 g/s to 8.17 g/s with the average of 2.04 g/s. This

estimation of NO2 emission rate obtained through

calculation either from the sampling data flow rate

(g/m3) or from natural gas combustion rate (kg/hr) as

mentioned on Section II - C. The summary input of the

point source specification in the ISCT3 software is

summarized as follows.

TABLE I SUMMARY OF DATA INPUT INTO ISCT3

Min. Max. Avg.

*Emission rate (g/s) 0.02 8.17 2.04

*Release height

(m)

15 87 45.5

*Temperature (oC) 310 600 434

*Exit velocity(m/s) 4.13 28.00 13.95

*Inside dia.(m) 0.52 3.40 1.46

NO2 flow rate(g/m3)

0.01 0.09 0.04

Natural gas flow

rate (kg/hr)

115 1989 743

* Direct input into ISCT3

Finally, combination input of the meteorological data

file, point source specification and NO2 emission

estimation into the ISCT3 will generate the predicted on

ground concentration of NO2 of which need to be

verified and optimized over the actual data accordingly.

D. Verification and Optimization Analysis

The verification analysis of the predicted on ground

concentration of NO2 simulated by the ISCT3 is gauged

to the actual NO2 data obtained accordingly. The initial

ISCT3 simulation of the monthly average shows lower

relationship of correlation coefficient value of the

predicted against actual NO2 which is at 0.68 (Figure 6).

By applying the optimization process through simulation

of wind direction at the increment of 5o and finalize at

the 1o rotation (Figure 7), the correlation coefficient is

discovered improve to 0.91 at 29o angle (Figure 8).

(a)

(b)

Fig. 6. (a- b) Time series comparison of monthly predicted

against actual NO2 shows low correlation coefficient at „0‟ angle

wind direction



Fig. 7. Optimization process through simulation of wind direction

(a)

(b)

Fig. 8. (a-b) Time series comparison of monthly predicted against actual NO2

shows improve correlation coefficient at the optimum

angle of wind direction

The above optimum condition of the predicted over

actual NO2 is applied to simulate the average on ground

distribution of monthly and yearly NO2 surroundings the

petrochemical plants. The distribution contours are used

to identify the magnitude of the NO2 distribution

accordingly (Figure 9). From the contours obtained, it is

discovered that the maximum on ground concentration

of monthly and yearly NO2 is at 13.97 ug/m3

and 6.91

ug/m3

respectively. The yearly value is much below

than the WHO guidelines which is at 40 ug/m3

respectively. No benchmarking could be gauged to the

monthly value since no guideline is available.

(a)

DATE :

5/15/2011

MODELER :

OUTPUT TYPE :

CONC

MODELING OPTIONS :

CONC, RURAL, FLAT, DFAULT

MAX :

13.97428

COMPANY NAME :

UNITS :

ug/m**3

MAX

0.00 20000.0010000.00

20

00

0.0

00

.00

10

00

0.0

0

D:\FURTHER\ISCT3PROTOCOL&TESTING\PROISCP.IS\MOH1GALL.PLTISC-AERMOD View by Lakes Environmental Software

PROJECT NAME :

Kertih Petrochemical PlantsPLOT FILE OF HIGH 1ST HIGH MONTH VALUES FOR SOURCE GROUP: ALL

RECEPTORS :

1682

COMMENTS :

Year 2008Wind Angle -29 degMax concentration at (15000, 10000)

PROJECT/PLOT NO. :

13

.97

7

.41

0.8

6

0 4 km



(b)

Fig. 9. The distribution contour of NO2 according to monthly (a) and yearly (b) average of year

2008

IV. CONCLUSION

As of the conclusion, this paper has successfully

achieved the objective of performing the atmospheric

dispersion modeling within the acceptable and much

improve correlation coefficient comparing to the

research previously conducted [8][30][34]. It is also

revealed that the maximum on ground concentration of

NO2 is much lower compare to the local and

international guidelines and this value should not pose

appreciable risk of harmful effects to human health.

This paper also reveals that the application of remote

meteorological data is relevant to research site with

similar meteorological parameter that leads to a more

flexible technique and less cost for future research.

V. ACKNOWLEDGMENT

The authors are grateful to the Malaysian

Meteorological Department and Malaysian Department

of Environment for providing data to this study.

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DATE :

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MODELER :

OUTPUT TYPE :

CONC

MODELING OPTIONS :

CONC, RURAL, FLAT, DFAULT

MAX :

6.9147

COMPANY NAME :

UNITS :

ug/m**3

MAX

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200

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0

D:\FURTHER\ISCT3PROTOCOL&TESTING\PROISCP.IS\AN00GALL.PLTISC-AERMOD View by Lakes Environmental Software

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Kertih Petrochemical PlantsPLOT FILE OF ANNUAL VALUES FOR SOURCE GROUP: ALL

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1682

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Year 2008Wind Angle -29 degMaximum concentration at (17000, 11500)

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