journal of occupational safety and health

December 2012, Vol. 9, No. 03ISSN 1675-5456

PP13199/12/2012(032005)

Journal ofOCCUPATIONALSAFETY AND HEALTH

National Institute of Occupational Safety and Health

National Institute of Occupational Safety and Health (NIOSH)

Ministry of Human Resources Malaysia

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Journal of OccupationalSafety and Health

Editor-in-chiefIr. Haji Rosli bin Hussin

Executive DirectorNIOSH, Malaysia

Secretariat

Editorial Board

Prof. Dr. Krishna Gopal RampalUniversiti Kebangsaan Malaysia

NIOSH, MalaysiaIr. Daud Sulaiman

Fadzil OsmanNIOSH, MalaysiaRaemy Md. ZeinNIOSH, Malaysia

The Journal

- Aims to serve as a forum for the sharing of research findings and information across broad areas in Occupational Safety and Health.

- Publishes original research reports, topical article reviews, book reviews, case reports, short communications, invited editorial and letters to editor.

- Welcomes articles in Occupational Safety and Health related fields.

Associate Editors

Prof. Dr. Ismail BahriUniversiti Kebangsaan MalaysiaDr. Jeffereli Shamsul BahrinBASF East Asia Regional Headquartes Ltd.Dr. Abu Hasan SamadPrince Court Medical Centre

Mohd Rashidi RohmadRoslina Md HusinNor Akmar Yussuf

Idayu Kassim


December 2012

ContentsVol. 9, No. 3

A Study of The Effectiveness of Local Exhaust Ventilation (LEV) 1 - 9 Using Computational Fluid Dynamics (CFD) ApproachC. S. Ng1, a, A. M. Leman2, b and N. Asmuin3, c

Assessment of Sitting Pressure on Malaysian Bus Drivers 10 - 161Ahmad Rasdan Ismail, 1Mohd Afiq Zainal Rosli, 2Isa Halim, 3Baba Md. Deros, 3Mohd Nizam Ab Rahman, 4Md. Mustafizur Rahman

Call Center Ergonomics Issues: A Case Study 17 - 22T.Hari Krishnan*, Raemy Md Zein*

Comparison of Air Conditioning Ducting Measurement Data and Effect of Indoor 23 - 30Air Data at Office Building M.D. Amir Abdullah1a, A.M.Leman2b, A. Norhidayah3c , M.M.Syafiq Syazwan4d

Comparison of Indoor Air Contaminants In Different Stages of New Building 31 - 38Occupancy: Training and Office Setting Nor Mohd Razif Noraini¹·², A.M. Leman², Ahmad Sayuti Zainal Abidin³, Ruslina Mohd. Jazar¹,Laila Shuhada Mat Zin¹, Rasdan Ismail4 and Nor Hidayah Abdull4

Compliances of Airborne Microbe in Different Phases of Building Commisioning 39 - 44Ahmad Sayuti Zainal Abidin1 and A.M. Leman2 Nor Mohd Razif Noraini3

Data Comparison on Fumes Local Exhaust Ventilation: Examination and Testing 45 - 54Compliance to USECHH Regulation 20001Nor Halim Hasan, 2Mohd Radzai Said, 3Abdul Mutalib Leman, 4B.Norerama D.Pagukuman and 5Jaafar Othman

Exposure to pm2.5 and Respiratory Health Among Traffic Policemen in Kuala Lumpur 55 - 64Ahmad Syazrin Muhammad, Juliana Jalaludin and Nur Aqilah M. Yusof

Indoor Thermal Comfort Study: A Case Study at Higher Institution in 65 - 72East Coast of Malaysia1Rosli Abu Bakar, 1Ahmad Rasdan Ismail, 1Norfadzilah Jusoh, 2Abdul Mutalib Leman

Laboratory OSH Compliance Status Among Chemical Testing Laboratory 73 - 82in Lembah KlangA. Suhaily, M. Mohd Norhafsam, Z.A. Ahmad Sayuti, M.H. Nor Husna, T.A. Naemah, J. Nurzuhairah

Response Surface Method in Modelling the Environmental Factors Toward 83 - 90Workers’ ProductivityAhmad Rasdan Ismail1, Mat Rebi Abdul Rani2, Baba Md. Deros3,Zafir Khan Mohamed Makhbul4, Mohd Yusri Mohd Yusof3

The Reaction of Nigerian School Children to Back Pain Due to Backpack Usage 91 - 94Ademola James Adeyemi*, Jafri Mohd. Rohani, Mat Rebi Abdul Rani

The Study of Respirable Dust Concentration in Paper Based Industry 95 - 102N. Azreen P1, A.M. Leman2, A. Norhidayah3, Ismail M4

Whole Body Vibration Exposure: An Experimental Study to Malaysian Bus Driver 103 - 1081Siti Nur Atikah Abdullah, 1Ahmad Rasdan Ismail, 2Abdul Mutalib Leman, 3Isa Halim, 1Nor Hidayah Abdull

Associations of Blood Lead and Disciplinary Behavior among Male Adolescents in 109 - 116Selangor, MalaysiaMohd Rafee B,B.,1 Asilah, A.,1 Rumaya, J.,2 and Shamsul Bahari, S3.

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1

Original Article J. Occu. Safety & Health 10 : 1 - 9, 2012

A Study Of The Effectiveness Of Local Exhaust Ventilation (LEV) Using Computational Fluid Dynamics (CFD) Approach

C. S. Ng1, a, A. M. Leman2, b and N. Asmuin3, c

1,3Department of Plant and Automotive EngineeringFaculty of Mechanical and Manufacturing Engineering

Universiti Tun Hussein Onn Malaysia86400 Parit Raja

Batu Pahat, Johor, Malaysia

2Department of Mechanical Engineering TechnologyFaculty of Engineering Technology

Universiti Tun Hussein Onn Malaysia86400 Parit Raja

Batu Pahat, Johor, Malaysia

[email protected], [email protected], [email protected]

ABSTRACT

Local exhaust ventilation (LEV) is used in industries to capture contaminants such as gases, dusts, mists, vapours or fumes out of workstations to protect occupants’ exposure to contaminants. LEV is allocated and installed by employers, however it doesn’t work accordingly. LEV design is often overlooked and underappreciated. Effectiveness of LEV system can be achieved if more attention is focused to proper design of LEV system. To solve this issue, computational fluid dynamics (CFD) can be performed. CFD is a software tool to predict and simulate fluid dynamic phenomena. CFD is used to forecast or reconstruct the behaviour of an engineering product under assumed or measure boundary conditions. However, CFD is just a prediction tool, which can lead to inaccuracy of predicting airflow due to problems with pre-processing, solver and post-processing with parameter from actual experimental results. Therefore, validation is needed to help minimizing percentage error of CFD methods. In this research, measurements of airflow parameter of LEV system at National Institute of Occupational Safety and Health (NIOSH) Bangi, Selangor were conducted. Control Speed panel found at NIOSH Bangi, which is used to increase or decrease speed of fan, was performed using Control Speed of 20%, 40% 60% and 80%. Upon validation, average absolute error obtained from four different control speeds ranges from 3.372% to 4.862%. Validity of CFD modelling is acceptable, which is less than 5% and good agreement is achieved between actual experimental results and CFD simulation results. Therefore, it can be concluded that CFD software tool can be performed to simulate air velocity in LEV system. CFD methods can save labour costs and time consumption when it is used during earliest stage of LEV design, before actual construction is implemented. The outcome of this paper can be used as a baseline for factories equipped with LEV system to protect occupants’ exposure to contaminants.

Keywords: local exhaust ventilation (LEV), computational fluid dynamics (CFD), simulation, validation, airflow

INTRODUCTION

LEV captures contaminants close to the generation

point of emission. It is achieved using inlet hood, duct,

air cleaner, fan and discharge. Figure 1 shows the basic

components of LEV system. An overall exposure

reduction of 92% was achieved by using LEV system

[1]. However, this reduction highly depends on the

way it is installed and used by occupants.

To take advantage of LEV design so that higher

efficiency can be achieved, CFD can be conducted. In the past studies, CFD has been performed to simulate

air velocities and temperatures in indoor environment

such as kitchen hood system [2], hospital room [3],

food court center [4] and aircraft cabin [5].

2

A Study of The Effectiveness of Local Exhaust Ventilation (LEV) Using Computational Fluid Dynamics (CFD) Approach

Figure 1: Basic components of LEV system

Figure 2: Overview of LEV system of Ventilation Laboratory, NIOSH Bangi

Figure 3: Nine locations where measurements were performed

METHODOLOGY

The scope of this research includes actual

experiments and CFD simulations. In this research,

actual experiments were performed at NIOSH Bangi,

Selangor. Figure 2 shows an overview of LEV system

of NIOSH Bangi.

A total number of nine locations were conducted

in this research. Figure 3 shows the nine locations

where measurements were performed. Each actual

experiment was performed using Control Speed of

20%, 40%, 60% and 80%.

while pitot tube and anemometer were used together

to obtain velocity pressure (VP).

Main equipment used in this research is

anemometer and pitot tube, shown in Figure 4.

Anemometer was used to obtain inlet hood velocity,

Air mover

DutingAir cleaner

Discharge

Hood

Inlet

3


Figure 4: Anemometer VelociCalc Plus Meter Model 8386 and pitot tube

Figure 5: Insertion depths for round ducts (DOSH guideline, 2008) [6]

Insertion depths for 10-pL traverses

Table 1: Recommended traverse insertion depths for round ducts (DOSH guideline, 2008)

Figure 6: Insertion measurement points for rectangular points (DOSH guideline, 2008) [6]

Based on Department of Occupational Safety and Health (DOSH) guideline, to obtain VP readings, the number and location of measuring points within duct depend on the duct size and shape, which are round and rectangular ducts. Unit measurement of VP is inches of water gauge (“wg). The methods to obtain VP are as follows:

(a) For round ducts: horizontal or vertical traverse insertion depths can be considered, as shown in Figure 5 and Table 1.

(b) For rectangular ducts: the cross section is divided into equal areas and at least sixteen readings must be taken. The distance between measuring points should not exceed six inches, as shown in Figure 6.

Duct shape of LEV of NIOSH Bangi is round, as shown in Figure 7. Therefore, the recommended measurement for round duct suggested by DOSH guideline was conducted.

Number of insertion

Distance from wall in fraction of a duct diameter, log-linear rule Traverse Position

1 2 3 4 5 6 7 8 9 10

4 0.043 0.290 0.710 0.957

6 0.032 0.135 0.321 0.679 0.865 0.968

8 0.021 0.117 0.184 0.345 0.655 0.816 0.883 0.979

10 0.019 0.077 0.153 0.217 0.361 0.639 0.783 0.847 0.923 0.981

4


Figure 7: Round duct of LEV of NIOSH Bangi

Figure 8: LEV geometry design of NIOSH Bangi

Based on DOSH guideline, velocity, V of LEV system can be calculated from VP readings using equation as shown below:

where:V is velocity value of LEV systemVP is velocity pressure readings

After actual experiments were conducted, the next step is to perform CFD simulations. SolidWorks was used to model LEV geometry design. The geometry model drawn using SolidWorks had to be converted to IGS file format first before importing to CFD software, ANSYS. This is to allow ANSYS to be able to read and run geometry design of LEV. Turbulence Model k-Ɛ was used in this research, while for boundary conditions, airflow of inlet hood and discharge obtained from actual experiments were used in CFD simulations. Default meshing was used in the entire CFD simulations methodology.

RESULTS

Figure 8 shows geometry design of LEV of NIOSH Bangi modelled using SolidWorks, while Figure 9, Figure 10, Figure 11 and Figure 12 represent CFD simulations using four different Control Speeds modelled using ANSYS.

where:EABS is absolute errorX is airflow parameter, which in this case, it is air velocity is the absolute difference between CFD simulation values and actual measurement values for variable X.

V = 4005 VP

EABS = x 100%XCFD - Xexp

Xexp

XCFD - Xexp

5


Figure 12: CFD simulation of LEV NIOSH Bangi using Speed Control of 80%




Velocity of Air Flow [Speed Control: 20%]

3.227e+000

2.421e+000

1.614e+000

8.075e-001

9.960e-004[m sA-1]


6.347e+000

4.761e+000

3.174e+000

1.588e-001

1.078e-003[m sA-1]


9.507e+000

7.134e+000

4.761e+000

2.388e-000

1.454e-002[m sA-1]

Y

XZ

Y

XZ

Y

XZ

Y

XZ


1.222e+001

9.175e+000

6.125e+000

3.075e-000

2.576e-002[m sA-1]

0 1.500 3.000 (m)

0.750 2.250

0 1.500 3.000 (m)

0.750 2.250

0 1.500 3.000 (m)

0.750 2.250

0 1.500 3.000 (m)

0.750 2.250

6


Table 2: Validation of air velocity results using Speed Control of 20%



Table 2, Table 3, Table 4 and Table 5 represent

validation of air velocity results using four different

Speed Controls. From Table 2, it shows that absolute

error ranges from 1.476% to 8.715%; from Table 3,

it shows that absolute error ranges from 0.149% to

8.722%; from Table 4, it shows that absolute error

ranges from 0.188% to 8.204%; and from Table 5,

it shows that absolute error ranges from 0.179% to

9.732%.

Table 6 represents average absolute error of

air velocity results using four different Speed

Controls. From Table 6, it shows that average

absolute error ranges from 3.372% to 4.862%. The

range obtained is very small, which is less than 5%.

Therefore, it can be concluded that CFD modelling can

be accepted to simulate air velocity in LEV system.

Location

Location

Location

1 488.561 473.878 14.683 3.100 2 310.945 322.893 11.948 3.700 3 318.543 310.226 8.317 2.681 4 319.232 335.082 15.850 4.730 5 329.815 353.712 23.897 6.756 6 342.126 369.243 27.117 7.344 7 364.285 335.082 29.203 8.715 8 377.051 358.218 18.833 5.257 9 252.659 256.445 3.786 1.476

1 956.183 962.867 6.684 0.694 2 600.986 588.612 12.374 2.102 3 605.772 587.248 18.524 3.154 4 605.020 566.393 38.627 6.820 5 620.516 660.522 40.006 6.057 6 640.498 639.547 0.951 0.149 7 660.366 607.388 52.978 8.722 8 673.535 648.266 25.269 3.898 9 447.929 442.367 5.562 1.257

1 1428.370 1514.504 86.134 5.687 2 904.356 891.056 13.300 1.493 3 911.841 876.537 35.304 4.028 4 917.100 857.107 59.993 6.999 5 943.106 922.020 21.086 2.287 6 976.819 957.857 18.962 1.980 7 1030.077 951.978 78.099 8.204 8 1062.703 1017.144 45.559 4.479 9 712.156 710.817 1.339 0.188

Simulated Velocity, VCFD (ft/min)



Actual Velocity,Vexp (ft/min)



VCFD - Vexp

Absolute Velocity Difference

VCFD - Vexp


VCFD - Vexp


Absolute Error, EABS

(%)


(%)


(%)

7



Table 6: Average absolute error of air velocity results using four different Speed Controls

LocationSimulated Velocity,

VCFD (ft/min) Actual

Velocity, Vexp (ft/min)


Absolute Error, (%)

1 1834.813 1745.739 89.074 5.102 2 1171.014 1159.377 11.637 1.004 3 1185.945 1188.075 2.130 0.179 4 1197.467 1134.200 63.267 5.578 5 1235.238 1125.683 109.555 9.732 6 1281.244 1322.257 41.013 3.102 7 1383.230 1398.886 15.656 1.119 8 1444.457 1471.531 27.074 1.840 9 1126.181 1157.299 31.118 2.689

Description Overall Average Error (%) NIOSH Bangi, Selangor with Speed

Control of 20% 4.862

NIOSH Bangi, Selangor with SpeedControl of 40%

3.650


3.927


3.372

CONCLUSION

In this research, it is proven that airflow parameter, such as air velocity can be modelled and simulated

in LEV system using CFD software tool. The findings in this research found that average absolute error ranges

from 3.372% to 4.862%. Hence, good agreement is

achieved between actual experimental results and

CFD simulation results. Therefore, CFD can be

performed as an engineering tool during the beginning

stage of LEV development prior to actual construction,

which saves labour costs and time consumption.

Location

1 1834.813 1745.739 89.074 5.102 2 1171.014 1159.377 11.637 1.004 3 1185.945 1188.075 2.130 0.179 4 1197.467 1134.200 63.267 5.578 5 1235.238 1125.683 109.555 9.732 6 1281.244 1322.257 41.013 3.102 7 1383.230 1398.886 15.656 1.119 8 1444.457 1471.531 27.074 1.840 9 1126.181 1157.299 31.118 2.689


Actual Velocity,Vexp (ft/min) VCFD - Vexp

Absolute Velocity Difference Absolute Error, EABS

(%)

8


REFERENCES

[1] Croteau, G.A., Flanagan, M.E., Camp, J.E.,

Seixas, N.S., 2004. The efficacy of local exhaust ventilation for controlling dust

exposures during concrete surface grinding.

Annals of Occupational Hygiene 48, page 509 -

518.

[2] Lim, K., Lee, C., 2008. A numerical study on

the characteristics of flow field, temperature and concentration distribution according to changing

the shape of separation plate of kitchen hood

system. Energy and Buildings 40, page 175 -

184.

[3] Mendez, C., San Jose, J.F., Villafruela, J.M.,

Castro, F., 2008. Optimization of a hospital

room by means of CFD for more efficient ventilation. Energy and Buildings 40, page 849 -

854.

[4] Wong, N.H., Song, J., Istiadji, A.D., 2006. A

study of the effectiveness of mechanical

ventilation systems of a hawker center in

Singapore using CFD simulations. Building and

Environment 41, page 726 - 733.

[5] Yan, W., 2009. Experimental and CFD study of

unsteady airborne pollutant transport within an

aircraft cabin mock-up. Building and

Environment 44, page 34 - 43.

[6] Department of Occupational Safety and Health

(DOSH) Guidelines on Occupational Safety

and Health for Design, Inspection, Testing and

Examination of LEV System, 2008.

9


Assessment of Sitting Pressure on Malaysian Bus Drivers 1Ahmad Rasdan Ismail, 1Mohd Afiq Zainal Rosli, 2Isa Halim, 3Baba Md. Deros,

3Mohd Nizam Ab Rahman, 4Md. Mustafizur Rahman

¹Faculty of Technology, Universiti Malaysia Pahang, 26300 Gambang, Pahang, Malaysia.²Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,

76100 Durian Tunggal, Melaka, Malaysia³Dept. of Mechanical & Materials Engineering, Faculty of Engineering and Built Environment,

Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia,4Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia

Corresponding Author: [email protected]

ABSTRACT:

The main purpose of this study was to establish the comfort zone for bus drivers in a seated position. In addition, this study is to investigate the seated pressure distribution among Malaysian bus drivers. The study consists of 10 bus drivers randomly selected to be a part of this study. The FSA pressure mat was utilized in order to investigate the force distribution of buttock to the seat pan of the drivers’ seat. This device is placed on the driver seat and backrest. Later, the subject would sit on for several minute. The finding reveals that most of the bus drivers feel discomfort by having low back pain and musculoskeletal disorder. The seat pressure distribution of Malaysian busses indicated that the seat not able to absorb high pressure generated from buttock that later may cause the discomfort and restricted the performance of drivers.

Keywords: pressure low back pain, comfort, musculoskeletal problem

INTRODUCTION

Comfort is best defined as the absence of discomfort. People feel uncomfortable when they

are too hot or too cold, or when the air is odorous

and stale. Positive comfort conditions are those that

do not distract by causing unpleasant sensations of

temperature, drafts, humidity, or other aspects of the

environment. Ideally, in a properly conditioned space,

people should not be aware of equipment noise, heat,

or air motion. The feeling of comfort or discomfort

is based on the integrated network of sensing organs:

the eyes, ears, nose, tactile sensors, heat sensors, and

brain.

Many studies on ride comfort and seat

convenience produced in the past contributed to the

improvement of seat design and convenience (Corlett,

1976). (Branton, 1969) Study on ride comfort suggested

that ride comfort was related to the deficiency of passengers’ experiences or the low quality of seats.

Thus, the ride comfort of the seats was evaluated

with various methods. These evaluations focused on

assessing the degrees of discomfort.

Assessment of Sitting Pressure on Malaysian Bus Drivers

10

Several other studies tried to evaluate positive seat

comfort (Zhao et al., 1994).Zhang et al., (1996) studied

a model for the perception of comfort and discomfort

based on the results of Zhao and Tang’s study, as well

as their own assumption that discomfort was related

to the lack of satisfaction from biomechanical factors

such as joint angles, muscle contractions and pressure

distribution that generates pain, soreness, numbness,

and fatigue Some common seating guidelines,

applicable to all types of chairs, are the following

(Delia, 1987):

a) Avoid compression of the thigh, which may

restrict blood flow to the lower extremities and pinch nerves, causing pain and numbness

(Tichauer, 1978)

b) Avoid flattening the lumbar spine by providing a backrest and lower back support.

c) Distribute weight equally on the weight-bearing

bony prominences (ischial tuberosities) in the

buttock.

d) Allow adjustment to be made in the dimensions

of the chair, such as height and angle of

inclination, in order to accommodate a variety of

user sizes.

Pain disorders at the lower back are a worldwide

concern in sedentary occupations such as office works, vehicle driving and dealing with heavy

industrial equipment. Long-term sitting in confined settings have been associated with an increased

risk for low back pain (LBP). Although no clear

epidemiological proof exists, literature reveals

that prolonged sitting in awkward postures and in

combination with exposure to whole body vibration

(WBV) potentially facilitates the process for low back

discomfort.

There is evidence that the sensation of

discomfort in persons who suffer from LBP reduces

when sitting with a lordotic spinal posture (Williams

et al., 1991). However, many persons adopt a flexed spinal curvature with the posterior tilted pelvis while

doing sedentary work. Such awkward spinal postures

have often been associated with aggravation of back

discomfort due to a reduction of back muscle activity.

(Callaghan and Dunk, 2002)

Beach et al., (2005) found that the passive flexion stiffness of the lumbar spine increases within the first two hours of sitting. This demonstrates that spinal

tissue characteristics already change after actively

short periods of exposure. They associated this stiffness

increase with an increased passive resistance of back

muscles, which they assumed being the primary

flexion-resisting tissues. Beach et al., 2005; Parkinson et al., 2004 reported that these findings suggest an increased risk for low back injury when individuals

perform full lumbar flexion tasks after prolonged sitting with a flexed lumbar posture. The influence of seat cushion designs on the seating comfort and

driver posture has been evaluated through a number

of subjective and objective studies. A study performed

by (Ng et al, 1995) reported that an adequate driver-

seat support could reduce the stresses in muscles of

the back, buttocks, and legs caused by prolonged

sitting during daily driving activities.

Bower et al, (1995) performed a subjective survey

on the heavy duty truck operators and identified back pain, neck pain, muscle stiffness, and sore buttocks

and legs as the most commonly reported ailments,

related to inadequate seat cushion design. Thakurta

et al, (1995) evaluated the seating comfort related to

four specific seat zones, including shoulders, lumbar, ischium tuberosity, and thighs and showed good

correlation between the measured static pressure

distribution and discomfort.

Body posture seems to have a complex and non linear

effect on acceleration transmissibility; the apparent

mass of the seated human body, posture influence is less than the acceleration magnitude effect. Our


11

model aims to analyze in-depth such a relationship.

Many professional drivers report that posture (torso

positioned almost vertically) would improve ride

comfort. Different pressure levels between bilateral

lower body parts in a driving posture are expected

due to the different task and postural requirements

placed on each lower extremity. For example, the

right foot, used to control pedals, is required to take

more restricted postures with less consistent support,

while the left foot, unless a clutch pedal is considered,

is relatively free and consistently supported by the car

floor or the foot rest. Due to this, the left foot (and the left lower limb) might be involved more dominantly

in postural balance, which would result in a bilaterally

asymmetric posture and pressure. Indeed, the preferred

driving posture has been shown to be asymmetric

(Hanson et al, 2006).

2.0 METHODOLOGY

2.1 Demographics Data

Many studies have found that the risk of back

pain increases with age (H. C. Boshuizen, 1992; M.

Bovenzi, 1994). In the studies reviewed here, only two

found the prevalence of low back pain to be greater

with increased age (G.E. Hedberg, 1988; J.C. Chen,

2004), while three studies found that age did not

increase the risk of low back pain (F. Pietri, 1992; T.

Videman 2001; J.M. Porter 2002). In the remaining

studies, age was not investigated as a risk factor for

low back pain. The influence of age is likely to be complex. For example, older drivers with back pain

may be more likely to leave their job. Age may co-

vary with the characteristics of the car. Older drivers

may drive cars of a different size or with different

features, such as automatic gearboxes.

2.2 Experiment Protocols

Experiment of seat pressure distribution objective

is to obtain the maximum pressure, minimum pressure,

average pressure, standard deviation and coefficient of variation. From these values, the comfort of the driver

while seating can be determined. Two experiments

conducted, one is for buttock pressure, and another

one is for back rest pressure.

The experiments protocol for this experiment

is to reduce the error during the data recorded. All

data collection was following these protocols. During

the data collection, the subject is assumed a driving

posture with both hands gripping the steering wheel,

the right foot on the accelerator pedal and the left

foot on the dead pedal or rest pedal, (Ng et al, 1995).

According to Kyung et al, (2007), during the initial

seat and posture adjustment make sure the suspect in a

comfort sitting. A pressure mat was placed on seat and

back rest secured with the masking tape. The subject

needs to sit carefully to minimize the wrinkles on the

pressure mat. Once the subject preferred posture and

inclination of the backrest was found.

2.3 Data and Statistical Analysis

In analyze the data collection from the FSA

pressure mapping sensor. FSA4.0 software was used

in other to show the data collection. The recorded data

will be display during the data was recorded. From

the FSA4.0 we can read the pressure contour plot on

the map. The software also can calculate minimum

pressure, maximum pressure, average, variance,

standard deviation, coefficient of variation (%) and other parameters. All pressure units are in mmHg.

Statistical analysis was performed by using SPSS 15.0

Evaluation for Windows. The correlation between the

results from perception studies and pressure mapping

are using the SPSS 15.0. The Pearson correlation

coefficient was used to examine the correlation between the parameter.


12

Table 3.2: Bus seats properties.

Table 3.1: Demographic characteristics of the sample analyzed (n = 10).

3.0 RESULTS AND DISCUSSIONS

3.1 The Personal Information Data

Table 3.1 showed that the demographic

characteristics of the bus driver. All samples consist of

male population. Most of the participants age ranged

are from 28 to 58 years old with mean 41.20 years old

with body weight from 50 to 98 kg with mean of 68.97

kg.

Variable Results

Sex (Men) 100 Age (Year) 41.20 Weight (kg) 68.97 Height (m) 164.82 BMI (kg/m²) 25.23

*data show the average for each variable.

Seat Type of seat cushion Type of back cushion Seat cover material

A low firm foam low firm foam polyester B low firm foam high firm foam cotton C low-profile sponge low-profile sponge cotton D low firm foam high firm foam polyester E low firm foam low firm foam polyester F low-profile sponge low-profile sponge cotton G high-profile sponge high-profile sponge polyester H low firm foam high firm foam polyester I low firm foam low firm foam polyester J low firm foam low firm foam polyester

3.2.1 Static Seat Pressure Distribution Data

Table 3.3 shows that the result of pressure

distribution for a seat. The minimum pressure for all

seats is (Pmin = 0 mmHg) because there are some point

that not involving in pressure. The pressure for the seat

is actually from thigh to buttock. The results show that

seat G (high- profile sponge with polyester material cover) the best mechanical performance (Pmax = 144.81

mmHg; Pmean = 25.6 mmHg; Psd = 30.99 mmHg)

with regard to distribution of pressure and contact

3.2 Pressure Distribution Analysis

Experiment was tested on ten different buses,

with different type of seat cushion, back cushion and

seat cover material. Two types of seat cushion and

back cushion material, which is sponge and foam with

the low and high level. The details of each seat are in

Table 3.2

surface compared to nine other seats. Otherwise, the

maximum pressure distribution (by compared the

Pmean values because of Pmax is almost same) recorded

is for seat J with the mechanical performance (Pmax =

200.0 mmHg; Pmean = 40.06 mmHg; Psd = 51.25 mmHg)

is the highest pressure compared to nine others. Figure

3.1 shows the distribution for the contact area for the

tight and buttock.


13

Figure 3.1: Contact Area for the Tight and Buttock.

Table 3.3: Buttock Seats Pressure Distribution.

Type of Variance Standard Coefficient of deviation variation seat minimum maximum average (mmHg) (mmHg) (%)

A 0 200 36.08 1887.91 43.45 120.42 B 0 200 26.72 1200.04 34.64 129.63 C 0 200 27.59 1837.11 42.86 155.35 D 0 200 36.85 2665.48 51.63 140.1 E 0 200 33.23 2375.59 48.74 146.67 F 0 182.57 27.54 1183.81 34.41 126.75 G 0 144.81 25.6 960.31 30.99 108.34 H 0 200 30.22 2012.13 44.86 148.42 I 0 200 34.73 2432.45 49.32 142.02 J 0 200 40.06 2626.57 51.25 127.94

* N= 10. Experiment of pressure distribution with same body mass index (BMI).

3.2.2 Static Back Pressure Distribution Data

Table 3.4 shows that the result of pressure

distribution for the seat. The minimum pressure for

all seats is (Pmin = 0 mmHg) because there is some

point that not involving in pressure. The pressure

for the seat is actually from thigh to buttock. The

maximum pressure that can be applied to the mating

sensor is (Pmax = 200 mmHg). Show that seat H (high-

profile sponge with polyester material cover) the best mechanical performance (Pmax= 56.11 mmHg; Pmean

= 5.07 mmHg; Psd = 9.22 mmHg) with regard to

Minimum (mmHg) 0.00Maximum (mmHg) 200.00Average (mmHg) 31.33Variance (mmHg²) 2079.89Standard deviation (mmHg) 45.61Coefficient of variation (¼) 145.58

distribution of pressure and contact surface compared

to nine other seats. Otherwise, the maximum pressure

distribution (by compared the Pmean values)

recorded is for seat A with the mechanical

performance (Pmax = 94.4 mmHg; Pmean = 10.33 mmHg;

Psd = 17.55 mmHg) is the highest pressure compared to

nine others. Figure 3.2 shows the distribution contact

area for the back rest.

200

180

160

140

120

100

80

60

40

20

0

mmHg

Pressure (mmHg)


14

Figure 3.2: Contact Area for Back Rest

Table 3.4: Back Seats Pressure Distribution.

3.3 Correlations between Cushion and Pressure

Distributions.

There is significant different between type of seat material which is sponge and foam type. The

average of mean pressure for a seat with sponge filling material (Pmean = 26.91 mmHg) and mean pressure for

a seat with firm foam filling is (Pmean = 33.98 mmHg).

The correlations between these two types are r =

-0.540.

However, by referring to Figure 3.3, there are

very small differences of pressure between sponge

filling and foam filling. Taking the average of mean

pressure, Pmean, for both types, the sponge filling average pressure for back support is (Pavg = 6.07

mmHg) meanwhile the foam filling is slightly high, which is (Pavg = 6.36 mmHg). These finding inline with the Gil et al., (2009) finding whereas they had conducted studies for several types of seat cushion.

Their result indicated that the dual-compartment air

cushion had the lowest mean pressure, Pmean, which is

34.9 mmHg and the gel and firm foam cushion had the highest Pmean value is 41.9 mmHg.

Minimum (mmHg) 0.00Maximum (mmHg) 100.00Average (mmHg) 9.32Variance (mmHg²) 262.30Standard deviation (mmHg) 16.20Coefficient of variation (¼) 173.69

200

180

160

140

120

100

80

60

40

20

0

mmHg

Type of Variance Standard Coefficient of deviation variation seat minimum maximum average (mmHg) (mmHg) (%)

A 0 94.4 10.33 308.07 17.55 169.98 B 0 59.75 5.33 127.51 11.29 211.81 C 0 113.32 8.38 336.36 18.34 218.97 D 0 78.67 5.61 170.23 13.05 232.58 E 0 107.1 7.16 280.09 16.74 233.84 F 0 103.34 4.76 202.4 14.23 298.62 G 0 81.36 5.07 134.88 11.61 229.23 H 0 56.11 4.03 84.98 9.22 228.66 I 0 84.83 7 169.74 13.03 186.24 J 0 143.13 5.05 225.88 15.03 297.64

* N= 10. Experiment of pressure distribution with same body mass index (BMI).

Pressure (mmHg)

Spongefilling

Foamfilling

40

30

20

10

0

Pres

sure

(m

mH

g)

Buttock

Back support


15

Figure 3.3: Different between two type of cushion filling

The finding of this particular study also indicated

that the lowest mean pressure for high profile sponge

is Pmean = 26.91 mmHg then follow by the firm

foam cushion which is Pmean =33.98 mmHg and the

highest pressure cushion goes to gel and firm foam

cushion at Pmean = 41.9 mmHg. According to Lakes

et al., (2000), peak pressure is more problematical

in a person suffering paralysis, since that pressure

may be prolonged, giving rise to pressure sores.

Prolonged pressure can inhabit blood flow where the

critical pressure for the blood capillary pressure is at

32 mmHg (4.3 kPa).

4.0 CONCLUSIONS

From this study, it can be conclude that pressure

distribution for seated bus driver contributed to the

discomfort and may lead to the symptom of back pain

and musculoskeletal disorder. The study also revealed

that the design of seat for the Malaysian bus driver

should be revaluated for comfort and reduction of

back pain symptom.

REFERENCES

Beach, T.A., Mooney, S.K., Callaghan, J.P.,

2003. The effects of a continuous passive motion

device on myoelectric activity of the erector spine

during prolonged sitting at a computer workstation.

Work 20, 237-244.

Bowers-Carnahan, R., Carnahan, T., Tallis-

Crump, R., Crump, R., Faulkner, D., Martin,

P.,Sanford,L.,Walters, J., 1995. User perspectives

on seat design. Int. Truck and Bus Meeting. North

Carolina, SAE Paper No. 952679.

Branton, P. (1969). Behaviour, body mechanics

and discomfort. Ergonomics,- 12(2), 3 16-327.

Callaghan, J.P., Dunk, N.M., 2002. Examination

of the flexion relaxation phenomenon in erector spine muscles during short duration slumped sitting.

Clinical Biomechanics 17, 353-360.

Corlett, E.N.,Bishop, R.P., 1976.A technique for

assessing postural discomfort. Applied Ergonomics

19, 175-182.

Delia T, W.S.Marras (1987) Measurement of seat

pressure distributions. Human factors, 1987, 29(5),

563-575.

F. Pietri, A. Leclerc, L. Boitel, J. Chastang, J.

Morcet, M. Blondet, Low back pain in commercial

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and Health 18 (1992) 52-58.

H.C. Boshuizen, P.M. Bongers, C.T.J. Hulshof,

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container tractor drivers exposed to whole-body

vibration, Spine 17 (1992) 59-65.


16

Hanson, L., Sperling, L., Akselsson, R., 2006.

Preferred car driving posture using 3-D information.

International Journal of Vehicle Design 42 (1–2), 154-

169.

G.E. Hedberg, The period prevalence of

musculoskeletal complaints among Swedish

professional drivers, Scandinavian Journal of Work,

Social Medicine 16 (1988) 5-13.

Gil-Agudo A, A. De la Peña-González, A. Del

Ama-Espinosa E. Díaz-Domínguez, A. Sánchez-

Ramos, E. Pérez-Rizo (2009) Comparative study of

pressure distribution at the user-cushion interface with

different cushions in a population with spinal cord

injury. Clinical Biomechanics 24 (2009) 558-563.

J.C. Chen, W.P. Chan, W.P. Chang, D.C. Christiani,

Occupational factors associated with low back pain

in urban taxi drivers, Occupational Medicine 55

(2005) 535-540.

J.M. Porter, D.E. Gyi, The prevalence of

musculoskeletal troubles among car drivers,

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Kyung, G. and M.A. Nussbaum, 2008. Driver

sitting comfort and discomfort (part II): Relationships

with and prediction from interface pressure. Int. J.

Ind. Ergon., 38:526538.DOI:10.1016/j.ergon. 2007.0

8.011.

Lakes, R. S. And Lowe, A. “Negative Poisson’s

Ratio Foam As Seat Cushion Material”,Cellular

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M. Bovenzi, A. Betta, Low-back pain disorders

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Ng, D., Cassar, T., Gross, C.M., (1995). Evaluation

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Thakurta, K., Koester, D., Bush N., Bachle, S.,

1995. Evaluating short and long term seating comfort.

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Van Wijmen, P.M., 1991. A comparison of the effects

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Identifying factors and discomfort. Human Factors 38

(3), 377-389.


17

Call Center Ergonomics Issues: A Case Study

T. Hari Krishnan*, Raemy Md Zein**Ergonomics Excellence Center, South Regional Office (SRO), National Institute of Occupational Safety and Health (NIOSH),

Corresponding author:T. Hari Krishnan

Ergonomics Excellence Center, South Regional Office (SRO), National Institute of Occupational Safety and Health (NIOSH), No. 10, Jalan Persiaran Teknologi, Taman Teknologi Johor, 81400 Senai, Johor Darul Takzim

Email: [email protected] / [email protected] : +60 7-599 1200 Fax: +60 599 0200

INTRODUCTIONS

Call center has been defined as a working environment in which uses telephone and computer for

the purpose of marketing and manage communication

with prospect clients or existing clients (Rocha, Glina,

Morinho and Nakasato, 2005; Sprigg, Smith and

Jackson, 2003).

METHODOLOGY

The study was conducted via observation of

working condition and face to face interview with call

center operators. Measurement of anthropometrics

was also conducted.

RESULTS

Ergonomics issues found at call center were

inappropriate work condition and workstation which

lead to awkward sitting posture (sitting with forward

leaning posture, raised shoulder, feet not supported

on floor). Besides that organizational policy which required high job demand and subsequently lead to

prolonged sitting and static posture (very minimal

posture changes). Combination all these factors lead to

musculoskeletal symptoms and the operators reported

of having neck, shoulder, upper back and lower back

pain compared to other body parts

CONCLUSION

The management should embark on organization

wide ergonomics management program and should

review the current policy and create safe and

healthy working environment by providing suitable

workstation for the operators in order to prevent

musculoskeletal.

KEYWORDS

Call center, musculoskeletal symptoms,

ergonomics, workstation, Malaysia


18

INTRODUCTIONS

Call center has been defined as a working environment in which uses telephone and computer for

the purpose of marketing and manage communication

with prospect clients or existing clients (Rocha, Glina,

Morinho and Nakasato, 2005; Sprigg, Smith and

Jackson, 2003).

Call center operators has been reported to

have high workload demands that require accuracy

and speed, but have limited control over their work

process (Knoll, 2010). Due to nature of the work

which required the operators to work long hours while

seated (Sudhashree, Rohith and Shrinivas, 2005)

it leads to health illness. Short-term effects could

be divided into symptoms such as eye discomfort,

headache, neck/shoulder, arm/hand, upper back and

lower back and other health related effect such as

stress-related somatic or mental symptoms, stress and

energy (Norman, 2005). Norman (2005) also reported

that in the longer perspective this could lead to long-

term effects such as disability and medical leave. The

situation is worse by performance monitoring system

where every single second recorded (Sudhashree et

al., 2005)

Organizational management also plays significant role for the stress experienced by the operators. Krause,

Burgel and Rempel (2010) reported that significant relationship was found between the average effort

reward imbalance ratio and right upper extremity pain

after adjusting for other confounding factors.

Apart from that Norman, Toomingas and Wigaeus

Tornqvist published the finding from their research in year 2004, which reported that call center operators

facing poor support from their immediate superior and

the operators have low control to influence their work. The objective of this study was to identify ergonomics

issues in call center.

METHODOLOGY

The study was conducted via structured

observations in accordance with an ergonomic checklist

of working condition during call center operators

performing routine tasks and face to face interview

with call center operators. Sixteen operators were

interviewed for the purpose of gathering the details of

task description, work activity and symptom of general

and localized body discomfort. These symptoms were

reported on the scale of 0 (Discomfort) to 4 (Pain).

Anthropometric measures were also obtained in order

to make comparison with the workstation currently

used in the call center.

RESULTS

The results obtained for the working condition

is presented in Table 1. As for the chair fit for the operators, sixty nine percent of the seat pans do not

fit properly to the observed operators. The situation is worse by the inadequate lumbar support for 63%

of the operators. The height of the keyboard is

inappropriate for 82% of the operators. Eighty one

percent of the mouse found to be positioned too far

from the operators. Other work condition issues found

in the call center were works without taking breaks

(81%), minimal or no posture changes (88%) and the

operators were not taking initiatives for vision or eye

relaxation breaks (88%).

Turning now to the results of body symptoms

survey, the results are shown in Table 2. It can be

observed that neck, shoulder upper back and lower

back having more pain compared to other body parts.


19

Table 1. Characteristics of Working Condition of Call Center Operators

Table 2. Symptom of General and Localized Body Discomfort

DISCUSSION

The current study further support the research

by Norman (2005) and Norman et al. (2004). Due to

inappropriate working condition such as unsuitable

seat pan and design of chair not according to the body

size of the user population, the operators adapt to

the given workstation. Anthropometric comparison

between the physical body size and dimension of

workstation further corroborates the survey results.

The average elbow height (sitting position) of the

operators in the call center is 0.65 meter while the

height of the table is 0.75 meter. Thus, the situation

lead the operators to work in awkward condition such

as sitting with forward leaning posture, raised shoulder

and feet not supported on floor. Khalid and Helander (2012) reported many furniture manufactures apply

American National Standards Institute (ANSI)

standards to produce workstation and not adopting the

design to meet the local population.

Chair Fit %

Seat pan does not fit user correctly 69 Inadequate lumbar support 63 Armrest do not adjust to correct height 63 Feet not supported on floor or footrest 13 Keyboard Height incorrect 82 Mouse Positioned too far from the shoulder 81 Not at the same height as keyboard 6 Writing Surface too high/low 25 Not enough leg clearance under surface 13 Others Bend neck to look down at copy 69 Works without taking task breaks 81 Minimal or no posture changes 88 No vision or eye relaxation breaks 88

Body Parts 0 1 2 3 4

Neck 13 25 25 31 6Elbow 50 12 19 19 0Forearms 50 19 19 12 0Wrist/hand 37.5 12.5 25 19 6Thighs 50 31 0 13 6Lower Legs 38 50 0 6 6Shoulders 12 25 25 19 19Upper Back 12.5 31 37.5 0 19Lower back 6 19 25 12.5 37.5Hips 25 31 19 6 19Knees 31 25 19 19 6Ankle/feet 37.5 37.5 6 6 13

Rating (%)


20

The operators reported that they work in static

condition with very minimal or no postural changes.

Main operation of call center is to handle calls and

the importance consideration is given to the numbers

of calls an operator handles. To cater this need,

performance monitoring system has been implemented

to continuously record the calls and the operators need

to fulfill the minimum numbers of call in a day. This organization policy leads the operator to be static at

their workstation and the operators claimed they leave

their seat during lunch break and for rest room break

only. This situation will lead to interruption of blood

flow.

Call centers companies are recommended to

review the organizational policy by adopting human

centered approach in order to create better working

condition. Ergonomics consideration based on the user

population also should be given equal emphasizing

during procurement procedures to avoid unsuitable

workstation for the users. Lacaze, Sacco, Rocha,

Bragança Pereira and Casarotto (2010) indicates that

compare to rest break, exercise during the work shift

are more effective. Thus, exercise programs should be

introduced to the call center operators.

Further research should be done to investigate

the correlation of musculoskeletal symptoms, job

satisfaction, design of workstation and environment

conditions.

Conclusion

The main reason for the ergonomics issues in

call center were due to inappropriate workstation design

and organizational policy which lead the operators

to be seated almost all the time in awkward posture.

The management should embark on organization

wide ergonomics management program and should

review the current policy and create safe and healthy

working environment by providing suitable

workstation for the operators in order to prevent

musculoskeletal.

Reference

Khalid, H.M., and Helander M.G. (2012).

Ergonomics collaboration in the oil and gas industry in

Southeast Asia. Ergonomics in Design: The Quarterly

of Human Factors Applications, 20 (4), 34-38.

Knoll Inc. (2010). A call center case study:

The impact of workstation design and work tools on

performance. Retrieved December 12, 2012, from

www.knoll.com/research/downloadsCallCenterCase

Study.pdf

Krause, N., Burgel, B., & Rempel, D. (2010).

Effort-reward imbalance and one-year change in neck-

shouldeer and upper extremity pain among call center

computer operators. Scan J Work Environ Health,

36(1), 42-53.


21

Lacaze, D.H.C., Sacco, I.C.N., Rocha, L.E.,

Bragança Pereira C.A., & Casarotto, R.A. (2010).

Stretching and joint mobilization exercises reduce

call-center operators’ musculoskeletal discomfort and

fatigue. Clinics, 65(7), 657-62.

Norman, K. (2005). Call centre work-characteristics, physical and psychosocial exposure

and health related outcomes. Linkoping University.

Norman, K., Toomingas, A., & Wigaeus

Tornqvist., E. (2004). Working conditions in a selected

sample of call centre companies in Sweden. American

Journal of Industrial Medicine, 46, 55-62.

Rocha, L.E., Glina, D.M.R., Morinho, M.D.F., &

Nakasato, D. (2005). Risk Factors for Musculoskeletal

Symptoms among Call Center Operators of a Bank in

Sao Paulo, Brazil. Industrial Health, 43, 637-646.

Sprigg, C.A., Smith, P.R., & Jackson, P.R.

(2003). Psychosocial risk factors in call centres: An

evaluation of work design and well being (Research

Report 169). Retrieved from Health and Safety

Executive website: http://www.hse.gov.uk/research/

rrpdf/rr169.pdf

Sudhashree, V.P., Rohith, K., & Shrinivas, K.

(2005). Issues and concerns of health among call

center employees. Indian J Occup Environ Med, 9,

129-32. Retrieved from http://www.ijoem.com/text.

asp?2005/9/3/129/19179


23

Comparison of Air Conditioning Ducting Measurement Data and Effect of Indoor Air Data at Office Building

M.D. Amir Abdullah1a, A.M.Leman2b, A. Norhidayah3c, M.M.Syafiq Syazwan4d

1,4Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, 86400 Batu Pahat, Malaysia

2Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, 86400 Batu Pahat, Malaysia

3Faculty of Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang, Malaysia.

[email protected], [email protected], [email protected], [email protected]

ABSTRACT

A poor Indoor Air Quality (IAQ) is a crucial problem which produces by the improper maintenance of Mechanical Ventilation and Air Conditioning (MVAC) ducting. A budget constraint intimidates for the practise of monitoring of the MVAC ducting. Thus IAQ measurements were conducted at the room where the air supplied by centralized air conditioning. It has been performed at four different offices that supply by two different Air Handling Unit (AHU). Walkthrough survey was conducted and the area samplings were selected for data collection. This paper examines the result of comparison of air ducting and air quality at academic office building, Universiti Tun Hussein Onn Malaysia (UTHM). The parameters involved were Temperature (°C), Relative Humidity (RH), Carbon Dioxide (CO2) and Carbon Monoxide (CO). Pictures were also captured to demonstrate the real conditions inside the ducting by using Mechanical Robot. Thus, duct cleaning was recommended to be an exceptional platform for the IAQ improvement.

Keywords: Indoor Air Quality (IAQ), Mechanical Ventilation and Air Conditioning (MVAC), Ducting, Mechanical Robot, Safety

I. INTRODUCTION

Since ducting were the primary source to deliver

air to the user, ducting must be maintained properly.

Indoor pollutant was identified by several physical and chemical parameters [1]. There are several ways

to create and maintain good indoor air including

monitoring ducting. To monitor the ducting some

allocation of budget were need and building owner will

think twice to pay contractor to clean their ducting.

Failing to proper maintain the duct will lead to several

sick syndrome and effect occupant productivity [2].

Since Malaysia weather warm and humid along the

year, it also will contribute to pollutant inside ducting

since every air conditioning system need to take fresh

air before deliver to end user [3]. The indoor emission

also should be considered to evaluate the IAQ [4]. For

the data capture, robots were widely used to do duct

cleaning system. But, research on monitoring ducting

still limited in Malaysia. Therefore, these papers focus

on the result of ducting that supply to multi office room by single Air Handling Unit (AHU). Jiaming Li in his

paper believed that reliable and optimal monitoring

and control ventilation system important to maintain

good IAQ. That mean, by good maintaining of ducting

either ventilation or air conditioning system will

definitely effect to the conservation of energy [5].


24

Figure 1: Basic Monitoring Steps for Duct Monitoring (SMACNA 1998) [10]

II. METHODOLOGY

2.1 Building Information

This study selects an office building which consists of administration office for Faculty of Mechanical Engineering (FKMP), Centre of Academic Development (CAD), Faculty of Electrical and Electronic Engineering (FKEE) and Postgraduate Centre (PPS). The building was built in 2001. There is 3 floor of office in this building, but due to several problems, this study were focuses on ground floor and first floor office building. The ground floor was supply with one Air Handling Unit (AHU) that supply to FKEE administration office and PPS Office. Same as ground floor, FKMP administration office and CAD also sharing AHU that located at first floor. Several parameters were involved during monitoring the air ducting in office building. Every floor was served by one AHU that are located near the staircase. Each AHU were having cooling capacity of 146.54 KW.

2.3 Walkthrough Inspection

In conducting IAQ monitoring in each department, inspection and observation were made to gain information at the department that significance to the indoor air quality problem. The walkthrough inspection form was referring to the DOSH 2010 walkthrough inspection form. The purposes of these procedures are to help facilitate in analysis and discussion in the IAQ data and comparison from ducting side and indoor side [1].

2.4 Ducting Site Monitoring

Figure 1 above show basic monitoring step that suggest by Sheet Metal and Air Conditioning Contractors National Association (SMACNA 1998). From this basic step, data collection then precedes to other procedure. To be more specific and precise, building map and duct design were very helpful to

The office were divided into two type that are closed type that occupied by Lecturer. While open type offices were occupied by administration occupants. Only open type office was sharing up to 10 people of occupants. While closed type office only for single person.

2.2 Data Collection

Data collection was divided into two parts that indoor monitoring and duct monitoring. The measurement for indoor monitoring and duct monitoring were held in three working days. Parameter involved:1. Temperature ( °C)2. Relative Humidity ( RH)3. Carbon dioxide ( COƐ)4. Carbon Monoxide ( CO)

ensure the exact location to get the sampling point.2.4.1 Duct Sampling Strategy

There is no specific strategy to obtain data in the ducting. The current standard for collecting data in the ducting was focuses on air flow and static pressure. ASHRAE recommend a minimum of 25 point for obtaining air flow data in rectangular duct [7, 8, 10, and 12]. By that concept, this study comply it to perform data collection inside the ducting. Three sample data were taken at each point.

2.4.2 Duct Sample Procedure

The ducting was drill to make a hole at the side of the ducting. Then the sensor was put and obtains three sample test data collection that is 2 tests at the tip of the duct and one more at the middle of the duct. The time interval for ducting sampling is 1 second, and every test at each point are 1 minute for every test.

Set upEquipment

DataCollection

DataAnalysis

DataTransfer

GenerateReport

Result

SelectDuct

Location/Place

Total floor area( Served by MVAC system) (m )

Minimum number of sampling points

<3000 1 per 500m

8 0005<-0003

21 00001<-0005

51 00051<-00001

81 00002<-00051

12 00003<-00002

30000 1 per 1200m


25

Table 1: Recommend minimum number sampling points for indoor air quality assessment (DOSH 2010).

That mean, there are 3 tests at each point. The points were selected at the main duct, and branch that supply to the room. For each Air Handling Unit (AHU), the air is supply to two departments. Each department should have 12 sampling point.

The step for obtaining data from ducting:Step 1: Drilling the ductingStep 2: Sampling tagStep 3: Data Collection

The picture inside the ducting will be capture to provide proof that which location that has the higher problem that will lead to poor IAQ.

2.5 Indoor Monitoring

IAQ monitoring was conducted in indoor offices. There is 8 points of indoor monitoring were selected. Since one AHU are supplying air conditioning for two departments, each department will have 4 sampling point. This study put 2 point at open type administration office, one point at middle of the duct, that is single office room, and one more point were put at the end of the supply duct that is at the end of the room. The step was repeated at every department [1]. The sample position also follows guidelines from DOSH 2010. The sampling period for indoor monitoring is 8 hours and real time measurement. The parameter involved also same as the parameter that obtain from the ducting.

III. RESULT AND DISCUSSION

3.1 Walkthrough Inspection

Walkthrough inspection was performing during indoor monitoring. As for general, there is present of odor at department FKMP, FKEE and PP. The odor are noticeable while morning. There are no places that dirty or unsanitary conditions since the housekeeping always in good schedule. There is no visible fungal growth or moldy odor that could see at the department except some of the diffuser. There is no unsanitary condition at cooling tower since the AHU for both floor are using packaged unit that did not need cooling tower. Inadequate ventilation and variable temperature really could be seen for the department. Air filter that installed at the AHU room also not maintained well for both AHU. Since the system is free return AHU, therefore the mechanical room must be maintaining in clean condition. But, at first floor AHU, the mechanical room is not in good condition. The administration room for all departments allocates more than 8 person in the offices. Meanwhile, lecturer office all is alone. And most of the workers also around 8 hours at their workstation. In the building, the temperature is not controlled by the thermostat. Therefore, the temperature, relative humidity and air flow rates were not checked regularly. The air also not reaches all the spaces in the office and make the occupant did not receive enough air flow rates. There is no renovation or maintenance that done during the monitoring and inspection. All the room also no regularly vacuum.

Comparison of Air Conditioning Ducting Measurement Data and Effect of Indoor Air Data at Offi ce Building

26

Figure 2: Image taken by Mechanical Robot at ground fl oor ducting.

Figure 4: Comparison chart for CO2 for all department

Carbon Dioxide ppm

Figure 3: Image taken by Mechanical Robot at fi rst fl oor ducting.

And some of the room carpet produces odor especially single offi ce room. All of the department complete with photocopy machine that can contribute gasses or fumes during operation. The air conditionings for both AHU were set by timer, and it automatically will turn of at 7 pm. The room also has enough supply air grilles, but the return air grilles are not enough. Fresh air intake at AHU room also locate to near car parking, but not at street level. There is no heavy industries that located near to the building. Furthermore, there is no construction work that going on near the building. Basically, there is regular schedule for cleaning and maintaining the air conditioning system. But, there is problem with AHU ground fl oor and still waiting for repair.

3.2 Duct Monitoring Using Mechanical Robot

The mechanical robot was used to capture visual picture inside the ducting. The robot goes through the ducting by access door in the AHU room. Figure below shows sample picture that were taken while monitoring the ducting. The robot were access while the AHU not turning on to avoid any interruption between the high velocity air. From the picture taken, after 2 years of duct cleaning perform in these ducting, the suspended particles were clearly defi ned in ducting for both AHU [6].

From the observation, both AHU shows clearly suspended particles and web that clearly will affect the IAQ. Internal insulation that used for sound and vibration, have been clearly detached from the ducting. This study also proves that inside the ducting are high in humidity that will create the growth of fungi and mold inside the ducting. Apart from that, the return system for both AHU is free return.

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Temperature °C

Figure 5: Comparison chart for Temperature for all department.

3.3 Carbon Dioxide Comparison

Figure 4 show the chart comparison between

indoor and ducting of CO2 for all departments. The

concentrations of CO2 in the ducting were higher

compared to the indoor concentration of CO2. The

indoor concentration of CO2 was in the range of 380-

442 ppm. While ducting concentration of CO2 were

in the range of 430-507 ppm. Most of administration

office having high concentration of CO2 compare to

other room. Administration offices were occupied by more than 7 person per office. Their offices are in open type position. Meanwhile, FKMP end rooms are

sharing with two occupants. But, the size of room and

lack of ventilation makes the CO2 concentration are

high compare to other room. CAD end room shows

the lowest CO2 among other room in CAD. The room

basically use for office, but due to their department arrangement, the room now is empty. FKEE end

room shows the highest indoor level of CO2 compare

to other room in FKEE. The room also was sharing

with 2 people, with lack of ventilation. While, PPS

administration room shows the highest concentration

of CO2. The offices were tightly arranged with more than 7 people sharing the office. The administration of the office also really busy with attendance of postgraduate students. From data collected, CO2

concentration was higher than indoor concentration.

3.4 Temperature Comparison

Figure 5 shows comparison of temperature

between indoor and ducting. Ducting temperature

exactly shows lower temperature for all room

compared to indoor temperature. From the data obtain,

T are between 3-5°C. Therefore, the temperature

difference is in right value (ASHRAE 2007). To make

sure get lower temperature at indoor, the ducting

temperature must be lower than current temperature.

From the chart, the temperature at obviously higher

than duct temperature. Majorly, most end room that

all four departments’ shows higher temperature

compare to other room. From the ducting temperature

that was collected also shows that the end of the

room, the temperature will increase, significant to the indoor temperature. With air supply temperature

that increases, the indoor activity also influenced to the data measures. Mostly, indoor temperature

was exceeding the comfort temperature [1]. Most

occupants in the office have to bring stand fan to have better airflow since the temperature is unacceptable range. FKMP and FKEE end room is the printing room

for the department. Most of machine that can produce

additional heat are located. Due to maintenance at the

building, one the compressor the supply to the ground

floor AHU was in faulty. The AHU were supply with 4 compressor using air cooled packaged unit. The faulty

compressor may influence the cooling load supply to the department

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28

Figure 6: Comparison chart for RH for all department

Figure 7: Comparison chart for CO for all department.

3.5 Relative Humidity Comparison

Figure 6 refer shows the comparison at RH between indoor and ducting. From the data obtain, ducting measurement were exactly higher than indoor RH. The level of RH inside the ducting were recorded between 73- 80% of humidity. While indoor humidity was measured lower than ducting RH that are around 60-70% of RH. From the chart also, the humidity of the ducting majorly from 70-80% of RH. Meanwhile, the indoor humidity to make people feel comfort is around 70%. Ground floor AHU that supplies to FKEE and PPS shows higher humidity compare to first floor AHU. The chart show all ground floor department having high humidity compare to the first floor department. It is also significant with the RH in the ducting that supply to the ground floor department. There are no water leaks at the department that can contribute to the high indoor humidity [10, 12].

Figure 7 shows the chart comparison of CO between indoor and ducting. From the data obtain, PPS show the highest concentration of CO in the ducting. While FKMP the end room, shows the highest concentration of CO at indoor condition. The indoor CO varies from 1.5ppm to 2.0 ppm. While 1.6 to 2.1 ppm of carbon dioxide is refer to the concentration in ducting. At the end room of FKMP department, the CO are measured high at indoor since it is printing room. The ozone was come from the photocopy machine and smoke that may access to the room since the room near to the car parking.

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29

IV. CONCLUSION

From this study, it can be concluded temperature, RH, CO2 and CO have been identified in the ducting. By using mechanical robot as tools for visual inspection, both AHU that supply for FKEE, PPS, FKMP and CAD have the potential that influence IAQ in the office. From the image taken, the ducting shows suspended particles and dust even after two years of duct cleaning process have been done. AHU at first floor shows the most clear suspended web and particles. The concentration of CO2 in the ducting also shows higher compare to the concentration of CO2 at indoor. The indoor concentration was limit until 442 ppm, while CO2 have been exceed to 507 ppm. But, the concentrations of CO2 were still under recommend by DOSH Malaysia that put the ceiling limit 1000ppm. Indoor Concentration of CO2 clearly shows significant to the number of occupant in any indoor spaces. PPS shows the highest concentration of CO2 among other department. Meanwhile, the CO2 concentration inside ducting is high at FKMP ducting. Temperature in the ducting shows the difference between duct temperatures to the indoor temperature around 3°C. But, due to problem of the AHU, it affect the duct temperature that supply to the room. The room temperature exceeds human comfort which is 26°C. This would affect employee comfort and productivity.

V. RECOMMENDATION

There are few recommendation for this study to improve the indoor air quality in this particular building

5.1.1 Maintaining the System

In order to get appropriate temperature in all places, building maintenance must practice and follow the maintenance guidelines regularly. By repairing the compressor, the cooling capacity that supply to the room may increase. From that, the duct temperature will decrease and make sure the indoor temperature also maintain at comfortable level.

5.1.2 Source Control

By control the sources it would give great impact and increase the quality of air. Every each personal may apply good discipline in order to maintain any inappropriate gases or dust that may affect the air quality problem. Prevention method always the best key to make sure air quality in good condition. By prevent such as do not smoke and do any unnecessary activities that may contribute to the high concentration of CO and CO2

5.1.3 Duct Cleaning

After 2 years of duct cleaning process have been done, administration could suggest duct cleaning regularly. It might take expensive cost, but to maintain good air quality, duct cleaning must be done to ensure ducting in clean condition. Then, the supply air will be much better and avoid any cases that related to the mold in the ducting and suspended dust and particles there.

5.1.4 Ventilation System

By increase the ventilation effectiveness, air quality problem may decrease. With walkthrough inspection, not enough ventilation has made the room become stuffy. While adding more ventilation may take cost, engineer or building maintenance may add more return grille in the office since the office are free return system. The adding of ventilation may an effective way to decreased the IAQ problem.


30

REFERENCES

[1] Industry Code of Practice On Indoor Air Quality 2010, Department of Occupational Safety and Health Ministry of Human Recourses, Malaysia. JKKP DP(S) 127/379/4-39

[2] R.Kosonen, F. Tan, The Effect of Perceived Indoor Air Quality on Productivity Loss, Energy and Building Vol 36 pp 981-986, 2004.

[3] Wan Rong and Kong Dequan “Analysis on Influencing Factors of Indoor Air Quality and Measures of Improvement on Modern Buildings” IEEE, pp.3959-3962, 2008

[4] Guoqing Cao, Effect of Ventilation on Indoor Airborne Microbial Pollution Control, IEEE, pp.390-394, 2008

[5] Jiaming Li, et al, Indoor Air Quality Control of HVAC System, Proc. of 2010 International Conference on Modeling, Identification and Control, Okayama, Japan, July 17-19,2010, pp.756-761.

[6] A.M. Leman, K.A.A. Rahman, M.Z.M Yusof and A.Hariri The Development of Mechanical Robot For Ducting Cleaning And Monitoring: Solution Steps of Indoor Air Quality Problems. Scientific Conference on Occupational Safety and Health (Sci-COSH 2011) 13-14 (December 2011) National Institute of Occupational Safety and Health (NIOSH), Bangi Selangor

[7] American Conference of Governmental Industrial Hygienists (ACGIH), Industrial Ventilation: A Manual of Recommended Practice for Operation and Maintenance.

[8] American Conference of Governmental Industrial Hygienists (ACGIH), Industrial Ventilation: A Manual of Recommended Practice 23rd Edition 1998

[9] Irtishad Ahmad,Berring Tansel, & Jose D Mitrani, Effectiveness of HVAC duct cleaning procedures in improving IAQ, 2000, Environmental Monitoring and Assessment 2001 pp 265-276

[10] Sheet Metal and Air Conditioning Contractors National Association (SMACNA) Indoor Air Quality: A System Approach, Third Edition, 1998

[11] World Health Organization Guidelines for Indoor Air Quality 2009

[12] American Society of Heating, Refrigerating and Air Conditioning Engineers,Fundamentals (ASHRAE 2009)

[13] American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE 2007)


31

Comparison of Indoor Air Contaminants in Different Stages of New Building Occupancy: Training And Office Setting

Nor MohdRazifNoraini¹·², A.M. Leman², Ahmad SayutiZainalAbidin³, RuslinaMohd. Jazar¹, LailaShuhada Mat Zin¹, Rasdan Ismail 4 and Nor Hidayah Abdull4

¹Industrial Hygiene Division, National Institute of Occupational Safety and Health (NIOSH), Bandar Baru Bangi, Malaysia

²Faculty of Engineering Technology, University Tun Hussein Onn Malaysia,86400 Parit Raja, Batu Pahat, Johor, Malaysia

³Industrial Hygiene Analytical Laboratory Division, National Institute of Occupational Safety and Health (NIOSH), Bangi, Malaysia

4Faculty of Technology, University Malaysia Pahang,LebuhrayaTunRazak 26300, Gambang, Pahang, Malaysia

ABSTRACT:

This study has been conducted in a new constructed building of NIOSH Malaysia located at Bandar Baru Bangi, Selangor. The goal of the case study is focusing on the level of Indoor Air Contaminants (IAC) including chemical contaminants within three consequent stages which are before furniture install, after furniture install and during one month occupancy. This study was divided the sampling area into two main facilities which are training and office setting. The contaminants has been measured consist of sixparameters such as Carbon Dioxide (CO2), Carbon Monoxide (CO), Total Volatile Organic Compounds (TVOC), Formaldehyde, Respirable Particulates (PM10) and Ozone. The result of Carbon Monoxide (CO), Total Volatile Organic Compound (TVOC), Respirable Particulates (PM10) and Ozone show an increasing trend across the three sampling stages. The Formaldehyde show an increasing trend in the first and second stages but were reduced significantly the last stage of sampling. These finding indicates that furniture and fittings installed might be a potential sources of indoor air contaminants. The management should be aware to their indoor air status to protect the occupant from the risk of unwanted exposure especially during the early stage of building occupancy.

1.0 INTRODUCTION

Indoor air quality (IAQ) has to be maintained

in a certain limit and standard as comply with the

Occupational Safety and Health Act 1994 (Act

514), Malaysia. Indoor Air Quality Code of Practice

(IAQ, COP) provides the needs to assure high safety

level for employer. Albeit the code does not yet

compulsory at the moment, it still can be used to

establish a good practice in a court of law. (E. Uhde

et. al, 2006) provided some sources for poor indoor

air quality, one of the reasons causes depletion of

indoor air quality in new building is the presence of

chemical substances in modern building products,

household products and furnishing. Substandard of

IAQ leads into many health problems, examples,

one of the chemical parameters existing in poor

IAQ like TVOC can cause Sick Building Syndrome

(SBS) like headache, fatigue and dizziness (Syazwan

et.al, 2009). Moreover, organic indoor pollutants are

suspected to be allergic, carcinogenic, neurotoxic,

immunotoxicand irritant (Shen et. al, 2010). In some

case study, high level of Fomaldehyde is classified as potential human carcinogen (Martin et.al, 2012).

In addition, some product especially ozone-initiated

terpene reaction products may be of concern in ozone-

enriched environments (≥0.1 mg/m³) and elevated limonene concentrations, partly due to the production

of formaldehyde. Ambient particles may cause cardio-

pulmonary effects, especially in susceptible people

such elderly and sick people (Walkoff, 2012).

Comparison of Indoor Air Contaminants In Different Stages of New Building Occupancy: Training and Office Setting

32

Concerning on this problem, a case study was

performed to investigate and identify the level of six

indoor air pollutants as classified to be the sources of IAQ chemical parameters. This study was conducted

inside the new building of National Institute of

Occupational Safety and Health (NIOSH), Malaysia.

Six chemical parameters which are Carbon Dioxide

(CO2), Carbon Monoxide (CO), Total Volatile Organic

Compounds (TVOC), Formaldehyde, Respirable

Particulates (PM10) and Ozone were measured and

analysed throughout the year 2011 until end of year

2012. These chemical parameters were compared

within two large categories, training and office setting. The objectives of this study were to measure

the mean data of an indoor air chemical and physical

contaminants in NIOSH building and comparing

the level of Indoor Air Quality (IAQ) parameters

including chemical and physical contaminants

between three consequent stages which are before

furniture install, after furniture install and during one

month occupancy.

2.0 METHODOLOGY

2.1 Sampling Site

This study was conducted inside a new building

at NIOSH, Malaysia. The new building was facilitated

with training rooms and office rooms. This building was selected as an ideal sampling site since it nearly

accomplished while this case study was proposed.

Starting on October 2011, many measurements of

chemical and physical parameters were collected and

analysed. However, the sampling points were decided

upon measurements were taken. Table 1 represent the

allocation space inside the building and air conditioning

systems were selected according to the function of the

space as concerning on energy efficiency.

2.2 Sampling Method

Sampling methods were performed at 0800 am

until 0530 pm, in order to imitate the same real working

situation. The sampling mechanism for this study was

measured by calibrated direct reading instruments.

The results were presented in part per million (ppm)

for Formaldehyde, CO and CO2. The TVOC values

were recorded as part per billion (ppb) and PM10 in

mg/m³. All the equipments as listed in Table 2 have

accuracy of ± 10% and were accepted by ICOP-IAQ,

DOSH Malaysia 2010.

2.3 Indoor Air Quality (IAQ) Monitoring

Measurement of IAQ was established

according to ICOP-IAQ, DOSH Malaysia 2010.

Carbon Dioxide (CO2), Carbon Monoxide (CO), Total

Volatile Organic Compounds (TVOC), Formaldehyde

and Respirable Particulates (PM10) were measured to

determine air indoor pollutants (IAPs). These chemical

parameters were then compared with acceptable

limit as stated by ICOP-IAQ, DOSH Malaysia 2010.

Acceptable limit of these chemicals were summarized

in the Table 2.

Direct reading measurement was used to measure

these five chemical parameters and calibration process was conducted on site before measurements

were taken. Figure 1 represent the consequences of

monitoring procedure implemented in gathering the

data on-site. Partial period of consecutive sampling

was performed in three times measurements (morning,

noon, and evening) to obtain IAP status inside the

building throughout the day.

All instruments were located at the centre of

every sampling location and placed 75 cm above the

ground. Five chemical parameters as listed above will

be measured using instruments as specified in Table 3. The location of all sampling spot was recorded

on the layout plan and all instruments were run

simultaneously using specific procedure by ICOP-IAQ, DOSH Malaysia 2010.

Table 1: summarized air conditioning systems inside the new building

DESIGN SETTING LEVEL SPACEAIR-CONDITIONING

SYSTEM

Manager’s room

Open Office 1st Floor

Receptionist

Open Office 2nd Floor

Document store

3rd Floor Examination Office

4th Floor Human Resource Office

5th Floor Finance Offices

Manager’s Room

Meeting Room

Office

6th Floor

Open Space

Centralize

3rd Floor Examination Rooms

4th Floor Training Rooms and Computer

LaboratoryTraining Centre

5th Floor Training Rooms

Split Unit

Table 2: summarized five chemical parameters and its acceptable limit as comply with ICOP-DOSH, Malaysia.

ITEM CHEMICAL PARAMETER ACCEPTABLE LIMIT ICOP (DOSH,2010)1 Carbon Dioxide (CO2) 1000 ppm (ceiling)

)gnilieC( mpp 01 )OC( edixonoM nobraC 23 Total Volatile Organic Compounds (TVOC) 3 ppm

)gnilieC( mpp 1.0 )OHCH( edyhedlamroF 45 Respirable Particulates (PM10) 0.15 mg/m3

6 mpp 50.0 enozO

Figure 1: Sampling procedure


ITEM CHEMICAL PARAMETER ACCEPTABLE LIMIT ICOP (DOSH,2010)1 Carbon Dioxide (CO2) 1000 ppm (ceiling)

)gnilieC( mpp 01 )OC( edixonoM nobraC 23 Total Volatile Organic Compounds (TVOC) 3 ppm

)gnilieC( mpp 1.0 )OHCH( edyhedlamroF 45 Respirable Particulates (PM10) 0.15 mg/m3

6 mpp 50.0 enozO



33

Table 1: summarized air conditioning systems inside the new building




34

2.4 Data Analysis and Interpretation

Purposely for data analysis and interpretation,

SPSS Statistic 20.0.0 was used to obtain the clear

figure of data collected on-site. The appropriate mean ± SD of the test was calculated using this software. The

control charts was used to present the data and to be

differentiate by plotting the control limits. Purpose of

the control chart is to allow simple detection of events

which indicate to actual process change (Olesen et. al,

2006).

3.0 RESULT AND DISCUSSION

3.1 Measurement of mean, μ data

Measuring the mean for indoor air chemical

contaminants was taken place for both setting in a new

NIOSH’s building. The data was recorded by using

SPSS Statistic 20.0.0 to calculate the mean value for

each contaminant.

Table 4 shows the mean reading of indoor air

chemical contaminants and physical parameters

within three phases of sampling period in the new

building. The mean reading of carbon dioxide and

carbon monoxide has the highestrecorded value with

occupancy of 837.87 ppm and 3.253 ppm respectively.

The concentration of formaldehyde after furniture has

been installed is higher than other stageswith 0.083

ppm. The level of PM10 shows the highest reading

with occupancy, with reading of 0.037 mg/m³. The

mean reading of total volatile organic compounds and

ozone was highest recorded with occupancy which are

3869.13 ppb and 0.0092 ppm.

3.2 Comparison of indoor air contaminants concentration between three consequential stages (Before Furniture Install, After Furniture install, 1 Month Occupancy) for training and office setting

3.2.1 Concentrations of Carbon Dioxide (CO2)

The results of Carbon Dioxide (CO2)

measurements are summarized graphically in figure 2.Carbon Dioxide (CO2) concentrations sampled

from the three stages (before furniture install, after|

furniture install, and 1 month occupancy) are

ranged from 384 ppm to 2486 ppm. The mean

reading for the first stage (before installation of furniture), second stage (after installation of

furniture) and final stage (after a month of occupancy) is 477.35p ppm, 466.31 ppm

and 837.87 ppm respectively. It is noted that the

concentrations of 1 month occupancy is slightly

higher than before furniture has been installed

and after furniture install. Figure also showed

that the Carbon Dioxide (CO2) measurement for

1 month occupancy is higher than the others.

3.2.2 Carbon Monoxide (CO) Concentrations

Figure 3 provides the results of Carbon

Monoxide (CO) measurements. Carbon Monoxide

(CO) concentrations sampled ranged for the 3

consequent stages are between 0.0 ppm until 4.6

ppm. The average for each stage is 2.168 ppm for

before furniture install, 2.778 ppm for after furniture

install and 3.253 ppm for 1 month occupancy. From

the figure, it was found that the reading at several locations was detected with 0.0 ppm concentration of

Carbon Monoxide (CO) for before and after furniture

install.

Table 3: represent the list of instruments used in measurement section.

ITEM CHEMICAL PARAMETER INSTRUMENT

OC( edixoiD nobraC 1 2)

)OC( edixonoM nobraC 2

3 Respirable Particulates (PM10)

Portable TSI IAQ Meter

TSI 9555-P

TSI Dust-Trac Particle Monitor

TSI 8534

4 Total Volatile Organic Compounds (TVOC)

Portable RAE VOCs Gas Detector

(ppbRAE 3000)

PGM 7340

)OHCH( edyhedlamroF 5

Portable Environment Sensor’s

Formaldehyde Meter

YES AIR

lauqoreA enozO 6

Table 4: represent the mean of chemical contam inants and physical data monitored in each phase.

Mean,Parameters Without

furnitureWith furniture

With occupancy

Carbon Dioxide (CO2) 477.35 466.31 837.87

Carbon Monoxide (CO) 2.168 2.778 3.253

Total Volatile Organic Compounds ( TVOC) (ppb) 32.25 259.84 3869.13 Formaldehyde (ppm) 0.0182 0.0829 0.0531

Ozone (ppm) 0.00436 0.00536 0.00918 Respirable Particulates, PM10 ( g/m3) 0.01785 0.01975 0.03651


ITEM CHEMICAL PARAMETER INSTRUMENT

OC( edixoiD nobraC 1 2)

)OC( edixonoM nobraC 2

3 Respirable Particulates (PM10)

Portable TSI IAQ Meter

TSI 9555-P

TSI Dust-Trac Particle Monitor

TSI 8534

4 Total Volatile Organic Compounds (TVOC)

Portable RAE VOCs Gas Detector

(ppbRAE 3000)

PGM 7340

)OHCH( edyhedlamroF 5

Portable Environment Sensor’s

Formaldehyde Meter

YES AIR

lauqoreA enozO 6

Table 4: represent the mean of chemical contam inants and physical data monitored in each phase.

Mean,Parameters Without

furnitureWith furniture

With occupancy

Carbon Dioxide (CO2) 477.35 466.31 837.87

Carbon Monoxide (CO) 2.168 2.778 3.253

Total Volatile Organic Compounds ( TVOC) (ppb) 32.25 259.84 3869.13 Formaldehyde (ppm) 0.0182 0.0829 0.0531

Ozone (ppm) 0.00436 0.00536 0.00918 Respirable Particulates, PM10 ( g/m3) 0.01785 0.01975 0.03651

Figure 2: Carbon Dioxide (CO2) concentrations

Figure 3: Carbon Monoxide (CO) concentrations


35


Table 4: represent the mean of chemical contaminants and physical data monitored in each phase.

Figure 2: Carbon Dioxide (CO2) concentrations



Figure 7: Formaldehyde concentrations

Figure 8: Ozone concentrations

Figure 9: Respirable Particulates (PM10) concentrations




36

Figure 6: Total Volatile Organic Compounds (TVOC) concentrations

Figure 7: Comparison chart for CO for all department.




37

3.2.3 Total Volatile Organic Compounds (TVOC) Concentrations

The results of Total Volatile Organic Compounds

(TVOC) monitoring are summarized graphically

in figure 6 respectively. The Total Volatile Organic Compounds (TVOC) concentrations measured

generally varied between 0 ppb and 33665 ppb. The

mean concentrations for 3 consequent stages were

calculated as 32.25 ppb for before furniture install

259.84 ppb for after furniture install and 3869.13 ppb

for 1 month occupancy. It noted that high TVOC at

new building due to after furniture install and after 1

month occupancy.

3.2.4 Formaldehyde Concentrations

Figure 7 showed the concentration of

Formaldehyde for 3 consequent stages. The data

recorded ranged between 0.00 ppm to 0.24 ppm as

the highest reading monitored. The average

concentration for before furniture install is 0.0182

ppm, after furniture install is 0.0829 ppm and 1 month

occupancy is 0.0531 ppm. High concentration of

Formaldehyde due to after furniture install which is

furniture could be a source of Formaldehyde.

3.2.5 Ozone Concentrations

Figure 8 provides the results of Ozone

measurements. The Ozone concentrations sampled

ranged for the 3 consequent stages are ranged from

0.00 ppm until 0.073 ppm. The average for each

stage is 0.0044 ppm for before furniture install, 0.0054

ppm for after furniture install and 0.0092 ppmfor 1

month occupancy. From the figure, it was found that the reading at the two locations after 1 month

occupancy was detected high which is 0.073 ppm and

0.058 ppm.

3.2.6 Respirable Particulates (PM10)

Concentrations

Code of Practice on Indoor Air Quality (IAQ)

published by Department of Occupational Safety

and Health Malaysia has set the maximum standard

for the particulate at 0.15 mg/m³. Figure 9 refer to

the Respirable Particulates (PM10) concentrations

measurement. The Respirable Particulates (PM10)

concentrations sampled ranged for the 3 consequent

stages between 0.03 mg/m³ and 0.18 mg/m³ as the

highest reading monitored. Meanwhile the mean

concentration for each stage were calculated as 0.0179

mg/m³ for before furniture install, 0.0198 mg/m³

for after furniture install, and 0.0365 mg/m³ for 1

month occupancy. The results show the readings

at all locations complied with COP Standard Limit

except 2 locations at level 6 which is 0.18 mg/m³ and

0.156 mg/m³. This due to the occupant activities

like improper storage at new building and some of

the cleaning works was performed during the day of

monitoring nearby. All results detected were less than

the COP limit less than 0.15 mg/m³.This can be related

to the practice and humidity level detects within the

buildings itself.

4.0 RECOMMENDATIONS

One of the principal methods to mitigate IAQ

problems is improving ventilation system, which is

mainly composed of active IAQ control by heating,

ventilation and air conditioning (HVAC) and passive

IAQ control by natural ventilation (Sungho Lee et. al,

2011). Moreover, Sungho Lee stated pollutant control

sources in the design stage of finishing materials can improve air quality.

5.0 CONCLUSION

In conclusion, concentration of Formaldehyde

in the new building is exceeding the acceptable

limit as comply by ICOP-IAQ, Malaysia 2010.

Installation of furniture and fittings in the new building is the main reason behind this situation.

However, other chemical parameters’ level such as

Carbon Dioxide (CO2), Carbon Monoxide (CO), Total

Volatile Organic Compound (TVOC), and Respirable

Particulates (PM10) are below the acceptable limit.

The development of future IAQ commissioning

guideline is important to improve health standard

and safety of the occupants.


38

6.0 ACKNOWLEDGEMENT

This project was funded by National Institute of

Occupational Safety and Health (NIOSH) Malaysia.

It was conducted by Industrial Hygiene Division,

Consultation, Research and Development Department

starting on year 2011 until end of year 2012.

7.0 REFERENCES

1.) Wolkoff P.,2012, ‘Indoor air pollutants in office environments: Assessment of comfort, health and

performance’, International Journal of Hygiene

and Environmental Health, Elsevier.

2.) Sungho Lee, Gideoc Kwon, JinkyuJoo, Jeong

Tai Kim, Sunkuk Kim,2012, ‘A finish material management system for poor air quality of

apartment building (FinIAQ)’, Energy and

Buildings 46, SciVerseScienceDirect, pp 68-79.

3.) E.Uhde, T.Salthammer, ‘Impact of reaction

products from building materials and furnishing

on indoor air quality - A review of recent advances

in indoor chemistry’, Atmospheric Environment

41, Elsevier, pp 3111-3128.

4.) Dols W S, 1995, ‘Indoor Air Quality

Commissioning of a New Office Building’, National Institute of Standards and Technology

(NIST), pp 1-7.

5.) Sun Sook Kim, Dong Hwa Kang, Dong Heechoi,

Myoung Souk Yeo, Kwang Woo Kim,2006,

‘Comparison of strategies to improve indoor air quality at the pre-occupancy stage in new

apartment buildings’, Building and Environment

43, Elsevier, pp 320-328.

6.) Martin B., Mohamed Z.M.S, Jaromir S.,2012,

Journal of Hazardous Materials, ‘Formaldehyde

emission monitoring from a variety of solid

wood, plywood, blockboard and flooring products manufactured for building and

furnishing materials’, Elsevier, pp 68-79.

7.) Syazwan A. I, Juliana J., Norhafizalina O., Azman Z.A, Kamaruzaman J., 2009, ‘ Indoor Air Quality

and Sick Building Syndrome in Malaysian

Buildings’, Global Journal of Health Science, pp

126-135.

8.) Xiaozhong S., Zhenqian C., 2010, Building

and Environment, ‘Coupled heat and formaldehyde migration in dry porous building

materials’, Elsevier, pp 1470-1476.

9.) Code of Practice on Indoor Air Quality, 2005,

Department of Occupational Safety and Health

Ministry of Human Resources Malaysia, 2005.


39

Compliances of Airborne Microbe In Different Phases Of Building Commisioning

Ahmad Sayuti Zainal Abidin¹ and A.M. Leman² Nor Mohd Razif Noraini³

¹Industrial Hygiene Analytical Laboratory, National Institute of Occupational Safety and Health (NIOSH), Bangi, Malaysia

²Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

³Industrial Hygiene Division, National Institute of Occupational Safety and Health (NIOSH), Bangi, Malaysia

ABSTRACT:

This study intended to investigate the level on airborne microbe in indoor air for new constructed building. It was divided by three different phase of building commissioning in Bandar Baru Bangi, Selangor. The first phase of the sampling was carried out after the building fully handed over from the main contractor to the building owner. Second phase of the sampling take place after the building is equipped with furniture. Phase three sampling is conducted after one month of building occupancy. Airborne microbes’ concentrations were determined by using a single stage impactor (Biosampler) as per requirement of National Institute of Occupational Safety and Health (NIOSH) method, NIOSH Manual Analytical Method MAM 0800. The total concentration of airborne bacteria and fungi were average to 641 and 338 CFU/m³ in the first phase, 133 and 117 CFU/m³ in the second phase, and 389 and 52 CFU/m³ in the third phase. These findings indicate that although a new constructed building should be having a significant background level of airborne microbe (total bacteria and total fungi). The building owner should be aware to their indoor air status to protect the occupant from the safety and health problem (risk) especially for ventilated building.

1.0 INTRODUCTION

Exposure to indoor air pollution is now

becoming serious public health problem in a wide

variety of nonindustrial setting such as residences,

offices, schools, hospital and vehicles. Increasing concern regarding this issue is due to most people

spending their working time in indoor environment.

Among the indoor air pollutant identified, airborne microbe is one of the most contaminant that

addressing major issue in defining poor indoor air quality.

2.0 RELATED WORK

In Malaysia, comprehensive guidelines were

produce in order to provide guidance on improving the

indoor air quality (IAQ) and to set minimum standard

for selected parameter that will avoid discomfort and

adverse health effect among employees and other

occupants of indoor or enclosed environment served by

mechanical ventilating and air conditioning (MVAC)

system including cooled split unit. The selected

parameter include three thermal comfort parameter;

air temperature, relative humidity and air movement;

and eight common indoor environmental parameters

which been divided into two type of air contaminant.

Carbon Monoxide, Formaldehyde, Ozone, respirable

particulate and Total Volatile Organic Compounds

(TVOC) is classified as chemical contaminant, total bacteria counts and total fungi count is categorised as

biological contaminant (Department of Occupational

Safety and Health (DOSH), Industrial Code of

Practices for Indoor Air Quality ICOP-IAQ 2010).

One of the best parameter to evaluate poor indoor

environment quality is airborne microorganisim (L.T

Wong et al. 2006) a wide variety of microorganism

such as fungi (moulds, yeasts), bacteria, viruses, and

amoebae can be found in the indoor environment.

Contamination of indoor air with microorganisms

can occur under many circumstances. Such

contamination most often occurs when a fault in

the building that utilizing Heating Ventilation Air

Conditioning HVAC, or other system that allows the

germination of micro-organisms (Teija Meklin et al

2003 and T. Kalamees et al. 2009).


40

Table 1 Information on indoor environment sampled

Table 2 Airborne Bacteria and Fungi detected in Non Carpeted Office at Different Phases

However, this guideline was produce as reactive

limits for the place of works such as office building in dealing with the indoor pollutant exposure in indoor

environment. It is noted that people spend almost 80

to 90 percent of their time stay indoors (Hai-Qiao

Wang et al 2001). With the range of 10 000 to 30 000

litre of air breath by normal person, it is essential to

ensure that the air we breathe is clean for any pollutant

that may harm our health.

Location No of Sampling Point Phase 1 Phase 2 Phase 3 Office Non Carpeted 8 / / /Office Carpeted 8 / / /Classroom 12 / / /Classroom Corridor 6 / / /Total Sampling Point 34 x 3 phases = 102

Mean(SD) Min Max Mean(SD) Min Max

HQ ONC Phase 1 7 346 (285) 35 777 270 (185) 0 565

HQ ONC Phase 2 8 157 (113) 0 318 221 (272) 18 760

HQ ONC Phase 3 8 320 (154) 141 583 62 (49) 0 141

Sampling Location N Airborne Bacteria (CFU/m³) Airborne Fungi(CFU/m³)

3.0 METHODOLOGY

3.1 Sampling Location

A new constructed building was selected for this

study. Sampling location was selected in each level

according to the procedure specified in the Department of Occupational Safety and Health (DOSH),

Industrial Code of Practices for Indoor Air Quality

(ICOP-IAQ) 2010, recommended minimum number

of sampling points for indoor air quality assessment.

It was divided by three different phase of building

commissioning in Bandar Baru Bangi, Selangor. The

first phase of the sampling was carried out after the building fully handed over from the main contractor to

the building owner. Second phase of the sampling take

place after the building is equipped with furniture.

Phase three sampling is conducted after one month

of building occupancy. Table 1 below show the total

number of sample collected from each phase.


41

Table 3 Airborne Bacteria and Fungi detected in Carpeted Office at Different Phases


HQ ONC Phase 1 8 1530 (2284) 0 6360 433 (410) 0 1140

HQ OC Phase 2 8 73(52) 0 141 53(69) 0 212

HQ OC Phase 3 8 99(84) 0 242 12(13) 0 35


3.2 Sampling

Airborne microbes’ concentrations were

determined by using an Anderson single stage

impactor and operated at a flow rate of 28.3 L/min as per requirement of National Institute of Occupational

Safety and Health (NIOSH) method NIOSH Manual

Analytical Method NMAM 0800. The impactor

was located at the centre of the sampling location

at a height of 1.0 to 1.5 meter above the floor. The sample was obtained over 2 minute periods to prevent

overloading of the substrate. Airborne microbe was

collected on specific nutrient media in Petri-dishes placed on the impactor. Trypcase soy agar (TSA) was

used to sample airborne bacteria and Malt extract agar

(MEA) for airborne fungi. After sampling completed,

the agar plate was immediately seal and kept in the

disinfected cool box filled with ice pack to inhibit microbe growth. The entire sample collected was

delivered immediately to an accredited laboratory

which was Industrial Hygiene Analytical Laboratory

(IHAL), NIOSH Malaysia within 18 hour. The bacteria

sample was incubated at 37oC and counting was

done after 2 days. Counted microbe was calculated

as colonies forming units per cubic meter of air

(CFU/m³). The sample was analyzed for total fungi

count by incubating them at 25oC for 5 days.

4.0 RESULT AND DISCUSSION

Table 2 below show the average of total bacteria

and total fungi in non-carpeted office collected at different phases of building commissioning. The total

bacteria concentration in non-carpeted office for all the phase fell within the range of 0 to 777 CFU/m³.

The concentration of total fungi fell within the range

of 0 till 760 CFU/m³.

Total sample collected is 23 samples at all

three phases. It was observed that the average

concentration of airborne bacteria detected at all

three phases was significantly lower than maximum exposure limit 500 CFU/m³. The average

concentrations of airborne fungi were found far below

the maximum exposure limit 1000CFU/m³. This

indicate that the office setting without carpet yield low result of airborne microorganism furthermore reduce

the risk of unwanted exposure that can lead to poor

health condition.


42

Table 4 Airborne Bacteria and Fungi detected in Classroom at Different Phases

Table 5 Airborne Bacteria and Fungi detected in Classroom Corridor at Different Phases

Total sample collected is 24 at all three phases.

The mean value for total bacteria and total fungi at

the first phase of the sampling project is 1530 CFU/m3and 433 CFU/m³ respectively. It was found that

the concentration of total bacteria was significantly higher compare to maximum exposure limit

500 CFU/m³ as stipulated under ICOP IAQ 2010.

The airborne microbes were tremendously reduced

during the second and third phase of sampling. High

concentration of airborne microorganism in the

early phase of the sampling might be hazardous to

the building occupant thus might result increase in

building complaint related to health issues.

It was found that the average concentration of

airborne bacteria at the third phase was slightly higher

compare to maximum exposure limit 500 CFU/m³ as

stipulated in ICOP IAQ 2010. The concentration of

total fungi at each phase was found not much different

with a concentration of 159, 91 and 71 CFU/m³ at

each sampling phases.

Total sample collected from the classroom

corridor is 18 at all three phases. It was found that

the average concentration of airborne bacteria at the

third phase of sampling was slightly higher compare

to maximum exposure limit 500 CFU/m³ as stipulated

in ICOP IAQ 2010. The concentration of total fungi

at each phase was found not much different with a

concentration of 185, 53 and 133 CFU/m³ at each

sampling phases. Since the main function of this

building is to facilitate the training, thus the higher the

participant flow, might introduce higher concentration of airborne microorganism (J. Karbowska-Berent et

al. 2011).


HQ Class Phase 1 12 197 (201) 0 777 159 (90) 35 389

HQ Class Phase 2 12 158(166) 18 583 91(99) 0 336

HQ Class Phase 3 12 627(548) 106 1837 71(64) 0 230


HQ CorPhase 1 6 274 (113) 71 406 185 (76) 53 247

HQ CorPhase 2 6 377(602) 18 1590 53(56) 0 159

HQ CorPhase 3 6 591(537) 18 1590 133(127) 0 371




43

Table 6 Compliance result on total bacteria and total fungi concentration

Total bacteria detected in all sampling phases

yielded 5.9% of noncompliance result which was

above the maximum exposure limit. The maximum

bacteria concentration detected during the sampling

was 6360 CFU/m³. The result was obtained during

the first phase of sampling in the office equipped with carpet. For total fungi concentration, only 2.0% of

fungi concentration was above the maximum exposure

limit. The maximum fungi concentration detected

during the sampling was 1140CFU/m³. The result

was also obtained during the first phase of sampling in the office equipped with carpet. The microbe detected at all sampling phase’s yielded significant number of colony forming unit of bacteria and fungi

in the airborne, thus might lead to unhealthy indoor

environment. The risk become more severe especially

to those how involve in early stage of building

commissioning. Building owners should consider

the entire factor that might potentially introduce

high exposure of airborne microorganism in a new

building starting from the building design and the

construction processes. Furthermore the maintenance

of the building is crucial in order to avoid building

damage or water intrusion (F.Fung and W.G. Hughson

2010). Support from the building occupant is also

an important to ensure maximum protection against

any unwanted pathogenic or hazardous contaminants

(Leung M et al 2006).

5.0 CONCLUSION

In general, findings from sampling conducted at new constructed 8 stories buildings that consist of

office without carpet, office with carpet and classroom setting indicate that although a new constructed

building should be having a significant background level of airborne microbe (total bacteria and total

fungi). The reported microbe count vary within the

building levels area depending on the cleanliness

from the dust residual on that level, the outdoor and

indoor air movement either mechanical or natural and

type of flouring used whether carpet or not. Therefore, it is necessary to consider the establishment of

recommended value for acceptable indoor microbe

levels in a new constructed building. As illustrated in

this study, there is a significant figure in determining the background level of this new constructed building.

Airborne Microbe Frequency Per cent (%) StandardAirborne Bacteria (CFU/m³) Comply 96 94.1 500 (CFU/m³) Not Comply 6 5.9

Total 102 100.0

Airborne Fungi(CFU/m³) Comply 100 98.0 1000 (CFU/m³)

Not Comply 2 2.0

Total 102 100


44

6.0 REFERENCES

1. L.T. Wong, K.W Mui, P.S. Hui, W.Y. Chan and A.K.Y. Law (2008); Thermal Environmental Interference with Airborne bacteria and Fungi Level in Air-Conditioning Offices, Indoor and Built Environment, 17;2:2:122-127.

2. Ronald E.Gots, Nancy J. Layton and Suellen W. Pirages(2003): Indoor Health: Background Level of Fungi, AIHA Jurnal, 64:4, 427-438.

3. Teija Meklin, Anne Hyvaarinen, Mika Toivola, Tina Reponen, Virpi Koponen, Tuula Husman, Taina Taskinen, Matti Korppi and Aino Nevalainen (2003): Effect of Building Frame and Moisture Damage on Microbiological Indoor Air Quality in School Building, AIHA Jurnal, 64:1, 108-116.

4. Kate T.H. Durand, Michael L. Muilenberg, Harriet A. Burge and Noah S.Seixas (2001): Effect of Sampling Time on the Culturability of Airborne Fungi and Bacteria Sampled by Filteration, Oxford University Press.

5. Stephen J Reynolds, Donald W. Black, Stanley S. Borin, George Breuer, Leon F. Burmeister, Laurence J. Fuortes, Theodore F. Smith, Matthew A. Stein, P Subramaniam, Peter S. Thorne & Paul Whitten (2001): Indoor Environmental Quality in Six Commercial Office Buildings in the Midwest United States, Applied Occupational and Environmental Hygiene, 16:11, 1065-1077.

6. Department Safety and Health, Industrial Code of Practices for Indoor Air Quality 2010. 7. Miller, D.P. Haisley and H. Reinhardy (2000); Air sampling results in relation to extent of fungal colonization of building materials in some water damaged building, Indoor Air 10:146-151.

8. Danuta O.Lis, Krzysztof Ulfig, Agnieszka Wlazlo & Jozef S. Pastuszka (2004; Microbial Air Quality in Offices at Manucipal Landfills, Journal of Occupational and Environmental Hygiene, 1:2, 62-68.

9. Winjnand Eduarda & Disck Heederik (1998); Method for Quantitative Assessment of Airborne Levels of Non-infectious Microorganisms in Highly Contaminated Work Environment, American Industrial Hygiene Association Journal, 59:2, 113-127.

10. David L. Maclntosh, Howard S. Bringtman, Brian J.Baker, Theodore A. Myatt, James H.Stewart & John F. McCarthy (2006): Airborne

Fungal Spores in a Cross-Sectional Study of Office Building, Journal of Occupational and Environment Hygiene, 3:7, 379-389

11. Anne Korpi, Anna-Liisa Pasanen, and Pertti Pasanen (1998): Volatile Compounds Originating from Mixed Microbial Cultures on Building Materials under Various Humidity Condition, Applied and Environmental Microbiology, 2914-2929.

12. W. Stuart Dols, Andrew K. Persily, Steven J. Nabinger (1994): Development and Application of an Indoor Air Quality Commissioning Program in a New Office Building, Engineering Indoor Environment.

13. Martin S. Favero, John R. Puleo, James H. Marshall, & Gordon S. Oxborrow (1966): Comparative Levels and Types of Microbial Contamination Detected in Industrial Clean Rooms, Applied Microbiology, Vol 14, No. 4.

14. Kwok Wai Mui, Wai Yee Chan, Ling Tim Wong and Pui Shan Hui (2010): Scoping indoor airborne fungi in an excellent indoor air quality office building in Hong Kong, Building Services Engineering Research and Technology 31,2 191- 199.

15. Hai-Qiao Wang, Jin-Duan Chen and Hao Zhang (2001): Ventilation, Air Conditioning and the Indoor Air Environment, Indoor and Built Environment 10:52-57.

16. Joanna Karbowska-Berent, Rafal L. Gorny, Alicja B. Strzelczyk, Agnieszka Wlazlo (2011): Airborne and Dust borne microorganisms in selected Polish libraries and archives, Indoor and Built Environment 46:1872-1879.

17. Micheal Leung and Alan H.S. Chan (2006): Control and Management of Hospital indoor Air Quality, Med Sci Monit 12(3):SR17-23.

18. Targo Kalamess, Minna Korpi, Juha Vinha adn Jarek Kurnitski (2009): The effect of ventilation systems and building fabric on the stability of indoor temperature and humidity in Finnish detached houses, Building and Environment 44: 1643-1650.

19. Ki Youn Kim and Chi Nyon Kim (2007): Airborne microbiological characteristic in public buildings of Korea, Building and Environment 42:2188-2196.

20. L.T Wong, K.W. Mui and P.S. Hui (2006): A statistical model for characterizing common air pollutants in air-conditioned offices.


45

Data Comparison on Fumes Local Exhaust Ventilation: Examination and Testing Compliance to USECHH Regulation 2000

¹Nor Halim Hasan, ²Mohd Radzai Said, ³Abdul Mutalib Leman, 4B.Norerama D.Pagukuman and 5Jaafar Othman1,2Faculty of Mechanical, Universiti Teknikal Melaka, Malaysia

3,4Faculty of Engineering Technology, Universiti Tun Hussien Onn, Malaysia5LCMS Consultancy Sdn Bhd

ABSTRACT:

The paper focused on the examination and testing of local exhaust ventilation (LEV) systems at one of Electrical Company to check the transport velocity whether it meet the recommended American Governmental Industrial Hygienist (ACGIH) Standard. The industrial hygiene approaches, AREC (Anticipating, Recognize, Evaluate and Control) were adopted in this study. This is to ensure that the LEV system installed has the optimum efficiency to extract out the contaminants from the workstation. Objective of this study is to make comparison with previous and current monitoring data. The efficiency and the other parameter measured will be the main source to analyze for the particular applications. The differential of data was discussed and several recommendations are proposed to make sure the LEV system performance is excellent.

Keywords: Local Exhaust Ventilation (LEV), USECHH Regulation 2000, Contaminants, Occupational Safety and Health.

1. Introduction

Worker in Electronics Company are exposed to

hazardous chemical (chemical hazard) and physical

hazard. (Koh, Chan, & Yap, 2004). They are suggested

significant measures is the application of ventilation and enclosure systems where ineffective removal

of chemicals and recycling of air could result in its

stagnation and concentration. A regularly assess

levels of selected substances to ensure engineering

controls are implementation Workforce increases

in electronics industries and a requirement to the

management to recognize possible hazards, and to

implement appropriate control measures to workers

on occupational health effects.

Meanwhile study by (Bluff, 1997) found that

training on safety and health was less commonly

provided and for better control measures implemented

on personal protective measures and administrative

controls, rather than on measures which control

chemical exposures at source. Areas for improvement

in the management of hazardous chemicals were

identified and baseline information was obtained against which the impact of proposed regulatory

reforms to control workplace hazardous substances

can be evaluated.

A local ventilation design solution for the mould

casting area was designed (Kulmala, Hynynen, Welling,

& Saamanen, 2007) and dimensioned with the aid of

computational fluid dynamic (CFD) calculations. The prototype of the push–pull ventilation system was

built and tested in actual operation at the foundry. The

capture efficiency of the prototype was determined by the tracer gas method varied between 40 and 80%.

Push-pull ventilation design was developed as

an alternative. Tested in laboratory that the systems

capture efficiency was carried out using nitrous oxide tracer gas and capture efficiency was generally greater than 90% (Watson, Cain, Cowie, & Cherrie, 2001).

Without the push airflow, capture efficiency decreased sharply with increasing distance from the exhaust

hood (between 38 and 58% at 420 mm from the front

of the exhaust hood with the same exhaust airflow used by the push-pull system). Only a small amount of

soldering was carried out both the in-house and push-

pull systems in their study suggested that the in-house

systems were relatively inefficient.

46


Table 1: Range of Minimum Velocity

a. Compliance to Legislations

Compliance to the Use and Standard Exposure of Chemical Hazardous to Health (USECHH) Regulation (Dept. of Occupational Safety and Health Malaysia, 2006) is an approach to reduce and maintain the exposure level of employees to chemicals hazardous to health below the permissible exposure limits or to the lowest practicable level. Engineering Control Equipment (Regulations 2 of USECHH) means any equipment, which is used to control exposure of employees to chemicals hazardous to health and includes local exhaust ventilation equipment, water spray or any other airborne chemical removal and containment equipment. The equipment shall be maintained and operated at all times while any machinery or plant is in operation, and for such time (Regulation 17 of USECHH). Design, construction and commissioning of local exhaust ventilation equipment. Regulation 18 of USECHH: any local exhaust ventilation equipment installed shall be designed according to an approved standard by a registered professional engineer and constructed according to the design specifications; and tested by a registered professional engineer after construction and installation to demonstrate that the equipment meets the design specifications. Regulation 17(1)(b) of the USECHH Regulations related with the DOSH compliance monitoring.

b. ACGIH recommendation

American Conference of Governmental Industrial Hygienist (ACGIH) (American Conference of Govenrnmental Industrial Hygienists, 2009) on 23rd Edition used as a references to get the range of minimum velocity for capture velocity and face velocity as a baseline in this study to obtain compliance of these guidelines.

For low-activity radioactive laboratory work, a laboratory fume hood may be acceptable. For such hoods, an average face velocity of 0.4 -0.5 m/s is recommended (Section 3.7.2, ACGIH). When significant quantities of heat are transferred to the air above and around the process by conduction and convection, a thermal draft is created which causes an upward air current with air velocities as high as 2 m/s. (Section 3.9,ACGIH).

Objective of this study is to make comparison with previous and current monitoring data. The efficiency and the other parameter measured will be the main source to analyze for the particular applications. The differential of data was discussed and several recommendations are proposed to make sure the LEV system performance is excellent.

Nature of Contaminant Examples

Vapors, gases, smoke All vapors, gases, and smoke

Fumes Welding

Very fine light dust Cotton lint, wood flour, litho powder Dry dusts & powders Fine rubber dust, Bakelite molding powder dust, jute lint, cotton

dust, shavings (light), soap dust, leather shavings

Average industrial dust Grinding dust, buffing lint (dry), wool jute dust (shaker waste), coffee beans, shoe dust, granite dust, silica flour, general material handling, brick cutting, clay dust, foundry (general), limestone dust,

packaging and weighing asbestos dust in textile industries

Heavy dusts Sawdust (heavy and wet), metal turnings, foundry tumbling barrels

and shake-out, sand blast dust, wood blocks, hog waste, brass

turnings, cast iron boring dust, lead dust

Heavy or moist Lead dusts with small chips, moist cement dust, asbestos chunks

from transite pipe cutting machines, buffing lint (sticky), quick-lime dust

Source: Table 3-2, American Conference of Governmental Industrial Hygienist (ACGIH) - 23rd Edition.

Design Velocity

Any desired velocity

(Economic optimum

velocity usually 5-10

m/s)

10-13 m/s

12-15 m/s

15-20 m/s

18-20 m/s

20-23 m/s

23 m/s


47

c. Case study description

The periodic testing and evaluation of the LEV

system was conducted on 1st and 2nd November 2011.

Generally, the purposed of the testing was done

to obtain the actual airflow values of the existing systems to determine their performance. The testing

and evaluation were done at Local Exhaust Ventilation

System in the electronic plant.

The purposed of examination and testing of an

LEV System to identify the effectiveness of the LEV

as an engineering control measure so as to reduce

the exposure of employees to chemical hazardous to

health to below the permissible exposure limits or it

is at the lowest practicable exposure level. The others

purpose of examination and testing an LEV system is

to prepare a periodic data for comparing it with the

last monitoring data to determine the effectiveness of

the LEV system by a hygiene technician at appropriate

intervals of not more than 12 months after the last

periodic monitoring.

The diagram 1 and 2 of LEV System in the plant

show below are to remove welding fume. The number

indicated the data measurement taken from 1 until

number 52. Only 1 (one) enclosure fan is used and

apply to the system located at point 1.

2. Research Methodology

Preparation of a periodic data for comparison

with the baseline examination and testing data, by

the hygiene technician and check if the design is

according to an approved standard by a registered

professional engineer and constructed according to

the design specifications. At last check if a registered professional engineer has tested the LEV system after

construction and installation to demonstrate that the

equipment meets with the design specifications.

a. Apparatus

The equipment used in the course of this study,

namely as recommended in the ACGIH guidelines.

Explanations of the use of each instrument are

as follows. Airflow Meter is used for airflow measurements. Thermal Anemometer is used for

airflow and temperature. Smoke Tube is used for identifying the direction of airflow and duct leakages. Thermo hygrometer is used to measure temperature

and humidity. Tachometer is used for determining the

fan and motor speed (rpm).

Vane anemometer is used for airflow measurements. A measuring tape is used to measure

the length and distance. To cover-up the holes on the

duct, Adhesive Tape are used. Pitot tube is used for

pressure measurements. Clamp Meter to measure

current and voltage. Manometer is used for airflow measurements.

Table 2 : Range Of Capture Velocities

Source: Table 3-1, American Conference of Governmental Industrial Hygienist (ACGIH) - 23rd Edition.

Condition of Dispersion

of Contamination

Released with practically no velocity

into quite air.

Released at low velocity into moderately

still air.

Active generation into zone of rapid air

motion.

Released at high initial velocity into

zone at very rapid air motion.

Examples

Evaporation from tanks, degreasing, etc.

Spray booths; intermittent container air filling; low speed conveyor transfer; welding; plating; pickling.

Spray painting in shallow booths; barrel filling; conveyor loading; crusher

Grinding; abrasive blasting; tumbling.

Capture Velocity, m/s

0.25 - 0.50 m/s

0.5 - 1.0 m/s

1.0 - 2.5 m/s

2.5 - 10.0 m/s

48


Diagram 2: Sketch diagram for LEV system (No indicated the measurement position from no 15 to 26 and no 31 to 52)

Diagram 1: Sketch diagram for LEV system (No indicated the measurement position from no 1 to 14 and no 27 to 30)


49

Measured; 1.08

Standard; 0.5

0.000.200.400.600.801.001.201.401.60

Velo

city

Mea

sure

d (m

/s)

Face Velocity vs Location

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Hoo

d C

aptu

re V

eloc

ity (m

/s)

Capture Velocity vs. Location

Measured; 1.08

Standard; 0.5

0.000.200.400.600.801.001.201.401.60

Velo

city

Mea

sure

d (m

/s)

Face Velocity vs Location

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Hoo

d C

aptu

re V

eloc

ity (m

/s)

Capture Velocity vs. Location

b. Inspection and Testing

Pre-preparation

Identify or tracing the LEV systems in the plant according to the drawing and physical examination and the operating characteristic of the systems. A walk through survey is carried out to determine the number of points to be tested along the hood, ducting, suction fan and exhaust stack.

Hood Measurement and Monitoring

Observation was made around the hood with respect to the following factors such as the physical condition of the hood, type of hood installed and its suitability for the process and condition of the work area around the hood, e.g. accumulation of the dust, cross drought etc. Hood velocity measurement (face/capture velocity) was carried out using smoke tubes and anemometer (to determine the average flow rate of the hood). Hood static pressure measurement was carried out (where possible) using anemometer.

Duct Measurement and Monitoring

Observation was made along the ducting system with respect to the physical condition of the ducting, unnecessary losses along the systems and location of the dampers or blast gates. Location for static

pressure testing was determined for the purpose of baseline measurement. Static pressure measurement was conducted by using anemometer. Location for the velocity pressure testing was determined for the purpose of baseline/annual measurement. Velocity pressure measurement was conducted by using anemometer.

Traverse measurement (to measure velocity pressure and air velocity) was conducted on the identified points by using pitot tube and anemometer.

Air Cleaner Measurement and Monitoring

Inlet and outlet static pressure of the air cleaners were determined by using pitot tube and anemometer.

Fan Measurement and Monitoring

Inlet and outlet static/velocity pressure (and the exhaust flow rate) of the fan by using pitot tube and anemometer. Non-contact tachometer was used to determine RPM of the motor.

Point Static Pressure (Sp) Velo city Pressure (Vp) Flow rate m( )aP( )aP( 3 / hr.) cfm

88.2107 46.42911 73.46 165 - telnI 04.7885 88.01001 83.54 653 teltuO

FSP = SPOUT – SPIN - VPIN (1) = 356 – 561 – 64 = 853 Pa = 3.42 in wg

TP = FSP + VPOUT (2) = 853 + 45.38 = 898 Pa = 3.60 in wg

(3)

Where ME = 0.65

Result measured; 10.58

Minimum Std; 10

Maximum Std; 13 Ve

loci

ty M

easu

red

(m/s

) Ducting Velocity vs. Location

50


Table 3: Static Pressure (Sp), Velocity Pressure (Vp) and Flow rate at fan.

All measurement for face velocity at working area are above std setting in ACGIH.Average of ducting velocity is 10.11m/s.Measurement shown in graph is fluctuate but over standard setting at range 10 m/s to 13 m/s.

FanThe static pressure, velocity pressure and flowrate are measure for inlet and outlet position at fan for Local Exhaust Ventilation System at plant the table below shown the data taken.

Table 3: Static Pressure (Sp), Velocity Pressure (Vp) and Flow rate at fan. Point StaticPressure (Sp) VelocityPressure (Vp) Flow rate

(Pa) (Pa) (m3 / hr.) cfm Inlet - 561 64.37 11924.64 7012.88

Outlet 356 45.38 10010.88 5887.40 Calculation of Fan Static Pressure (FSP) and Total Pressure are shown as formula (American Conference of Govenrnmental Industrial Hygienists, 2009)below:



Calculation of Brake Horse Power (BHP) shown as formula below: (3)

Where ME = 0.65

Summarize of data are shown in table 4. From the calculation above the data are available only for measurement only. No data are available for design and can make the comparisons and to determine the performance of fan after operate for certain time. Unable to measured the RPM because the fan and motor was built in line of ducting (enclosed type)

Result measured, 10.58

Minimum Std, 10

Maximum Std, 13

Vel

ocity

Mea

sure

d (m

/s)

Ducting Velocity vs. Location

3. Results

Current measurement against ACGIH Standard

This section will be discussed in relation to the measured data for Local Exhaust Ventilation (LEV) system and comparison with American of Governmental Industrial Hygienists (ACGIH)

Hood (Face Velocity and Capture Velocity)

Measurements for face velocity are taken at all point of workstation shown in diagram 1 and diagram 2.

All measurement for face velocity at working area are above std setting in ACGIH. Average of hood face velocity is 1.06 m/s. 3 type of hood size measured i.e. size 813x356mm at location 10/11, size 813x533mm at location 39/40/44/45 and other location size are 150x100mm. Measurement shown in graph is fluctuate but over standard setting at min 0.5m/s.

Trend of graph for capture velocity almost the same with face velocity. Average of capture velocity is 0.81m/s. Measurement shown in graph is fluctuate but over standard setting at min 0.5m/s.

Ducting

Using the thermal Anemometer to measure direct reading of velocity at position shown in Diagram 1 and Diagram 2 for the whole system of Local Exhaust Ventilation at the plant.

All measurement for face velocity at working area are above std setting in ACGIH. Average of ducting velocity is 10.11 m/s. Measurement shown in graph is fluctuate but over standard setting at range 10 m/s to 13 m/s.

Fan

The static pressure, velocity pressure and flowrate are measure for inlet and outlet position at fan for Local Exhaust Ventilation System at plant the table below shown the data taken.

Calculation of Fan Static Pressure (FSP) and Total Pressure are shown as formula (American Conference of Govenrnmental Industrial Hygienists, 2009) below:

Point Static Pressure (Sp) Velocity Pressure (Vp) Flow Rate

(Pa) (Pa) (m³ / hr.) cfmInlet - 561 64.37 11924.64 7012.88

Outlet 356 45.38 10010.88 5887.40

Description RPM FSP (in wg)

TP (in wg) BHP Flow Rate (m3 / hrs.)

Design NA NA NA NA NA Test NA 3.42 3.60 6.1 11925 Difference % NA NA NA NA NA NOTE: NA = NOT AVAILABLE

2008; 2.97

2011; 10.58

MINIMUM STD; 10

MAXIMUM STD; 13

Velo

city

(m/s

)

Velocity Measurement at location of LEV and comparison with standard and years


51

All measurement for face velocity at working area are above std setting in ACGIH.Average of ducting velocity is 10.11m/s.Measurement shown in graph is fluctuate but over standard setting at range 10 m/s to 13 m/s.

FanThe static pressure, velocity pressure and flowrate are measure for inlet and outlet position at fan for Local Exhaust Ventilation System at plant the table below shown the data taken.

Table 3: Static Pressure (Sp), Velocity Pressure (Vp) and Flow rate at fan. Point StaticPressure (Sp) VelocityPressure (Vp) Flow rate

(Pa) (Pa) (m3 / hr.) cfm Inlet - 561 64.37 11924.64 7012.88

Outlet 356 45.38 10010.88 5887.40 Calculation of Fan Static Pressure (FSP) and Total Pressure are shown as formula (American Conference of Govenrnmental Industrial Hygienists, 2009)below:



Calculation of Brake Horse Power (BHP) shown as formula below: (3)

Where ME = 0.65


Result measured, 10.58

Minimum Std, 10

Maximum Std, 13

Vel

ocity

Mea

sure

d (m

/s)

Ducting Velocity vs. Location

Calculation of Brake Horse Power (BHP) shown as formula below:


Comparison measurement current and previous

Data were comparing for the performance of the local exhaust ventilation system of the velocity of difference years i.e. 2008, 2009 and 2011.

Measurements are taken at location of working area from position 5 to 52. Data measured are comparing for ducting velocity shown for the year 2008, 2009 and 2011. Standard are used refer to ACGIH on fume. As a result velocity data taken for the year 2008 are below the Standard requirement. Improvement of the Local Exhaust Ventilation system are made by the management and the result measured in the year

2009 shows the increment compare previous years and position between the standard of ACGIH. In the year of 2011 the result taken slightly lower compare to 2009 but the system of LEV are between the standard settings.

4. Recommendations

Improvement on velocity is to maintain for better performance of Local Exhaust Ventilation system. For those data are above and within standard recommended to maintain transport velocity to the existing system by having a periodical inspection and any further tapping / connecting need to recalculate the efficiency on the respective systems.

It is also recommended to the management to conduct schedule inspection and maintenance to improve or maintain the overall exhaust ventilation system required under USECHH Regulation 2000. Generally the low transport velocity and pressure losses could be due to several factors such as; clog in the ducting system, duct friction losses, duct losses in elbows, contraction, expansion, and orifice, entry losses in branch entries or cleaner entries, hood entry losses due to turbulence, shock losses and vena contract. Special fitting losses such as blast gates, valves, orifices, and exhaust cap and exhaust stack losses.

Description RPM FSP TP BHP Flow Rate

(in wg) (in wg) (m3 / hrs.)

Design NA NA NA NA NA

Test NA 3.42 3.60 6.1 11925

Difference % NA NA NA NA NA

NOTE: NA = NOT AVAILABLE

52


Photo 1: Too many bent along flexible ducting (may cause many loses).

Photo 2: Flexible ducting was dented.

Computerized Fluid Dynamic (CFD) software is recommended for future work to verify and validate the data from measurement and compare with simulations.

5. Conclusions

The airflow measurements, visual assessment and other tests conducted, the overall performance of the local exhaust ventilation System was found to be satisfactory. At this present working condition, the system was effective to remove chemical contaminants from workplace. Therefore, the workplace was clean and safe for the workers to work for longer hours without any serious exposure to the chemical hazardous to health.

The airflow performance of the LEV system was quite lower as compared to the last monitoring but still within the range of standard of ACGIH. The management is advised to practice the following steps to maintain/ improve the systems performance. The LEV system shall be serviced regularly to maintain / improve its performance. Maintenance and servicing schedules should be followed regularly to maintain the performance and detect early sign of deterioration of the systems. Yearly evaluation of LEV system by

any DOSH Registered Hygiene Technician and the management must kept the LEV report for 5 years for any further action.

As a result with both fan measured and compare with previous data and design calculation show that LEV system performance are good and remove the contaminant at workplace. Measurement and monitoring showed that the LEV system are comply with both regulation enforced by the Department of Occupational Safety and Health Malaysia such as on engineering control equipment, design, construction and commissioning of local exhaust ventilation equipment and records of engineering control equipment USECHH Regulation 2000 and followed according to the American Conference of Governmental Industrial Hygienists (ACGIH) Guidelines.

Some defect observed during inspection and observation and possible lead to performance of local exhaust ventilation system at plant. The flexible ducting hose found too many bent (photo 1) and flexible duct dented (photo 2). Improve of the ducting are recommended to improve the airflow and velocity for better capture of fume at workstation.


53

6. Acknowledgement

The authors would like to acknowledge the following organizations and individual for their contributions and supports: Government of Malaysia, Department of Public Services, Department of Occupational Safety and Health Malaysia. Faculty of Mechanical Engineering, University of Technical Malaysia, Melaka. University Tun Hussien Onn, Malaysia. Also to LCMS assist in providing information and co-operation on this study.

7. References

American Conference of Govenrnmental Industrial Hygienists. (2009). Industrial Ventiltion: A Manual of Recommended Practice for Operation and Maintenance. Cincinnati: ACGIH.

Bluff, E. (1997). The Use and Management of Hazardous Chemicals in South Australian Workplaces. Safety Science, 25 (1-3), 123-136.

Dept. of Occupational Safety and Health Malaysia. (2006). Occupational Safety and Health (Use and Standards of Exposure of Chemicals Hazadrous to Health, Regulation 2000.) Kuala Lumpur: MDC.

Koh, D., Chan, G., & Yap, E. (2004). World at work: The electronics industry. Occupational Environment Medicine, 61, 180-183.

Kulmala, I., Hynynen, P., Welling, I., & Saamanen, A. (2007). Local Ventilation Solution for Large, Warm Emission Sources. Annal Occupational Hygiene, 51 (1), 35-43.

Watson, S., Cain, J., Cowie, H., & Cherrie, J. (2001). Development of a Push-pull Ventilation System to Control Solder Fume. Annal Occupatioanl Hygiene, 45 (8), 669-676.


55

Exposure to PM2.5 and Respiratory Health Among Traffic Policemen in Kuala Lumpur

Ahmad Syazrin Muhammad, Juliana Jalaludin and NurAqilah M. Yusof

Environmental and Occupational Health, Community Health Department

Faculty of Medicine and Health Sciences, Universiti Putra Malaysia

ABSTRACT:

Exposure to traffic air pollutant have shown a significant health effect on respiratory systems and decreased in lung function among traffic policemen. The main objective of this study was to determine the relationships between personal exposure levels to PM2.5 and respiratory health among traffic policemen working at Traffic Police Station in Kuala Lumpur and general duty policemen attached to Police Headquarters, Bukit Aman. A cross sectional comparative

study was conducted among 50 traffic policemen from Traffic Police Station Kuala Lumpur and 50 general duty policemen from Police Headquarters Bukit Aman as comparative group. A purposive sampling method was used to select

the respondents based on inclusive criteria such as age between 25 to 60 years, no history of respiratory disease and

working not less than 3 years as traffic policemen. Questionnaire based on ATS (1978) was used to collect information on socio-demographic and respiratory symptoms. Spirometer (Spirolab II Model) was used to perform lung function tests.

Personal Air Sampling Pump (Aircheck 52) was used to measure personal exposure level to PM2.5. The mean exposure

level of PM2.5 among the traffic policemen was 22.33 ± 8.54µg/m³ compared to only 5.60 ± 4.29µg/m³ for comparative group. There was a significant difference in all lung function parameters between the exposed group and comparative group.From the finding, it shows that there was significant relationship between working duration (years) and lung function parameters among both exposed and comparative group. The result from this research shows that traffic policemen were determined as having lower lung function parameters due to their nature of work and the environment. Also, there was

a significant association between exposure to fine particle (PM2.5) and lung function among the exposed group. Finding

from this study indicated that exposure to elevated concentration level to traffic related air pollutant was the risk factors in the development of respiratory diseases as shown by the higher prevalence of respiratory symptoms and the reduction

in lung function among traffic policemen.

Keyword: fine particle (PM2.5);lung function; respiratory symptoms; traffic air pollutant


56

INTRODUCTION

Nowadays, most of air pollutants came from

vehicle emissions. According to Lioy & Zhang (1999),

air pollution occurs because of physical, chemical and

dynamic process thatultimately lead to the gases or

particles being emitted by a source and then being

accumulated in the atmosphere. The movement of air

pollutant in the atmosphere was governed by the wind

speed and direction, temperature and topographic

factors. One of the reasons that air pollution was

such a threat to human health was that human had no

choice over the air that being breathe as compared to

other needs of life such as food and water. According

to WHO (2000) and EPA (2006), motor vehicle

contributed, by far the largest amounts of air pollution

in many big cities of development countries. This

was partly because of the relatively high densities of

the road networks that have been built in the cities of

developing countries and also because of the increasing

population in the cities and increase of numbers of

motor vehicle ownership. These developments have

resulted in rapid deteriorating air quality in many

big cities. Data obtained from the Department of

Environment, Malaysia (2000) showed that from 1995

to 1999, motor vehicle have contributed approximately

from 73% to 84% of the population loads in Malaysia.

According to Anne Maître et al, (2002), respirable

particles levels of the policemen in the study were

higher in particulate concentrations reported for traffic policemen when total dust was collected. Based on

the United Stated Environment Protection Agency

(USEPA), National Ambient Air Quality Standard

(NAAQS) concerned about the fine particle that being transmitted by vehicle emission. So, they established

the standard for fine particle (PM2.5) in 1997 and

being revised in 2006. Based on the standard, daily

exposure of PM2.5 was 65 microgram per meter cube

(65µg/m³) to 35 microgram per meter cube (35µg/m³) and retained the current annual fine particle standard at 15 microgram per meter cube (15µg/m³). The short-term standard (24 hours / daily average) was 35

microgram per meter cube (35 µg/m³) while the long-term standard (annual average) was 15 microgram per

meter cube (15 µg/m³) (EPA, 2010).

The present study aimed to determine the

relationship between personal exposure level to traffic air pollutant (PM2.5) and respiratory health among

traffic policemen working in Kuala Lumpur area and general duty policemen attached to Police

Headquarter Bukit Aman. By assisting in regulating

traffic flow as their routine services, traffic policemen were considered to be among the high risk group

that exposed to traffic air pollutant. In addition, this study was important to evaluate lung function (FVC

and FEV1) and respiratory symptoms among traffic policemen, particularly on the effects of the exposure

towards fine particle (PM2.5). The consequences

of impairment or reduction of lung function and

respiratory health problems can affect their services

in term of lost in working days, reduced productivity

of working quality, increased in health cost as well

as other socio-demographic aspects of their life.

Findings of the study were important to assess lung

function and respiratory health problems among the

traffic policemen and also their personal exposure level of the fine particle (PM2.5). From the findings, it might be useful for the management to take actions

in order to minimize exposure to traffic air pollutant such as reducing exposure duration and applying

suitable respiratory protective equipment. Health

education should also be provided to alarm the

importance of respiratory health among subject

concerned.


57

METHODOLOGY

Respondents were interviewed by using

questionnaires develop based on ATS (1978) to

obtain information on socio-demographic

characteristics. Information obtained included history

of health, disease or illness, smoking habits and

acute and chronic respiratory symptoms namely

chronic cough, chronic phlegm, chest tightness and

wheezing. Then, lung function test was run using

Spirometer to determine lung abnormalities of the

respondents. The lung function parameters consisted

of Forced Vital Capacity (FVC), Forced Expiratory

Volume in 1 second (FVC1) and Forced Capacity Ratio

(FVC/FEV1). Then, concentrations of fine particle (PM2.5) were measured using Personal Air Sampling

Pump (Air Check 52) in order to obtained personal

exposure level of PM2.5. The method for collecting air

samples was based on NIOSH Manual of Analytical

Methods (NMAM), Fourth edition (Method 0600).

The duration of air sampling was 4 hours.

Data collected were analyzed using SPSS

(Statistical Package for Social Science)version

18. Descriptive analysis was conducted to analyze

descriptive variables from socio-demographic

information including age, duration of exposure,

body mass index, prevalence of respiratory symptoms

and lung function parameters. Continuous data

were presented in mean (SD). Bivariate analysis

was performed to determine association between

study variables and concentration of exposure

level to fine particle (PM2.5). Then, Kolmogorov-

Smirnov test was used to test normality for all

continuous variables and T-test was used to

make comparisons of mean difference for all

quantitative study variables. Chi-square test was used

to compare the prevalence of respiratory symptoms

between traffic policemen and comparative group. Spearman Rho test was performed to determine the

relationship between exposure levels, duration of

exposure to air pollutant and lung function among the

respondents.

RESULTS

Background Information

50 traffic policemen were recruited as exposed group while 50 general duty policemen were chosen for

comparative group giving total number of respondents

of 100. Routine task of traffic policemen which was assisting in regulating traffic flow started as early 6.00 a.m.in the morning as it was the time of people started

going to work and also during office lunch hour at 12.00 p.m. Each session took about 4 hours of duty at

the highly congested road. Comparative group were

administrative workers from the Polis Diraja Malaysia

(PDRM) at Bukit Aman, Kuala Lumpur who did not

involved in regulating traffic flow.

Referring to Table 1, number of male respondents

in exposed group was 42 (84%) while 8 respondents

(16%) were female. While in comparative group,

male respondents comprised of 46 (92%) while

female respondents were 4 (8%) persons. Ages were

categorized into four groups. From exposed group, 27

(54%) persons were aged from 20 to 30 years old. 15

(30%) respondents were aged between 31 to 40 years

old. Next, 5 (10%) were in age range of 41 to 50 years

old. There were 3(6%) respondents who aged more

than 50 years old.

As for comparative group, there were 19 (38%)

of respondents who aged in range of 20 to 30 years

old. There was a same number of respondents who

in age category of 31 to 40 years old and 41 to 50

years old which were 11 (22%) while 9 (18%) were

more than 50 years old. Most of the respondents were

Malay comprised of 50 (100%) from comparative

group and 47 (94%) persons from exposed group. The

rest of the respondents were non Malay which were 3

(6%) persons. As for education level, it shows that 30

(60%) of the respondents in comparative group had

degree and remaining of 15 (30%) persons finished their A-Level As for O-Level levels for comparative

group were about 5 (10%) persons. From exposed

group, about 12 (24%) number of respondent had their

A-Level, 37 (74%) persons had A-Level and 1 (2%)

person had PMR.

Table 1: Demographic data of the respondents

puorG ydutS Frequency (%)

Variables Exposed Group (n=50)

Comparative Group(n=50)

Total(n=100)

Gender Male 42 (84) 46 (92) 88 Female 8 (16) 4 (8) 12 Age 20-30 27 (54) 19 (38) 46 31-40 15 (30) 11 (22) 26 41-50 5 (10) 11 (22) 16

21 )81( 9 )6( 3 05>Race Malay 47 (94) 50 (100) 97

2 )4( 2 esenihC 1 )2( 1 naidnI

N=100

Table 2: Fine particles (PM2.5) concentration exposure among respondents

Study Group

Exposed Group (n=50)

Comparative Group (n=50)

Variable

Mean ± SD Range Mean ± SD Range

Z value p value

Fine Particle concentration( g/m3)

22.33 ± 8.54 3.92 – 40.44

5.60 ± 4.29 0.00 – 20.83

-7.760 <0.001

N = 100 Mann Whitney U test *Significant at p 0.05


puorG ydutS Frequency (%)

Variables Exposed Group (n=50)

Comparative Group(n=50)

Total(n=100)

Gender Male 42 (84) 46 (92) 88 Female 8 (16) 4 (8) 12 Age 20-30 27 (54) 19 (38) 46 31-40 15 (30) 11 (22) 26 41-50 5 (10) 11 (22) 16

21 )81( 9 )6( 3 05>Race Malay 47 (94) 50 (100) 97

2 )4( 2 esenihC 1 )2( 1 naidnI

N=100


Study Group

Exposed Group (n=50)

Comparative Group (n=50)

Variable

Mean ± SD Range Mean ± SD Range

Z value p value

Fine Particle concentration( g/m3)

22.33 ± 8.54 3.92 – 40.44

5.60 ± 4.29 0.00 – 20.83

-7.760 <0.001



58



Analysis was conducted based on the second

study objective. Table 2 shows mean of fine particle concentration in both groups. Mean ± SD of fine particle concentration for exposed group was 22.33

± 8.54µg/m³ while for the comparative group was 5.60 ± 4.29µg/m³. Mean concentration for exposed group was higher than comparative group. There was

a significant difference in the PM2.5 concentration

between exposed and comparative group.

Table 3 shows respiratory symptoms among

exposed and comparative group. In exposed group,

the highest symptom reported was phlegm (24%)

followed by cough (18%), chest tightness (10%)

and wheezing (10%). While in comparative group,

the highest symptom reported was cough (14%)

which the percentage was lower than in exposed

group. Followed by chest tightness (12%) which had

higher percentage than exposed group, phlegm (10%)

has lower percentage than in exposed group and

wheezing (4%) which also had lower percentage than

exposed group. There was no significance difference between all respiratory symptoms in exposed and

comparative groups.


59

Comparison of Lung Function

Level among Respondents

Referring to Table 4, mean± SD for FVC (litre)

was 4.14 ± 0.88 and mean± SD for FEV1 (litre) was

3.46 ± 0.73 in exposed group. In comparative group,

the mean ± SDfor FVC (litre) was 4.51 ± 0.72 and

the mean ± SDfor FEV1 (litre) was 3.79 ± 0.60. For

FVC% predicted and FEV1% predicted, the mean ±

SD was 76.92 ± 13.40 and 68.26 ± 12.92 in exposed

group respectively while in comparative group was

83.49 ± 10.35 and 74.40 ± 10.15 respectively. There

was a significant difference in all lung function parameters which were FVC (litre) with t = - 2.272, p

< 0.05, FEV1 (litre) with t = -2.469, p < 0.05, FVC%

predicted with t = -2.046, p < 0.05, FEV1% predicted

with t = -2.213, p < 0.05 and FEV1/FVC % predicted

with t = -2.365, p < 0.05 between the exposed group

and comparative group.

Comparison of Lung Function

Status among Respondents

From the result, it shows that 24 of the

respondents (48%) from exposed group and 14 (28%)

from comparative group werehaving abnormal lung

function status while 26 (52%) from exposed group

and 36 (72%) from comparative group were having

normal FVC% predicted. For FEV1% predicted,

39 (78%) from exposed group and 34 (68%) from

comparative group were having abnormal lung

function status while 11 (22%) from exposed group

and 16 (32%) from comparative group were having

normal lung function status. There wasa significant difference in FVC% predicted among exposed and

comparative group.

Relationship between Exposure Level of Fine

Particles (PM2.5) and Lung Function Parameters

among Respondents

The relationship between personal exposure

levels to fine particles and lung function parameters (FVC% predicted, FEV1% predicted and FEV1/

FVC % predicted) in the study was determined by

using Spearman Rho correlation test. Based on

the result in Table 5, there were no significant relationships in fine particles exposure level and lung function status between both exposed and comparative

group.

Relationship between Working Duration (years) and

Lung Function Parameters among Respondents

In order to determine the relationship between

working duration (years) and lung function

parameters (FVC, FEV1, FVC% predicted, FEV1%

predicted and FEV1/FVC % predicted) in the study

group, statistical analysis of Spearman Rho test was

performed. From the finding, it shows that there were significant relationships between working duration (years) and the lung function parameters in both

exposed and comparative group.


60

Table 3: Comparison of respiratory symptoms among respondents


Study Group Frequency (%)

Variables ExposedGroup(n=50)

ComparativeGroup(n=50)

2 pvalue

OR(95% CI)

CoughYes 9 (18) 7 (14) No 41 (82) 43 (86)

0.298 0.585 1.438(0.460 – 3.956)

PhlegmYes 12 (24) 5 (10) No 38 (76) 45 (90)

3.473 0.62 2.842(0.919 – 8.790)

ChestTightness

Yes 5 (10) 6 (12) No 45 (90) 44 (88)

0.102 0.749 0.812(0.232 – 2.865)

WheezingYes 5 (10) 2 (4) No 45 (90) 48 (96)

1.382 0.240 2.667(0.492 – 14.445)


Table 4: Comparison of lung function level among respondents

Study Groups Mean ± SD

VariablesExposedGroup(n=50)


t/zvalue

pvalue

FVC (litre) 4.14 ± 0.88 4.51 ± 0.72 -2.272 0.025* FEV1 (litre) 3.46 ± 0.73 3.79 ± 0.60 -2.469 0.015* FVC% predicted 76.92 ± 13.40 83.49 ± 10.35 -2.046 0.016* FEV1% predicted 68.26 ± 12.92 74.40 ± 10.15 -2.213 0.027* FEV1/FVC % predicted

88.54 ± 4.32 88.96 ± 1.88 -2.365 0.018*

Statistic Mann Whitney U test *Significant at p 0.05

DISCUSSION

Fine particle was one of the airborne particulate matters (PM) and was a complex chemical mixture of extremely small solid particles and liquid droplets that can derive from both natural and anthropogenic sources (USEPA, 2008). Result from this study shows that traffic policemen were determined having lower lung function parameters compared to comparative group due to the working environment which was constantly exposed to traffic air pollutant.

Traffic policemen working at Traffic Police Station Kuala Lumpur were exposed to 4 times higher concentration level of PM2.5 (22.33 ± 8.54µg/m³) compared to only 5.60 ± 4.29µg/m3among comparative group. Result from statistical analysis showed that there was a significant difference (p< 0.001) in the concentration level of PM2.5 between the two groups. The PM2.5 concentration level ranged from 3.92 - 40.44µg/m³ and 0.00 - 20.83 µg/m3for the exposed and the comparative group respectively. Unfortunately in this study, concentration level of fine particle (PM2.5) cannot be compared with OSHA or other work standard for Time Weighted Average (TWA) for exposure level because there was no specific standard for PM2.5. Instead, the mean value of PM2.5 recorded in this study did not exceeding the National Ambient Air Quality Standard (NAAQS)

by US Environmental Protection Agency which was 35µg/m³ for 24 hours.

The mean value of PM2.5 recorded among traffic policeman in this present study was lower compared to mean concentration (31.39 ± 14.81µg/m³) recorded in the study conducted by Fairuz (2010) among road side hawker in Kelantan, Malaysia. In another study conducted by Mukram (2010) among postmen in Kuala Lumpur, the mean value (32.29± 5.70µg/m³) recorded was higher compared to this present study. Besides that, study conducted by Cao et al., (2006) at roadside microenvironment in Hong Kong, China recorded also higher mean concentration (41.73 ± 12.63µg/m³) compared to this study. In another study conducted by Kanaeet al., (2001), traffic policemen who work in busy roads in Bangkok were exposed to continuously higher level (>100 µg/m³) of PM2.5 concentration compared to this present study.

Lung function test were conducted for both exposed and comparative groups using Spirometer Chestgraph based on the standard from American Thoracic Society ATS (2005). From the findings, values of FVC and FEV1 have been calculated to get the value of FVC% predicted and FEV1% predicted. Table 3 shows prevalence of respiratory symptoms of exposed and comparative group. The results shows that phlegm was symptom most reported in exposed


61

group while in comparative group, cough symptom was most reported by the respondents. Based on the result, there was no significance relationship between respiratory symptoms between the respondents. To protect our respiratory system, human lung was equipped with several types of defense mechanism in order to prevent foreign substance such as particulate matters from invading the lung. Based on the mechanistic understanding of non-genotoxic health effects induced by particles, the existence of a threshold because of these defenses mechanism was biologically plausible. However, the effectiveness towards the defense mechanism was difference between individual. Therefore, adverse effects may be limited at low pollution levels in sensitive subgroup. Individuals may have threshold for specific responses, but they may difference within population due to inter-individual differences in sensitivity. According to WHO (2004), it was not clear which susceptibility characteristics from a toxicological point of view were the most important although it has been shown that there were large difference in antioxidant defenses in lung lining fluid between healthy subjects. From the study result, there was a possibility that some of the respondent who experienced adverse respiratory symptoms, avoiding any unfavorable effects on the employment prospect made the respiratory symptoms among the exposed group and comparative group did not have significant relationships.

Referring to Table 4, the result shows that there was a significant reduction in all lung function parameters among exposed group compared to the comparative group (p<0.05). This was due to the fact that exposed group was directly exposed to fine particle (PM2.5) while regulating traffic flow. From Table 5, result of lung function status shows that abnormalities in FVC% predicted were significantly higher in exposed group compared to the comparative group (p<0.05). Result of this study were consistent with the study perform by Kanaeet al., (2001) among traffic policemen in busy roads in Bangkok. The FEV1 was significantly lower in subjects who exposed to the more polluted working environment as compared to the other less polluted working environment. In another study conducted by Kumar et al., (2000) among urban population of Hyderabad city in India also shows similar finding with this study. He found that the percentage predicted of FVC was significantly lower (p<0.005) among the subjects from commercial areas with massive traffics compared to the residential areas. Their study revealed that the percentage of prevalence abnormalities of FVC was 29.7% and FEV1 was 35.9% among the subjects from the commercial areas compared to lower percentage from the residential areas which were 12.2% for FVC and 10.8% for FEV1.


Study Group Frequency (%)

Variables ExposedGroup(n=50)


2 pvalue

OR(95% CI)

CoughYes 9 (18) 7 (14) No 41 (82) 43 (86)

0.298 0.585 1.438(0.460 – 3.956)

PhlegmYes 12 (24) 5 (10) No 38 (76) 45 (90)

3.473 0.62 2.842(0.919 – 8.790)

ChestTightness

Yes 5 (10) 6 (12) No 45 (90) 44 (88)

0.102 0.749 0.812(0.232 – 2.865)

WheezingYes 5 (10) 2 (4) No 45 (90) 48 (96)

1.382 0.240 2.667(0.492 – 14.445)



Study Groups Mean ± SD

VariablesExposedGroup(n=50)


t/zvalue

pvalue

FVC (litre) 4.14 ± 0.88 4.51 ± 0.72 -2.272 0.025* FEV1 (litre) 3.46 ± 0.73 3.79 ± 0.60 -2.469 0.015* FVC% predicted 76.92 ± 13.40 83.49 ± 10.35 -2.046 0.016* FEV1% predicted 68.26 ± 12.92 74.40 ± 10.15 -2.213 0.027* FEV1/FVC % predicted

88.54 ± 4.32 88.96 ± 1.88 -2.365 0.018*

Statistic Mann Whitney U test *Significant at p 0.05



62

Table 5: Comparison of lung function status among respondents

Study Groups Frequencies (%)

Variables StatusExposedGroup(n=50)


2p

value

FVC%predicted

Abnormal 24(48) 14(28) 5.191 0.023*

Normal 26(52) 36(72)

FEV1%predicted

Abnormal 39(78) 34(68) 1.268 0.260

Normal 11(22) 16(32)

N=100*Significant at p 0.05

Table 6: Correlation between exposure of fine particles (PM 2.5) and the lung function parameters among respondents

( noitartnecnoc selcitraP eniF g/m3) puorG desopxE

(n=50)Comparative Group

(n=50)Variables r

valuep

valuer

valuep

valueFVC (litre) -0.077 0.594 0.08 0.958 FVC% predicted -0.099 0.495 0.061 0.672 FEV1 (litre) -0.063 0.662 -0.048 0.743 FEV1%predicted

-0.117 0.440 0.073 0.612

FEV1/FVC%predicted

-0.033 0.818 -0.054 0.710

N=100


Based on Table 6, there was no significant relationship between fine particles exposure and lung function in both exposed and comparative group. In a study conducted in Hong Kong where comparison was made the Non-air conditioned bus (NACB) drivers and Air-conditioned bus (ACB) drivers. From this study, it was found that there was a significance difference in lung function status, FVC and FEV1 between both groups. It also shows that there was a relationship between lung function and concentration of fine particles (PM2.5). Besides that, in study by Alice et al.(2008), the lung function status, FVC and FEV1/FVC % had a significant difference between the exposed group (roadside vendor) and comparative group (university personnel).

Result shows that there was a significant association between working duration (years) and the lung function among all the respondents as shows in Table 7. The study by Sopanet al., (2005) stated that, between the two groups of traffic policemen, those who work more than 10 years were reported having higher (66%) respiratory disease than those who worked less than 10 years (33%). In addition, among 60 policemen, 40% of the traffic policemen were suffering from frequent coughing, 10% from shortness of breath and 29% from irritation in respiratory tract. The data on the length of service shows that 67% of the traffic policemen were in traffic service for more than 10 years. The long term exposure to pollution may be a reason for respiratory symptoms among the subjects.


Study Groups Frequencies (%)

Variables StatusExposedGroup(n=50)


2p

value

FVC%predicted

Abnormal 24(48) 14(28) 5.191 0.023*

Normal 26(52) 36(72)

FEV1%predicted

Abnormal 39(78) 34(68) 1.268 0.260

Normal 11(22) 16(32)

N=100*Significant at p 0.05

Table 6: Correlation between exposure of fine particles (PM 2.5) and the lung function parameters among respondents

( noitartnecnoc selcitraP eniF g/m3) puorG desopxE


(n=50)Variables r

valuep

valuer

valuep

valueFVC (litre) -0.077 0.594 0.08 0.958 FVC% predicted -0.099 0.495 0.061 0.672 FEV1 (litre) -0.063 0.662 -0.048 0.743 FEV1%predicted

-0.117 0.440 0.073 0.612

FEV1/FVC%predicted

-0.033 0.818 -0.054 0.710

N=100

Table 6: Correlation between exposure of fine particles (PM2.5) and the lung function parameters among respondents


63

Table 7: Correlation between working duration (years) and the lung function parameters among respondents

Table 7: Correlation between working duration (years) and the lung function parameters among respondents

Working Duration (years) Variables Exposed Group


(n=50)r value p value r value p value

FVC% predicted -0.489 <0.001 -0.450 <0.001 FEV1%predicted

-0.514 <0.001 -0.486 <0.001

FEV1/FVC%predicted

-0.515 <0.001 -0.530 <0.001

CONCLUSION

The result from this research showed that traffic policemen were determined as having lower lung

function parameters as their working environment

exposed to higher traffic pollutant. There was a significant association between the exposure to fine particle (PM2.5) and lung function among the exposed

group. Besides that, there was also a significant association between working duration (years) and lung

function among the exposed group. Results of this

study were consistent with the findings of the other air pollutant studies conducted by other researchers

as the previous study. Traffic policemen that exposed to higher concentration levels of PM2.5 have shown a

significant reduction in FVC and FEV1 compared to

the comparative group. As for conclusion, this study

showed that working as a traffic policemen lower the lung function parameters and have hazards of exposure

to the fine particles (PM2.5).

As for recommendations, the administration can

use certain ways in order to increase awareness and

knowledge towards working risk as traffic policemen. Firstly, job schedule may constantly be rotated from

congested (more polluted) areas to less congested(less

polluted) areas. Secondly, health monitoring should

be conducted based on the personal exposure level

of air pollutants inhaled by traffic policemen. Results obtained from the monitoring would be compared

to the permissible exposure limit (PEL) value in

the standard of existing guidelines. If the individual

exceeded the PEL value, the management needs

to take action such as shorten the working period.

Besides monitoring procedures, education and training

also can be one of the recommendations. For example,

conduct an awareness program to educate traffic policemen on the prevention and identification of the symptoms related to the exposure to air pollutants.

Finally, management can conduct periodic medical

examination in order to early detect the status of their

respiratory health and wellbeing. From the finding of the medical examination, the individual that had

severe respiratory health problem may be transferred

to other police department.

ACKNOWLEDGEMENT

The author would like to express his utmost

gratitude to all traffic policemen from Traffic Station Kuala Lumpur and general duty police from Police

Headquarter Bukit Aman who willingly involved in

this study.


64

REFERENCES

Alice Y.M Jones, P. K. (2008). Respiratory Health of Road-Side Vendors in a Large Industrialized City. Env Sci Pollut Res, Vol. 15 (2): pp 150-154.

American Thoracic Society. (1978). Lung Function Testing: Selection of Reference Values and Interpretive Strategies. American Review of Respiratory Disease, Vol. 85: pp 762-768.

Cao J.J, H. K. (2006). Source Apportionment of PM2.5 in Urban area of Hong Kong. Journal of Hazardous Materials, Vol. 138: pp 73-85.

Daud, S. F. (2010). Exposure to PM2.5 and Lung Function Among Roadside Hawkers in Kota Bharu, Kelantan. Final Year Project. B. Sc (Environmental and Occupational Health), Faculty of Medicine and Health Science, Universiti Putra Malaysia.

Kumar. K. (2000). Respiratory Symptoms and Spirometric Observation in Relation to Atmospheric Pollutants in a Sample of Urban Population. Asia Pac J Public Health, Vol. 12 (2): pp 58-64.

Kanae Karita, E. Y. (2002). Roadside Particulate Air Pollution in Bangkok. Journal of Air and Waste Management, Vol. 52: pp 1102-1110.

Maitre, A. (2002). Exposure to Carcinogenic Air Pollutants among Policemen Working Close to Traffic in Urban Area. Scand J Work Environmental Health, Vol. 28 (6): pp 402-410.

Lioy P.L, J. Z. (1999). Respiratory Exposure to Air Pollutant. In Air Pollutant and Respiratory Tract. Edited by D. L Swift, W.M Foster, Vol. 128.

Musa, M. M. (2011). Exposure to Fine Particle (PM2.5) and Lung Function of Postmen In Kuala Lumpur and Selangor Areas. Final Year Project. B. Sc (Environmental and Occupational Health), Faculty of Medicine and Health Science, Universiti Putra Malaysia.

Sopan T, B. G. (2005). Exposure to Vehicular Pollution and Respiratory Impairment of Traffic Policemen in Jalgaon City, India. Industrial Health, Vol. 43: pp 656-663.

US EPA. (2006). Emissions Monitoring and Analysis Division Monitoring & Quality Assurance Group. Development of the Particulate Matter (PM2.5) quality system for the chemical speciation monitoring trend sites.

US EPA. (2010). US Environmental Protection Agency. Retrieved April 17, 2010, from Particulate Matter: Based on Data Through 2009: www.epa.gov/airtrends/pm.html

World Health Organization. (2000). Health Aspects of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxide. Report of WHO Working Group. WHO Regional Office for Europe.

World Health Organization. (2004). Outdoor Air Pollution: Assessing the Environment Burden of Disease at National and Local Levels. Environment Burden and Disease Series, Vol. 1 (5): pp 1-62.


65

Indoor Thermal Comfort Study: A Case Study at Higher Institution in East Coast of Malaysia

¹Rosli Abu Bakar, ¹Ahmad Rasdan Ismail, ¹Norfadzilah Jusoh, ²Abdul Mutalib Leman,

¹Faculty of Technology, Universiti Malaysia Pahang, 26300 Gambang, Pahang, Malaysia.

²Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia,

86400 Parit Raja, Batu Pahat, Johor, Malaysia

Corresponding Author: [email protected]

ABSTRACT

This paper discuss thermal comfort studies of an under air conditioning in hot and humid climate which at one

of the higher institution in East Coast of Malaysia. Indoor thermal environment is important as it affects the health

and productivity of building occupants. The paper reports on an experimental investigation of indoor thermal comfort

characteristics under the control of air conditioning. Firstly, the well known Fanger’s thermal comfort model was

simplified for the current experimental investigation. This is followed by reporting the experimental results of indoor

thermal comfort characteristics under the control of temperature, with eight different of temperatures which are 22oC to

29oC. Finally, indoor thermal comfort was merely affected by the increment ventilation and outdoor climate. PMV value

was higher when near from the window because of the effects of the wall radiations and the metabolic heat.

Keywords: Thermal comfort, air conditioner, climate

INTRODUCTION

In Malaysia, the use of air conditioning has been

steadily raising not only in services building but also

in other building types such as the residential sector.

Aun (2004) summarize about the affecting energy

use in the buildings which is the amount of energy

used in buildings depends on what it is used for, a

typical Malaysia Office Building consumes about 250 kWh/m2/year of energy of which about 64% is

for air conditioning, 12% lighting and 24% general

equipment, the major non design factors influencing energy use in buildings are:

1. Occupancy & Management,

2. Environmental Standards,

3. Climate

and major building related factors influencing energy requirements can be classified under the following headings:

• Size and Shape• Orientation

• Planning and Organization• Thermo physical properties - thermal resistance & thermal capacity

• Window systems• Construction detailing.

The purpose of air conditioning in a building is

to provide a safe and comfortable environment for

its occupants. Satisfaction with the environment is

composed of many components, the most important

of which is thermal comfort (Sherman, 1985).

Sherman (1985) stated thermal comfort is a

topic which is by nature multidisciplinary; it involves

aspects of engineering and of human physiology.

Because the human body has its own temperature

regulating responses (e.g., sweating, vasodilation/

constriction, shivering, etc.), an occupant’s response

to (and hence sensation of) the environment will be a

strong function of his/her physical condition; a young,

healthy body recovers more quickly and therefore can

respond to changes in thermal stress more quickly

than can an older, ill-conditioned one.

66


Window systems Construction detailing.

The purpose of air conditioning in a building is to provide a safe and comfortable environment for its occupants. Satisfaction with the environment is composed of many components, the most important of which is thermal comfort (Sherman, 1985).

Sherman (1985) stated thermal comfort is a topic which is by nature multidisciplinary; it involves aspects of engineering and of human physiology. Because the human body has its own temperature regulating responses (e.g., sweating, vasodilation/constriction, shivering, etc.), an occupant's response to (and hence sensation of) the environment will be a strong function of his/her physical condition; a young, healthy body recovers more quickly and therefore can respond to changes in thermal stress more quickly than can an older, ill-conditioned one.

Thermal comfort has a great influence on the health and productivity of building occupants. A person’s sense of thermal comfort is primarily a result of the body’ heat exchange with environment, which is influenced by two personal and four environmental parameters: metabolic rate, clothing insulation, and air temperature, mean radiant temperature (MRT), air speed and humidity. Two indexes proposed by Fanger have been extensively used and accepted in assessing indoor thermal comfort. The first one is predicted mean vote (PMV) (Fanger, 1970:1982) that expresses the quality of thermal environment as a mean value of votes of a large group of persons according to the ASHRAE thermal sensation scale. The other is the predicted percentage dissatisfied (PPD)(Fanger, 1980) which expresses the thermal comfort level as a percentage of thermally dissatisfied people, and is directly determinable by PMV. The two indexes can be evaluated by

LMPMV 028.0036.0exp303.024 2179.003353.0exp95100 PMVPMWPPD

Figure 1 shows some basic features of man's thermoregulatory system (Hensel, 1981). The controlled variable is an integrated value of internal temperatures (i.e. near the central nervous system and other deep body temperatures) and skin temperatures. The controlled system is influenced by internal (e.g. internal heat generation by exercise) and external (e.g. originating from environmental heat or cold) thermal disturbances. External thermal disturbances are rapidly detected by thermoreceptors in the skin. This enables the thermoregulatory system to act before the disturbances reach the body core. Important in this respect is the fact that the thermoreceptors in the skin respond to the temperature as well as to the rate of a temperature change. According to Madsen (1984), the latter is actually done by sensing heat flow variations through the skin.

Autonomic thermoregulation is controlled by the hypothalamus. There are different autonomic control actions such as adjustment of: heat production (e.g. by shivering), internal thermal resistance (by vasomotion; i.e. control of skin blood flow), external thermal resistance (e.g. by control of respiratory dry heat loss), water secretion and evaporation (e.g. by sweating and respiratory evaporative heat loss). The associated ternperatures for these autonomic control actions need not necessarily be identical nor constant or dependent on each other.

Thermal comfort has a great influence on the health and productivity of building occupants. A

person’s sense of thermal comfort is primarily a result

of the body’ heat exchange with environment, which

is influenced by two personal and four environmental parameters: metabolic rate, clothing insulation, and

air temperature, mean radiant temperature (MRT), air

speed and humidity. Two indexes proposed by Fanger

have been extensively used and accepted in assessing

indoor thermal comfort. The first one is predicted mean vote (PMV) (Fanger, 1970:1982) that expresses

the quality of thermal environment as a mean value

of votes of a large group of persons according to the

ASHRAE thermal sensation scale. The other is the

predicted percentage dissatisfied (PPD) (Fanger,

1980) which expresses the thermal comfort level as

a percentage of thermally dissatisfied people, and is directly determinable by PMV. The two indexes can

be evaluated by

Figure 1 shows some basic features of man’s

thermoregulatory system (Hensel, 1981). The

controlled variable is an integrated value of internal

temperatures (i.e. near the central nervous system

and other deep body temperatures) and skin

temperatures. The controlled system is influenced by internal (e.g. internal heat generation by exercise)

and external (e.g. originating from environmental

heat or cold) thermal disturbances. External thermal

disturbances are rapidly detected by thermoreceptors

in the skin. This enables the thermoregulatory

system to act before the disturbances reach the

body core. Important in this respect is the fact

that the thermoreceptors in the skin respond to the

temperature as well as to the rate of a temperature

change. According to Madsen (1984), the latter is

actually done by sensing heat flow variations through the skin.

Autonomic thermoregulation is controlled by

the hypothalamus. There are different autonomic

control actions such as adjustment of: heat production

(e.g. by shivering), internal thermal resistance (by

vasomotion; i.e. control of skin blood flow), external thermal resistance (e.g. by control of respiratory dry

heat loss), water secretion and evaporation (e.g. by

sweating and respiratory evaporative heat loss). The

associated ternperatures for these autonomic control

actions need not necessarily be identical nor constant

or dependent on each other.

This paper reports on an experimental investigation

of indoor thermal comfort characteristics under the

control of air conditioning. Firstly, the well known

Fanger’s thermal comfort model was simplified for the current experimental investigation. This is followed

by reporting the experimental results of indoor

thermal comfort characteristics under the control

of temperature, with eight different of temperatures

which are 22oC to 29oC. Finally, a discussion on

indoor thermal comfort with through varying

temperature of air conditioning affecting thermal

comfort is given.


67

Figure 1 Schematic diagram of autonomic and behavioural temperature regulation in man

RESEARCH METHODOLOGY

Model Descriptions

The experimental work has been carried out using

equipment as shown as in Figure 2. Measurements

were carried out with the sensors at a height of 2 m,

which corresponds to the height recommended in ISO

7726-1985 (Thermal environments - instruments and

methods for measuring thermal comfort) for head

level for a sedentary occupant. While measuring

the environmental parameters, the two personal

parameters, metabolic rate and clothing insulation

are estimate in accordance with ASHRAE Standard

55–92. This equipment can measure of all the various

parameters defining the quality of an environment from the thermal, sound, illumination and chemical.

Currently, the most frequently cited thermal comfort

standard are ASHRAE 55-2004 and ISO 7730 are

both based on Fanger model, which solves the heat

balance equations between human body and its

surroundings represented as a uniform environment.

In this experimental study, the measuring

points inside the air conditioned room are shown

in Figure 3. There were totally seven measuring

points. At these seven measuring points, indoor air

temperature was measured at eight different levels,

which are 22oC to 29oC. Figure 3 show the schematic

design and measuring point of a real room in house

which is presently used in this study. This room has

one door and window to facilitate the well lighting,

view and in-out convenience. The dimensions of this

room are 3.2 m (L) x 4.4 m (D) x 2.6 m (H). The air

conditioner used as a ventilation system in this room.

Controller Temperature sensation

Skin thermoreceptors Internal thermoreceptors

Internal disturbance

External disturbance Adjustment of

Heat exchange:Adjustment of

Heat production:

Autonomicregulation

Behaviouralregulation

Thermal comfort

Hypothalamus

Body shell

Vascomotion sweating Metabolism shivering

Clothing

Signal path Heat transfer path

Voluntary movements

Body core

Feedbackelements

Controlledsystem

Controlaction

68


Measurements

The measuring PMV concentration is to estimate

thermal comfort in the case study. Experiments were

carried out for three processes as shown in Figure 4.

Thermal comfort was analyzed through collecting

comfort variables of the room. The experiment for

each case was repeated eight times which is with

different setting temperature of air conditioner and

different setting points. To perform the simultaneous

study, all experiments were conducted during office hour. Seven measuring points as shown in Figure

3 were selected to analyze the local or the room

averaged performance for thermal comfort. Sampling

PMV was conducted for about 10 min at every point.

Thermal comfort equipment data logger with sensors

for indoor air temperature, radiant temperature, air

velocity and relative humidity, measurements was

used for estimate PMV. 1.2 met and 0.5 clo values

are using and considering the sedentary activity and

the summer clothing. This equipment was accurately

calibrated according to manufacturer specifications.

RESULTS AND DISCUSSION

Considering the human body to be an open

thermodynamics system, the term “thermal stability

of the human body” has been introduced. It is related

to the environmental conditions and is presented with

evaporative resistance parameters. This is presented

as a criterion of the human body thermal stability.

The results lead to the following question: in

each thermal state of the human body, what

combination of environmental parameters defining thermal stability is fixed?

It was necessary to relate the classification of the environmental parameters to the thermal stability

of the human body. The thermal stability, depending

on the air velocity and altitude. Therefore the rate

of air movement and the altitude are mediators of

the thermal effect of the body thermal stability. This

means that the field of air velocity is the space where it is propagating the effect due to the temperature and

humidity changes; the field where the enthalpy is propagating (ASHRAE, 1985).

The indoor air temperature values in the case

studies for all the points are shown in Figure 5

respectively. Data from different point are obtained

during different days. Field experiments were

conducted during the months of Jun-September

2012 and the weather during that period (the

months of Jun to September) is usually quite uniform

and similar. Whilst it is noted that the ventilation

rates would be different for different days due to the

air velocity, it is also to be noted that the air velocity

do not vary much during typical periods of hot and

humid months. It is seen that the temperature level

remains changed with the different point of measuring.

The effect of near the window location make the indoor

air temperature increase as shown as on point

measuring five in Figure 5. The point of measuring three shows the decreasing of indoor air temperature.

This is because the location is far away from the

entrance and window.

The temperature of the air conditioning

was requested to be set from 22oC to 29oC all

the experiments. However, it is seen that the air

temperature for all the cases did not reach what

the set point of air conditioning. The indoor air

temperature different with temperature set point. The

reasons for measured temperatures not meeting the

set point temperature may be several folds, one of

which is infiltration caused by leakages through the window. The type of window of case study is double

glass window. Another possible reason is different

of location point measuring. The points measuring

extreme affecting comfort are point far away and near

the window and air conditioning.


69

Figure 2 Thermal comfort equipment

Figure 3 Set points in the air conditioned room of the case study

Figure 4 Research process of study

ComfortVariables

MeasuringPoints

Temperatureof Air

Conditioner

22oC 23oC 24oC 25oC 26oC 27oC 28oC 29oCPMV PPD PMV PPD PMV PPD PMV PPD PMV PPD PMV PPD PMV PPD PMV PPD

1 -0.6 12.5 -0.3 6.9 -0.1 5.2 0.2 5.8 0.4 8.3 0.6 12.5 0.5 10.2 0.8 18.52 -0.6 12.5 -0.4 8.3 0 5 0.2 5.8 0.5 10.2 0.2 5.8 0.3 6.9 0.7 15.33 -0.7 15.3 -0.4 8.3 -0.1 5.2 0.2 5.8 0.4 8.3 0.6 12.5 0.6 12.5 0.8 18.54 -0.6 12.3 -0.4 8.3 0 5 0.2 5.8 0.6 12.5 0.7 15.3 0.7 15.3 0.9 22.15 -0.9 22.1 -0.2 5.8 0.1 5.2 0.4 8.3 0.7 15.3 0.8 18.5 0.9 22.1 1 26.16 -0.6 12.5 -0.2 5.8 0 5 0.3 6.9 0.6 12.5 0.5 10.2 0.8 18.5 0.7 15.37 -0.5 10.5 -0.2 5.8 0 5 0.4 8.3 0.7 15.3 0.7 15.3 0.7 15.3 1 26.1

70


Figure 5 Effect of indoor air temperature under different temperature of air conditioning

Table 1 The calculated PMV and PPD under different temperature of air conditioning

Table 1 shows the result of thermal comfort

sensation vote. The measured values of indoor

air temperature, indoor relative humidity, radiant

temperature and air velocity are the used in the

computation of PMV and PPD values. The PMV

values indicate that range neutral and slightly warm,

while almost every mean vote along the measuring is

termed cold. Generally as seen in Table 1, the thermal

sensation of the occupants in the room is noted to be

within the range of neutral to slightly warm. No warm

discomfort is experienced.

During the experiments, the outdoor air

temperature was in a range of 25-33oC and the

outdoor relative humidity was in a range of

50-60%. But the indoor air temperature, the radiant

temperature and the indoor relative humidity

were measured at 21-27oC, 22-28oC and 55-78%,

respectively. When the discharge airflow is varied, the average PMV measured were in the range of -0.9 to

1 and most of PMV values at all points, except only

temperature 29oC of air conditioner, was not in the

acceptable comfort zone to satisfy of thermal comfort.

And PMV values of the point of (5) was higher than

the other points. This is because points are relatively

near from the window, so the effects of the wall

radiations and the metabolic heat are higher than ones

at the other points.


71

CONCLUSION

The study focused on a university campus

in Malaysia and discussed the thermal comfort

conditions of indoor building in a context of hot and

humid climate. The conducted field study investigated the indoor thermal comfort in terms of environmental

conditions and human comfort level.

A study is carried on the air conditioning with

an aim of examining its impact on the indoor thermal

comfort. Experiments were conducted in one of room

to measure the indoor air temperature, indoor relative

humidity, radiant temperature and air velocity at

selected point within a total of seven sampling points.

Based on the empirical data, the PMV and PPD are

computed. Besides this, simple linear graph are also

attempted to derive the relationship between point

measuring and indoor air temperature. The results

of PMV and PPD also reveal that cold discomfort is

always felt at sampling points that are closed with the

air conditioner (supply) and far away from window.

Therefore, unless occupants are seated away from the

supply, localized thermal discomfort is unavoidable.

Our theoretical knowledge concerning thermal

comfort in transient conditions is still limited. At

present, results of thermal comfort experiments seem

to be the only source of information on thermal

acceptability in changing environmental conditions.

The present study is restricted to conditions

characteristic for homes, offices, etc. The following conclusions are supplementary to the steady state

comfort criteria which are usually associated with

those conditions; i.e. sedentary or slightly active

persons, wearing normal indoor clothing in an

environment with low air movement (<0.15 ms-1) at

50% relative humidity.

The amount of air conditioning load required

and thus air conditioning energy used depends very

much on the indoor air temperature maintained in the

building. Some office buildings and hotels maintain indoor temperatures as low as 18 to 20oC when

the comfortable temperature is about 24oC. There

are many office buildings in Malaysia where the indoor temperature is so low that the occupants wear

sweaters at the work desk. It is obvious the owners

are no aware of the cost implications of their actions.

It should also be noted that the average outdoor air

temperature in Malaysia is only about 4oC above the

comfort range (Sekhar and Goh, 2011).

The outcomes of the experimental study could

possibly lead to the potential development of using

indoor thermal comfort indexes, rather than indoor

environmental parameters, such as indoor air dry-

bulb temperature, for control purpose.

72


REFERENCE

ASHRAE, 1985. ASHRAE Handbook:

Fundamentals, Capt. 6, Psychrometrics. American

Society of Heating, Refrigerating, and Air-

Conditioning Engineers, Atlanta, GA.

ASHRAE Standard 55-2004. 2004. Thermal

environmental conditions for human occupancy.

ASHRAE Inc. Atlanta.

Aun, C. S. 2004. Energy efficiency designing low energy buildings using Energy 10. CPD Seminar 7th August, Pertubuhan Arkitek Malaysia, 1-18.

Fanger, P.O. 1970. Thermal comfort: Analysis

and application of environmental engineering. Danish

Technical Press, Copenhagen.

Fanger, P.O. 1982. Thermal Comfort, Robert E.

Krieger Publishing Company, Malabar, FL.

Hensel, H. 1981. Thermoreception and

temperature regulation. Academic Press, London.

ISO 7730. 1994. Moderate thermal

environments-determination of the PMV and PPD

indices and specification of the conditions for thermal comfort, International Standard Organization,

Geneva.

Madsen, T.L. 1984. Why low air velocities may

cause thermal discomfort? Proceedings of the 3rd

International Conference on Indoor Air Quality and Climate, Stockholm, pp. 331-336.

Sekhar, S.C. & Goh, S.E. 2011. Thermal comfort

and IAQ characteristics of naturally/mechanically

ventilated and air conditioned bedrooms in a hot and

humid climate. Building and Environment. 46, 1905-

1916.

Sherman, M. 1985. A simplified model of thermal comfort. Energy and Buildings, 8, 37-50.


73

Laboratory OSH Compliance Status Among Chemical Testing Laboratory in Lembah Klang

A. Suhaily, M. Mohd Norhafsam, Z.A. Ahmad Sayuti, M.H. Nor Husna, T.A. Naemah, J. Nurzuhairah;

Laboratory Division,

Consultation Research and Development Department,

National institute of Occupational Safety and Health (NIOSH)

Lot 1, Jalan 15/1, Seksyen 15, 43650 Bandar Baru Bangi. Selangor

Email: [email protected]

ABSTRACT

NIOSH Malaysia was awarded by Pertubuhan Keselamatan Sosial (SOCSO) to conduct a study on Laboratory

OSH Compliance Status among Chemical Testing Laboratory in Lembah Klang, Selangor, Malaysia. In this program, 20

chemical testing laboratories were participated on voluntary basis. The study focused on Occupational Safety and Health

(Use and Standard of Exposure of Chemical Hazardous to Health) Regulations 2000 or in short USECHH Regulation

2000. The objective of this study is to determine the gap between current practices and implementation of chemical

related legislation under Occupational Safety and Health Act 1994.The study was conducted based on site interview with

participant using checklist established by NIOSH. The checklist based on detail requirement of USECHH regulation 2000.

Beside requirement of the USECHH Regulation 2000, the checklist also included other general requirement of laboratory

safety management. The step taken in this study are divided into 3 main phase. The phases are (1) Onsite gathering of

information, (2) Off-site analysis of finding, and (3) presentation of report. A total of 20 laboratories participated in the

program. 95% of the participants are ISO/IEC 17025 Accredited Laboratory. 40% are industry laboratories while 60%

are commercial laboratory. All laboratories are performing chemical testing activities involving chemical hazardous to

health. Based on this study 100% of laboratories are equipped with chemical register; 30% of the laboratories complied

with Permissible Exposure Limit; 50% of the laboratories performed the CHRA, 20% of laboratory comply with personel

protective equipment (PPE) requirement; 30% conduct monthly inspection of engineering control by in-house technician

while only 40% of the laboratories conduct yearly inspection by hygiene technician; 30% of the laboratories conducted

chemical monitoring; 25% of the laboratories performed medical surveillance for their workers; 100% of the laboratory

management provide worker with CSDS and label but only in English version; 50% of the laboratories employers conduct

proper training in safe handling of the chemical; 45% of the laboratories management posting appropriate warning sign in

the laboratories; and 45% of the laboratory kept report appropriately according to the requirement. Based on the results,

it was found that most of the laboratories did not comply with the USECHH Regulation 2000.

74


BACKGROUND

Currently there are many chemical testing

laboratories in Malaysia which are operated for

commercial purposes. However, most of these

laboratories are not emphasizing on occupational safety

and health (OSH) aspects while handling chemical.

This situation increases the risk of occupational

accidents and diseases caused by chemical exposure.

This condition may be due to lack of awareness

among employers and employees; or caused by lack

of monitoring and promotion by the authorities.

NIOSH Malaysia was awarded by Sosial

Security Organization (SOCSO) to conduct a study on

Laboratory OSH Compliance among Chemical Testing

Laboratory in Lembah Kelang, Selangor, Malaysia.

In this program, 20 chemical testing laboratories

participated on voluntary basis.

This study will be the initial step by NIOSH and

SOCSO to assist chemical testing laboratory towards

OSH legal compliance on chemical management.

These activities directly may improve the OSH

standards among the participants and indirectly may

protect workers from chemical exposure. The program

is expected to increase OSH awareness among

participant and may reduce the risk of occupational

accidents and diseases at work, increase productivity,

reduce sick leave and ultimately reduce the payment

of compensation by SOCSO

1.1 Occupational Safety and Health (Use and

Standard of Exposure of Chemical

Hazardous to Health) Regulations 2000

Workplace safety and health laws establish

regulations designed primarily to eliminate personal

injuries and at the same time preventing it to recur at

the workplace. The main statute protecting health and

safety of workers at the workplace in Malaysia is the

Occupational Safety and Health Act 1994 (Act 514).

The Act was promulgated based on the philosophy

of self-regulations with the primary responsibility

of ensuring safety and health at the workplace lies

with those who create the risk and work with the risk

(Hanum et al, 2008) in order to ensure safety, healthy

and a favorable working environment, proper safety

procedures at the workplace must be in place and

practice.

At this juncture, both the employer and employees

ought to be well-informed of some of the laws

which have bearing on laboratory safety such as the

“Occupational Safety and Health (Use and Standards

of Exposure of Chemicals Hazardous to Health)

Regulations 2000, Occupational Safety and Health

(Classification, Packaging and Labeling of Hazardous Chemicals) Regulation 1997 and Environmental

Quality Act, 1974 vis-à-vis the Environmental Quality

(Scheduled Wastes) (Amendment) Regulations 2000

(Hanum et al, 2008)

Occupational Safety and Health (Use and

Standard of Exposure of Chemical Hazardous to

Health) Regulations 2000 or in short USECHH

Regulation 2000 was established to provide a legal

framework for the employer to control hazardous

“industrial chemicals” use at the workplace. Beside

that, the requirement under the regulations set

workplace exposure standard in order to protect the

health of the employees and other persons at the place

of work. Indirectly, USECHH Regulation 2000 plays

as a tool to promote excellence in management of

those chemicals that are known to be hazardous to

health.

The USECHH Regulation 2000 Clearly

Stipulated the responsibility of employer to protect

OSH of their employee and other person from being

affected by chemical hazardous at workplace (CHTH)

(Yon, 2007). USECHH Regulation 2000 detail out

responsibilities need to carried out by employer

regarding managing chemical used in the workplace.

The regulation change the employer approached

from reactive to proactive. Proactive means that the

organisation have to anticipate accident or near miss,

and to introduce procedure and system to tackle

chemical hazard in the workplace (Yon, 2007). Among

the duties of employers in USECHH Regulation

2000 are registration of chemical hazardous to

health, complying with permisible exposure standard,

conducting chemical health risk assessment, conduct

chemical monitoring and medical surveillance, hazard

comunication and many others.


75

1.2 OSH Compliance Study

Hazard in the workplace can be identified by several method such as studying accident and ill health statsistics, incident and accident investigation report, and site or compliance audit report. Audit report can be produce from audit activities using requirement checklist. Audit is defined as a review and evaluation of record and activities conducted to evaluate the control system in order to ensure its consistent with the policies and procedure that have been determine(Kadir. Et al qoauted from Dang, 2004). Kadir et al state that audit has also been seen as an independent body which conducts an objective assessmnet and consultation activities which aims at adding value and enhancing the organisation operation.

According to Hasnan dan Rasidi (1996) qouted by Anuar (2008), Environmental, health and safey auditing activities in industries date back to mid 1970s as internal tools to review and evaluate environmental problem at operating unit level. Anuar (2008) also qout from Linda et all (1999) that audit can be used as key management tools in assessing the strength and weakness of management system for health and safety in order to to promote continuous improvement. It is vital to document and bring them the attention of all concern in the laboratory, in order to prevent major accident and also because of medico-legal impli cation (Norain and Choe, 2000).

Safety audit checklist are frequently used by safety and health practioner, especially safety and health officer (SHO) as basic tools to identify gaps between practice and requirement. This approched are considered as very cost effective, and only required basic skill and knowledge. Beside that, this tools can be easily modified to suit any type of requirement. Findings from the audit may assist management to make decisions regarding the implementation of OSH at workplace.

1.3 Laboratory Accident and Occupational Disease

Laboratory workplace must be safe and conducive for all workers to work. Workers in chemical laboratories or are designated as Chemical Technologist and Technicians are exposed to lots of hazards(Hanum et al, 2008). In specific, laboratory testing workers exposed to variety of chemical such as solvent, acid, alkali during sample preparation and analysis. Beside that, they also posed higher risk from chemical incident such as spillage, fire and explosion.

The knowledge and research on occupatrional injuries among Malaysian laboratory workers are unexplored (Anuar et al, 2008). It must be appreciate that several factors can contribute in the increased number of accidents such as the number of personnel involved, the space in the unit and the nature of chemical (Norain and Choe,2000).

Testing laboratory environment requires workers to continously exposed to chemical hazardous to health. Since contact with chemical are so frequent and sometimes intense, the probability or the risk of exposure become higher and greater. Beside that the frequency and intensity of contact also increase potential of accident and incident in the laboratory setting. Health risk in laboratory setting may arise from two(2) major situation which are thorough chemical exposure or chemical accident. Chemical exposure may arise from the routine contact with chemical while handling the testing work. The effect may be acute or chronic. Acute effect are such as as irritation, dizziness, or breathing difficulties; while chronic effect such as development of cancer, or mutagenic effect to unborne child. Chemical properties such as exothermic reaction, flammability limit and vapour presure may increse risk of chemical accident such as fire, explosion. Beside that, mishandling of chemical may cause spillage or splashing which also lead to injury and property damage. Based on the study conducted by Anuar et all (2008) in 3 medical laboratories, in 2003 25% of occupational accident in the laboratories are due to exposure to chemical followed by 1.3% and 35.3% in year 2004 and 2005. Anuar also elaborated that incident can be viewed as an indication of something lacking in the system. Similar studies conducted by Norain and Choe in 2000 showed that 27% of accident in laboratory are due to splash and squired by fluid such as chemical and blood. 67% of the accident mainly involving laboratory technician. Naorain and Choe also stated that certain hazard such as smarting of eyes, secondary exposure to chemical, breathless ness, allergic rhinitis and contact allergy by contact to chemical such as formaline were considered as occupational hazard rather than accident. Hand were the most commonly involved in injuries followed by face .

Keywords: Laboratory Safety, USECHH Regulation 2000

76


2.0 METHODOLOGY

NIOSH-SOCSO Laboratory Safety Audit was

conducted in accordance to audit checklist established

by NIOSH. The audit checklist is based on detail

requirement under USECHH Regulation 2000. Beside

requirement of the USECHH Regulation 2000, the

audit checklist also includes other general requirement

of laboratory safety management. The audit was carried

out by NIOSH staff. The audit program was offered

to about 100 chemical testing laboratory operated

in Lembah Kelang, Selangor Malaysia but only 20

laboratories agreed to participate in the program

The step taken in this NIOSH-SOCSO Laboratory

Safety Audit are divided into 3 main phase. The phases

are (1) Onsite gathering of information, (2) Off-site

analysis of finding, and (3) presentation of report.

The audit started by opening meeting by the

lead auditor, the laboratory personnel were informed

about the objectives and scope of the audit. After that,

the audit are continued with document review and

followed by inspection of the laboratory facilities

and interviewed with relevant personnel. The audit

checklist was used as main tool to ensure that all

requirements of the USECHH 2000 were asked and

inspected accordingly. The onsite audit was ended

by closing meeting by the lead auditor. During the

closing meeting the laboratory personnel were briefed

of the major finding in the assessment and a copy of audit summary are submitted to the laboratory

management.

Data gathered during the onsite audit were

analyzed, and report is prepared for individual

laboratory. The report covered details of finding gathered during the onsite audit. Whichever

appropriate, NIOSH recommended action to be taken

in order to comply with the legal requirement or to

improved workplace condition.

Report on audit findings were presented to all laboratory representatives. In the session, each

laboratory was given the final report and a certificate of participation.

The presentation is focused on introduction to

the USECHH requirements, descriptive statistic of the

audit program, and recommendations to improve the

workplace condition.

3.0 FINDING AND DISCUSSION

20 laboratories were participating in the program. 95% were ISO/IEC 17025 Accredited Laboratory. 40% were industrial laboratories which performed internal analysis for quality control purposes while another 60% were commercial laboratory. All laboratories perform chemical testing activities involving usage of chemical hazardous to health.

Audit criteria are focus on employer’s responsibilities under USECHH 2000. Mainly there are 11 duties of employers as described in the USECHH 2000. The eleven duties are (1)Identify chemicals hazardous to health, (2)Comply with permissible exposure limits (PELs), (3)Conduct chemical health risk assessment, (4)Action to control hazard exposure, (5)Labeling and re-labeling of chemicals hazardous to health, (6)Monitor exposure at the place of work, (7) Carry out health surveillance, (8) Medical removal protection, (9) Provide information, instruction and training, (10) Exhibit warning signs and (11)Keep relevant record.


77

3.1 Identify chemicals hazardous to health

Under the USECHH Regulations 2000, an

employer is required to identify and register all

chemical hazardous to health used at the workplace and

record them in a register known as chemical register.

Chemical Register of Chemical Hazardous to Health

need to be prepared and maintained with complete list

of chemical; CSDS, average quantity used, produced

,stored and disposed, processed and work area where

the chemicals are used, and name and address of the

supplier of each chemical.

Based on this study, 100% of laboratories were

equipped with chemical registered but only 60% of

the laboratories follow the format as per Guideline

for Preparation of Chemical Register published

by Department of Occupational Safety and Health

(DOSH 2000).

3.2 Comply with permissible exposure limits

(PELs)

Under the USECHH Regulations 2000, an

employer is required to ensure that all workers exposed

to chemical as listed in Schedule II are not exposed to

level above permissible exposure limit (PEL); only

30% of the laboratories comply with the requirement.

Status of other laboratories cannot be determined due

to unavailability of monitoring report.

PEL is intended to be used as general guidelines

and do not define an exact level of safety. In general, exposure measurements which approach or exceed

PEL criteria indicate the need to improve control and

further evaluation. Moreover, PEL is used as a guide

to protect working population and not to general

public which comprise people of various age groups

from infant up to very old people. Some people are

more susceptible than others to the effects of exposure

to chemicals for many reasons including inherited

genetic disorder; tobacco smoking; alcohol and

drug consumption; nutritional deficiencies; parasitic diseases and pre-existing diseases such as bronchial

asthma or chronic bronchitis.

3.3 Conduct chemical health risk

assessment(CHRA)

Under the USECHH Regulations 2000, employer

is required to assess the risk to health arising from the

use of chemical hazardous to health at work and to

review it whenever there is any reasonable changes

occur; to prevent or control the risk; to ensure that

control measures are used and maintained; to monitor

exposure and carry out health surveillance when

necessary; to inform, instruct and train employees

about the risks and the precautions needed; and to

keep records where required. The audit scope is based

on USECHH 2000 and additional laboratory general

requirement

This study found that only 50% of the laboratories

perform the CHRA. According to USECHH Regulation

2000, CHRA must be conducted before starting any

work involve chemical hazardous to health. All these

laboratories are heavily used CHTH in their work

process. By besides complying with legal requirement,

CHRA finding may assist employer on relevant action to be taken in order to improve chemical management

in the work place alongside with compliance to legal

requirement.

78


3.4 Action to Control Hazard Exposure

Under the USECHH Regulations 2000, the

employer shall control chemicals hazardous to health

through control measures:

a) Elimination of chemicals hazardous to health

from place of work;

b) Substitution of less hazardous chemicals for

chemicals hazardous to health;

c) Total enclosures of the process and handling

systems;

d) Isolation of the work area to control the emission

of chemicals hazardous to health;

e) Modification of the process parameters;

f) Application of engineering control equipment;

g) Adoption of safe work systems and practices that

eliminate or minimize the risk to health; or

h) Provision of approved personal protective

equipment.

Application of the hierarchy of control measures

involves firstly assessing whether a hazardous chemical or process can be eliminated. Where this is

not practicable, consideration should be given to each

of the other control measures (isolation, engineering

control, safe work practices and use of personal

protective equipment), until a control measures or

combination of measures are identified which can achieve the required reduction in exposure

The study find out that only 20% of laboratory comply with personel Protective Equipment (PPE)

requirement. According to the USECHH Regulations

2000, where the approved personal protective

equipment (PPE) is used to control exposure to

chemicals hazardous to health, the employer shall

establish and implement procedures on the issuance,

maintenance, inspection and training in the use of the

approved PPE. Appropriate program should include

PPE selection, fit testing, training, medical evaluations, use and Maintenance and Program evaluation

Under the USECHH Regulations 2000; any

engineering control equipment including general

ventilation shall be inspected at an appropriate intervals

by the employer, each interval being no longer than

one month; examined and tested for its effectiveness

by a hygiene technician at each interval being no

longer than 12 months; design according to approved

standard by registered professional engineer and tested

by professional engineer (PE) after construction and

installation.

Only 30% conduct monthly inspection by in-

house technician while only 40% of the laboratories

conduct yearly inspection by hygiene technician. No

record of approved design a by PE was found during

the study.

3.5 Labeling and re-labeling of chemicals

hazardous to health

Management of laboratories shall ensure that

all hazardous chemicals to be stored and should be

labeled and/or relabeled as per the Occupational

Safety and Health (Classification, Packaging and Labeling of Hazardous Chemicals) Regulation 1997

or in short CPL regulation 1997. Most of the chemical

are only label according to manufacturer labeling

system. None of the laboratories take any initiative to

relabeling chemical according to CPL 1997.

In the laboratories practice, chemical are

routinely being mixed or diluted to form stock,

standard, diluents or buffer for testing procedure.

During the process the hazard classification may be changed to more or less hazardous. In this situation,

USECHH Regulation 2000 required management

to classify and reliable chemical according to CPL

Regulation 1997 specification and requirement. None of the laboratories practice relabeling for their

chemical


79

3.6 Monitor exposure at the place of work

Monitoring of exposure is required for ensuring

the maintenance of adequate control of the exposure of

employees to CHTH. Generally Chemical Monitoring

will be performed base on recommendation of

CHRA report. According to USECHH Regulation

2000. If the workers exposed to chemical listed in

Schedule II of the regulation, the monitoring must be

perform by registered hygiene technician at interval

of not more than 6 months or shorter as determine

by CHRA assessor. Only 30% of the laboratories

conducted chemical monitoring while 10% fail to

performed chemical monitoring even though has been

recommended by assessor. Compliance status for the

rest 60% laboratories cannot be determine due to in

availability of CHRA or monitoring report

3.7 Carry out health surveillance and Medical

removal protection

According to USECHH regulation 2000, where

an assessment indicates that the health surveillance

is necessary for the protection of the health of

empoyees exposed or likely to be exposed to CHTH,

the employer shall carry out health surveillance

program. And if the employee is exposed or likely

to be exposed to CHTH listed in schedule II, health

surveillace must be conducted at interval of not more

than 12 months or shorter as determine by Occupational

Health Doctor (OHD). Only 25% of the laboratories

performed medical surveillance for their workers

while 10% fail to conduct medical surveillance even

though has been determine by assessor. The Compliance

for other 75% laboratories cannot be determined

due to unavailability of CHRA report or medical

surveillance report. No record medical removal

protection found during this study.

3.9 Exhibit warning signs

Where a CHTH is used in any area employer

must post warning sign at every entrance of the area

to warn persons entering the area. Only 45% of the

laboratories management posted appropriate warning

sign in the laboratories.

Table 3.1: Compliance Status to USECHH Regulation 2000 Among 20 laboratories

Requirement Regulation Comply (%)

NonComply (%)

5(1) 100 0 Identify chemicals hazardous to health,

5(2) 60 40 Comply with permissible exposure limits (PELs), 6,7,8 - - Conduct chemical health risk assessment, 9, 11, 12, 13 50 50 Action to control hazard exposure, 16,

1718 a 18 b 19

203004040

80301006060

001 0 12,02 gnilebal-er dna gnilebaLMonitor exposure at the place of work, 26 30 10

01 52 72 ,ecnallievrus htlaeh tuo yrraC - - 82 ,noitcetorp lavomer lacideM

Provide information, instruction and training, 22, 23, 24,25

-50

-50

55 54 92 sngis gninraw tibihxE 55 54 03 drocer tnaveler peeK

80


3.10 Keep relevant record

All record or report related to USECHH

regulations 2000 must be kept for specified duration as determined by relevant Part in these regulations.

This study indicates only 45% of the laboratory kept

report appropriately according to the requirement.

Summary of compliance status for each regulation are

simplified in Table 3.1.

4.0 DISCUSSION AND RECOMMENDATION

USECHH Regulation 2000 should be used as

baseline reference for laboratory safety and health

practice. Since Occupational Safety and Health (Use

and Standard of Exposure of Chemical Hazardous to

Health) Regulations 2000 is a self regulation approach,

employer initiative play a big role toward compliance

and implementation of safety and health practice in

the workplace. Laboratories may set up a task force to

implement safety and health practice into their work

culture.

Most off participant in these studies are MS

ISO/IEC 17025 accredited laboratory; it showed

that accreditation has a major influence to laboratory management to upgrade their awareness in safety and

health issue.

Aggressive promotion and communication by

Department of Occupational safety and health (DOSH),

NIOSH and other Safety and Health practitioner need

to be done in order to improve the compliance level of

laboratory.

5.0 CONCLUSION

Based on the results, it was found that most of

the laboratories were not comply with the USECHH

Regulation 2000 and accreditation to MS ISO/IEC

17025 has a major influence to laboratory management to upgrade their awareness and practice in safety and

health requirement and implementation.

Table 3.1: Compliance Status to USECHH Regulation 2000 Among 20 laboratories


81

REFERENCES

1. A.Kadir, A.Kadaruddin, P.latifah, J. Azhar.

(2010). An Audit of occupational Safety and

health at the Workplace: A case study at Faculty

of Social Sciences and Humanity (FSKK), UKM.

Research Jurnal of Applied Sciences, 5(6): 4040-

41

2. H. Hanum, B. Aizuddin,W. Faridah. (2008).

Laboratory Safety Guidelines. University

Malaysia Perlis.1-40

3. I. Anuar, F. Zahedi , A. Kadir , A.B Mokhtar.

(2008) Laboratory Acquired Injuries in Medical

Laboratory: A Survey of Three Referral Medical

Laboratories from Year 2001 to 2005. Jurnal of

Community Health, 14: 132-3

4. N. Karim, C.K.Choe. (2000) Laboratory

Accident- A Matter of Attitute. Malaysian Jurnal

of Pathology, 22(2): 85-89

5. Occupational Safety and Health and Regulation;

Occupational Safety and Health (Use and

Standard of Exposure Chemical Hazardous to

Health) Regulation 2000, 10th edition; Kuala

Lumpur, MDC Publisher Sdn. Bhd, 2003

6. Y. Hazlina. (2007) Factors Associated With

Chemical Safety Status In Small And Medium

Printing Enterprises In Penang. Unpublished

Master Thesis. Universiti Sains Malaysia,


83

Response Surface Method in Modelling the Environmental Factors Toward Workers’ Productivity

Ahmad Rasdan Ismail1, Mat Rebi Abdul Rani2, Baba Md. Deros3,Zafir Khan Mohamed Makhbul4,

Mohd Yusri Mohd Yusof3

1Faculty of Technology, Universiti Malaysia Pahang, 26300 Gambang, Kuantan, Pahang, Malaysia.2Dept of Manufacturing & Industrial Engineering, Faculty of Mechanical Engineering

Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia.4School of Business Management, Faculty of Economy and Business, Universiti Kebangsaan Malaysia,

43600 Bangi, Selangor, Malaysia. 3Department of Mechanical & Materials Engineering, Faculty of Engineering & Built Environment

Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.

Corresponding author : [email protected]

ABSTRACT

Environmental factors can enhance workers’ working comfort while performing their tasks. Past researches had

shown uncomfortable working environment could lowered workers’ productivity and higher exposure to work related

health problems. Many studies were carried out to determine the optimal level of environmental factors. However, there is

still lack of research conducted to investigate the relationship between various environmental factors towards productivity

level. The three major objectives of this study are: to study the influence of environment factors; to model relationships

between environmental factors; and to predict the environmental levels that leads to optimum productivity rates in manual

assembly of automotive products. Twelve subjects were involved in this study; 6 subjects took part in the data acquisition

and the other 6 subjects took part in the result validation process. The data acquisition process took 90 days; it was

done during the daytime (morning) shift. In one work shift (4 hours), all the studied factors and number of completed

assembled units were recorded at an interval of 10 minutes. Later, the data was analysed using Response Surface Methods

(RSM) to determine the optimum productivity values, which is equal to 1.0. In this study, the optimum productivity

value (value ≈ 1.0) was obtained when the (WBGT) temperature was at 23.63°C, relative humidity was at 54.43% RH and illumination was at 604.36 Lux. During the study, it was observed when the subjects were exposed to extreme

environmental parameters; they felt uncomfortable and resulted in lower productivity values. In addition, RSM analysis

was used to model the mathematical relationship between temperature (WBGT), relative humidity, and illumination with

productivity values.

Keywords - environmental; factors; productivity; human workers; optimum; manual; assembly component

84


I. INTRODUCTION

The automotive manufacturing sector is a very

important industry to the Malaysian economy. It

provides significant contribution to the Malaysian economy and closely related to manufacturing

and service sectors. Manpower is a vital resource

that contributes towards high productivity. Higher

productivity means we could produce more output

by using the same amount input sources. Quality of

work, management, and working conditions are the

three critical factors that could be used to increase

the workers’ productivity [1]. In this study, the

environmental factors were comprises of illuminance,

relative humidity and Wet-Bulb Globe temperature

(WBGT). It is believed these three factors have

significant influence on employees’ safety, health, and performance in the workplace and daily life [2].

Dua [3] found that lower emotional health could be

manifested by psychological distress, depression and

anxiety; meanwhile, lower physical health could be

manifested as heart disease, insomnia, headaches

and infections. These environmental factors could

influence the production operators comfort level while they are performing their daily tasks. Ettner

and Grzywacz [4] in their study found that work

environments can be associated with perceived effects

of work with respect to workers’ health, safety and

productivity. Therefore, there is need to conduct a

study to determine the relationships between the

effects of these environmental factors on the workers’

productivity. Response Surface Method (RSM) and

MINITAB software were used while performing

this study to determined the values of environmental

parameters that optimize the workers’ productivity.

According to Myers et al. [5], RSM is an empirical

model that can be utilized to obtain the relationship

between illuminance, relative humidity and Wet Bulb

Globe Temperature (WBGT) parameters

II. MATERIALS AND METHODS

A body line switch with wire harness assembly

of automotive parts production line was chosen to

be simulated in a control room. The control room

was designed to be similar with the actual assembly

line workstation in an automotive component

manufacturing industry. The study was conducted in

a control room which has an area of 17 meter squares

and it was equipped with environmental control

systems such as air-conditioning systems, variable

control lighting switch, and dehumidifier. These control systems can be used to control environmental

factors such as temperature, illuminance and relative

humidity. Equipment arrangement and placement

were based on previous studies [6; 7; 8].

The room temperature was controlled by using

the air conditioning system. Based on a past study

conducted by Lan et al. [6], the air-conditioning system

was mounted on the upper side of the wall and facing

the subject for controlling the room temperature (i.e.

WBGT).

The illuminance can be controlled by using a

variable control switch lighting system, which is

connected to a set of fluorescent lamps. By using a variable control switch, illuminance levels in the

control room can be adjusted to achieve the desired

levels. Based on a previous study conducted by Juslén

et al. [7], the fluorescent lamps were mounted on the side of the subject. This was done to ensure the light

source can directly illuminate the subject.

Dehumidifier equipment was used to control the amount of water vapour present in the air, inside

the control room. When the amount of water present

in the air can be controlled, the relative humidity

inside the control room can also be controlled at the

desired levels. Based on Tsutsumi et al. [8] study,

the dehumidifier device was placed near the subject so that he/she can control the relative humidity in the

air.

Fluorescent lamps were placed above the

subject and Heavy Duty Light Meter tool was used to

measure the illuminance was placed perpendicular to

the subject’s eye position. This was done to provide

better data readability. Air-conditioning system and

dehumidifier were installed in-front of the subject. Thermal Quest Environment Monitor was used to

record temperature and humidity readings, it was

placed on the left side of the table.

Later, data was analyzed using Response

Surface Method (RSM) first order analysis to study the relationship of each factor towards worker’s


85

productivity. Further optimization of environmental

factors were carried out through MINITAB to

determine the optimum values of environmental

factors that can produce productivity of 1.0.

In this study, 9 subjects had volunteered to take

part in the experiments. They were asked to install

contact switch spring to the body and connect them

to the wire harness. As a token of appreciation all the

subjects were paid for taking part in the experiments.

Subjects were divided into two groups: 6 subjects

participate for obtaining raw data and the other 3

subjects used for validation purpose. The numbers

of subjects involved in this experiment are in-line

with Goldman [9] suggestion, which states six is the

minimum number of subjects for conducting research

on human. Goldman [9] has 50 years of experience

in the field of human thermal comfort. Physical parameters such as age, height, weight, and gender were

also recorded. The subjects had no prior experience in

performing the task and they were trained for about a

week before performing the actual study.

To ensure the data were accurate, all measuring

equipment were placed at an appropriate distance

with respect to the subject without disturbing their

movement, while they are performing their assembly

tasks. The study was conducted for 90 days during

daytime period. All the environmental factors and

productivity were recorded at 10 minutes time intervals

for 4 hours working shift.

Air velocity factor in the control room are

designated as null and held constant throughout the

experiment. Tsutumi et al. [8] in their study had

controlled the air velocity to ensure it was maintained

at constant level throughout the experiment. In this

experiment, motivational factors such as family or

financial problems were not taken into consideration.

Prior to start performing their tasks in the experiment,

all the subjects were told to settle their family or

financial problems and they would get same pay even though they achieve high or low productivity.

During the experiment, the subjects worked

normally and the equipment for experimental

measurement did not restrict their movements while

they were doing their work. The equipment was

mounted near the operator, with a maximum range

of 3 meters. All environmental factors values were

recorded at every 10 minutes time intervals.

In this study, productivity is determined by

comparing the real output value with the target

output. Productivity will be calculated as the ratio of

actual output (output) to target output (output). The

measuring equipment were calibrated, prior to starting

the data collection process. In this study, the target

time to complete a unit is 1.8 minutes. Therefore to

assembly 10 complete units would take 18 minutes.

Productivity of workers is the ratio of actual output

to targeted output. Therefore, in this experiment

productivity was calculated using Equation 1.

The Design of Experiments (DOE) data must

be generated first before starting the experiment. The experiments were carried out from this generated

data to obtain the productivity values. DOE data was

generated through the RSM analysis. DOE data was

developed by referring to parameters values that have

been studied by previous researchers [6; 7; 8].

Productivity = (1)output

18

Sequent WBGT Relative Humidity

Illuminance

0C lux %12 345 6789101112131415

P TermConstant

WBGT

Relative humidity

Illuminance

WBGT x WBGT

Relative humidity x Relative humidity

Illuminance x Illuminance

WBGT x Relative humidity

WBGT x Illuminance

Relative humidity x Illuminance

0.000

0.000

0.000

0.000

0.013

0.000

0.000

0.556

0.000

0.000

T201.038

-17.431

-12.152

25.398

-2.544

3.798

5.482

-0.592

-9.264

24.514

SE coefficient0.0049

0.0029

0.0029

0.0029

0.0044

0.0044

0.0044

0.0042

0.0042

0.0042

2= 96.09%

19.025.525.532.025.525.525.5 25.5 32.032.019.019.032.0 19.025.5

557055405555404055555540707070

10001000

600600600600

1000200

1000200200600600600200

86


Table 1 Design of experiment details

In their study Tsutsumi et al. [8] had used four

levels of relative humidity at: 30% RH, 40% RH, 50%

RH and 70% RH. Meanwhile, Lan et al. [6] conducted

their study using four levels of room temperature at:

19°C, 24°C, 27°C and 32°C. For lighting, the authors

had adopted Juslén et al. [7] methodology. Juslén

et al. [7] found there were significant activities at illumination levels between 200 lux and 1000 lux.

All parameter values were added and the authors

take only the minimum and maximum parameter

values for generating the DOE data as shown in

Table 1. The DOE data was based on Box-Behnken

design because it has fewer factors.

III. RESULTS AND DISCUSSIONS

Data were analyzed using second order analysis

of Response Surface Method (RSM) via MINITAB

software to study the relationship of each factor

towards worker’s productivity. Further optimization to

determine the optimum values for three environmental

factors were carried out using MINITAB software to

achieve productivity of 1.0.

RSM analysis using quadratic modeling was

performed to present the interaction effect for each

environmental parameter. The quadratic model was

analyzed using Analysis of Variance (ANOVA).

The mathematical model was generated by using

MINITAB software. Table 2 shows the coefficients of

the quadratic regression model.

All coefficients in the model were later converted into a real terms mathematical equation:

Where y is the productivity, x1 is WBGT (°C),

x2 is relative humidity (%), and x3 is lighting level

(lux).

Table 2 shows the P value for illuminance;

p = 0.000, the P value for WBGT; p = 0.000, the P

value for relative humidity; p = 0.000, the P value for

WBGT x WBGT; p = 0.013, the P value for relative

x humidity relative humidity; p = 0.000, the P value

for illumination x lighting; p = 0.000, the P value for

WBGT x lighting; p = 0.000 and P value for relative

humidity x illumination; p = 0.000, all parameters

showed a significant effect (i.e. p <0.05) at 95% confidence interval except WBGT x relative humidity (p = 0.556). Indeed, the value of p below the 0.05 also

implies that there are significant interactions effects between the environmental parameters studied. It

was found the coefficient for WBGT x relative humidity (p = 0.556) did not has significant impact on productivity because the p value was higher than 0.05

(i.e. p > 0.05).

DOE data was developed by referring to parameters values that have been studied by previous researchers [6; 7; 8].

In their study Tsutsumi et al. [8] had used four levels of relative humidity at: 30% RH, 40% RH, 50% RH and 70% RH. Meanwhile, Lan et al. [6] conducted their study using four levels of room temperature at: 19 °C, 24 °C, 27 °C and 32 °C. For lighting, the authors had adopted Juslén et al. [7] methodology. Juslén et al. [7] found there were significant activities at illumination levels between 200 lux and 1000 lux. All parameter values were added and the authors take only the minimum and maximum parameter values for generating the DOE data as shown in Table 1. The DOE data was based on Box-Behnken design because it has fewer factors.

Data were analyzed using second order analysis of Response Surface Method (RSM) via MINITAB software to study the relationship of each factor towards worker’s productivity. Further optimization to determine the optimum values for three environmental factors were carried out using MINITAB software to achieve productivity of 1.0.

RSM analysis using quadratic modeling was performed to present the interaction effect for each environmental parameter. The quadratic model was analyzed using Analysis of Variance (ANOVA). The mathematical model was generated by using MINITAB software. Table 2 shows the coefficients of the quadratic regression model.

effect (i.e. p <0.05) at 95% confidence interval except WBGT x relative humidity (p = 0.556). Indeed, the value of p below the 0.05 also implies that there are significant interactions effects between the environmental parameters studied. It was found the coefficient for WBGT x relative humidity (p = 0.556) did not has significant impact on productivity because the p value was higher than 0.05 (i.e. p > 0.05).

Meanwhile, the value for coefficient of Regression (R2) is 0.9609. This value indicates high validity value of the mathematical model prediction by using RSM technique. In this case, the R2 value was very close to 1.0, which indicates there is a very strong relationship between the true values found in the experiment with the values proposed by the quadratic model. In short, this quadratic model can represent

y = 1.961908 + 0.00573566x1 -0.0203483x2 -5.58281e-4x3 -2.6557e-4 + 7.4446e-5 +1.51106e-7 -2.57204e-5x1x2-1.50982e-5x1x3 +1.73129e-5x2x3 (2)

Where y is the productivity, x1 is WBGT (0C), x2 is relative humidity (%), and x3 is lighting level (lux).

Table 2 shows the P value for illuminance; p = 0.000, the P value for WBGT; p = 0.000, the P value for relative humidity; p = 0.000, the P value for WBGT x WBGT; p = 0.013, the P value for relative x humidity relative humidity; p = 0.000, the P value for illumination x lighting; p = 0.000, the P value for WBGT x lighting; p = 0.000 and P value for relative humidity x illumination; p = 0.000, all parameters showed a significant

Table 2 Coefficients of the quadratic regression model

All coefficients in the model were later converted into a real terms mathematical equation:

III. RESULTS AND DISCUSSIONS

Sequent WBGT Relative Humidity

Illuminance

0C lux %12 345 6789101112131415

P TermConstant

WBGT

Relative humidity

Illuminance

WBGT x WBGT

Relative humidity x Relative humidity

Illuminance x Illuminance

WBGT x Relative humidity

WBGT x Illuminance

Relative humidity x Illuminance

0.000

0.000

0.000

0.000

0.013

0.000

0.000

0.556

0.000

0.000

T201.038

-17.431

-12.152

25.398

-2.544

3.798

5.482

-0.592

-9.264

24.514

SE coefficient0.0049

0.0029

0.0029

0.0029

0.0044

0.0044

0.0044

0.0042

0.0042

0.0042

2= 96.09%

19.025.525.532.025.525.525.5 25.5 32.032.019.019.032.0 19.025.5

557055405555404055555540707070

10001000

600600600600

1000200

1000200200600600600200


87

Table 2 Coefficients of the quadratic regression model

Table 3 Analysis of variance for quadratic model

Meanwhile, the value for coefficient of Regression (R²) is 0.9609. This value indicates high

validity value of the mathematical model prediction

by using RSM technique. In this case, the R² value

was very close to 1.0, which indicates there is a very

strong relationship between the true values found in

the experiment with the values proposed by the

quadratic model. In short, this quadratic model can

represent the true values for each environmental

factor and thus can be adopted and used to generate

the optimization value for each environmental factor

to obtain the optimum productivity.

Analysis of variance was also conducted to

determine the reliability level for the RSM analysis.

Table 3 shows the analysis of variance for the

quadratic model. In this study the confidence level used was at 95%, all source has a p-value less than

0.05 thus showed that there is a significant value for mathematical model except the p value for lack of fit model; p = 0.151. In summary, the p value for lack of

fit model; p = 0.0151, which indicates the model is not significant due to lack fit factor. In other words, this model is suitable and will generate less reading error.

The interaction between one respective

parameter with the other respective was illustrated by

using RSM. For example, the interactions between

productivity with the relative humidity and WBGT

environmental factors are illustrated in Figure 1,

Figure 2 and Figure 3.

WBGT (°C)

WBGT (°C)

Relative Humidity (%)

Illu

min

ance

(lu

x)

40

1000

900

800

700

600

500

400

300

20045 50 55 60 65 70

Illu

min

ance

(lu

x)

20

1000

900

800

700

600

500

400

300

20022 24 26 28 30 32

20

70

65

60

55

50

45

4022 24 26 28 30 32

Rel

ativ

e H

umid

ity (

%)

WBGT (°C)

WBGT (°C)


Illu

min

ance

(lu

x)

40

1000

900

800

700

600

500

400

300

20045 50 55 60 65 70

Illu

min

ance

(lu

x)

20

1000

900

800

700

600

500

400

300

20022 24 26 28 30 32

20

70

65

60

55

50

45

4022 24 26 28 30 32

Rel

ativ

e H

umid

ity (

%)

88


Figure 2 Graph for relative humidity and WBGT versus productivity

Figure 1 Graph for relative humidity and WBGT versus productivity

Relative humidity fixed at 40 %

Referring to Figure 2, it appears that maximum productivity occurred when the WBGT was below 23°C with illuminance higher than 960 lux and also when the illuminance was lower than 350 lux. On the other hand, the minimum productivity value was achieved when the WBGT values were in the range between 30°C to 32°C and illuminance level between 750 lux to 1000 lux. In summary, it can be concluded that productivity level dropped lower when WBGT increase. Meanwhile, lower illuminance levels (at high value of WBGT) would lower the productivity level. This result is in-line with Niemelä et al. [10] findings shows that lower productivity between 5 to 7 % when the WBGT value exceed 25°C.

WBGT fixed at 19°C

Figure 3 show that maximum productivity level was observed when the relative humidity higher than 60 % and illuminance exceeds 900 lux. On the other hand, productivity level would be at the minimum if the illuminance provided was lower than 400 lux and relative humidity at higher than 58%. In general,

higher relative humidity values gave negative impact to productivity except when the illuminance is at a high level. As for illuminance, higher productivity was achieved by increasing illuminance level. This relationship indicates that illuminance could bring significant impact on productivity. This result was in-line with the Juslén et al. [7] findings that illuminance gave positive correlation and impact towards productivity. It was observed during the experiment that subjects felt less sleepy and performed their tasks more effectively in high level of illuminance.

From the Equation 2, RSM had predicted the optimal productivity values were achieved by combining WBGT 23.63°C, relative humidity 54.43%, and illuminance 604.36 lux, with the strong interaction relationship between the parameters. In summary, the WBGT comfort level lies between 24°C to 27°C. The results found in this study were in-line with the results found by ISO 7730:2005 [11]; Tsutsumi et al. [8] that had recommended the comfort levels for relative humidity were in the range betweem 40 % to 50%; Juslén et al. [7], that had recommended the minimum requirement for illuminance at an assembly line for electrical industry was at 500 lux.

WBGT (°C)

WBGT (°C)


Illu

min

ance

(lu

x)

40

1000

900

800

700

600

500

400

300

20045 50 55 60 65 70

Illu

min

ance

(lu

x)

20

1000

900

800

700

600

500

400

300

20022 24 26 28 30 32

20

70

65

60

55

50

45

4022 24 26 28 30 32

Rel

ativ

e H

umid

ity (

%)


89

Figure 3 Graph for relative humidity and Illuminance versus productivity

IV. CONCLUSION

The overall objective to study the effects of

environmental factors (i.e. WBGT, relative humidity,

illuminance) towards the productivity of workers at the

assembly production line in automotive industry was

fully achieved through the research. A mathematical

model was developed to relate the relationship of

each environmental factor on worker’s productivity.

It was found in this study the environmental factors

(i.e. WBGT, relative humidity, illuminance) that

could achieved the optimal productivity value of 1.0

were: WBGT 23.63°C, relative humidity 54.43%, and

illuminance 604.36 lux.

ACKNOWLEDGMENT

The authors would like to thank Universiti

Kebangsaan Malaysia under UKM-GUP-2011-039

for their support in providing a research grant for a

project Design of an Ergonomics Car Driver’s Seat

Using Malaysian Anthropometrics Data.

REFERENCE

[1] Prokopenko, J. 1987. Productivity management:

A practical handbook. International Labour

Organisation. Switzerland.

[2] Dul, J. & Weerdmeester, B.A. 2008. Ergonomics

for Beginners. Third Edition, Taylor & Francis

Group.

[3] Dua, J.K. 1994. Job stressors and their effects on

physical health, emotional health and job

satisfaction in a university. Journal of

Educational Administration, 32: 59-78, doi:

10.1108/09578239410051853

[4] Ettner, S.L. and Grzywacz, J.G. 2001. Workers’

perceptions of how jobs affect health: A social

ecological perspective. Journal of Occupational

and Health Psychology, 6: 101-131.

[5] Myers, R.H., Montgomery, D.C., Cook, C.M.A.

2009. Response Surface Methodology:

Process and Product Optimization Using

Designed Experiments. Third Edition, John

Wiley & Sons, Inc, New Jersey.

90


[6] Lan, L., Lian, Z., Pan, L., & Ye, Q. (2009).

Neurobehavioral approach for evaluation of

office workers’ productivity: The effects of room temperature. Building and Environment 44(8):

1578-1588.

[7] Juslén, H.T., Verbossen, J. & Wouters, M.C.H.M.

2007. Appreciation of localized task lighting

in shift work- a field study in the food industry. International Journal Of Industrial Ergonomics

37(5): 433-443.

[8] Tsutsumi, H., Tanabe, S., Harigaya, J., Iguchi, Y.

& Nakamura, G. 2007. Effect of humidity on

human comfort and productivity after step

changes from warm and humid environment.

Building and Environment 42(12): 4034-4042.

[9] Goldman, R. F. 2005. Environmental

Ergonomics: Whence What Wither. Proceeding

of the 11th International Conference on Environmental Ergonomics, Ystad, Sweden, pp.

39-47.

[10] Niemela, R., Hannula, M., Rautio, S., Reijula,

K., Railio, J. 2002. The effect of air temperature

on labour productivity in call centres-a case

study. Energy and Buildings 34: 759-764.

[11] ISO7730:2005. Ergonomics of the Thermal

Environment: Analytical Determination and

Interpretation of Thermal Comfort Using

Calculation of the PMV And PPD Indices and

Local Thermal Comfort Criteria.


91

The Reaction Of Nigerian School Children to Back Pain Due to Backpack Usage

Ademola James Adeyemi*, Jafri Mohd. Rohani, Mat Rebi Abdul Rani

Faculty of Mechanical Engineering

Universiti Teknologi Malaysia

Johor, Malaysia

*E-mail: [email protected]

ABSTRACT

Studies across the world have shown high prevalence of back pain among backpack users in schools but little information

is available on how the children react to such pain. That is what this paper is aimed at investigating. Cross section study

was carried out among pupils from 4 schools in Nigeria. The participants’ age ranged from 8 to 12 years (mean 10.29±1.21

S.D) with gender composition even at 97 for boys and girls. Children’s opinion on pain occurrence and their reaction

was obtained through questionnaires. Standiometer and digital scales were also used to obtain the height and weight of

the children respectively. The relationship was investigated by One-way ANOVA and Bivariate correlation using PASW

statistic 18. Only age and backpack weight was significantly associated with their reaction to pain. The study highlights

the role of age and weight of school bag towards proffering solution to the back pain problem among school children.

Keyword: Schoolchildren, Back pain, Backpack, Reaction

INTRODUCTION

Back pain occurrence among school children

is no longer a controversy as literature provides

evidence of high prevalence rate of back pain among

school children [1, 2, 3]. The situation has a global

dimension as it cuts across economical (advanced

and developing countries) or regional (Europe, Asia,

America and Africa) classification. Initial research was mainly carried out in the developed countries such

as Europe, Australia and America. These studies have

recommended a safe weight of 10-15% of body weight.

Since ergonomic studies are based on preponderance

of facts, repeated studies in many developing countries

have found such recommendation inadequate for the

elimination of the problem [4, 5]. Follow up studies

in countries where recommendations have been made

has also identified that other factors play major role

in the occurrence of back pain [1, 6, 7]. Various bag

design features have also been recommended [5]

and ergonomic interventions to promote awareness

have been implemented [8]. The seriousness of the

problem has led to countries developing guidelines and

standards based on these existing guidelines [3].This is

because studies have highlighted the possibility of the

high prevalence of back pain in school children as a

contributory factor towards back pain in adulthood [9,

10]. Despite these studies, literature is scanty on the

reaction of children to the pain when it occurs. This

might have psychological effects on the children’s

perception of pain which is believed to be subjective.

This paper is therefore aimed at investigating the

reaction of children to back pain and its association

with individual variables in children.

92


Table 1: Descriptive Statistics of the dependent variables investigated against children’s reaction

METHOD

A cross sectional study was conducted between

May and June, 2012 in Nigeria. 194 pupils from

4 schools participated in the study. The age of

the participants ranged from 8 to 12 years (mean

10.29±1.21 S.D). The gender composition was even at

97 for boys and girls. The study was approved by the

management of the schools and pupils whose parents

did not give consent were excluded from the study.

Questionnaires were given to the pupils and they

were expected to provide demographic information

such as age, sex and class. A specific question, “while carrying my bag, I normally feel………” was asked.

The children were expected to tick from a list of four

responses: (1) Nothing (2) Tired (3) Aches/Pain

and (4) Tired and Pain. The heights of the children

were measured using a SECA 213 standiometer with

an accuracy of 0.01cm. The pupils were instructed

to stand upright with their hands by their sides. The

measurement was taken without their shoes on. The

weight was measured with a Beurer diagnostic scale

BF 20 with an accuracy of 0.01kg while the weight

of the bag was measured with a digital hanging scale

having an accuracy of 0.01kg. Hanging scale was

used to ensure accuracy. The contents of the bag were

not itemized since a question in the questionnaire

was asked to indicate the contents of their bag. Data

analysis was carried out using Statistical Package

for Social Science PASW statistic 18. The data was

initially screened for homogeneity of variances and

Analysis of Variance (ANOVA) was conducted to

investigate the significance of participants’ reaction to the measured parameters. The significance level was set at p<0.05. Bivariate correlation analysis was then

carried out on the significant variables.

RESULTS AND DISCUSSION

The descriptive summary of the variables are

presented in Table 1. The Levene statistic test shows

that apart from sex (p=0.031), other variables; age

(p=0.274), height (p=0.839), weight (p=0.714) and

weight of bag (p=0.212) all demonstrated homogeneity

of variances. The ANOVA table presented as table 2

shows that only age ((F(3,190)=3.344, p=0.018) and

the weight of bag (F(3,190)= 2.567, p=0.046) shows

any underlying relationship with the pupils’ reaction to

pain. While that of the age is obvious, weight of the bag

has a close call which requires further investigation.

Table 3 shows a bivariate correlation results between

the significant variables and reaction to pain. There is a significant correlation between age and weight of bag (r=0.389, p<0.001) and also between age and

reaction to pain (r= -0.196, p=0.006).

Variables Number Maximum Minimum Mean Standard Deviation

Sex 194 1.00 2.00 1.5000 .50129

Age 194 8.00 12.00 10.2938 1.21359

Height 194 120.00 167.20 143.4046 9.51479

Weight 194 21.70 78.30 37.7670 11.26044

weight of bag 194 0.55 10.84 3.9328 1.84551


93

Table 3: Bivariate correlation coefficient among the significant variables and children’s reaction

Table 2: ANOVA table showing the level of significant difference among the variables

Age

The study shows that there is significant difference in the reaction to pain among the various

ages. Age being significant might be associated with adaptation to pain. While larger percentage of all the

age groups reported doing “nothing” when they felt

back pain, a larger percentage among the lower age (8

years-23% and 9years-36%) complain and takes drugs

at home compared to the older ones (10years-14%,

Variables SS df MS F Sig

Sex Bet Groups 1.096 3 0.366 1.467 0.225

Within Groups 47.402 190 0.249

Total 48.500 193

Age Bet Groups 14.644 3 4.881 3.440 0.018

Within Groups 269.608 190 1.419

Total 284.253 193

Height Bet Groups 495.458 3 165.153 1.848 0.140

Within Groups 16777.068 190 89.353

Total 17472.526 193

Weight Bet Groups 186.783 3 62.261 0.487 0.692

Within Groups 24285.126 190 127.816

Total 14471.909 193

Weight of bag Bet Groups 25.603 3 8.534 2.567 0.046

Within Groups 631.734 190 3.325

Total 657.337 193

Age p Type

Reaction after pain -0.196 0.006 Spearman

Weight of bag 0.389 0.000 Pearson

11years-16% and 12 years-6%). This couldn’t be as a

result of the weight of the load because table 3 shows

that the children’s age is positively correlated with

the load of the bag and yet negatively correlated with

reaction after pain. This is an indication of the problem

in proffering a general solution across different age

groups.

94


Backpack weight

Backpack weight being slightly significant is an indication of different reaction to the varying degree of the load. Yet the level of significance might be associated with no distinct weight of bag for a particular age although table 3 reveals that age is positively correlated with the weight of the bag. As expected, the weight of the bag content increases with class as the work load generally increases from one class to another [2]. Yet there is no clear distinction in backpack weight between two subsequent classes as other factors such parents and pupils’ individual differences also contribute to the weight of the bag. This might justify why there are still high prevalence rate of back pain complaint despite biomechanical analysis indicating that 10% of percentage body weight is safe. It justifies the argument that backpack weight is not a sufficient index to justify the safe weight but other contributing factors require consideration also [3, 11]. The finding also discloses that height, weight and sex of individuals do not determine how he or she reacts to pain as they were also found to be insignificant.

A similar study recently carried out in Malaysia also has similar outcome. The findings have brought to the limelight the increment in pain tolerance as children grow older.

CONCLUSION

The need to be pragmatic has led to the investigation of the reaction of school children to the problem of back pain due to backpack use. This study has identified age and weight of the school bag as two vital variables to consider when investigating the back pain problem among school children in order to arrive at a comprehensive solution to the problem.

REFERENCE

1. Gilkey, D.P., Keefe, T.J., Peel, J.L., Kassab, O.M and Kennedy, C.A. 2010. Risk factors associated with back pain: A cross-sectional study of 963 college students. Journal of Manipulative and Physiological Therapeutics. 33(2): 88-95.

2. Grimmer, K. and Williams, M. 2000. Gender- age environmental associates of adolescent low back pain. Applied Ergonomics. 31: 343-360.

3. Adeyemi Ademola James, Jafri Mohd Rohani, Mat Rebi Abdul Rani. 2012. Development of a Holistic Backpack-Back Pain Model. Presented at Southeast Asian Network of Ergonomics Societies conference, 9-12 July, 2012, Langkawi, Malaysia.

4. Al-Hazzaa H.M. 2006. School backpack: how much load do Saudi school boys carry on their shoulder. Saudi Medical Journal, 27(10):1567-1571.

5. Amiri, M., Dezfooli, M.S. and Mortezaei, S.R. 2012. Designing an ergonomics backpack for student aged 7-9 with user-centred design approach. Work: 41:1193-1201.

6. Negrini, S. and Carabalona, R. 2002. Backpacks on! School children’s perceptions of load, associations with back pain and factors determining the load, Spine (Phila Pa 1976). 27 (2):187-195.

7. Sheir-Neiss, G.I., Kruse, R.W., Rahman, T. Jacobson, L.P. and Pelli, J.A. 2003. The association of backpack use and back pain in adolescents, Spine (Phila Pa 1976). 28 (9): 922- 930.

8. Syazman, A.I., Tamrin, S.B., Baharudin, M.R., Noor, M. A., Juni, M. H., Jalaludin, J. and Hashim, Z. 2010. Evaluation of two ergonomics intervention programs in reducing ergonomic risk factors of musculoskeletal disorder among school children. Research journal of medical sciences. 4 (1):1-10.

9. Murphy,S., Buckie, P. and Stubbs, D. 2004. Classroom posture and self-reported back and neck pain in school children. Applied Ergonomics 35: 113-120.

10. Korovessis, P., Repantis, T. and Baikousis, A. 2010. Factors affecting low back pain in adolescents. Journal of spinal disorders and techniques. 23 (8):513-520.

11. Marras, W.S. 2008. The working back: A systems view,” New Jersey: John Wiley and sons Inc.


95

The Study of Respirable Dust Concentration in Paper Based Industry

N. Azreen P1, A.M. Leman2, A. Norhidayah3, Ismail M4

1Faculty of Mechanical & Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia (UTHM)2Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia (UTHM)

3Faculty of Technology, University Malaysia Pahang (UMP)4Faculty of Science and Technology, University Malaysia Terengganu (UMT)

ABSTRACT

Work environment factor such as air quality in industry become public concern recently especially due to issues

related to respirable dust. Most of the workers from paper based industry were exposed to dust during on their daily work

activities. A preliminary study and measurement was conducted at tissue mill and packaging area in one of the selected

paper based mill in Malaysia to monitor the personal exposure of respirable dust. Series of a direct reading measurement

for area sampling of respirable dust (PM10), carbon dioxide, temperature and relative humidity were also conducted at

the same time. Questionnaires were administrated in purposed to determine the respiratory health symptoms. The result

of the study showed most of the workers are exposed to respirable dust when the TWA result was above the permissible

exposure limit which is 5 mg/m³ and 3 mg/m³ from OSHA’s and ACGIH standard respectively. From the survey feedbacks

several workers sometimes exposed with the symptoms but claims that it was happen with no noticeable trend they relief

when they leave the building for both TM and KLU2 workers. For respiratory symptoms problem, seem like majority

of workers never experienced a prolonged cough. However, for a better mankind in future, some engineering control

and approach has been suggested to the safety and health team to control the machine that fully operated and consider

contribute to the dust concentration. Lung tests need to be done due to workers respiratory health status.

Keywords: Respirable dust, personal sampling, industrial hygiene.

INTRODUCTION

Various studies have provided evidence that particulate matter (PM), which represents the size range of particles likely to pass through the nose and mouth, is associated with a range of effects on human [1-4]. This fine particulate air pollutant, derived from both human and natural activities such as road and agricultural dust, tire wear emissions, wood combustion, construction, demolition works, and also from cement industry [5-9]. Work environment factor such as air quality in industry become public concern recently especially due to issues related to respirable dust. Most of the workers from paper based industry were exposed to dust during on their daily work activities. However, in fabrication of paper product, starting from selection of raw material till the end of production dust was generated.

Current study on the effects of particulate matter and dust exposures to the workers in industry had been conducted in many parts of worlds and variety of ages including India, US, and China [1, 2, 6, 7 and 12-15]. These researchers had published proves of significant epidemiological effects of respiratory symptoms towards wild land firefighters, cricket bat manufacturing workers, school children, rural and urban residential area that exposed to the particulates. Most study concluded that the weather also plays their roles in influencing the concentration of dust and particulate matters in atmosphere. The epidemiological evidence shows adverse effects of particles associated with both short-term and long-term exposures. Adverse health effects have been demonstrated at levels just above background concentrations which have been estimated at 3–5 μg/m³ in the United States and Western Europe for PM2.5 [10-11].

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Figure 1: Step Measurement Figure

Currently, there is still very limited study that discussed the effects of long time exposures of dust and particle matters in industry at Malaysia. Thus, this preliminary study was conducted in a tissue paper fabrication to get the basic experiences and fundamental knowledge on respirable dust issues and their effects to human. The purpose of this study is to monitor the concentration of personal exposure of paper based industrial worker to the respirable dust. Some qualitative feedbacks of workers health status was conducted through the questionnaire in relation of respiratory symptoms issue.

METHODOLOGY

This study was conducted during a typical eight hours working shift from 8.00 a.m. until 5.30 p.m. with morning breaks starting from 10.00 a.m. till 10.20 a.m. and lunch breaks at 1.00 p.m. to 2.00 p.m. In production process of tissue paper, there are several steps starting from raw material which is virgin pulp, recycle paper or mix of both material, till the end product where it ready for user. A walk through inspection and study of the process were done by a site visit and interviewing person in-charge and relevant personnel. The assessment was focused in the following work area which believe contribute to the respirable dust emission source are Tissue Mill (TM) and packaging area (KLU2) which are contain rewinder and converting proses. These three processes had been selected for this study regarding to highest risk area as consideration.

This is because these locations were generating dust due to tissue paper fabrication processes.

Meanwhile, the location of these processes were inside the building far from the main entrance and windows and also adjacent with others two sections.

Out of 15 workers at tissue paper fabrication, there are ten workers that direct handling with the machines and four workers in control room were selected as respondent. While in packaging area, 14 workers was selected. All the welders were wearing protective gears such as safety boots, safety goggles, ear plug and arm cover. However none of the welders were using respirators during work. The workplace does not have any extractor but uses fan at certain area workstation by means of controlling dust. Based on visual inspection, this workplace seems highly exposed with the dust especially during the process and end of work shift whereby the workers blow the dust from machineries in purpose of clealiness. The situation is getting ‘snowy’ when windy and rainy day. This is because the wind blows back the dust into the building from the existence window. Based on Figure 1, there are three main measurements conducted in this study are as follows:

2.3 Questionnaire Survey Set of questionnaire were drafted to seek information from workers in term of:

(a) Demographic background

(b) Basic information on current health status (c) Persistence symptoms experiences by the respondent for past three (3) months

(d) Respiratory health symptoms


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Figure 2: Distribution of Respirable dust PM10 concentration in (a) tissue mill (TM) and (b) packaging (KLU2)

(a) (b)

The questionnaires were then translate to Malay language by fluent speaker and try out with 2 to 4 native speakers. The questionnaires were translated back to its original language using certified translator. These questionnaires were distributed to all the workers during the beginning of the work and were collected at the end of the work shift.

RESULT AND DISCUSSION

3.1 Personal Sampling Monitoring

Based on the data collected, its showed that the highest concentration in TM was 21.51 mg/m³ per Time Weighted Average (TWA) and the lowest concentration is 0.44 mg/m³. Meanwhile the highest

concentration of respirable dust concentration in KLU2 was 63.08 mg/m³ per TWA and the lowest was 2.29 mg/m³. In TM and KLU2 area, based on the result obtained in Figure 2 (a) and (b), shows that most of the workers are exposed to respirable dust when the TWA result was above the permissible exposure limit which is 5 mg/m³ and 3 mg/m³ from OSHA’s and ACGIH standard respectively.

However, between TM and KLU2 areas, the concentration of respirable dust for respondent at KLU2 is higher than TM. This is due to the main activities of each area. Even TM is the production line of tissue and always scattered with paper tissue dust, the higher concentration was found in KLU2.

Figure 3: Distribution of Respirable dust PM10 concentration in (a) tissue mill (TM) and (b) packaging (KLU2)

(a) (b)

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Figure 4: Distribution of relative humidity concentration in (a) tissue mill (TM) and (b) packaging (KLU2)

(a) (b)

Figure 5: Distribution of carbon dioxide concentration in (a) tissue mill (TM) and (b) packaging (KLU2)

(a) (b)

3.2 Area Sampling Monitoring

a) PM10

The profile of PM10 concentration levels of the selected sampling point is shown in Figure 3. The figure depicts the temporal distribution of PM10 concentrations, where the concentration varied with four (4) slot time depending on the corresponding changes of daily work activities inside the plant. The profile for each plant appears to be different from each other. Overall, the PM10 mass concentration distribution in the six sampling point in each three sampling area appear to be different from each other. In Figure 3 (a), there is a trend of respirable dust distribute high during morning and afternoon session. This resulted obtained due to the location of sampling point is near with the Tissue Mill Kitchen Reel and the motion of workers doing their daily work activities.

b) Relative Humidity

The profile of RH levels of the sampling point in each plant area is shown in Figure 4. The trend of RH level concentration for each area shows that the

high level was in Slot 1 and Slot 4. This is because of weather factor which can be the interruption of the data. However, from all data collected, the result displayed that the RH level in that area is within the permissible amount which are between 40% till 70% of RH level recommended by ICOP-IAQ for office premises but yet still no reference standard can be refered for manufacturer and factory.

c) Carbon Dioxide

The profile of CO2 concentration levels of the sampling point in each plant area is shown in Figure 5. The profile for each sampling point appears to be different from each other. There is no such trend that obviously obtained through this result. However, compared among all these sampling area, CO2 concentration level is higher in packaging (KLU2) area compare the others. As per walk through inspection that had been done, KLU2 consist of more workers compare to TM.

Even the number of sample group was 14 workers for each sampling area, hence the existence of others workers that exist during at that sampling


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Figure 6: Distribution of temperature level in (a) tissue mill (TM) and (b) packaging (KLU2)

(a) (b)

also contribute the increasing amount of CO2 concentration. As human as the main factor who contribute the increasing of CO2 concentration, this would be support this idea.

d) Temperature

The profile of temperature levels of the sampling point in each plant area is shown in Figure 6. The profile for each sampling point appears to be different from each other. From all slot time of monitoring, the data obtained demonstrate at TM seems fluctuate and show no trend. Meanwhile in KLU2 the levels of temperature are between 27°C till 34°C. There is no significant trend among all sampling point of both sampling area.

3.3 Respiratory Survey Feedback

A total of eighteen (18) survey questionnaires were conducted for the workers at TM and KLU2 area respectively. With reference to the area distribution, there were nine (9) workers responded to the survey for both area. The age group of the workers are ranged between 31 to 40 years old (66.7%) in TM and 55.6% in range group of 21 to 30 years old in KLU2.It was noted that majority of workers (88.9 % and 100%) in SPM level of academic for both mill.

Most of KLU W#2 workers have been working in the mill for more than 1 to 10 year (33.3%). While in TM, there were 33.3% of worker was served to the mill for 1 to 10 years and 20 to 30 years. The feedbacks received were deemed to be valid and representative since they have been repeatedly working at the same activities and mill for more than eight hours respectively.

In general, majority of the office staffs spent more than 30 hours in a week at their work station and was surrounded with dusty condition at working area. Most of the workers in TM satisfied with the cleanliness in their work area compare with KLU2 workers. It was noted that majority of the workers were in healthy condition. Majority of the workers experienced the itchiness at eyes, nose throat during breathing in mill (66.7% for both mill). There is a possible risk that those respondents exposed to the dust and dry air.

In general, the survey feedbacks for symptoms occurrences for past three months before seem that it is majority rarely got headache, feeling heavy-headed, fatigue/lethargy, drowsiness, dizziness, nausea/vomiting, cough, irritated, stuffy nose, hoarse, dry throat, skin rash/itchiness, irritation of the eyes and scaling/itching scalp or ears.

Health Symptom Mean Yes %

No%

Mean Criteria

Smoking 50 50 1.50 No

Dust Allergic 33.3 66.7 1.67 No

Breath Difficulty 16.7 83.3 1.83 No

Chest Problem 22.2 77.8 1.78 No

Long Cough 33.3 66.7 1.67 No

Itchy at eyes, nose

and throat

66.7 33.3 1.33 Yes

Nausea/Dizziness 5.6 94.4 1.94 No

Itchy at Skin and

eyes

27.8 72.2 1.72 No

Fatigue/Lethargy 33.3 66.7 1.67 No

Shortness of breath 22.2 77.8 1.78 No

Eyes Irritation 0 100 2.00 No

Cleanliness Satisfied 50 50 1.50 No

Dust Emission 88.9 11.1 1.11 Yes

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Table 1: Questionnaire analysis for health symptom

Several workers sometimes exposed with the

symptoms but that claims that it was happen with

no noticeable trend they relief when they leave the

building for both TM and KLU2 workers. They also

said that majority of the rarely or never experiences the

extraordinary of fatigue, hard to focus or remembering

something, depression, feeling uneasy at stomach,

body coordination and imbalance problem, stress,

nervousness, pain at finger or swollen at hand and leg or difficulty or sleep disturbance. The symptoms were likely associated with prolonged working hours,

respirable dust exposure and factor of sick building

syndrome as most of the respondents were reported to

get better when they were away from building.

CONCLUSION

As the conclusion, this study will prove a

better understanding regarding the association of

respirable dust concentration with health symptom

at the industrial area and consequences happen for

prolonged exposure of air pollutants. The result that

obtained is very significantly to be used as part of the characteristic in risk management process.

To get solving the problem occurs, a better

understanding the pollutant source have to be done.

For get a crystal clear procedure, the process flow of manufacturing production had to be understood

and identified the source of emission of particulate matter. There was several size of particulate matter

(PM) especially respirable dust that can give adverse

health impact to exposed personal. There were a

few assessment will be done due to determination of

concentration of PM at industry.


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Table 2: Questionnaire analysis on health symptom for last past three months

Health Symptom

Yes,

Always,

caused by

working

environment

(YAY)

%

Yes,

Sometimes,

caused by

working

environment

(YSY)

%

Yes,

Always, not

caused by

working

environment

(YAN)

%

Yes,

Sometime

not caused

by working

environment

(YSN)

%

No, Never

%

Mean Mean

Criteria

Headache 0 22.2 0 16.7 61.1 4.17 YSN

Feeling Heavy-headed 0 16.7 0 0 83.3 4.50 YSN

Fatigue/Lethargy 0 27.8 0 5.6 66.7 4.11 YSN

Drowsiness 11.1 16.7 0 27.8 44.4 3.78 YSN

Dizziness 0 5.6 0 5.6 88.9 4.78 No, Never

Nausea/Vomiting 0 5.6 0 0 94.4 4.83 No, Never

Cough 16.7 27.8 0 11.1 44.4 3.39 YSN

Irritated, stuffy nose 11.1 27.8 0 0 61.1 3.72 YSN

Hoarse, dry throat 22.2 33.3 0 5.6 38.9 3.06 YAN

Skin rash/itchiness 0 38.9 0 0 61.1 3.83 YSN

Irritation of eyes 0 0 0 0 100 5.00 No, Never

Scaling/itching scalp or ears 5.6 44.4 0 0 50 3.44 YSY

Over fatigues 0 5.6 0 0 94.4 6.78 No, Never

Headache 0 33.3 0 0 66.7 5.67 YSN

Focus Difficulty 16.7 0 0 11.1 72.2 6.22 YSN

Depression 0 11.1 0 0 88.9 6.56 No, Never

Feel uneasy in stomach 5.6 0 0 11.1 83.3 6.56 No, Never

Body Imbalance Problem 0 0 0 0 100 7.00 No, Never

Stress, nervous 16.7 33.3 0 0 50 4.78 YAN

Pain in finger or toe 0 0 0 0 100 7.00 No, Never

Sleep Disturbance 22.2 33.3 0 5.6 38.9 4.39 YAN

ACKNOWLEDGMENT

The authors would like to express our

gratitude to the Office of Research, Innovation and Commercialization and Consultancy for the funding.

The authors would like to thank the Faculty of

Mechanical and Manufacturing Engineering

Universiti of Tun Hussein Onn Malaysia (UTHM) for

the morale support during this project.

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Whole Body Vibration Exposure: An Experimental Study to Malaysian Bus Driver

1Siti Nur Atikah Abdullah, 1Ahmad Rasdan Ismail, 2Abdul Mutalib Leman, 3Isa Halim, 1Nor Hidayah Abdull

1Faculty of Technology, Universiti Malaysia Pahang, 26300 Gambang, Pahang, Malaysia.2Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,

76100 Durian Tunggal, Melaka, Malaysia3Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia,

86400 Parit Raja, Batu Pahat, Johor, Malaysia

ABSTRACT:

Bus drivers are among the most important in ensuring that every trip smoother. All the way to the destination, the driver of his or her regular bus will have more time to sit on the driver’s seat. This is because they have to drive. A bus driver is more prone to suffer from low back pain. Therefore, it is very important to do research about how whole body vibration exposure along the journey. Bus drivers were lacked of knowledge about the whole body vibration exposure and how whole body vibration exposure can affect their way of driving. Data collected will be compared to the exposure limits as per ISO 2631 standard. The study will include one of the national bus express. The route is from Kuantan to Johor Bharu. About 20 bus drivers from terminal Kuantan will be asked to fill a questionnaire form. Collaboration with local agencies such as SIRIM and NIOSH also will be the most important. The route to Johor Bharu is quite long. Therefore, the decided time is day and night. It is to compare if the vibration during the daytime is longer than night time. According to ISO 2631, for the time worked 4hours; RMS value is 4 m/s². Thus, the value obtained shall not exceed the value of ISO 2631. In conclusion, the higher the value of the vibration in a bus, a bus driver will feel back pain or aching muscles or joints frequently. Therefore, the vibration must always review and monitored at all times.

Key words: Whole-body vibration, vibration dose value, low back pain.

INTRODUCTION

Whole body vibration (WBV) is when we applied through a supporting surface such as a seat or a platform. This statement also defined the basicentric coordinates system used in the standards, (O. Bruyere, et al. 2003). Exposure of the body and vibration or shock of this kind produces a complex distribution of oscillation motions and forces within the body which can degrade health, impair activities, impair comfort and cause motion sickness,(O. O. Okunribido et al. 2006).

Degraded health includes back ache and spinal damage resulting from exposure to seat vibration. Almost any part of the body can be damaged by vibration or shock, in some cases by a single event, in others by long term exposure. Vibration can disturb one’s comfort. Low frequency vibration can cause motion sickness syndrome. WBV measurements were

performed according to the International Standard ISO guidelines using a tri-axial seat accelerometer, (I J H Tiemessen et al. 2008). Noise is unwanted sound and measured in dB or sound power level to avoid hearing damage and to fulfil regulations. (P. H. T. Zannin, 2008).

Whole body vibration is an oscillation that is a movement back and forth as time passes. An example is a swinging pendulum. The source of all vibrations is forces. A force causes initial movement and force sustains the continued motion. A heavy spot on a rotor causes a centrifugal force as it rotates. This force going around during rotation creates a strain on the shaft which transmits through the bearings to the housing. Mass imbalanced is just one force causing vibration in machinery. There are other forces that can set machinery into oscillatory motion. (N. K. Kittusamy et al. 2004).

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Figure 1.0: Accelerometer sensor used for measuring the vibration.

Figure 2.0: (a) Profiling for whole body vibration exposure for x-axis, (b) Profiling for whole body vibration exposure for y-axis, and (c) Profiling for whole body vibration exposure for z-axis.

Note: The red line is referring to the Permissible Exposure Level (PEL).

(a) (b) (c)

Exposure to whole body vibration can cause physiological changes to the cardiovascular, respiratory and musculoskeletal system. Clinical affects attributed to whole body vibration include headache, motion sickness, sleep and visual disturbance. The only effect with reasonable evidence is low back pain. In drivers, low back pain may occur as a result of vibration, poor posture within the vehicle cab and other work duties. (D.J. Oborne, 2005).

Although vibration may produce undesirable side effects, several studies have shown the positive impacts of vibration (i.e. On the bone density of postmenopausal women and disabled children), back pain, stroke, multiple sclerosis and muscle spasticity of cerebral palsy sufferers.(A.D.Woolf et al. 2010).

METHODOLOGY

Materials and method

Bus driver will sit on accelerometer as in figure 1.0. Location for measuring the whole body vibration is all the way from Kuantan to Johor Bahru. Tools to measure whole body vibration is called accelerometer sensor. The accelerometer used in this study is the VI-400Pro. This measuring device consists of nine keyboards which is every keyboard plays an important role.

Each collected data can be viewed through displays screen on the device. However, for a more appropriate data, software Quest Suite Professional II is used because it is much easier. This is because the software will summarize the entire job right from the first one task of determining the unit, retrieve data and data review. Before using the device, the device must be calibrated until it reaches the exact value of 114dB. Bus is one of the public transports that produce a high magnitude of vibration.

Exposure Limit RMS Acceleration

8 h 2.8 m sec-2

4 h 4.0 m sec-2

2.5 h 5.6 m sec-2

1 h 11.2 m sec-2

30 min 16.8 m sec-2

5 min 27.4 m sec-2

1 min 61.3 m sec-2

noitidnoC timil erusopxE

Less than 0.315 m sec-2 Not uncomfortable 0.315 m sec-2 to 0.63 m sec-2 A little uncomfortable

0.5 m sec-2 to 1 m sec-2 Fairy uncomfortable 0.8 m sec-2 to 1.6 m sec-2 uncomfortable 1.25 m sec-2 to 2.5 m sec-2 Very uncomfortable

Greater than 2 m sec-2 Extreme uncomfortable

Exposure Limit RMS Acceleration

8 h 2.8 m sec-2

4 h 4.0 m sec-2

2.5 h 5.6 m sec-2

1 h 11.2 m sec-2

30 min 16.8 m sec-2

5 min 27.4 m sec-2

1 min 61.3 m sec-2

noitidnoC timil erusopxE

Less than 0.315 m sec-2 Not uncomfortable 0.315 m sec-2 to 0.63 m sec-2 A little uncomfortable

0.5 m sec-2 to 1 m sec-2 Fairy uncomfortable 0.8 m sec-2 to 1.6 m sec-2 uncomfortable 1.25 m sec-2 to 2.5 m sec-2 Very uncomfortable

Greater than 2 m sec-2 Extreme uncomfortable


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DISCUSSION

According to ISO Standard 2631, they are actually a table to refer the standard value of RMS acceleration and also the standard value of comfort reaction to vibration environment.

The first reference must be the exposure limit. The easier words to describe the exposure limit are the total of time for a bus driver to finish their work. Therefore, they are interpreting in hours. Since the route from Johor Bharu to Kuantan takes about 5 to 6 hours every journey, the RMS acceleration that should be referred is 8hours of exposure limit. Therefore, the value of RMS acceleration that should be measured is 2.8 m sec-2

The overall result that manages to get is 0.5467m sec2. Therefore, by comparing the value of the RMS

acceleration which is 2.8m sec-2, the result did not exceed the standard value of RMS acceleration given.

A.R Ismail (2010) stated that, the basic method (frequency weighted R.M.S. method) in ISO 2631-1 is primarily applicable to assessment of health risks from stationary vibrations not containing severe multiple or single event shocks. Single event shocks can be analysed with the additional method running R.M.S. in 2631-1, although there is no information on health risk levels. The additional method VDV (frequency weighted fourth power vibration dose value) is more sensitive to shocks than the basic method, but it will still underestimate the health risk of vibration containing severe shocks in comparison to the health risk of vibration not containing severe shocks. The EU Physical Agents Directive uses the basic method for assessment of health risk with VDV as an alternative.

Table 1.0: Standard value of RMS acceleration

Table 2.0: Standard value of comfort reaction to vibration environment

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Still, there are ranges to know either the condition is comfortable enough or not comfortable. For the value of RMS less than 0.315m sec2, the condition is stated as not uncomfortable. The value of RMS acceleration in between 0.315 m sec-2 to 0.63 m sec2, it is stated as a little uncomfortable. For the extreme uncomfortable is when the value of RMS acceleration is greater than 2m sec-2.

Therefore, according to result obtain the value for RMS acceleration that manages to collect during the measurement is 0.567m sec-2. By refereeing to table 3.0, it says that the value in between 0.5 m sec-2 to 1 m sec-2 the condition is fairy uncomfortable. There are a lot of reasons to contribute to this condition. According to T.C.Fai et al (2007), seats are one of the most important components of vehicles and they are the place where professional drive spend most of their time on the seat while working. M.J.Yu et al (2002), comfortable chair means that posture on body is the most close to natural state in it.

Other reasons that may lead to this uncomfortable condition are the condition of the road. Theoretically the best way to reduce most vibration is to control it at source by ensuring that all roads and work surfaces are smooth. This should be the aim especially for transport vehicles such as trucks and light vehicles.

The resulting vibration magnitude higher than the bus can cause a bus driver experience low back pain and musculoskeletal also rates. if low back pain experienced at the critical point, the driver can also experience problems in psychology and health problems for a long time. Low back pain is a condition in which a driver has pain in the lower back, and this can last up to several weeks. Musculoskeletal hand, is a condition in musculoskeletal system is injured depends on time. This inability occurs when one is working over time. These problems can have an impact in the blink of an eye. However, if the problem happens too often it can cause permanent disability.

A.R Ismail et al (2010), said that experimental studies have found that resonance frequencies of most of the organs or other parts of the body lie between 1 and 10 Hz, which are in the range of frequencies found in occupational machines and vehicles. 6 million workers are exposed to WBV typically while in a

seated position including delivery vehicles drivers, forklift operators, helicopters pilots and construction equipment operators (Griffin, 2006).

A study from Noorloos.D et al (2006) stated that BMI does not influence the risk of low back pain complaints in a population of occupational participants already exposed to whole body vibration exposure.

By referring to journal done by A.R. Ismail et al (2010), the methods are almost the same. But in term of software, it is different because they decided to use MATLAB. They conducted the measurement for train passenger in three different routes which is from Kajangto Seremban, from Seremban to Gemas and also from Segamat to Tampin. Compare to researcher, the researcher only manage to get one data from Johor Bharu to Kuantan. The values of daily exposure to vibration A (8) and Vibration Dose Value (VDV) were 0.3749 m sec-2 and 1.2513 m sec1.75 respectively. This is very different from value that the researcher have. Their result are slightly different because the journal are measuring the whole body vibration exposure to a passenger and not the driver, while the researcher are actually measuring the whole body vibration exposure to the bus driver which is said to have the highest bad condition in the bus. Furthermore, usually a train is design accordingly to follow the railway that they use to move. But the bus, it is unexpected, because the road can be really bad at certain time especially during rainy days.

M. Fukonashi et al (2004) reported that studied a research about the whole body vibration exposure in taxi drivers. Their objectives in this research are to measure whole body vibration (WBV) on the driver’s seat pan of 12 taxis operating under actual working condition. The objectives are exactly similar to researcher objective. Their attained result of health which is by using formula from ISO 2631-1: 1997 0.44 ms-2. The result is different from what the researcher manages to obtain during the measurement. This is due to the difference in time of exposure. As stated in the journal, the time exposure for the taxi drivers is 8 hours. But for the researcher, the time exposure is slightly short which is equal to 5 hour and 30 minutes. Therefore, the time to exposure is longer, the higher value for the whole body vibration. Furthermore, they have quite more sample of driver compared to the researcher which makes their value more accurate.


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CONCLUSION

The conclusion from this study are unexpected results are obtained. Even so, the result still can be explained. As stated in the literature review, maximum vibration will result in low back pain and also other pain such as motion sickness. The increasing in whole body vibration exposure will lead to higher low back pain. In other words, LBP due to exposure from WBV should be considered a chronic condition, and should be studied accordingly.

For the bus driver, the workplace for the bus driver should be more space. This is because the BMI for the bus driver is different. So, a bigger bus driver will need a bigger space for them to feel comfortable and drive the bus calmly. According to the survey studies that have been held before, they agree that the seat is not suitable. So, they ask to redesign their seat. For the passenger, they agree that the bus should be redesigned so that the inside of the bus can absorb the noise inside the bus along the journey.

REFERENCES

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Bovenzi. M, Rui. F, Negro. C, Angotzi.F.D.G, Bianchi. S, Bramanti. L, Festa. G, Gattib. S, Pinto. S.I, Rondina.L &Stacchini.N (2006). An epidemiological study of low back pain in professional drivers. Journal of Sound and Vibration 298 (2006) 514-539.

Bovenzi.M (2005). Health effects of mechanical vibration. Clinical Unit of Occupational Medicine, Department of Public Health Sciences, University of Trieste, Italy.

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Gallais .L & Griffin.M.J (2008). Modelling resonances of the standing body exposed to vertical whole-body vibration: Effects of posture. Journal of Sound and Vibration 317 (2008) 400-418.

Gallais.L & Griffin.M.J (2005). Low back pain in car drivers: A review of studies published 1975 to 2005. Journal of Sound and Vibration 298 (2006) 499-513.

Ismail A.R., Nuawi M.Z., How C.W., Kamaruddin N.F., Nor M.J.M. & Makhtar N.K. (2010). Whole Body Vibration Exposure to Train Passenger.2010Science Publications.

Kittusamy.N.K. & Buchholz.B (2004).Whole-body vibration and postural stress among operators of construction equipment: A literature review. Journal of Safety Research 35 (2004) 255-261.

Mingjiu. Y, Jun. Y, Quan. Z &Changde.(2002) L. Ergonomics Analysis for sitting posture and chair. College of Mechanical and Electrical Engineering, Northwestern Polytechnical University

Noorloos. D, Tersteeg. L, Tiemessen. I. J. H, Hulshof.C. T. J & Monique H. W (2008). Does body mass index increase the risk of low back pain in a population exposed to whole body vibration? Applied Ergonomics 39 (2008) 779-785.

Oborne D.J. (2005). Vibration and passenger comfort. Department of Psychology, University College of Swansea

Okunribido.O.O, Shimbles.S.J, Magnusson.M & Pope.M (2006). City bus driving and low back pain: A study of the exposures to posture demands, manual materials handling and whole-body vibration. Department of Environmental and Occupational Medicine, Liberty

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Okunribido.O.O, Magnusso. M & Pope. M (2006). Low back pain in drivers: The relative role of whole-body vibration, posture and manual materials handling. Journal of Sound and Vibration 298 (2006) 540-555.

Okunribido.O.O, Magnusso.M & Pope.M (2006). Delivery drivers and low-back pain: A study of the exposures to posture demands, manual materials handling and whole-body vibration. International Journal of Industrial Ergonomics 36 (2006) 265-273.

Tiemessen.I.J.H, Hulshof.C.T.J & Monique. H.W (2008).Vibration:Analysis of a dose response pattern Low back pain in drivers exposed to whole body. Coronel Institute of Occupational Health, Academic Medical Center (AMC), University of Amsterdam, Amsterdam, The Netherlands.

Tiemessen. I. J. H, Hulshof. C. T. J & Monique. H. W. (2007). The development of an intervention programme to reduce whole-body vibration exposure at work induced by a change in behavior: a study protocol. Coronel Institute of Occupational Health, Academic Medical Centre.

Zannin. P. H. T (2008). Occupational noise in urban buses. International journal of Industrial Ergonomics 38(2008) 232-237.

Erik W. Gregory. Whole-Body Vibration and the Lower Back: The Effect of Whole-Body Vibration on Pain in the Lower Back. College of Engineering and Mineral Resources.

Original Article J. Occu. Safety & Health 9 : 109 - 116 , 2012

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Associations of Blood Lead and Disciplinary Behavior among Male Adolescents in Selangor, Malaysia

Mohd Rafee B,B.,1 Asilah, A.,1 Rumaya, J.,2 and Shamsul Bahari, S3.

1Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.

2Department of Human Development and Family Studies, Faculty of Human Ecology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.

3Department of Community and Family Medicine, School of Medicine, Universiti Malaysia Sabah, Malaysia.

ABSTRACT:

A cross sectional comparative study was conducted to determine the relationship between blood lead levels and disciplinary behaviour among adolescent males. This study involved 194 secondary school adolescents ranging from 14 to 16 years old in both Petaling and Hulu Langat district. Respondent sampling frame was obtained from the Ministry of Education. Finger-prick method was applied to obtain capillary blood specimen. Blood lead was determined using an atomic absorption spectrometer equipped with graphite furnace. Both background and environmental profile were obtained from self-administered questionnaires. Disciplinary behaviour of each respondent was then assessed by using Self-Reporting Disciplinary Behaviour (SRDB). This assessment comprise 86 items on disciplinary actions and divided into eight subscales of offences which include crime, obscenity, self-cleanliness, time management, disrespect, vandalism, dishonesty and absenteeism. Total score for each items assessed were then calculated for behaviour score. Results showed that the mean of blood lead concentration is 4.6134 µg/dL (95% CI : 4.0146 - 5.2122 µg/dL). The mean of behavior scores calculated is 40.94. There is no significant difference found in the mean blood lead concentrations between adolescents with disciplinary actions and adolescents with no disciplinary actions (t = 0.708; p = 0.480). Findings showed that blood lead has no significant correlation between blood lead and behavior score (r = 0.74, p>0.05). There are significant correlation between PbB concentrations and both eating (r = 0.166*, p<0.05) and damaging canteen property (r=0.163*, p<0.05) respectively. In conclusion, this study revealed that the PbB concentration has no significant statistical correlation with disciplinary behavior among respondents.

INTRODUCTION

Throughout the years, numerous studies were conducted on lead and linking it with behavior disturbances [3, 26], antisocial behaviour [5, 13, 14, 15], delinquency [5, 14, 15], criminal behavior [6, 27], and crime rates [11, 16, 22]. In Malaysia, lead studies often emphasize on children and workers that exposed to lead. Considering the evidence linking lead with behavior problems and the limited resource of lead-behavior study in Malaysia, the purpose of this study was to appraise the relationship between blood lead and disciplinary behavior among adolescents in Selangor.

METHODOLOGY

A cross sectional comparative study was conducted on 194 adolescents in Petaling and Hulu Langat districts of Selangor. The sampling frame was obtained from the Ministry of Education which comprise a list of students studying in form two (14 years old) and form four (16 years old) from the selected schools. Adolescents were selected based on exclusive criteria (female; other than Malay ethnicity; 13, 15 and 17 years old) and inclusive criteria (case: school records in discipline problems within a period of 12 months; control: no discipline records).

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Finger-prick method was applied to obtain capillary blood specimen of adolescents. The blood specimen was collected into a none-lead-free micro-collection container containing pre-mixed sample diluent (dilation of 10mL 10% Triton X-100, 0.3g Ethylene Diamine Tetra Acetic Acid (EDTA) and 5.0g Ammonium Dihydrogen Phosphate (NH4H2PO4) with 1L distilled de-ionized water) to avoid blood specimen to clog while in storage. The ratio of blood specimen to sample diluent of the final dilution is 1:5 (100µL of blood:500µL of sample diluent). Following preparation for analysis, lead concentration in the blood specimen was determined using an atomic absorption spectrometer equipped with graphite furnace.

Both background and environmental profile were obtained from self-constructed questionnaires. Students Discipline System [10] was adapted into a self-report instrument (Self-report of Disciplinary Behavior) to assess disciplinary acts of adolescents. Adolescents were given the Self-Report of Disciplinary Behavior which consists of 86 items inventory. The disciplinary acts are divided into eight subscales of offences such as crime, obscenity, self-cleanliness, time management, disrespect, vandalism, dishonesty, and absenteeism. Each item required a response in one of five scales (1 = never, 2 = seldom, 3 = sometimes, 4 = often, and 5 = always) depending on frequency of acts committed during the last 12 months. The score for total items were then calculated for behavior score.

A pre-test were performed before actual study was conducted to evaluate the realibility of the questionnaire. Blood sampling was conducted through a series of Standard Operating Procedures. Before analysis, all glassware and plastic wares (including auto sampler cups, pipette tips, and microtainer tubes) were soaked in acid bath for 24 hours with 10% and 5% HNO3 respectively and rinsed with distilled water to ensure they were not significantly contaminated. Equipment used in the data collection and analysis processing including digital scale and atomic absorption spectrometer were handled as described in the Standard Operating Procedures Manual and calibrated on a regularly scheduled basis.

RESULTS

Both paternal and maternal highest formal education is under secondary education with 37.6% and 40.2% respectively, and 53.1% of the adolescents’ mothers are unemployed. For cigarette consumption, 27.7% of households and 62.7% of adolescents are non-smokers. About 41% of total adolescents are living in housing estates followed by 30% in flat houses. Based on districts, 60.1% of adolescents in Hulu Langat live in housing estates while 51.1% of adolescents in Petaling live in flat houses. About 47.9% of adolescents are living less than 100 meter to the main road, with 37 and 28 of them are living in housing estates and flat houses respectively. Some of the schools and respondents’ houses are within an industrial area which comprises tyre, car, paint, and chocolate factory respectively. About 21.1% of adolescents reported the distance between house and factory are as close as less than 500 meters. Table 1 represents background and environmental profile for this study.

Table 2 shows the mean ± SD of blood lead (PbB) concentrations for both groups before and after logarithmic (log) transformation. The mean of PbB of all adolescents in this study was 4.61µg/dL (95% CI : 4.01 - 5.21 µg/dL). The range of PbB was from 0.49µg/dL to 25.74µg/dL. Both groups showing mean PbB less than 5µg/dL.

N=194

PbB were divided into two categories as 10µg/dL indicated the cutoff level using the baseline from the Centers for Disease Control (CDC). In this study, about 94.3% of total adolescents indicated PbB below safety limit. Six out of ten (5.7%) showed PbB above 10µg/dL were from the comparative group. The range for these extreme cases was 10.25 - 25.74µg/dL. Only 8.2% of adolescents showed PbB concentrations below 1µg/dL (Table 3).

Mean ± SD of total behavior scores for both groups using log transformation was given in Table 4. With the behavior scores ranging from 1 - 146, the mean of behavior scores for all adolescents in this study was 40.94.


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Total Case Comparative group

Characteristic (n=194) (n=98) (n=96)

Parents’ Marital Status Married 176 92 84 Divorced 12 3 9 Widowed 5 2 3 Others 1 1 0 Unemployed/housewife 103 56 47

No. of cigarettes consumed per day More than 10 4 2 2 6 - 10 5 4 1 3 - 5 31 23 8 1 - 2 32 15 17 0 121 54 67

Length of time reside < 5 years 64 29 35 5-10 years 42 21 21 > 10 years 78 45 33

Distance between house and main road < 100 meter 93 50 43 101 - 500 meter 61 30 31 501 - 1000 meter 15 4 11 > 1000 meter 21 11 10

Distance between house and factory

< 500 meter 41 16 25 501 - 1000 meter 25 11 14 > 1000 meter 34 23 11 No factory 92 47 45

Total Case Comparative Group

Mean ± SD Mean ± SD Mean ± SD

PbB 4.61 ± 4.22 4.45 ± 4.23 4.77 ± 4.23log PbB 0.52 ± 0.34 0.51 ± 0.35 0.54 ± 0.34

PbB (µg/dL) Total Case Comparative Group

(n=194) (n=98) (n=96)

≤ 10 183 (94.3%) 93 90 > 10 11 (5.7%) 5 6

Total sample Case Comparative Group

Mean ± SD Mean ± SD Mean ± SD

Behavior 40.94 ± 29.77 51.41 ± 32.190 30.47 ± 22.91log Behavior 1.47 ± 0.390 1.58 ± 0.40 1.36 ± 0.341

Table 1: Background and environmental profile

Table 2 : Blood lead concentrations for case and comparative group, before and after log

Table 3: Blood lead levels (PbB) for total sample, case and comparative group

Table 4: Behavior score for case and comparative group, before and after log

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Based on the statistical analysis, significant differences was found in the mean of all subscales between case and comparative group, with case group has higher mean scores in all subscales than comparative group. The mean scores of each subscale in SRDB for both groups are shown in Table 5. Disrespect subscale and dishonesty subscale are the highest and lowest mean scores for both groups. While zero conductivity was found in absent from examination only for case group, zero conductivity was found in distributing/dealing with drug for both groups. Among the highest conducted offences for case group were using harsh language, loitering, using obscene language and keeping long hair.

The difference between blood lead levels of case group and comparative group

An independent-samples t-test was conducted to compare blood lead concentration for case and comparative group. Based on the analysis of parametric test, there was no statistically significant difference in the blood lead levels for case group and control (t =0.70; p = 0.48). The statistical analysis described that the mean value of blood lead concentration for case group was significantly lower than comparative group.

There was no statistically significant correlation between PbB and behavior score (r = 0.74). While no correlation found between PbB and all subscales of SRDB, two items were found to be weakly correlated with PbB which are eating other than recess hour (r = 0.16, p<0.05) and damaging canteen property (r = 0.16, p<0.05) which is from time-management subscale and vandalism subscale respectively.

Variables used in the analysis were tested for correlation with both PbB and behavior score. The results are given in Table 6. Maternal education, paternal education, and family income have a negative significant association with PbB. Of all the environmental variables, only type of residence is correlated with PbB. While for behavior score, only age and cigarettes consumption by household and adolescent were correlated. All correlations found from the analysis, ranging from 0.160 - 0.406, indicate a weak relationship.

Case Comparative Group

Subscales of SRDB Mean ± SD Mean ± SD

Crime 5.10 ± 5.20 3.23 ± 4.49 Obscenity 2.59 ± 2.54 1.46 ± 1.61 Self-cleanliness 4.81 ± 3.81 2.63 ± 2.53 Time management 6.01 ± 3.88 4.01 ± 2.96 Disrespect 9.75 ± 7.47 5.79 ± 5.84 Vandalism 2.21 ± 2.61 1.08 ± 1.47 Dishonesty 1.39 ± 1.65 0.54 ± 0.84 Absenteeism 6.38 ± 5.18 3.29 ± 3.65

Table 5: The mean scores for each subscale in SRDB for case and comparative group


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CONCLUSION

The mean PbB for all the adolescents in this study is below the safety limit for children as set by the Centers for Disease Control (10µg/dL). Only 5.7% of total respondents indicated PbB concentrations exceeding 10µg/dL, compared to other local study with 11.73% [29]. In a few months after the government’s regulation on reduction of lead in petrol (0.84g/L to 0.40g/L) was enforced, lead in ambient air in Kuala Lumpur was reduced as high as 40% from 0.45±0.17µg/m3 to 0.24±0.08µg/m3 for the following nine months [12]. Following the introduction of unleaded petrol in 1991 and the total phase-out of leaded petrol in 1998, the lead level in the atmosphere had declined significantly, especially from 1989 to 1990 [4]. This decreasing pattern was also found in other countries [1, 25], when the using of leaded gasoline was stopped.

There is no representative data exists on PbB of adolescents in Malaysia that can be used for comparison. However, the mean PbB concentrations for all the adolescents is slightly higher than the mean blood lead concentrations in primary school students from local studies, which reported below 4µg/dL [20, 29]. This increasing trends of lead as the age increases were found in other studies as well [7, 19, 28, 30]. Compared to children, adolescents are more exposed to lead sources from the environment as adolescents spend more time outdoor resulting a higher PbB concentration found in adolescents.

Research on lead and behavior varies from study designs (cross-sectional vs. chronological), methods (lead sampling), and subject’s age (infancy to 18 years of age). Despite these variations, many studies agreed on the same conclusion that lead was associated with behavior [3, 5, 13, 15, 26]. On the contrary, this study suggests that PbB has no statistically significant correlation with self-report of disciplinary behavior. In a recent study, it was found that in a lead-behavior study on adolescents, it was found that dental lead levels were not significantly associated with Self-Reported Delinquency Scale [18].

The apparent differences in findings between this study and others may be due to differences in study design, limitation of inclusion criteria and the lack of measurement on behavior data. Because of most public schools have low students with high disciplinary records, restriction to sample case group among adolescents with severe disciplinary records was not applied. All students with official school records, regardless of the severity (mild, moderate or severe cases) were included in sampling selection. However, this limitation (the limitation to severe cases) can be applied on blacklisted schools where the students with disciplinary records are relatively higher.

PbB Behavior score

Variables r p r p

Background Profile Age 0.05 0.48 0.19** 0.01 Parents’ marital status 0.03 0.62 0.01 0.86 Maternal education 0.18* 0.01 0.07 0.32 Paternal education 0.18** 0.01 0.10 0.15 Mother’s occupation 0.06 0.39 0.10 0.15 Father’s occupation 0.08 0.25 0.05 0.48 Family income 0.16* 0.02 0.11 0.13 No. of cigarettes consumed per day (household) 0.04 0.56 0.20** 0.01 No. of cigarettes consumed per day (adolescent) 0.05 0.43 0.40** 0.01

Environmental Profile Type of residence 0.20** 0.01 0.02 0.73 Length of residency 0.07 0.30 0.06 0.37 Age of house 0.04 0.59 0.04 0.63 House paint 0.10 0.13 0.06 0.40 Type of pipe 0.12 0.08 0.06 0.40 Distance between house and main road 0.03 0.64 0.09 0.20 Distance between house and factory 0.04 0.51 0.02 0.73

N=194; *p<0.05; **p<0.01

Table 6: Correlations of variables with PbB and behavior score

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In this study, even though adolescents were assigned into case-comparative group based solely on school record of disciplinary problems, there were inconsistencies between self-reports and school records. There is a possibility that adolescents from comparative group are actually belong in case group, where in actuality these adolescents might be reserved at school but problematic outside school compound, hence the undocumented misconducts. This discrepancy raises question on the reliability on using school records to distinguish the subject’s group and the sufficiency on relying solely based on adolescent’s self-report to gather behavior data.

Because of the inconsistency between self-reports and school records found in the study, in future research, it is suggested to combine multiple informants in behavior measurement. Having additional sources of information from several points of views and settings such as parent reports and teacher reports may strengthen and increase the validity of the findings.

CONCLUSION

The findings of this study concluded that the mean PbB concentration for all the adolescents in this study is under safety limit. From statistical analysis, it was found that PbB has no association with self-report of disciplinary behavior. No statistically significant difference found in PbB concentration between adolescents with school records and adolescents without school records. In conclusion, differences in findings between this study and previous studies may be due to differences in study design, limitation of inclusion criteria and the lack of measurement on behavior data.

ACKNOWLEDGEMENT

The authors thank Research University Grant Scheme, UPM, Malaysia for funding. The authors great fully acknowledge participants, parents, and teachers who co-operated in the study as well as staff nurses and enumerators for assistance during sampling sessions.

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Secretariat

Editorial Board

Prof. Dr. Krishna Gopal RampalUniversiti Kebangsaan Malaysia

NIOSH, MalaysiaIr. Daud Sulaiman

Fadzil OsmanNIOSH, MalaysiaRaemy Md. ZeinNIOSH, Malaysia

The Journal

- Aims to serve as a forum for the sharing of research findings and information across broad areas in Occupational Safety and Health.

- Publishes original research reports, topical article reviews, book reviews, case reports, short communications, invited editorial and letters to editor.

- Welcomes articles in Occupational Safety and Health related fields.

Associate Editors

Prof. Dr. Ismail BahriUniversiti Kebangsaan MalaysiaDr. Jeffereli Shamsul BahrinBASF East Asia Regional Headquartes Ltd.Dr. Abu Hasan SamadPrince Court Medical Centre

Mohd Rashidi RohmadRoslina Md HusinNor Akmar Yussuf

Idayu Kassim

December 2012, Vol. 9, No. 03ISSN 1675-5456

PP13199/12/2012(032005)

Journal ofOCCUPATIONALSAFETY AND HEALTH

National Institute of Occupational Safety and Health

National Institute of Occupational Safety and Health (NIOSH)

Ministry of Human Resources Malaysia

journal of occupational safety and health

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