FIELD EVALUATION OF PARTICULATE MATTER MEASUREMENTS USING TEOM AT MONITORING STATIONS IN MALAYSIA By MAIZATUL HUSNA BINTI ISMAIL Thesis submitted in partial fulfillment of the requirement for the award of the degree of Bachelor of Technology

Thesis submitted in partial fulfillment of the
requirement for the award of the degree of
Bachelor of Technology (Environment)
This is acknowledged and confirmed that thesis entitled: Field Evaluation of Particulate Matter Measurements using TEOM at monitoring stations in Malaysia by Maizatul Husna Binti Ismail Matric No.: UK34061 have been checked and all the suggested corrections have been done. The thesis is submitted to School of Ocean Engineering, Universiti Malaysia Terengganu in partial fulfillment of the requirements for the award of the degree of Bachelor of Technology (Environment).
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Main Supervisor
Dr. Hajah Noor Zaitun Binti Yahaya
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Dr Asmadi Ali @ Mahmud
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I hereby declare that this thesis is the result of my own research except as cited in the references.

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Name: Maizatul Husna Binti Ismail
Matric No.: UK34061
At the end of my thesis, I would like to thank all those people who made this thesis possible and an enjoyable experience for me. All kinds of supports and help of many individuals and organizations are the strengths of completing this project.

First of all, I wish to express my sincere gratitude to Dr. Hajah Noor Zaitun Binti Haji Yahaya for the constant supervision and her Master students as well as for the guidance regarding in this project.

I am grateful to my friends for their encouragement and support, especially to my FYP group. The experiences, joys, time and hardships during this project are one of the valuable memories of getting graduated.

Finally, I would like also to express my deepest gratitude for a constant support, emotional understanding, and love that I received from my family in term of prays for their daughter.

ABSTRACTThe tapered element oscillating microbalance (TEOM) utilized to measure ambient particulate matter concentrations around the world. The US Environmental Protection Agency (US EPA) identifies particulate matter as a mixture of solid particles and liquid droplets that found in the air. Particulate matter measured in concentration, µg/m3 expressed as micrograms per cubic meter with a size 10 micron. The purpose of the study is to determine the correction factor between TEOM instruments at monitoring stations in Malaysia. The instrument designed with a correction factor required by the US Environmental Protection Agency (EPA) to represent the difference between the TEOM instruments. The difference between PM10 as measured by TEOM and the FDMS measurement is analyzed. The difference between these two measurements will show to be proportional to the FDMS’s measurement of volatile particulate matter. This has provided a technique for keeping up measurement continuity if TEOMs upgraded to FDMS. A non-linear relationship between these two instruments found to give the best outcome.

Tapered Element Oscillating Microbalance (TEOM) digunakan sebagai alat untuk mengukur kepekatan zarah-zarah di sekeliling dunia. Agensi Perlindungan Alam Sekitar Amerika Syarikat (US EPA) mengenal pasti bahan zarahan sebagai titisan campuran yang terdapat di udara. Zarah diukur dalam ketumpatan, µg/m3 dinyatakan sebagai mikrogram per meter padu. Tujuan kajian ini adalah untuk menentukan faktor pembetulan antara instrumen TEOM di stesen pemantauan di Malaysia. Instrumen ini direka bentuk dengan faktor pembetulan yang diperlukan oleh Agensi Perlindungan Alam Sekitar Amerika Syarikat (US EPA) untuk menunjukkan perbezaan antara instrumen-instrumen TEOM. Perbezaan antara PM10 yang diukur oleh TEOM dan pengukuran FDMS dianalisa. Perbezaan di antara kedua-dua ukuran ini menunjukkan bahawa ia berkadar dengan pengukuran FDMS bagi zarah-zarah yang tidak menentu. Teknik telah disediakan untuk meneruskan kesinambungan pengukuran jika TEOM ditingkatkan kepada FDMS. Hubungan tidak linier antara kedua-dua instrumen ini memberikan hasil yang terbaik
1.2 Problem Statement 13
1.3 Aim and Objectives 13
1.4 Scopes of Study 14
1.5 Significance of Study 14
CHAPTER 2 LITERATURE REVIEW 2.1 Air Quality Monitoring in Malaysia 15
2.2 Particulate Matter 17
2.3 Effect of Particulate Matter on Human Health and Environment 18
2.4 Instrumentation for Measuring Particulate Matter 18
2.4.1 Federal Reference Method (FRM) 18
2.4.2 Beta Attenuation Monitor (BAM) 19
2.5 Tapered Element Oscillating Microbalance (TEOM) 19
2.6 The Filter Dynamics Measurement System (FDMS) 20
2.7 Statistical Software R Package 21
CHAPTER 3 METHODOLOGY 3.1 Site Description 22
3.2 Instrument Selection 23
3.2.1 Tapered Element Oscillating Microbalance (TEOM) 23
3.3 Research Flowchart 24
3.4 Correlation and Linear Regression 25
Table No. Page
2.1 Air Pollution Index (API) 16
2.2 Malaysia Air Quality Guidelines 16

LIST OF FIGURES(2 x single spacing)
Figure No. Page
2.1 Schematic Diagram of TEOM 20
2.2 Schematic Diagram of FDMS 21
3.1 The station at Pakar Scieno TW Sdn. Bhd. 22
3.3 Tapered Element Oscillating Microbalance 23
3.4 Flowchart of the research study on PM10 monitoring using TEOM instruments 24
LIST OF ABBREVIATIONS(2single spacing)
Abbreviations API Air Pollution Index
BAM Beta Attenuation Monitor
CO Carbon Monoxide
DOE Department of Environment
FDMS Filter Dynamics Measurement System
FRM Federal Reference Method
H2SO4 Sulphuric acid
MAAGs Malaysia Air Quality Guidelines
NEDC National Evironmental Data Centre
NO2 Nitrogen Dioxide
O3 Ozone
PM10 Particulate matter smaller than 10 micron in diameter
PSTW Pakar Scieno TW Sdn. Bhd.

SO2 Sulphur Dioxide
TEOM Tapered Element Oscillating Microbalance
US EPA US Environmental Agency
INTRODUCTION1.1Research Background
Clean air is an essential necessity of human health and prosperity because it has impacts to the natural and to the human health, particularly in short and long period. The particulate issue in air contaminant will cause health problem on human, especially respiratory problems, cardiovascular disease, difficulty in breathing, harm internal organs, for example, lung and heart, cancer, mortality and premature death ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.5487/TR.2014.30.2.071”, “ISBN” : “1976-8257”, “ISSN” : “1976-8257”, “PMID” : “25071915”, “abstract” : “Ambient air pollution (AAP) and particulate matters (PM) have been closely associated with adverse health effects such as respiratory disease and cardiovascular diseases. Previous studies have examined the adverse health effects associated with short- and long-term exposure to AAP and outdoor PM on respiratory disease. However, the effect of PM size (PM2.5 and PM10) on cardiovascular disease has not been well studied. Thus, it remains unclear how the size of the inhalable particles (coarse, fine, or ultrafine) affects mortality and morbidity. Airborne PM concentrations are commonly used for ambient air quality management worldwide, owing to the known effects on cardiorespiratory health. In this article, we assess the relationship between cardiovascular diseases and PM, with a particular focus on PM size. We discuss the association of PM2.5 and PM10, nitrogen dioxide (NO2), and elemental carbon with mortality and morbidity due to cardiovascular diseases, stroke, and altered blood pressure, based on epidemiological studies. In addition, we provide evidence that the adverse health effects of AAP and PM are more pronounced among the elderly, children, and people with preexisting cardiovascular and respiratory conditions. Finally, we critically summarize the literature pertaining to cardiovascular diseases, including atherosclerosis and stroke, and introduce potential studies to better understand the health significance of AAP and PM on cardiovascular disease.”, “author” : { “dropping-particle” : “”, “family” : “Lee”, “given” : “Byeong-Jae”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Kim”, “given” : “Bumseok”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Lee”, “given” : “Kyuhong”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Toxicological research”, “id” : “ITEM-1”, “issue” : “2”, “issued” : { “date-parts” : “2014” }, “page” : “71-5”, “title” : “Air pollution exposure and cardiovascular disease.”, “type” : “article-journal”, “volume” : “30” }, “uris” : “” } , “mendeley” : { “formattedCitation” : “(Lee et al., 2014)”, “plainTextFormattedCitation” : “(Lee et al., 2014)”, “previouslyFormattedCitation” : “(Lee et al., 2014)” }, “properties” : { “noteIndex” : 0 }, “schema” : “” }(Lee et al., 2014).

Particulate matter is a criterion that is directed by the U.S. Environmental Protection Agency. According to the New Malaysia Ambient Air Quality Standard, standard that has been set for particulate matter with the size of less than 10 microns (PM10) is 100µg/m3 for averaging time of 24 hours and 40µg/m3 for averaging time of 1 year.
The Tapered Element Oscillating Microbalance (TEOM) was developed as an automatic PM10 monitor in the US. TEOM offer advantages over more labor-intensive manual gravimetric methods in terms of measurement time resolution and operating costs besides it able to measure particulate matter in real-time (Green, 2006). TEOM is designed with a correction factor required by the US Environmental Protection Agency (EPA) to represent the difference between TEOM and gravimetric method (Green and Fuller, 2006).

The Filter Dynamics Measurement System (FDMS) also gives an estimation of volatile particulate matter, which instructive for the comprehension of sources of particulate matter and measurements methodologies. FDMS is a redesign that can be fitted to most TEOM instruments (or provided new), which give comes about that are proportionate to the European reference technique (Green, 2006).

1.2Problem Statement
Issues on air pollution are one of the serious problems that concern people. Particulate matter significantly effects to haze case. In spite of the fact that the TEOM is viewed as the standard for measuring particulate matter, past research shows that there could be some collaboration between particle size distribution and the accuracy of measurements (Wanjura et al., 2005). Due to many difficulties related to the utilization of filter-based samplers, implementation of filter-based sampling techniques in a routine monitoring network is a difficult task. Some of the problems related to routine observing for PM2.5 could be kept away from by the utilization of real-time continuous instrument capable of measuring PM2.5 concentrations (Chung et al., 2001). Even though Department of Environment has set up their monitoring station, the number of monitoring stations is limited. With the use of simple instruments and cost-effective, the air quality in areas without monitoring stations can be monitored and assessed.
1.3Aim and Objectives
The aim of this study carried out is to determine the correction factor between TEOM instruments at monitoring stations in Malaysia.

Below are three objectives that need to achieve in this study.

To identify the trend and pattern of PM10 at different sites.

To determine the percent correlation between equipment.

To establish correction factor of TEOM instruments
1.4Scopes of Study
This study involved these scopes that were highlighted as below:
This study collaborated with the company of air quality, Pakar Scieno TW Sdn. Bhd (PSTW) that located at Shah Alam, Selangor.

Data collection
PSTW provides continuous ambient air quality monitoring data to the National Environmental Data Centre (NEDC). Parameters of continuous air quality monitoring stations include PM10 and PM2.5. Meteorological parameters such as wind speed, wind direction, relative humidity, and temperature will also integrate with the monitoring of air quality.

Data analysis
Data compilation analyzed using R Software Package to identify the trend and pattern of PM10.

1.5Significance of Study
This study would determine the correction factor between TEOM instruments at monitoring stations in Malaysia. The measurements of particulate matter in Malaysia did not correction factor the instruments, made the result of measurements became not accurate with the ambient air quality in Malaysia. The number of research that been conducted on the particulate matter concentration by using TEOM has not greatly studied in Malaysia. It was good opportunity to conduct the research on this so that it could help to further understanding more of the instrumentation and technique used. Besides, this study brought benefits to another scholar that has the same field in discovering air quality in Malaysia.

2.1Air Quality Monitoring in Malaysia
Department of Environment (DOE) Malaysia has 52 monitoring stations classified as commercial, metropolitan and sub-urban places, purpose to check constantly 5 main contaminants, specifically Particulate Matter (PM10), Ozone (O3), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2) and Carbon Monoxide (CO). PM10 is the main parameter to determine the particulate matter and has used as specification measurement in the calculation. Status of air quality in Malaysia is defined based on Air Pollution Index (API).

Air Pollutant Index is an index developed that closely follows the United States Environmental Protection Agency (US EPA) Pollution Standards Index in providing easily comprehensible information about the air pollution level. API system includes 5 major air pollutants which could cause potential harm to human health should they reach unhealthy levels. The air pollutants included in Malaysia’s API are (O3), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) and particulate matter with a diameter of less than 10 microns (PM10). The API value gives an indication of the air quality as shown in Table 2.1.
Table 2.1 Air Pollution Index (API)
API Status Level of Pollution
< 50 Good Low pollution without any bad effect on health
51-100 Moderate Moderate pollution that does not pose any bad effect on health
> 300 Unhealthy
Very Unhealthy
Hazardous Worsen the health condition of high-risk people who are the people with heart and lung complications
Worsen the health condition and low tolerance of physical exercises to people with heart and lung complications. Affect public health
Hazardous to high-risk people and public health
(Department of Environmental Malaysia, 2017)
The Malaysia Air Quality Guidelines (MAAGs) as shown in Table 2.2 which form the basis for calculating API. These guidelines have been derived from available scientific and human health data, and represent “safe levels” below which no adverse health effects have been observed. The MAAGs are generally comparable to the corresponding air quality standards recommended by the World Health Organization and other countries.

Table 2.2 Malaysia Air Quality Guidelines, Adopted in Air Pollution Index Calculation
(ppm) (ug/m3)
OZONE 1 HOUR 0.10 200
8 HOUR 0.06 120
8 HOUR 9 10
8 HOUR 0.04 75
1 HOUR 0.13 350
24 HOUR 0.04 105
PM10 24 HOUR   150
1 YEAR 50
2.2Particulate Matter
The US Environmental Protection Agency (US EPA) identifies particulate matter as a mixture of solid particles and liquid droplets that found in the air. Some particles are large and or dark enough to be seen with naked eyes such as dust, smoke or dirt. While others are so small so they can only be detected by using electron microscopes. There are two types of particulate matter, which are PM10 and PM2.5. PM10 is inhalable particles that have 10 micrometers in diameter size while PM2.5 is fine inhalable particles with diameters that are generally 2.5 micrometers and smaller.

Particulate matter originated from two sources which are natural such as volcanoes, lightning and tornadoes and anthropogenic (man-made) such as sea spray, road dust, soil, biomass burning, industrial and domestic activities (Shaadan et al., 2015). Particulate matters can be both primary and secondary pollutants. Particles that known, as a primary pollutant is a pollutant that is directly emitted from a source, such as carbonaceous compounds from exhaust emission and combustion processes. Secondary pollutants form when there are chemical reactions or by the condensation of the vapor such as atmospheric oxidation of SO2 to H2SO4 (Hamid et al., 2016).

2.3Effect of Particulate Matter on Human Health and Environment
Both PM10 and PM2.5 particles can cause health problems, specifically respiratory health. Small particles less than 10 micrometers in diameter pose the greatest problems because they can penetrate into lungs and also bloodstream. Particle pollution exposure mostly affected to the people with heart or lung disease, children, and older adults.
For environment effects, particulate matter can reduce the atmospheric chemistry and air quality. For example, dry and wet deposition, visibility and cloud formation (Khoder et al., 2002). According to US EPA (2017), the main cause of reduced visibility (haze) is a fine particle which is PM2.5.

2.4Instrumentation for Measuring Particulate Matter

There are several instruments for measuring different characteristics of particulate matter. The most important measurements of particles are particle concentration and particle size. Instruments that measured particle size distribution use the behavior of particles, for example, diffusion, aerodynamics, and optical and electrical mobility (Kulkarni et al, 2011). The common mass-only instruments were Federal Reference Method, Beta Attenuation Monitor and Tapered Element Oscillating Microbalance
2.4.1Federal Reference Method (FRM)
The FRM sampler comprises of a good impactor ninety-six (WINS impactor) followed by a Teflon filter. Particles in the specimen stream with an aerodynamic diam more than 2.5 µm are captured in the WINS impactor, while smaller particles are gathered on the downstream 47-mm Teflon filter (Chung et al., 2001). This method gives administrative satisfactory outcomes. However, it experiences from a time lag of few days to as long as 3-4 months in cases where chemical analyses are also conducted from possible inaccuracies (David, 2005)
2.4.2Beta Attenuation Monitor (BAM)
The BAM indirectly calculates PM2.5 mass by measuring the absorption, by PM, of beta radiation on a filter. The automatic BAM consists of a size-selective inlet, a filter tape, a beta radiation source, and beta radiation detector. Continuous measurements can be achieved, however, the accuracy of the instrument is fundamentally enhanced by measuring the mass accumulated more than 24-hour time span.

2.5Tapered Element Oscillating Microbalance (TEOM)
The TEOM is a real-time particulate mass monitor; its mass measurement method depends on a microbalance, which comprises of a hollow glass tapered tube, clamped at one end free to oscillate at the other: an exchangeable filter is put on the free end (Green, 2006). TEOM are generally utilized all through the United States, permitting brisk detailing of pollution episodes, giving high temporal resolution (hourly) estimations, and reducing the time and effort required to achieve a point estimation compared with the gravimetric method (Clements et al., 2013). A schematic of the entire system is shown in Figure 2.1. However, the TEOM has gotten US EPA certification as an equivalent method for PM10 observing. The tapered element oscillating microbalance monitor measures PM10 or PM2.5 mass concentration comprises of a TEOM mass sensor and control unit in a network ready configuration.

Figure 2.1 Schematic diagram of TEOM
(Green, 2006)
2.6The Filter Dynamics Measurement System (FDMS)
The FDMS aims to measure the mass concentration of airborne particulate matter and evaluate the mass changes of the filter because of evaporative and condensation process that will influence the measurements. This system depended on TEOM innovation, utilizing same microbalance (Green, 2006). Figure 2.2 showed a schematic diagram of the FDMS system. According to Rupprecht & Patashnick Co, the FDMS is designed to provide high quality, representative particulate matter mass concentration readings for both short-term averages (1 hour) and 24-hour averages.

Figure 2.2 A schematic diagram of FDMS
(Green, 2006)
2.7Statistical Software R Package
The R software is free open-source software developed by Development Core Group R (2008). R is an open-source programming condition, which was picking up quickly in utilization over a numerous extensive variety of orders. Packages were accessible to complete an analysis including generalized additive models, linear and non-linear modeling, regression trees, Bayesian statistics and so on (Carslaw et al., 2012). To work on for air pollution purposes, R Package was an ideal system. Besides, R has advantages, which are managing large data sets, supporting wide varieties of graphical plots and applying new statistical analysis methods.

3.1Site Description

Figure 3.1 Station at Pakar Scieno TW Sdn. Bhd.

(Google Earth, 2017)
The continuous monitoring instrument intercomparison study described in this study conducted at Pakar Scieno TW Sdn. Bhd. The station located at 25, Jalan Pengacara U1/48, Kawasan Perindustrian Temasya, 40150 Shah Alam, Selangor (latitude 3°05′ N and longitude 101°34′ E). Pakar Scieno TW (PSTW) provides continuous ambient air quality monitoring at 65 strategic locations across Malaysia. The locations approved by the Malaysia Department of Environment (DOE), strategically located in residential, traffic and industrial areas to detect any significant change in the air quality which may be harmful to human health and the environment.

3.2Instruments Selection
Air quality monitoring of PM10 measurements had undertaken using TEOM.

3.2.1Tapered Element Oscillating Microbalance (TEOM)
TEOM as shown in Figure 3.2, is a real-time instrument for measuring the particulate concentration of particulate smaller than 10 microns in diameter in outdoor and indoor ambient air. It is a true gravimetric instrument that draws air through a filter at a constant flow rate, continuously weighing the filter and calculating near real-time mass concentration (Malaysian Meteorological Department, 2017). The frequency of oscillation measured and recorded by a microprocessor at two-second intervals.

Figure 3.2 Tapered Element Oscillating Microbalance (TEOM)
3.3Research Flowchart
155765564135Study on the literature review
Experimental layout/ setting
Performance test between instruments
Data capture
‘Correction factor’
Produce equation of:-
y = mx+ c
Correlation analysis regression between instruments
Data result (particulate matter concentration & meteorological data)
00Study on the literature review
Experimental layout/ setting
Performance test between instruments
Data capture
‘Correction factor’
Produce equation of:-
y = mx+ c
Correlation analysis regression between instruments
Data result (particulate matter concentration & meteorological data)

Figure 3.4 Flowchart of the research study on PM10 monitoring using TEOM instruments
3.4Correlation and Linear Regression
Correlation indicates whether there is any association between two quantitative variables and the quality of the association. Linear regression is statistical devices that allow us to examine the relationship between a causal variable and a variable of interest. In order to see whether two variables are correlated, we can access by using Pearson correlation coefficient r, which expresses the strength of the linear relationship between two variables, and it takes values from -1 to 1. If there is a perfect linear relationship with the positive slope between the variable, r = 1. If there is a perfect linear relationship with the negative slope between the two variables r = -1. A correlation coefficient of zero means that is no linear relationship between the variables (Pandis, 2016). According to Green et al. (2001), the regression equations produced by correlating the two methods are used as the ‘correction factors’ and applied to the TEOM PM10 data.

Adams, K., Greenbaum, D. S., Shaikh, R., Erp, A. M., & Russell, A. G. (2014). Particulate matter components, sources, and health: Systematic approaches to testing effects. Journal of the Air & Waste Management Association, 544-558.

Carslaw, D. C., & Ropkins, K. (2012). open-air – An R package for air quality data analysis. Environmental Modelling & Software, 52-61.

Chung, A., Chang, D. P., & Kleeman, M. J. (2001). Comparison of Real-Time Instruments Used To Monitor Airborne Particulate Matter. Journal of the Air & Waste Management Association, 109-120.

Clements, N., Milford, J. B., Miller, S. L., Navidi, W., Peel, J. L., & Hannigan, M. P. (2013). Errors in coarse particulate matter mass concentrations and spatiotemporal characteristics when using subtraction estimation methods. Journal of the Air & Waste Management Association, 1386-1398.

Cowen, K., Kelly, T., Coutant, B., & Riggs, K. (2001). Rupprecht &Patashnick Co. Series 1400a TEOM® Particle Monitor with Sample Equilibration System. Ohio: Battelle.

Green, D., Fuller, G., & Barratt, B. (2001). Evaluation of TEOMTM ‘correction factors’ for assessing the EU Stage 1 limit values for PM10. Atmospheric Environment,35 (14), 2589-2593.

Green, D. (2004). Measurements of Particulate Matter Volatility. London: Environmental Research Group.

Green, D. (2006). Particulate Matter Measurements Made using the Filter Dynamics Measurement System (FDMS), 2005. Environmental Research Group.

Green, D., & Fuller, G. W. (2006). The implications of tapered element oscillating microbalance (TEOM) software configuration on particulate matter measurements in the UK and Europe. Atmospheric Environment, 5608-5616.

Green, D. C., Fuller, G. W., & Baker, T. (2009). Development and validation of the volatile correction model for PM10 – An empirical method for adjusting TEOM measurements for their loss of volatile particulate matter. Atmospheric Environment, 2132-2141.

Jimoda, L. A. (2012). Effects Of Particulate Matter On Human Health, The Ecosystem, Climate, And Materials: A Review. Working and Living Environmental Protection, 27-44.

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Lee, B.-J., Kim, B., & Lee, K. (2014). Air pollution exposure and cardiovascular disease. Toxicological Research, 30(2), 71–5.
Li, Q.-F., Wang-Li, L., Liu, Z., & Heber, A. J. (2012). Field evaluation of particulate matter measurements using tapered element oscillating microbalance in a layer house. Journal of the Air & Waste Management Association, 322-335.

Pandis, N. (2016). Correlation and linear regression. American Journal of Orthodontics and Dentofacial Orthopedics, 298-299.

R Development Core Team (2008). R: A language and environment for
statistical computing. R Foundation for Statistical Computing,
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Tarmizi, S. N., Asmat, A., & Sumari, S. M. (2014). Temporal and spatial PM10 concentration distribution using an inverse distance weighted method in Klang Valley, Malaysia. IOP Conference Series: Earth and Environmental Science.

Williams, M., & Bruckmann, P. (2002). Guidance To Member States Guidance To the Member States With The Reference Method. Ec Working Group.

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Name : Maizatul Husna Binti Ismail
Permanent Address : Lot 1693, Kg. Kedai Baru, Banggu, 16150 Kota Bharu, Kelantan
Telephone Number : 017-9065706
Email : [email protected]
Nationality : Malaysia
Sex : Female
2002-2004 Sek. Ren. Keb. Paya Taram
2004-2007 Sek. Ren. Keb. Kerdau
2008-2009 Sek Men. Keb. Kerdau
2010-2012 Sek. Men. Keb. Kubang Kerian 1
2013-2014 Kolej Matrikulasi Melaka
2014-2018 Universiti Malaysia Terengganu
Education :