Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric

Azid, Azman; Juahir, Hafizan; Ezani, Ezureen; Toriman, Mohd Ekhwan; Endut, Azizah; Rahman, Mohd Nordin Abdul; Yunus, Kamaruzzaman; Kamarudin, Mohd Khairul Amri; Hasnam, Che Noraini Che; Saudi, Ahmad Shakir Mohd; Umar, Roslan

doi:10.4209/aaqr.2014.04.0073

Cited by 31 publications

(40 citation statements)

References 44 publications

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“…CA is an unsupervised pattern recognition identification method, used to split a large group into smaller ones [12] based on homogeneity data. The homogeneous sub-groups will be obtained within the population and gather them into clusters based on similarity of the data [38,40,41].…”

Section: Cluster Analysis (Ca)mentioning

confidence: 99%

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Monthly Analysis of Pm10 in Ambient Air of Klang Valley, Malaysia

Jamalani¹,

Abdullah²,

Azid³

et al. 2016

MJAS

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The urbanization in Klang Valley, Peninsular Malaysia over the last decades has induce the atmospheric pollution's risk resulted to negative impact on the environment. The aims of this paper are to identify the spatial-temporal relationship of particulate matter (PM 10 ), to determine the characteristic of each location and to classify hierarchical of the location in relation to their impact on PM 10 concentration in Klang Valley. The Spearman correlation test indicate that there was strong significant relationship between all the locations (> 0.7; p < 0.001) and moderate relationship between Petaling Jaya-Kajang and Kajang-Shah Alam (< 0.7; p < 0.001). The principal component analysis (PCA) identifies all four locations have been affected by PM 10 which were determined as one of the pollutant that deteriorated the air quality. Cluster analysis (CA) has classified the PM 10 pattern into three (3) different classes; Class 1 (Klang), Class 2 (Petaling Jaya and Kajang) and Class 3 (Shah Alam) based on location. Further analysis of CA would be able to classify the PM 10 classes into groups depending on their dissimilarities characteristic. Thus, possible period of extreme air quality degradation could be identified. Therefore, statistical and envirometric techniques have proved the impact of the various location on increasing concentration of PM 10 . Keywords: particulate matter, Spearman correlation test, principal component analysis, cluster analysis AbstrakProses pembandaran di Lembah Klang, Semenanjung Malaysia sedekad lalu telah mendorong kepada risiko pencemaran atmosfera yang memberi impak negatif kepada alam sekitar. Kajian ini dilakukan bertujuan untuk mengenalpasti hubungkait antara ruang dan tempoh bagi partikel terampai (PM 10 ), menentukan ciri -ciri setiap lokasi dan menentukan pengkelasan hirarki lokasi berhubungan dengan impak kepekatan PM 10 di Lembah Klang. Ujian korelasi Spearman menunjukkan hubungkait yang kuat antara semua lokasi (> 0.7; p < 0.001) dan hubungan

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Section: Cluster Analysis (Ca)mentioning

confidence: 99%

“…In this study, CA was used for clustering data with the similarities in a group. CA is employed on the normal distribution dataset through the Ward's method by means of Euclidean distances, as a measure of the relationship [11,12,16]. The outcome of this method will be demonstrated in a dendrogram form.…”

Section: Cluster Analysis (Ca)mentioning

confidence: 99%

Monthly Analysis of Pm10 in Ambient Air of Klang Valley, Malaysia

Jamalani¹,

Abdullah²,

Azid³

et al. 2016

MJAS

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“…Metal species with characteristic values of over 1 in Principal Component Analysis (PCA) (SPSS v.12.0) can be classified into several groups by their sources (Allen et al, 2001;Marcazzan et al, 2001;Manoli et al, 2002;AlMomani, 2003;Azid et al, 2015;Chen et al, 2015;Liang et al, 2015). In each sampling period, the metal species, based on their characteristic values, exhibited three groups in PCA (Table 3).…”

Section: Analysis Of Particulate Metal Sourcesmentioning

confidence: 99%

Characteristics of Respirable Particulate Metals Emitted by a Beehive Firework Display in YanShuei Area of Southern Taiwan

Lin

Tsai

Huang

et al. 2016

Aerosol Air Qual. Res.

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This study investigates metals in the PM 1.0 and PM 2.5 collected using a micro-orifice uniform deposition impactor (MOUDI) sampler in the YanShuei area of southern Taiwan during a beehive firework display. The results of sample analyses indicate that during the beehive firework display, the ratios of metal concentrations in PM 2.5 (D) to the background level (B) at leeward sampling site were 1,828 for Ba, 702 for K, 534 for Sr, 473 for Cu, 104 for Mg, 121 for Al, and 98 for Pb. The corresponding data for PM 1.0 were 3036, 838, 550, 676, 594, 190, and 126, respectively. According to the results of metal composition ratio, Principal Component Analysis (PCA), and upper continental crust (UCC) analyses, the concentrations of particle-bound Al, Ba, Cu, K, Mg, Pb, and Sr increased during the beehive firework displays, suggesting that firework-display aerosols contained abundant metal elements of Al, Ba, Cu, K, Mg, Pb, and Sr. Before (background), trial, during, and after the beehive firework display, the Ba, K, Cu, Mg, Pb, and Sr (commonly regarded as firework display indicator elements) accounted for 0.520, 2.45, 26.4 and 0.849% mass of PM 1 , respectively, while for PM 2.5 the corresponding data were 0.777, 2.32, 23.8, and 0.776%, respectively.

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“…AHC is an unsupervised pattern recognition approach to separate a large group of datasets into smaller or simpler ones [16,23]. AHC were performed based on the normal distribution of datasets through Ward's method by means of Euclidean distances, as a measure of the connection between the datasets or variables [24]. In this study, AHC was used to classify the cluster groups from the selected IAQ office building.…”

Section: Lda and Agglomerative Hierarchical Clustering (Ahc) For Compmentioning

confidence: 99%

Assessing Indoor Air Quality Using Chemometric Models

Azid¹,

Amran²,

Samsudin³

et al. 2018

Pol. J. Environ. Stud.

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The objectives of this study are to identify the significant variables and to verify the best statistical method for determining the effect of indoor air quality (IAQ) at 7 different locations in Universiti Sultan Zainal Abidin, Terengganu, Malaysia. The IAQ data were collected using in-situ measurement. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discrimination analysis (LDA), and agglomerative hierarchical clustering (AHC) were used to classify the significant variables as well as to compare the best method for determining IAQ levels. PCA verifies only 4 out of 9 parameters (PM 10 , PM 2.5 , PM 1.0 , and O 3 ) and is the significant variable in IAQ. The PLS-DA model classifies 89.05% correct of the IAQ variables in each station compared to LDA with only 66.67% correct. AHC identifies three cluster groups, which are highly polluted concentration (HPC), moderately polluted concentration (MPC), and low-polluted concentration (LPC) area. PLS-DA verifies the groups produced by AHC by identifying the variables that affect the quality at each station without being affected by redundancy. In conclusion, PLS-DA is a promising procedure for differentiating the group classes and determining the correct percentage of variables for IAQ.

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Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric

Cited by 31 publications

References 44 publications

Monthly Analysis of Pm10 in Ambient Air of Klang Valley, Malaysia

Monthly Analysis of Pm10 in Ambient Air of Klang Valley, Malaysia

Characteristics of Respirable Particulate Metals Emitted by a Beehive Firework Display in YanShuei Area of Southern Taiwan

Assessing Indoor Air Quality Using Chemometric Models

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