Azman Azid1, Hafizan Juahir 1, Ezureen Ezani1, Mohd Ekhwan Toriman1, Azizah Endut1, Mohd Nordin Abdul Rahman2, Kamaruzzaman Yunus3, Mohd Khairul Amri Kamarudin1, Che Noraini Che Hasnam1, Ahmad Shakir Mohd Saudi1, Roslan Umar1
Cite this article: Azid, A., Juahir, H., Ezani, E., Toriman, M.E., Endut, A., Rahman, M.N.A., Yunus, K., Kamarudin, M.K.A., Hasnam, C.N.C., Saudi, A.S.M. and Umar, R. (2015). Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric.
Aerosol Air Qual. Res.
15: 1545-1558. https://doi.org/10.4209/aaqr.2014.04.0073
Chemometric techniques were used for this study.
The stations have been categorized into three clusters; LPS, MPS and SHPS.
Fuel combustion is the main contributors of air pollutions in the study location.
PM10 is the highest pollution contributor to Malaysian air quality.
This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition. The data sets of air quality for 12 months (January–December) in 2007, consisting of 14 stations around Peninsular Malaysia with 14 parameters (168 datasets) were applied. Three significant clusters - low pollution source (LPS) region, moderate pollution source (MPS) region, and slightly high pollution source (SHPS) region were generated via HACA. Forward stepwise of DA managed to discriminate 8 variables, whereas backward stepwise of DA managed to discriminate 9 out of 14 variables. The method of PCA and FA has identified 8 pollutants in LPS and SHPS respectively, as well as 11 pollutants in MPS region, where most of the pollutants are expected derived from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 categorize as the primary pollutant in Malaysia. From the study, it can be stipulated that the application of chemometric techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novel design of air quality monitoring network for better management of air pollution can be achieved.
Keywords: Air quality; PCA; Chemometric; Pattern recognition; HACA; DA; FA; MLR
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