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

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

doi:10.4209/aaqr.2014.04.0073

Abstract

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.

Highlights

Nowadays, the ambient air quality is a matter of serious concern to both developed and developing countries
The findings have shown that the deficiency of the model for original air quality parameters, low pollution source (LPS), moderate pollution source (MPS), and slightly high pollution source (SHPS), which the data sets indicate a great difference in the range of –8 to 4, –3 to 4, –0.6 to 0.8, and –6 to 2, respectively
It can be concluded that the spatial variations of air quality data in Peninsular Malaysia were successfully studied by applying chemometric procedures, namely, hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR)

Summary

Introduction

The ambient air quality is a matter of serious concern to both developed and developing countries. The status of air quality is described according to biological, chemical and physical properties. The index is important in evaluating the air quality of different sources (Azid et al, 2014a). Once the compliance or lack of compliance determined, the data can be used to advise or caution the public in lieu of health effects (Kamal et al, 2006; Azid et al, 2014a). Poor air quality has both acute and chronic effects, Malaysia strives to achieve industrialized country status by 2020, which is highly correlated with rapid economic growth.

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Results

Conclusion