Air quality pattern assessment in Peninsular Malaysia using statistical approach

The causes of air quality problems detected are the high increase in the number of vehicles, the rapidly growing urbanization and industrialization that indirectly poses a threat to the environment and public health. The primary objective of this research was to analyse air quality pattern in Peninsular Malaysia utilizing cluster analysis. The sub-objectives of this study were to classify the air quality pattern using hierarchical cluster analysis, to differentiate between clusters using discriminant analysis and also to identify sources of air pollution using principal component analysis. The data was provided by the Department of Environment (DOE) of Malaysia for the years 2013 until 2016 by monthly basis. 5 parameters of air pollution index (API) namely nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), sulphur dioxide (SO2) and particulate matter less than 10 microns (PM10) were involved in this study. Therefore, statistical approach which is cluster analysis (CA) was used in this study. It cluster three smaller classes compared to 48 air monitoring stations which has the same characteristics. Discriminant analysis (DA) was used to differentiating between the three classes where it used standard, backward and forward stepwise technique. It was shown that 2015 has more stand out and different pattern. Wilks’ lambda test also is utilize to measure how well each variables separates into groups. Principal component analysis (PCA) found that CO and PM10 are the major pollutants that have contributed to degradation of air quality in the Peninsular Malaysia based on the three classes due to combustion process from vehicles and industries. Hence, by using the statistical approach for analysing huge data sets become better understanding on air quality pattern and clearly identified the significant air pollutant parameters.