Air pollution is becoming a major environmental issue in Malaysia. This study focused on the identification of potential sources of variations in air quality around the study area based on the data obtained from the Malaysian Department of Environment (DOE).  Eight air quality parameters in ten monitoring stations for seven years (2006 – 2012) were gathered.  The Principal Component Analysis (PCA) method from chemometric technique was applied to identify the source identification of pollution around the study area. The PCA method has identified methane (CH4), non-methane hydrocarbon (NmHC), total hydrocarbon (THC), ozone (O3) and particulate matter under 10 microns (PM10) are the most significant parameters around the study area.  From the study, it can be concluded that the application of the PCA method in chemometric techniques can be applied for the source apportionment purpose. Hence, this study indicated that for the future and effective management of the Malaysian air quality, an effort should be placed as a priority in controlling point and non-point pollution sources.

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 PM 10 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.

The spatial water quality data (281x22) obtained from 12 sampling stations located along the Terengganu River and its main tributaries were evaluated with environmetric methods. Principal component analysis was used to investigate the origin of each variable due to land use and human activities based on the three clustered regions obtained from the hierarchical agglomerative cluster analysis. Six principal components (PCs) were obtained, where six varimax factor (VF) of values more than 0.70 that considered strong loading are discussed. The possible pollution sources identified are of anthropogenic sources, mainly municipal waste, surface runoff, agricultural runoff, organic pollution and urban storm runoff. As a conclusion, the application of environmetric methods could reveal important information on the spatial variability of a large and complex river water quality data in order to control pollution sources.

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