Source Apportionment of Air Pollution: A Case Study In Malaysia

Abstract

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.

Assessing Indoor Air Quality Using Chemometric Models

“…Every component in PCA is orthogonal between each other. The variance of a large set of interrelated variables can be transformed into a new (smaller set of uncorrelated (independent) variables when applying this method [16][17]. In this study, the most significant parameter contributing to IAQ can be recognized by this method.…”

“…Influencing Indoor Air Quality PCA was developed by Pearson [15], where each principal component (PCs) is an evaluation of a linear combination of original multiple responses [16]. Every component in PCA is orthogonal between each other.…”

“…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].…”

Spatial assessment on ambient air quality status: a case study in Klang, Selangor

“…Port Klang as the busiest port in Malaysia might contribute to NO 2 emission as it was located approximately 3 kilometers from the monitoring station [12]. The presence of high CO in Klang was largely due to the incomplete combustion of fossil fuels in industries and motor vehicles [4,6]. According to [13], the average daily road traffic between Klang to Port Klang had been gradually increased form 46,690 in 2009 to 56,513 in 2013.…”

“…According to [13], the average daily road traffic between Klang to Port Klang had been gradually increased form 46,690 in 2009 to 56,513 in 2013. Moreover, PM 10 was potentially came from the industrial emissions, power station activity, heavy construction works, motor vehicle exhaustions, re-suspension of soil dust and open burning activity in the study area [4]. It was also attributed by the transboundary haze pollution due to the forest fires from Indonesia [14][15].…”