Application of chemometric techniques an spatial and temporal distribution of selected heavy metals of Langat River Basin, Malaysia

This study investigates the spatial and temporal patterns of heavy metals concentration in Langat River based on primary data. Application of different multivariate approaches namely cluster analysis (CA), discriminant analysis (DA), and principal components analysis (PCA) were used in interpreting complex environmental data matrices of Langat River in Malaysia. This research was conducted from February 2012 to January 2013 at the Langat River Basin which is located in Selangor, Peninsular Malaysia. The concentrations of heavy metals were determined by using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). Nine heavy metals elements selected in this study include arsenic (As), mercury (Hg), cadmium (Cd), chromium (Cr), lead (Pb), zinc (Zn), iron (Fe), manganese (Mn), and nickel (Ni). The Hierrachical Agglomeraitve Cluster Analysis (HACA) successfully grouped the Langat River data into three spatial clusters based on monitoring stations with low concentration metals (LCM), moderate concentration metals (MCM) and high concentration metals (HCM). Similarities and dissimilarities among heavy metals from each monitoring stations were studied using Cluster Analysis (CA). Five out of the nine heavy metals namely Hg, Fe, Zn, Pb and Mn were found to be significant in influencing the water characteristic by means of stepwise forward and stepwise backward DA with classification matrix accuracy of 89.00% for spatial variations. Seven out of nine heavy metals assigning 84% include Hg, Fe, Zn, Cr, Pb, Ni and Mn as the most critical for the temporal discrimination based on three different water level conditions (Low, Normal, and High). PCA was conducted to identify the possible main sources of heavy metal of each monitoring station that may affect the river water quality especially attributed from anthropogenic activities such as industries, mining activities, sewage treatment plant, landfills and others based on the three clustered regions. Out of the nine parameters resulted in three PCs explaining approximately 81.59% of the cumulative variances for HCM. Meanwhile, four PCs obtained 65.61% of the total variance for MCM region. Last but not least, for LCM, three PCs are obtained with 78.04% of total variance. The finding of this study showed that the applications of chemometrics techniques are valuable in assisting the Department of Environment (DOE) in reporting the status of Langat River water quality and can be utilized as a reference for future studies in monitoring heavy metals in rivers.

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