Artificial Neural Network (ANN) approach in predicting arsenic and mercury species in Kinta River / Norshidah Baharuddin

Surface water is most exposed to pollution from chemical, physical and biological contaminants by anthropogenic activities. Identifying the variables contributing to the deterioration of water quality is crucial and predicting the future status is vital in managing the ecosystem. As Kinta River is an ex-mining area, heavy metals contamination is expected to be the major pollution contributor particularly arsenic and mercury. Confirmation on the significant contribution of arsenic and mercury species was done by applying selected chemometrics techniques namely cluster analysis (CA) and principal component analysis (PCA) on the physicochemical variables monitored by the Department of Environment (DOE), Malaysia during the period from 1997 – 2006. Thirty physicochemical variables were selected as input variables for CA and PCA in an attempt to identify the significant variables by the factor loadings obtained from PCA. From CA, the physicochemical variables were classified into four main clusters based on the similarities and dissimilarities. PCA applied to the dataset resulted in ten varifactors with a total variance of 78.06%. Arsenic and mercury were classified as moderately significant variables that affect the Kinta River water with a factor loadings of 0.561 and 0.643, respectively. The pollution of the river due to these metals could be contributed by industrial discharge, agricultural activities and residential waste. Since the toxicity of arsenic and mercury depended on the specific species of the metals, speciation analysis is therefore important to study the toxicity of these metals species due to their significant risks to human health and to the environment…