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Updated: Apr 14, 2026

Clean Sampling and Analysis of River and Estuarine Waters for Trace Metal Studies
Published on: July 1, 2016
Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for
Mayank Pandey1, Ashutosh Kumar Pandey1, Ashutosh Mishra1
1Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, U.P., 221005, India.
Abstract:
Present study deals with the river Ganga water quality and its impact on metal speciation in its sediments. Concentration of physico-chemical parameters was highest in summer season followed by winter and lowest in rainy season. Metal speciation study in river sediments revealed that exchangeable, reducible and oxidizable fractions were dominant in all the studied metals (Cr, Ni, Cu, Zn, Cd, Pb) except Mn and Fe. High pollution load index (1.64-3.89) recommends urgent need of mitigation measures. Self-organizing Map-Artificial Neural Network (SOM-ANN) was applied to the data set for the prediction of major point sources of pollution in the river Ganga.
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