Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment.

Journal: Environmental pollution (Barking, Essex : 1987)
Published Date: Apr 26, 2015

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.

Authors

  • Mayank Pandey
    Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, U.P., 221005, India. Electronic address: mayankacademics@gmail.com.
  • Ashutosh Kumar Pandey
    Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, U.P., 221005, India. Electronic address: ashutosh.cest@gmail.com.
  • Ashutosh Mishra
    Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, U.P., 221005, India. Electronic address: amevsbhu@gmail.com.
  • B D Tripathi
    Department of Botany, Banaras Hindu University, Varanasi, U.P., 221005, India. Electronic address: tripathibd@gmail.com.

Keywords

External Resources

Popular Topics
Recent Journals