Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment.
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.
Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, U.P., 221005, India. Electronic address: mayankacademics@gmail.com.
Mayank
M
Pandey
Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, U.P., 221005, India. Electronic address: ashutosh.cest@gmail.com.
Ashutosh Kumar
AK
Pandey
Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, U.P., 221005, India. Electronic address: amevsbhu@gmail.com.
Ashutosh
A
Mishra
Department of Botany, Banaras Hindu University, Varanasi, U.P., 221005, India. Electronic address: tripathibd@gmail.com.
B D
BD
Tripathi
D004784:Environmental Monitoring; D019015:Geologic Sediments / Q000032:analysis*; D007194:India; D019216:Metals, Heavy / Q000032:analysis*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D045483:Rivers; D012621:Seasons; D014874:Water Pollutants, Chemical / Q000032:analysis*; D060753:Water Quality
D019216:Metals, Heavy; D014874:Water Pollutants, Chemical
Chemometric analysis; Metal speciation; River Ganga; Self-organizing map-artificial neural network; Sequential extraction process