Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment. (September 2015)

• Record Type: Journal Article
• Authors: Pandey, Mayank; Pandey, Ashutosh Kumar; Mishra, Ashutosh; Tripathi, B.D.
• Is Part Of: Environmental pollution. Volume 204(2015)
• Page Start: 64
• Page End: 73
• Publication Date: 2015-09
• Subjects: River Ganga -- Metal speciation -- Sequential extraction process -- Chemometric analysis -- Self-organizing map-artificial neural network; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2015.04.007
• Languages: English
• ISSNs: 0269-7491

Abstract

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. Highlights: Impact of river water quality on metal speciation in its sediments. Sequential Extraction Process was opted for metal speciation study. Total Acid Digestion was opted for total metal concentration assessment. Chemometric and Self-Organizing Map-Artificial Neural Network were applied. Abstract : Metal speciation study helps to assess the risk caused by metals in river sediments. ANN-SOM along with other chemometric tools can act as an effective source apportionment model.