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

Pandey, Mayank; Pandey, Ashutosh Kumar; Mishra, Ashutosh; Tripathi, B.D.

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

2015

Pandey, Mayank | Pandey, Ashutosh Kumar | Mishra, Ashutosh | Tripathi, B.D.

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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AGROVOC Keywords

cadmium chromium copper data collection iron lead manganese neural networks nickel physicochemical properties prediction rivers summer water quality wet season winter zinc

Bibliographic information

Published in

Environmental pollution

ISSN 0269-7491

Publisher

American Institute of Nutrition

Other Subjects

Self-organizing map-artificial neural network; Sediments; Chemometrics; Metal speciation; Sequential extraction process; Chemometric analysis; Pollution load; River ganga

Language

English

Type

Journal Article; Text

In AGRIS since: 2024-02-29

Format: MODS

Data Provider

This bibliographic record has been provided by National Agricultural Library

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Links

DOI http://dx.doi.org/10.1016/j.envpol.2015.04.007

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