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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Ключевые слова АГРОВОК

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

Библиографическая информация

Опубликовано в

Environmental pollution

ISSN 0269-7491

Издатель

American Institute of Nutrition

Другие темы

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

Язык

Английский

Тип

Journal Article; Text

В АГРИСе с: 2024-02-29

Формат: MODS

Поставщик данных

Эту запись предоставил National Agricultural Library

Откройте коллекцию этого поставщика данных в AGRIS

Ссылки

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

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