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  4. Prediction of arsenic vulnerable zones in the groundwater environment of a rapidly urbanizing setup, Guwahati, India
 
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Prediction of arsenic vulnerable zones in the groundwater environment of a rapidly urbanizing setup, Guwahati, India

Source
Chemie Der Erde
ISSN
00092819
Date Issued
2020-12-01
Author(s)
Singh, Ashwin
Patel, Arbind Kumar
Deka, Jyoti Prakash
Das, Aparna
Kumar, Abhay
Kumar, Manish  
DOI
10.1016/j.chemer.2019.125590
Volume
80
Issue
4
Abstract
The present study, through a multi model assessment approach emphasizes the relevance of prior urban settlement planning with respect to the changing land use pattern. A coupled approach integrating satellite data products of Land Use and Land Cover (LULC) change along with chemical evaluation of As toxicity were modelled to evaluate the groundwater vulnerability. Change detection analysis was incorporated using the satellite images of 2013 and 2016 which shows 10 % increase in the buildings as compared to the total area of the study in given period. The LULC class that has been most compromised in this expansion and got reduced were the vegetation (∼6 %) and the barren areas (∼4 %) and the total surface water sources (0.5 %), all contributing in increased urban sprawl. As per the origin of As contamination in the area it seems that high sediment deposition along the braided Brahmaputra River stretch leads to As contamination which was associated with Fe-(oxy)-hydroxide dissolution. The vulnerability assessment using Artificial Neural Network technique in this region shows that As is present relatively in lesser concentration in the east direction. The continuous expansion of the urban habitation towards the greater As concentration zone will further make the situation critical for the people dependent on groundwater. The study suggests that prior settlement planning must be carried out in accordance to the presence of any toxic constituent in drinking water source to avoid future crisis for potable water.
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URI
https://d8.irins.org/handle/IITG2025/23062
Subjects
Artificial Neural Network (ANN) | Groundwater | Guwahati | LULC | Smart city | Urbanisation | Vulnerability
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