Arsenic Pollution in the Environment

Author(s):  
Munawar Sultana ◽  
Santonu Kumar Sanyal ◽  
Mohammed Anwar Hossain

Arsenic (As) pollution in drinking water and soils poses a threat to over 100 million people worldwide, making it one of the largest environmental catastrophes particularly in Bangladesh and West Bengal- where more than one-third of the population are at risk. Microbial As metabolism and mobilization in aqua system is relatively a recent issue.The presence of the arsenic oxidation, reduction, and extrusion genes (aioA, arrA, arsB, and acr3) are explored within microorganisms retrieved from As-contaminated environments. However, the nature of microbiome involved within a certain As transformation environment is still an area of research, specifically how microbial redox transformations occur, that can be exploited to mitigate the longstanding problem. The present chapter overviews the mechanism of As pollution in various environment, microbial diversity in such environment, correlation of their activities to the biogeochemistry of As and finally application of microbes as a bioremediation tool for As detoxification and bioremediation.

RSC Advances ◽  
2021 ◽  
Vol 11 (41) ◽  
pp. 25484-25496
Author(s):  
Wei Zhou ◽  
Weiying Li ◽  
Jiping Chen ◽  
Yu Zhou ◽  
Zhongqing Wei ◽  
...  

Drinking water microbial diversity influence in full-scale water supply systems.


2006 ◽  
Vol 163 (7) ◽  
pp. 662-669 ◽  
Author(s):  
O. S. von Ehrenstein ◽  
D. N. Guha Mazumder ◽  
M. Hira-Smith ◽  
N. Ghosh ◽  
Y. Yuan ◽  
...  

The Analyst ◽  
1995 ◽  
Vol 120 (3) ◽  
pp. 643 ◽  
Author(s):  
Amit Chatterjee ◽  
Dipankar Das ◽  
Badal K. Mandal ◽  
Tarit Roy Chowdhury ◽  
Gautam Samanta ◽  
...  

BioMetals ◽  
2013 ◽  
Vol 26 (5) ◽  
pp. 855-862 ◽  
Author(s):  
Somnath Paul ◽  
Pritha Bhattacharjee ◽  
Prafulla K. Mishra ◽  
Debmita Chatterjee ◽  
Anirban Biswas ◽  
...  

2010 ◽  
Vol 8 (3) ◽  
pp. 532-542 ◽  
Author(s):  
Colin R. Hayes

Computational modelling methods have been used to predict the risks from lead in drinking water across a simulated supply zone, for a range of plumbosolvency conditions and a range of extents of occurrence of houses having a lead pipe, on the basis of five risk benchmarking methods. For the worst case modelled (very high plumbosolvency and 90% houses with a lead pipe) the percentage of houses at risk in the simulated zone ranged from 34.1 to 73.3%. In contrast, for a simulated phosphate-treated zone and 10% houses with a lead pipe, the percentage of houses at risk in the simulated zone ranged from 0 to 0.4%. Methods are proposed for using computational modelling for different levels of risk assessment, for both water supply zones and individual houses. These risk assessment methods will inform policy, help to set improvement priorities and facilitate a better understanding of corrective options.


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