ISSN 1842-4562
Member of DOAJ

Statistical Evaluation of Highly Arsenic Contaminated Groundwater in South-Western Bangladesh

Akira MANO
Keiko UDO


groundwater, arsenic, multiple regression, principal component, prediction


High Arsenic (As) in natural groundwater in most of the shallow sandy aquifers of the South-Western part of Bangladesh has recently been focused as a serious environmental concern. This paper is aiming to illustrate the statistical evaluation of the Arsenic polluted groundwater to identify the correlation of that As with other participating groundwater parameters so that the As contamination level can easily be predicted by analyzing only those parameters. Multivariate data analysis done with the collected groundwaters from the 67 tube-wells of the contaminated aquifer suggests that As may have substantial positive correlations with Fe, Mn, Al, DOC, HCO3 and PO4 whereas noticeable negative relationships have also been observed with SO4, Cl and NO3-N. Based on these relationships, a multiple linear regression model has been developed that incorporates seven most influential groundwater parameters as the independent predictor variables to estimate the As contamination level in the polluted groundwater. This model could also be a suggestive tool while designing the As removal scheme for the affected groundwater.