Assessment of trace elements of groundwater and their spatial distribution in Rangpur district, Bangladesh

  • A. R. M. Towfiqul Islam
  • Shuanghe Shen
  • Md. Bodrud-Doza
  • M. Atiqur Rahman
  • Samiran Das
Original Paper


Descriptive statistics, correlation, regression, and geostatistical modeling are applied to assess the trace elements of groundwater and their spatial distribution at the Rangpur district of Bangladesh. A total number of 47 water samples have been collected from wells at depth ranging from 10 to 53 m. The descriptive statistics results show that the mean concentrations of iron (Fe), manganese (Mn), and barium (Ba) have exceeded the permissible limits and those concentrations are alarming to human health and their surrounding environments. Furthermore, Mn, Zn, Al, and Ba concentrations reveal the highly positive skewed and are considered to be extreme. The statistical results demonstrate that groundwater trace element quality is mainly related to natural/geogenic sources followed by anthropogenic sources in the study area even though they show significant correlations among them. The multiple regression models are developed for prediction of each trace element of groundwater samples. The spatial analysis of groundwater trace elements is performed by geostatistical modeling. The cross validation results reveal that kriging models are produced to show the most accurate spatial distribution maps for all trace elements except Ba concentration. The semivariogram models have demonstrated that most of the elements have shown moderate to strong spatial dependence suggesting less agronomic/residential area influences. The findings of the multiple regression model and the correlation matrix are also consistent with the spatial analysis results. It is anticipated that outcomes of this study will provide insights for decision makers taking adaptive measures for groundwater trace element monitoring in Rangpur district, Bangladesh.


Groundwater quality Trace elements Geostatistics Regression model Rangpur district 



The authors are thankful to Nanjing University of Information Science and Technology, China, and to the Department of Environmental Sciences, Jahangirnagar University, Bangladesh, for different forms of support. The authors thank the Chinese Scholarship Council (CSC) for the financial support. The authors are also thankful to the Chemistry Division, Atomic Energy Center of Dhaka, Bangladesh, for providing some technical supports during this study.

Supplementary material

12517_2017_2886_MOESM1_ESM.xlsx (28 kb)
ESM 1 (XLSX 27 kb)


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Copyright information

© Saudi Society for Geosciences 2017

Authors and Affiliations

  • A. R. M. Towfiqul Islam
    • 1
    • 2
  • Shuanghe Shen
    • 1
  • Md. Bodrud-Doza
    • 3
  • M. Atiqur Rahman
    • 1
    • 4
  • Samiran Das
    • 5
  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Department of Disaster ManagementBegum Rokeya UniversityRangpurBangladesh
  3. 3.Department of Environmental SciencesJahangirnagar UniversitySavarBangladesh
  4. 4.Department of Geography and Environmental StudiesUniversity of ChittagongChittagongBangladesh
  5. 5.School of HydrometeorologyNanjing University of Information Science and TechnologyNanjingChina

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