Skip to main content

Predicting arsenic concentration in groundwater of Bangladesh using Bayesian geostatistical model


The pattern of the spatial variation in arsenic concentration in groundwater of Bangladesh is usually needed for the planning of safe drinking water. Often a model-based prediction is required for this purpose. In this paper, we fit a Bayesian hierarchical geostatistical model by utilizing data from the project, ‘Groundwater studies of arsenic concentration in Bangladesh’ conducted by the British Geological Survey and the Department of Public Health Engineering of Bangladesh. We also develop a predictive model for arsenic concentration at different levels of well-depth using the same approach. The resulting predictive model has been cross-validated by appropriate statistical tools. Finally, we obtained reliable spatially continuous predictive maps and predictive probability maps showing the areas with high probability of arsenic concentration for different levels of well-depth. Results indicate that our model fits the data well and captures a substantial amount of spatial variation. Moreover, well-depth is found to have a significant contribution in explaining the observed variation in arsenic concentration. The predictive maps that have been produced are observed to be different for various levels of well-depths and are expected to be helpful to the policy makers in preparing proper regional planning for safe drinking water.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  • Amini M, Abbaspour K, Berg M, Winkel L, Hug S, Hoehn E, Yang H, Johnson C (2008) Statistical modeling of global geogenic arsenic contamination in groundwater. Environ Sci Technol 42(10):3669–3675

    CAS  PubMed  Article  Google Scholar 

  • Banerjee S, Gelfand AE, Finley AO, Sang H (2008) Gaussian predictive process models for large spatial data sets. J R Stat Soc Ser B (Stat Methodol) 70(4):825–848

    Article  Google Scholar 

  • Cressie N, Johannesson G (2008) Fixed rank kriging for very large spatial data sets. J R Stat Soc Ser B (Stat Methodol) 70(1):209–226

    Article  Google Scholar 

  • Cressie N, Wikle CK (2011) Statistics for spatio-temporal data. Wiley, New York

    Google Scholar 

  • Cressie NA (1993) Statistics for spatial data, revised edn. Wiley, New York

    Google Scholar 

  • Diggle P, Ribeiro P (2007) Model-based geostatistics. Springer, New York

    Google Scholar 

  • Diggle P, Thomson M, Christensen O, Rowlingson B, Obsomer V, Gardon J, Wanji S, Takougang I, Enyong P, Kamgno J et al (2007) Spatial modelling and the prediction of loa loa risk: decision making under uncertainty. Ann Trop Med Parasitol 101(6):499–509

    CAS  PubMed  Article  Google Scholar 

  • Finley AO, Banerjee S, Carlin BP (2007) spbayes: an R package for univariate and multivariate hierarchical point-referenced spatial models. J Stat Softw 19(4):1

    PubMed Central  PubMed  Google Scholar 

  • Gelman A, Carlin JB, Rubin DB (2003) Bayesian data analysis. CRC Press, Boca Raton, FL

    Google Scholar 

  • Harvey C, Ashfaque K, Yu W, Badruzzaman A, Ali M, Oates P, Michael H, Neumann R, Beckie R, Islam S et al (2006) Groundwater dynamics and arsenic contamination in Bangladesh. Chem Geol 228(1):112–136

    CAS  Article  Google Scholar 

  • Harvey C, Swartz C, Badruzzaman A, Keon-Blute N, Yu W, Ali M, Jay J, Beckie R, Niedan V, Brabander D et al (2002) Arsenic mobility and groundwater extraction in Bangladesh. Science 298(5598):1602–1606

    CAS  PubMed  Article  Google Scholar 

  • Hossain F, Sivakumar B (2006) Spatial pattern of arsenic contamination in shallow wells of Bangladesh: regional geology and nonlinear dynamics. Stoch Environ Res Risk Assess 20(1):66–76

    Article  Google Scholar 

  • Karthik B (2001) Spatial variability of groundwater arsenic in Bangladesh: an evaluation of geologic and physical controls. PhD thesis, University of Cincinnati

  • Kinniburgh D, Smedley P (2001) Arsenic contamination of groundwater in Bangladesh. Technical report, British Geological Survey

    Google Scholar 

  • McArthur J, Ravenscroft P, Safiulla S, Thirlwall M (2001) Arsenic in groundwater: testing pollution mechanisms for sedimentary aquifers in Bangladesh. Water Resour Res 37(1):109–117

    CAS  Article  Google Scholar 

  • Nickson R, McArthur J, Ravenscroft P, Burgess W, Ahmed K (2000) Mechanism of arsenic release to groundwater, Bangladesh and West Bengal. Appl Geochem 15(4):403–413

    CAS  Article  Google Scholar 

  • Smedley P, Kinniburgh D et al (2002) A review of the source, behaviour and distribution of arsenic in natural waters. Appl Geochem 17(5):517–568

    CAS  Article  Google Scholar 

  • Wendelberger J, Campbell K (1994) Non-detect data in environmental investigations. Technical report, Los Alamos National Lab., NM (United States)

  • Yu W, Harvey C, Harvey C (2003) Arsenic in groundwater in Bangladesh: a geostatistical and epidemiological framework for evaluating health effects and potential remedies. Water Resour Res 39(6):1146

    Article  Google Scholar 

Download references


This research was partially supported by United Nations Population Fund (UNFPA) which provided a research grant to complete the MS thesis work of first author. We also thank DPHE and BGS for provision of the data on which these analyses were based. The authors also acknowledge the valuable comments/suggestions made by the reviewers which were helpful in improving the quality of the paper.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Paritosh K. Roy.

Additional information

Handling Editor: Pierre Dutilleul.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Roy, P.K., Hossain, S.S. Predicting arsenic concentration in groundwater of Bangladesh using Bayesian geostatistical model. Environ Ecol Stat 21, 583–597 (2014).

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI:


  • Arsenic concentration
  • Bayesian prediction
  • Geostatistical model
  • Predictive probability map
  • Spatially continuous map
  • Spatial process