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Predicting arsenic concentration in groundwater of Bangladesh using Bayesian geostatistical model

Abstract

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.

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Acknowledgments

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.

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Correspondence to Paritosh K. Roy.

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Handling Editor: Pierre Dutilleul.

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Roy, P.K., Hossain, S.S. Predicting arsenic concentration in groundwater of Bangladesh using Bayesian geostatistical model. Environ Ecol Stat 21, 583–597 (2014). https://doi.org/10.1007/s10651-013-0269-9

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  • DOI: https://doi.org/10.1007/s10651-013-0269-9

Keywords

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