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<p>Assessment and prediction of the spatial variability of groundwater fluoride contamination are essential for developing an effective management plan for ensuring safe drinking water and mitigating health risks. The spatial analysis tool helps predict the variability in groundwater fluoride content, significantly reducing labour, time, and cost associated with a conventional technique. In this study spatial variability of fluoride in the Gaisilet Block of the Bargarh District of western Odisha was analyzed using a comparative semi-variogram model, and Kriging techniques to identify the most accurate prediction model for the study region. The study found that the Gaussian semi-variogram model predicted spatial variability more precisely than circular, spherical, and exponential models in pre-monsoon season. In contrast, the exponential model is the best-fit model for the post-monsoon season. The root means square error is found lowest (0.533) for the Gaussian model in pre-monsoon and the exponential model (0.642) in post-monsoon. The spatial quantification reveals that 242.26&#xa0;km² of Gaisilet Block exceeds the permissible fluoride limit of 1.5&#xa0;mg/L during the pre-monsoon season, while 336.89&#xa0;km² surpasses this threshold in the post-monsoon period. These findings may assist policymakers and health officials in developing more effective groundwater resource management strategies.</p>

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Geostatistical modelling of groundwater fluoride contamination in Western Odisha, India: insights for spatial prediction and risk management

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月20日

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  • Krishna Manjari Jena,
  • Prasanta Kumar Patra,
  • Duryadhan Behera,
  • Shreerup Goswami,
  • Muralitharan Jothimani

摘要

Assessment and prediction of the spatial variability of groundwater fluoride contamination are essential for developing an effective management plan for ensuring safe drinking water and mitigating health risks. The spatial analysis tool helps predict the variability in groundwater fluoride content, significantly reducing labour, time, and cost associated with a conventional technique. In this study spatial variability of fluoride in the Gaisilet Block of the Bargarh District of western Odisha was analyzed using a comparative semi-variogram model, and Kriging techniques to identify the most accurate prediction model for the study region. The study found that the Gaussian semi-variogram model predicted spatial variability more precisely than circular, spherical, and exponential models in pre-monsoon season. In contrast, the exponential model is the best-fit model for the post-monsoon season. The root means square error is found lowest (0.533) for the Gaussian model in pre-monsoon and the exponential model (0.642) in post-monsoon. The spatial quantification reveals that 242.26 km² of Gaisilet Block exceeds the permissible fluoride limit of 1.5 mg/L during the pre-monsoon season, while 336.89 km² surpasses this threshold in the post-monsoon period. These findings may assist policymakers and health officials in developing more effective groundwater resource management strategies.