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Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
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Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater.

Erum Zahid1, Ijaz Hussain1, Gunter Spöck2

  • 1Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan.

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|September 30, 2016
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Summary

High sodium in drinking water can lead to hypertension. This study models sodium distribution and optimizes sampling in Punjab, Pakistan, using advanced geostatistical methods for better health monitoring.

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Area of Science:

  • Environmental Science
  • Public Health
  • Geostatistics

Background:

  • Excessive sodium in drinking water is a significant risk factor for high blood pressure and hypertension.
  • Understanding the spatial distribution of sodium concentration is crucial for public health interventions.

Purpose of the Study:

  • To model the spatial distribution of sodium concentration in drinking water across three divisions in Punjab, Pakistan.
  • To develop optimized sampling designs for effective monitoring of sodium levels.

Main Methods:

  • Utilized Universal Kriging and Bayesian Universal Kriging for predicting sodium concentrations.
  • Employed Spatial Simulated Annealing for generating optimized sampling designs.
  • Estimated variogram model parameters using Maximum Likelihood, Restricted Maximum Likelihood, Ordinary Least Squares, and Weighted Least Squares.

Main Results:

  • Bayesian Universal Kriging demonstrated a superior fit compared to Universal Kriging.
  • The Universal Kriging predictor achieved minimum mean variance for sampling location selection (both additions and deletions).

Conclusions:

  • Bayesian Universal Kriging offers a more accurate approach for modeling sodium distribution in drinking water.
  • Optimized sampling designs derived from geostatistical methods enhance the efficiency of health monitoring programs.