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Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data

Author

Listed:
  • Hongxing Li

    (National Center for Rural Water Supply Technical Guidance, Chinese Center for Disease Control and Prevention, Beijing 102200, China)

  • Charlotte D. Smith

    (Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA 94720, USA)

  • Li Wang

    (National Center for Rural Water Supply Technical Guidance, Chinese Center for Disease Control and Prevention, Beijing 102200, China)

  • Zheng Li

    (National Center for Rural Water Supply Technical Guidance, Chinese Center for Disease Control and Prevention, Beijing 102200, China)

  • Chuanlong Xiong

    (National Center for Rural Water Supply Technical Guidance, Chinese Center for Disease Control and Prevention, Beijing 102200, China)

  • Rong Zhang

    (National Center for Rural Water Supply Technical Guidance, Chinese Center for Disease Control and Prevention, Beijing 102200, China)

Abstract

Drinking water monitoring is essential for identifying health-related risks, as well as for building foundations for management of safe drinking water supplies. However, statistical analyses of drinking water quality monitoring data are challenging because of non-normal (skewed distributions) and missing values. Therefore, a new method combining a water quality index (WQI) with spatial analysis is introduced in this paper to fill the gap between data collection and data analysis. Water constituent concentrations in different seasons and from different water sources were compared based on WQIs. To generate a WQI map covering all of the study areas, predicted WQI values were created for locations in the study area based on spatial interpolation from nearby observed values. The accuracy value of predicted and measured values of our method was 0.99, indicating good predication performance. Overall, the results of this study indicate that this method will help fill the gap between the collection of large amounts of drinking water data and data analysis for drinking water monitoring and process control.

Suggested Citation

  • Hongxing Li & Charlotte D. Smith & Li Wang & Zheng Li & Chuanlong Xiong & Rong Zhang, 2019. "Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data," IJERPH, MDPI, vol. 16(3), pages 1-9, January.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:3:p:357-:d:201121
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    References listed on IDEAS

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    2. Chao Chen & Jamie Twycross & Jonathan M Garibaldi, 2017. "A new accuracy measure based on bounded relative error for time series forecasting," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-23, March.
    3. Le, Nhu D. & Zidek, James V., 1992. "Interpolation with uncertain spatial covariances: A Bayesian alternative to Kriging," Journal of Multivariate Analysis, Elsevier, vol. 43(2), pages 351-374, November.
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