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Transforming Complex Water Quality Monitoring Data into Water Quality Indices

Author

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  • Nashwa A. Shaaban

    (Alexandria University)

  • David K. Stevens

    (Utah State University)

Abstract

Unplanned urbanization and economic development can deteriorate water quality (WQ) and alter its beneficial usage. Continuous monitoring of biotic and abiotic parameters describing the WQ is essential to track changes and classify water resources to protect public health. Various invest significant effort, money, and time in monitoring programs. Using data from those sources is challenging due to the large number of observations, and inconsistencies in sampling time, date, stations, and gaps. This study aims to design different water quality index (WQI) models to provide policymakers, stakeholders, and water managers with a more comprehensive assessment by converting complex datasets from over 10 years, processed with the statistical software R, into consistent data sets. These datasets are then transformed into small principal components. WQ datasets of lakes and reservoirs in the western USA were chosen as case studies. The strategy of data processing is explained, and the results organized as a descriptive summary of the 12,000 observations for 31 parameters are discussed. Outputs of principal component analysis (PCA) are used to create relative and absolute WQI models for water irrigation usage and protecting cold- and warm-water species of game fish. Weighted arithmetic water quality indices are applied, and the relation between different models is examined.

Suggested Citation

  • Nashwa A. Shaaban & David K. Stevens, 2025. "Transforming Complex Water Quality Monitoring Data into Water Quality Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(8), pages 3883-3899, June.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:8:d:10.1007_s11269-025-04135-4
    DOI: 10.1007/s11269-025-04135-4
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    References listed on IDEAS

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    1. Qingqing Zhang & Xue-yi You, 2024. "Recent Advances in Surface Water Quality Prediction Using Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 235-250, January.
    2. Zehai Gao & Yang Liu & Nan Li & Kangjie Ma, 2022. "An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2685-2702, June.
    3. Wenjie Yang & Yue Zhao & Dong Wang & Huihui Wu & Aijun Lin & Li He, 2020. "Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China," IJERPH, MDPI, vol. 17(8), pages 1-14, April.
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    Cited by:

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