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<p>Declining water quality poses significant challenges to ecosystems, public health, and sustainable development. The Water Quality Index (WQI) provides a practical framework for converting complex water quality data into a single interpretable score for assessment and decision-making. This review synthesizes the evolution of WQI research from 1973 to 2025 based on 5,852 publications. Bibliometric analysis highlights substantial global growth in WQI studies, with major contributions from India and China. The review critically examines key components of WQI development, including parameter selection, sub-index formation, weighting schemes, and aggregation methods, while identifying major limitations such as subjectivity, uncertainty, and aggregation bias. Emerging approaches, including fuzzy logic, machine learning, and GIS-based assessment, are evaluated for their potential to enhance WQI performance. The study further highlights research gaps and future priorities, emphasizing the need for standardized, transparent, and adaptive WQI frameworks to support sustainable water resource management and evidence-based policy development.</p>

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A Systematic Review of Water Quality Indexing Approaches

  • 期刊论文

  • 发表日期：

    2026年6月27日

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  • Deborshee Sinha,
  • Mayur Shirish Jain

摘要

Declining water quality poses significant challenges to ecosystems, public health, and sustainable development. The Water Quality Index (WQI) provides a practical framework for converting complex water quality data into a single interpretable score for assessment and decision-making. This review synthesizes the evolution of WQI research from 1973 to 2025 based on 5,852 publications. Bibliometric analysis highlights substantial global growth in WQI studies, with major contributions from India and China. The review critically examines key components of WQI development, including parameter selection, sub-index formation, weighting schemes, and aggregation methods, while identifying major limitations such as subjectivity, uncertainty, and aggregation bias. Emerging approaches, including fuzzy logic, machine learning, and GIS-based assessment, are evaluated for their potential to enhance WQI performance. The study further highlights research gaps and future priorities, emphasizing the need for standardized, transparent, and adaptive WQI frameworks to support sustainable water resource management and evidence-based policy development.