A framework for assessing the adequacy of Water Quality Index – Quantifying parameter sensitivity and uncertainties in missing values distribution

Hui Ying Pak, C. Joon Chuah, Mou Leong Tan, Ee Ling Yong, Shane A. Snyder

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Water quality monitoring is a pillar in water resource management, but it can be resource intensive, especially for developing countries with limited resources. As such, Water Quality Indices (WQI) are developed to summarise general water quality, but efforts to assess the utility, flexibility, and practicality of WQI have been limited. In this study, we introduced an additional step to the traditional WQI development framework by introducing an adjusted form of WQI (WQIADJUSTED) to handle missing values, and capitalise on the remaining available information for the development of a WQI. A Sub-WQI was also developed to address local water quality conditions. WQI results (weighted and non-weighted) developed using different parameter optimisation methods, namely Multivariate Linear Regression and Principal Component Analysis were compared. To build upon the current framework, a new procedure was developed to assess the adequacy of WQI based on the sensitivity analysis of parameters and uncertainties associated with each parameter's missing values distribution. The number of observations needed for the development of a robust WQI was optimised with respect to user-defined acceptable change in WQI, based on Monte Carlo probabilistic simulation. The Johor River Basin (JRB), Malaysia is used as a case-study for the application of this new framework. The JRB serves as an important resource for Johor, one of the most populous state in Malaysia, and Singapore, a country south of Johor. WQIMLR performed better in explaining the general water quality than WQIPCA for weighted water quality parameters. Optimisation of sampling frequency revealed that around 130 samples will be required if a 2% change in WQI can be tolerated. The results (specific to the JRB) also revealed that total coliform is the most sensitivity parameter to missing values, and the distribution of sensitive parameters are similar for both WQINON-ADJUSTED and WQIADJUSTED.

Original languageEnglish (US)
Article number141982
JournalScience of the Total Environment
StatePublished - Jan 10 2021


  • Johor
  • Multivariate linear regression
  • Principal component analysis
  • Sensitivity analysis
  • Statistical decision theory
  • Water Quality Index

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution


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