Towards a unified principal indicator system for global water quality assessment

Water quality is a multidimensional concept. In the global water quality monitoring framework proposed by the United Nations Environment Programme (UNEP), water quality indicators are categorized into 11 subcategories, including nutrients, organic and inorganic chemicals, suspended solids, organisms, and pigments, and serve as key indicators of aquatic ecosystem health. Effective water quality monitoring is crucial for global water resource management, yet national monitoring systems often suffer from inconsistent standards and incomplete coverage. Although the UNEP has proposed a comprehensive framework of 598 indicators, its large scale makes national implementation challenging, and a streamlined indicator set that balances information representativeness and monitoring feasibility is still lacking. To address this issue, this study integrates water quality data from 134 countries, over 20,000 monitoring stations, spanning 1906-2024, and develops a "data-knowledge-cost" driven framework to identify 231 principal indicators. This set retains over 90% of the variance explained by the UNEP indicator system based on a subset selection framework, while substantially reducing monitoring burdens. It also demonstrates strong robustness, explaining >90% of the variance across indicators in over 95% of countries, and suggests potential applicability in long-term monitoring scenarios. Case studies further show that adopting a unified principal indicator system can substantially improve the comparability and accuracy of water quality assessments. This study provides a scientific basis for optimizing and standardizing global water quality monitoring.