Development of a PCA-derived water quality index model for monitoring the urban river systems in Dhaka City, Bangladesh

• PCA-based WQI model optimizes parameters, explaining 74.06% of overall variance. • Buriganga faces severe damage (mean WQI: 46.85), needing urgent pollution control. • WQI shows strong seasonal changes, lowest in the dry season (29.78). • TS, COD, and chloride were the most sensitive WQI parameters. • WQI model offers a scalable, optimized tool for urban river quality monitoring. In this study, a Water Quality Index (WQI) model was developed to assess the water quality of the major rivers in Dhaka city, including Buriganga, Balu, Turag, and Sitalakhya, based on a comprehensive dataset collected across four seasons (dry, pre-monsoon, monsoon, post-monsoon) in 2024. A total of 144 water samples were analyzed for 16 water quality parameters, and principal component analysis (PCA) combined with correlation analysis was applied to identify eight key parameters: pH, dissolved oxygen (DO), total solids (TS), chemical oxygen demand (COD), chloride, ammonia–nitrogen, E. coli, and total coliform. Sub-indexing was performed using quality rating curves provided by NSF-WQI and a linear interpolation function to transform parameter values into standardized scores. PCA was used again to assign weights based on eigenvalues, a data-driven approach to parameter weighting, with TS (0.215) and ammonia–nitrogen (0.199) contributing most to pollution levels. The final WQI, calculated using a weighted aggregation function, ranged from 29.78 to 77.96, with Buriganga exhibiting the poorest water quality (mean WQI: 46.85) and Sitalakhya the best (mean WQI: 57.10). Seasonal variations revealed that the dry season WQI reached as low as 29.78, while dilution during the monsoon season improved water quality to a maximum of 74.29. Statistical analysis confirmed significant temporal ( p < 0.001) and spatial ( p < 0.001) variations in water quality. Sensitivity analysis identified TS, COD, and chloride as the most influential parameters. This study presents a robust, data-driven WQI model for efficient monitoring and management of water quality in Dhaka.