Beyond the Water Quality Index: Parameter-Level and Multivariate Assessment of an Urban Lake System Across Two Sampling Campaigns

This study evaluated whether Water Quality Index (WQI) classifications adequately represent parameter-specific degradation signals in a degraded urban lake system under contrasting hydrological conditions. Water samples were collected at 15 monitoring sites in the Ribeirão Cambé catchment, southern Brazil, during rainy-season and dry-season campaigns in 2021. Physicochemical and microbiological parameters were interpreted together with WQI classifications, regulatory assessment, correlation analysis, and Principal Component Analysis (PCA). The results showed marked spatial and seasonal variability in individual parameters. During the rainy season, increased turbidity, biochemical oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP), and thermotolerant coliforms indicated runoff-driven transport, diffuse pollution, organic matter enrichment, nutrient mobilization, and microbiological contamination. In contrast, the dry-season pattern was characterized by higher phosphorus and dissolved-ion-related signals, suggesting the influence of reduced dilution, localized inputs, sediment-water interactions, and potential internal loading processes. Despite these parameter-level differences, WQI classifications remained relatively stable across most sampling sites, with mean values classified as good in both campaigns. PCA revealed distinct seasonal degradation structures: rainy-season variability was strongly associated with runoff-related gradients, whereas dry-season variability reflected more heterogeneous and localized controls. These findings indicate that WQI is useful for communication and broad classification, but may smooth or partially mask seasonal and parameter-specific degradation signals when interpreted in isolation. Therefore, WQI-based assessments in degraded urban lake systems should be complemented by parameter-level, regulatory, and multivariate analyses to support more robust, risk-aware monitoring and management decisions.