Integrated water quality assessment of heterogeneous animal ponds under contrasting hydraulic and operational regimes

Water quality management in animal facilities is increasingly challenged by non-conventional wastewater matrices, driven by heterogeneous enclosure designs and species-specific behaviors. Although zoo facilities are widespread and consume large volumes of water, very few studies have examined pond water quality, and even fewer have integrated physicochemical, microbiological, hydraulic, and operational variables in a single framework. This study evaluated sixteen animal ponds at Barcelona Zoo by integrating physicochemical, microbiological, and operational data using Multiple Factor Analysis (MFA) and Principal Component Analysis (PCA) combined with Hierarchical Clustering (HC), providing an exploratory approach for water quality assessment in heterogeneous aquatic systems. MFA explained 66% of variance, distinguishing open-exchange systems with high water renewal and consumption (> 7,900 m 3 /year) from recirculated systems with low hydraulic turnover (< 400 m 3 /year). PCA-HC revealed two dominant gradients: (i) organic matter accumulation and eutrophication potential (COD, BOD₅, TSS, PO₄ 3 ⁻), and (ii) nitrogen speciation, characterized by low NH₄⁺ and NO₃⁻ concentrations and a substantial fraction of total nitrogen not explained by the measured inorganic forms, contrasting with typical domestic wastewater. Microbial profiles were strongly species- and system-specific, with flamingo and hippopotamus ponds exhibiting high fecal indicator loads, while reptile and bear systems were enriched in environmentally persistent proxies ( Clostridium perfringens ). Hierarchical clustering identified exploratory operational groups based on water quality and enclosure characteristics, supporting comparative interpretation of site-specific management needs. Overall, the results indicate that enclosure-specific hydraulic and operational conditions were associated with distinct water quality patterns, suggesting that variability cannot be explained by pollutant loads alone. Consequently, this study provides an exploratory basis for tailored water management, prioritizing solids control, aeration, nitrogen transformation processes, and disinfection barriers in accordance with the identified cluster profiles. Future research should integrate quantitative microbial risk assessment (QMRA) and techno-economic analysis to align treatment strategies with health-based targets and sustainability objectives.