Evaluation of activated sludge and treated water at wastewater treatment plant Gaia coastline

: performance indicators using protozoa and filamentous bacteria

Abstract

This study analyzed the operational dynamics of the Wastewater treatment plant (WWTP) SIMDOURO S.A. in Águas de Gaia, focusing on microbial communities in the activated sludge system. Design aids odor control, but efficiency depends on the microbial population. Initially, the activated sludge process was described using the Sludge Biotic Index (SBI) to evaluate how various factors influence microfauna. The goal was to find reliable indicators for daily monitoring and assess seasonal variations in microbial communities. Sludge, Affluent, and effluent samples were collected in fall, winter, and spring, while sensors monitored dissolved oxygen (O2) levels, temperature (Temp), and other parameters. Microscopic analysis quantified protozoan and metazoan populations, and laboratory analyses provided data on Total Suspended Solids (TSS), pH, Biochemical Oxygen Demand (COB), Chemical Oxygen Demand (COD), and nutrients like Ammonium (NH₄⁺) and Nitrate (NO₃⁻). Statistical analyses included t-tests, Wilcoxon tests, and Principal Component Analysis (PCA). Biological reactors 1 and 2 (T1 and T2) showed no significant differences, justifying their grouping. Seasonal shifts in microbial composition were noted: mobile ciliates dominated in autumn, while sessile ciliates prevailed in winter and spring. Key taxa such as Epistylis and Microthrix were identified, with SBI consistently at an ideal score of 10. PCA showed distinct seasonal clusters, and Modelo Linear Geral (GLM) indicated significant influences from seasonality and protozoan abundance on key parameters. Dominant filamentous bacteria included Nocardia spp. and Microthrix parvicella, correlated with specific Affluent conditions. Reliable ciliate indicators of system efficiency were identified, while flagellates and other organisms appeared sporadically. Future research should focus on advanced bacterial quantification methods, explore Artificial Intelligence (AI) and machine learning models, and extend the study into summer for a comprehensive understanding of microbial ecology.

Date of Award	30 Jul 2025
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Ermelinda de Lurdes Lopes Barreiro (Supervisor)