Optimizing Parasitic And Microbial Communities For Enhanced Biological Wastewater Treatment Efficiency

Abstract

Background: Microbial and parasitic  communities play a central role in the biological treatment of wastewater, but their natural dynamics can lead to inconsistent treatment outcomes. Objectives: To assess whether Microbial and parasitic  community optimization including bioaugmentation, operational parameter adjustments,[1] and real-time Microbial and  parasitic  monitoring can improve the treatment efficiency and operational stability of activated sludge systems. Methods: A 12-week, prospective, controlled interventional trial was conducted at two municipal wastewater treatment plants. The intervention arm received targeted Microbial and parasitic  optimization (bioaugmentation with functional strains, adjusted sludge retention time, dissolved oxygen control, and nutrient balancing), while the control arm maintained standard protocols. Weekly samples were analyzed for treatment performance (COD, TN, TP removal), Microbial and  parasitic  diversity (16S rRNA sequencing), functional gene expression (amoA, nirK, ppk via qPCR), and sludge settleability (SVI). Results: The intervention group achieved significantly higher removal efficiencies for COD (91.5% vs. 77.2%, p < 0.001), TN (75.8% vs. 55.6%, p < 0.001), and TP (70.1% vs. 49.5%, p < 0.001) compared to controls. Microbial and  parasitic  diversity (Shannon Index) increased from 3.72 to 4.45 (p < 0.001) in the intervention group. Gene expression of amoA, nirK, and ppk was markedly higher in the intervention group. No operational disturbances were recorded, and SVI remained stable at 96.4 mL/g, contrasting with bulking in the control unit. RDA indicated that DO, SRT, and nutrient ratios explained 67% of the variance in Microbial and  parasitic  structure (p = 0.002). Conclusion: It is concluded that Microbial and  parasitic  community optimization significantly improves biological wastewater treatment efficiency, system resilience, and operational stability. This approach represents a promising shift toward microbiome-guided environmental engineering and supports the development of high-performance, sustainable treatment systems.