Operational Factors Associated with Saline Activated Sludge Performance: Insights from Explainable Machine Learning

Numerous studies have explored how environmental and process parameters shape microbial community composition in activated sludge. However, a critical gap remains in identifying which operational and environmental conditions are most strongly associated with microbiome characteristics linked to wastewater treatment performance, particularly under real-world operational conditions where multiple variables exert simultaneous and interacting influences. In this study, we leverage high-throughput metatranscriptomic data combined with machine learning–based feature selection to identify the most informative measured process and environmental parameters associated with activated sludge microbiome structure of highest removal performance. Unlike traditional approaches that rely on controlled laboratory experiments with replicated conditions, our method enables the analysis of complex, full-scale systems without the need for extensive experimental replication, thereby capturing the intrinsic variability of real-world wastewater treatment processes. Using data from a saline wastewater treatment plant which was previously shown to benefit from microbial communities with high functional gene diversity and evenness, we evaluated the relative importance of measured operational variables. Recursive feature elimination, using a Random Forest model with repeated 10-fold cross-validation, identified blower operational load, mixed liquor suspended solids, and temperature as the most informative predictors of functional gene diversity among the operational and environmental variables evaluated.