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High-flow events that significantly impact Water Resource Recovery Facility (WRRF) operations are rare, but accurately predicting these flows could improve treatment operations. Data-driven modeling approaches could be used; however, high flow events that impact operation are an infrequent occurrence, providing limited data from which to learn meaningful patterns. The performance of a statistical model (logistic regression) and two machine learning (ML) models (support vector machine and random forest) were evaluated to predict high flow events one-day-ahead to two plants located in different parts of the United States, Northern Virginia and the Gulf Coast of Texas, with combined and separate sewers, respectively. We compared baseline models (no synthetic data added) to models trained with synthetic data added from two different sampling techniques (SMOTE and ADASYN) that increased the representation of rare events in the training data. Both techniques enhanced the sample size of the very high-flow class, but ADASYN, which focused on generating synthetic samples near decision boundaries, led to greater improvements in model performance (reduced misclassification rates). Random forest combined with ADASYN achieved the best overall performance for both plants, demonstrating its robustness in identifying one-day-ahead extreme flow events to treatment plants. These results suggest that combining sampling techniques with ML has the potential to significantly improve the modeling of high-flow events at treatment plants. Our work will prove useful in building reliable predictive models that can inform management decisions needed for the better control of treatment operations. 

Phage emit communication signals that inform their lytic and lysogenic life cycles. However, little is known regarding the abundance and diversity of the genes associated with phage communication systems in wastewater treatment microbial communities. This study focused on phage communities within two distinct biochemical wastewater environments, specifically aerobic membrane bioreactors (AeMBRs) and anaerobic membrane bioreactors (AnMBRs) exposed to varying antibiotic concentrations. Metagenomic data from the bench-scale systems were analyzed to explore phage phylogeny, life cycles, and genetic capacity for antimicrobial resistance and quorum sensing. Two dominant phage families, Schitoviridae and Peduoviridae, exhibited redox-dependent dynamics. Schitoviridae prevailed in anaerobic conditions, while Peduoviridae dominated in aerobic conditions. Notably, the abundance of lytic and lysogenic proteins varied across conditions, suggesting the coexistence of both life cycles. Furthermore, the presence of antibiotic resistance genes (ARGs) within viral contigs highlighted the potential for phage to transfer ARGs in AeMBRs. Finally, quorum sensing genes in the virome of AeMBRs indicated possible molecular signaling between phage and bacteria. Overall, this study provides insights into the dynamics of viral communities across varied redox conditions in MBRs. These findings shed light on phage life cycles, and auxiliary genetic capacity such as antibiotic resistance and bacterial quorum sensing within wastewater treatment microbial communities. 

ABSTRACT Wastewater-based epidemiology (WBE) expanded rapidly in response to the COVID-19 pandemic. As the public health emergency has ended, researchers and practitioners are looking to shift the focus of existing wastewater surveillance programs to other targets, including bacteria. Bacterial targets may pose some unique challenges for WBE applications. To explore the current state of the field, the National Science Foundation-funded Research Coordination Network (RCN) on Wastewater Based Epidemiology for SARS-CoV-2 and Emerging Public Health Threats held a workshop in April 2023 to discuss the challenges and needs for wastewater bacterial surveillance. The targets and methods used in existing programs were diverse, with twelve different targets and nine different methods listed. Discussions during the workshop highlighted the challenges in adapting existing programs and identified research gaps in four key areas: choosing new targets, relating bacterial wastewater data to human disease incidence and prevalence, developing methods, and normalizing results. To help with these challenges and research gaps, the authors identified steps the larger community can take to improve bacteria wastewater surveillance. This includes developing data reporting standards and method optimization and validation for bacterial programs. Additionally, more work is needed to understand shedding patterns for potential bacterial targets to better relate wastewater data to human infections. Wastewater surveillance for bacteria can help provide insight into the underlying prevalence in communities, but much work is needed to establish these methods. IMPORTANCEWastewater surveillance was a useful tool to elucidate the burden and spread of SARS-CoV-2 during the pandemic. Public health officials and researchers are interested in expanding these surveillance programs to include bacterial targets, but many questions remain. The NSF-funded Research Coordination Network for Wastewater Surveillance of SARS-CoV-2 and Emerging Public Health Threats held a workshop to identify barriers and research gaps to implementing bacterial wastewater surveillance programs.