Forecasting and Optimizing Dual Media Filter Performance via Machine Learning.

Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of a multi-media filter operating as a function of raw water quality and plant operating variables. The models were trained using data collected over a seven year period covering water quality and operating variables, including true colour, turbidity, plant flow, and chemical dose for chlorine, KMnO, FeCl, and Cationic Polymer (PolyDADMAC). The machine learning algorithms have shown that the best prediction is at a 1-day time lag between input variables and unit filter run volume (UFRV).

Wastewater-based monitoring reveals geospatial-temporal trends for antibiotic-resistant pathogens in a large urban community.

Antimicrobial resistance (AMR) is one of the top ten global health threats, and current surveillance programs rarely monitor it outside healthcare settings. This limits our ability to understand and manage the spread of AMR. Wastewater testing has the potential to simply, reliably and continuously survey trends in AMR outside the healthcare settings, as it captures biological material from the entire community.

Management of biogenic taste and odour: From source water, through treatment processes and distribution systems, to consumers.

Biogenic taste and odour (T&O) have become a global concern for water utilities, due to the increasing frequency of algal blooms and other microbial events arising from the combined effects of climate change and eutrophication. Microbially-produced T&O compounds impact source waters, drinking water treatment plants, and drinking water distribution systems. It is important to manage across the entire biogenic T&O pathway to identify key risk factors and devise strategies that will safeguard the quality of drinking water in a changing world, since the presence of T&O impacts consumer confidence in drinking water safety.

Novel crAssphage marker genes ascertain sewage pollution in a recreational lake receiving urban stormwater runoff.

Considerable efforts have been made in recent years in developing novel marker genes for fecal pollution tracking in environmental waters. CrAssphage are recently discovered DNA bacteriophage that are highly abundant in human feces and untreated sewage. In this study, we evaluated the host-sensitivity and -specificity of the newly designed crAssphage qPCR assays (Stachler et al.