Predicting wastewater treatment plant influent in mixed, separate, and combined sewers using nearby surface water...

For wastewater sample collection approaches supporting public health applications, few high hydrologic activity normalizing guidelines currently consider readily available environmental flow data that may earlier capture information regarding periods of influent mixing and dilution of wastewater with groundwater and runoff. This study aimed to identify wastewater sampling rules for high hydrological activity events, allowing for an earlier decision point in the control of dilution before sample collection. We defined the sampling rules via data-driven models (Random Forest and linear regression) using environmental data (i.e., wastewater treatment facility influent rates, nearby stream discharge flow, and precipitation). These models were applied to five treatment plants in Jefferson County, Kentucky (USA) in mixed, separate, and combined sewers with different population sizes. We proposed cutoffs of 10 %, 25 %, and 50 % flow conditions for orientation towards public health samples. The results showed a strong nonlinear relationship between nearby stream discharge and treatment facility flow rates, which was used to infer the hydrological conditions that produce high volumes of diluted wastewater in the sewer system. Accumulated Local Effects and SHapley Additive exPlanations aided in deciphering the relationship between the predictors and response variables of the Random Forest models. The influent rate to the treatment plant from the previous day and two USGS stream gages were needed to adequately predict the degree of infiltration and inflow mixing on a given day. Surface water discharge data can be used to provide an earlier workflow decision point during wet weather periods to improve understanding of flow conditions for wastewater-based epidemiological studies to inform laboratory analysis and data interpretation. Not only total flow, but also the specific proportions of infiltration and inflow to wastewater volume in influent should be considered when analyzing data for normalization purposes, and our method provides a starting point for doing so rapidly and at low cost.