Using multilinear regressions developed from excitation-emission matrices to estimate the wastewater content in urban streams impacted by sanitary sewer leaks and overflows

Abstract

Failing sewer infrastructure introduces unknown quantities of raw wastewater into urban streams, raising human and ecological health concerns. To address this problem, we developed multilinear regressions that relate fluorescent dissolved organic matter to wastewater content. The models were constructed with the area-normalized regional volumes of excitation-emission matrices measured for mixtures of deionized water, surface water from a wastewater-impacted stream, wastewater from a sanitary sewer adjacent to the stream, and Suwannee River natural organic matter. The best performing multilinear regression had a standard error of 0.55 % wastewater. A matrix-matched calibration was used to internally validate the approach and confirm the wastewater content of select samples. The multilinear model was externally validated through (i) comparison to concentrations of contaminants of emerging concern in surface water and wastewater and (ii) extension to samples from previous campaigns that employed alternative wastewater indicators. Using the validated model, we estimated an average wastewater content of 2.4 ± 4.0 % in 165 samples collected from 14 locations in the Gwynns Falls watershed (USA) between April 2019 and April 2023. The maximum wastewater content was 35 % at a site where sanitary sewer leaks and overflows have been previously documented. The reported approach represents a cost-effective and scalable technique to estimate wastewater content in urban streams through analysis of fluorescent dissolved organic matter.