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Onsite wastewater treatment systems (OWTSs) are important nonpoint sources (NPSs) of pollution to consider in watershed management. However, limited OWTS data availability makes it challenging to account for them as an NPS of water pollution. In this study, we succeeded in obtaining OWTS permits and integrated them with environmental data to model the pollution potential from OWTSs at the watershed scale using GIS-based multicriteria decision analysis. Then, in situ water quality parameters─ Escherichia coli (E. coli) , total nitrogen, total phosphorus, temperature, and pH─were measured along the main tributary at base-flow conditions. Three general linear models were developed to relate E. coli to water quality parameters and OWTS pollution indicators. It was found that the model with the OWTS pollution potential had the lowest corrected Akaike information criterion (AICc) value (35.01) compared to the models that included classified OWTS pollution potential input criteria (AICc = 36.76) and land cover (AICc = 36.74). These results demonstrate that OWTSs are a significant contributor to surface water pollution, and future efforts should be made to improve access to OWTS data (i.e., location and age) to account for these systems as an NPS of water pollution.

Fecal pollution in surface waters is a major threat to recreational and drinking water resources, with Escherichia coli being a primary concern. The best way to mitigate fecal pollutant loading is to identify the sources and tailor remediation strategies to reduce loading. Tracking E. coli back to its source is notoriously difficult in a mixed-use watershed where input from humans, wildlife, and livestock all contribute to E. coli loading. One proposed tracking method for E. coli contamination is the use of fecal sterols and sterol ratios. This study uses fecal sterol data published globally to assess how well sterol compositions for different species clusters along with the effectiveness of sterol ratios as tracking tools. Hierarchical cluster analysis produces stronger clusters based on sterol ratios than raw sterol concentration, but the global dataset results in clustering of the same species in different levels. The accuracy of the sterol ratios was also compared to understand the rate of false negatives and false positive assignments. Overall, these ratios did not have a high success rate for determining the correct source, which was also reflected in the poor clustering trends observed. Establishing local end-member sterol profiles is essential when using sterol signatures to unravel fecal loading., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)