Insights from Explainable AI-Assisted Microbial Source Tracking in an Urban Watershed

Abstract Microbial contamination in urban watersheds poses significant challenges for public health, yet traditional monitoring offers limited insight into identifying contamination sources or environmental drivers. This study integrates chip-based digital polymerase chain reaction (cdPCR), a sensitive microbial source tracking (MST) assay, with explainable artificial intelligence (AI) to identify dominant fecal sources and key environmental drivers in the Rock Creek watershed, Washington, DC. Biweekly water samples were collected over 13 months (2021–2022) at three sites and analyzed for four host-associated MST markers (HF183, Rum2Bac, DG3, and GFD). After fitting Random Forest models to E. coli concentration data, SHapley Additive exPlanations (SHAP), an explainable AI tool, was used to quantify variable importance and local effects. Although HF183 exhibited the highest concentration by cdPCR, SHAP identified the canine-associated DG3 marker as the key predictor, particularly during storm events, suggesting that canine-associated fecal inputs were a major contributor to elevated E. coli concentrations. Storm events, turbidity, and minimum vapor pressure deficit were the most influential environmental drivers. SHAP further revealed substantial temporal and spatial heterogeneity in fecal contamination, indicating episodic contributions from multiple sources. This novel integration of digital PCR and explainable AI enhances understanding of fecal contamination dynamics and delivers actionable insights for precision water quality management.