Probabilistic fecal pollution source profiling and microbial source tracking for an urban river catchment.

We developed an innovative approach to estimate the extent of fecal pollution sources for urban river catchments. The methodology consists of 1) catchment surveys complemented by literature data where needed for probabilistic estimates of daily produced fecal indicator (FIBs, E. coli, enterococci) and zoonotic reference pathogen numbers (Campylobacter, Cryptosporidium and Giardia) excreted by human and animal sources in a river catchment, 2) generating a hypothesis about the dominant sources of fecal pollution and selecting a source targeted monitoring design, and 3) verifying the results by comparing measured concentrations of chemical tracers, C. perfringens, and host-associated genetic microbial source tracking (MST) markers in the river, and by multi-parametric correlation analysis. We tested the approach at a study area in Vienna, Austria. The daily produced microbial particle numbers according to the probabilistic estimates indicated that, for the dry weather scenario, the discharge of treated wastewater (WWTP) was the primary contributor to fecal pollution. For the wet weather scenario, 80-99 % of the daily produced FIBs and pathogens resulted from combined sewer overflows (CSOs) according to the probabilistic estimates. When testing our hypothesis in the river, the measured concentrations of the human genetic fecal marker were log10 4 higher than for selected animal genetic fecal markers. Our analyses showed for the first-time statistical relationships between C. perfringens and a human genetic fecal marker (i.e. HF183/BacR287) with the reference pathogen Giardia in river water (Spearman rank correlation: 0.78-0.83, p < 0.05. The developed approach facilitates urban water safety management and provides a robust basis for microbial fate and transport models and microbial infection risk assessment