Quantitative microbial human exposure model for faecal indicator bacteria and risk assessment of pathogenic Escherichia coli in surface runoff following application of dairy cattle slurry and co-digestate to grassland.

Animal waste contains high numbers of microorganisms and therefore can present a potential biological threat to human health. During episodic rainfall events resulting in runoff, microorganisms in the waste and soil may migrate into surface runoff, contaminating surface water resources. A probabilistic human exposure (HE) model was created to determine exposure to faecal indicator bacteria (FIB): total coliforms (TC), E. coli and enterococci following application of bio-based fertiliser (dairy cattle slurry, digestate) to grassland; using a combination of experimental field results and literature-based data. This step was followed by a quantitative microbial risk assessment (QMRA) model for pathogenic E. coli based on a literature-based dose-response model. The results showed that the maximum daily HE (HEdaily) is associated with E. coli for unprocessed slurry (treatment T1) on day 1, the worst-case scenario where the simulated mean HEdaily was calculated as 2.84 CFU day -1. The results indicate that the overall annual probability of risk (Pannual) of illness from E. coli is very low or low based on the WHO safe-limit of Pannual as 10 -6. In the worst-case scenario, a moderate risk was estimated with simulated mean Pannual as 1.0 × 10 -5. Unpasteurised digestate application showed low risk on day 1 and 2 (1.651 × 10 -6, 1.167 × 10 -6, respectively). Pasteurised digestate showed very low risk in all scenarios. These results support the restriction imposed on applying bio-based fertiliser if there is any rain forecast within 48 h from the application time. This study proposes a future extension of the probabilistic model to include time, intensity, discharge, and distance-dependant dilution factor. The information generated from this model can help policymakers ensure the safety of surface water sources through the quality monitoring of FIB levels in bio-based fertiliser.