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
Rajat Nag, Stephen Nolan, Vincent O’Flaherty, Owen Fenton, Karl G. Richards, Bryan Markey, Paul Whyte, Declan Bolton, Enda Cummins
Abstract
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.