The treatment efficacy for reducing Campylobacter concentrations by a drinking water treatment plant was assessed using a stochastic Monte Carlo model. The goal of the study was to reduce uncertainty of the results by combining microbiological and non-microbiological data in an advanced treatment assessment. Combining raw water Campylobacter and E. coli data reduced the uncertainty on raw water (peak) concentrations five-fold. Similar improvement was achieved for rapid sand filtration. Ozone disinfection was modelled based on ozone concentrations, contact time and temperature. Since this data was available, whereas most microbiological analyses at this point were negative, uncertainty was reduced three-fold. The slow sand filtration assessment could not be improved; however, since previous steps contained less uncertainty, this did not increase uncertainty by much. The study showed that using appropriate data for each treatment step can greatly reduce uncertainty in treatment assessment.

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