A stochastic sewer model to quantify health risks to residents from sewer flooding with quantitative microbial risk assessment

Abstract

This work presents a stochastic sewer model (SIMDEUM-WW) to predict dry weather sewer flows and pollutant loading within a sewer network; by generating probabilistic household demand patterns based on information about inhabitants and appliance usage. The stochastic outputs were fed into MIKE URBAN (DHI) for hydrodynamic and water quality simulations under several weather conditions. The MIKE URBAN model includes a 2D overland flow model and a 1D sewer network model that flows are exchanged via manholes. The model was validated against field measurement data and the results show that the SIMDEUM-WW can adequately calculate wastewater and pollutant loading. A quantitative microbial risk assessment model was developed to assess the infection risk due to E. coli contamination from combined sewer overflow. It was predicted that that the probability of infection from E. coli was high.