A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways.
Overflows from sanitary sewers during wet weather, which occur when the hydraulic capacity of the sewer system is exceeded, are considered a potential threat to the ecological and public health of the waterways which receive these overflows. As a result, water retailers in Australia and internationally commit significant resources to manage and abate sewer overflows. However, whilst some studies have contributed to an increased understanding of the impacts and risks associated with these events, they are relatively few in number and there still is a general lack of knowledge in this area. A Bayesian network model to assess the public health risk associated with wet weather sewer overflows is presented in this paper. The Bayesian network approach is shown to provide significant benefits in the assessment of public health risks associated with wet weather sewer overflows. In particular, the ability for the model to account for the uncertainty inherent in sewer overflow events and subsequent impacts through the use of probabilities is a valuable function. In addition, the paper highlights the benefits of the probabilistic inference function of the Bayesian network in prioritising management options to minimise public health risks associated with sewer overflows.
RMIT University, School of Civil, Environmental and Chemical Engineering, GPO Box 2476, Melbourne, Victoria 3001, Australia. Rebecca.Goulding@rmit.edu.au
R
R
Goulding
N
N
Jayasuriya
E
E
Horan
D001315:Australia; D001499:Bayes Theorem; D055868:Floods / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D062486:Public Health Surveillance / Q000379:methods*; D011891:Rain; D018570:Risk Assessment; D012722:Sewage / Q000009:adverse effects*; D014876:Water Pollution / Q000009:adverse effects* / Q000706:statistics & numerical data
D012722:Sewage