Urban flooding and health risk analysis by use of quantitative microbial risk assessment : Limitations and improvements

Abstract

Extreme rainfall overloads combined sewers, thereby causing flooding in urban areas, and if the public is exposed to flooding, they are at risk of acquiring gastrointestinal diseases. This is a known problem and is expected to increase because the frequency and intensity of extreme rainfall are expected to increase in the future. To ensure public health during extreme rainfall, solutions are needed, but limited knowledge on microbial water quality, and related health risks, makes it difficult to implement microbial risk analysis as a part of the basis for decision making. The main aim of this PhD thesis is to identify the limitations and possibilities for optimising microbial risk assessments of urban flooding through more evidence-based solutions, including quantitative microbial data and hydrodynamic water quality models. The focus falls especially on the problem of data needs and the causes of variations in the data. Essential limiting factors of urban flooding QMRAs were identified as uncertainty regarding ingestion volumes, the limited use of dose-response models and low numbers of microbial parameters measurements and absent validation of the risk assessments. Because improving knowledge of ingestion volumes and of dose-response models involves many difficulties, including ethical considerations, they are therefore less manageable than measuring microbial parameters. But when aiming at predicting the risk of infection from exposure to urban floodwater, measurements from flooding episodes are difficult to acquire, and so it is actually more feasible to use dynamic water quality models, which has the advantage of including dilution, spatial distribution and changes over time. However, determining inputs into water quality models is a challenge, because wastewater composition varies greatly according to location, as demonstrated for microbial, chemical and physical parameters between sub-catchments. Variations in wastewater quality, as well as rainfall, system and environmental effects (solar radiation), cause knock-on variations in floodwater composition, because they are functions of these parameters. Variations between locations have been demonstrated through measurements of microbial concentrations in flooding episodes, while changes in microbial concentrations over time have been demonstrated through a survival and decay study, where decay was substantial in the presence of UV light (potential sunlight) and varied according to turbidity and depth. Risk analysis could be integrated for use in risk management more than they are today, which involves the use of measurements along with hydrodynamic water quality models. In this PhD thesis two risk assessments were conducted with employment of hydrodynamic water quality models. One risk assessment examined contaminated bathing water, and here dilution and transport were modelled through a drainage model and a hydrodynamic bathing water model. The maximum concentrations of pathogens in wastewater found in the literature were used to estimate risk, and by comparing the model results with an epidemiological study of the same event, the concept of using hydrological models to estimate water quality – and thereby estimate risk – was improved. Another urban flooding risk assessment used average measured concentrations of pathogens in wastewater as inputs into a drainage model, to estimate the pathogen concentration in the floodwater. Compared to the risk assessment for the contaminated bathing water, the concept was improved, because actual pathogen measurements rather than literature-based values were used as inputs into the drainage model. Furthermore, the drainage model was validated by comparison of modelled and measured microbial concentrations in CSOs. The model result was used in the analysis of the risk of infection from exposure to urban flooding which resulted in a risk of 10.3 to 10-1 from exposure to flooding, both from cleaning up flooding, but also when wading through a flooded area. The results in this thesis have brought microbial risk assessments one step closer to more uniform and repeatable risk analysis by using actual and relevant measured data and hydrodynamic water quality models to estimate the risk from flooding caused by overloaded combined sewers. This approach is useful in supporting decision making regarding optimising sewer systems in the future to ensure public health.