Modelling of quantitative microbiological risk assessment in bathing waters

Identifier:  TFM:1847

Handle: https://hdl.handle.net/20.500.11797/TFM1847

Authors:  Munschke, Sarah

Abstract:
This thesis focuses on the modelling of quantitative microbiological risk assessment (QMRA) in bathing waters, with a specific case study on Bogatell beach in Barcelona. The study is motivated by the increasing frequency of extreme weather events due to climate change, which can lead to the contamination of water bodies by microbial pathogens from faecal matter and pose a risk to human health. The objectives of this thesis are to develop a QMRA tool in Python to assess the risk of waterborne diseases following Combined Sewer Overflow (CSO) events, considering various environmental conditions like temperature, pH, salinity, and UV radiation. This tool aims to assist local authorities and beach managers in making informed decisions about beach safety after CSO events. Firstly, using Python the distribution of Faecal Indicator bacteria (FIB) such as E. coli and Enterococci is simulated under consideration of environmental factors for each season and their impacts on pathogen degradation. Secondly the QMRA is conducted to assess the risk for children or adults swimming as well as people participating in recreational activities on the water such as kayaking or rowing. The results indicate that the pathogen distribution and associated health risks vary depending on seasonal changes in environmental conditions and sea currents, although the speed and direction of the currents has been shown to have the biggest impact. Based on the outcomes of the simulation and risk assessment potential impacts of the progressing climate change have been discussed as well. In conclusion, the developed QMRA tool provides a basis for assessing the risks of waterborne diseases in bathing waters after CSO events, contributing to improved public health safety and sustainable water management practices. The focus on Bogatell beach shows the potential utility in real-world scenarios and emphasizes the importance of predictive modelling and risk assessment in the decision-making processes for public health.