Karina Gin Yew-Hoong
University of Singapore, Singapore
Title: Monitoring and modeling pathogens in recreational waters
Biography
Biography: Karina Gin Yew-Hoong
Abstract
Rapid urbanisation has increased the pressure to meet the water demands of an expanding population for whom drinking water, drainage, waste water and sanitation services have to be provided. However, greater economic growth generally also increases contamination of water resources and impairs water quality. As a result, many waterborne or water-related infectious illness have been reported worldwide. Current water quality monitoring schemes are based on the detection of fecal indicator bacteria (i.e. E. coli and Enterococcus) but this has been questioned due to the lack of correlation with several pathogens especially in tropical areas. In Singapore, approximately two-thirds of the land surface is used as water catchment: rainwater is collected through a comprehensive network of drains, canals, rivers and stormwater collection ponds before it is channeled to 17 reservoirs for storage. Field data was collected over a period of 5 years from different reservoirs and catchments in Singapore to study the occurrence and distribution of pathogens and microbial indicators. A total of 25 targets were tested, including fecal indicator bacteria, coliphages (somatic and male specific coliphages, FRNA G1-G4), human specific markers (Bacteroides thetaiotaomicron, Methanobrevibacter smithii, human polyomavirus), bacterial pathogens (Salmonella spp., Pseudomonas aeruginosa), enteric viruses (Adenovirus, Norovirus G1 & G2, Rotavirus, Astrovirus, Enterovirus, Hepatitis A virus, Hepatitis E virus, Aichi virus, Sapovirus, Influenza A virus), parasitic pathogens (Naegleria fowleri, Microsporidia) and a plant virus (Pepper mild mottle virus). Predictive models for the occurrence of pathogens have been developed through several mathematical approaches, including machine learning (e.g. Bayesian network and decision tree). Quantitative microbial risk assessment (QMRA) offers a framework to assess the possible health risk brought by each pathogen. Through the integration of QMRA with a suitable predictive model for the occurrence of pathogens, a better evaluation of human health risks associated with the usage of surface waters can be made.