AbstractThe presence of faecal material in rivers and bathing waters can degrade water quality and impact on drinking water supply, fishing, aquaculture and recreational uses. In developing countries, infectious diseases related to the faecal contamination of water resources can constitute a major burden on human health. Determining the origin of contamination is not only important when assessing the degree of risk posed to public health, but also in deciding how best to target remediation measures. This study set out to develop and implement a low-cost antibiotic resistance analysis (ARA) method for determining the sources of faecal pollution present within aquatic environments.
ARA is a potentially useful method for distinguishing faecal bacteria by host source. ARA has been used successfully to predict the origin of faecal contamination in river catchments in the U.S.A. This phenotypic approach has cost benefits over genotypic approaches but existing protocols are time-consuming and manual data handling is open to human error. A simplified, low-cost approach to the ARA technique was developed that uses automated data recording combined with simple statistical analyses to compare bacteria of the genus Enterococcus from various faecal sources. In total, 3675 isolates were analysed for their sensitivity to a combination of antibiotic concentrations, creating a database of over 250,000 test reactions. Discriminant function analysis (DFA) revealed how many of the isolates were correctly grouped into each source category, on the basis of their antibiotic resistance patterns. When the database was split into ‘municipal wastewater (MW) vs. livestock vs. wild bird’ 73% the database isolates were correctly classified. This rate fell slightly to 68% when the database was split into ‘MW vs. cattle and sheep vs. pigs vs. wild birds vs. dogs vs. poultry’. Isolates of porcine origin demonstrated the highest levels of resistance to the chosen antibiotic concentrations and the best rates of classification. Once the most discriminant antibiotics had been identified, it was possible to reduce the number of concentrations from 80 to just 31.
The most common sources of faecal pollution present in river water samples were livestock and wild birds. Whilst isolates classified as originating from municipal wastewaters were also detected, they were not found to be the dominant source. The data presented in this thesis also suggest that patterns of antibiotic resistance amongst bacterial populations differ with respect to geographical location. Future faecal source tracking studies using ARA may therefore require the assembly of catchment specific databases. However, antibiotic resistance patterns did appear to remain stable for at least 36 months. Most importantly, the ARA technique has demonstrated that it is capable of distinguishing the human and animal populations responsible for a faecal pollution incident, thereby allowing a more cost-effective and targeted approach to the remediation of impaired surface waters in the future.
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