Faecal pollution source identification in an urbanising catchment using antibiotic resistance profiling, discriminant analysis and partial least squares regression

Abstract

Increasing urbanisation and changes in land use lead to adverse impacts on the quality of natural water resources. The specific sources of contamination are often difficult to identify using conventional water quality monitoring techniques. This acts as a significant constraint to the development of appropriate management techniques to protect natural water resources. Consequently, alternative means of identifying pollutant sources and their locality are necessary. In this study, Antibiotic Resistance Patterns (ARP) were established for a library of 1005 known Escherichia coli source isolates obtained from human and non-human (domesticated animals, livestock and wild) sources in an urbanising catchment in Queensland State, Australia. Discriminant Analysis (DA) was used to differentiate between the ARP of source isolates and to identify the sources of faecal contamination. Partial Least Square (PLS) regression was then utilised on identified human source isolates to correlate their locality with specified sampling locations within the catchment. The resulting ARP DA indicated that a majority of the faecal contamination in the rural areas was non-human. However, the percentage of human isolates increased significantly in urbanised areas using onsite systems for wastewater treatment. The PLS regression was able to develop predictive models which indicated a high correlation of human source isolates from the urban area. The study results confirm the feasibility of using ARP for source tracking faecal contamination in surface waters, as well as predicting their point of origin.