CLUSTERS ANALYSIS OF POLLUTION SOURCES BASED ON THE STEROL COMPOSITION

Pollution in waterbodies is a major issue, and researchers work to assess sources of pollution in order to mitigate its impact on the environment. A major source of pollution is from animal fecal matter and sewage effluents, contributing to water quality issues worldwide. Tracking the source of the fecal pollution is necessary, providing the foundation on which to build an effective solution. In this study, we propose to track the fecal pollution sources by the sterol composition and explore the cluster structure of fecal pollution sources according to their sterol signatures. The sterol composition of various animal fecal and effluents were sourced from different peer reviewed journals (n = 10). Various clustering methods including hierarchical clustering analysis are performed on the data to explore the similarity of different fecal pollutions and clusters based on the sterol composition. The results from the analysis reveal that human, dingo, and rosella fecal have the highest proportion of coprostanol, cholesterol and campesterol respectively. Rosella, wastewater effluents, humans, and pigs are shown to have a definite cluster. Overall, our results show that clusters produced by hierarchical clustering can be used in tracking source of fecal pollution and should be adopted in assessing source of fecal pollution.