Most research on wastewater treatment efficiency compliance focuses on physicochemical and microbial indicators; however, very little emphasis has been placed so far on determining suitable indicator organisms to predict the discharge level of pathogens from treatment plants. In this study, raw wastewater, activated sludge, and the resulting final effluents and biosolids in four municipal wastewater treatment plants (WWTPs A, B, C and D) were seasonally investigated for human-virulent water-borne pathogens Cryptosporidium parvum/hominis and Giardia duodenalis, and microsporidia (e.g. Encephalitozoon hellem, E. intestinalis, and Enterocytozoon bieneusi) between 2008 and 2009. A suite of potential microbial indicators for human-virulent protozoa and microsporidia was also determined. A combination of multiple fluorescent in situ hybridization and immunofluorescent antibody assays were applied to detect Cryptosporidium oocysts, Giardia cysts, and microsporidian spores. Escherichia coli, enterococci and Clostridium perfringens spores were cultivated in selective media. Positive correlations were found between the abundance of enterococci and E. coli and abundance of Cryptosporidium oocysts (rs > 0.47, p < 0.01) and Giardia cysts (rs > 0.44, p < 0.01) at WWTPs A–D. Clostridium perfringens spores were positively correlated to Cryptosporidium oocysts (rs = 0.40, p < 0.01) and Giardia cysts (rs = 0.46, p < 0.01). There was a strong positive correlation between abundance of Giardia cysts and that of Cryptosporidium oocysts (rs > 0.89, p < 0.01). To sum up, a suite of faecal indicator bacteria can be used as indicators for the presence of Cryptosporidium oocysts and Giardia cysts in these activated-sludge systems (WWTPs A, B and C). Overall, Giardia duodenalis was noted to be the best Cryptosporidium indicator for human health in the community-based influent wastewater and throughout the treatment process.
Keywords: Cryptosporidium oocysts, faecal indicator bacteria, Giardia cysts, microsporidian spores, multiple linear regression predictive model, wastewater treatment process